





Regulatory Impact Analysis for the Proposed Reconsideration of the National Ambient Air Quality Standards for Particulate Matter

                                                               EPA-452/P-22-001
                                                                    August 2022
                                                                               
                                                                               
                                                                               
                                                                               
                                                                               
                                                                               
                                       
Regulatory Impact Analysis for the Proposed Reconsideration of the National Ambient Air Quality Standards for Particulate Matter
                     U.S. Environmental Protection Agency
                 Office of Air Quality Planning and Standards
                   Health and Environmental Impacts Division
                          Research Triangle Park, NC

                              CONTACT INFORMATION
This document has been prepared by staff from the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. Questions related to this document should be addressed to U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, C439-02, Research Triangle Park, North Carolina 27711 (email: oaqpseconomics@epa.gov). Please submit comments on this document to the following docket for the regulatory impact analysis: EPA-HQ-OAR-2019-0587. The docket for the notice of proposed rulemaking is EPA-HQ-OAR-2015-0072.

                               ACKNOWLEDGEMENTS
In addition to EPA staff from the Office of Air Quality Planning and Standards, personnel from the Office of Policy's National Center for Environmental Economics contributed data and analysis to this document. 
                                       

TABLE OF CONTENTS
LIST OF TABLES	x
LIST OF FIGURES	xvi
EXECUTIVE SUMMARY	ES-1
Overview of the Proposal	ES-1
Overview of the Regulatory Impact Analysis	ES-2
ES.1	 Design of the Regulatory Impact Analysis	ES-3
ES.1.1	 Establishing the Analytical Baseline	ES-5
ES.1.2 	Estimating PM2.5 Emissions Reductions Needed for Annual and 24-hour Alternative Standard Levels Analyzed	ES-7
ES.1.3 	Control Strategies and PM2.5 Emissions Reductions	ES-10
ES.1.4 	Estimates of PM2.5 Emissions Reductions Still Needed after Applying Control Technologies and Measures	ES-11
ES.1.5 	Engineering Costs	ES-13
ES.1.6 	Human Health Benefits	ES-14
ES.1.7 	Welfare Benefits of Meeting the Primary and Secondary Standards	ES-19
ES.1.8 	Environmental Justice	ES-20
ES.2	Qualitative Assessment of the Remaining Air Quality Challenges	ES-22
ES.3	Results of Benefit-Cost Analysis	ES-24
ES.4	References	ES-29
CHAPTER 1: OVERVIEW AND BACKGROUND	1-1
Overview of the Proposal	1-1
Overview of the Regulatory Impact Analysis	1-1
1.1	Background	1-3
1.1.1	National Ambient Air Quality Standards	1-4
1.1.2	Role of Executive Orders in the Regulatory Impact Analysis	1-5
1.1.3	Nature of the Analysis	1-5
1.2	The Need for National Ambient Air Quality Standards	1-6
1.3	Design of the Regulatory Impact Analysis	1-7
1.3.1	Establishing the Baseline for Evaluation of Proposed and Alternative Standards	1-8
1.3.2	Cost Analysis Approach	1-10
1.3.3	Benefits Analysis Approach	1-10
1.3.4	Welfare Benefits of Meeting the Primary and Secondary Standards	1-10
1.4	Organization of the Regulatory Impact Analysis	1-11
1.5	References	1-12
CHAPTER 2: AIR QUALITY MODELING AND METHODS	2-1
Overview	2-1
2.1	PM2.5 Characteristics	2-2
2.1.1	PM2.5 Size and Composition	2-2
2.1.2	PM2.5 Regional Characteristics	2-5
2.1.3	PM2.5 Trends	2-7
2.2	Modeling PM2.5 in the Future	2-12
2.2.1	Air Quality Modeling Platform	2-13
2.2.1.1	Model Configuration	2-13
2.2.1.2	Emission Inventory	2-15
2.2.1.3	Model Evaluation	2-17
2.2.2	Future-Year PM2.5 Design Values	2-18
2.3	Calculating Emission Reductions for Meeting the Existing and Alternative Standard Levels	2-20
2.3.1	Developing Air Quality Ratios	2-20
2.3.2	Emission Reductions to Meet 12/35	2-24
2.3.3	Emission Reductions to Meet Alternative Standards	2-26
2.3.4	Limitations of Using Air Quality Ratios	2-28
2.4	Description of Air Quality Challenges in Select Areas	2-29
2.4.1	Delaware County, PA	2-29
2.4.2	Border Areas	2-31
2.4.2.1	Imperial County, CA	2-31
2.4.2.2	Cameron and Hidalgo County, TX	2-33
2.4.3	Small Mountain Valleys in the West	2-35
2.4.4	California Areas	2-39
2.4.4.1	San Joaquin Valley, CA	2-39
2.4.4.2	South Coast Air Basin, CA	2-43
2.4.4.3	San Luis Obispo and Napa, CA	2-47
2.5	Calculating PM2.5 Concentration Fields for Standard Combinations	2-49
2.5.1	Creating the PM2.5 Concentration Field for 2032	2-49
2.5.2	Creating Spatial Fields Corresponding to Meeting Standards	2-51
2.6	References	2-53
APPENDIX 2A: ADDITIONAL AIR QUALITY MODELING INFORMATION	2A-1
Overview	2A-1
2A.1	2016 CMAQ Modeling	2A-3
2A.1.1	Model Configuration	2A-3
2A.1.2	Model Performance Evaluation	2A-5
2A.2	Projecting PM2.5 DVs to 2032	2A-20
2A.2.1	Monitoring Data for PM2.5 Projections	2A-21
2A.2.2	Future-Year PM2.5 Design Values	2A-40
2A.3	Developing Air Quality Ratios and Estimating Emission Reductions	2A-46
2A.3.1	Developing Air Quality Ratios for Primary PM2.5 Emissions	2A-47
2A.3.2	Developing Air Quality Ratios for NOx in Southern California	2A-51
2A.3.3	Developing Air Quality Ratios for NOx in SJV, CA	2A-53
2A.3.4	Applying Air Quality Ratios to Estimate Emission Reductions	2A-55
2A.3.4.1 Emission Reductions Needed to Meet 12/35	2A-56
2A.3.4.2 Emission Reductions Needed to Meet 10/35, 9/35, 8/35, and 10/30	2A-61
2A.4	Calculating PM2.5 Concentration Fields for Standard Combinations	2A-66
2A.4.1	Creating the PM2.5 Concentration Field for 2032	2A-66
2A.4.2	Creating Spatial Fields Corresponding to Meeting Standards	2A-68
2A.5	Calculating DV Impacts for Further EGU Emission Reductions	2A-70
2A.5.1	Estimating the Influence of Additional Primary PM2.5 EGU Reductions	2A-70
2A.5.2	Estimating the Regional Influence of Additional SO2 and NOx EGU Emission Reductions	2A-72
2A.5.3	Estimating the Local Influence of Additional SO2 and NOx EGU Emission Reductions	2A-75
2A.6	References	2A-78
CHAPTER 3: CONTROL STRATEGIES AND PM2.5 EMISSIONS REDUCTIONS	3-1
Overview	3-1
3.1	Preparing the 12/35 g/m[3] Analytical Baseline	3-4
3.2	Illustrative Control Strategies and PM2.5 Emissions Reductions from the Analytical Baseline	3-5
3.2.1	Estimating PM2.5 Emissions Reductions Needed for Annual and 24-hour Alternative Standard Levels Analyzed	3-6
3.2.2	Applying Control Technologies and Measures	3-10
3.2.3	Estimates of PM2.5 Emissions Reductions Resulting from Applying Control Technologies and Measures	3-15
3.2.4	Potential Influence of EGU Emissions Reductions from Planned Retirements	3-22
3.2.5	Estimates of PM2.5 Emissions Reductions Still Needed after Applying Control Technologies and Measures	3-24
3.2.6	Qualitative Assessment of the Remaining Air Quality Challenges and Emissions Reductions Potentially Still Needed	3-31
3.2.6.1	Delaware County, Pennsylvania (Northeast)	3-32
3.2.6.2	Border Areas (Southeast, California)	3-34
3.2.6.3	Small Mountain Valleys (West)	3-37
3.2.6.4	California Areas	3-39
3.3	Limitations and Uncertainties	3-43
3.4	References	3-46
APPENDIX 3A: CONTROL STRATEGIES AND PM2.5 EMISSIONS REDUCTIONS	3A-1
Overview	3A-1
3A.1	Types of Control Measures	3A-1
3A.1.1	PM Control Measures for Non-EGU Point Sources	3A-1
3A.1.2	PM Control Measures for Non-point (Area) Sources	3A-2
3A.2	EGU Trends Reflected in EPA's Integrated Planning Model (IPM) v6 Platform, Summer 2021 Reference Case Projections	3A-3
3A.3	 Applying Control Technologies and Measures	3A-4
CHAPTER 4: ENGINEERING COST ANALYSIS AND QUALITATIVE DISCUSSION OF SOCIAL COSTS	4-1
Overview	4-1
4.1	Estimating Engineering Costs	4-2
4.1.1	Methods, Tools, and Data	4-3
4.1.2	Cost Estimates for the Control Strategies	4-4
4.2	Limitations and Uncertainties in Engineering Cost Estimates	4-11
4.3	Social Costs	4-13
4.4	References	4-17
APPENDIX 4A: ENGINEERING COST ANALYSIS	4A-1
Overview	4A-1
4A.1	Estimated Costs by County for Alternative Standard Levels	4A-1
CHAPTER 5: BENEFITS ANALYSIS APPROACH AND RESULTS	5-1
Overview	5-1
5.1	Updated Methodology Presented in the RIA	5-5
5.2	Human Health Benefits Analysis Methods	5-6
5.2.1	Selecting Air Pollution Health Endpoints to Quantify	5-6
5.2.2	Calculating Counts of Air Pollution Effects Using the Health Impact Function	5-8
5.2.3	Calculating the Economic Valuation of Health Impacts	5-10
5.3	Benefits Analysis Data Inputs	5-11
5.3.1	Demographic Data	5-11
5.3.2	Baseline Incidence and Prevalence Estimates	5-12
5.3.3	Effect Coefficients	5-14
5.3.3.1	PM2.5 Premature Mortality Effect Coefficients for Adults	5-14
5.3.4	Unquantified Human Health Benefits	5-18
5.3.5	Unquantified Welfare Benefits	5-20
5.3.5.1	Visibility Impairment Benefits	5-23
5.3.6	Climate Effects of PM2.5	5-23
5.3.6.1	Climate Effects of Carbonaceous Particles	5-24
5.3.6.2	Climate Effects: Summary and Conclusions	5-25
5.3.7	Economic Valuation Estimates	5-26
5.4	Characterizing Uncertainty	5-26
5.4.1	Monte Carlo Assessment	5-27
5.4.2	Sources of Uncertainty Treated Qualitatively	5-28
5.5	Benefits Results	5-29
5.5.1	Benefits of the Applied Control Strategies for the Alternative Combinations of Primary PM2.5 Standard Levels	5-29
5.6	Discussion	5-36
5.7	References	5-39
APPENDIX 5A: BENEFITS OF THE PROPOSED AND ALTERNATIVE STANDARD LEVELS5A-1
Overview	5A-1
5A.1	Benefits of the Proposed and More Stringent Alternative Standard Levels of Primary PM2.5 Standards	5A-2
5A.2	 References	5A-8
CHAPTER 6: ENVIRONMENTAL JUSTICE	6-1
Introduction	6-1
6.1	Analyzing EJ Impacts in This Proposal	6-3
6.2	EJ Analysis of Exposures Under Current Standard and Alternative Standard Levels	6-6
6.2.1	Total Exposure	6-7
6.2.1.1	National	6-7
6.2.1.2	Regional	6-12
6.2.2	Exposure Changes	6-15
6.2.2.1	National	6-15
6.2.2.2	Regional	6-17
6.2.3	Proportional Changes in Exposure	6-20
6.2.3.1	National	6-21
6.2.3.2	Regional	6-22
6.3	EJ Analysis of Health Effects under Current Standards and Alternative Standard Levels
	6-23
6.3.1	Total Mortality Rates	6-25
6.3.1.1	National	6-25
6.3.1.2	Regional	6-26
6.3.2	Mortality Rate Changes	6-27
6.3.2.1	National	6-28
6.3.2.2	Regional	6-28
6.3.3	Proportional Changes in Mortality Rates	6-30
6.3.3.1	National	6-31
6.3.3.2	Regional	6-31
6.4	EJ Case Study of Exposure and Health Effects in Impacted Areas	6-32
6.4.1	Exposures	6-34
6.4.2	Mortality Rates	6-37
6.5	Summary	6-39
6.6	Environmental Justice Appendix	6-44
6.6.1	Input Information	6-44
6.6.1.1	EJ Exposure Analysis Input Data	6-44
6.6.1.2	EJ Health Effects Analysis Input Data	6-44
6.6.2	EJ Analysis of Total Exposures Associated with Meeting the Standards	6-47
6.6.2.1	National	6-47
6.6.2.2	Regional	6-49
6.6.3	EJ Analysis of Exposure Changes Associated with Meeting the Standards	6-53
6.6.3.1	National	6-53
6.6.3.2	Regional	6-55
6.6.4	Proportionality of Exposure Changes Associated with Meeting the Standards	6-58
6.6.4.1	National	6-58
6.6.4.2	Regional	6-59
6.6.5	EJ Analysis of Total Mortality Rates Associated with Meeting the Standards	6-61
6.6.5.1	National	6-61
6.6.5.2	Regional	6-62
6.6.6	EJ Analysis of Mortality Rate Change Associated with Meeting the Standards	6-64
6.6.6.1	National	6-64
6.6.6.2	Regional	6-65
6.6.7	Proportionality of Mortality Rate Changes Associated with Meeting the Standards
	6-67
6.6.7.1	National	6-67
6.6.7.2	Regional	6-68
6.7	References	6-70
CHAPTER 7: LABOR IMPACTS	7-1
Overview	7-1
7.1	Labor Impacts	7-1
7.2	References	7-6
CHAPTER 8: COMPARISON OF BENEFITS AND COSTS	8-1
Overview	8-1
8.1	Results	8-2
8.2	Limitations of Present Value Estimates	8-10
8.3	References	8-13
 LIST OF TABLESTABLE ES-1	Summary of PM2.5 Emissions Reductions Needed by Area in 2032 to Meet Current Primary Annual and 24-hour Standards of 12/35 g/m[3] (tons/year)	ES-7
Table ES-2	By Area, Summary of PM2.5 Emissions Reductions Needed, In Tons/Year and as Percent of Total Reduction Needed Nationwide, for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032	ES-9
Table ES-3	Summary of PM2.5 Estimated Emissions Reductions from CoST by Area for the Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	ES-11
Table ES-4	Summary of PM2.5 Emissions Reductions Still Needed by Area for the Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	ES-12
Table ES-5	By Area, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	ES-14
Table ES-6 	Estimated Avoided PM-Related Premature Respiratory Mortalities and Illnesses of the Control Strategies for the Alternative Primary PM2.5 Standard Levels for 2032 (95% Confidence Interval)	ES-17
Table ES-7	Estimated Monetized Benefits of the Control Strategies for Alternative Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 ug/m[3] (billions of 2017$)	ES-18
Table ES-8	Estimated Monetized Benefits by Region of the Control Strategies for the Alternative Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 ug/m[3] (billions of 2017$)	ES-19
Table ES-9	Summary of Counties by Bin that Still Need Emissions Reductions for Proposed Alternative Primary Standard Levels of 10/35 g/m[3] and 9/35 g/m[3]	ES-23
Table ES-10	Estimated Monetized Benefits, Costs, and Net Benefits of the Control Strategies Applied Toward the Primary Alternative Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 for the U.S. (millions of 2017$)	ES-26
Table ES-11	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies Applied Toward the Proposed Primary Alternative Standard Level of 10/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount rates)	ES-27
Table ES-12	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies Applied Toward the Proposed Primary Alternative Standard Level of 9/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount rates)	ES-28
Table 2-1	Annual and 24-Hour Air Quality Ratios for Primary PM2.5 Emissions	2-23
Table 2-2	Information on areas with challenging residential wood combustion issues	2-39
Table 2A-1	Definition of Statistics Used in the CMAQ Model Performance Evaluation	2A-8
Table 2A-2	CMAQ Performance Statistics for PM2.5 at AQS Sites in 2016	2A-10
Table 2A-3	CMAQ Performance Statistics for PM2.5 Sulfate at CSN and IMPROVE Sites in 2016
	2A-12
Table 2A-4	CMAQ Performance Statistics for PM2.5 Nitrate at CSN and IMPROVE Sites in 2016
	2A-14
Table 2A-5	CMAQ Performance Statistics for PM2.5 EC at CSN and IMPROVE Sites in 2016	2A-16
Table 2A-6	CMAQ Performance Statistics for PM2.5 OC at CSN and IMPROVE Sites in 2016	2A-18
Table 2A-7	Wildfire Episodes Identified and Counties Impacted by Screening (if above extreme value threshold of 61 ug m[-3])	2A-24
Table 2A-8	PM2.5 DVs for 2032 Projection and 12/35 Analytical Baseline for the Highest DVs in the County for Counties with Annual 2032 DVs Greater 8 g m[-3] or 24-hour 2032 DVs Greater than 30 g m[-3]	2A-43
Table 2A-9	Annual and 24-Hour Air Quality Ratios for Primary PM2.5 Emissions	2A-49
Table 2A-10	County Groups for Calculating Air Quality Ratios for NOx Emission Changes in Southern California	2A-52
Table 2A-11	2032 PM2.5 DVs and NOx-adjusted PM2.5 DVs for the Highest Annual and 24-Hour DV Monitors in South Coast Counties	2A-53
Table 2A-12	2032 PM2.5 DVs and NOx-adjusted PM2.5 DVs for the Highest Annual and 24-Hour DV Monitors in SJV Counties	2A-55
Table 2A-13	Summary of Primary PM2.5 Emissions Reductions by County Needed to Meet the Existing Standards (12/35) for Counties with 2032a Annual DVs greater than 8 g m-3 or 24-Hour DVs Greater than 30 g m-3	2A-57
Table 2A-14	Primary PM2.5 Emission Reductions Needed to Meet the Alternative Standard Levels of 10/35, 10/30, 9/35, and 8/35 Relative to the 12/35 Analytical Baseline	2A-62
Table 2A-15	Primary PM2.5 Emission Reductions from EGUs Expected beyond 2032 Modeling Case and Estimated Impact on DVs for Counties Exceeding Alternative Standards in the 2032 Case	2A-72
Table 2A-16	SO2 and NOx Emission Reductions from EGUs Expected Beyond 2032 Modeling Case by County	2A-73
Table 2A-17	2032 PM2.5 DVs and Estimated Influence of Emission Reductions from EGUs in Franklin and Jefferson, MO, and Randolph, IL on DVs in Nearby Counties	2A-76
Table 2A-18	2032 PM2.5 DVs and Estimated Influence of Emission Reductions from EGUs in Clermont and Hamilton, OH on DVs in Nearby Counties	2A-77
Table 3-1	Summary of PM2.5 Emissions Reductions Needed by Area in 2032 to Meet Current Primary Annual and 24-hour Standards of 12/35 g/m[3] (tons/year)	3-5
Table 3-2	By Area, Summary of PM2.5 Emissions Reductions Needed, in Tons/Year and as Percent of Total Reductions Needed Nationwide, for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032	3-8
Table 3-3	By Inventory Sector, Control Measures Applied in Analyses of the Current Standards and the Alternative Primary Standard Levels	3-14
Table 3-4	Summary of PM2.5 Estimated Emissions Reductions from CoST by Area for the Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	3-15
Table 3-5	Summary of PM2.5 Emissions and Estimated Emissions Reductions from CoST by Inventory Sector for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	3-17
Table 3-6	Summary of Estimated Emissions Reductions from CoST by Inventory Sector and Control Technology for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	3-19
Table 3-7	Summary of Estimated PM2.5 Emissions Reductions from CoST by Inventory Source Classification Code Sectors for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	3-21
Table 3-8	Summary of PM2.5 Emissions Reductions Still Needed by Area for the Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	3-26
Table 3-9	Summary of PM2.5 Emissions Reductions Still Needed by Area and by County for the Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	3-27
Table 3-10	Summary of Counties by Bin that Still Need Emissions Reductions for Proposed Alternative Primary Standard Levels of 10/35 g/m[3] and 9/35 g/m[3]	3-32
Table 3-11	Summary of Estimated PM2.5 Emissions Reductions Needed and Emissions Reductions Identified by CoST for the West for the Proposed Primary Standard Level of 9/35 g/m[3] in 2032 (tons/year)	3-37
Table 3A-1	By Area and Emissions Inventory Sector, Control Measures Applied in Analyses of the Current Standards and Alternative Primary Standard Levels	3A-8
Table 3A-2	Summary of PM2.5 Estimated Emissions Reductions from CoST for the Northeast (57 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	3A-11
Table 3A-3	Summary of PM2.5 Estimated Emissions Reductions from CoST for the Adjacent Counties in the Northeast (75 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)
	3A-13
Table 3A-4	Summary of PM2.5 Estimated Emissions Reductions from CoST for the Southeast (35 counties) for Alternative Primary Standard Levels of 10/35g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	3A-16
Table 3A-5	Summary of PM2.5 Estimated Emissions Reductions from CoST for the Adjacent Counties in the Southeast (32 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)
	3A-17
Table 3A-6	Summary of PM2.5 Estimated Emissions Reductions from CoST for the West (36 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	3A-18
Table 3A-7	Summary of PM2.5 Estimated Emissions Reductions from CoST for California (26 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 (tons/year)	3A-19
Table 3A-8 	Remaining PM2.5 Emissions and Potential Additional Reduction Opportunities
	3A-20
Table 4-1	By Area, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4-5
Table 4-2	By Emissions Inventory Sector, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4-7
Table 4-3	By Area and by Emissions Inventory Sector, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4-7
Table 4-4	By Control Technology, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4-9
Table 4-5	By Emissions Inventory Sector and Control Technology, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4-10
Table 4A-1	Summary of Estimated Annual Control Costs for the Northeast (57 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4A-1
Table 4A-2	Summary of Estimated Annual Control Costs for Adjacent Counties in the Northeast (75 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4A-3
Table 4A-3	Summary of Estimated Annual Control Costs for the Southeast (35 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4A-5
Table 4A-4	Summary of Estimated Annual Control Costs for Adjacent Counties in the Southeast (32 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4A-6
Table 4A-5	Summary of Estimated Annual Control Costs for the West (36 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4A-7
Table 4A-6	Summary of Estimated Annual Control Costs for California (26 counties) for Alternative Primary Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] for 2032 (millions of 2017$)	4A-8
Table 5-1	Estimated Monetized Benefits of the Applied Control Strategies for the Proposed and Alternative Combinations of Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)	5-4
Table 5-2	Human Health Effects of Pollutants Potentially Affected by Attainment of the Primary PM2.5 NAAQS	5-8
Table 5-3 	Causal determinations for relationships between criteria pollutants and ecological effects from the 2020 ISA	5-21
Table 5-4 	Estimated Avoided PM-Related Premature Respiratory Mortalities and Illnesses of the Applied Control Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (95% Confidence Interval)	5-32
Table 5-5 	Monetized PM-Related Premature Respiratory Mortalities and Illnesses of the Applied Control Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 3% discount rate; 95% Confidence Interval)	5-33
Table 5-6 	Monetized PM-Related Premature Respiratory Mortalities and Illnesses of the Applied Control Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 7% discount rate; 95% Confidence Interval)	5-34
Table 5-7	Estimated Monetized Benefits of the Applied Control Strategies for the Proposed and More Stringent Alternative Combinations of Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)	5-35
Table 5-8	Estimated Monetized Benefits by Region of the Applied Control Strategies for the Proposed and More Stringent Alternative Combinations of Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)	5-36
Table 5A-1 	Estimated Avoided PM-Related Premature Mortalities and Illnesses of Meeting the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (95% Confidence Interval)	5A-3
Table 5A-2 	Monetized Avoided PM-Related Premature Mortalities and Illnesses of Meeting the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 3% discount rate; 95% Confidence Interval)	5A-4
Table 5A-3 	Monetized Avoided PM-Related Premature Mortalities and Illnesses of Meeting the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 7% discount rate; 95% Confidence Interval)	5A-5
Table 5A-4	Total Estimated Monetized Benefits of Meeting the Proposed and More Stringent Alternative Primary Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)	5A-6
Table 5A-5	Total Estimated Monetized Benefits by Region of Meeting the Proposed and More Stringent Alternative Primary Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)	5A-7
Table 6-1 	Populations Included in the PM2.5 Exposure Analysis	6-7
Table 6-2	Hazard Ratios, Beta Coefficients, and Standard Errors (SE) from Di et al., 2017	6-45
Table 7-1  	Baseline Industry Employment	7-3
Table 7-2  	Employment per $1 Million Output (2017$) by Industry (4-digit NAICS)	7-5
Table 8-1	Estimated Monetized Benefits, Costs, and Net Benefits of the Control Strategies Applied Toward Primary Alternative Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3] in 2032 for the U.S. (millions of 2017$)	8-3
Table 8-2	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs of the Control Strategies Applied Toward the Primary Alternative Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3] 8/35 g/m3 (millions of 2017$, 2032-2051, discounted to 2022, 3 percent discount rate)	8-5
Table 8-3	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs of the Control Strategies Applied Toward the Primary Alternative Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3] 8/35 g/m3 (millions of 2017$, 2032-2051, discounted to 2022, 7 percent discount rate)	8-6
Table 8-4	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Benefits of the Control Strategies Applied Toward the Primary Alternative Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3] 8/35 g/m3 (millions of 2017$, 2032-2051, discounted to 2022, 3 percent discount rate)	8-7
Table 8-5	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Benefits of the Control Strategies Applied Toward the Primary Alternative Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3] 8/35 g/m3 (millions of 2017$, 2032-2051, discounted to 2022, 7 percent discount rate)	8-8
Table 8-6	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies Applied Toward the Proposed Primary Alternative Standard Level of 10/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount rates)	8-9
Table 8-7	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies Applied Toward the Proposed Primary Alternative Standard Level of 9/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount rates)	8-10
 LIST OF FIGURESFIGURE ES-1	Geographic Areas Used in Analysis	ES-6
Figure ES-2	Counties Projected to Exceed in Analytical Baseline for Alternative Standard Levels of 10/35 g/m[3], 9/35 g/m[3], and 8/35 g/m[3]	ES-9
Figure ES-3	Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative Standard Level of 10/35 g/m[3]	ES-12
Figure ES-4	Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative Standard Level of 9/35 g/m[3]	ES-13
Figure 2-1	Annual Average PM2.5 Concentrations over the U.S. in 2019 Based on the Hybrid Satellite Modeling Approach of van Donkelaar et al. (2021)	2-5
Figure 2-2	Seasonally Weighted Annual Average PM2.5 Concentrations in the U.S. from 2000 to 2019 (406 sites)	2-8
Figure 2-3	National Emission Trends of PM2.5, PM10, and Precursor Gases from 1990 to 2017
	2-8
Figure 2-4	Annual Anthropogenic Source Sector Emission Totals (1000 tons per year) for NOx, SO2, and PM2.5 for 2016 and 2032	2-10
Figure 2-5	Gridded PM2.5 Concentrations over Selected Urban Areas Based on the 2032 Modeling Case Described Below with the Enhanced Voronoi Neighbor Averaging Approach	2-12
Figure 2-6	Map of the Outer 36US3 (36 x 36 km Horizontal Resolution) and Inner 12US2 (12 x 12 km Horizontal Resolution) Modeling Domains	2-14
Figure 2-7	Regional Groupings for Calculating Air Quality Ratios	2-22
Figure 2-8	Counties with Projected 2032 PM2.5 DVs that Exceed the 24-Hour (24-hr Only), Annual (Annual Only) or Both (Both) Existing Standards (12/35 g m[-3])	2-25
Figure 2-9	Counties with PM2.5 DVs that Exceed Alternative Annual (Annual Only), 24-Hour (24-hr Only), or Both (Both) Standards in the 12/35 Analytical Baseline	2-27
Figure 2-10	Total Primary PM2.5 Emission Reductions Needed to Meet the Alternative Standard Levels of 10/35, 10/30, 9/35, and 8/35 Relative to the 12/35 Analytical Baseline in the Eastern and Western U.S.	2-28
Figure 2-11	Location of the Chester Site in Relation to the Evonik Degussa and PQ Corporation Facilities	2-30
Figure 2-12	Imperial County and the Nonattainment Area	2-32
Figure 2-13	Nighttime Aerial View of Calexico, CA and Mexicali, MX	2-32
Figure 2-14	Annual Source Sector Emission Totals (1000 tons per year) for PM2.5 for 2016 and 2032 in Imperial County	2-33
Figure 2-15	Location of Mission and Brownsville Monitors in Hidalgo and Cameron County, respectively, with Annual Wind Patterns from Meteorological Measurements	2-34
Figure 2-16	Annual Source Sector Emission Totals (1000 tons per year) for PM2.5 for 2016 and 2032 in Cameron and Hidalgo County Combined	2-35
Figure 2-17	Air Pollution Layer Associated with a Temperature Inversion in Missoula, MT in November 2018	2-36
Figure 2-18	Plumas County, CA (Grey), Portola Nonattainment Area (Red), and City of Portola (Purple)	2-37
Figure 2-19	Lincoln County, MT (Grey), Libby Nonattainment Area (Red), and City of Libby (Purple)	2-37
Figure 2-20	San Joaquin Valley Nonattainment Area and Location of Highest PM2.5 Monitor in Bakersfield (06-029-0016)	2-40
Figure 2-21	Recent Annual PM2.5 DVs at the Highest SJV Monitor for Design Value Periods (e.g., 11-13: 2011-2013).  Dashed line is the 2012 Annual PM2.5 NAAQS Level (12 g m[-3])	2-41
Figure 2-22	Decrease in the Number of Days SJV Exceeded the 24-hr NAAQS Level (35 g m[-3])
	2-41
Figure 2-23	Annual Source Sector PM2.5 Emission Totals in SJV Counties for 2032 Modeling Case	2-43
Figure 2-24	South Coast Air Basin Nonattainment Area and Locations of Highest PM2.5 Monitors in Los Angeles (06-037-4008), Riverside (06-065-8005), and San Bernardino (06-071-0027)	2-44
Figure 2-25	Recent Annual PM2.5 DVs at the Highest South Coast Monitor for Design Value Periods (e.g., 11-13: 2011-2013).  Dashed line is the 2012 Annual PM2.5 NAAQS Level (12 g m[-3])	2-45
Figure 2-26	Annual Source Sector PM2.5 Emission Totals in the SoCAB Counties for 2032 Modeling Case	2-46
Figure 2-27	San Luis Obispo County and Location of Highest PM2.5 Monitor in Arroyo Grande (06-079-2007)	2-47
Figure 2-28	Recent and Projected Annual PM2.5 DVs at the Arroyo Grande Monitor (06-079-2007) in San Luis Obispo County for DV Periods (e.g., 11-13: 2011-2013; 32-32: Projected 2032 DV)	2-48
Figure 2-29	Napa County and Location of PM2.5 Monitor (06-055-0003)	2-49
Figure 2-30	PM2.5 Concentration for 2032 based on eVNA Method	2-51
Figure 2-31	PM2.5 Concentration Improvement Associated with Meeting 12/35 Relative to the 2032 case	2-52
Figure 2A-1	Map of the Outer 36US3 (36 x 36 km Horizontal Resolution) and Inner 12US2 (12 x 12 km Horizontal Resolution) Modeling Domains Used for the PM NAAQS RIA	2A-5
Figure 2A-2	U.S. Climate Regions (Karl and Koss, 1984) Used in the CMAQ Model Performance Evaluation	2A-8
Figure 2A-3	Comparison of CMAQ Predictions of PM2.5 and Observations at AQS Sites for County Highest PM2.5 Monitors with 2032 PM2.5 DVs Greater than 8/30	2A-9
Figure 2A-4	NMB in 2016 CMAQ Predictions of PM2.5 Components at CSN and IMPROVE Sites
	2A-11
Figure 2A-5	NMB in 2016 CMAQ Predictions of PM2.5 Components at CSN and IMPROVE Sites for Monitors in Counties with 2032 PM2.5 DVs Greater than 8/30	2A-12
Figure 2A-6	Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from the Camp Fire on 11/10/2018	2A-28
Figure 2A-7	Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from the North Bay/Wine Country Fires on 10/09/2017	2A-28
Figure 2A-8	Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from Fires Across the Pacific Northwest/Northern California on 08/29/2017	2A-29
Figure 2A-9 	Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from Fires in Washington and Oregon on 08/09/2018	2A-29
Figure 2A-10	Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from Fires in Montana on 08/19/2018	2A-30
Figure 2A-11	Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from Fires in Montana, Washington and Idaho on 08/22/2015	2A-30
Figure 2A-12	Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from the 416/Burro Complex Fires on 06/10/2018	2A-31
Figure 2A-13	Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from the Butte Fire on 09/11/2015	2A-31
Figure 2A-14	Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from the Carr/Mendocino/Ferguson Fires on 08/04/2018	2A-32
Figure 2A-15	Visible Satellite Imagery from NASA's Worldview Platform Showing Smoke from Fires in the Appalachians on 11/10/2016	2A-32
Figure 2A-16	Daily PM2.5 (in ug m[-3]) from a Subset of Monitors Impacted by the Camp Fire in November 2018	2A-33
Figure 2A-17	Daily PM2.5 (in ug m[-3]) from a Subset of Monitors Impacted by the North Bay/Wine Country Fires in October 2017	2A-34
Figure 2A-18	Daily PM2.5 (in ug m[-3]) from a Subset of Monitors Impacted by Fires in the Pacific Northwest/Northern California in August-September 2017	2A-35
Figure 2A-19	Daily PM2.5 (in ug m[-3]) from a Subset of Monitors Impacted by Fires in Washington and Oregon in July-August 2018	2A-36
Figure 2A-20 	Daily PM2.5 (in ug m[-3]) from the Monitors Impacted by Fires and Smoke in Montana in August 2018	2A-37
Figure 2A-21 	Daily PM2.5 (in ug m[-3]) from a Subset of Monitors Impacted by Fires in Montana, Washington and Idaho in August 2015	2A-38
Figure 2A-22	Daily PM2.5 (in ug m[-3]) from the Monitor in Plata, CO Impacted by the 416/Burro Fire Complex in June 2018	2A-38
Figure 2A-23	Daily PM2.5 (in ug m[-3]) from the Two monitors Impacted by the Butte Fire in September 2015	2A-39
Figure 2A-24	Daily PM2.5 (in ug m[-3]) from a Subset of Monitors Impacted by the Carr/Mendocino/Ferguson Fires in August 2018	2A-39
Figure 2A-25 	Daily PM2.5 (in ug m[-3]) from a Subset of Monitors Impacted by Fires in the Appalachians in November 2016	2A-40
Figure 2A-26	Counties with Projected 2032 PM2.5 DVs that Exceed the 24-Hour (24-hr Only), Annual (Annual Only) or Both the 24-Hour and Annual (Both) Standards for the Combination of Existing Standards (12/35)	2A-41
Figure 2A-27	Counties with PM2.5 DVs in the 12/35 Analytical Baseline that Exceed the 24-Hour (24-hr Only), Annual (Annual Only) or Both the 24-Hour and Annual (Both) Standards for the Combination of Existing Standards	2A-42
Figure 2A-28	Counties with 50% Reduction in Anthropogenic Primary PM2.5 Emissions in 2028 Sensitivity Modeling	2A-48
Figure 2A-29	Regional Groupings for Calculating Air Quality Ratios	2A-49
Figure 2A-30	Counties Used in Estimating the Relative Impact of Emissions in Core and Neighboring Counties	2A-50
Figure 2A-31	Counties with 50% Reduction in Anthropogenic NOx Emissions in 2028 Sensitivity Modeling	2A-52
Figure 2A-32	Total Primary PM2.5 Emission Reductions Needed to Meet the Alternative Standard Levels of 10/35, 10/30, 9/35, and 8/35 Relative to the 12/35 Analytical Baseline in the East and West	2A-62
Figure 2A-33	PM2.5 Concentration for 2032 based on eVNA Method	2A-68
Figure 2A-34	PM2.5 Concentration Improvement Associated with Meeting 12/35 Relative to the 2032 Case	2A-69
Figure 2A-35 	PM2.5 Counties with 50% Reductions of SO2 Emissions in the 2028 CMAQ Sensitivity Simulations (Green) and Eastern States Considered in the EGU Sensitivity Analysis (Red)	2A-74
Figure 2A-36	Distributions of the Estimated Changes in Annual PM2.5 DVs in the Eastern U.S. Associated with NOx and SO2 EGU Emission Reductions in the Eastern US Beyond the 2032 Modeling Case	2A-75
Figure 2A-37	County Group in 2028 Sensitivity Modeling Used in Estimating the Response of DVs to EGU Emission Changes in Franklin and Jefferson, MO, and Randolph, IL	2A-76
Figure 2A-38	County Group in 2028 Sensitivity Modeling Used in Estimating the Response of DVs to EGU Emission Changes in Clermont and Hamilton, OH	2A-77
Figure 3-1	Geographic Areas Used in Analysis	3-4
Figure 3-2	Counties Projected to Exceed in Analytical Baseline for Alternative Standard Levels of 10/35 g/m[3], 9/35 g/m[3], and 8/35 g/m[3]	3-8
Figure 3-3	Counties Projected to Exceed in Analytical Baseline for Alternative Standard Levels of 10/30 g/m[3]	3-9
Figure 3-4	PM2.5 Emissions Reductions and Costs Per Ton (CPT) in 2032 (tons, 2017$)	3-13
Figure 3-5	Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative Standard Level of 10/35 g/m[3]	3-29
Figure 3-6	Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative Standard Level of 9/35 g/m[3]	3-29
Figure 3-7	Counties that Still Need PM2.5 Emissions Reductions for More Stringent Alternative Standard Level of 8/35 g/m[3]	3-30
Figure 3-8	Counties that Still Need PM2.5 Emissions Reductions for More Stringent Alternative Standard Level of 10/30 g/m[3]	3-30
Figure 5-1	Data Inputs and Outputs for the BenMAP-CE Model	5-11
Figure 6-1	Heat Map of National Average Annual PM2.5 Concentrations (ug/m[3]) by Demographic for Current and Alternative PM NAAQS Levels (10/35, 10/30, 9/35, and 8/35) After Application of Controls	6-9
Figure 6-2	National Distributions of Annual PM2.5 Concentrations by Demographic for Current and Alternative PM NAAQS Levels After Application of Controls	6-12
Figure 6-3	Heat Map of Regional Average Annual PM2.5 Concentrations (ug/m[3]) by Demographic for Current (12/35) and Alternative PM NAAQS Levels (10/35, 10/30, 9/35, and 8/35) After Application of Controls	6-13
Figure 6-4	Regional Distributions of Annual PM2.5 Concentrations by Demographic for Current and Alternative PM NAAQS Levels After Application of Controls	6-14
Figure 6-5	Heat Map of National Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls	6-16
Figure 6-6	National Distributions of Annual PM2.5 Concentration Reductions for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls	6-17
Figure 6-7	Heat Map of Regional Reductions in PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls	6-18
Figure 6-8	Regional Distributions of Total PM2.5 for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls	6-19
Figure 6-9	Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls	6-22
Figure 6-10	Heat Map of Regional Percent Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current (12/35) to Alternative PM NAAQS Level (10/35, 10/30, 9/35, and 8/35 After Application of Controls	6-23
Figure 6-11	Heat Map of National Average Annual Total Mortality Rates (per 100K) for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls	6-26
Figure 6-12	National Distributions of Total Annual Mortality Rates for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls	6-26
Figure 6-13	Heat Map of Regional Average Annual Total Mortality Rates (per 100K) for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls	6-27
Figure 6-14	Regional Distributions of Total Annual Mortality Rates for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls	6-27
Figure 6-15	Heat Map of National Average Annual Mortality Rate Reductions (per 100k) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls	6-28
Figure 6-16	National Distributions of Annual Mortality Rate Reductions for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls	6-28
Figure 6-17	Heat Map of Regional Average Annual Mortality Rate Reductions (per 100k) for Demographic Groups When Moving from Current and Alternative PM NAAQS Levels After Application of Controls	6-29
Figure 6-18	Regional Distributions of Annual Mortality Rate Reductions for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls	6-30
Figure 6-19	Heat Map of National Average Percent Mortality Rate Reductions (per 100k People) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls	6-31
Figure 6-20	Heat Map of Regional Average Percent Mortality Rate Reductions (per 100k) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls	6-31
Figure 6-21	Map of Areas in which PM2.5 Concentrations Change when Moving from 12/35 to 9/35 After Application of Controls	6-33
Figure 6-22	Heat Map of National Average Annual PM2.5 Concentrations and Concentration Changes (ug/m[3]) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of Areas that Do and Do Not Experience Changes in Air Quality When Moving from 12/35 to 9/35	6-35
Figure 6-23	Heat Map of Regional Average Annual PM2.5 Concentrations and Concentration Changes (ug/m[3]) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of Areas that Do and Do Not Experience Changes in Air Quality When Moving from 12/35 to 9/35	6-36
Figure 6-24	Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations for Demographic Groups in the Subset of Areas in which PM2.5 Concentrations Change When Moving from 12/35 to 9/35	6-37
Figure 6-25	Heat Map of National Average Annual Total Mortality Rates and Mortality Rate Reductions (per 100K) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of Areas that Do and Do Not Experience Changes in Air Quality when Moving from 12/35 to 9/35	6-38
Figure 6-26	Heat Map of Regional Average Annual Total Mortality Rates and Mortality Rate Reductions (per 100K) by Demographic for 12/35 9/35, and 12/35-9/35, in the Subset of Areas that Do and Do Not Change When Moving from 12/35-9/35	6-38
Figure 6-27	Heat Map of National and Regional Percent Reductions in Average Annual Total Mortality Rates (per 100K) by Demographic in the Subset of Areas in which PM2.5 Concentrations Change When Moving from 12/35-9/35	6-39
Figure 6-28	Heat Map of National Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies (Controls) or with Meeting the Standards (Standards) by Demographic for Current (12/35) and Alternative PM NAAQS Levels (10/35, 10/30, 9/35, and 8/35)	6-48
Figure 6-29	National Distributions of Annual PM2.5 Concentrations Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels	6-49
Figure 6-30	Heat Map of Regional Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels	5-51
Figure 6-31	Regional Distributions of Annual PM2.5 Concentrations Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels	6-52
Figure 6-32	Heat Map of National Average Annual Reductions in PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels	6-53
Figure 6-33	National Distributions of Annual Reductions in PM2.5 Concentrations Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels	6-54
Figure 6-34	Heat Map of National Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels	6-56
Figure 6-35	National Distributions of Reductions in Annual PM2.5 Concentrations Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels	6-57
Figure 6-36	Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving From Current to Alternative PM NAAQS Levels	6-58
Figure 6-37	Heat Map of Regional Percent Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving From Current to Alternative PM NAAQS Levels	6-60
Figure 6-38	Heat Map of National Average Annual Total Mortality Rates (per 100K People) Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels	6-61
Figure 6-39	National Distributions of Total Mortality Rates Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels	6-62
Figure 6-40	Heat Map of Regional Average Annual Total Mortality Rates (per 100K People) Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels	6-62
Figure 6-41	Regional Distributions of Total Mortality Rates Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels	6-63
Figure 6-42	Heat Map of National Average Annual Total Mortality Rate Reductions (per 100K People) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels	6-64
Figure 6-43	National Distributions of Annual Total Mortality Rate Reductions Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels	6-65
Figure 6-44	Heat Map of Regional Average Annual Total Mortality Rate Reductions (per 100K People) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels	6-65
Figure 6-45	Regional Distributions of Average Annual Total Mortality Rate Reductions Associated Either with Control Strategies or with Meeting the Standards by Demographic for When Moving from Current to Alternative PM NAAQS Levels	6-66
Figure 6-46	Heat Map of National Percent Changes in Average Mortality Rate Reductions Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels	6-67
Figure 6-47	Heat Map of Regional Percent Reductions in Average Mortality Rate Reductions Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels	6-69
 EXECUTIVE SUMMARY
 Overview of the Proposal
In setting primary and secondary national ambient air quality standards (NAAQS), the Environmental Protection Agency's (EPA) responsibility under the law is to establish standards that protect public health and welfare. The Clean Air Act (CAA) requires the EPA, for each criteria pollutant, to set standards that protect public health with "an adequate margin of safety" and public welfare from "any known or anticipated adverse effects." As interpreted by the Agency and the courts, the CAA requires the EPA to base the decisions for primary standards on health considerations only; economic factors cannot be considered. The prohibition against considering cost in the setting of the primary air quality standards does not mean that costs, benefits, or other economic consequences are unimportant. The Agency believes that consideration of costs and benefits is an essential decision-making tool for the efficient implementation of these standards. The impacts of costs, benefits, and efficiency are considered by the States when they make decisions regarding what timelines, strategies, and policies are appropriate for their circumstances.
On June 10, 2021, the EPA announced its decision to reconsider the 2020 Particulate Matter (PM) NAAQS final action. The EPA is reconsidering the December 2020 decision because the available scientific evidence and technical information indicate that the current standards may not be adequate to protect public health and welfare, as required by the CAA. The EPA has concluded that the existing annual primary PM2.5 standard for PM, set at a level of 12.0 ug/m[3], is not requisite to protect public health with an adequate margin of safety. The EPA Administrator is proposing to revise the existing standard to provide increased public health protection. Specifically, the EPA Administrator is proposing to revise the level of the standard within the range of 9-10 ug/m[3], while soliciting comment on levels down to 8 ug/m[3] and up to 11 ug/m[3]. The primary 24-hour PM2.5 standard provides protection against exposures to short-term "peak" concentrations of PM2.5 in ambient air. The EPA Administrator is proposing to retain the primary 24-hour PM2.5 standard at its current level of 35 ug/m[3] and is soliciting comment on revising the level of the standard to as low as 30 ug/m[3].
The EPA has also concluded that the existing secondary PM standards are requisite to protect public welfare from known or anticipated effects and is proposing to retain the secondary standards for PM. Specifically, for the secondary annual PM2.5 standard, the EPA Administrator is proposing to retain the existing standard of 15.0 ug/m[3]. For the secondary 24-hour PM2.5 standard, the EPA Administrator is proposing to retain the existing standard of 35 ug/m[3]; however, the Administrator is soliciting comment on revising the level of the standard to as low as 30 ug/m[3]. For the secondary 24-hour PM10 standard, the EPA Administrator is proposing to retain the existing standard of 150 ug/m[3].

In addition, the EPA is proposing to change the Air Quality Index (AQI) xxxx and the monitoring expectations (from 5-7 days) and add the new criterion for monitor siting. These proposed changes are apart from the health based standard and the agency is providing separate analysis of potential effects apart from the illustrative analysis for the primary and secondary standards.
 Overview of the Regulatory Impact Analysis
Per Executive Orders 12866 and 13563 and the guidelines of the Office of Management and Budget's (OMB) Circular A-4, in this Regulatory Impact Analysis (RIA) we are analyzing the proposed annual and current 24-hour alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/m[3], as well as the following two more stringent alternative standard levels: (1) an alternative annual standard level of 8 g/ - m - [3] -  in combination with the current 24-hour standard (i.e., 8/35 g/ - m - [3] - ), and (2) an alternative 24-hour standard level of 30 g/ - m - [3] in combination with the proposed annual standard level of 10 g/ - m - [3] -  (i.e., 10/30 g/ - m - [3] - ). Because the EPA is proposing that the current secondary PM standards be retained, we did not evaluate alternative secondary standard levels. The RIA includes the following chapters: Chapter 2: Emissions, Air Quality Modeling and Methods; Chapter 3: Control Strategies and PM2.5 Emissions Reductions; Chapter 4: Engineering Cost Analysis and Social Costs; Chapter 5: Benefits Analysis Approach and Results; Chapter 6: Environmental Justice Impacts; Chapter 7: Labor Impacts;  Chapter 8: Comparison of Benefits and Costs; chapter 9: AQI, monitoring requirements and their effects. 
The RIA presents estimates of the costs and benefits of applying illustrative national control strategies in 2032 after implementing existing and expected regulations and assessing emissions reductions to meet the current annual and 24-hour particulate matter NAAQS (12/35 g/ - m - [3]). The selection of 2032 as the analysis year in the RIA does not predict or prejudge attainment dates that will ultimately be assigned to individual areas under the CAA. The CAA contains a variety of potential attainment dates and flexibility to move to later dates, provided that the date is as expeditious as practicable. For the purposes of this analysis, the EPA assumes that it would likely finalize designations for the proposed particulate matter NAAQS in late 2024. Furthermore, also for the purposes of this analysis and depending on the precise timing of the effective date of those designations, the EPA assumes that nonattainment areas classified as Moderate would likely have to attain in late 2032. As such, we selected 2032 as the primary year of analysis. 
      The analyses in this RIA rely on national-level data (emissions inventory and control measure information) for use in national-level assessments (air quality modeling, control strategies, environmental justice, and benefits estimation). However, the ambient air quality issues being analyzed are highly complex and local in nature, and the results of these national-level assessments therefore contain uncertainty. It is beyond the scope of this RIA to develop detailed local information for the areas being analyzed, including populating the local emissions inventory, obtaining local information to increase the resolution of the air quality modeling, and obtaining local information on emissions controls, all of which would reduce some of the uncertainty in these national-level assessments. For example, having more refined data would be ideal for agricultural dust and burning, prescribed burning, and non-point (area) sources due to their large contribution to primary PM2.5 emissions and the limited availability of emissions controls. 
 ES.1		Design of the Regulatory Impact Analysis
The goal of this RIA is to provide estimates of the potential costs and benefits of the illustrative national control strategies in 2032. Because States are ultimately responsible for implementing strategies to meet alternative standard levels, this RIA provides insights and analysis of a limited number of illustrative control strategies that states might adopt to implement a proposed standard level. 
We developed our projected baselines for emissions and air quality for 2032. To estimate the costs and benefits of the illustrative national control strategies for the proposed and more stringent annual and 24-hour PM2.5 alternative standard levels, we first prepared an analytical baseline for 2032 that assumes full compliance with the current standards of 12/35 g/m[3]. From that analytical baseline, we estimate PM2.5 emissions reductions needed to reach the proposed and alternative annual and 24-hour PM2.5 standard levels and then analyze illustrative control strategies that areas might employ. 
Because PM2.5 concentrations are most responsive to direct PM emissions reductions, for the illustrative control strategies we analyze direct, local PM2.5 emissions reductions by individual counties. For the eastern U.S. where counties are relatively small and terrain is relatively flat, we identified potential PM2.5 emissions reductions within each county and in adjacent counties within the same state, where needed. As discussed in Chapter 3, Section 3.2.2, when we applied the emissions reductions from adjacent counties, we used a g/m[3] per ton PM2.5 air quality ratio that was four times less responsive than the ratio used when applying in-county emissions reductions. Because the counties in the western U.S. are generally large and the terrain is more complex, we only identified potential PM2.5 emissions reductions within each county. 
We then prepare illustrative control strategies. We apply end-of-pipe control technologies to non-electric generating unit (non-EGU) stationary sources (e.g., fabric filters, electrostatic precipitators, venturi scrubbers) and control measures to nonpoint (area) sources (e.g., installing controls on charbroilers), to residential wood combustion sources (e.g., converting woodstoves to gas logs), and for area fugitive dust emissions (e.g., paving unpaved roads) in analyzing PM2.5 emissions reductions. The estimated PM2.5 emissions reductions from these control applications do not fully account for all the emissions reductions needed to reach the proposed and more stringent alternative standard levels in some counties in the northeast, southeast, west, and California. In Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6, we discuss the remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California for the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - ; the areas include a county in Pennsylvania affected by local sources, counties in border areas, counties in small western mountain valleys, and counties in California's air basins and districts. The characteristics of the air quality challenges for these areas include features of local source-to-monitor impacts, cross-border transport, effects of complex terrain in the west, and identifying wildfire influence on projected PM2.5 DVs that could potentially qualify for exclusion as atypical, extreme, or unrepresentative events (U.S. EPA, 2019a). Lastly, we estimate the engineering costs and human health benefits associated with the illustrative control strategies, as well as assess environmental justice considerations.
Chapter 2, Section 2.1.3, includes discussions of historical and projected emissions trends for direct PM2.5 and precursor emissions (i.e., SO2, NOx, VOC, and ammonia), as well as of the "urban increment" of consistently higher PM2.5 concentrations over urban areas. We did not apply controls to EGUs or mobile sources beyond what is reflected in the projections between 2016 and 2032. The projections reflect SO2 and NOX emissions decreases between 2016 and 2032 -- over this period (1) NOX emissions are projected to decrease by 3.8 million tons (40 percent), with the greatest reductions from mobile source and EGU emissions inventory sectors, and (2) SO2 emissions are projected to decrease by 1 million tons (38 percent), with the greatest reductions from the EGU emissions inventory sector. 
    ES.1.1	 Establishing the Analytical Baseline
To project air quality to the future, the Community Multiscale Air Quality Modeling System (CMAQ) model was applied to simulate air quality over the U.S. during 2016 and for a case with emissions representative of 2032. In the 2032 projections, PM2.5 design values (DVs) exceeded the current standards for some counties in the west. As described in Chapter 2, Section 2.3.2, we adjusted the PM2.5 DVs for 2032 to account for emissions reductions needed to attain the current annual and 24-hour PM2.5 standards of 12/35 g/m - [3] to form the 12/35 g/ - m - [3] -  analytical baseline; it is from this baseline that we estimate the incremental costs and benefits associated with control strategies for the proposed and more stringent alternative standard levels relative to the current standards. The analytical baseline reflects, among other existing regulations, the Revised Cross-State Air Pollution Rule Update, the Safer Affordable Fuel Efficient (SAFE) Vehicles Final Rule for Model Years 2021-2026, the Standards of Performance for Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary Sources: EGUs, and the Mercury and Air Toxics Standards. For a more complete list of regulations, please see Chapter 2, Section 2.2.1.   
We present results throughout the RIA by northeast, southeast, west, and California, and Figure ES-1 includes a map of the U.S. with these areas identified. Table ES-1 presents a summary of the PM2.5 emissions reductions needed by area to meet the current standards to form the 12/35 g/ - m - [3] -  analytical baseline.
                                       
Figure ES-1	Geographic Areas Used in Analysis

Table ES-1	Summary of PM2.5 Emissions Reductions Needed by Area in 2032 to Meet Current Primary Annual and 24-hour Standards of 12/35 g/ - m - [3] -  (tons/year)
Area
                                     12/35
Northeast
                                       0
Southeast
                                       0
West
                                     2,298
CA
                                     6,907
Total
                                     9,205
Eighteen counties need PM2.5 emissions reductions to meet the current standards in 2032  -  9 counties in California and 9 counties in the west. The counties in California include several counties in the San Joaquin Valley Air Pollution Control District and the South Coast Air Quality Management District, as well as Plumas County in Northern California and Imperial County in southern California. No counties in the northeast or southeast U.S. need PM2.5 emissions reductions to meet the current annual and 24-hour standards.
    ES.1.2 	Estimating PM2.5 Emissions Reductions Needed for Annual and 24-hour Alternative Standard Levels Analyzed 
      We apply regional PM2.5 air quality ratios to estimate PM2.5 DVs at air quality monitor locations and then again to estimate the emissions reductions needed to reach the proposed and more stringent annual and 24-hour alternative standard levels analyzed. To develop air quality ratios that relate the change in DV in a county to the change in primary PM2.5 emissions in that county, we performed air quality sensitivity modeling with reductions in primary PM2.5 emissions in selected counties. More specifically, we conducted a 2028 CMAQ sensitivity modeling simulation with 50 percent reductions in primary PM2.5 emissions from anthropogenic sources in counties with annual 2028 DVs greater than 8 g/m[3]. We divided the change in annual and 24-hour PM2.5 DVs in these counties by the change in emissions in the respective counties to determine the air quality ratio at individual monitors. 
      We developed representative air quality ratios for regions of the U.S. from the ratios at individual monitors as in the 2012 PM - 2.5 NAAQS review (U.S. EPA, 2012). These regions are shown in Chapter 2, Figure 2-7, and the air quality ratios for primary PM2.5 emissions used in estimating the emission reductions needed to just meet the alternative standard levels analyzed are listed in Chapter 2, Table 2-1. We estimated the emissions reductions needed to just meet the alternative standard levels analyzed using the primary PM2.5 air quality ratios in combination with the required incremental change in concentration. Chapter 2, Section 2.3.1 includes a brief discussion of developing air quality ratios and estimated emissions reductions needed to just meet the alternative standard levels analyzed, and Appendix 2A, Section 2A.3 includes more detailed discussions.
Table ES-2 presents a summary of the estimated emissions reductions needed by area to reach the annual and 24-hour alternative standard levels. For each alternative standard level, Table ES-2 also includes an area's percent of the total estimated emissions reductions needed nationwide to reach that alternative standard level in all locations. For example, for the proposed standard level of 10/35 g/m[3], California's 10,128 estimated tons needed is 81 percent of the total estimated emissions reductions needed nationwide to meet 10/35 g/m[3]. See Appendix 2A, Table 2A-14 for the estimated PM2.5 emissions reductions, from the analytical baseline, needed by county for the alternative standard levels analyzed. Figure ES-2 shows the counties projected to exceed the annual and 24-hour alternative standard levels of 10/35 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in the analytical baseline. Additional information on the air quality modeling, as well as information about projected future DVs, DV targets, and air quality ratios is provided in Chapter 2 and Appendix 2A. 
Table ES-2	By Area, Summary of PM2.5 Emissions Reductions Needed, In Tons/Year and as Percent of Total Reduction Needed Nationwide, for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032
Area
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
                                     1,068
                                     1,221
                                     6,996
                                    30,843
Southeast
                                      474
                                      474
                                     4,088
                                    18,028
West
                                      820
                                     7,852
                                     3,078
                                     9,708
CA
                                    10,128
                                    12,230
                                    17,750
                                    28,293
Total
                                    12,490
                                    21,776
                                    31,912
                                    86,872
 
 
 
 
 
Area
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
                                      9%
                                      6%
                                      22%
                                      36%
Southeast
                                      4%
                                      2%
                                      13%
                                      21%
West
                                      7%
                                      36%
                                      10%
                                      11%
CA
                                      81%
                                      56%
                                      56%
                                      33%
            
                                       
Figure ES-2	Counties Projected to Exceed in Analytical Baseline for Alternative Standard Levels of 10/35 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] - 
For each alternative standard level, Chapter 2, Section 2.3.3 includes a discussion of the number of counties that are projected to exceed in 2032, and Figure 2-9 includes maps of counties projected to exceed along with the number of counties. The following summarizes the number of counties, by alternative standard level, in each geographic area that need PM2.5 emissions reductions from the analytical baseline. 
 10/35 g/ - m - [3] - -- 24 counties need PM2.5 emissions reductions. This includes 4 counties in the northeast, 2 counties in the southeast, 3 counties in the west, and 15 counties in California.
 10/30 g/ - m - [3] - -- 47 counties need PM2.5 emissions reductions. This includes 4 counties in the northeast, 2 counties in the southeast, 23 counties in the west, and 18 counties in California. 
 9/35 g/ - m - [3] -  -- 51 counties need PM2.5 emissions reductions. This includes 14 counties in the northeast, 8 counties in the southeast, 8 counties in the west, and 21 counties in California.
 8/35 g/ - m - [3] -  -- 141 counties need PM2.5 emissions reductions. This includes 57 counties in the northeast, 35 counties in the southeast, 24 counties in the west, and 25 counties in California.
    ES.1.3 	Control Strategies and PM2.5 Emissions Reductions
We identified control measures using the EPA's Control Strategy Tool (CoST) (U.S. EPA, 2019b) and the control measures database. CoST estimates emissions reductions and engineering costs associated with control technologies or measures applied to non-electric generating unit (non-EGU) point, non-point (area), residential wood combustion, and area fugitive dust sources of air pollutant emissions by matching control measures to emissions sources by source classification code (SCC). For these control strategy analyses, to maximize the number of emissions sources we included a lower emissions source size threshold (5 tons per year) and a higher marginal cost per ton threshold ($160,000/ton) than reflected in prior NAAQS RIAs (25-50 tpy, $15,000-$20,000/ton). In Chapter 3, Figure 3-4 shows estimated PM2.5 emissions reductions for several emissions source sizes and cost thresholds up to the $160,000/ton marginal cost threshold. We selected the $160,000/ton marginal cost threshold because it is around that cost level that (i) road paving controls get selected and applied, and (ii) opportunities for additional emissions reductions diminish. 
By area, Table ES-3 includes a summary of the estimated emissions reductions from control applications for the alternative standards analyzed. These emissions reductions were used to create the PM2.5 spatial surfaces described in Appendix 2A, Section 2A.4.2 for the human health benefits assessments presented in Chapter 5. See Chapter 3, Tables 3-5 through 3-7 for additional summaries of estimated PM2.5 emissions reductions from CoST.
Table ES-3	Summary of PM2.5 Estimated Emissions Reductions from CoST by Area for the Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
 
                          PM2.5 Emissions Reductions
Area
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
                                     1,070
                                     1,222
                                     6,334
                                    19,142
Northeast (Adjacent Counties)
                                       0
                                       0
                                     1,737
                                    15,440
Southeast
                                      475
                                      475
                                     3,040
                                    12,212
Southeast (Adjacent Counties)
                                       0
                                       0
                                      194
                                     4,892
West
                                      224
                                     2,206
                                      947
                                     4,711
CA
                                     1,792
                                     2,481
                                     2,958
                                     4,925
Total
                                     3,561
                                     6,384
                                    15,210
                                    61,321
   Note: Totals may not match related tables due to independent rounding. In the northeast and southeast when we applied the emissions reductions from adjacent counties, we used a ppb/ton PM2.5 air quality ratio that was four times less responsive than the ratio used when applying in-county emissions reductions.
   
    ES.1.4 	Estimates of PM2.5 Emissions Reductions Still Needed after Applying Control Technologies and Measures
The estimated PM2.5 emissions reductions from the control strategies do not fully account for all the emissions reductions needed to reach the proposed and more stringent alternative standard levels in some counties in the northeast, southeast, west, and California. By area, Table ES-4 includes a summary of the estimated emissions reductions still needed after control applications for the alternative standards analyzed. See Chapter 3, Table 3-9 for an additional summary of estimated emissions reductions still needed. Figure ES-3 and Figure ES-4 show the counties that still need emissions reductions after control applications for the proposed alternative standard levels of 10/35g/m[3] and 9/35 g/m[3]. Section ES.2 below includes a qualitative discussion of the remaining air quality challenges. In addition, Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6 provide more detailed discussions of these air quality challenges.
Table ES-4	Summary of PM2.5 Emissions Reductions Still Needed by Area for the Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
Region
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
                                       0
                                       0
                                      238
                                     6,741
Southeast
                                       0
                                       0
                                      994
                                     4,780
West
                                      595
                                     5,651
                                     2,132
                                     5,023
CA
                                     8,336
                                     9,749
                                    14,793
                                    23,368
Total
                                     8,931
                                    15,400
                                    18,157
                                    39,912

                                       
Figure ES-3	Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative Standard Level of 10/35 g/ - m - [3] - 

                                       
Figure ES-4	Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative Standard Level of 9/35 g/ - m - [3] - 
    ES.1.5 	Engineering Costs
The EPA also used CoST and the control measures database to estimate engineering control costs. We estimated costs for non-EGU point, non-point (area), residential wood combustion, and area fugitive dust sources of air pollutant emissions. CoST calculates engineering costs using one of two different methods: (1) an equation that incorporates key operating unit information, such as unit design capacity or stack flow rate, or (2) an average annualized cost-per-ton factor multiplied by the total tons of reduction of a pollutant. The engineering cost analysis uses the equivalent uniform annual costs (EUAC) method, in which annualized costs are calculated based on the equipment life for the control measure and the interest rate incorporated into a capital recovery factor. Annualized costs represent an equal stream of yearly costs over the period the control technology is expected to operate. The cost estimates reflect the engineering costs annualized using a 7 percent interest rate.
By area, Table ES-5 includes a summary of estimated control costs from control applications for the alternative standard levels analyzed. See Chapter 4, Tables 4-2 through 4-5 for additional summaries of estimated control costs associated with the control strategies.
Table ES-5	By Area, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/m - [3] - , and 8/35 g/ - m - [3] -  for 2032 (millions of 2017$)
Area
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
                                     $7.3
                                     $12.8
                                    $183.5
                                    $560.2
Northeast (Adjacent Counties)
                                      $0
                                      $0
                                     $22.3
                                    $539.7
Southeast
                                     $4.1
                                     $4.1
                                     $50.4
                                    $250.6
Southeast (Adjacent Counties)
                                      $0
                                      $0
                                     $18.2
                                    $186.5
West
                                     $19.0
                                    $150.0
                                     $34.2
                                    $121.8
CA
                                     $64.1
                                     $90.4
                                     $84.7
                                    $162.9
Total
                                     $94.5
                                    $257.2
                                    $393.3
                                   $1,821.7

For the proposed alternative standard level of 10/35 g/ - m - [3] - , the majority of the estimated costs are incurred in California because 15 of the 24 counties that need emissions reductions are located in California. Looking at the more stringent alternative standard level of 10/30 g/m - [3] - , in the west an additional 20 counties need emissions reductions and estimated costs increase significantly; estimated costs for the proposed alternative standard level of 9/35 g/ - m - [3] are higher than for 10/35 g/ - m - [3] -  -  but lower than for 10/30 g/ - m - [3] -  -  in this area -  - . For alternative standard levels of 9/35 g/ - m - [3] -  and 8/35 g/m - [3] - , more controls are available to apply in the northeast and the southeast as compared to California and the west. Therefore, the estimated costs for the northeast and southeast are higher for 9/35 g/ - m - [3] -  and 8/35 g/m - [3].
In the northeast and southeast when we applied the emissions reductions from adjacent counties, we applied a ratio of 4:1. That is, four tons of PM2.5 emissions reductions would be required from an adjacent county to reduce one ton of emissions reduction needed in a given county. Application of this ratio contributes to the higher estimated cost estimates for alternative standard levels of 9/35 g/ - m - [3] -  and 8/35 g/m - [3] - .

    ES.1.6 	Human Health Benefits
We estimate the quantity and economic value of air pollution-related effects using a "damage-function." This approach quantifies counts of air pollution-attributable cases of adverse health outcomes and assigns dollar values to those counts, while assuming that each outcome is independent of one another. We construct this damage function by adapting primary research -- specifically, air pollution epidemiology studies and economic value studies -- from similar contexts. This approach is sometimes referred to as "benefits transfer."
We use the environmental Benefits Mapping and Analysis Program -- Community Edition (BenMAP-CE) software program to quantify counts of premature deaths and illnesses attributable to photochemical modeled changes in annual mean PM2.5 for the year 2032 using a health impact function (Sacks et al., 2018). A health impact function combines information regarding: the concentration-response relationship between air quality changes and the risk of a given adverse outcome; the population exposed to the air quality change; the baseline rate of death or disease in that population; and the air pollution concentration to which the population is exposed.
After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure is therefore ex ante willingness-to-pay (WTP) for changes in risk. However, epidemiological studies generally provide estimates of the relative risks of a particular health effect avoided due to a reduction in air pollution. A convenient way to use this data in a consistent framework is to convert probabilities to units of avoided statistical incidences. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. 
Applying the impact and valuation functions to the estimated changes in PM2.5 yields estimates of the changes in physical damages (e.g., premature mortalities, cases of hospital admissions and emergency department visits) and the associated monetary values for those changes. Table ES-6 presents the estimated avoided incidences of PM-related illnesses and premature mortality resulting from emissions reductions associated with the application of the illustrative control strategies  for each of the alternative standard levels in 2032.  Table ES-7 and Table ES-8 present a summary of the monetized benefits associated with  emissions reductions from the application of the illustrative control strategies for each of the alternative standard levels, both nationally and by region, thereby allowing the comparison of cost and benefits of the application of the illustrative controls. As mentioned above and discussed in Chapter 3, Section 3.2.5, the estimated PM2.5 emissions reductions from control applications do not fully account for all the emissions reductions needed to reach the proposed and more stringent alternative standard levels in some counties in the northeast, southeast, west, and California. In Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6, we discuss the remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California for the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - . In Appendix 5A a set of tables summarizes the benefits associated with identifying all of the emissions reductions needed to reach the proposed and more stringent alternative standard levels. For Table ES-7 and Table ES-8, the monetized value of unquantified effects is represented by adding an unknown "B" to the aggregate total. This B represents both uncertainty and a bias in this analysis, as it reflects health and welfare benefits that we are unable to quantify. Note that not all known PM health effects could be quantified or monetized. 
Table ES-6 	Estimated Avoided PM-Related Premature Respiratory Mortalities and Illnesses of the Control Strategies for the Alternative Primary PM2.5 Standard Levels for 2032 (95% Confidence Interval)
Avoided Mortality[a]
                           10/35 g/ - m - [3] - 
                             10/30 g/m - [3] - 
                            9/35 g/ - m - [3] - 
                            8/35 g/ - m - [3] - 
Pope III et al., 2019 (adult mortality ages 18-99 years)
                                     1,700
                               (1,200 to 2,100)
                                     1,900
                               (1,400 to 2,400)
                                     4,200
                               (3,000 to 5,300)
                                     9,200
                               (6,600 to 12,000)
Wu et al., 2020 (adult mortality ages 65-99 years)
                                      810
                                 (710 to 900)
                                      920
                                (810 to 1,000)
                                     2,000
                               (1,800 to 2,200)
                                     4,400
                               (3,900 to 4,900)
Woodruff et al., 2008 (infant mortality)
                                      1.6
                                (-0.99 to 4.0)
                                      1.8
                                 (-1.1 to 4.6)
                                      4.7
                                 (-3.0 to 12)
                                      11
                                 (-6.9 to 28)
Avoided Morbidity 
                          10/35g/ - m - [3] - 
                          10/30g/ - m - [3] - 
                           9/35g/ - m - [3] - 
                           8/35g/ - m - [3] - 
Hospital admissions -- cardiovascular (age > 18)
                                      140
                                 (100 to 170)
                                      150
                                 (110 to 190)
                                      310
                                 (230 to 400)
                                      660
                                 (480 to 840)
Hospital admissions -- respiratory
                                      93
                                  (31 to 150)
                                      100
                                  (35 to 170)
                                      210
                                  (74 to 350)
                                      460
                                 (160 to 740)
ED visits--cardiovascular
                                      260
                                 (-100 to 610)
                                      290
                                 (-110 to 670)
                                      630
                                (-240 to 1,500)
                                     1,400
                                (-530 to 3,200)
ED visits -- respiratory
                                      490
                                 (95 to 1,000)
                                      530
                                (100 to 1,100)
                                     1,200
                                (240 to 2,600)
                                     2,700
                                (540 to 5,700)
Acute Myocardial Infarction
                                      29
                                  (5.9 to 17)
                                      32
                                  (19 to 45)
                                      67
                                  (39 to 94)
                                      143
                                  (83 to 200)
Cardiac arrest
                                      15
                                 (-5.9 to 33)
                                      16
                                 (-6.6 to 37)
                                      34
                                  (-14 to 76)
                                      72
                                 (-29 to 160)
Hospital admissions-- Alzheimer's Disease
                                      360
                                 (270 to 440)
                                      390
                                 (300 to 480)
                                      850
                                (640 to 1,000)
                                     1,900
                               (1,500 to 2,400)
Hospital admissions-- Parkinson's Disease
                                      48
                                  (25 to 70)
                                      54
                                  (28 to 79)
                                      120
                                  (63 to 180)
                                      270
                                 (140 to 390)
Stroke
                                      55
                                  (14 to 94)
                                      61
                                  (16 to 110)
                                      130
                                  (33 to 220)
                                      270
                                  (71 to 470)
Lung cancer
                                      65
                                  (20 to 110)
                                      73
                                  (22 to 120)
                                      150
                                  (46 to 250)
                                      320
                                  (99 to 530)
Hay Fever/Rhinitis
                                    15,000
                               (3,500 to 25,000)
                                    16,000
                               (4,000 to 28,000)
                                    35,000
                               (8,500 to 60,000)
                                    75,000
                              (18,000 to 130,000)
Asthma Onset
                                     2,200
                               (2,100 to 2,300)
                                     2,500
                               (2,400 to 2,600)
                                     5,400
                               (5,100 to 5,600)
                                    11,000
                              (11,000 to 12,000)
Asthma symptoms  -  Albuterol use
                                    310,000
                             (-150,000 to 750,000)
                                    350,000
                             (-170,000 to 850,000)
                                    740,000
                            (-360,000 to 1,800,000)
                                   1,600,000
                            (-780,000 to 3,900,000)
Lost work days
                                    110,000
                              (97,000 to 130,000)
                                    130,000
                             (110,000 to 150,000)
                                    270,000
                             (230,000 to 310,000)
                                    580,000
                             (490,000 to 660,000)
Minor restricted-activity days
                                    680,000
                             (550,000 to 800,000)
                                    750,000
                             (610,000 to 890,000)
                                   1,600,000
                           (1,300,000 to 1,900,000)
                                   3,400,000
                           (2,700,000 to 4,000,000)
Note: Values rounded to two significant figures. 
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-term exposure to PM2.5.  These values should not be added to one another.
Table ES-7	Estimated Monetized Benefits of the Control Strategies for Alternative Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 ug/m[3] (billions of 2017$)
                               Benefits Estimate
                 10 ug/m[3] Annual &
35 ug/m[3] 24-hour
                 10 ug/m[3] Annual &
30 ug/m[3] 24-hour
                  9 ug/m[3] Annual &
35 ug/m[3] 24-hour
                  8 ug/m[3] Annual &
35 ug/m[3] 24-hour
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Pope III et al., 2019
  3% discount rate
                                    $17 + B
                                    $20 + B
                                    $43 + B
                                    $95 + B
  7% discount rate
                                    $16 + B
                                    $18 + B
                                    $39 + B
                                    $86 + B
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Wu et al., 2020
  3% discount rate
                                   $8.5 + B
                                   $9.6 + B
                                    $21 + B
                                    $46 + B
  7% discount rate
                                   $7.6 + B
                                   $8.6 + B
                                    $19 + B
                                    $41 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not all possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and welfare benefits.

Table ES-8	Estimated Monetized Benefits by Region of the Control Strategies for the Alternative Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 ug/m[3] (billions of 2017$)
                               Benefits Estimate
                                    Region
                                 10 ug/m[3] 
                       Annual &
35 ug/m[3] 24-hour
                                 10 ug/m[3] 
                       Annual &
30 ug/m[3] 24-hour
                                  9 ug/m[3] 
                       Annual &
35 ug/m[3] 24-hour
                  8 ug/m[3] Annual &
35 ug/m[3] 24-hour
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Pope III et al., 2019
  3% discount rate
                                  California
                                    $13 + B
                                    $14 + B
                                    $17 + B
                                    $23 + B
  
                                   Northeast
                                   $2.3 + B
                                   $2.6 + B
                                    $15 + B
                                    $40 + B
  
                                   Southeast
                                   $1.8 + B
                                   $1.8 + B
                                   $8.8 + B
                                    $22 + B
  
                                     West
                                  $0.018 + B
                                   $1.1 + B
                                   $2.2 + B
                                    $11 + B
  7% discount rate
                                  California
                                    $12 + B
                                    $13 + B
                                    $16 + B
                                    $21 + B
  
                                   Northeast
                                    $2 + B
                                   $2.3 + B
                                    $13 + B
                                    $36 + B
  
                                   Southeast
                                   $1.6 + B
                                   $1.6 + B
                                   $7.9 + B
                                    $20 + B
  
                                     West
                                  $0.016 + B
                                    $1 + B
                                    $2 + B
                                   $9.5 + B
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Wu et al., 2020
  3% discount rate
                                  California
                                   $6.5 + B
                                   $6.9 + B
                                   $8.4 + B
                                    $11 + B
  
                                   Northeast
                                   $1.1 + B
                                   $1.3 + B
                                   $7.3 + B
                                    $19 + B
  
                                   Southeast
                                   $0.84 + B
                                   $0.84 + B
                                   $4.1 + B
                                    $10 + B
  
                                     West
                                  $0.0092 + B
                                   $0.56 + B
                                   $1.1 + B
                                   $5.1 + B
  7% discount rate
                                  California
                                   $5.8 + B
                                   $6.2 + B
                                   $7.5 + B
                                    $10 + B
  
                                   Northeast
                                    $1 + B
                                   $1.2 + B
                                   $6.6 + B
                                    $17 + B
  
                                   Southeast
                                   $0.75 + B
                                   $0.75 + B
                                   $3.6 + B
                                   $9.2 + B
  
                                     West
                                  $0.0082 + B
                                   $0.5 + B
                                   $0.97 + B
                                   $4.6 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and welfare benefits.

    ES.1.7 	Welfare Benefits of Meeting the Primary and Secondary Standards
Even though the primary standards are designed to protect against adverse effects to human health, the emissions reductions would have welfare benefits in addition to the direct health benefits. The term welfare benefits covers both environmental and societal benefits of reducing pollution. Welfare benefits of the primary PM standard include reduced vegetation effects resulting from PM exposure, reduced ecological effects from particulate matter deposition and from nitrogen emissions, reduced climate effects, and changes in visibility. This RIA does not assess welfare effects quantitatively; this is discussed further in Chapter 5.  
    ES.1.8 	Environmental Justice
Environmental justice (EJ) concerns for each rulemaking are unique and should be considered on a case-by-case basis, and EPA's EJ Technical Guidance states that "[t]he analysis of potential EJ concerns for regulatory actions should address three questions: 
 Are there potential EJ concerns associated with environmental stressors affected by the regulatory action for population groups of concern in the baseline? 
 Are there potential EJ concerns associated with environmental stressors affected by the regulatory action for population groups of concern for the regulatory option(s) under consideration? 
 For the regulatory option(s) under consideration, are potential EJ concerns created or mitigated compared to the baseline?" 
To address these questions, EPA developed an analytical approach that considers the purpose and specifics of the proposed rulemaking, as well as the nature of known and potential exposures and impacts. For the proposal, we quantitatively evaluate the potential for disparities in PM2.5 exposures and mortality effects across different demographic populations under illustrative control strategies associated with implementation of the current standard (12/35 g/m[3], or baseline) and potential alternative PM2.5 standard levels (10/35 mg/m[3], 10/30 g /m[3], 9/35 g /m[3], and 8/35 g /m[3]) at the national and regional levels. Specifically, we provide information on totals, changes, and proportional changes in 1) exposures, in terms of annual average PM2.5 concentrations and 2) premature mortality, in terms of rates per 100,000 individuals across and within various demographic populations. Each type of analysis has strengths and weaknesses, but when taken together, can respond to the above three questions from EPA's Environmental Justice (EJ) Technical Guidance. 
Beginning with the first question, under the 12/35g/m[3] analytical baseline, some populations are predicted to experience disproportionately higher annual PM2.5 exposures nationally than the reference (overall) population, both in terms of aggregated average exposure and across the distribution of air quality. Specifically, Hispanics, Asians, Blacks, and those less educated (no high school) have higher national annual exposures, on average and across the distributions, than both the overall reference population or other populations (e.g., non-Hispanic, White, and more educated). In particular, the Hispanic population is estimated to experience the highest exposures, both on average and across PM2.5 concentration distributions, of all demographic groups analyzed. These disproportionalities are also observed at the regional level, though to different extents.  
In response to the second question, while a lower standard level would be predicted to reduce PM2.5 exposures and mortality rates across all demographic groups, disparities seen in the baseline are also reflected in the standard levels under consideration. However, as to the third question, for most populations assessed, PM2.5 exposure disparities are mitigated in the illustrative air quality scenarios reflecting control strategies (10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3]) as compared to the baseline (12/35 g/m[3]), and more so as the alternative standard levels become more stringent. At the national scale, Hispanics, Asians, and those less educated are estimated to see greater proportional reductions in PM2.5 concentrations than reference populations under all alternative standard levels evaluated, with proportional reductions increasing as the alternative standard levels decrease. However, exposures in the Black population are estimated to proportionally decrease on par with exposures in reference population. Considering the four geographic regions (northeast, southeast, west, and California), proportionally greater reductions in PM2.5 concentrations experienced by Asian, Hispanic, and less educated populations are most notable in the southeast and California, whereas PM2.5 concentration reductions among Black populations tend to be proportionally larger than among the reference population in California, the west, and the northeast, especially under the proposed alternative standard level of 9/35 g/m[3] and the more stringent alternative standard level of 8/35 g/m[3].  We also discuss the implications for exposures to the Hispanic population in the counties that are projected to fall short of attainment.
In terms of health effects, some populations are also predicted to experience disproportionately higher rates of premature mortality than the reference population under the baseline scenario (question 1). Black populations are estimated to have the highest national and regional mortality rates, both on average and across population distributions. Research has documented significant racial disparities in baseline mortality rates.  Differential PM2.5 exposures for this population in some parts of the country, which may contribute to higher magnitude concentration-response relationships between exposure and premature mortality, as well as other underlying health factors that may increase susceptibility to adverse outcomes among Black populations. Health disparities associated with the baseline scenario are also predicted for the proposed and more stringent standard levels (question 2), although as the alternative standard levels become increasingly stringent, differences in mortality rates across demographic groups decline, particularly for the proposed and more stringent alternative standard levels evaluated (9/35 g/m3 and 8/35g/m[3]) (question 3). 
 ES.2	Qualitative Assessment of the Remaining Air Quality Challenges
For the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3], the analysis indicates that some areas in the northeast and southeast, as well as in the west and California may still need emissions reductions (Figure ES-3 and Figure ES-4). As discussed in Chapters 2 and 3, the remaining air quality challenges for the proposed alternative standard levels can be grouped into the following "bins": Delaware County, Pennsylvania, border areas, small mountain valleys, and California areas. By bin, Table ES-9 below summarizes the counties that may need additional emissions reductions for the proposed alternative standard -  levels. 
Table ES-9	Summary of Counties by Bin that Still Need Emissions Reductions for Proposed Alternative Primary Standard Levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - 
Bin
Area
Counties[a] for
10/35 mg/ - m - [3]
Additional Counties[a] for 
9/35 mg/ - m - [3] - 
Delaware County, Pennsylvania
Northeast
--
Delaware County, PA
Border Areas
Southeast
--
Cameron County, TX
Hidalgo County, TX

California
Imperial County, CA
--
Small Mountain Valleys
West
Plumas County, CA
Lemhi County, ID
Shoshone County, ID
Lincoln County, MT
Benewah County, ID
California Areas

Fresno County, CA (SJVAPCD)
Kern County, CA (SJVAPCD)
Kings County, CA (SJVAPCD)
Los Angeles County, CA (SCAQMD)
Madera County, CA (SJVAPCD)
Merced County, CA (SJVAPCD)
Riverside County, CA (SCAQMD)
San Bernardino County, CA (SCAQMD)
Stanislaus County, CA (SJVAPCD)
Tulare County, CA (SJVAPCD)
Napa County, CA (BAAQMD)
San Joaquin County, CA (SJVAPCD)
San Luis Obispo County, CA
Note: For California counties that are part of multi-county air districts, the relevant district is indicated in parentheses; BAAQMD = Bay Area Air Quality Management District, SCAQMD = South Coast Air Quality Management District, and SJVAPCD= San Joaquin Valley Air Pollution Control District.
[a] The following counties have no identified PM2.5 emissions reductions because available controls were applied for the current standard of 12/35 g/m[3] and additional controls were not available: Imperial, Kern, Kings, Lemhi, Plumas, Riverside, San Bernardino, Shoshone, and Tulare.

The characteristics of the air quality challenges for these areas include features of local source-to-monitor impacts, cross-border transport, effects of complex terrain in the west and California, and identifying wildfire influence on projected PM2.5 DVs that could potentially qualify for exclusion as atypical, extreme, or unrepresentative events (U.S. EPA, 2019a). For bin-specific detailed discussions of these air quality challenges, see Chapter 2, Section 2.4. Further, for each bin for discussions of the estimated PM2.5 emissions reductions needed, the control strategy analyses and controls applied, the estimated PM2.5 emissions reductions still needed after the application of controls, and the bin-specific air quality challenges, see Chapter 3, Section 3.2.6. 
For Delaware County, Pennsylvania, a more detailed local analysis of the local source emissions reductions impacts is needed. For the border areas that may be influenced by cross-border emissions, more detailed analyses of international transport emissions are needed to assess the relevance of Section 179B of the Clean Air Act. For the small mountain valleys in the west that are influenced by the temperature inversions, residential wood combustion, and wildfire smoke additional detailed analyses that reflect local PM2.5 response factors, emissions inventory information, and control measure information are needed. In addition, more detailed analyses are needed to characterize the influence of wildfires on PM2.5 concentrations and the potential for some wildfires to qualify for exclusion as atypical, extreme, or unrepresentative events.
Lastly, the air quality in the SJVAPCD and SCAQMD is influenced by complex terrain and meteorological conditions that are best characterized with a high-resolution air quality modeling platform developed for the specific conditions of the air basins. Specific, local information on control measures to reduce emissions from agricultural dust and burning, prescribed burning, and many of the non-point (area) emissions sources (e.g., commercial and residential cooking) is needed given the magnitude of emissions from these sources in these areas. Further, more detailed analyses are needed to characterize the influence of wildfires on PM2.5 concentrations and the potential for some wildfires to qualify for exclusion as atypical, extreme, or unrepresentative events.
We also discuss the implications for exposures to the Hispanic population in the counties that are projected to fall short of attainment.  Because of the uncertainties associated with projecting potential sources and control technologies, this analysis cannot provide precise illustrative cost estimates for attainment of these counties thereby reduced exposures to the Hispanic population, especially those who reside in southern and central California.  If the type of sources and the available technologies remain constant as assumed by the illustrative analysis, the potential costs to reduce emissions and associated exposure to this population may be [tens of million/hundreds of million/billions] of dollars.  While the potential costs may be [minimal/substantial/significant], the potential benefits may be [minimal/substantial/significant].
 ES.3	Results of Benefit-Cost Analysis
As discussed above and in Chapter 3, Section 3.2.5, the estimated PM2.5 emissions reductions from control applications do not fully account for all the emissions reductions needed to reach the proposed and more stringent alternative standard levels in some counties in the northeast, southeast, west, and California. In Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6, we discuss the remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California for the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - . The EPA calculates the monetized net benefits of the proposed alternative standard levels by subtracting the estimated monetized compliance costs from the estimated monetized benefits in 2032. These estimates do not fully account for all of the emissions reductions needed to reach the proposed and more stringent alternative standard levels. In 2032, the monetized net benefits of the proposed alternative standard level of 10/35 g/m3 are approximately $8.4 billion and $17 billion using a 3 percent real discount rate for the benefits estimates, and the monetized net benefits of the proposed alternative standard level of 9/35 g/m3 are approximately $20 billion and $43 billion using a 3 percent real discount rate for the benefits estimates (in 2017$). The benefits are associated with two point estimates from two different epidemiologic studies discussed in more detail in Chapter 5, Section 5.3.3. Table ES-10 presents a summary of these impacts for the proposed alternative standard levels and the more stringent alternative standard levels for 2032. 
Table ES-10	Estimated Monetized Benefits, Costs, and Net Benefits of the Control Strategies Applied Toward the Primary Alternative Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 for the U.S. (millions of 2017$)
                                       
                                     10/35
                                     10/30
                                     9/35
                                     8/35
                                  Benefits[a]
                              $8,500 and $17,000
                              $9,600 and $20,000
                              $21,000 and $43,000
                              $46,000 and $95,000
                                   Costs[b]
                                      $95
                                     $260
                                     $390
                                    $1,800
                                 Net Benefits
                              $8,400 and $17,000
                              $9,300 and $19,000
                              $20,000 and $43,000
                              $44,000 and $93,000
Notes: Rows may not appear to add correctly due to rounding. We focus results to provide a snapshot of costs and benefits in 2032, using the best available information to approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
[a] We assume that there is a cessation lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion over the 20 years following exposure, which affects the valuation of mortality benefits at different discount rates. Similarly, we assume there is a cessation lag between the change in PM exposures and both the development and diagnosis of lung cancer. The benefits are associated with two point estimates from two different epidemiologic studies, and we present the benefits calculated at a real discount rate of 3 percent. The benefits exclude additional health and welfare benefits that could not be quantified (see Chapter 5, Sections 5.3.4 and 5.3.5).
b The costs are annualized using a 7 percent interest rate.
As part of fulfilling analytical guidance with respect to E.O. 12866, the EPA presents estimates of the present value (PV) of the monetized benefits and costs over the twenty-year period 2032 to 2051. To calculate the present value of the social net benefits of the proposed alternative standard levels, annual benefits and costs are discounted to 2022 at 3 percent and 7 percent discount rates as directed by OMB's Circular A-4. The EPA also presents the equivalent annualized value (EAV), which represents a flow of constant annual values that, had they occurred in each year from 2032 to 2051, would yield a sum equivalent to the PV. The EAV represents the value of a typical cost or benefit for each year of the analysis, in contrast to the 2032-specific estimates.
For the twenty-year period of 2032 to 2051, for the proposed alternative standard level of 10/35 g/m3 the PV of the net benefits, in 2017$ and discounted to 2022, is $200 billion when using a 3 percent discount rate and $90 billion when using a 7 percent discount rate. The EAV is $13 billion per year when using a 3 percent discount rate and $8.5 billion when using a 7 percent discount rate. For the twenty-year period of 2032 to 2051, for the proposed alternative standard level of 9/35 g/m3 the PV of the net benefits, in 2017$ and discounted to 2022, is $490 billion when using a 3 percent discount rate and $220 billion when using a 7 percent discount rate. The EAV is $33 billion per year when using a 3 percent discount rate and $21 billion when using a 7 percent discount rate. The comparison of benefits and costs in PV and EAV terms for the proposed alternative standard levels can be found in Table ES-11 and Table ES-12. Estimates in the tables are presented as rounded values.   
Table ES-11	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies Applied Toward the Proposed Primary Alternative Standard Level of 10/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount rates)
                                       
                                  Benefits[a]
                                   Costs[b]
                                 Net Benefits
                                     Year
                                      3%
                                      7%
                                      3%
                                      7%
                                      3%
                                      7%
                                     2032
                                    $13,000
                                    $8,000
                                      $70
                                      $48
                                    $13,000
                                    $7,900
                                     2033
                                    $13,000
                                    $7,500
                                      $68
                                      $45
                                    $13,000
                                    $7,400
                                     2034
                                    $12,000
                                    $7,000
                                      $66
                                      $42
                                    $12,000
                                    $6,900
                                     2035
                                    $12,000
                                    $6,500
                                      $64
                                      $39
                                    $12,000
                                    $6,500
                                     2036
                                    $12,000
                                    $6,100
                                      $63
                                      $37
                                    $11,000
                                    $6,100
                                     2037
                                    $11,000
                                    $5,700
                                      $61
                                      $34
                                    $11,000
                                    $5,700
                                     2038
                                    $11,000
                                    $5,300
                                      $59
                                      $32
                                    $11,000
                                    $5,300
                                     2039
                                    $11,000
                                    $5,000
                                      $57
                                      $30
                                    $10,000
                                    $4,900
                                     2040
                                    $10,000
                                    $4,600
                                      $56
                                      $28
                                    $10,000
                                    $4,600
                                     2041
                                    $9,900
                                    $4,300
                                      $54
                                      $26
                                    $9,900
                                    $4,300
                                     2042
                                    $9,700
                                    $4,100
                                      $52
                                      $24
                                    $9,600
                                    $4,000
                                     2043
                                    $9,400
                                    $3,800
                                      $51
                                      $23
                                    $9,300
                                    $3,800
                                     2044
                                    $9,100
                                    $3,500
                                      $49
                                      $21
                                    $9,100
                                    $3,500
                                     2045
                                    $8,800
                                    $3,300
                                      $48
                                      $20
                                    $8,800
                                    $3,300
                                     2046
                                    $8,600
                                    $3,100
                                      $47
                                      $19
                                    $8,500
                                    $3,100
                                     2047
                                    $8,300
                                    $2,900
                                      $45
                                      $17
                                    $8,300
                                    $2,900
                                     2048
                                    $8,100
                                    $2,700
                                      $44
                                      $16
                                    $8,000
                                    $2,700
                                     2049
                                    $7,900
                                    $2,500
                                      $43
                                      $15
                                    $7,800
                                    $2,500
                                     2050
                                    $7,600
                                    $2,400
                                      $41
                                      $14
                                    $7,600
                                    $2,300
                                     2051
                                    $7,400
                                    $2,200
                                      $40
                                      $13
                                    $7,400
                                    $2,200
                                 Present Value
                                   $200,000
                                    $91,000
                                    $1,100
                                     $540
                                   $200,000
                                    $90,000
                          Equivalent Annualized Value
                                    $13,000
                                    $8,500
                                      $72
                                      $51
                                    $13,000
                                    $8,500
Notes: Rows may not appear to add correctly due to rounding. The annualized present value of costs and benefits are calculated over a 20-year period from 2032 to 2051. 
[a] The benefits values use the larger of the two avoided premature deaths estimates presented in Chapter 5, Table 5-7, and are discounted at a rate of 3 percent over the SAB-recommended 20-year segmented lag. The benefits exclude additional health and welfare benefits that could not be quantified (see Chapter 5, Sections 5.3.4 and 5.3.5).
[b] The costs are annualized using a 7 percent interest rate.

Table ES-12	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies Applied Toward the Proposed Primary Alternative Standard Level of 9/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount rates)
                                       
                                  Benefits[a]
                                   Costs[b]
                                 Net Benefits
                                     Year
                                      3%
                                      7%
                                      3%
                                      7%
                                      3%
                                      7%
                                     2032
                                    $32,000
                                    $20,000
                                     $290
                                     $200
                                    $32,000
                                    $20,000
                                     2033
                                    $31,000
                                    $18,000
                                     $280
                                     $190
                                    $31,000
                                    $18,000
                                     2034
                                    $30,000
                                    $17,000
                                     $280
                                     $170
                                    $30,000
                                    $17,000
                                     2035
                                    $29,000
                                    $16,000
                                     $270
                                     $160
                                    $29,000
                                    $16,000
                                     2036
                                    $29,000
                                    $15,000
                                     $260
                                     $150
                                    $28,000
                                    $15,000
                                     2037
                                    $28,000
                                    $14,000
                                     $250
                                     $140
                                    $27,000
                                    $14,000
                                     2038
                                    $27,000
                                    $13,000
                                     $250
                                     $130
                                    $27,000
                                    $13,000
                                     2039
                                    $26,000
                                    $12,000
                                     $240
                                     $120
                                    $26,000
                                    $12,000
                                     2040
                                    $25,000
                                    $11,000
                                     $230
                                     $120
                                    $25,000
                                    $11,000
                                     2041
                                    $25,000
                                    $11,000
                                     $220
                                     $110
                                    $24,000
                                    $11,000
                                     2042
                                    $24,000
                                    $10,000
                                     $220
                                     $100
                                    $24,000
                                    $9,900
                                     2043
                                    $23,000
                                    $9,400
                                     $210
                                      $95
                                    $23,000
                                    $9,300
                                     2044
                                    $23,000
                                    $8,800
                                     $210
                                      $89
                                    $22,000
                                    $8,700
                                     2045
                                    $22,000
                                    $8,200
                                     $200
                                      $83
                                    $22,000
                                    $8,100
                                     2046
                                    $21,000
                                    $7,700
                                     $190
                                      $78
                                    $21,000
                                    $7,600
                                     2047
                                    $21,000
                                    $7,200
                                     $190
                                      $72
                                    $20,000
                                    $7,100
                                     2048
                                    $20,000
                                    $6,700
                                     $180
                                      $68
                                    $20,000
                                    $6,600
                                     2049
                                    $19,000
                                    $6,300
                                     $180
                                      $63
                                    $19,000
                                    $6,200
                                     2050
                                    $19,000
                                    $5,800
                                     $170
                                      $59
                                    $19,000
                                    $5,800
                                     2051
                                    $18,000
                                    $5,500
                                     $170
                                      $55
                                    $18,000
                                    $5,400
                                 Present Value
                                   $490,000
                                   $220,000
                                    $4,500
                                    $2,300
                                   $490,000
                                   $220,000
                          Equivalent Annualized Value
                                    $33,000
                                    $21,000
                                     $300
                                     $210
                                    $33,000
                                    $21,000
Notes: Rows may not appear to add correctly due to rounding. The annualized present value of costs and benefits are calculated over a 20-year period from 2032 to 2051.
[a] The benefits values use the larger of the two avoided premature deaths estimates presented in Chapter 5, Table 5-7, and are discounted at a rate of 3 percent over the SAB-recommended 20-year segmented lag. The benefits exclude additional health and welfare benefits that could not be quantified (see Chapter 5, Sections 5.3.4 and 5.3.5).
[b] The costs are annualized using a 7 percent interest rate.
 ES.4	References
Pope III, CA, Lefler, JS, Ezzati, M, Higbee, JD, Marshall, JD, Kim, S-Y, Bechle, M, Gilliat, KS, Vernon, SE and Robinson, AL (2019). Mortality risk and fine particulate air pollution in a large, representative cohort of US adults. Environmental health perspectives 127(7): 077007.
Sacks, JD, Lloyd, JM, Zhu, Y, Anderton, J, Jang, CJ, Hubbell, B and Fann, N (2018). The Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP - CE): A tool to estimate the health and economic benefits of reducing air pollution. Environmental Modelling Software 104: 118-129
U.S. EPA (2012). Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-12-005. Available at: https://www.epa.gov/sites/default/files/2020-07/documents/naaqs-pm_ria_final_2012-12.pdf.
U.S. EPA (2019a). Additional Methods, Determinations, and Analyses to Modify Air Quality Data Beyond Exceptional Events. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, EPA-457/B-19-002. Available: https://www.epa.gov/sites/default/files/2019-04/documents/clarification_memo_on_data_modification_methods.pdf
U.S. EPA (2019b). CoST v3.7 User's Guide. Office of Air Quality Planning and Standards, Research Triangle Park, NC. November 2019. Available at < https://www.cmascenter.org/help/documentation.cfm?model=cost&version=3.7>.
Woodruff, TJ, Darrow, LA and Parker, JD (2008). Air pollution and postneonatal infant mortality in the United States, 1999 - 2002. Environmental Health Perspectives 116(1): 110-115.
Wu, X, Braun, D, Schwartz, J, Kioumourtzoglou, M and Dominici, F (2020). Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly. Science advances 6(29): eaba5692.



OVERVIEW AND BACKGROUND 
 Overview of the Proposal
On June 10, 2021, the Environmental Protection Agency (EPA) announced its decision to reconsider the 2020 Particulate Matter (PM) National Ambient Air Quality Standards (NAAQS) final action. In this reconsideration, the EPA has concluded that the existing annual primary PM2.5 standard for PM, set at a level of 12.0 ug/m[3], is not requisite to protect public health with an adequate margin of safety. The EPA Administrator is proposing to revise the level of the standard within the range of 9-10 ug/m[3], while soliciting comment on levels down to 8 ug/m[3] and up to 11 ug/m[3]. The primary 24-hour PM2.5 standard provides protection against exposures to short-term "peak" concentrations of PM2.5 in ambient air. The EPA Administrator is proposing to retain primary 24-hour PM2.5 standard at its current level of 35 ug/m[3] and is soliciting comment on revising the level of the standard to as low as 30 ug/m[3].
The EPA has also concluded that the existing secondary PM standards are requisite to protect public welfare from known or anticipated effects and is proposing to retain the secondary standards for PM. Specifically, for the secondary annual PM2.5 standard, the EPA Administrator is proposing to retain the existing standard of 15.0 ug/m[3]. For the secondary 24-hour PM2.5 standard, the EPA Administrator is proposing to retain the existing standard of 35 ug/m[3]; however, the Administrator is soliciting comment on revising the level of the standard to as low as 30 ug/m[3]. For the secondary 24-hour PM10 standard, the EPA Administrator is proposing to retain the existing standard of 150 ug/m[3]. The docket for the proposed rulemaking is EPA-HQ-OAR-2015-0072.
 Overview of the Regulatory Impact Analysis
This chapter summarizes the purpose and background of this Regulatory Impact Analysis (RIA). In this RIA, we are analyzing the proposed annual and current 24-hour alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/m[3], as well as the following two more stringent alternative standard levels: (1) an alternative annual standard level of 8 g/ - m - [3] -  in combination with the current 24-hour standard (i.e., 8/35 g/ - m - [3] - ), and (2) an alternative 24-hour standard level of 30 g/ - m - [3] in combination with the proposed annual standard level of 10 g/ - m - [3] -  (i.e., 10/30 g/ - m - [3] - ). The RIA presents estimated costs and benefits of the control strategies analyzed for the proposed and more stringent alternative standard levels. According to the Clean Air Act ("the Act"), the Environmental Protection Agency (EPA) must use health-based criteria in setting the NAAQS and cannot consider estimates of compliance cost.
The analyses in this RIA rely on national-level data (emissions inventory and control measure information) for use in national-level assessments (air quality modeling, control strategies, environmental justice, and benefits estimation). However, the ambient air quality issues being analyzed are highly complex and local in nature, and the results of these national-level assessments therefore contain uncertainty. It is beyond the scope of this RIA to develop detailed local information for the areas being analyzed, including populating the local emissions inventory, obtaining local information to increase the resolution of the air quality modeling, and obtaining local information on emissions controls, all of which would reduce some of the uncertainty in these national-level assessments. For example, having more refined data would be ideal for agricultural dust and burning, prescribed burning, and non-point (area) sources due to their large contribution to primary PM2.5 emissions and the limited availability of emissions controls.  
To maximize the number of emissions sources included and controls analyzed in the analyses, we included a lower emissions source size threshold (5 tons per year) and a higher marginal cost per ton threshold ($160,000/ton) than reflected in prior NAAQS RIAs (25-50 tpy, $15,000-$20,000/ton). As discussed in Chapter 2, Section 2.1.3, given historical and projected trends in NOX and SO2 emissions reductions (reducing background PM concentrations and increasing the importance of urban PM concentrations), we analyze direct PM emissions reductions because our modeling indicates that these reductions will be the most effective at reducing PM concentrations in counties projected to exceed the proposed standard levels.  The spatial distributions of PM2.5 concentrations in the U.S. are characterized by an "urban increment" of consistently higher PM2.5 concentrations over urban than surrounding areas. Monitored concentrations are highest in urban areas and relatively low in rural areas. Conceptually, PM2.5 concentrations in urban areas can be viewed as the superposition of the urban increment and the contributions from regional and natural background sources. The decreases in anthropogenic SO2 and NOX emissions in recent decades have reduced regional background concentrations and increased the relative importance of the urban increment. The projections of additional large reductions in SO2 and NOX emissions in the 2032 case further motivate the need for control of local primary PM2.5 sources to address the highest PM2.5 concentrations in urban areas. Lastly, Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6 discuss the remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California for the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - ; the areas include a county in Pennsylvania affected by local sources, border areas, counties in small western mountain valleys, and counties in California's air basins and districts. The characteristics of the air quality challenges for these areas include features of local source-to-monitor impacts, cross-border transport, effects of complex terrain in the west, and identifying wildfire influence on projected PM2.5 DVs that could potentially qualify for exclusion as atypical, extreme, or unrepresentative events (U.S. EPA, 2019).   
The remainder of this chapter provides a brief background on the NAAQS, the need for the NAAQS, and an overview of structure of this RIA. The EPA prepared this RIA both to provide the public with information on the benefits and costs of meeting a revised PM2.5 NAAQS and to meet the requirements of Executive Orders 12866 and 13563.
Background
In setting primary ambient air quality standards, the EPA's responsibility under the law is to establish standards that protect public health, regardless of the costs of implementing those standards. As interpreted by the Agency and the courts, the CAA requires the EPA to create standards based on health considerations only. The prohibition against the consideration of cost in the setting of the primary air quality standards, however, does not mean that costs or other economic consequences are unimportant or should be ignored. The Agency believes that consideration of costs and benefits is essential to making efficient, cost-effective decisions for implementing these standards. The impact of cost and efficiency is considered by states during the implementation process, as they decide what timelines, strategies, and policies are appropriate for their circumstances. This RIA is not part of the standard setting and is intended to inform the public about the potential costs and benefits that may result when new standards are implemented.
National Ambient Air Quality Standards
Sections 108 and 109 of the CAA govern the establishment and revision of the NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify pollutants that "may reasonably be anticipated to endanger public health or welfare" and to issue air quality criteria for them. These air quality criteria are intended to "accurately reflect the latest scientific knowledge useful in indicating the kind and extent of all identifiable effects on public health or welfare which may be expected from the presence of [a] pollutant in the ambient air." PM is one of six pollutants for which the EPA has developed air quality criteria. 
Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate "primary" and "secondary" NAAQS for pollutants identified under section 108. Section 109(b)(1) defines a primary standard as an ambient air quality standard "the attainment and maintenance of which in the judgment of the Administrator, based on [the] criteria and allowing an adequate margin of safety, [is] requisite to protect the public health." A secondary standard, as defined in section 109(b)(2), must "specify a level of air quality the attainment and maintenance of which in the judgment of the Administrator, based on [the] criteria, is requisite to protect the public welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the ambient air." Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)] include but are not limited to "effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal comfort and well-being." 
Section 109(d) of the CAA directs the Administrator to review existing criteria and standards at 5-year intervals. When warranted by such review, the Administrator is to retain or revise the NAAQS. After promulgation or revision of the NAAQS, the standards are implemented by the states.
Role of Executive Orders in the Regulatory Impact Analysis
While this RIA is separate from the NAAQS decision-making process, several statutes and executive orders still apply to any public documentation. The analyses required by these statutes and executive orders are presented in the proposed rule preamble, and below we briefly discuss requirements of Orders 12866 and 13563 and the guidelines of the Office of Management and Budget (OMB) Circular A-4 (U.S. OMB, 2003). 
In accordance with Executive Orders 12866 and 13563 and the guidelines of OMB Circular A-4, the RIA presents the estimated benefits and costs associated with control strategies for a range of annual and 24-hour PM2.5 alternative standard levels. The estimated benefits and costs associated with emissions controls are incremental to a baseline of attaining the current standards (annual and 24-hour PM2.5 standards of 12/35 g/ - m - [3] -  in ambient air). OMB Circular A-4 requires analysis of one potential alternative standard level more stringent than the proposed standard and one less stringent than the proposed standard. The Agency is proposing to revise the current annual PM2.5 standards to a level within the range of 9-10 g/ - m - [3] -  and is soliciting comment on an alternative annual standard level down to 8 g/m[3] and a level up to 11 g/m[3]. The Agency is also proposing to retain the current 24-hour standard of 35 g/m[3] and is soliciting comment on an alternative 24-hour standard level of 30 g/m[3]. In this RIA, we are analyzing the proposed annual and  current 24-hour alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/m[3], as well as the following two more stringent alternative standard levels: (1) an alternative annual standard level of 8 g/ - m - [3] -  in combination with the current 24-hour standard (i.e., 8/35 g/ - m - [3] - ), and (2) an alternative 24-hour standard level of 30 g/ - m - [3] in combination with the proposed annual standard level of 10 g/ - m - [3] -  (i.e., 10/30 g/ - m - [3]).  
Nature of the Analysis
The control strategies presented in this RIA are an illustration of one possible set of control strategies states might choose to implement in response to the proposed standards. States -- not the EPA -- will implement the proposed NAAQS and will ultimately determine appropriate emissions control strategies and measures. State Implementation Plans (SIPs) will likely vary from the EPA's estimates provided in this analysis due to differences in the data and assumptions that states use to develop these plans. Because states are ultimately responsible for implementing strategies to meet the proposed standards, the control strategies in this RIA are considered hypothetical. The hypothetical strategies were constructed with the understanding that there are inherent uncertainties in estimating and projecting emissions and applying control measures to specific emissions or emissions sources. Additional important uncertainties and limitations are documented in the relevant chapters of the RIA.
The EPA's national program rules require technology application or emissions limits for a specific set of sources or source groups. In contrast, a NAAQS establishes a standard level and requires states to identify and secure emissions reductions to meet the standard level from any set of sources or source groups. To avoid double counting the impacts of NAAQS and other national program rules, the EPA includes previously promulgated federal regulations and enforcement actions in its baseline for this analysis (See Section 1.3.1 below for additional discussion of the baseline). The benefits and costs of the proposed standards will not be realized until specific control measures are mandated by SIPs or other federal regulations.    
The Need for National Ambient Air Quality Standards
OMB Circular A-4 indicates that one of the reasons a regulation such as the NAAQS may be issued is to address a market failure. The major types of market failure include externality, market power, and inadequate or asymmetric information. Correcting market failures is one reason for regulation, but it is not the only reason. Other possible justifications include improving the function of government, removing distributional unfairness, or promoting privacy and personal freedom.
Environmental problems are classic examples of externalities -- uncompensated benefits or costs imposed on another party as a result of one's actions. For example, the smoke from a factory may adversely affect the health of local residents and soil the property in nearby neighborhoods. If bargaining was costless and all property rights were well defined, people would eliminate externalities through bargaining without the need for government regulation.
From an economics perspective, setting an air quality standard is a straightforward remedy to address an externality in which firms emit pollutants, resulting in health and environmental problems without compensation for those incurring the problems. Setting a standard with an adequate margin of safety attempts to place the cost of control on those who emit the pollutants and lessens the impact on those who suffer the health and environmental problems from higher levels of pollution. For additional discussion on the PM2.5 air quality problem, see Chapter 2 of the Policy Assessment for the Reconsideration of the National Ambient Air Quality Standards for Particulate Matter (U.S. EPA, 2022a).
Design of the Regulatory Impact Analysis
The RIA presents the estimates of costs and benefits of applying hypothetical national control strategies for the proposed and more stringent alternative annual and 24-hour standard levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3], and 8/35 g/m[3], incremental to attaining the current PM2.5 standards and implementing existing and expected regulations. We assume that potential nonattainment areas everywhere in the U.S. will be designated such that they are required to attain the proposed standards by 2032. 
The selection of 2032 as the analysis year in the RIA does not predict or prejudge attainment dates that will ultimately be assigned to individual areas under the CAA. The CAA contains a variety of potential attainment dates and flexibility to move to later dates, provided that the date is as expeditious as practicable. For the purposes of this analysis, the EPA assumes that it would likely finalize designations for the proposed particulate matter NAAQS in late 2024. Furthermore, also for the purposes of this analysis and depending on the precise timing of the effective date of those designations, the EPA assumes that nonattainment areas classified as Moderate would likely have to attain in late 2032. As such, we selected 2032 as the primary year of analysis. States with areas classified as Moderate and higher are required to develop attainment demonstration plans for those nonattainment areas.    
The EPA recognizes that areas designated nonattainment for the proposed PM2.5 NAAQS and classified as Moderate will likely incur some costs prior to the 2032 analysis year. States with nonattainment areas designated as Moderate are required by the CAA to develop SIPs demonstrating attainment by no later than the assigned attainment date. The CAA also requires these states to address Reasonably Available Control Technologies (RACT) for sources in the Moderate nonattainment area, which would lead to additional point source controls in an area beyond existing federal emissions control measures. Additionally, the CAA requires some Moderate areas with larger populations to implement basic vehicle inspection and maintenance in the area. Should these federal programs and CAA required programs prove inadequate for the area to attain the proposed standards by the attainment date, the state would need to identify additional emissions control measures in its SIP to meet attainment requirements. 
 Establishing the Baseline for Evaluation of Proposed and Alternative Standards
To develop and evaluate control strategies, it is important to estimate PM2.5 levels in the future after attaining the current standards of 12/35 g/m[3], taking into account projections of future air quality reflecting on-the-books Federal regulations, enforcement actions, state regulations, population and where possible, economic growth. Establishing this baseline for the analysis then allows us to estimate the incremental costs and benefits associated with the alternative standard levels. For the purposes of this analysis and depending on the precise timing of the effective date of designations, the EPA assumes that areas will be designated such that they are required to reach attainment by 2032, and we developed our projected baselines for emissions and air quality for 2032.
Attaining the current standards of 12/35 g/m[3] reflects emissions reductions (i) already achieved as a result of national regulations, (ii) expected prior to 2032 from recently promulgated national regulations (i.e., reductions that were not realized before promulgation of the previous standard but are expected prior to attainment of the current PM2.5 standards), and (iii) from additional controls that the EPA estimates need to be included to reach the current standard. Additional emissions reductions achieved as a result of state and local agency regulations and voluntary programs are reflected to the extent that they are represented in emissions inventory information submitted to the EPA by state and local agencies.
We took two steps to develop the baseline for this analysis. First, national PM2.5 concentrations were projected to the analysis year (2032) based on forecasts of population and where possible, economic growth and the application of emissions controls resulting from national rules promulgated prior to this analysis, as well as state programs and enforcement actions. Second, we estimated additional emissions reductions needed to meet the current standards of 12/35 g/m[3]. Below is a list of some of the national rules reflected in the baseline. For a more complete list, please see Chapter 2, Section 2.2.1 (Air Quality Modeling Platform) and the technical support document (TSD) for the 2016v2 emissions modeling platform titled Preparation of Emissions Inventories for the 2016v2 North American Emissions Modeling Platform (U.S. EPA, 2022b). If the national rules reflected in the baseline result in changes in PM2.5 concentrations or actual emissions reductions that are lower or higher than those estimated, the costs and benefits estimated in this RIA would be higher or lower, respectively.
 Revised Cross-State Air Pollution Rule Update (RCU), (U.S. EPA, 2021)
 The Standards of Performance for Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary Sources: EGUs (U.S. EPA, 2015)
 Mercury and Air Toxics Standards (U.S. EPA, 2011) 
 Safer Affordable Fuel Efficient (SAFE) Vehicles Final Rule for Model Years 2021-2026 (U.S. EPA, U.S. DOT, 2020)
 Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles  -  Phase 2 (U.S. EPA, U.S. DOT, 2016)
 Tier 3 Motor Vehicle Emission and Fuel Standards (U.S. EPA, 2014)
We did not conduct this analysis incremental to controls applied as part of previous NAAQS analyses because the data and modeling on which these previous analyses were based are now considered outdated and are not compatible with this PM2.5 NAAQS analysis. 
Cost Analysis Approach
The EPA estimated the costs of applying hypothetical national control strategies. Where available, we apply end-of-pipe controls to achieve emissions reductions and present the costs associated with these PM2.5 emissions reductions. These cost estimates reflect only engineering costs, which generally include the costs of purchasing, installing, and operating the referenced control technologies. The technologies and control strategies selected for analysis illustrate one way in which nonattainment areas could reduce emissions. As mentioned above, the air quality issues being analyzed are highly complex and local in nature, and the results of these national-level assessments contain uncertainty. The EPA anticipates that state and local governments will consider programs that are best suited for local conditions.
Benefits Analysis Approach
The EPA estimated the number and economic value of the avoided PM2.5-attributable premature deaths and illnesses associated with the control strategies analyzed for the proposed alternative standard levels. We quantified an array of mortality and morbidity effects using the BenMAP-CE tool (U.S. EPA 2018), which has been used in recent RIAs. As compared to the 2012 PM NAAQS RIA (U.S. EPA, 2012), the Agency applied concentration-response relationships from newer epidemiologic studies, assessed a wider array of human health endpoints and updated other economic and demographic input parameters. Each of these updates is fully described in Chapter 5, the benefits analysis approach and results chapter. Unquantified health benefits, welfare benefits, and climate benefits are also discussed in Chapter 5.
Welfare Benefits of Meeting the Primary and Secondary Standards
Even though the primary standards are designed to protect against adverse effects to human health, the emissions reductions would have welfare benefits in addition to the direct health benefits. The term welfare benefits covers both environmental and societal benefits of reducing pollution. Welfare benefits of the primary PM standard include reduced vegetation effects resulting from PM exposure, reduced ecological effects from particulate matter deposition and from nitrogen emissions, reduced climate effects, and changes in visibility. This RIA does not assess welfare effects quantitatively; this is discussed further in Chapter 5.
Organization of the Regulatory Impact Analysis
This RIA is organized into the following remaining chapters:
 Chapter 2:  Air Quality Modeling and Methods. The data, tools, and methods used for the air quality modeling are described in this chapter, as well as the post-processing techniques used to produce a number of air quality metrics for input into the analysis of benefits and costs.
 Chapter 3: Control Strategies and PM2.5 Emissions Reductions. The chapter presents the hypothetical control strategies and estimated emissions reductions in 2032 after applying the control strategies.
            
 Chapter 4: Engineering Cost Analysis and Qualitative Discussion of Social Costs. The chapter summarizes the methods, tools, and data used to estimate the engineering costs of the alternative standard levels analyzed. The chapter also provides a qualitative discussion of social costs.
 Chapter 5: Benefits Analysis Approach and Results. The chapter quantifies the estimated health-related benefits of the PM-related air quality improvements associated with the control strategies for the proposed and alternative standard levels analyzed. The chapter also presents qualitative discussions of welfare benefits and climate benefits.
 Chapter 6: Environmental Justice. This chapter includes an assessment of environmental justice impacts associated with the control strategies for the proposed and alternative standard levels analyzed.
 Chapter 7: Labor Impacts. This chapter provides a qualitative discussion of potential labor impacts.
 Chapter 8: Comparison of Benefits and Costs. The chapter compares estimates of the benefits with costs and summarizes the net benefits of the proposed and alternative standard levels analyzed.
References 
U.S. EPA (2011). Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards. Research Triangle Park, NC. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact Division. U.S. EPA. EPA-452/R-11-011. December 2011. Available at: https://www.epa.gov/sites/default/files/2020-07/documents/utilities_ria_final-mats_2011-12.pdf. 
U.S. EPA (2012). Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. Research Triangle Park, NC. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact Division. U.S. EPA. EPA-452/R-12-005. December 2012. Available at: https://www3.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf.
U.S. EPA (2014). Control of Air Pollution from Motor Vehicles: Tier 3 Motor Vehicle Emission and Fuel Standards Final Rule Regulatory Impact Analysis. U.S. Environmental Protection Agency, Office Transportation and Air Quality. EPA-420/R-14-005. March 2014. Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi/P100ISWM.PDF?Dockey=P100ISWM.PDF.
U.S. EPA (2015). Regulatory Impact Analysis for the Final Standards of Performance for Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary Sources: Electric Utility Generating Units. Research Triangle Park, NC. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact Division. U.S. EPA. EPA-452/R-15-005. August 2015. https://www.epa.gov/sites/default/files/2020-07/documents/utilities_ria_final-nsps-egus_2015-08.pdf. 
U.S. EPA., U.S. DOT (2016). Greenhouse Gas Emissions and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles - Phase 2 Regulatory Impact Analysis. U.S. Environmental Protection Agency, Office Transportation and Air Quality. U.S. Department of Transportation, National Highway Traffic Safety Administration. EPA-420/R-16-900. August 2016. Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi/P100P7NS.PDF?Dockey=P100P7NS.PDF 
U.S. EPA (2018). Environmental Benefits Mapping and Analysis Program  -  Community Edition User's Manual. Office of Air Quality Planning and Standards. Research Triangle Park, NC. U.S. EPA. Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf.



U.S. EPA (2019). Additional Methods, Determinations, and Analyses to Modify Air Quality Data Beyond Exceptional Events. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, EPA-457/B-19-002. Available: https://www.epa.gov/sites/default/files/2019-04/documents/clarification_memo_on_data_modification_methods.pdf
U.S. EPA., U.S. DOT (2020). Final Regulatory Impact Analysis, The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Year 2021  -  2026 Passenger Cars and Light Trucks. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact Division. U.S. Department of Transportation, National Highway Traffic Safety Administration. March 2020. Available at: https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/final_safe_fria_web_version_200701.pdf 
U.S. EPA (2021). Regulatory Impact Analysis for the Final Revisions Revised Cross-State Air Pollution Rule (CSAPR) Update for the 2008 Ozone NAAQS. Research Triangle Park, NC. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact Division. U.S. EPA. EPA-452/R-21-002. March 2021. Available at: https://www.epa.gov/sites/default/files/2021-03/documents/revised_csapr_update_ria_final.pdf.
U.S. EPA (2022a). Policy Assessment for the Reconsideration of the National Ambient Air Quality Standards for Particulate Matter. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-452/R-22-004. May 2022. Available at: https://www.epa.gov/naaqs/particulate-matter-pm-standards-policy-assessments-current-review-0.
U.S. EPA (2022b). Technical Support Document (TSD): Preparation of Emissions Inventories for the 2016v2 North American Emissions Modeling Platform. Research Triangle Park, NC. Office of Air Quality Planning and Standards, Air Quality Assessment Division. U.S. EPA. EPA-452/B-22-001. February 2022. Available at: https://www.epa.gov/system/files/documents/2022-02/2016v2_emismod_tsd_february2022.pdf.
U.S. OMB (2003). Circular A-4, September 17, 2003, Available at: https://www.whitehouse.gov/wp-content/uploads/legacy_drupal_files/omb/circulars/A4/a-4.pdf.


AIR QUALITY MODELING AND METHODS
 Overview
To evaluate the incremental costs and benefits of meeting the alternative PM2.5 standard levels relative to meeting the existing standards, models were used to predict PM2.5 concentrations and emissions associated with the standard levels. Air quality was simulated using a 2016-based modeling platform with the Community Multiscale Air Quality (CMAQ) model. The modeling platform paired a 2016 CMAQ simulation with a corresponding CMAQ simulation with emissions representative of 2032 that reflect effects of finalized rules and other factors. 
Air quality ratios, which relate a change in PM2.5 design values (DVs) to a change in emissions, were used to estimate the emission reductions needed to meet the existing and alternative NAAQS in areas projected to exceed the standards in 2032. These emission estimates are used in identifying controls and associated costs of meeting the alternative standard levels relative to meeting the existing standards. A PM2.5 concentration field was developed using the 2032 CMAQ modeling and was adjusted according to the required change in PM2.5 concentrations to create PM2.5 fields associated with meeting standard levels. These PM2.5 concentration fields are used in calculating the health benefits associated with meeting the standard levels.
      The overall steps in the process are as follows:
 Project annual and 24-hour PM2.5 DVs to 2032 using a CMAQ simulation for 2016 and a corresponding CMAQ simulation with emissions representative of 2032 that reflects effects of finalized rules and other factors.
 Develop air quality ratios that relate a change in PM2.5 DV to a change in emissions for use in estimating the emissions reductions needed to just meet the existing and alternative NAAQS. The air quality ratios are developed using CMAQ sensitivity modeling with reductions in anthropogenic emissions in select counties.
 Using the air quality ratios from Step 2, estimate the emission reductions beyond the 2032 modeling case that are needed to meet the existing standards and adjust PM2.5 DVs accordingly. The resulting PM2.5 DVs define the 12/35 analytical baseline that is used as the reference case in estimating the incremental costs and benefits of meeting alternative standard levels relative to existing standards. Note that emission reductions applied to meet the existing standards do not contribute to incremental costs and benefits in the Regulatory Impact Analysis (RIA).
 Using the air quality ratios from Step 2, estimate the primary PM2.5 emission reductions needed to meet the alternative standard levels beyond the 12/35 analytical baseline. These emission reduction estimates are used in developing controls to meet the alternative standard levels. 
 Develop a gridded national PM2.5 concentration field associated with the 2032 case by fusing the 2032 CMAQ modeling with projected monitor concentrations. Adjust the 2032 concentration field according to the changes in PM2.5 DVs needed to meet standard levels to create PM2.5 fields associated with each standard level. These PM2.5 concentration fields are used in calculating the health benefits associated with meeting alternative standard levels.
      In the remainder of this chapter, contextual information on PM2.5 and its characteristics in the U.S. is first provided in Section 2.1. The projection of air quality from 2016 to 2032 is then described in Section 2.2. In Section 2.3, the development of air quality ratios and their application to estimating emission reductions is described. In Section 2.4, the air quality challenges in select areas are described in terms of highly local influences on PM2.5 concentrations. Finally, the development of the PM2.5 concentration fields associated with meeting the existing and alternative standards is described in Section 2.5. 
PM2.5 Characteristics
PM2.5 Size and Composition
     As described in the Integrated Science Assessment (US EPA, 2019a) and Policy Assessment (US EPA, 2022a), PM (particulate matter) refers to the mass concentration of suspended particles in the atmosphere. Atmospheric particles range in size from less than 1 nanometer (10[-9] meter) to over 100 micrometers (um, or 10[-6] meter) in diameter. For reference, a typical strand of human hair is 70 um in diameter and a grain of salt is about 100 um. Atmospheric particles are often classified into size ranges associated with the three distinct modes evident in measured ambient particle size distributions. The size ranges include ultrafine particles (<0.1 um), accumulation mode or fine particles (0.1 to ~3 um), and coarse particles (>1 um). For regulatory purposes, fine particles are measured as PM2.5, which refers to the total mass concentration of particles with aerodynamic diameter less than 2.5 um.
     PM is made up of many different chemical components. The major components include carbonaceous matter (elemental and organic carbon) and inorganic species such as sulfate, nitrate, ammonium, and crustal species. PM includes solid and liquid particles as well as multiphase particles (e.g., particles with a solid core surrounded by an inorganic aqueous solution with an organic coating). The phase state and composition of an atmospheric particle can vary with atmospheric conditions. For example, the aqueous phase of a particle may effloresce (i.e., crystallize) when the atmospheric relative humidity falls below a threshold. Similarly, as gas-phase concentrations and meteorological conditions (e.g., temperature and relative humidity) change, chemical species can condense and evaporate from particles to maintain or approach equilibrium with their gas-phase counterparts (Seinfeld and Pandis, 2016).
PM can be directly emitted into the atmosphere or formed in the atmosphere through chemical and physical processes. PM that is directly emitted into the atmosphere by sources is referred to as primary PM. Elemental carbon and crustal species are examples of primary PM components. PM that is formed in situ through atmospheric processes is referred to as secondary PM. Secondary PM is formed through pathways including new particle nucleation, condensation and reactive uptake of gas-phase species, and cloud and fog evaporation (Seinfeld and Pandis, 2016). Nucleation of new particles occurs when molecular clusters formed from gas-phase species grow into stable particles. Condensation of atmospheric gases onto preexisting particles occurs when gas-phase concentrations exceed the equilibrium vapor concentrations of the particle constituents. PM formation from cloud and fog processes occurs when semi- and non-volatile chemical species formed via aqueous chemistry in cloud and fog remain suspended in ambient particles following cloud/fog evaporation.
Gaseous SO2 emissions lead to PM2.5 formation following SO2 oxidation to sulfuric acid in the gas and aqueous phases (Seinfeld and Pandis, 2016). Sulfuric acid is essentially non-volatile under atmospheric conditions and leads to PM2.5 sulfate formation by contributing to new particle formation, condensation onto preexisting particles, and remaining in particles following cloud/fog evaporation. Enhanced particle acidity due to PM2.5 sulfate formation reduces the equilibrium vapor concentration of ammonia (the primary atmospheric base) and promotes condensation of ammonia onto particles, thereby forming PM2.5 ammonium. PM2.5 sulfate and associated water and acidity also influence chemical pathways for the formation of secondary organic aerosol (SOA). 
Gaseous NOx emissions lead to PM2.5 formation following NOx oxidation to nitric acid, which is semi-volatile under atmospheric conditions (Seinfeld and Pandis, 2016). Condensation of nitric acid onto particles tends to be favorable under cool, humid conditions with abundant ammonia, and results in PM2.5 nitrate formation. Due to effects of nitric acid on particle acidity, ammonia often co-condenses with nitric acid to yield PM2.5 ammonium. NOx emissions also influence secondary PM concentrations by modulating many atmospheric oxidation processes and by contributing to the production of organic nitrates. Monoterpene nitrates and isoprene nitrates are examples of PM2.5 species that can be formed from products of anthropogenic NOx emissions and biogenic volatile organic compound (VOC) emissions. SOA formation occurs following the oxidation of VOC emissions in the atmosphere. SOA formation is an active area of research and involves myriad species and reactions occurring in the gas, particle, and aqueous phases. Gaseous ammonia emissions can influence PM concentrations by affecting cloud and aerosol acidity in addition to condensing on particles to form PM2.5 ammonium. 
The emission sources of primary PM2.5 and the gaseous precursors of PM2.5 have recently been summarized in the PM NAAQS Policy Assessment (USEPA, 2022a). EGUs make up the largest emissions source sector for SO2. The largest NOx emissions sectors include mobile sources (on-road and non-road) and EGUs. Ammonia emissions are greatest from the agricultural sector (fertilizer and livestock waste) and from fires. VOC emissions are largest from mobile sources, industrial processes, fires, and biogenic sources. Primary PM2.5 emissions are largest from fires, fugitive dust (paved/unpaved road dust and construction dust), and area sources (e.g., residential wood combustion). Fires are an important source of particulate organic matter. Note that some PM2.5 components (e.g., elemental carbon and crustal species) occur due to direct emissions alone while other PM2.5 components (e.g., organic carbon and sulfate) occur due to a combination of direct emissions and secondary formation in the atmosphere. 
PM2.5 Regional Characteristics
     PM2.5 concentrations vary in magnitude and composition over the U.S. with distinct regional and seasonal features. The characteristics of PM2.5 concentrations in the U.S. have recently been summarized in the Integrated Science Assessment (USEPA, 2019a), and the spatial distribution of PM2.5 over the U.S. is shown in Figure 2-1 based on a hybrid satellite modeling method (van Donkelaar et al., 2021). In the Eastern U.S., organic carbon and sulfate have the highest contribution to total PM2.5 concentrations in most locations. In the Upper Midwest and Ohio Valley, nitrate can also be an important contributor to PM2.5, due to the cool, humid conditions in winter and influence of ammonia that promotes ammonium nitrate formation. In the Southeastern U.S., organic carbon concentrations are relatively high due to the abundance of biogenic VOC emissions that contribute to SOA formation following oxidation in the presence of anthropogenic emissions. Areas of relatively high PM2.5 concentrations within the Eastern U.S. are associated with urban centers.
                                       
Figure 2-1	Annual Average PM2.5 Concentrations over the U.S. in 2019 Based on the Hybrid Satellite Modeling Approach of van Donkelaar et al. (2021)
     
     The Western U.S. is characterized by some of the lowest and highest PM2.5 concentrations in the country, with relatively sharp spatial gradients in PM2.5 compared to the east. The complex terrain of the Western U.S. has an important influence on air pollution processes as does the relative abundance of wildfires (and prescribed burning). In the Northwest, meteorological temperature inversions often occur in small mountain valleys in winter and trap pollution emissions in a shallow atmospheric layer at the surface. Emissions from home heating with residential wood combustion can build up in the surface layer and produce episodically high PM2.5 concentrations in winter. Elevated wintertime PM2.5 in these mountain valleys can approach or sometimes exceed the 24-hour PM2.5 standard, which is based on a 98[th] percentile form. 
     In large western air basins (e.g., San Joaquin Valley, CA; South Coast Air Basin, CA; and Salt Lake Valley, UT), emission sources are more diverse than in the small mountain valleys and include NOx emissions from urban centers and ammonia from agriculture. Meteorological conditions are also more complex than in the smaller valleys and can include a persistent aloft temperature inversion from high-pressure-driven air subsidence in addition to a near-surface temperature inversion from nighttime radiative cooling. The near-surface inversion has the effect of concentrating primary PM2.5 emissions near the ground, whereas the aloft inversion caps the nighttime residual air layer, in which NOx is converted to nitrate through heterogeneous aerosol chemistry. In the morning, when the near-surface inversion breaks and the surface mixed layer grows due to surface heating, the PM2.5 nitrate and ammonium formed overnight in the residual layer are entrained to the surface. This entrainment has the effect of diluting primary PM2.5 concentrations near the surface and enhancing surface concentrations of secondary PM2.5. PM2.5 concentrations in the South Coast Air Basin are also affected by the land-sea breeze circulation and a semi-permanent high-pressure cell. Due to the large populations, diverse emission sources, and terrain-driven meteorological features, the San Joaquin Valley and South Coast Air Basin experience elevated annual-average PM2.5 concentrations as well as short-term PM2.5 enhancements. These characteristics can create challenges for meeting both the annual and 24-hour PM2.5 standards.
     PM2.5 concentrations in the Western U.S. are also strongly influenced by emissions from wildfires, which are relatively common in summer but increasingly occur year-round. In the Southwest, dust emission sources are prevalent, and windblown dust makes substantial contributions to PM2.5 concentrations under dry, windy conditions. Organic carbon is often the largest PM2.5 contributor in the west due to the influence of combustion sources such as wildfire and residential wood combustion. Crustal species are also important contributors in dust-prone areas, and ammonium nitrate is a major PM2.5 component in large air basins during meteorological stagnation periods in fall and winter. Along the border with Mexico, western areas also experience important cross-border transport contributions to PM2.5 (e.g., Calexico, CA experiences contributions from the much the larger city of Mexicali, MX, which is in the same airshed just across the border). 
PM2.5 Trends
	Over the last several decades, PM2.5 concentrations have decreased on average over the U.S. (Figure 2-2). As described in the recent PM NAAQS Policy Assessment (USEPA, 2022a), the reductions in PM2.5 concentrations correspond to the reductions in PM2.5 precursor emissions illustrated in Figure 2-3. Among the PM2.5 precursors (i.e., SO2, NOx, VOC, and ammonia), the largest emission reductions occurred for SO2 and NOx. SO2 emissions decreased by 84% between 2002 and 2017, and NOx emissions decreased by 60%. Reductions in SO2 emissions were relatively large from stationary sources such as EGUs in the Eastern U.S. NOx emission reductions were driven by reduced emissions from mobile sources and EGUs. Compared with SO2 and NOx, emissions of primary PM2.5 and ammonia have been relatively flat in recent decades. The small changes in primary PM2.5 emissions in Figure 2-3 are likely due to changes in emission estimation methods for source sectors over time. Wildfire emissions are not included in the data for Figure 2-3, but an upward trend in PM2.5 emissions is evident in estimates generated for National Emission Inventory years (i.e., 2005, 2008, 2011, 2014, and 2017). Studies have also predicted that climate change presents increased potential for very large fires in the contiguous U.S. in the future (e.g., Barbero et al., 2015).
                                       
Figure 2-2	Seasonally Weighted Annual Average PM2.5 Concentrations in the U.S. from 2000 to 2019 (406 sites) 
Note: The white line indicates the mean concentration while the gray shading denotes the 10th and 90th percentile concentrations.

                                       
Figure 2-3	National Emission Trends of PM2.5, PM10, and Precursor Gases from 1990 to 2017 
Note: Data do not include wildfire emissions.

	As described in the PM NAAQS Policy Assessment (USEPA, 2022a), PM2.5 precursor emission reductions have altered the seasonal variation in PM2.5 concentrations over the U.S. Through 2008, the peak in the national average PM2.5 concentration occurred during summer, largely due to sulfate formation from summertime increases in EGU SO2 emissions in the Eastern U.S. and wildfires in the West. However, starting in 2009, the summertime peaks in PM2.5 concentrations have been smaller than those in winter as PM2.5 sulfate concentrations have decreased (Chan et al., 2018). The decrease in sulfate in the Eastern U.S. has increased the relative contribution of organic carbon and sources of primary PM2.5, whose emissions have remained flat as SO2 emissions have decreased. Primary PM2.5 sources in urban centers contribute to the "urban increment" of consistently higher PM2.5 concentrations in urban than surrounding areas (Chan et al., 2018). 
	To explore how emission trends may persist into the future, models are applied to project emission inventories accounting for expected future emission changes from finalized rules and other factors. Air quality models are then used to simulate pollutant concentrations under conditions of the projected future emissions. For the purposes of the RIA, model projections from 2016 to 2032 were developed for air quality analyses as described in section 2.2. As shown in Figure 2-4, the trends in NOx, SO2, and primary PM2.5 emissions from the recent past (Figure 2-3) are projected to continue into the near future. From 2016 to 2032, anthropogenic NOx emissions are projected to decrease by 3.8 million tons (40%), with the greatest reductions from mobile-source sectors (nonroad and onroad) and EGUs. SO2 emissions are projected to decrease by 1 million tons (38%), with the greatest reductions from the EGU sector. For primary PM2.5, emissions are relatively flat from 2016 to 2032, with a decrease of 100k tons (3%) mainly due to reductions from mobile sources and EGUs. Primary PM2.5 emissions from the largest emitting sectors (e.g., dust, agricultural and prescribed fires, residential wood combustion, and areas sources) are essentially constant or slightly increasing (e.g., dust) (Figure 2-4). This projected behavior is consistent with past trends, in which NOx and SO2 emissions declined steadily while primary PM2.5 emissions were relatively constant (Figure 2-3).

                                       
Figure 2-4	Annual Anthropogenic Source Sector Emission Totals (1000 tons per year) for NOx, SO2, and PM2.5 for 2016 and 2032 
Note that AgPrFire: agricultural and prescribed fire; Nonpt: non-point area sources; O&G: oil and gas; Other: airports, commercial marine vehicles, rail, and solvents; NonIPM: remaining non-EGU point sources; RWC: residential wood combustion. 
      As mentioned above, spatial distributions of PM2.5 concentrations in the U.S. are characterized by an "urban increment" of consistently higher PM2.5 concentrations over urban than surrounding areas. Monitored concentrations are highest in urban areas and relatively low in rural areas. Conceptually, PM2.5 concentrations in urban areas can be viewed as the superposition of the urban increment and the contributions from regional and natural background sources. The decreases in anthropogenic SO2 and NOx emissions in recent decades have reduced regional background concentrations and increased the relative importance of the urban increment. The projections of additional large reductions in SO2 and NOx emissions in the 2032 case further motivates the need for control of local primary PM2.5 sources to address the highest PM2.5 concentrations in urban areas.
      In Figure 2-5, PM2.5 concentrations are shown over four urban areas in the Eastern U.S. based on the 2032 modeling case described in section 2.2. A common feature of these diverse locations is the relatively high PM2.5 concentrations over the urban area and lower concentrations just outside of the urban core. PM2.5 concentrations in the urban core of these Eastern U.S. areas exceed alternative standards levels considered in the RIA, whereas concentrations surrounding the urban core are below the alternative standard levels. In the illustrative control strategy analysis of the RIA, the urban exceedances are addressed by focusing on primary PM2.5 emission controls in the local county. This approach is consistent with the exceedances being driven by the urban PM2.5 increment, the relatively high responsiveness of PM2.5 concentrations to primary PM2.5 emission reductions (e.g., Appendix 2A.5), and the reductions in regional PM2.5 concentrations from the large SO2 and NOx emission reductions in recent decades and in the 2032 projection. Patterns may vary in the Western U.S. where the spatial extent of the PM2.5 increment may be influenced by complex terrain that defines distinct air basins..

                                       
 Figure 2-5	Gridded PM2.5 Concentrations over Selected Urban Areas Based on the 2032 Modeling Case Described Below with the Enhanced Voronoi Neighbor Averaging Approach

Modeling PM2.5 in the Future
To evaluate the incremental costs and benefits of meeting the alternative PM2.5 standard levels proposed in this RIA relative to meeting the existing standards, models were used to predict PM2.5 concentrations associated with emissions representative of a 2032 future year to inform subsequent analyses. The projections were performed using a 2016-based modeling platform with the Community Multiscale Air Quality (CMAQ) model (www.epa.gov/cmaq). The modeling platform paired a 2016 CMAQ simulation with a corresponding CMAQ simulation based on emissions representative of 2032. The 2032 emission projections account for numerous factors including the effects of finalized rules. This modeling platform was chosen because it represents the most recent, complete set of emissions information currently available for national-scale modeling. The approach used for projecting future-year air quality with the platform is described in this section. 
Air Quality Modeling Platform
To project air quality to the future, the CMAQ model was applied to simulate air quality over the U.S. during 2016 and for a case with emissions representative of 2032. Other than the differences in emissions inventories for the 2016 and 2032 CMAQ simulations, all other model inputs specified for the 2016 base year remained unchanged in the 2032 modeling case. Inputs for CMAQ simulations include files with emissions, meteorology, and initial and boundary condition data.
Model Configuration
CMAQ is a three-dimensional grid-based Eulerian air quality model designed to estimate the formation and fate of oxidant precursors, primary and secondary PM2.5 concentrations, and deposition over regional spatial scales (e.g., over the contiguous U.S.) (Appel et al., 2021, Appel et al., 2018, Appel et al., 2017). CMAQ simulates the key processes (e.g., emissions, transport, chemistry, and deposition) that affect primary (directly emitted) and secondary (formed by atmospheric processes) PM2.5 using state-of-the-science process parameterizations and input data for emissions, meteorology, and initial and boundary conditions. CMAQ's representation of the chemical and physical mechanisms that govern the formation and fate of air pollution enable simulations of the impacts of emission controls on PM2.5 concentrations. 
CMAQ version 5.3.2 (www.epa.gov/cmaq) was used to simulate air quality for 2016 to provide a reference simulation for the 2032 air quality projection. The geographic extents of the outer and inner air quality modeling domains are shown in Figure 2-6. The outer domain covers the 48 contiguous states along with most of Canada and Mexico using a horizontal resolution of 36 x 36 km. Air quality modeling for the 36-km domain was used to provide chemical boundary conditions for the simulation on the nested 12-km domain used in air quality analyses in the RIA.
Gas-phase chemistry in the CMAQ simulations was based on the Carbon Bond 2006 mechanism (CB6r3) (Emery et al., 2015), and deposition was modeled with the M3DRY parameterization. Aerosol processes were parameterized with the AERO7 module using ISORROPIA II for inorganic aerosol thermodynamics (Fountoukis and Nenes, 2007) and the non-volatile treatment for primary organic aerosol (Appel et al., 2017, Simon and Bhave, 2012). Emissions of biogenic compounds were modeled with the Biogenic Emission Inventory System (BEIS) (Bash et al., 2016). Anthropogenic emissions were based on 2016 version 2 emissions modeling platform (USEPA, 2022b), which included emissions for 2016 and the projected 2032 case. Meteorological data were based on a 2016 simulation with version 3.8 of the Weather Research Forecasting (WRF) model (Skamarock et al., 2008). The meteorological fields include hourly-varying horizontal wind components (i.e., speed and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical layer. Additional details on the model configuration are available in section 2A.1.1 of Appendix 2A.
                                     36US3
12US2
36US3
12US2
Figure 2-6	Map of the Outer 36US3 (36 x 36 km Horizontal Resolution) and Inner 12US2 (12 x 12 km Horizontal Resolution) Modeling Domains

Emission Inventory
The future-year emission inventory is projected from the 2016 version 2 emissions modeling platform. The projected emission case is labeled 2032, although the emission projections are based on a combination of projection years. The development of the 2016 base-year inventory, the projection methodology, and the controls applied to create the projected inventory are described in detail in the emissions Technical Support Document (TSD): Preparation of Emissions Inventories for the 2016v2 North American Emissions Modeling Platform (USEPA, 2022b). The types of sources included in the emission inventory include stationary point sources such as EGUs and non-EGUs; non-point emissions sources including those from oil and gas production and distribution, agriculture, residential wood combustion, fugitive dust, and residential and commercial heating and cooking; mobile source emissions from onroad and nonroad vehicles, aircraft, commercial marine vessels, and locomotives; wild, prescribed, and agricultural fires; and biogenic emissions from vegetation and soils.
The EGU emissions were developed using the Summer 2021 version of the Integrated Planning Model (IPM) (USEPA, 2021). The IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector. The EGU projected inventory represents demand growth, fuel resource availability, generating technology cost and performance, and other economic factors affecting power sector behavior. It also reflects environmental rules and regulations, consent decrees and settlements, plant closures, and newly built units for the calendar year 2030. In this analysis, the projected EGU emissions include the 2021 Revised Cross-State Air Pollution Rule Update (RCU), the 2016 Standards of Performance for Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary Sources, the Mercury and Air Toxics Rule (MATS) finalized in 2011, and other finalized rules. Full documentation and results of the Summer 2021 Reference Case for EGUs are available at https://www.epa.gov/power-sector-modeling/results-using-epas-power-sector-modeling-platform-v6-summer-2021-reference.
      Regulations for non-EGU point sources and non-point sources reflected in the inventories include:
 New Source Performance Standards (NSPS) for oil and natural gas sources (2016), process heaters (2013), natural gas turbines (2012), and reciprocating internal combustion engines;
 NSPS for residential wood combustion (2015);
 Fuel sulfur rules in mid-Atlantic and northeast states (current through 2019);
 NSPS and Emission Guidelines for Commercial and Industrial Solid Waste Incineration (CISWI) from March 2011;
 NSPS Subpart JA for Standards of Performance for Petroleum Refineries from June 2008;
 Specific consent decrees; and
 Ozone Transport Commission controls for Portable Fuel Containers, consumer products, architectural and industrial maintenance coatings, and various other solvents.
Note that the Boiler MACT is assumed to be fully implemented by 2016 except for North Carolina, in which it was fully implemented by 2017. Known closures are also implemented for non-EGU point sources.
Onroad and nonroad mobile source emissions were developed using the Motor Vehicle Emission Simulator version 3 (MOVES3). The SMOKE-MOVES emissions modeling framework was used that leverages MOVES-generated emission factors, county and SCC-specific activity data, and hourly meteorological data. MOVES3 was run in emission rate mode to create emission factor tables for the 2032 future modeling year for all representative counties and fuel months. These emissions represent the effects the Safer Affordable Fuel Efficient (SAFE) Vehicles Final Rule for Model Years 2021-2026 (March 2020); Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles  -  Phase 2 (October 2016); Tier 3 Vehicle Emission and Fuel Standards Program (March 2014) and other finalized rules. A full discussion of the future year base inventory is provided in USEPA (2022b). Nonroad emissions rules related to nonroad spark-ignition engines, equipment, and vessels from October 2008 are reflected. 
      Emissions for commercial marine vessels and locomotive engines reflect the rules finalized in 2010 and 2008: 
 Growth and control from Locomotives and Marine Compression-Ignition Engines Less than 30 Liters per Cylinder: March 2008
 Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards: April 2010
 Growth and control from Locomotives and Marine Compression-Ignition Engines Less than 30 Liters per Cylinder: March 2008
            
Model Evaluation
An operational model performance evaluation for PM2.5 and its speciated components (e.g., sulfate, nitrate, elemental carbon, and organic carbon) was performed to estimate the ability of the CMAQ modeling system to replicate the 2016 base year concentrations. This evaluation includes statistical assessments of model predictions versus observations from national monitoring networks paired in time and space. Details on the evaluation methodology and the calculation of performance statistics are provided in section 2A.1.2 of Appendix 2A. Overall, the performance statistics for PM2.5 and its components from the CMAQ 2016 simulation are within or close to the ranges found in other recent applications. These model performance results provide confidence that our use of the 2016 modeling platform is a scientifically credible approach for assessing PM2.5 concentrations for the purposes of the RIA.
Future-Year PM2.5 Design Values
To evaluate the incremental costs and benefits associated with meeting alternative standard levels relative to the existing standard, PM2.5 DVs were first projected to 2032 accounting for emission reductions expected from finalized rules. The air quality and emission changes associated with meeting the existing and alternative standard levels were then estimated as described below in Section 2.3. PM2.5 DVs were projected to 2032 using the air quality model results in a relative sense, as recommended by the EPA modeling guidance (USEPA, 2018), by projecting monitoring data with relative response factors (RRFs) developed from the 2016 and 2032 CMAQ modeling.
PM2.5 RRFs were calculated as the ratios of modeled PM2.5 species concentrations in the future year (2032) to the base year (2016) for each PM2.5 component (i.e., sulfate, nitrate, organic carbon, elemental carbon, crustal material, and ammonium). The 2032 PM2.5 DVs were calculated by applying the species-specific RRFs to ambient PM2.5 species concentrations from the PM2.5 monitoring network. Observed PM2.5 concentrations were disaggregated into species concentrations by applying the SANDWICH method (Frank, 2006) and through interpolation of PM2.5 species data from the Chemical Speciation Network (CSN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network. The RRF method for projecting PM2.5 DVs was implemented using EPA's Software for Modeled Attainment Test-Community Edition (SMAT-CE) version 1.8 (USEPA, 2018, Wang et al., 2015). More details on the PM2.5 projection method using RRFs are provided in the user's guide for the predecessor to the SMAT-CE software (Abt, 2014).
Ambient PM2.5 measurements from the 2014-2018 period centered on the 2016 CMAQ modeling period were used in projecting PM2.5 DVs. PM2.5 species measurements from the IMPROVE and CSN networks during 2015 - 2017 were used to disaggregate the measured total PM2.5 concentrations into components. In addition to exclusion of EPA-concurred exceptional events, limited exclusion of wildfire and fireworks influence on PM2.5 concentrations was applied to the 2014-2018 PM2.5 monitoring data. Monitoring data were evaluated (i.e., screened) for potential wildfire and fireworks influence because PM2.5 concentrations may be influenced by atypical, extreme, or unrepresentative events such as wildfires or fireworks that may be appropriate for exclusion as described in EPA's memorandum Additional Methods, Determinations, and Analyses to Modify Air Quality Data Beyond Exceptional Events (USEPA, 2019b).  The steps in implementing the limited screening of major wildfire and fireworks influence on PM2.5 concentrations are as follows.
 An extreme-concentration cutoff of 61 ug m[-3] was identified based the 99.9[th] percentile value from all daily PM2.5 concentrations across all sites in the long-term AQS observations (2002-2018). 
 Specific states and months where wildfires frequently occur were screened for instances of monitors exceeding the cutoff concentration. Potential wildfire periods were identified as those with PM2.5 concentrations above the cutoff concentration in June-October in CA, WA, OR, MT, ID, and CO. 
 For potential wildfire periods, the presence of visible wildfire smoke was examined using satellite imagery from NASA's Worldview platform (https://worldview.earthdata.nasa.gov). Timeseries of PM2.5 concentrations at individual sites were also examined to confirm that the PM2.5 enhancements are temporally consistent with wildfire events. 
 For wildfire periods confirmed by the satellite imagery and timeseries analysis, PM2.5 concentrations above the cutoff concentration of 61 ug m[-3] occurring during the identified wildfire episode window at impacted sites were excluded. If the satellite imagery and timeseries analysis did not corroborate the wildfire event, data from the period were retained.
 In addition to the screening criteria above, data for the Camp Fire in northern CA during November 2018 and the Appalachian Fires in NC, TN, and GA during November 2016 were evaluated for exclusion if concentrations exceeded the extreme value threshold of 61 g m[-3]. These large fire episodes produced obvious PM2.5 concentration impacts across multiple monitors and were clearly evident in the satellite imagery. 
 In addition to the limited exclusion of major wildfire influence, data were evaluated to identify days for potential exclusion due to the influence of isolated fireworks events on PM2.5 concentrations. The 99.9[th] percentile concentration of 61 ug m[-3] was applied as the cutoff value across all sites for New Year's Eve and the Fourth of July. 
The excluded site-day combinations represent a small fraction (0.4%) of the total site-day combinations for the flagged sites. Since the cutoff value (61 g m[-3]) is much greater than the 24-hour and annual standard levels, wildfire contributions to PM2.5 concentrations above the standard levels likely persists in the data following screening. Comprehensive identification and exclusion of such wildfire impacts would require detailed analyses that are beyond the scope of this national assessment. More information on the wildfire and fireworks screening are provided in section 2A.2.1 of Appendix 2A.
Calculating Emission Reductions for Meeting the Existing and Alternative Standard Levels
To estimate the tons of emissions reductions needed to reach attainment of the existing and proposed alternative standard levels, we calculated air quality ratios based on how modeled concentrations changed with changes in emissions in CMAQ sensitivity modeling. Air quality ratios represent an estimate of how the DVs at a monitor would change in response to emissions reductions and have been used in prior PM NAAQS RIAs (USEPA, 2012a, USEPA, 2012b). Air quality ratios have units of g m[-3] per 1000 tons of emissions. The remainder of this section describes the development of air quality ratios and their application to estimating emission reductions for meeting the existing and alternative standards. 
Developing Air Quality Ratios
In the illustrative control strategy analysis in the RIA, the alternative standard level exceedances are addressed by focusing on primary PM2.5 emission controls in the local county. This approach is consistent with the exceedances generally being driven by the urban PM2.5 increment, the relatively high responsiveness of PM2.5 concentrations to primary PM2.5 emission reductions (e.g., Appendix 2A.5), and the reductions in regional PM2.5 concentrations from the large SO2 and NOx emission reductions in recent decades and in the 2032 projection (section 2.1.3). To develop air quality ratios that relate the change in DV in a county to the change in primary PM2.5 emissions in that county, CMAQ sensitivity modeling was performed with reductions in primary PM2.5 emissions in selected counties. The modeling was conducted using CMAQ version 5.2.1 for a 2028 modeling case similar to that of recent regional haze modeling (USEPA, 2019c) due to the availability of the 2028 (but not 2032) modeling at the time of the work. Since air quality ratios reflect the sensitivity of air quality to emission changes (rather than absolute concentrations), the air quality ratios based on the 2028 modeling are suitable for application to our 2032 modeling case. 
      To develop air quality ratios for primary PM2.5 emissions, we used the following method:
 A CMAQ sensitivity simulation was conducted with 50% reductions in primary PM2.5 emissions from anthropogenic sources in counties with annual 2028 DVs greater than 8 g m[-3]. 
 The change in annual and 24-hour PM2.5 DVs at monitors in counties where emission reductions were applied was calculated using projected DVs from the 2028 modeling with the SMAT-CE software.
 The change in DVs at individual monitors was divided by the change in emissions in the respective county to determine the air quality ratio (g m[-3] per 1000 tons) for the individual monitors. 
 The responsiveness of air quality at a specific monitor location to primary PM2.5 emission reductions depends on several factors including the specific meteorology and topography in an area and the nearness of the emissions source to the monitor. As described in a previous PM NAAQS RIA (USEPA, 2012a), the strong local influence of changes in directly emitted PM2.5 on air quality produces large variability in air quality ratios that can result in non-representative values for general application. To address this issue, representative air quality ratios for regions of the U.S. were developed from the ratios at individual monitors. The five regions are illustrated in Figure 2-7. The Northeast region was defined by combining the Upper Midwest, Ohio Valley, and Northeast U.S. climate regions (Karl and Koss, 1984); the Southeast region was defined by combining the Southeast and South U.S. climate regions (Karl and Koss, 1984); and California was separated into southern and northern regions as done previously (USEPA, 2012a) due to differences in PM2.5 responsiveness in those areas. For each region, representative air quality ratios were calculated as the 75[th] percentile of air quality ratios for individual monitors within the region. The 75[th] percentile was selected to avoid use of extreme values while accounting for the relatively high responsiveness of the highest-DV monitors that are most relevant to our application. 

                                         
                                         
Figure 2-7	Regional Groupings for Calculating Air Quality Ratios

The air quality ratios for primary PM2.5 emissions used in estimating the emission reductions needed to meet standard levels at monitors in the five regions are shown in Table 2-1. These data give an estimate of how PM2.5 DVs at a monitor would change if 1000 tons of primary PM2.5 emissions were reduced in the county in which the monitor is located. Additional details on the development of the air quality ratios are available in section 2A.3.1 of Appendix 2A.

Table 2-1	Annual and 24-Hour Air Quality Ratios for Primary PM2.5 Emissions
Region
                           Annual Air Quality Ratio
                             (g m[-3] per kton)
                           24-hour Air Quality Ratio
                             (g m[-3] per kton)
Northeast
                                     1.37
                                     4.33
Southeast
                                     1.22
                                     3.51
West
                                     2.14
                                     8.70
Northern California
                                     3.15
                                     9.97
Southern California
                                     1.18
                                     2.56

The air quality ratios in Table 2-1 relate the change in DV in a county to a change in emissions in that county. The ratios are developed for local spatial scales because concentrations are most responsive to changes in local emissions. However, emission controls may not always be identified in the local county, and emission reductions in neighboring counties may sometimes be appropriate, such as in the Eastern U.S. where counties are relatively small and terrain is relatively flat. To apply emission reductions in the neighboring counties in the Eastern U.S., the responsiveness of annual PM2.5 DVs for emission reductions within the county was compared to the responsiveness of DVs in the neighboring counties using the 2028 sensitivity modeling. Annual DVs were estimated to be 4 times more responsive on average for emission reductions in the county containing the monitor than for emission reductions in a neighboring county in the Eastern U.S. Primary PM2.5 emission reductions were not applied in neighboring counties in the Western U.S. (including California) due to the large size of the counties and the complex terrain that often isolates the influence of primary PM2.5 emissions to the local air basin. Additional information related to air quality ratios for neighboring counties is available in section 2A.3.1 of Appendix 2A.
At monitors in the South Coast Air Basin and San Joaquin Valley (SJV) of California, PM2.5 DVs exceeded the existing standards in the 2032 modeling case. Air quality management plans apply reductions in NOx emissions in addition to reductions in primary PM2.5 emissions to meet the existing NAAQS in these air basins (SCAQMD, 2017, SJVAPCD, 2018). The NOx emission reductions help in meeting the existing standards by reducing concentrations of PM2.5 ammonium nitrate in the air basins as described in section 2.1.2. In creating the 12/35 analytical baseline of DVs associated with meeting existing standards, we applied 75% reductions in NOx emissions in SJV and South Coast in addition to primary PM2.5 emission reductions. To apply the NOx emission reductions, air quality ratios for NOx emissions were developed for South Coast and SJV monitors. Air quality ratios for South Coast were developed using 2028 sensitivity modeling for NOx emissions similar to the approach described above for the primary PM2.5 air quality ratios. For SJV, air quality ratios were developed from sensitivity modeling results presented in the SJV air quality management plan (SJVAPCD, 2018), which was based on a fine-scale CMAQ modeling platform. Additional details on the South Coast and SJV air quality ratios for NOx are available in section 2A.3.2 and 2A.3.3 of Appendix 2A. Note that the NOx emission reductions were applied in attaining the existing standards and therefore do not contribute to the incremental costs and benefits of meeting alternative standard levels relative to meeting the existing standards.
Emission Reductions to Meet 12/35
PM2.5 DVs from the 2032 projection were adjusted using air quality ratios to correspond with just meeting the existing standard level to create the 12/35 analytical baseline. The 12/35 analytical baseline is used as the reference case for estimating the incremental costs and benefits of meeting the alternative standard levels relative to the existing 12/35 standard combination. 
The counties with projected 2032 PM2.5 DVs that exceed the existing standard levels and require air quality adjustments to meet 12/35 are shown in Figure 2-8. Counties that exceed only the 24-hour standard are in northern California, Oregon, Washington, Idaho, Utah, and Montana. Elevated PM2.5 episodically occurs in winter in these areas due to meteorological temperature inversions that concentrate PM2.5 in shallow layers near the ground in complex terrain. In California, multiple counties exceed both the annual and 24-hour standards, and three counties (Los Angeles, San Bernardino, and Imperial) exceed only the annual standard. Los Angeles and San Bernardino are in the South Coast Air Basin along with Riverside County, which exceeds both the annual and 24-hour standard. 
     
                                       
                                       
Figure 2-8	Counties with Projected 2032 PM2.5 DVs that Exceed the 24-Hour (24-hr Only), Annual (Annual Only) or Both (Both) Existing Standards (12/35 g m[-3])

To create the PM2.5 DVs for the 12/35 analytical baseline, the reductions in primary PM2.5 emissions needed to just meet 12/35 at the highest DV monitor by county were calculated using the air quality ratios in Table 2-1. The emission reductions were calculated as follows:
      ∆Emissionstd=DVModel,std-DVTarget,stdAQratiostdx1000			(2-1)
where Emissionstd is the emission reduction required to meet the annual or 24-hour standard; DVTarget,std is the level of the annual or 24-hour standard to be met; DVModel,std is the modeled PM2.5 design value for the annual or 24-hour standard at the county highest monitor; AQratiostd is the air quality ratio for that standard; and the factor of 1000 converts units from kton to ton. 
For example, the highest annual PM2.5 DV in Kern County is 14.54 g m[-3] at site 06-029-0016 after applying the 75% NOx emission reduction to the 2032 DVs in SJV. The annual air quality ratio for primary PM2.5 emissions in Northern California is 3.15 g m[-3] per 1000 tons. Therefore, to meet an annual standard of 12 g m[-3], a total of 794 tons of primary PM2.5 emissions is needed (i.e., (14.54-12.04)/3.15 x 1000). The highest 24-hour PM2.5 DV in Kern County is 40.4 g m[-3] at site 06-029-0010 after applying the 75% NOx emission reduction to the 2032 DVs. The 24-hour air quality ratio for primary PM2.5 emissions in Northern California is 9.97 g m[-3] per 1000 tons. Therefore, to meet a 24-hour standard of 35 g m[-3], a total of 502 tons of primary PM2.5 emissions would be needed (i.e., (40.4-35.4)/9.97 x 1000). To determine the overall emission reductions needed to meet the combination of annual and 24-hour standards, the maximum needed reduction across standards is calculated. For the Kern County example, a total 794 tons of primary PM2.5 emission reductions are needed to meet the 12/35 standard combination (i.e., the maximum of 794 tons and 502 tons). 
After the emission reductions needed to meet a standard combination are identified, the PM2.5 DVs are adjusted to correspond with the emission reductions. The PM2.5 DVs associated with meeting a standard combination at the highest monitor in a county are calculated as follows:
      DVstd.combo=DVinitial-∆Emissionstd.comboxAQratio/1000		(2-2)
In the Kern County example, the adjusted annual DV for the 12/35 case is 12.04 g m[-3] (i.e., 14.54 - (794 x 3.15 / 1000)) and the adjusted 24-hour DV is 32.5 g m[-3] (40.4  -  (794 x 9.97 / 1000)).
Emission Reductions to Meet Alternative Standards
PM2.5 DVs in the 12/35 analytical baseline exceed the levels of the alternative standards in some areas of the country. The emission reductions needed to resolve these exceedances and the associated air quality improvements contribute to the incremental costs and benefits of the alternative standard levels. 
Exceedances of the alternative standard levels in the 12/35 analytical baseline are shown by county in Figure 2-9. Since the PM2.5 DVs have been adjusted to meet the 24-hour standard level of 35 g m[-3] in the analytical baseline, there are no exceedances of the 24-hour standard for the cases of 10/35, 9/35, and 8/35. For the 10/35 case, six counties in the east, three in the northwest, and fifteen in California have annual PM2.5 DVs greater than 10 g m[-3] in the 12/35 analytical baseline. For the 10/30 case, twenty-three counties have 24-hr DVs greater than 30 g m-3 with annual DVs less than 10 g m[-3], and eleven counties exceed both the 24-hr and annual standards. For the 9/35 case, twenty-two counties exceed the annual standard in the Eastern U.S., compared with six for the 10/35 and 10/30 cases. The total number of counties exceeding the standards increases from 51 to 141 when moving from 9/35 to 8/35. Additional information on PM2.5 DVs for the 2032 projection and 12/35 analytical baseline are available in section 2A.2.2 of Appendix 2A.
     
                                       
                                       
Figure 2-9	Counties with PM2.5 DVs that Exceed Alternative Annual (Annual Only), 24-Hour (24-hr Only), or Both (Both) Standards in the 12/35 Analytical Baseline 

The primary PM2.5 emission reductions needed to meet the alternative standard levels of 10/35, 10/30, 9/35, and 8/35 relative to the 12/35 analytical baseline were calculated using Equation 2-1 and the air quality ratios in the Table 2-1. The emission reductions needed to meet the standard levels in the Eastern and Western U.S. are shown in Figure 2-10. These emission estimates are used to inform identification of emission controls for meeting the standard levels analyzed. Additional information on estimating the emission reductions needed to meet alternative standards is available in section 2A.3.4.2 of Appendix 2A.

                                          
Figure 2-10	Total Primary PM2.5 Emission Reductions Needed to Meet the Alternative Standard Levels of 10/35, 10/30, 9/35, and 8/35 Relative to the 12/35 Analytical Baseline in the Eastern and Western U.S.

Limitations of Using Air Quality Ratios
There are important limitations to the methodology of calculating and using air quality ratios to predict the response of air quality to emissions changes. The air quality ratios are calculated with results from only two CMAQ model runs and assume that the monitor DVs would decrease with additional reductions in the future similar to how the CMAQ model runs predicted changes in air quality concentrations. In addition, the model response to emissions changes is analyzed at the county-level and air quality concentrations at a monitor are assumed to decrease linearly with emission reductions in a county. Due to the strong local influence of changes in primary PM2.5 emissions on air quality, the generalized air quality ratio approach may not capture the specific features of how the DV at a monitor in a county would respond to changes in specific primary PM2.5 emissions in the county. Ideally, direct modeling would be applied to account for the location of the source relative to the location of the monitor using a model configuration designed to capture the local features near the source. Such source-specific, high-resolution modeling is beyond the scope of this national assessment.
The exact impact of using the air quality ratio methodology to estimate the emission reductions needed for attainment and the associated effect on the cost and benefits is uncertain and may vary from monitor-to-monitor. We do not believe that this methodology tends towards any general trend or results systematically in either an underestimation or overestimation of the costs and benefits of attaining the alternative standard levels. 
Description of Air Quality Challenges in Select Areas
Several groups of areas have air quality characteristics that limit our ability to characterize how standard levels might be met given highly local influences that require more specific information beyond what is available for this type of national analysis. The challenging air quality characteristics include features of local source-to-monitor impacts, cross-border transport, effects of complex terrain in the west, and identifying wildfire influence on projected PM2.5 DVs that could potentially qualify for exclusion as atypical, extreme, or unrepresentative events (USEPA, 2019b). In particular, we note that our analysis is limited in its ability to evaluate potential air quality improvements in border counties, major California air basins, small western mountain valleys, and an area in Pennsylvania affected by local sources. As a result, we have treated these areas differently in the control strategy analysis as described in Chapter 3. In this section, we describe the nature of the air quality conditions in these areas and the challenges they present for our national assessment.
Delaware County, PA
PM2.5 concentrations at the Chester monitor (site ID: 42-045-0002) in Delaware County, Pennsylvania appear to be strongly influenced by one or two nearby facilities. As described in the PA Department of Environmental Protection (PADEP) 2014 Annual Ambient Air Monitoring Network Plan (PADEP, 2014), the Chester monitor is located on the property of Evonik Degussa Corporation (Figure 2-11). The neighboring PQ Corporation produces sodium silicate and provides it to Evonik Degussa Corporation to undergo a drying process. Speciation data discussed in the 2014 monitoring plan demonstrated an anomalously high amount of silicon at the Chester speciation monitor that suggests PM2.5 concentrations are strongly influenced by local emissions from the PQ and Evonik facilities. To confirm the source influence, additional PM2.5 monitoring was performed at the Marcus Hook site about 2.5 miles from the Chester site. In PADEP's 2018 monitoring plan (PADEP, 2018), the state concluded that local sources are impacting the Chester monitoring site based on comparison of PM2.5 concentrations from the Chester and Marcus Hook sites. Our 2032 DV projections are consistent with a local source influence on the Chester monitor. For instance, the annual 2032 DV at Chester is 9.96 g m[-3] and is 8.61 g m[-3] at the Marcus Hook site about 2.5 miles away. Given the local nature of the source-to-monitor influence at the Chester site, controllable emissions likely exist at the facilities to resolve the air quality issue. However, specifically quantifying the impacts of the near-monitor controls would require a detailed local analysis beyond the scope of the national RIA.
     
                                          
Figure 2-11	Location of the Chester Site in Relation to the Evonik Degussa and PQ Corporation Facilities 
Source: PADEP, 2018

Border Areas
Imperial County, CA
As described in the Clean Air Act Section 179B Technical Demonstration by the California Air Resources Board (CARB, 2018b), the Imperial County PM2.5 nonattainment area is an agricultural community located in the southeast corner of California that shares a southern border with Mexicali, Mexico. Imperial County includes three PM2.5 monitoring sites, located in the cities of Calexico (site ID: 06-025-0005), El Centro (site ID: 06-025-1003), and Brawley (site ID: 06-025-0007) (Figure 2-12). Although these three cities are of similar size and have similar emission sources, the PM2.5 DV at the Calexico monitor closest to the U.S.-Mexico border is much greater than the other two monitors. The projected 2032 annual PM2.5 DV is 12.45 g m[-3] in Calexico, 9.13 g m[-3] in Brawley, and 8.02 g m[-3] in El Centro. The Calexico monitor is in an airshed that includes both Calexico and Mexicali and is less than one mile from the international border. Previous analysis has demonstrated that Mexicali emissions have a daily influence on PM2.5 concentrations in Calexico and can contribute to PM2.5 NAAQS exceedances there (CARB, 2018a, CARB, 2018b).  
The city of Mexicali has a population of about 700,000 (CARB, 2018a) and Calexico has a population of 38,633 (2020 U.S. Census). The nighttime aerial view of Calexico and Mexicali in Figure 2-13 illustrates the much larger scale of urban activity in Mexicali than Calexico. Substantially greater emissions have been estimated for Mexicali than Calexico (i.e., 3.4x greater for NOx, 13.7x greater for combined SO2 and sulfate, and 57% greater for primary PM2.5, CARB, 2018b). PM2.5 emissions in Imperial County are dominated by dust with limited contribution from other controllable sectors (Figure 2-14). Considering the influence of Mexicali emissions on PM2.5 concentrations in Calexico, the limited emissions available for control in Imperial County, and the relatively lower concentrations predicted at the two Imperial County monitors away from the border, EPA believes it is reasonable to assume that a significant portion of the emissions affecting this area cannot be controlled in California. However, a detailed local analysis beyond the scope of the RIA would be needed to evaluate this possibility.
      
                                       
Figure 2-12	Imperial County and the Nonattainment Area 
Source: CARB, 2018a

                                       
Figure 2-13	Nighttime Aerial View of Calexico, CA and Mexicali, MX 
Source: CARB, 2018b

                                       
Figure 2-14	Annual Source Sector Emission Totals (1000 tons per year) for PM2.5 for 2016 and 2032 in Imperial County 
Note: Sector names defined in Figure 2-4

Cameron and Hidalgo County, TX
The Brownsville monitor in Cameron County, TX (site ID: 48-061-0006) and the Mission monitor in Hidalgo County, TX (site ID: 48-215-0043) are in the Lower Rio Grande Valley, which includes the northern portion of the state of Tamaulipas, Mexico. Addressing the exceedances of the 9/35 standard level at the monitors in Cameron (2032 annual DV: 9.75 g m[-3]) and Hidalgo (2032 annual DV: 10.30 g m[-3]) is challenging due to the location of these areas along the U.S.-Mexico border. The Brownsville monitor is within one mile of the Mexican metropolitan area of Matamoros (population: 540,000; datamexico.org) and the Mission monitor is about nine miles from the Mexican metropolitan area of Reynosa (population: 700,000; datamexico.org). Due to the southeast to northwest wind pattern (Figure 2-15), emissions from these local metropolitan areas in Mexico might influence PM2.5 concentrations at the Brownsville and Mission monitors. Studies have also identified long-range transport of emissions from agricultural burning and wildfire in the southwestern states of Mexico and Central America as major regional sources that influence air quality along the U.S.-Mexico border (Karnae and John, 2019, TCEQ, 2015). Long-range transport of Saharan dust also episodically influences concentrations in this area based on speciation data, satellite imagery, and wind-flow back trajectories (TCEQ, 2015).  
Dust makes up the largest fraction of primary PM2.5 emissions in Hidalgo and Cameron County in the 2016 and 2032 modeling cases (Figure 2-16). Paved-road dust emissions are projected to increase in these counties between 2016 and 2032 due to projected increases in the vehicle miles travelled. Non-point (area source) emissions are also projected to increase due to population-based emission projection factors. Increases in dust and non-point emissions from 2016 to 2032 offset the decreases in primary PM2.5 emissions projected for EGUs and mobile (onroad/nonroad) sources in Cameron and Hidalgo County (Figure 2-16). A local area analysis would be better suited than the national RIA to understand the potential growth in dust and area source emissions as well as the potential contributions of international transport to projected exceedances in this area.   
      
                                       
Figure 2-15	Location of Mission and Brownsville Monitors in Hidalgo and Cameron County, respectively, with Annual Wind Patterns from Meteorological Measurements 
Source: TCEQ, 2015


                                       
Figure 2-16	Annual Source Sector Emission Totals (1000 tons per year) for PM2.5 for 2016 and 2032 in Cameron and Hidalgo County Combined 
Note: Sector names defined in Figure 2-4

Small Mountain Valleys in the West
As described in section 2.1.2, meteorological temperature inversions often occur in small northwestern mountain valleys in winter and trap pollution emissions in a shallow atmospheric layer at the surface. Primary PM2.5 emissions, particularly from home heating with residential wood combustion, can build up in the surface layer and produce high PM2.5 concentrations in winter (e.g., Figure 2-17). The mountain valleys are often very small in size relative to the area of the surrounding county and the scales resolved by photochemical air quality models. For instance, the Portola nonattainment area for the 2012 PM2.5 NAAQS and the city of Portola are shown within Plumas County, CA in Figure 2-18. The Libby nonattainment area for the 1997 PM2.5 NAAQS and the city of Libby are shown within Lincoln County, MT in Figure 2-19.  
     
                                          
Figure 2-17	Air Pollution Layer Associated with a Temperature Inversion in Missoula, MT in November 2018  
Source: Tommy Martino, Missoulian
                                       
Figure 2-18	Plumas County, CA (Grey), Portola Nonattainment Area (Red), and City of Portola (Purple)
Source: Map Data (C)2022 Google.
                                       
Figure 2-19	Lincoln County, MT (Grey), Libby Nonattainment Area (Red), and City of Libby (Purple)
Source: Map Data (C)2022 Google.

Due to the small size of the urban areas within the western mountain valleys, air quality planning is commonly based on linear rollback methods (rather than air quality process modeling) for these areas (e.g., LRAPA, 2012, NSAQMD, 2017). The linear rollback approach relates wood-smoke contribution estimates at the exceeding monitor to the local (sub-county) wood combustion emission totals to estimate the tons of emission reductions needed to meet the standard. Due to the high effectiveness of reducing PM2.5 emissions near monitors under stagnant meteorological conditions, the PM2.5 response factors from linear rollback methods estimate that relatively small emission reductions can greatly influence PM2.5 concentrations in the mountain valleys. For instance, based on the linear rollback analysis in the Portola, CA state implementation plan (NSAQMD, 2017), a reduction of 100 tons of primary PM2.5 emissions would reduce the annual DV by about 6.6 g m[-3]. This responsiveness is about 30x more efficient than photochemical modeling estimates of PM2.5 responsiveness for county-wide emission reductions under typical meteorological conditions (i.e., outside of mountain valley stagnation conditions). Our national RIA analysis did not apply linear rollback-based response factors for the mountain valleys because emission and control information are available only at the county level, and therefore controls cannot be targeted to the local communities in our analysis. To address standard exceedances in the small mountain valleys, a detailed analysis would be necessary that considers local PM2.5 response factors and applies controls in the local community.    
Challenges due to the wood-smoke issues just described occur in five western counties including Plumas, CA; Lincoln, MT; Shoshone, ID; Lemhi, ID; and Benewah, ID. The populations of the relevant cities within these counties range from 1,913 to 3,182 (Table 2-2). In addition to challenges related to residential wood combustion and meteorological temperature inversions, PM2.5 concentrations in these areas may also be influenced by wildfire smoke that could potentially qualify as atypical, extreme, or unrepresentative events. Some wildfire influence likely persists in the projected 2032 PM2.5 DVs despite the removal of EPA-concurred exceptional events and the wildfire screening described in section 2.2.2. Sensitivity projections with lower cutoff concentrations and broader temporal screening of wildfire influence were performed to explore the potential for wildfire impacts to affect attainment of the standards. The sensitivity projections (Table 2-2) suggest that the elevated concentrations in Benewah County may be driven largely by wildfires and that annual DVs in Lemhi, Shoshone, and Lincoln could be up to 0.8 to 1 g m[-3] lower if detailed analyses led to additional data exclusion. However, a detailed local analysis would be needed to fully characterize the wildfire influence on attainment in these areas as well as the wood-smoke issues discussed above.
Table 2-2	Information on areas with challenging residential wood combustion issues
County,
State
                             City (Population[a])
                                Annual 2032 DV
                                 (g m[-3])
                Annual 2032 DV Alternative Fire Screening I[b]
                                 (g m[-3])
                Annual 2032 DV Alternative Fire Screening II[c]
                                 (g m[-3])
Plumas, CA
Portola (1,913)
                                     14.52
                                     14.49
                                     14.23
Lincoln, MT
Libby (2,845)
                                     11.08
                                     10.79
                                     10.04
Shoshone, ID
Pinehurst (1,620)
                                     11.04
                                     10.57
                                     10.10
Lemhi, ID
Salmon (3,182)
                                     11.03
                                     10.59
                                     10.21
Benewah, ID
St. Maries (2,465)
                                     9.61
                                     8.83
                                     8.58
[a] Population from Census.gov (https://www.census.gov/programs-surveys/popest/technical-documentation/research/evaluation-estimates/2020-evaluation-estimates/2010s-cities-and-towns-total.html)
[b] Screening based on a cutoff concentration of 25 g m[-3] (rather than the default value of 61 g m[-3]) 
[c] Screening based on a cutoff concentration of 20 g m[-3] (rather than the default value of 61 g m[-3]) and inclusion of all days in June-October (rather than the flagged fire periods alone).
     
California Areas
Several areas in California present challenges in the RIA analysis in addition to the Imperial and Plumas County areas discussed above. The additional areas, described in this section, are SJV, South Coast Air Basin, and two relatively isolated counties (San Luis Obispo and Napa).
San Joaquin Valley, CA
SJV is a large inter-mountain air basin covering approximately 25,000 square miles (SJVAPCD, 2018) that makes up the southern portion of California's Central Valley. SJV is formed by the Sierra Nevada mountains in the east, the coastal mountain ranges in the west, and the convergence of mountain ranges at the Tehachapi mountains in the south. The SJV nonattainment area (Figure 2-10) includes eight counties with a combined population of about 4.3 million. Due to the typical north to south wind pattern (Ying and Kleeman, 2009) and wintertime meteorological inversions, PM2.5 concentrations tend to be highest in the south near Bakersfield and the convergence of the mountain ranges.  
                                       
Figure 2-20	San Joaquin Valley Nonattainment Area and Location of Highest PM2.5 Monitor in Bakersfield (06-029-0016) 
Source: Map Data (C)2022 Google.

SJV is currently in nonattainment of the 1997 and 2012 annual PM2.5 NAAQS and the 2006 24-hr PM2.5 NAAQS. The ambient DVs at the highest SJV monitor for the 2009-2011 to 2019-2021 DV periods are shown in Figure 2-21. Discerning progress from the SJV DVs over this period is complicated by the year-to-year variability in wildfire activity and meteorological conditions that strongly influence PM2.5 concentrations. However, the effectiveness of SJV control strategies has previously been demonstrated in terms of reductions in the annual number of days that exceed the 24-hr standard level of 35 g m[-3] (Figure 2-22; SJVAPCD, 2018). SJV control strategies focus on reducing NOx emissions to lower ammonium nitrate concentrations and reducing primary PM2.5 emissions to lower carbonaceous and crustal PM2.5 concentrations (SJVAPCD, 2018). These strategies are based on decades of modeling research and multiple intensive field measurement campaigns such as the 1995 Integrated Monitoring Study (IMS), the 2000/2001 California Regional PM10/PM2.5 Air Quality Study (CRPAQS), the 2010 California Research at the Nexus of Air Quality and Climate Change (CalNex) study, and the 2013 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) study. The effectiveness of NOx reduction for control of ammonium nitrate in SJV has also been demonstrated using data from the long-term ambient monitoring record (Pusede et al., 2016). 
                                       
Figure 2-21	Recent Annual PM2.5 DVs at the Highest SJV Monitor for Design Value Periods (e.g., 11-13: 2011-2013).  Dashed line is the 2012 Annual PM2.5 NAAQS Level (12 g m[-3])

                                       
Figure 2-22	Decrease in the Number of Days SJV Exceeded the 24-hr NAAQS Level (35 g m[-3]) 
Source: SJVAPCD, 2018

SJV air quality is influenced by emissions from large cities such as Bakersfield (population: 400,000) and Fresno (population: 540,000), an extremely productive agricultural region, dust exacerbated by drought, major goods transport corridors (i.e., Interstate-5 and Highway 99), and wildfire. Primary PM2.5 emission totals are shown for SJV in our 2032 modeling case in Figure 2-23. PM2.5 emissions are largest from agricultural dust from the production of crops and livestock, agricultural burning, paved and unpaved road dust, and prescribed burning. Wildfire also contributed 22,000 tons of PM2.5 emissions to SJV based on 2016 levels. 
The highest projected 2032 annual DV in SJV is 16.20 g m[-3] in Bakersfield (site ID: 06-029-0016). To address standard exceedances in SJV in the RIA, we applied 75% NOx emissions reductions beyond the 2032 modeling case and pursued emission reductions of primary PM2.5. However, the RIA is not well suited to identifying the specific measures needed to meet standards in SJV given the nature and magnitude of the air quality challenges. Challenges include air quality influenced by complex terrain and meteorological conditions that would be best characterized with a high-resolution modeling platform developed for the specific conditions of the valley. Also, specific local information on measures for reducing emissions from agricultural dust and burning and prescribed burning would be valuable given the magnitude of those emissions in SJV.  Characterizing the influence of wildfire on PM2.5 concentrations and potential atypical, extreme, or unrepresentative events in SJV would also benefit from a local analysis. Wildfire screening is particularly complex in California because different parts of the state have different wildfire seasonality (e.g., Barbero et al., 2014), and severe wildfire episodes can occur during periods where anthropogenic PM2.5 concentrations may also be high. Progress toward meeting the alternative standards in SJV will likely occur as an outgrowth of existing efforts to meet the 1997, 2006, and 2012 PM2.5 NAAQS.  

                                       
                                       
Figure 2-23	Annual Source Sector PM2.5 Emission Totals in SJV Counties for 2032 Modeling Case
Note that DustAgProd: Dust from Agricultural Production; AgBurn: Open Agricultural Burning; DustRoad: Paved and Unpaved Road Dust; NonIPM: Non-EGU Point Sources; Onroad: Onroad Mobile Sources; ResWoodComb: Residential Wood Combustion; Cooking: Commercial Cooking and Residential Grilling; Other: Airports, Commercial Marine Vehicles, Rail, Solvents, and Other Non-Point Area Sources; Nonroad: Nonroad Mobile Sources; WasteBurn: Open Waste Burning; DustConstruct: Construction Dust; GasComb: Gas Combustion; and DustMineQrry: Dust from Mining and Quarrying.  Wildfire emissions (Not Shown) are 22,000 tons. Point Source Emissions for NonIPM, EGU, and Oil&Gas Reflect Levels in the Nonattainment Area.

South Coast Air Basin, CA
The South Coast Air Basin (SoCAB) is formed by mountain ranges on three sides and the Pacific Ocean in the west (Figure 2-24). SoCAB includes all or part of four counties (LA, Riverside, San Bernardino, and Orange) with a combined population of over 17 million and diverse emission sources associated with the large population, the ports of LA and Long Beach, wildfire, and transportation of goods. The semi-permanent Pacific high-pressure system leads to subsidence temperature inversions over SoCAB that can influence air pollution processes by capping vertical mixing over the basin (Jacobson, 2002, Lu and Turco, 1995). The sea-breeze circulation transports emissions from coastal ports and Los Angeles to inland areas such as Riverside and San Bernardino (Lu and Turco, 1995, Neuman et al., 2003, Pilinis et al., 2000). This transport, along with concurrent formation of secondary PM2.5 and limited ventilation due to terrain blocking and temperature inversions, causes the highest PM2.5 concentrations to occur downwind of LA in Riverside and San Bernardino. For instance, the projected 2032 annual DV at the highest site in LA is 12.73 g m[-3] (site ID: 06-037-4008) and is 14.10 g m[-3] in Riverside (site ID: 06-065-8005) and 14.96 g m[-3] in San Bernardino (site ID: 06-071-0027). 
                                       
Figure 2-24	South Coast Air Basin Nonattainment Area and Locations of Highest PM2.5 Monitors in Los Angeles (06-037-4008), Riverside (06-065-8005), and San Bernardino (06-071-0027)
Source: Map Data (C)2022 Google.

PM2.5 DVs in SoCAB exceed the 2012 annual PM2.5 NAAQS and the 2006 24-hr PM2.5 NAAQS. As in SJV, limited progress is evident in the trend of recent annual DVs in SoCAB (Figure 2-25). However, year-to-year variability in wildfire emissions and meteorology might mask air quality management progress. The 2016 Air Quality Management Plan demonstrates the effectiveness of control programs during the 1999 to 2015 period in which SoCAB experienced significant population growth (SCAQMD, 2017). Emission control programs for SoCAB focus on reducing NOx emissions to lower ammonium nitrate concentrations and primary PM2.5 emissions to lower carbonaceous PM2.5 concentrations. Ammonium nitrate tends to be elevated in Riverside and San Bernardino due to the mixing of NOx emissions from LA with ammonia emissions from dairy facilities near Chino during transport inland (Neuman et al., 2003, Nowak et al., 2012). The largest primary PM2.5 emission sources in our 2032 modeling are commercial and residential cooking, onroad mobile sources, and paved and unpaved road dust (Figure 2-26). PM2.5 control strategies in SoCAB are based on decades of study including intensive measurement and modeling campaigns such as the 1987 Southern California Air Quality Study (SQAQS) and the 2010 CalNex campaign.
                                       
Figure 2-25	Recent Annual PM2.5 DVs at the Highest South Coast Monitor for Design Value Periods (e.g., 11-13: 2011-2013).  Dashed line is the 2012 Annual PM2.5 NAAQS Level (12 g m[-3])

                                       
                                       
Figure 2-26	Annual Source Sector PM2.5 Emission Totals in the SoCAB Counties for 2032 Modeling Case
Note: See Figure 2-23 for Label Definitions. Wildfire emissions (Not Shown) are 8,000 Tons. 
      
To address standard exceedances in SoCAB in the RIA, we applied 75% NOx emission reductions beyond the 2032 modeling case and pursued emission reductions of primary PM2.5. However, the RIA is not well suited to identifying the specific measures needed to meet standards in SoCAB given the nature and magnitude of the air quality challenges. Challenges include air quality influenced by complex terrain and meteorological conditions that would be best characterized with a high-resolution modeling platform developed for the specific conditions of the air basin. Also, specific local information on measures for reducing emissions from the major area sources would be valuable given the magnitude of these emissions in SoCAB. Characterizing the influence of wildfire on PM2.5 concentrations and potential atypical, extreme, or unrepresentative events in SoCAB would also benefit from a local analysis. Progress toward meeting the alternative standards in SoCAB will likely occur as an outgrowth of existing efforts to meet the 2006 and 2012 PM2.5 NAAQS.  
San Luis Obispo and Napa, CA
The RIA analysis identified challenges in meeting the 9/35 standard at the Arroyo Grande site (06-079-2007) in San Luis Obispo County. Local sources and wildfires could be the main contributors to PM2.5 concentrations at this site based on the coastal situation and surrounding mountains (Figure 2-27). In recent years, the PM2.5 DVs have decreased at the Arroyo Grande site such that the annual PM2.5 DVs for the 2018-2020 and 2019-2021 periods are 8.0 and 7.7 g m[-3], respectively (Figure 2-28). The projected 2032 annual DV (9.63 g m[-3]) is based on monitoring from the 2014-2018 period and does not capture the recent air quality improvements. Based on the ambient data for the two most recent DV periods, the Arroyo Grande site may not require additional emission reductions to meet alternative standard levels.
                                       
                                       
Figure 2-27	San Luis Obispo County and Location of Highest PM2.5 Monitor in Arroyo Grande (06-079-2007)
Source: Map Data (C)2022 Google.

                                       
Figure 2-28	Recent and Projected Annual PM2.5 DVs at the Arroyo Grande Monitor (06-079-2007) in San Luis Obispo County for DV Periods (e.g., 11-13: 2011-2013; 32-32: Projected 2032 DV)

The RIA analysis also identified challenges in meeting alternative standard levels in Napa County. The projected 2032 annual DV at the highest-DV site in Napa (06-055-0003) is 10.09 g m[-3]. Since the site is located in a valley (Figure 2-29), PM2.5 concentrations may have relatively large contributions from local emission sources. Contributions from regional sources in the Bay Area, Central Valley, and wildfire are also possible. For instance, severe wildfires occurred in Napa during the Wine Country Fires in Fall 2017. A previous study reported that modeled concentrations of carbonaceous PM2.5 at the Napa site were underestimated, often by a factor of two to three (BAAQMD, 2009). The analysis suggested that carbonaceous PM2.5 emissions, possibly from wood burning, may have been strongly underrepresented in the Napa emission inventory. Additional work to develop local emission inventories and modeling for the area would be needed to identify appropriate emission reductions in Napa.
                                       
Figure 2-29	Napa County and Location of PM2.5 Monitor (06-055-0003)
Source: Map Data (C)2022 Google.

Calculating PM2.5 Concentration Fields for Standard Combinations
National PM2.5 concentration fields corresponding to meeting the existing and alternative standard levels were developed to inform health benefit calculations. First, a gridded PM2.5 concentration field for the 2032 CMAQ modeling case was developed using the enhanced Voronoi Neighbor Average (eVNA) method. Next, the incremental difference in annual PM2.5 DVs between the 2032 case and cases of meeting standard combinations was calculated at monitors and interpolated to the spatial grid. The resulting field of incremental PM2.5 concentration was then subtracted from the 2032 eVNA field to create the gridded field for the standard combination. The steps in developing the PM2.5 concentration fields are described further below.
Creating the PM2.5 Concentration Field for 2032
The gridded field of annual average PM2.5 concentrations for 2032 was developed using the eVNA method that combines information from the model and monitors to predict PM2.5 concentrations. The eVNA approach was applied using SMAT-CE, version 1.8, and has been previously described in EPA's modeling guidance document (USEPA, 2018) and the user's guide for the predecessor software to SMAT-CE (Abt, 2014). Briefly, the steps in developing the eVNA PM2.5 concentration field for 2032 are as follows:
 Quarterly average PM2.5 component concentrations measured during the 2015-2017 period were interpolated to the spatial grid using inverse distance-squared-weighting of monitored concentrations that were further weighted by the ratio of the 2016 CMAQ value in the prediction grid cell to CMAQ value in the monitor-containing grid cell. The weighting by CMAQ predictions adjusts the interpolation of monitor data to account for spatial gradients in the CMAQ fields. This step results in an interpolated spatial field of gradient-adjusted observed concentrations for each PM2.5 component and each quarter representative of 2016. 
 The 2016 eVNA component concentration in each grid cell is multiplied by the corresponding ratio (i.e., RRF) of the quarterly-average CMAQ concentration predictions in 2032 and 2016. This step results in spatial concentration fields for each PM2.5 component in each quarter of 2032.
 The 2032 PM2.5 component concentrations are summed to give the total PM2.5 concentration for each quarter in 2032. The quarterly PM2.5 concentrations are then averaged to create the 2032 PM2.5 concentration field. The resulting PM2.5 concentration field for 2032 is shown in Figure 2-30. 

                                       
                                       
Figure 2-30	PM2.5 Concentration for 2032 based on eVNA Method

Creating Spatial Fields Corresponding to Meeting Standards
To create spatial fields corresponding to meeting standard levels, the 2032 concentration field was adjusted according to the change in PM2.5 concentrations associated with the difference in annual PM2.5 DVs between the 2032 case and the cases where standards are met. To implement this adjustment, the following steps were applied:
 The difference in annual PM2.5 DVs was calculated at the county highest monitor between the 2032 case and cases of meeting the 12/35, 10/30, 10/35, 9/35, and 8/35 standard combinations. For the county non-highest monitors, the difference in PM2.5 DVs was estimated by proportionally adjusting DVs according to the percent change in PM2.5 DV at the highest monitor. 
 The difference in DVs between the 2032 case and the cases of meeting the standard combinations were then interpolated to the spatial grid using inverse-distance-squared VNA interpolation (Abt, 2014, Gold et al., 1997). The interpolated field was clipped to grid cells within 50 km of monitors whose DVs changed in meeting the standard level (USEPA, 2012b). 
 National PM2.5 concentration fields were developed for each standard combination by subtracting the corresponding spatial field of PM2.5 concentration differences from Step 2 from the 2032 eVNA concentration field. 
An example of a spatial field for the incremental change in PM2.5 concentration between the 2032 case and the case of meeting the existing standard combination, 12/35, is shown in Figure 2-31. Additional details on the method for developing PM2.5 concentration fields are available in section 2A.4 of Appendix 2A. 

                                       
Figure 2-31	PM2.5 Concentration Improvement Associated with Meeting 12/35 Relative to the 2032 case


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 CONTROL STRATEGIES AND PM2.5 EMISSIONS REDUCTIONS
 Overview
The current annual primary PM2.5 standard is 12 g/m[3], and the current 24-hour standard is 35 g/m[3]. The Agency is proposing to revise the current annual PM2.5 standard to a level within the range of 9-10 g/ - m - [3] -  and is soliciting comment on an alternative annual standard level down to 8 g/m[3] and a level up to 11 g/m[3]. The Agency is also proposing to retain the current 24-hour standard of 35 g/m[3] and is soliciting comment on an alternative 24-hour standard level of 30 g/m[3]. In this Regulatory Impact Analysis (RIA), we are analyzing the proposed annual and current 24-hour alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/m[3], as well as the following two more stringent alternative standard levels: (1) an alternative annual standard level of 8 g/ - m - [3] -  in combination with the current 24-hour standard (i.e., 8/35 g/ - m - [3] - ), and (2) an alternative 24-hour standard level of 30 g/ - m - [3] in combination with the proposed annual standard level of 10 g/ - m - [3] -  (i.e., 10/30 g/ - m - [3] - ). Because the EPA is proposing that the current secondary PM standards be retained, we did not evaluate alternative secondary standard levels in this RIA.
As discussed in Chapter 1 in the Overview of the Regulatory Impact Analysis, the analyses in this RIA rely on national-level data (emissions inventory and control measure information) for use in national-level assessments (air quality modeling, control strategies, environmental justice, and benefits estimation). However, the ambient air quality issues being analyzed are highly complex and local in nature, and the results of these national-level assessments therefore contain uncertainty. It is beyond the scope of this RIA to develop detailed local information for the areas being analyzed, including populating the local emissions inventory, obtaining local information to increase the resolution of the air quality modeling, and obtaining local information on emissions controls, all of which would reduce some of the uncertainty in these national-level assessments. For example, having more refined data would be ideal for  agricultural dust and burning, prescribed burning, and non-point (area) sources  due to their large contribution to primary PM2.5 emissions and the limited availability of emissions controls.               
We assume that areas will be designated such that they are required to reach attainment by 2032, and we developed our projected baselines for emissions and air quality for 2032. To estimate the costs and benefits of the proposed and more stringent annual and 24-hour PM2.5 alternative standard levels, we first prepared an analytical baseline for 2032 that assumes full compliance with the current standards of 12/35 g/m[3]. From that baseline, we then analyze illustrative control strategies that areas might employ toward attaining the proposed and more stringent annual and 24-hour PM2.5 alternative standard levels.  Because PM2.5 concentrations are most responsive to direct PM emissions reductions, as discussed in Chapter 2, Section 2.1.3, we analyze direct, local PM2.5 emissions reductions by individual counties.  Section 2.1.3 also includes a discussion of historical and projected emissions trends for direct PM2.5 and precursor emissions (i.e., SO2, NOx, VOC, and ammonia), as well as a discussion of the "urban increment" of consistently higher PM2.5 concentrations over urban areas. The projections of additional, large reductions in SO2 and NOX emissions in the 2032 case further motivate the need for control of local primary PM2.5 sources to address the highest PM2.5 concentrations in urban areas.
For the eastern U.S. where counties are relatively small and terrain is relatively flat, we identified potential PM2.5 emissions reductions within each county and in adjacent counties within the same state, where needed. As discussed below in Section 3.2.2, when we applied the emissions reductions from adjacent counties, we used a g/m[3] per ton PM2.5 air quality ratio that was four times less responsive than the ratio used when applying in-county emissions reductions. Because the counties in the western U.S. are generally large and the terrain is more complex, we only identified potential PM2.5 emissions reductions within each county. 
Next, we prepare illustrative control strategies. We apply end-of-pipe control technologies to non-electric generating unit (non-EGU) stationary sources (e.g., fabric filters, electrostatic precipitators, venturi scrubbers) and control measures to nonpoint  (area) sources (e.g., installing controls on charbroilers), to residential wood combustion sources (e.g., converting woodstoves to gas logs), and for area fugitive dust emissions (e.g., paving unpaved roads) in analyzing PM2.5 emissions reductions needed toward attaining the alternative standard levels. We did not apply controls to EGUs or mobile sources;  Chapter 2, Section 2.1.3 includes a discussion of SO2 and NOX emissions decreases reflected in the projections between 2016 and 2032, noting that over the period (1) NOX emissions are projected to decrease by 3.8 million tons (40 percent), with the greatest reductions from mobile source and EGU emissions inventory sectors, and (2) SO2 emissions are projected to decrease by 1 million tons (38 percent), with the greatest reductions from the EGU emissions inventory sector. In addition, Chapter 2, Section 2.2.1.2 includes a discussion of the EGU and non-EGU rules reflected in the projections for this analysis. Further, Appendix 2A, Section 2A.5 includes a discussion of EGU NOX, SO2, and PM2.5 emissions reductions that are expected to occur from firm retirements between 2016 and 2030; these reductions are beyond those included in the air quality modeling for this analysis. Lastly, Section 2A.5 includes a discussion of the potential influence of the reductions from these firm EGU retirements on future PM2.5 design values (DVs) regionally in the east, as well as locally.
The illustrative control strategy analyses indicate that counties in the northeast and southeast U.S. do not need additional emissions reductions after the application of controls to meet alternative standard levels of 10/35 g/m[3] and 10/30 g/m[3]; however, these counties would need additional PM2.5 emissions reductions to meet alternative standard levels of 9/35 g/m[3] and 8/35 g/m[3]. Also, the analysis indicates that counties in the west and California would need additional PM2.5 emissions reductions after the application of controls to meet all of the alternative standard levels analyzed. 
The remainder of this chapter is organized into four sections. Section 3.1 provides a summary of the steps that we took to create the analytical baseline. Section 3.2 presents the illustrative control strategies identified to assess the proposed and more stringent annual and 24-hour alternative standard levels in the continental U.S., along with the resulting estimated emissions reductions. Section 3.3 includes a summary of the key limitations and uncertainties associated with the control strategy analyses. Finally, Section 3.4 includes the references for the chapter. We present the costs associated with the estimated PM2.5 emissions reductions in Chapter 4. 
Preparing the 12/35 g/ - m - [3] -  Analytical Baseline
In the 2032 projections, PM2.5 DVs exceeded the current standards for some counties in the west. As a result, we adjusted the PM2.5 DVs for 2032 to correspond with just meeting the current standards to form the 12/35 g/ - m - [3] -  analytical baseline used in estimating the incremental costs and benefits associated with control strategies for the proposed and more stringent alternative standard levels relative to the current standards. Figure 3-1 includes a map of the U.S. with the areas identified as northeast, southeast, west, and California; results are summarized for these areas. Table 3-1 presents a summary of the PM2.5 emissions reductions needed by area to meet the current standards. 
                                       
Figure 3-1	Geographic Areas Used in Analysis
Table 3-1	Summary of PM2.5 Emissions Reductions Needed by Area in 2032 to Meet Current Primary Annual and 24-hour Standards of 12/35 g/ - m - [3] -  (tons/year)
Area
                                     12/35
Northeast
                                       0
Southeast
                                       0
West
                                     2,298
CA
                                     6,907
Total
                                     9,205

Eighteen counties need PM2.5 emissions reductions to meet the current standards in 2032  -  9 counties in California and 9 counties in the west. The counties in California include several counties in the San Joaquin Valley Air Pollution Control District and the South Coast Air Quality Management District, as well as Plumas County in Northern California and Imperial County in southern California. No counties in the northeast or southeast U.S. need PM2.5 emissions reductions to meet the current annual and 24-hour standards.
Illustrative Control Strategies and PM2.5 Emissions Reductions from the Analytical Baseline
To analyze counties projected to exceed the proposed and more stringent annual and 24-hour alternative standard levels in 2032, we estimate total PM2.5 emissions reductions needed by county for the alternative standard levels analyzed. To estimate the PM2.5 emissions reductions needed, we start with projected future DVs, DV targets for each area, and the sensitivity of PM2.5 DVs to direct PM2.5 emissions reductions. For each of the alternative standard levels, we estimate PM2.5 emissions reductions needed by county and then identify control technologies and measures that can achieve PM2.5 emissions reductions. In Section 3.2.1, we discuss the approach for estimating the direct PM2.5 emissions reductions needed and present them by area for the alternative standard levels analyzed. In Sections 3.2.2 and 3.2.3, respectively, we present information on the controls and the estimated emissions reductions, from the analytical baseline, associated with applying controls by area for the alternative standard levels analyzed. In Section 3.2.4, we discuss EGU emissions reductions from planned retirements and their potential influence in some areas. In Sections 3.2.5 and 3.2.6, we discuss areas with other types of influences affecting PM2.5 concentrations. As noted in Chapter 2, Section 2.4, there are certain types of areas for which our illustrative control strategies may not capture important local emissions and air quality dynamics. For these areas, we note that local emissions inventory information and information on potential additional controls for emissions inventory sectors that are traditionally challenging to control may be needed. Sections 3.2.5 presents the emissions reductions still needed, and for each area Section 3.2.6 includes a qualitative discussion of the remaining area-specific air quality challenges. Appendix 3A, Tables 3A.2 through 3A.7 summarize estimated PM2.5 emissions reductions by county for the alternative standard levels for the northeast, the adjacent counties in the northeast, the southeast, the adjacent counties in the southeast, the west, and California. 
Estimating PM2.5 Emissions Reductions Needed for Annual and 24-hour Alternative Standard Levels Analyzed
      We apply regional PM2.5 air quality ratios to estimate PM2.5 DVs at air quality monitor locations and then again to estimate the emissions reductions needed to reach the proposed and more stringent annual and 24-hour alternative standard levels analyzed. To develop air quality ratios that relate the change in DV in a county to the change in primary PM2.5 emissions in that county, we performed air quality sensitivity modeling with reductions in primary PM2.5 emissions in selected counties. More specifically, we conducted a 2028 Community Multiscale Air Quality Modeling System (CMAQ) sensitivity modeling simulation with 50 percent reductions in primary PM2.5 emissions from anthropogenic sources in counties with annual 2028 DVs greater than 8 g/m[3].  We divided the change in annual and 24-hour PM2.5 DVs in these counties by the change in emissions in the respective counties to determine the air quality ratio at individual monitors. 
      We developed representative air quality ratios for regions of the U.S. from the ratios at individual monitors as in the 2012 PM - 2.5 NAAQS review (U.S. EPA, 2012). We calculated regional ratios as the 75[th] percentile of air quality ratios at monitors within five regions: Northeast, Southeast, Northern California, Southern California, and West. The Northeast region was defined by combining the Upper Midwest, Ohio Valley, and Northeast U.S. climate regions; the Southeast region was defined by combining the Southeast and South climate regions; and California was separated into Southern and Northern regions as done previously. (These regions are shown in Figure 2-7 in Chapter 2, and the air quality ratios for primary PM2.5 emissions used in estimating the emission reductions needed to just meet the alternative standard levels analyzed are listed in Table 2-1 in Chapter 2.) We estimated the emissions reductions needed to just meet the alternative standard levels analyzed using the primary PM2.5 air quality ratios in combination with the required incremental change in concentration. (Chapter 2, Section 2.3.1 includes a brief discussion of developing air quality ratios and estimated emissions reductions needed to just meet the alternative standard levels analyzed, and Appendix 2A, Section 2A.3 includes more detailed discussions.)
Table 3-2 presents a summary of the estimated emissions reductions needed by area to reach the annual and 24-hour alternative standard levels. For each alternative standard level, Table 3-2 also includes an area's percent of the total estimated emissions reductions needed nationwide to reach that alternative standard level in all locations. For example, for the proposed standard level of 10/35 g/m[3], California's 10,128 estimated tons needed is 81 percent of the total estimated emissions reductions needed nationwide to meet 10/35 g/m[3]. (See Appendix 2A, Table 2A-14 for the estimated PM2.5 emissions reductions, from the analytical baseline, needed by county for the alternative standard levels analyzed.) Figure 3-2 shows the counties projected to exceed the annual and 24-hour alternative standard levels of 10/35 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in the analytical baseline. Figure 3-3 shows the counties projected to exceed the annual and 24-hour alternative standard levels of 10/30 g/ - m - [3] -  in the analytical baseline. Additional information on the air quality modeling, as well as information about projected future DVs, DV targets, and air quality ratios is provided in Chapter 2 and Appendix 2A. 
Table 3-2	By Area, Summary of PM2.5 Emissions Reductions Needed, in Tons/Year and as Percent of Total Reductions Needed Nationwide, for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032
Area
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
                                     1,068
                                     1,221
                                     6,996
                                    30,843
Southeast
                                      474
                                      474
                                     4,088
                                    18,028
West
                                      820
                                     7,852
                                     3,078
                                     9,708
CA
                                    10,128
                                    12,230
                                    17,750
                                    28,293
Total
                                    12,490
                                    21,776
                                    31,912
                                    86,872
 
 
 
 
 
Area
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
                                      9%
                                      6%
                                      22%
                                      36%
Southeast
                                      4%
                                      2%
                                      13%
                                      21%
West
                                      7%
                                      36%
                                      10%
                                      11%
CA
                                      81%
                                      56%
                                      56%
                                      33%
            
                                       
Figure 3-2	Counties Projected to Exceed in Analytical Baseline for Alternative Standard Levels of 10/35 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] - 


	
                                       
Figure 3-3	Counties Projected to Exceed in Analytical Baseline for Alternative Standard Levels of 10/30 g/ - m - [3] - 

As presented previously, for each alternative standard level, Chapter 2, Section 2.3.3 includes a discussion of the number of counties that are projected to exceed in 2032, and Figure 2-9 includes maps of counties projected to exceed along with the number of counties. The following summarizes the number of counties, by alternative standard level, in each geographic area that need PM2.5 emissions reductions from the analytical baseline. 
 10/35 g/ - m - [3] - -- 24 counties need PM2.5 emissions reductions. This includes 4 counties in the northeast, 2 counties in the southeast, 3 counties in the west, and 15 counties in California.
 10/30 g/ - m - [3] - -- 47 counties need PM2.5 emissions reductions. This includes 4 counties in the northeast, 2 counties in the southeast, 23 counties in the west, and 18 counties in California. 
 9/35 g/ - m - [3] -  -- 51 counties need PM2.5 emissions reductions. This includes 14 counties in the northeast, 8 counties in the southeast, 8 counties in the west, and 21 counties in California.
 8/35 g/ - m - [3] -  -- 141 counties need PM2.5 emissions reductions. This includes 57 counties in the northeast, 35 counties in the southeast, 24 counties in the west, and 25 counties in California.
Applying Control Technologies and Measures
To identify controls and estimate emissions reductions, we used information about the emissions reductions needed, by county, in the northeast, southeast, west, and California. Given the different county sizes between eastern and western states, as well as different terrain or other topographical features, we estimated potential PM2.5 emissions reductions for the eastern U.S. and western U.S. as detailed below. Note that we included a total of 154 counties in the analyses. The total number of counties below (154 counties) does not directly match the number of counties that would need emissions reductions for the more stringent alternative standard level of 8/35 g/ - m - [3] -  (141 counties) in Section 3.2.1 above. This difference is because there are thirteen counties that do not need PM2.5 emissions reductions to meet alternative standard levels of 9/35 g/ - m - [3] -  and 8/35 g/ - m - [3] -  but do need PM2.5 emissions reductions to meet an alternative standard level of 10/30 g/ - m - [3] - .
 Northeast (57 counties) and Southeast (35 counties)  -  In the eastern U.S. where counties are relatively small, we were not always able to identify controls within a given county. We identified controls and emissions reductions from neighboring counties because the terrain is relatively flat, and the application of these controls is appropriate in such cases. Any emissions reductions from neighboring counties were identified in adjacent counties within the same state. 
      To apply emissions reductions in the neighboring counties in the eastern U.S., we compared the responsiveness of annual PM2.5 DVs to emissions reductions within a county to the responsiveness for neighboring counties. The resulting impact ratio suggests that primary PM2.5 emissions reductions in neighboring counties would be 4 times less effective as in the core county. (Appendix 2A, Section 2A.3.1 includes a more detailed discussion of the comparison.) As such, when we applied the emissions reductions from adjacent counties, we used a g/m[3] per ton PM2.5 air quality ratio that was four times less responsive than the ratio used when applying in-county emissions reductions (i.e., we applied four tons of PM2.5 emissions reductions from an adjacent county for one ton of emissions reduction needed in a given county).
 West (36 counties) and California (26 counties) - Because these counties are generally large and the terrain is complex, we only identified potential PM2.5 emissions reductions within each county.
We identified control measures using the EPA's Control Strategy Tool (CoST) (U.S. EPA, 2019) and the control measures database. CoST estimates emissions reductions and engineering costs associated with control technologies or measures applied to non-electric generating unit (non-EGU) point, non-point (area), residential wood combustion, and area fugitive dust sources of air pollutant emissions by matching control measures to emissions sources by source classification code (SCC). For these control strategy analyses, to maximize the number of emissions sources included we applied controls to emissions sources with greater than 5 tons per year of PM2.5 emissions at a marginal cost threshold of up to a $160,000/ton. Figure 3-4 presents estimated PM2.5 emissions reductions for 5 tons per year (tpy), 10 tpy, 15 tpy, 25 tpy, and 50 tpy emissions unit/source sizes up to the $160,000/ton marginal cost threshold; the figure includes all emissions inventory and control measure data for the counties in the analysis. We selected the $160,000/ton marginal cost threshold because it is around that cost level that (i) road paving controls get selected and applied (as seen by the slight uptick in the curves), and (ii) opportunities for additional emissions reductions diminish (as seen by the flattening of the curve around that cost threshold). While the 2012 PM NAAQS RIA used a $20,000/ton marginal cost threshold and a 50 tpy emissions source size threshold, this analysis uses a higher cost per ton threshold and a lower source size threshold in recognition of the challenges that some areas will experience in identifying controls to meet both the current and alternative standard levels analyzed (U.S. EPA, 2012). The estimated costs of the control measures are presented in Chapter 4.
In some cases, more emissions reductions are selected by CoST than may be needed for some areas to meet the alternative standard levels. There are two primary reasons this may occur. First, because CoST employs a least cost algorithm to determine the bundle of controls that achieves the required emissions reductions at the lowest possible cost, there are instances when a non-point or area fugitive dust source will be selected for control due to its cost-effectiveness. Because the emissions from these sources are summarized at the county level and the controls specify a percent reduction, selection of these sources for control can sometimes lead to overshooting the emissions reduction target. 
Second, for counties in the northeast and southeast, we considered emissions reductions from adjacent counties. There are some instances where a neighboring county may be adjacent to multiple counties that need reductions. Furthermore, it is sometimes the case that one of the multiple counties to which a neighboring county is adjacent needs substantially more reductions than the other counties. In these cases, an adjacent (neighboring) county may be called upon to provide reductions to help the county that needs the most reductions, and in so doing it may cause the other counties to which it is adjacent to overshoot their emissions reductions targets. 
                                       
Figure 3-4	PM2.5 Emissions Reductions and Costs Per Ton (CPT) in 2032 (tons, 2017$)
We identified control technologies and measures for non-electric generating unit point sources (non-EGU point, oil & gas point), non-point (area) sources, residential wood combustion sources, and area fugitive dust emissions. Controls applied for the analyses of the current standards of 12/35 g/ - m - [3] -  and the annual and 24-hour PM2.5 alternative standard levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  are listed in Table 3-3 by emissions inventory sector, with an "X" indicating which control technologies were applied in analyzing each standard level. See Appendix 3A, Table 3A.1 for a more detailed presentation of control technologies applied for the alternative standard levels both by geographic area and by emissions inventory sector, as well as a discussion of some of the control measures. 
Non-EGU point source controls are applied to individual point sources. Non-point (area), residential wood combustion, and area fugitive dust emissions data are generated at the county level, and therefore controls for these emissions inventory sectors were applied at the county level. Control measures were applied to non-EGU point, non-point (area), residential wood combustion, and area fugitive dust sources of PM2.5 emissions including: industrial, commercial, and institutional boilers; industrial processes located in the cement manufacturing, chemical manufacturing, pulp and paper, mining, ferrous and non-ferrous metals, and refining industries; commercial cooking; residential wood combustion; and fugitive construction and road dust. (Also, see Appendix 2A, Section 2A.5 for a discussion of electric generating unit NOX, SO2, and PM2.5 emissions reductions that are expected to occur between 2016 and 2030 beyond those included in the 2032 air quality modeling simulation for this analysis. These additional emissions reductions will result from planned EGU retirements that were not known when we developed the 2032 emissions projections.)
Table 3-3	By Inventory Sector, Control Measures Applied in Analyses of the Current Standards and the Alternative Primary Standard Levels
Inventory Sector
Control Technology
                                     12/35
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Non-EGU Point
Electrostatic Precipitator-All Types

                                       x
                                       
                                       x
                                       x

Fabric Filter-All Types
                                       x
                                       x
                                       x
                                       x
                                       x

Install new drift eliminator at 10% RP
                                       x
                                       
                                       
                                       x
                                       x

Install new drift eliminator at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Venturi Scrubber
                                       x
                                       x
                                       x
                                       x
                                       x
Oil & Gas Point
Fabric Filter-All Types
                                       x
                                       
                                       
                                       
                                       x

Install new drift eliminator at 25% RP
                                       
                                       
                                       
                                       
                                       x
Non-Point (Area)
Add-on Scrubber at 25% RP

                                       x
                                       x
                                       
                                       

Annual tune-up at 10% RP

                                       
                                       x
                                       x
                                       x

Annual tune-up at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Biennial tune-up at 10% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Biennial tune-up at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Catalytic oxidizers at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Electrostatic Precipitator at 10% RP

                                       
                                       
                                       x
                                       x

Electrostatic Precipitator at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Fabric Filter-All Types

                                       
                                       
                                       x
                                       x

HEPA filters at 10% RP

                                       x
                                       x
                                       x
                                       x

HEPA filters at 25% RP

                                       x
                                       
                                       x
                                       x

Smokeless Broiler at 10% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Smokeless Broiler at 25% RP

                                       
                                       
                                       x
                                       x

Substitute chipping for burning
                                       x
                                       x
                                       x
                                       x
                                       x
Residential Wood Combustion
Convert to Gas Logs at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x

EPA-certified wood stove at 10% RP

                                       
                                       
                                       
                                       x

EPA Phase 2 Qualified Units at 10% RP

                                       
                                       
                                       x
                                       x

EPA Phase 2 Qualified Units at 25% RP

                                       x
                                       x
                                       
                                       x

Install Cleaner Hydronic Heaters at 10% RP

                                       
                                       x
                                       
                                       

Install Cleaner Hydronic Heaters at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Install Retrofit Devices at 10% RP
                                       x
                                       
                                       
                                       x
                                       x

Install Retrofit Devices at 25% RP

                                       x
                                       x
                                       
                                       x

New gas stove or gas logs at 10% RP
                                       x
                                       x
                                       x
                                       x
                                       x

New gas stove or gas logs at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x
Area Source Fugitive Dust
Chemical Stabilizer at 10% RP

                                       x
                                       x
                                       x
                                       x

Chemical Stabilizer at 25% RP
                                       x
                                       
                                       
                                       x
                                       x

Dust Suppressants at 10% RP

                                       
                                       
                                       
                                       x

Dust Suppressants at 25% RP

                                       
                                       
                                       
                                       x

Pave existing shoulders at 10% RP

                                       
                                       
                                       
                                       x

Pave existing shoulders at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Pave Unpaved Roads at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x
Note: The 10% RP and 25% RP indicate the rule penetration (RP) percent, or the percent of the non-point (area), residential wood combustion, or area fugitive dust inventory emissions that the control measure is applied to at a specified percent control efficiency.

Estimates of PM2.5 Emissions Reductions Resulting from Applying Control Technologies and Measures
By area, Table 3-4 includes a summary of the estimated emissions reductions from control applications for the alternative standards analyzed. These emissions reductions were used to create the PM2.5 spatial surfaces described in Appendix 2A, Section 2A.4.2 for the human health benefits assessments presented in Chapter 5. 
Table 3-4	Summary of PM2.5 Estimated Emissions Reductions from CoST by Area for the Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
 
                          PM2.5 Emissions Reductions
Area
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
                                     1,070
                                     1,222
                                     6,334
                                    19,142
Northeast (Adjacent Counties)
                                       0
                                       0
                                     1,737
                                    15,440
Southeast
                                      475
                                      475
                                     3,040
                                    12,212
Southeast (Adjacent Counties)
                                       0
                                       0
                                      194
                                     4,892
West
                                      224
                                     2,206
                                      947
                                     4,711
CA
                                     1,792
                                     2,481
                                     2,958
                                     4,925
Total
                                     3,561
                                     6,384
                                    15,210
                                    61,321
   Note: Totals may not match related tables due to independent rounding. In the northeast and southeast when we applied the emissions reductions from adjacent counties, we used a ppb/ton PM2.5 air quality ratio that was four times less responsive than the ratio used when applying in-county emissions reductions.

By emissions inventory sector, Table 3-5 includes a summary of PM2.5 emissions and estimated emissions reductions from control applications for the alternative standard levels analyzed. The PM2.5 emissions in Table 3-5 are the total emissions associated with the emissions units/sources that get controls applied within each of the inventory sectors for each of the alternative standard levels (not the total emissions associated with the entire inventory sector). Across the alternative standard levels analyzed, overall total emissions reductions are approximately 30 percent of the PM2.5 emissions from the sources selected by CoST for control. In general, a large percentage of the emissions are being controlled for the alternative standard levels analyzed, while additional reductions may be possible in some areas and different inventory sectors are selected for control in different areas.
The emissions inventory sector with the highest percent of emissions reductions relative to total potentially controllable emissions for that sector is the non-EGU point sector  -  the estimated emissions reductions are between 65 and 92 percent of total PM2.5 emissions from the sources selected for control, with that percent increasing as the alternative standard level gets more stringent. The emissions inventory sector with the lowest percent of emissions reductions relative to total potentially controllable emissions for that sector is the area fugitive dust sector  -  the estimated emissions reductions are between 15 and 19 percent of total PM2.5 emissions from the sources selected for control, with that percent decreasing as the alternative standard level gets more stringent. The residential wood combustion sector's emissions reductions relative to total potentially controllable emissions are between 21 and 23 percent across the alternative standard levels analyzed. It is worth noting that the control efficiencies associated with control measures for the non-point (area), area fugitive dust, and residential wood combustion sectors are generally lower than control efficiencies associated with control measures for the non-EGU point and oil and gas point inventory sectors, and many of the controls for these sectors are only applied to a portion of the inventory. As noted in Table 3-3, controls for emissions from these inventory sectors are applied to a percent of the relevant inventory (rule penetration) at a specified percent control efficiency. For the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - , the inventory sectors with the most potentially controllable emissions are the non-point (area) and area fugitive dust sectors. The inventory sectors with the most estimated emissions reductions are the non-point (area) and non-EGU point sectors.
Table 3-5	Summary of PM2.5 Emissions and Estimated Emissions Reductions from CoST by Inventory Sector for Alternative Primary Standard Levels of 10/35 g/m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
Emissions Inventory Sector
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Non-EGU Point

                                       
                                       
                                       
                                                                PM2.5 Emissions
                                     1,384
                                     1,823
                                     6,824
                                    19,832
                                                     PM2.5 Emissions Reductions
                                      901
                                     1,326
                                     6,035
                                    18,289
Oil & Gas Point

                                       
                                       
                                       
                                                                PM2.5 Emissions
                                       0
                                       0
                                       0
                                      83
                                                     PM2.5 Emissions Reductions
                                       0
                                       0
                                       0
                                      60
Non-Point (Area)

                                       
                                       
                                       
                                                                PM2.5 Emissions
                                     6,994
                                     9,987
                                    23,770
                                    80,265
                                                     PM2.5 Emissions Reductions
                                     1,771
                                     2,572
                                     6,269
                                    27,352
Residential Wood Combustion

                                       
                                       
                                       
                                                                PM2.5 Emissions
                                     1,262
                                     2,635
                                     5,808
                                    17,963
                                                     PM2.5 Emissions Reductions
                                      296
                                      556
                                     1,276
                                     4,193
Area Source Fugitive Dust

                                       
                                       
                                       
                                                                PM2.5 Emissions
                                     3,175
                                    10,198
                                     9,127
                                    74,034
                                                     PM2.5 Emissions Reductions
                                      593
                                     1,930
                                     1,630
                                    11,427
Total

                                       
                                       
                                       
                                                                PM2.5 Emissions
                                    12,816
                                    24,643
                                    45,529
                                    192,176
                                                     PM2.5 Emissions Reductions
                                     3,561
                                     6,384
                                    15,210
                                    61,321
        Note: The PM2.5 emissions in the table are for the emissions sources that get controls applied within each of the inventory sectors (not the total emissions associated with the entire inventory sector) for each of the standard levels. 

By emissions inventory sector and by control technology, Table 3-6 includes a summary of estimated PM2.5 emissions reductions from control applications for the alternative standard levels analyzed. Across alternative standard levels analyzed, estimated PM2.5 emissions reductions from control applications in the (i) non-EGU point and oil and gas point inventory sectors account for between 21 and 40 percent of estimated reductions; (ii) non-point (area) inventory sector account for between 41 and 50 percent of estimated reductions; (iii) residential wood combustion inventory sector account for between 7 and 9 percent; and (iv) area fugitive dust inventory sector account for between 11 and 30 percent. 
Also, across alternative standard levels analyzed, six control technologies and measures comprise between approximately 81 and 87 percent of the estimated emissions reductions. Those control technologies and measures include:
 Fabric Filter- All Types (non-EGU point inventory sector)  -  the control technology is generally applied to industrial, commercial, and institutional boilers and industrial processes located in the cement manufacturing, chemical manufacturing, pulp and paper, mining, ferrous and non-ferrous metals, and refining industries.  
 Electrostatic Precipitator at 25% RP (non-point (area) inventory sector)  -  the control measure is applied to area source commercial cooking emissions.
 Substitute Chipping for Burning (non-point (area) inventory sector)  -  the control measure is applied to area source waste disposal emissions.
 Convert to Gas Logs at 25% RP (residential wood combustion inventory sector)  -  the control measure is applied to area source residential wood combustion emissions.
 Pave Existing Shoulders at 25% RP (area fugitive dust inventory sector)  -  the control measure is applied to road dust emissions.
 Pave Unpaved Roads at 25% RP (area fugitive dust inventory sector)  -  the control measure is applied to road dust emissions.
The three control measures that result in the most emissions reductions for alternative standard levels of 10/35 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  are Fabric Filter- All Types, Electrostatic Precipitator at 25% RP, and Substitute Chipping for Burning. The three control measures that result in the most emissions reductions for alternative standard levels of 10/30 g/ - m - [3] -  are Fabric Filter- All Types, Substitute Chipping for Burning, and Pave Unpaved Roads at 25% RP. The 10% RP and 25% RP indicate the rule penetration (RP) percent, or the percent of the area source inventory emissions that the control measure is applied to at a specified percent control efficiency.
Table 3-6	Summary of Estimated Emissions Reductions from CoST by Inventory Sector and Control Technology for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
Inventory Sector
Control Technology
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Non-EGU Point
Electrostatic Precipitator-All Types
                                      16
                                       0
                                      27
                                      20

Fabric Filter-All Types
                                      713
                                     1,071
                                     5,026
                                    16,511

Install new drift eliminator at 10% RP
                                       0
                                       0
                                       5
                                       2

Install new drift eliminator at 25% RP
                                      115
                                      115
                                      144
                                      292

Venturi Scrubber
                                      56
                                      139
                                      833
                                     1,464
Oil & Gas Point
Fabric Filter-All Types
                                       0
                                       0
                                       0
                                      55

Install new drift eliminator at 25% RP
                                       0
                                       0
                                       0
                                       5
Non-Point (Area)
Add-on Scrubber at 25% RP
                                       5
                                       5
                                       0
                                       0

Annual tune-up at 10% RP
                                       0
                                       1
                                       1
                                       1

Annual tune-up at 25% RP
                                      83
                                      96
                                      450
                                     1,589

Biennial tune-up at 10% RP
                                       1
                                       1
                                       0
                                      44

Biennial tune-up at 25% RP
                                      24
                                      58
                                      53
                                      347

Catalytic oxidizers at 25% RP
                                      42
                                      53
                                      151
                                      183

Electrostatic Precipitator at 10% RP
                                       0
                                       0
                                      11
                                       1

Electrostatic Precipitator at 25% RP
                                      849
                                     1,038
                                     1,615
                                     6,395

Fabric Filter-All Types
                                       0
                                       0
                                      77
                                      199

HEPA filters at 10% RP
                                       0
                                       1
                                       1
                                       2

HEPA filters at 25% RP
                                       1
                                       0
                                       6
                                      27

Smokeless Broiler at 10% RP
                                      53
                                      79
                                      142
                                      39

Smokeless Broiler at 25% RP
                                       0
                                       0
                                      411
                                      177

Substitute chipping for burning
                                      712
                                     1,240
                                     3,351
                                    18,349
Residential Wood Combustion
Convert to Gas Logs at 25% RP
                                      219
                                      369
                                      805
                                     2,446

EPA-certified wood stove at 10% RP
                                       0
                                       0
                                       0
                                       1

EPA Phase 2 Qualified Units at 10% RP
                                       0
                                       0
                                      16
                                       3

EPA Phase 2 Qualified Units at 25% RP
                                      15
                                      20
                                       0
                                      66

Install Cleaner Hydronic Heaters at 10% RP
                                       0
                                       1
                                       0
                                       0

Install Cleaner Hydronic Heaters at 25% RP
                                      22
                                      42
                                      285
                                      901

Install Retrofit Devices at 10% RP
                                       0
                                       0
                                      12
                                       6

Install Retrofit Devices at 25% RP
                                      11
                                      11
                                       0
                                       9

New gas stove or gas logs at 10% RP
                                       3
                                      54
                                      45
                                      86

New gas stove or gas logs at 25% RP
                                      25
                                      58
                                      111
                                      675
Area Source Fugitive Dust
Chemical Stabilizer at 10% RP
                                      22
                                      71
                                      42
                                     1,524

Chemical Stabilizer at 25% RP
                                       0
                                       0
                                      52
                                     1,488

Dust Suppressants at 10% RP
                                       0
                                       0
                                       0
                                       0

Dust Suppressants at 25% RP
                                       0
                                       0
                                       0
                                      126

Pave existing shoulders at 10% RP
                                       0
                                       0
                                       0
                                      49

Pave existing shoulders at 25% RP
                                      200
                                      611
                                      769
                                     4,854

Pave Unpaved Roads at 25% RP
                                      371
                                     1,248
                                      767
                                     3,384
Total
 
                                     3,561
                                     6,384
                                    15,210
                                    61,321

By emissions inventory sector and by inventory source classification code (SCC) sector, Table 3-7 includes a summary of estimated PM2.5 emissions reductions from control applications for the alternative standard levels analyzed. As seen in Table 3-6, across alternative standard levels analyzed, estimated PM2.5 emissions reductions from control applications in the (i) non-EGU point and oil and gas point inventory sectors account for between 21 and 40 percent of estimated reductions; (ii) non-point (area) inventory sector account for between 41 and 50 percent of estimated reductions; (iii) residential wood combustion inventory sector account for between 7 and 9 percent; and (iv) area fugitive dust inventory sector account for between 11 and 30 percent. 
Across alternative standard levels analyzed, estimated PM2.5 emissions reductions from control applications in the Industrial Processes  -  Ferrous Metals, Industrial Processes  -  Not Elsewhere Classified, and Industrial Processes  -  Petroleum Refineries inventory SCC sectors account for between 62 percent and 69 percent of reductions from the non-EGU point and oil and gas point inventory sectors. Estimated PM2.5 emissions reductions from control applications in the Commercial Cooking and Waste Disposal  -  All Categories inventory SCC sectors account for between 78 percent and 88 percent of reductions from the non-point (area) inventory sector. Estimated PM2.5 emissions reductions from control applications in the Fuel Combustion  -  Residential  -  Wood inventory SCC sector account for all of the reductions from the residential wood combustion inventory sector, and estimated PM2.5 emissions reductions from control applications in the Dust  -  Paved Road Dust and Dust  -  Unpaved Road Dust inventory SCC sectors account for all of the reductions from the area source fugitive dust inventory sector.
Table 3-7	Summary of Estimated PM2.5 Emissions Reductions from CoST by Inventory Source Classification Code Sectors for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
Sector
SCC Sector
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Non-EGU Point
Agriculture - Livestock Waste
                                       0
                                      6.2
                                      6.8
                                      6.8

Fuel Combustion - Commercial/Institutional Boilers - Biomass
                                       0
                                       0
                                       0
                                     15.6

Fuel Combustion - Commercial/Institutional Boilers - Coal
                                       0
                                       0
                                      8.0
                                      8.0

Fuel Combustion - Commercial/Institutional Boilers - Natural Gas
                                       0
                                       0
                                       0
                                     85.9

Fuel Combustion - Commercial/Institutional Boilers - Other
                                     64.7
                                     64.7
                                     64.7
                                     69.8

Fuel Combustion - Industrial Boilers, ICEs - Biomass
                                       0
                                     76.0
                                      5.2
                                     402.2

Fuel Combustion - Industrial Boilers, ICEs - Coal
                                       0
                                       0
                                     16.4
                                     211.2

Fuel Combustion - Industrial Boilers, ICEs - Natural Gas
                                      6.1
                                     75.4
                                     81.7
                                     405.8

Fuel Combustion - Industrial Boilers, ICEs - Oil
                                       0
                                       0
                                       0
                                     18.1

Fuel Combustion - Industrial Boilers, ICEs - Other
                                     110.9
                                     140.7
                                     689.5
                                    1,023.9

Industrial Processes - Cement Manufacturing
                                       0
                                       0
                                     89.8
                                     688.5

Industrial Processes - Chemical Manufacturing
                                     29.3
                                     40.3
                                     136.5
                                     953.8

Industrial Processes - Ferrous Metals
                                     142.8
                                     150.1
                                     836.0
                                    2,378.0

Industrial Processes - Mining
                                       0
                                      7.4
                                     239.4
                                     326.9

Industrial Processes - Non-ferrous Metals
                                     55.9
                                     55.9
                                     502.1
                                     918.0

Industrial Processes - Not Elsewhere Classified
                                     304.3
                                     456.1
                                    2,169.9
                                    6,818.0

Industrial Processes - Petroleum Refineries
                                     178.5
                                     216.6
                                     875.8
                                    2,204.2

Industrial Processes - Pulp & Paper
                                       0
                                     18.3
                                     119.5
                                     848.1

Industrial Processes - Storage and Transfer
                                      8.9
                                     18.0
                                     186.7
                                     887.4

Waste Disposal - Excavation/Soils Handling
                                       0
                                       0
                                       0
                                      5.8

Waste Disposal - General Processes
                                       0
                                       0
                                      7.0
                                      7.0

Waste Disposal - Landfill Dump
                                       0
                                       0
                                       0
                                      5.5
Oil & Gas Point
Industrial Processes - Not Elsewhere Classified
                                       0
                                       0
                                       0
                                      3.6

Industrial Processes - Oil & Gas Production
                                       0
                                       0
                                       0
                                     54.9

Industrial Processes - Petroleum Refineries
                                       0
                                       0
                                       0
                                      1.8


                                       
                                       
                                       
                                       
Non-Point (Area)
Commercial Cooking
                                     950.2
                                    1,176.5
                                    2,336.9
                                    6,823.5

Fuel Combustion - Commercial/Institutional Boilers - Biomass
                                     16.3
                                     20.2
                                     52.8
                                     258.6

Fuel Combustion - Commercial/Institutional Boilers - Coal
                                       0
                                       0
                                       0
                                      0.5

Fuel Combustion - Commercial/Institutional Boilers - Natural Gas
                                     18.9
                                     22.2
                                     49.8
                                     95.5

Fuel Combustion - Commercial/Institutional Boilers - Oil
                                       0
                                       0
                                      3.0
                                     14.4

Fuel Combustion - Industrial Boilers, ICEs - Biomass
                                     66.0
                                     103.3
                                     345.0
                                    1,499.0

Fuel Combustion - Industrial Boilers, ICEs - Coal
                                       0
                                      2.4
                                     17.8
                                     39.1

Fuel Combustion - Industrial Boilers, ICEs - Natural Gas
                                      4.0
                                      4.0
                                     32.7
                                     65.5

Fuel Combustion - Industrial Boilers, ICEs - Oil
                                      1.0
                                      1.0
                                      1.0
                                      5.4

Fuel Combustion - Industrial Boilers, ICEs - Other
                                      2.0
                                      2.0
                                      2.0
                                      2.0

Industrial Processes - Chemical Manufacturing
                                       0
                                       0
                                     77.4
                                     199.1

Waste Disposal - All Categories
                                     603.2
                                     880.0
                                    2,641.3
                                   14,623.5

Waste Disposal - Residential
                                     109.2
                                     360.5
                                     709.2
                                    3,725.4
Residential Wood Combustion
                     Fuel Combustion - Residential - Wood
                                     296.2
                                     555.6
                                    1,275.9
                                    4,193.4
Area Source Fugitive Dust
Dust - Paved Road Dust
                                     199.9
                                     611.0
                                     768.9
                                    4,903.3

Dust - Unpaved Road Dust
                                     392.7
                                    1,319.3
                                     861.3
                                    6,523.6
Total
 
                                    3,561.0
                                    6,383.7
                                   15,210.0
                                   61,320.7

Potential Influence of EGU Emissions Reductions from Planned Retirements

      As indicated in Appendix 2A and the Overview section above, we did not apply controls and estimate emissions reductions and costs for EGUs; however, Appendix 2A, Section 2A.5 includes a discussion of EGU NOX, SO2, and PM2.5 emissions reductions from planned EGU retirements that are expected to occur between 2016 and 2030 beyond those included in the air quality modeling for this analysis. Section 2A.5 discusses the potential influence of these EGU emissions reductions on PM2.5 DVs in three ways  -  (i) local impact of the direct PM2.5 emissions reductions from EGUs on DVs for counties with 2032 PM2.5 DVs that exceed the alternative standard levels, (ii) regional impact of the total EGU SO2 and NOX emissions reductions in the eastern U.S. on 2032 PM2.5 DVs, and (iii) relatively local impact of the EGU NOX and SO2 emissions reductions on 2032 PM2.5 DVs in nearby counties for two cases with large SO2 reductions. The emissions reductions from the planned EGU retirements are not expected to have large impacts on PM2.5 DVs in the areas that need emissions reductions in this analysis. We include brief discussions below; for more detailed discussions see Appendix 2A, Section 2A.5.
      In assessing the local impact of direct PM2.5 emissions reductions on DVs for counties with 2032 PM2.5 DVs that exceed the alternative standard levels, ten counties had PM2.5 reductions from the planned EGU retirements (see Table 2A-15). The direct PM2.5 EGU emissions reductions from just three counties (out of the ten counties) account for 95 percent of these EGU PM2.5 reductions from these ten counties. In these three counties, either emissions reductions were not needed for, or the control strategy analysis identified sufficient non-EGU emissions reductions for, the alternative standard levels of 10/35 g/m[3], 10/30 g/m[3], and 9/35 g/m[3]; in all three counties the control strategy analysis did not identify sufficient non-EGU reductions for an alternative standard level of 8/35 g/m[3]. If the EGU PM2.5 emissions reductions from the planned retirements were directly included in the control strategy analyses, these reductions may have offset the need for some of the controls applied for all of the alternative standard levels. In particular, we note that for Hamilton County, Ohio, Jefferson County, Missouri, and Allegheny County, Pennsylvania, the planned retirements could offset the need for some of the other non-EGU controls identified in this analysis.
      In assessing the regional impact of the total EGU NOX and SO2 emissions reductions (see Table 2A-16) on annual 2032 PM2.5 DVs, across monitors in the eastern states the estimated median annual 2032 PM2.5 DV change is 0.06 g/m[3]. See Figure 2A-36 for the distributions of annual 2032 PM2.5 DV changes from the NOX and SO2 emissions reductions.  Therefore, these NOX and SO2 emissions reductions from planned retirements could have a small impact on PM2.5 DVs regionally across the eastern U.S. but are unlikely to have a substantial impact on the need for the additional non-EGU controls identified in this analysis.
      For the areas with the largest SO2 reductions expected near monitors with 2032 PM2.5 DVs that exceed alternative standard levels, we combined the NOX and SO2 EGU emissions reductions from the relevant counties and estimated their impact on the annual 2032 PM2.5 DVs. For one area, the EGU emissions reductions are estimated to impact the 2032 annual PM2.5 DV at each of the five monitoring sites listed in Table 2A-17 by approximately 0.5 g/m[3]. For the other area, the emissions reductions are estimated to impact the 2032 annual PM2.5 DV at the two monitoring sites listed in Table 2A-18 by approximately 0.3 g/m[3]. For a few counties in these two areas, the NOX and SO2 reductions could offset the need for some of the controls applied in the analysis, particularly for a standard level of 8/35g/m[3].

Estimates of PM2.5 Emissions Reductions Still Needed after Applying Control Technologies and Measures
The percent of total PM2.5 emissions reductions estimated from CoST (shown in Table 3-4 above) relative to total PM2.5 emissions reductions needed (shown in Table 3-2 above) varies by alternative standard level and by area. Note that in the northeast and southeast when we applied the emissions reductions from adjacent counties, we used a g/m[3] per ton PM2.5 air quality ratio that was four times less responsive than the ratio used when applying in-county emissions reductions (i.e., we applied four tons of PM2.5 emissions reductions from an adjacent county for one ton of emissions reduction needed in a given county).
 For the proposed alternative standard level of 10/35 g/m[3] -  - , the northeast and southeast have sufficient estimated emissions reductions. For the west, the estimated emissions reductions are approximately 27 percent of the total needed, and for California the estimated emissions reductions are approximately 18 percent of the total needed. 
 For the proposed alternative standard level of 9/35 g/m - [3] - , for the northeast we were able to identify approximately 97 percent of the reductions needed. For the southeast we were able to identify approximately 76 percent of the reductions needed. For the west, we were able to identify approximately 31 percent of the reductions needed, and for California the percentage is approximately 17 percent.  
The higher percent of estimated emissions reductions relative to needed reductions in the northeast and southeast is likely because as the alternative standard level becomes more stringent, more controls from counties projected to exceed and their adjacent counties are available and applied. See Appendix 3A, Tables 3A.2 through 3A.7 for more detailed summaries of PM2.5 emissions reductions by county for the alternative standard levels for the northeast, the adjacent counties in the northeast, the southeast, the adjacent counties in the southeast, the west, and California. Table 3A.7 for California presents the counties organized by air districts. 
As indicated, the estimated PM2.5 emissions reductions from control applications do not fully account for all the emissions reductions needed to reach the proposed and more stringent alternative standard levels in some counties in the northeast, southeast, west, and California. By area, Table 3-8 includes a summary of the estimated emissions reductions still needed after control applications for the alternative standards analyzed. By area and by county, Table 3-9 includes a more detailed summary of the estimated emissions reductions still needed after control applications for the alternative standards analyzed. As seen in Table 3-9, some counties need emissions reductions to meet a standard level of 10/30 g/m[3] that did not need emissions reductions to meet a standard level of 10/35 g/m[3]. These counties are in the west and California, where there are small valleys with mountainous terrain and wintertime inversions, along with residential woodsmoke emissions and some wildfire influence, and need emissions reductions to meet the more stringent 24-hour standard level of 30 g/m[3]. Figure 3-5 through Figure 3-8 show the counties that still need emissions reductions after control applications for the alternative standards analyzed.
The analysis indicates that counties in the northeast and southeast U.S. do not need additional emissions reductions to meet alternative standard levels of 10/35 g/ - m - [3] -  and 10/30 g/ - m - [3] - . For the northeast, 1 (out of 14) county needs additional emissions reductions to reach attainment of the proposed alternative standard level of 9/35 g/ - m - [3] - , and 22 (out of 57) counties need additional emissions reductions to reach attainment of the more stringent alternative standard level of 8/35 g/ - m - [3] - . For the southeast, 2 (out of 8) counties need additional emissions reductions to reach attainment of the proposed alternative standard level of 9/35 g/ - m - [3] - , and 10 (out of 35) counties need additional emissions reductions to reach attainment of the more stringent alternative standard level of 8/35 g/ - m - [3] - . 
The analysis also indicates that counties in the west and California need additional emissions reductions after the application of controls to meet all of the alternative standard levels. For the west, 3 (out of 3) counties need additional emissions reductions to reach attainment of the proposed alternative standard level of 10/35 g/ - m - [3] - , 16 (out of 23) counties need additional emissions reductions to reach attainment of the more stringent alternative standard level of 10/30 g/ - m - [3] - , 4 (out of 8) counties need additional emissions reductions to reach attainment of the proposed alternative standard level of 9/35 g/ - m - [3] - , and 8 (out of 24) counties need additional emissions reductions to reach attainment of the more stringent alternative standard level of 8/35 g/ - m - [3] - . For California, 12 (out of 15) counties need additional emissions reductions to reach attainment of the proposed alternative standard level of 10/35 g/ - m - [3] - , 14 (out of 18) counties need additional emissions reductions to reach attainment of the more stringent alternative standard level of 10/30 g/ - m - [3] - , 15 (out of 21) counties need additional emissions reductions to reach attainment of the proposed alternative standard level of 9/35 g/ - m - [3] - , and 21 (out of 25) counties need additional emissions reductions to reach attainment of the more stringent alternative standard level of 8/35 g/ - m - [3] - .
Table 3-8	Summary of PM2.5 Emissions Reductions Still Needed by Area for the Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
Region
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
                                       0
                                       0
                                      238
                                     6,741
Southeast
                                       0
                                       0
                                      994
                                     4,780
West
                                      595
                                     5,651
                                     2,132
                                     5,023
CA
                                     8,336
                                     9,749
                                    14,793
                                    23,368
Total
                                     8,931
                                    15,400
                                    18,157
                                    39,912

Table 3-9	Summary of PM2.5 Emissions Reductions Still Needed by Area and by County for the Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
Area
Area Name
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
Saint Clair County, IL
                                       0
                                       0
                                       0
                                      13
                                       
Marion County, IN
                                       0
                                       0
                                       0
                                      390
                                       
St. Joseph County, IN
                                       0
                                       0
                                       0
                                      207
                                       
Vigo County, IN
                                       0
                                       0
                                       0
                                      63
                                       
Wayne County, MI
                                       0
                                       0
                                       0
                                      286
                                       
St. Louis City County, MO
                                       0
                                       0
                                       0
                                      77
                                       
Camden County, NJ
                                       0
                                       0
                                       0
                                      608
                                       
Union County, NJ
                                       0
                                       0
                                       0
                                      76
                                       
New York County, NY
                                       0
                                       0
                                       0
                                      266
                                       
Butler County, OH
                                       0
                                       0
                                       0
                                      410
                                       
Cuyahoga County, OH
                                       0
                                       0
                                       0
                                      436
                                       
Hamilton County, OH
                                       0
                                       0
                                       0
                                      36
                                       
Jefferson County, OH
                                       0
                                       0
                                       0
                                      680
                                       
Allegheny County, PA
                                       0
                                       0
                                       0
                                      382
                                       
Armstrong County, PA
                                       0
                                       0
                                       0
                                      294
                                       
Cambria County, PA
                                       0
                                       0
                                       0
                                      129
                                       
Delaware County, PA
                                       0
                                       0
                                      238
                                      970
                                       
Lancaster County, PA
                                       0
                                       0
                                       0
                                      600
                                       
Lebanon County, PA
                                       0
                                       0
                                       0
                                      523
                                       
Philadelphia County, PA
                                       0
                                       0
                                       0
                                      51
                                       
Brooke County, WV
                                       0
                                       0
                                       0
                                      119
 
Marshall County, WV
                                       0
                                       0
                                       0
                                      124
Southeast
Bibb County, GA
                                       0
                                       0
                                       0
                                      154
                                       
Clayton County, GA
                                       0
                                       0
                                       0
                                      304
                                       
Floyd County, GA
                                       0
                                       0
                                       0
                                      15
                                       
Fulton County, GA
                                       0
                                       0
                                       0
                                      396
                                       
Muscogee County, GA
                                       0
                                       0
                                       0
                                      265
                                       
Caddo Parish, LA
                                       0
                                       0
                                       0
                                      359
                                       
West Baton Rouge Parish, LA
                                       0
                                       0
                                       0
                                      55
                                       
Cameron County, TX
                                       0
                                       0
                                      427
                                     1,244
                                       
El Paso County, TX
                                       0
                                       0
                                       0
                                      603
 
Hidalgo County, TX
                                       0
                                       0
                                      567
                                     1,385
West
Pinal County, AZ
                                       0
                                      272
                                       0
                                       0
                                       
Santa Cruz County, AZ
                                       0
                                       0
                                       0
                                      431
                                       
Denver County, CO
                                       0
                                       0
                                       0
                                      323
                                       
Benewah County, ID
                                       0
                                      419
                                      134
                                      601
                                       
Lemhi County, ID
                                       3
                                      575
                                      471
                                      939
                                       
Shoshone County, ID
                                      330
                                      575
                                      797
                                     1,265
                                       
Lewis and Clark County, MT
                                       0
                                      487
                                       0
                                       0
                                       
Lincoln County, MT
                                      262
                                      262
                                      730
                                     1,197
                                       
Ravalli County, MT
                                       0
                                      514
                                       0
                                       0
                                       
Silver Bow County, MT
                                       0
                                       0
                                       0
                                      148
                                       
Crook County, OR
                                       0
                                      352
                                       0
                                       0
                                       
Harney County, OR
                                       0
                                       0
                                       0
                                      119
                                       
Lake County, OR
                                       0
                                      575
                                       0
                                       0
                                       
Cache County, UT
                                       0
                                      29
                                       0
                                       0
                                       
Davis County, UT
                                       0
                                       1
                                       0
                                       0
                                       
Salt Lake County, UT
                                       0
                                      413
                                       0
                                       0
                                       
Weber County, UT
                                       0
                                       7
                                       0
                                       0
                                       
Kittitas County, WA
                                       0
                                      575
                                       0
                                       0
                                       
Okanogan County, WA
                                       0
                                      22
                                       0
                                       0
 
Yakima County, WA
                                       0
                                      575
                                       0
                                       0
CA
Alameda County, CA
                                       0
                                       0
                                       0
                                      175
                                       
Fresno County, CA
                                      192
                                      253
                                      509
                                      826
                                       
Imperial County, CA
                                     1,701
                                     1,701
                                     2,551
                                     3,402
                                       
Kern County, CA
                                      634
                                      634
                                      951
                                     1,268
                                       
Kings County, CA
                                      634
                                      634
                                      951
                                     1,268
                                       
Los Angeles County, CA
                                      542
                                      542
                                     1,393
                                     2,243
                                       
Madera County, CA
                                      67
                                      67
                                      384
                                      702
                                       
Merced County, CA
                                      136
                                      136
                                      453
                                      770
                                       
Napa County, CA
                                       0
                                       0
                                      300
                                      617
                                       
Plumas County, CA
                                      176
                                      502
                                      493
                                      810
                                       
Riverside County, CA
                                     1,701
                                     1,701
                                     2,551
                                     3,402
                                       
Sacramento County, CA
                                       0
                                       0
                                       0
                                      168
                                       
San Bernardino County, CA
                                     1,701
                                     1,701
                                     2,551
                                     3,402
                                       
San Diego County, CA
                                       0
                                       0
                                       0
                                      337
                                       
San Joaquin County, CA
                                       0
                                       0
                                      161
                                      478
                                       
San Luis Obispo County, CA
                                       0
                                       0
                                      59
                                      376
                                       
Siskiyou County, CA
                                       0
                                      43
                                       0
                                       0
                                       
Solano County, CA
                                       0
                                       0
                                       0
                                      167
                                       
Stanislaus County, CA
                                      218
                                      218
                                      535
                                      852
                                       
Sutter County, CA
                                       0
                                       0
                                       0
                                      56
                                       
Tulare County, CA
                                      634
                                      634
                                      951
                                     1,268
 
Ventura County, CA
                                       0
                                      983
                                       0
                                      783
Total
 
                                     8,931
                                    15,400
                                    18,157
                                    39,912
Note: The table includes only those counties that still need reductions (e.g., in the Northeast there were 57 counties that needed emissions reductions, and only the 22 counties still need emissions reductions for an alternative standard level of 8/35 g/ - m - [3] - ).

                                       

Figure 3-5	Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative Standard Level of 10/35 g/ - m - [3] - 
                                       
Figure 3-6	Counties that Still Need PM2.5 Emissions Reductions for Proposed Alternative Standard Level of 9/35 g/ - m - [3] - 


                                       
Figure 3-7	Counties that Still Need PM2.5 Emissions Reductions for More Stringent Alternative Standard Level of 8/35 g/ - m - [3] - 
                                       
Figure 3-8	Counties that Still Need PM2.5 Emissions Reductions for More Stringent Alternative Standard Level of 10/30 g/ - m - [3] - 

Qualitative Assessment of the Remaining Air Quality Challenges and Emissions Reductions Potentially Still Needed

The sections below discuss the remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California for the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - ; the areas include a county in Pennsylvania potentially affected by local sources, counties in border areas, counties in small western mountain valleys, and counties in California's air basins and districts. The characteristics of the air quality challenges for these areas include features of local source-to-monitor impacts, cross-border transport, effects of complex terrain in the west and California, and identifying wildfire influence on projected PM2.5 DVs that could potentially qualify for exclusion as atypical, extreme, or unrepresentative events (USEPA, 2019b).   	
      Consistent with Chapter 2, Section 2.4, to discuss the remaining air quality challenges for the proposed alternative standard levels of 10/35 g/ - m - [3] and 9/35 g/ - m - [3], we group  - counties into the following "bins": Delaware County, Pennsylvania, border areas, small mountain valleys, and California areas. By bin, Table 3-10 below summarizes the counties that need additional emissions reductions for the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - . 

Table 3-10	Summary of Counties by Bin that Still Need Emissions Reductions for Proposed Alternative Primary Standard Levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - 
Bin

Area
Counties[a] for
10/35 mg/ - m - [3]
Additional Counties[a] for 
9/35 mg/ - m - [3] - 
Delaware County, Pennsylvania
Northeast
--
Delaware County, PA
Border Areas
Southeast
--
Cameron County, TX
Hidalgo County, TX

California
Imperial County, CA
--
Small Mountain Valleys
West
Plumas County, CA
Lemhi County, ID
Shoshone County, ID
Lincoln County, MT
Benewah County, ID
California Areas

Fresno County, CA (SJVAPCD)
Kern County, CA (SJVAPCD)
Kings County, CA (SJVAPCD)
Los Angeles County, CA (SCAQMD)
Madera County, CA (SJVAPCD)
Merced County, CA (SJVAPCD)
Riverside County, CA (SCAQMD)
San Bernardino County, CA (SCAQMD)
Stanislaus County, CA (SJVAPCD)
Tulare County, CA (SJVAPCD)
Napa County, CA (BAAQMD)
San Joaquin County, CA (SJVAPCD)
San Luis Obispo County, CA
Note: For California counties that are part of multi-county air districts, the relevant district is indicated in parentheses; BAAQMD = Bay Area Air Quality Management District, SCAQMD = South Coast Air Quality Management District, and SJVAPCD= San Joaquin Valley Air Pollution Control District.
[a] The following counties have no identified PM2.5 emissions reductions because available controls were applied for the current standard of 12/35 g/m[3] and additional controls were not available: Imperial, Kern, Kings, Lemhi, Plumas, Riverside, San Bernardino, Shoshone, and Tulare.

Delaware County, Pennsylvania (Northeast)
      As shown in Table 3-9 above, the analysis indicates that counties in the northeast do not need additional emissions reductions for the proposed alternative standard level of 10/35 g/ - m - [3] - ; Delaware County, Pennsylvania county needs additional emissions reductions for the proposed alternative standard level of 9/35 g/ - m - [3].
      In analyzing the proposed alternative standard level of 9/35 g/ - m - [3], we estimated Delaware County would need 673 tons of PM2.5 emissions reductions. The control strategy analysis identified 277 tons of reductions within Delaware County from the application of several controls, including a potential control at one of the facilities adjacent to a monitor. Some additional control applications within Delaware County included: Electrostatic Precipitator at 25% RP applied to commercial cooking emissions in the non-point (area) inventory sector; Pave Existing Shoulders at 25% RP applied to road dust emissions in the area fugitive dust inventory sector; Fabric Filter  -  All Types applied to industrial, commercial, and institutional boilers and industrial processes in the non-EGU point inventory sector; and Convert to Gas Logs at 25% RP and New Gas Stove or Gas Logs at 25% RP applied to area source residential wood combustion emissions in the residential wood combustion inventory sector. 
      To analyze the 396 tons of PM2.5 emissions reductions still needed, we identified 633 tons of PM2.5 emissions reductions from adjacent counties, which is the equivalent of 158 tons of in-county emissions reductions after adjusting for the 4:1 ratio of adjacent county reductions identified to in-county reductions needed. This left 238 tons of PM2.5 emissions reductions still needed. As shown in Table 3A-8, Delaware County has area fugitive dust (afdust), non-point (area) (nonpt), non-electric generating unit point source (ptnonipm), and residential wood combustion (rwc) emissions remaining in the inventory if additional controls beyond the scope of this analysis can be identified. In addition, Philadelphia County and Montgomery County, which are adjacent to Delaware County, have emissions remaining in those inventory sectors if additional controls beyond the scope of this analysis can be identified. 
      In Chapter 2, Section 2.4.1 we discuss a monitor located on the property of Evonik Degussa Corporation in Delaware County, Pennsylvania. The state, in their Commonwealth of Pennsylvania Department of Environmental Protection 2018 Annual Ambient Air Monitoring Network Plan, concluded that local emissions sources are impacting this monitor (Chester monitor) based on comparisons of PM2.5 concentrations from the Chester monitor and a monitor approximately 2.5 miles away (Marcus Hook monitor). The EPA's 2032 DV projections are consistent with a local source influence on the Chester monitor. It is possible that controls applied in the illustrative control strategy analysis at one of the facilities adjacent to the Chester monitor might result in sufficient emissions reductions for the proposed alternative standard level of 9/35 g/m[3] at that monitor because PM2.5 concentrations are more responsive to primary PM2.5 emission reductions located close to a monitor. However, specifically quantifying the impacts of the CoST-recommended control at one of the facilities adjacent to the Chester monitor would require a more detailed local analysis. In addition, the CoST-recommended control may not be applicable if the underlying emissions inventory did not accurately reflect existing controls at the facility adjacent to the Chester monitor. 
Border Areas (Southeast, California)
      As shown in Table 3-9 above, the analysis indicates that counties in the southeast do not need additional emissions reductions for the proposed alternative standard level of 10/35 g/ - m - [3] - ; Cameron County and Hidalgo County, Texas need additional emissions reductions for the proposed alternative standard level of 9/35 g/ - m - [3].
      We estimated Cameron County would need 581 tons of PM2.5 emissions reductions. The control strategy analysis identified 148 tons of reductions within Cameron County from the application of several controls. The control applications within Cameron County included: Electrostatic Precipitator at 25% RP applied to commercial cooking emissions in the non-point (area) inventory sector; Pave Existing Shoulders at 25% RP and Pave Unpaved Roads at 25% RP applied to road dust emissions in the area fugitive dust inventory sector; Convert to Gas Logs at 25% RP applied to area source residential wood combustion emissions in the residential wood combustion inventory sector; and Substitute Chipping for Burning applied to waste disposal emissions in the non-point (area) inventory sector. 
      To analyze the 433 tons of PM2.5 emissions reductions still needed, we identified 22 tons of PM2.5 emissions reductions from adjacent counties, which was the equivalent of 5.5 tons of in-county emissions reductions after adjusting for the 4:1 ratio of adjacent county reductions identified to in-county reductions needed. This left 427 tons of PM2.5 emissions reductions still needed. As shown in Table 3A-8, Cameron County has area fugitive dust (afdust), point source agriculture fire (ptagfire), non-point (area) (nonpt), non-electric generating unit point source (ptnonipm), and residential wood combustion (rwc) emissions remaining in the inventory if additional controls beyond the scope of this analysis can be identified; the majority of the emissions remaining are area fugitive dust emissions. 
      In addition, we estimated Hidalgo County would need 1,022 tons of PM2.5 emissions reductions. The control strategy analysis identified 406 tons of reductions within Hidalgo County from the application of several controls. Some of the control applications within Hidalgo County included: Electrostatic Precipitator at 25% RP applied to commercial cooking emissions in the non-point (area) inventory sector; Fabric Filter  -  All Types applied to industrial, commercial, and institutional boilers in the non-EGU point inventory sector; Pave Existing Shoulders at 25% RP and Pave Unpaved Roads at 25% RP applied to road dust emissions in the area fugitive dust inventory sector; Convert to Gas Logs at 25% RP and New Gas Stove or Gas Logs at 25% RP applied to area source residential wood combustion emissions in the residential wood combustion inventory sector; and Substitute Chipping for Burning applied to waste disposal emissions in the non-point (area) inventory sector. 
      To analyze the 616 tons of PM2.5 emissions reductions still needed, we identified 194 tons of PM2.5 emissions reductions from adjacent counties, which was the equivalent of 48.5 tons of in-county emissions reductions after adjusting for the 4:1 ratio of adjacent county reductions identified to in-county reductions needed. This left 567 tons of PM2.5 emissions reductions still needed. As shown in Table 3A-8, Hidalgo County has area fugitive dust (afdust), point source agriculture fire (ptagfire), non-point (area) (nonpt), non-point source oil and gas (np_oilgas), non-electric generating unit point source (ptnonipm), point source oil and gas (pt_oilgas), and residential wood combustion (rwc) emissions remaining in the inventory if additional controls beyond the scope of this analysis can be identified; the majority of the emissions remaining are area fugitive dust emissions.
      In Chapter 2, Section 2.4.2.1 we note that the monitors in Cameron County and Hidalgo County are in the Lower Rio Grande Valley, which includes the northern portion of the state of Tamaulipas, Mexico. Addressing emissions reductions needed for the proposed alternative standard level of 9/35 g/ - m - [3] at the monitors is challenging because of the location of these counties along the U.S.-Mexico border. 
      Area fugitive dust emissions make up the largest fraction of primary PM2.5 emissions in Hidalgo County and Cameron County in the 2016 and 2032 air quality modeling cases (Chapter 2, Figure 2-16). Paved-road dust emissions (in the area fugitive dust inventory sector) are projected to increase in these counties between 2016 and 2032 as a result of projected increases in the vehicle miles travelled; non-point (area) sources emissions are also projected to increase as a result of population-based emissions projection factors. Increases in area fugitive dust and non-point (area) emissions from 2016 to 2032 offset the decreases in primary PM2.5 emissions projected for EGUs and mobile sources in the counties. More detailed local analyses for these counties are needed to better understand the potential growth in area fugitive dust and non-point (area) source emissions, as well as the potential contributions of international transport. 
      Further, for Imperial County, California the control strategy analysis did not identify any emissions reductions from the application of controls. As shown in Table 3A-8, Imperial County has area fugitive dust (afdust), non-point (area) (nonpt), non-electric generating unit point source (ptnonipm), point source agriculture fire (ptagfire), and residential wood combustion (rwc) emissions remaining in the inventory if controls beyond the scope of this analysis can be identified; the majority of the emissions remaining are area fugitive dust emissions.
      As discussed in Chapter 2, Section 2.4.2, Imperial County is located in the southeast corner of California and shares a southern border with Mexicali, Mexico. Imperial County includes three PM2.5 monitoring sites, located in the cities of Calexico, El Centro, and Brawley (Chapter 2, Figure 2-12). While these three cities are of similar size and have similar emissions sources, the annual 2032 PM2.5 DV at the Calexico monitor, which is the southern-most monitor and is less than a mile from the U.S.-Mexico border, is much greater than the other two monitors (12.45 g/m[3], 9.13 g/m[3], and 8.02g/m[3], respectively). In addition, substantially greater NOx, SO2 and sulfate, and primary PM2.5 emissions have been estimated for Mexicali, Mexico than for Calexico, California. For the proposed alternative standard levels, Imperial County may not need the additional emissions reductions estimated because of the potential influence of Mexicali emissions on PM2.5 concentrations at the Calexico monitor and Section 179B of the Clean Air Act; however, a detailed local analysis is needed.
Small Mountain Valleys (West)
      As shown in Table 3-9 above, the analysis also indicates that counties in the west need additional emissions reductions after the application of controls for all of the alternative standard levels analyzed. For the small mountain valleys bin, Table 3-11 below summarizes the estimated PM2.5 emissions reductions needed and emissions reductions identified by CoST for each of these counties for the proposed alternative standard level of 9/35 g/ - m - [3].  
Table 3-11	Summary of Estimated PM2.5 Emissions Reductions Needed and Emissions Reductions Identified by CoST for the West for the Proposed Primary Standard Level of 9/35 g/ - m - [3] -  in 2032 (tons/year)
County/State
                      PM2.5 Emissions Reductions Needed 
            In-County PM2.5 Emissions Reductions Identified by CoST
Plumas, CA
                                     493.2
                                       0
Benewah, ID
                                     266.6
                                     132.8
Lemhi, ID
                                     471.0
                                       0
Shoshone, ID
                                     797.4
                                       0
Lincoln, MT
                                     954.0
                                     224.2
Note: As shown in Table 3A-8, for Plumas, CA and Lemhi and Shoshone, ID, CoST identified controls to apply toward the current standard of 12/35 g/m[3]. Additional controls in those counties were not available for the proposed or more stringent alternative standard levels.

      As shown in Table 3-11, the control strategy analysis identified emissions reductions for two of the counties. Some of the control applications in those counties included: Pave Existing Shoulders at 25% RP and Pave Unpaved Roads at 25% RP applied to road dust emissions in the area fugitive dust inventory sector; Install Cleaner Hydronic Heaters at 25% RP and New Gas Stove or Gas Logs at 25% RP applied to area source residential wood combustion emissions in the residential wood combustion inventory sector; and Substitute Chipping for Burning applied to waste disposal emissions in the non-point (area) inventory sector. 
      As shown in Table 3A-8, these counties have area fugitive dust (afdust), non-point (area) (nonpt), non-electric generating unit point source (ptnonipm), and residential wood combustion (rwc) emissions remaining in the inventory if additional controls beyond the scope of this analysis can be identified; for each of the counties the majority of the emissions remaining are area fugitive dust emissions. 
      Meteorological temperature inversions often occur in small northwestern mountain valleys in winter and trap pollution emissions in a shallow atmospheric layer at the surface (Chapter 2, Section 2.1.2). As discussed in Chapter 2, Section 2.4.3, primary PM2.5 emissions can build up in the surface layer and produce high PM2.5 concentrations in winter (Chapter 2, Figure 2-17). These mountain valleys are often very small in size relative to the area of the surrounding county and far smaller than the resolution of photochemical air quality models (e.g., 12km grid cells). See Chapter 2, Figures 2-18 and 2-19 for maps of the Portola nonattainment area (2012 PM2.5 NAAQS) relative to the city of Portola, California and the Libby nonattainment area (1997 PM2.5 NAAQS) relative to the city of Libby, Montana. PM2.5 concentrations in these small mountain valleys can be influenced by the temperature inversions, as well as by residential wood combustion and wildfire smoke.
     Also as discussed in Chapter 2, Section 2.4.3, because of the small size of the urban areas within the northwestern mountain valleys, air quality planning is commonly based on linear rollback methods. To estimate emissions reductions needed for a standard level, the linear rollback method relates wood-smoke contribution estimates at an exceeding monitor to the local, or sub-county, wood combustion emissions totals. The PM2.5 response factors from linear rollback methods estimate that relatively fewer residential wood combustion emissions reductions can greatly influence PM2.5 concentrations in many of these small mountain valleys. We did not apply linear rollback-based response factors for the mountain valleys in this RIA because emissions inventory and control measure information are available at the county level, preventing us from targeting residential wood combustion controls in the local communities identified in the analyses. To better assess the emissions reductions needed for the proposed standard levels - , more detailed analyses that include local PM2.5 response factors, emissions estimates, and controls for each local area are needed.    
      In addition to air quality challenges related to meteorological temperature inversions and residential wood combustion, PM2.5 concentrations in these small mountain valleys may also be influenced by wildfire emissions that could potentially qualify for exclusion as atypical, extreme, or unrepresentative events. We performed sensitivity projections to assess the potential for wildfire impacts. These projections suggest that Benewah County, Oregon may be largely affected by wildfires and that annual 2032 DVs in Lemhi County and Shoshone County, Oregon, and Lincoln County, Montana could be much lower if detailed analyses resulted in additional data exclusion. Detailed local analyses are needed to fully characterize the wildfire influence in these areas. For more detailed discussions of the residential wood combustion and wildfire smoke air quality challenges, see Chapter 2, Section 2.4.3.
California Areas
      As shown in Table 3-9 above, the analysis also indicates that counties in California need additional emissions reductions after the application of controls for all of the alternative standard levels analyzed. The sections below discuss the air quality challenges by each air basin and/or district.
      In the SJVAPCD, in analyzing the proposed alternative standard level of 9/35 g/ - m - [3], the District needed 5,636 tons of PM2.5 emissions reductions. The control strategy analysis identified 741 tons of reductions from the application of several controls. Some of the control applications included: Electrostatic Precipitator at 25% RP applied to commercial cooking emissions in the non-point (area) inventory sector; Fabric Filter  -  All Types applied to industrial, commercial, and institutional boilers and industrial processes in the non-EGU point inventory sector; Pave Existing Shoulders at 25% RP and Pave Unpaved Roads at 25% RP applied to road dust emissions in the area fugitive dust inventory sector; Convert to Gas Logs at 25% RP applied to area source residential wood combustion emissions in the residential wood combustion inventory sector; and Substitute Chipping for Burning applied to waste disposal emissions in the non-point (area) inventory sector. As discussed above, we did not attempt to identify additional PM2.5 emissions reductions in adjacent counties or air districts.
      As discussed in more detail in Chapter 2, Section 2.4.4, the air quality in SJVAPCD is influenced by complex terrain and meteorological conditions that are best characterized with a high-resolution air quality modeling platform developed for the specific conditions of the valley. Air quality in the valley is influenced by emissions from large cities such as Bakersfield and Fresno, a productive agricultural region, dust exacerbated by drought, major goods transport corridors, and wildfires. The largest share of 2032 PM2.5 emissions are from agricultural dust, the production of crops and livestock, agricultural burning, paved and unpaved road dust, and prescribed burning (Chapter 2, Figure 2-23); wildfire emissions also influence PM2.5 concentrations. 
      Specific, local information on control measures to reduce emissions from agricultural dust and burning and prescribed burning is needed given the magnitude of emissions from these sources. In addition, more detailed analyses are needed to characterize the influence of wildfires on PM2.5 concentrations and the potential for some of these wildfires to be considered as atypical, extreme, or unrepresentative events. Note that wildfire screening is particularly complex in California because different parts of the state have different wildfire seasons.
      In the SCAQMD, in analyzing the proposed alternative standard level of 9/35 g/ - m - [3], the District needed 7,654 tons of PM2.5 emissions reductions. The control strategy analysis identified 1,159 tons of reductions from the application of several controls. Some of the control applications included: Electrostatic Precipitator at 25% RP applied to commercial cooking emissions in the non-point (area) inventory sector; Fabric Filter  -  All Types applied to industrial, commercial, and institutional boilers and industrial processes in the non-EGU point inventory sector; Convert to Gas Logs at 25% RP applied to area source residential wood combustion emissions in the residential wood combustion inventory sector; and Substitute Chipping for Burning applied to waste disposal emissions in the non-point (area) inventory sector. We did not attempt to identify additional PM2.5 emissions reductions in adjacent counties or air districts.
      As discussed in more detail in Chapter 2, Section 2.4.4, the air quality in the SCAQMD is influenced by complex terrain and meteorological conditions that are best characterized with a high-resolution air quality modeling platform developed for the specific conditions of the air basin. Air quality is influenced by diverse emissions sources associated with the large population, the ports of Los Angeles and Long Beach, wildfires, and transportation of goods. The largest share of 2032 PM2.5 emissions are from commercial and residential cooking, on-road mobile sources, and paved and unpaved road dust (Chapter 2, Figure 2-26). 
      Specific, local information on control measures to reduce emissions from many of the non-point (area) emissions sources (e.g., commercial and residential cooking) is needed given the magnitude of emissions from these sources. In addition, more detailed analyses are needed to characterize the influence of wildfires on PM2.5 concentrations and the potential for some of these wildfires to be considered as atypical, extreme, or unrepresentative events.
      In the BAAQMD, in analyzing the proposed alternative standard level of 9/35 g/ - m - [3], the District needed 884 tons of PM2.5 emissions reductions. The control strategy analysis identified 586 tons of reductions from the application of several controls. Some of the control applications included: Smokeless Broiler at 25% RP, Catalytic Oxidizers at 25% RP, and Electrostatic Precipitator at 25% RP applied to commercial cooking emissions in the non-point (area) inventory sector; Fabric Filter  -  All Types and Venturi Scrubber applied to industrial, commercial, and institutional boilers and industrial processes in the non-EGU point inventory sector; Pave Existing Shoulders at 25% RP and Pave Unpaved Roads at 25% RP applied to road dust emissions in the area fugitive dust inventory sector; Convert to Gas Logs at 25% RP applied to area source residential wood combustion emissions in the residential wood combustion inventory sector; and Substitute Chipping for Burning applied to waste disposal emissions in the non-point (area) inventory sector. We did not attempt to identify additional PM2.5 emissions reductions in adjacent counties or air districts.
      As discussed in Chapter 2, Section 2.4.4, PM2.5 concentrations in Napa County may have relatively large contributions from local emissions sources, as well as contributions from wildfires and sources in nearby regions including the BAAQMD and the SJVAPCD. In addition, previous research reported that modeled concentrations of carbonaceous PM2.5 at the monitor in Napa County were underestimated. The research suggested that carbonaceous PM2.5 emissions, possibly from wood burning, may have been strongly underrepresented in the Napa County emissions inventory. Additional work to develop local emissions inventories and identify appropriate controls is needed.
      In San Luis Obispo County APCD, in analyzing the proposed alternative standard level of 9/35 g/ - m - [3], the District needed 187 tons of PM2.5 emissions reductions. The control strategy analysis identified 128 tons of reductions from the application of several controls. The control applications included: Electrostatic Precipitator at 25% RP applied to commercial cooking emissions in the non-point (area) inventory sector; Fabric Filter  -  All Types applied to industrial processes in the non-EGU point inventory sector; Convert to Gas Logs at 25% RP applied to area source residential wood combustion emissions in the residential wood combustion inventory sector; and Substitute Chipping for Burning applied to waste disposal emissions in the non-point (area) inventory sector. We did not attempt to identify additional PM2.5 emissions reductions in adjacent counties or air districts.
     As discussed in Chapter 2, Section 2.4.4, in recent years the PM2.5 DVs have decreased at the monitor in San Luis Obispo County APCD -- the annual PM2.5 DVs for the 2018-2020 and 2019-2021 periods are 8.0 and 7.7 g/m[3], respectively (Chapter 2, Figure 2-28). The projected 2032 annual DV (9.63 g/m[3]) at the monitor is based on data from the 2014-2018 period and does not capture these recent air quality improvements. Based on the data for these two most recent DV periods, the monitor may not need additional emissions reductions for the proposed alternative standard level of 9/35 g/ - m - [3].
Limitations and Uncertainties
The EPA's analysis is based on its best judgment for various input assumptions that are uncertain. As a general matter, the Agency selects the best available information from engineering studies of air pollution controls and has set up what it believes is the most reasonable modeling framework for analyzing the cost, emissions changes, and other impacts of emissions controls. However, the control strategies above are subject to important limitations and uncertainties. In the following, we summarize the limitations and uncertainties that are most significant.
 Illustrative control strategy: A control strategy is the set of control measures or actions that States may take to meet a standard, such as which industries should be required to install end-of-pipe controls or certain types of equipment and technology. The illustrative control strategy analyses in this RIA present only one potential pathway for controlling emissions. The control strategies are not recommendations for how a revised PM2.5 NAAQS should be implemented, and States will make all final decisions regarding implementation strategies for a revised NAAQS. We do not presume that the controls presented in this RIA are an exhaustive list of possibilities for emissions reductions.
 Emissions inventories and air quality modeling: These serve as a foundation for the projected PM2.5 DVs, control strategies, and estimated costs in this analysis and thus limitations and uncertainties for these inputs impact the results, especially for issues such as future year emissions projections and information on controls currently in place at many sources. Limitations and uncertainties for these inputs are discussed in previous chapters In addition, there are factors that affect emissions, such as economic growth and the makeup of the economy that introduce additional uncertainty.  
 Projecting level and geographic scope of exceedances:  Estimates of the geographic areas that would exceed alternative standard levels in a future year, and the level to which those areas would exceed, are approximations based on several factors. The actual nonattainment determinations that would result from a revised NAAQS will likely depend on the consideration of local issues, changes in source operations between the time of this analysis and implementation of a new standard, and changes in control technologies over time.
 Assumptions about the baseline: There is significant uncertainty about the illustration of the impact of rules on the baseline. In addition, the February 2022 Proposed Federal Implementation Plan Addressing Regional Ozone Transport for the 2015 Ozone National Ambient Air Quality Standard and the firm EGU retirements are not included in the 2032 projections. 
 Applicability of control measures: The applicability of a control measure to a specific source varies depending on a number of process equipment factors such as age, design, capacity, fuel, and operating parameters. These can vary considerably from source to source and over time. The applicability of control measures to area sources is also subject to the uncertainty of the area source emissions estimated. 
 Control measure advances over time: The control measures applied do not reflect potential effects of technological change that may be available in future years. All estimates of impacts associated with control measures applied reflect our current knowledge, and not projections, of the measures' effectiveness or costs.  
 Pollutants to be targeted: Local knowledge of atmospheric chemistry in each geographic area may result in a different prioritization of pollutants for potential control.

References 
Kelly, J. T., CJ Jang, B Timin, B Gantt, A Reff, Y Zhu, S Long, A Hanna. 2019. A System for Developing and Projecting PM2. 5 Spatial Fields to Correspond to Just Meeting National Ambient Air Quality Standards. Atmospheric Environment: X 100019. https://doi.org/10.1016/j.aeaoa.2019.100019
Kelly, J. T., K. R. Baker, S. N. Napelenok, and S. R. Roselle. 2015. Examining single-source secondary impacts estimated from brute-force, decoupled direct method, and advanced plume treatment approaches. Atmospheric Environment 111:10-19. https://doi.org/10.1016/j.atmosenv.2015.04.004
U.S. Environmental Protection Agency (U.S. EPA). 2019. CoST v3.7 User's Guide. Office of Air Quality Planning and Standards, Research Triangle Park, NC. November 2019. Available at < https://www.cmascenter.org/help/documentation.cfm?model=cost&version=3.7>.
U.S. Environmental Protection Agency (U.S. EPA). 2012. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-12-005. Available at: https://www.epa.gov/sites/default/files/2020-07/documents/naaqs-pm_ria_final_2012-12.pdf.

 APPENDIX 3A: CONTROL STRATEGIES AND PM2.5 EMISSIONS REDUCTIONS
 Overview
Chapter 3 describes the approach that EPA used in applying the illustrative control strategies for analyzing the following proposed and more stringent alternative annual and 24-hour standard levels -- 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] - . This Appendix contains additional information about the control technologies and measures that were applied, as well as additional details on the estimated PM2.5 emissions reductions. 
 3A.1	Types of Control Measures
Several types of control measures were applied in the analyses for the analytical baseline and alternative standard levels. We identified control measures using the EPA's Control Strategy Tool (CoST) (U.S. EPA, 2019) and the control measures database. A brief description of several of the control technologies and measures is below.
    3A.1.1	PM Control Measures for Non-EGU Point Sources
Non-EGU point source categories covered in this analysis include industrial boilers, as well as industrial processes in the cement manufacturing, chemical manufacturing, pulp and paper, mining, ferrous and non-ferrous metals, and refining industries. Several types of PM2.5 control technologies were applied for these sources, including venturi scrubbers, fabric filters, and electrostatic precipitators, which are the primary controls analyzed for non-EGU point sources. 
 Venturi scrubbers  -  Venturi scrubbers are one of several types of wet scrubbers that remove both acid gas and PM from waste gas streams of stationary point sources. The pollutants are removed primarily through the impaction, diffusion, interception and/or absorption of the pollutant onto droplets of liquid. The liquid containing the pollutant is then collected for disposal. 
 Fabric filters -- A fabric filter unit consists of one or more isolated compartments containing rows of fabric bags in the form of round, flat, or shaped tubes, or pleated cartridges. Particle-laden gas usually passes up along the surface of the bags then radially through the fabric. Particles are retained on the upstream face of the bags, and the cleaned gas stream is vented to the atmosphere. Fabric filters collect particles with sizes ranging from submicron to several hundred microns in diameter at efficiencies generally in excess of 99 or 99.9 percent.
 Electrostatic precipitators -- An ESP is a particle control device that uses electrical forces to move the particles out of the flowing gas stream and onto collector plates. The particles are given an electrical charge by forcing them to pass through a corona, a region in which gaseous ions flow. The electrical field that forces the charged particles to the walls comes from electrodes maintained at high voltage in the center of the flow lane. Once the particles are collected on the plates, they must be removed from the plates without re-entraining them into the gas stream. This is usually accomplished by knocking them loose from the plates, allowing the collected layer of particles to slide down into a hopper from which they are evacuated.
            
    3A.1.2	PM Control Measures for Non-point (Area) Sources
The non-point sector of the emissions inventory includes emissions sources that are generally too small and/or numerous to estimate emissions for individual sources (e.g., commercial cooking, residential woodstoves, commercial or backyard waste burning). We estimate the emissions from these sources for each county overall, typically using an emissions factor that is applied to a surrogate of activity such as population or number of houses. Control measures for non-point sources are applied to the county level emissions. Several control measures were applied to PM2.5 emissions from non-point sources, including catalytic oxidizers applied to charbroilers in commercial cooking, electrostatic precipitator applied to under-fire charbroilers in commercial cooking, substitute chipping for open burning in general and for households, converting to gas logs for residential wood combustion, chemical stabilizers to suppress unpaved road dust, and paving existing shoulders to suppress paved road dust.
 3A.2	EGU Trends Reflected in EPA's Integrated Planning Model (IPM) v6 Platform, Summer 2021 Reference Case Projections
      The EPA's Integrated Planning Model (IPM) v6 Platform Summer 2021 Reference Case projections were used in the air quality modeling done for this RIA. A high level summary of the input assumptions in the Summer 2021 Reference Case is below. This version features bottom-up comprehensive input data and assumption updates, including the following:
 Demand  -  Annual Energy Outlook (AEO) 2020
 Gas Market Assumptions  -  Updated as of September 2020
 Coal Market Assumptions  -  Updated as of September 2020
 Cost and Performance of Fossil Generation Technologies  -  AEO 2020
 Cost and Performance of Renewable Energy Generation Technologies  -  National Renewable Energy Lab Annual Technology Baseline 2020 mid-case
 Nuclear Unit Operational Costs  -  AEO 2020 with some adjustments
 Environmental Rules and Regulations (On-the-Books) -- Revised Cross-State Air Pollution Rule, Mercury and Air Toxics Standard, BART, California Assembly Bill 32, Regional Greenhouse Gas Initiative, various renewable portfolio standards and clean energy standards, non-air rules (Cooling Water Intake, Steam Electric Power Generating Effluent Guidelines, Coal Combustion Residuals), State Rules
 Financial Assumptions  -  Based on 2016-2020 data, reflects tax credit extensions from Consolidated Appropriations Act of 2021
 Transmission  -  Updated data with build options
 Retrofits  -  carbon capture and storage option for combined cycles
 Operating Reserves (in select runs) - greater detail in representing interaction of load, wind, and solar, ensuring availability of quick response of resources at higher levels of renewable energy penetration
 Fleet  -  NEEDS Summer 2021
      The Summer 2021 Reference Case projections show a gradual decline in national-level annual SO2, NOx, and primary PM emissions because of displacement of retired coal units with new natural gas generation and renewable energy. Greater near-term renewable energy penetration is due to increase in actual projects reflected in NEEDS prior to the IPM projections; long-term increase is largely driven by improved renewable energy technology costs.
California sees a significant decrease in projected emissions for all pollutants by 2030 due to the state's Clean Energy Standards (CES). California's Senate Bill No. 100 requires expansion of the Renewable Portfolio Standard through 2030 where generation from qualifying renewables must achieve a 50 percent share of retail sales by 2026 and 60 percent by 2030. California's legislation requires a transition from the RPS to CES where generation from qualifying "zero carbon resources" must equal 100 percent of retail sales by 2045. Our projections show a significant shift from fossil to renewable energy generation in California between 2025 and 2030 with the trend continuing thereafter.
 3A.3		Applying Control Technologies and Measures
As mentioned in Chapter 3, Section 3.2.2, controls applied for the analyses of the existing standards of 12/35 g/ - m - [3] -  and the proposed and more stringent annual and 24-hour PM2.5 alternative standard levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  are listed in Table 3A-1 by geographic area and by emissions inventory sector, with an "X" indicating which control technologies were applied for each standard level. 
Table 3A-2 through Table 3A-7 include detailed summaries of PM2.5 emissions reductions by county for the alternative standard levels for the northeast, the adjacent counties in the northeast, the southeast, the adjacent counties in the southeast, the west, and California. Table 3A-7 for California presents counties organized by air districts.
As shown in Table 3A-2 and Table 3A-3 for the northeast counties (57 counties) and the adjacent counties (75 counties), for the alternative standard levels of 10/35 g/ - m - [3] -  and 10/30 g/ - m - [3] - , controls were applied in 4 counties and no additional emissions reductions were needed in adjacent counties. For the alternative standard level of 9/35 g/ - m - [3] - , we estimated a total of 8,701 tons of PM2.5 emission reductions available from the application of controls  -  approximately 78 percent of that total is available from within a county and 22 percent is from an adjacent county. For the alternative standard level of 8/35 g/ - m - [3] - , we estimated a total of 34,582 tons of PM2.5 emission reductions  -  approximately 55 percent of that total is available from within a county and 45 percent is from an adjacent county. 
As shown in Table 3A-4 and Table 3A-5 for the southeast counties (35 counties) and the adjacent counties (32 counties), for the alternative standard levels of 10/35 g/ - m - [3] -  and 10/30 g/ - m - [3] - , controls were applied in two counties and no additional emissions reductions were needed in adjacent counties. For the alternative standard level of 9/35 g/ - m - [3] - , we estimated a total of 3,235 tons of PM2.5 emission reductions  -  approximately 94 percent of that total is available from the application of controls from within a county and six percent is from an adjacent county. For the alternative standard level of 8/35 g/ - m - [3] - , we estimated a total of 17,104 tons of PM2.5 emission reductions  -  approximately 71 percent of that total is available from within a county and 29 percent is from an adjacent county. 
As shown in Table 3A-6 for the west (36 counties), for the alternative standard level of 10/35 g/ - m - [3] -  controls were applied in one county. For the alternative standard level of 10/30 g/ - m - [3] -  controls were applied in 18 counties; for the alternative standard level of 9/35 g/ - m - [3] -  controls were applied in six counties; and for the alternative standard level of 8/35 g/ - m - [3] -  controls were applied in 22 counties. 
As shown in Table 3A-7 for California (26 counties) of the eight counties in the San Joaquin Valley Air Pollution Control District, we estimated that five need PM2.5 emissions reductions. For four counties, we identified some emissions reductions available for an alternative standard level of 10/35 g/ - m - [3] -  and no additional emissions reductions for lower alternative standard levels. For one county, we identified some emissions reductions available for an alternative standard level of 10/35 g/ - m - [3] -  and additional reductions available for an alternative standard level of 9/35 g/ - m - [3] - .  Of the four counties in the South Coast Air Quality Management District, we estimated that three need emissions reductions. For two counties we did not identify any emissions reductions from the application of controls for any of the alternative standard levels. For one county, we identified some emissions reductions available for an alternative standard level of 10/35 g/ - m - [3] - .
Table 3A-8 includes information on PM2.5 emissions by emissions inventory sector, on counties needing emissions reductions, and on estimated emissions reductions by alternative standard levels being analyzed. The column labeled Sector uses abbreviations for emissions inventory sectors from the National Emissions Inventory. The abbreviations and related sectors include: afdust or area fugitive dust emissions; nonpt or non-point (area) source emissions; np_oilgas or non-point (area) source oil and gas emissions; ptagfire or point source agriculture fire emissions; ptnonipm or non-electric generating unit, point source emissions; pt_oilgas or point source oil and gas emissions; and rwc or residential wood combustions emissions.
The first column includes names of adjacent counties and counties still needing emissions reductions. The second column lists any counties that need emissions reductions. The columns with annual PM2.5 emissions and the PM2.5 emissions reductions are related to the county in the first column. If the second column is blank, then the annual PM2.5 emissions serves as an indicator of the county's own PM2.5 emissions that might be controllable if a state or local jurisdiction knew how to control those emissions; in these cases the maximum PM2.5 emissions reductions should be equal to the selected PM2.5 emissions reductions for one of the alternative standards being analyzed (e.g., Pinal County, AZ). 
The table is intended to present information about potential nearby emissions reductions that might be available to help counties attain an alternative standard level. The list of PM2.5 emissions is not exhaustive, as inventory sectors with reported emissions less than 5 tons per year are excluded in general, and emissions from rail, airports, and wildfires of all types are excluded regardless of their emissions because either we do not have information on potential controls for these sectors or the emissions from these sectors are not necessarily controllable (i.e., wildfires). While we considered emissions from adjacent counties in the east, we did not do so in the west and California due to uncertainty about the air quality impacts of emissions reductions from adjacent counties. For the west and California, in addition to finding ways of controlling remaining emissions within a county or adjacent counties (or within the same air district in California), it will be necessary to determine how much emissions reductions in adjacent counties may impact the DV at a monitor of interest.   
Table 3A-1	By Area and Emissions Inventory Sector, Control Measures Applied in Analyses of the Current Standards and Alternative Primary Standard Levels
Area
Inventory Sector
Control Technology
                                     12/35
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
Non-EGU Point
Electrostatic Precipitator-All Types

                                       x
                                       
                                       x
                                       


Fabric Filter-All Types

                                       x
                                       x
                                       x
                                       x


Install new drift eliminator at 25% RP

                                       
                                       
                                       x
                                       x


Venturi Scrubber
                                       
                                       x
                                       x
                                       x
                                       x

Non-Point (Area)
Annual tune-up at 10% RP

                                       
                                       
                                       
                                       x


Annual tune-up at 25% RP

                                       x
                                       x
                                       x
                                       x


Biennial tune-up at 10% RP

                                       x
                                       x
                                       
                                       x


Biennial tune-up at 25% RP

                                       x
                                       x
                                       x
                                       x


Catalytic oxidizers at 25% RP

                                       x
                                       x
                                       x
                                       x


Electrostatic Precipitator at 10% RP

                                       
                                       
                                       x
                                       


Electrostatic Precipitator at 25% RP

                                       x
                                       x
                                       x
                                       x


HEPA filters at 10% RP

                                       x
                                       x
                                       
                                       x


HEPA filters at 25% RP

                                       x
                                       
                                       x
                                       x


Smokeless Broiler at 10% RP

                                       
                                       
                                       
                                       x


Smokeless Broiler at 25% RP

                                       
                                       
                                       x
                                       x


Substitute chipping for burning
                                       
                                       x
                                       x
                                       x
                                       x

Residential Wood Combustion
Convert to Gas Logs at 25% RP

                                       x
                                       x
                                       x
                                       x


EPA-certified wood stove at 10% RP

                                       
                                       
                                       
                                       x


EPA Phase 2 Qualified Units at 10% RP

                                       
                                       
                                       x
                                       x


EPA Phase 2 Qualified Units at 25% RP

                                       
                                       
                                       
                                       x


Install Cleaner Hydronic Heaters at 25% RP

                                       x
                                       x
                                       x
                                       x


Install Retrofit Devices at 10% RP

                                       
                                       
                                       x
                                       x


Install Retrofit Devices at 25% RP

                                       
                                       
                                       
                                       x


New gas stove or gas logs at 10% RP

                                       x
                                       x
                                       
                                       x


New gas stove or gas logs at 25% RP
                                       
                                       x
                                       x
                                       x
                                       x

Area Source Fugitive Dust
Chemical Stabilizer at 10% RP

                                       
                                       
                                       
                                       x


Chemical Stabilizer at 25% RP

                                       
                                       
                                       x
                                       x


Dust Suppressants at 10% RP

                                       
                                       
                                       
                                       x


Pave existing shoulders at 10% RP

                                       
                                       
                                       
                                       x


Pave existing shoulders at 25% RP

                                       
                                       
                                       x
                                       x


Pave Unpaved Roads at 25% RP
                                       
                                       
                                       
                                       x
                                       x
Northeast (Adjacent Counties)
Non-EGU Point
Fabric Filter-All Types

                                       
                                       
                                       x
                                       x


Install new drift eliminator at 25% RP

                                       
                                       
                                       x
                                       x


Venturi Scrubber
                                       
                                       
                                       
                                       x
                                       x

Oil & Gas Point
Fabric Filter-All Types
                                       
                                       
                                       
                                       
                                       x

Non-Point (Area)
Annual tune-up at 25% RP

                                       
                                       
                                       x
                                       x


Biennial tune-up at 10% RP

                                       
                                       
                                       x
                                       


Biennial tune-up at 25% RP

                                       
                                       
                                       x
                                       x


Catalytic oxidizers at 25% RP

                                       
                                       
                                       
                                       x


Electrostatic Precipitator at 25% RP

                                       
                                       
                                       x
                                       x


Fabric Filter-All Types

                                       
                                       
                                       x
                                       x


Smokeless Broiler at 10% RP

                                       
                                       
                                       
                                       x


Smokeless Broiler at 25% RP

                                       
                                       
                                       
                                       x


Substitute chipping for burning
                                       
                                       
                                       
                                       x
                                       x

Residential Wood Combustion
Convert to Gas Logs at 25% RP

                                       
                                       
                                       x
                                       x


Install Cleaner Hydronic Heaters at 25% RP

                                       
                                       
                                       x
                                       x


New gas stove or gas logs at 25% RP
                                       
                                       
                                       
                                       x
                                       x

Area Source Fugitive Dust
Chemical Stablizer at 10% RP

                                       
                                       
                                       x
                                       x


Chemical Stablizer at 25% RP

                                       
                                       
                                       x
                                       


Pave existing shoulders at 25% RP

                                       
                                       
                                       x
                                       x


Pave Unpaved Roads at 25% RP
                                       
                                       
                                       
                                       
                                       x
Southeast
Non-EGU Point
Electrostatic Precipitator-All Types

                                       
                                       
                                       
                                       x


Fabric Filter-All Types

                                       x
                                       x
                                       x
                                       x


Install new drift eliminator at 10% RP

                                       
                                       
                                       x
                                       x


Install new drift eliminator at 25% RP

                                       x
                                       x
                                       x
                                       x


Venturi Scrubber
                                       
                                       
                                       
                                       x
                                       x

Oil & Gas Point
Install new drift eliminator at 25% RP
                                       
                                       
                                       
                                       
                                       x

Non-Point (Area)
Annual tune-up at 25% RP

                                       
                                       
                                       x
                                       x


Biennial tune-up at 10% RP

                                       
                                       
                                       
                                       x


Biennial tune-up at 25% RP

                                       x
                                       x
                                       
                                       x


Catalytic oxidizers at 25% RP

                                       x
                                       x
                                       x
                                       x


Electrostatic Precipitator at 10% RP

                                       
                                       
                                       x
                                       x


Electrostatic Precipitator at 25% RP

                                       x
                                       x
                                       x
                                       x


HEPA filters at 10% RP

                                       
                                       
                                       
                                       x


HEPA filters at 25% RP

                                       
                                       
                                       
                                       x


Smokeless Broiler at 10% RP

                                       x
                                       x
                                       x
                                       x


Smokeless Broiler at 25% RP

                                       
                                       
                                       x
                                       x


Substitute chipping for burning
                                       
                                       x
                                       x
                                       x
                                       x

Residential Wood Combustion
Convert to Gas Logs at 25% RP

                                       x
                                       x
                                       x
                                       x


EPA Phase 2 Qualified Units at 25% RP

                                       x
                                       x
                                       
                                       x


Install Cleaner Hydronic Heaters at 25% RP

                                       
                                       
                                       x
                                       x


Install Retrofit Devices at 10% RP

                                       
                                       
                                       
                                       x


New gas stove or gas logs at 10% RP

                                       
                                       
                                       
                                       x


New gas stove or gas logs at 25% RP
                                       
                                       x
                                       x
                                       x
                                       x

Area Source Fugitive Dust
Chemical Stabilizer at 10% RP

                                       x
                                       x
                                       x
                                       


Chemical Stabilizer at 25% RP

                                       
                                       
                                       
                                       x


Pave existing shoulders at 10% RP

                                       
                                       
                                       
                                       x


Pave existing shoulders at 25% RP

                                       
                                       
                                       x
                                       x


Pave Unpaved Roads at 25% RP
                                       
                                       
                                       
                                       x
                                       x
Southeast (Adjacent Counties)
Non-EGU Point
Fabric Filter-All Types

                                       
                                       
                                       
                                       x


Install new drift eliminator at 25% RP
                                       
                                       
                                       
                                       
                                       x

Non-Point (Area)
Annual tune-up at 25% RP

                                       
                                       
                                       
                                       x


Electrostatic Precipitator at 25% RP

                                       
                                       
                                       x
                                       x


Substitute chipping for burning
                                       
                                       
                                       
                                       x
                                       x

Residential Wood Combustion
Convert to Gas Logs at 25% RP

                                       
                                       
                                       
                                       x


Install Cleaner Hydronic Heaters at 25% RP

                                       
                                       
                                       
                                       x


New gas stove or gas logs at 25% RP
                                       
                                       
                                       
                                       
                                       x

Area Source Fugitive Dust
Pave existing shoulders at 25% RP

                                       
                                       
                                       x
                                       x


Pave Unpaved Roads at 25% RP
                                       
                                       
                                       
                                       x
                                       x
West
Non-EGU Point
Fabric Filter-All Types
                                       x
                                       
                                       x
                                       x
                                       x


Install new drift eliminator at 10% RP

                                       
                                       
                                       
                                       x


Install new drift eliminator at 25% RP

                                       
                                       
                                       x
                                       x


Venturi Scrubber
                                       
                                       
                                       x
                                       x
                                       x

Non-Point (Area)
Annual tune-up at 10% RP

                                       
                                       x
                                       
                                       


Annual tune-up at 25% RP
                                       x
                                       
                                       x
                                       x
                                       x


Biennial tune-up at 10% RP

                                       
                                       
                                       
                                       x


Biennial tune-up at 25% RP
                                       x
                                       
                                       x
                                       x
                                       x


Catalytic oxidizers at 25% RP
                                       x
                                       
                                       x
                                       x
                                       x


Electrostatic Precipitator at 25% RP
                                       x
                                       
                                       x
                                       
                                       x


HEPA filters at 25% RP

                                       
                                       
                                       
                                       x


Smokeless Broiler at 10% RP
                                       x
                                       
                                       x
                                       x
                                       x


Smokeless Broiler at 25% RP

                                       
                                       
                                       x
                                       


Substitute chipping for burning
                                       x
                                       x
                                       x
                                       x
                                       x

Residential Wood Combustion
Convert to Gas Logs at 25% RP
                                       x
                                       
                                       x
                                       
                                       x


EPA Phase 2 Qualified Units at 25% RP

                                       
                                       x
                                       
                                       x


Install Cleaner Hydronic Heaters at 10% RP

                                       
                                       x
                                       
                                       


Install Cleaner Hydronic Heaters at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x


Install Retrofit Devices at 10% RP
                                       x
                                       
                                       
                                       
                                       


Install Retrofit Devices at 25% RP

                                       
                                       
                                       
                                       x


New gas stove or gas logs at 10% RP
                                       x
                                       
                                       x
                                       x
                                       x


New gas stove or gas logs at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Area Source Fugitive Dust
Chemical Stabilizer at 10% RP

                                       
                                       x
                                       
                                       x


Chemical Stabilizer at 25% RP
                                       x
                                       
                                       
                                       
                                       x


Dust Suppressants at 25% RP

                                       
                                       
                                       
                                       x


Pave existing shoulders at 25% RP
                                       x
                                       
                                       x
                                       x
                                       x


Pave Unpaved Roads at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x
CA
Non-EGU Point
Electrostatic Precipitator-All Types

                                       
                                       
                                       
                                       x


Fabric Filter-All Types
                                       x
                                       x
                                       x
                                       x
                                       x


Install new drift eliminator at 10% RP
                                       x
                                       
                                       
                                       
                                       


Install new drift eliminator at 25% RP
                                       x
                                       
                                       
                                       
                                       


Venturi Scrubber
                                       x
                                       x
                                       x
                                       x
                                       x

Oil & Gas Point
Fabric Filter-All Types
                                       x
                                       
                                       
                                       
                                       

Non-Point (Area)
Add-on Scrubber at 25% RP

                                       x
                                       x
                                       
                                       


Annual tune-up at 10% RP

                                       
                                       
                                       x
                                       


Annual tune-up at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x


Biennial tune-up at 10% RP
                                       x
                                       
                                       
                                       
                                       


Biennial tune-up at 25% RP
                                       x
                                       
                                       
                                       x
                                       


Catalytic oxidizers at 25% RP
                                       x
                                       
                                       
                                       x
                                       


Electrostatic Precipitator at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x


Fabric Filter-All Types

                                       
                                       
                                       
                                       x


HEPA filters at 10% RP

                                       
                                       
                                       x
                                       


HEPA filters at 25% RP

                                       
                                       
                                       x
                                       


Smokeless Broiler at 10% RP

                                       
                                       x
                                       x
                                       


Smokeless Broiler at 25% RP

                                       
                                       
                                       x
                                       x


Substitute chipping for burning
                                       x
                                       x
                                       x
                                       x
                                       x

Residential Wood Combustion
Convert to Gas Logs at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x


Install Retrofit Devices at 10% RP

                                       
                                       
                                       x
                                       


Install Retrofit Devices at 25% RP
                                       
                                       x
                                       x
                                       
                                       

Area Source Fugitive Dust
Chemical Stabilizer at 10% RP

                                       
                                       
                                       x
                                       


Chemical Stabilizer at 25% RP

                                       
                                       
                                       
                                       x


Pave existing shoulders at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x


Pave Unpaved Roads at 25% RP
                                       x
                                       x
                                       x
                                       x
                                       x

Table 3A-2	Summary of PM2.5 Estimated Emissions Reductions from CoST for the Northeast (57 counties) for Alternative Primary Standard Levels of 10/35 g/ - m[3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
County
                                     10/35
                                     10/30
                                     9/35
                                     8/35
New Castle County, DE
                                       0
                                       0
                                       0
                                      73
Cook County, IL
                                       0
                                       0
                                      285
                                      710
Madison County, IL
                                       0
                                       0
                                       0
                                      724
St. Clair County, IL
                                       0
                                       0
                                       0
                                      579
Allen County, IN
                                       0
                                       0
                                       0
                                      44
Clark County, IN
                                       0
                                       0
                                       0
                                      395
Elkhart County, IN
                                       0
                                       0
                                       0
                                      213
Floyd County, IN
                                       0
                                       0
                                       0
                                      40
Lake County, IN
                                       0
                                       0
                                       0
                                      644
Marion County, IN
                                       0
                                       0
                                      405
                                      405
St. Joseph County, IN
                                       0
                                       0
                                       0
                                      205
Vanderburgh County, IN
                                       0
                                       0
                                       0
                                      161
Vigo County, IN
                                       0
                                       0
                                       0
                                      206
Jefferson County, KY
                                       0
                                       0
                                       0
                                      552
Baltimore city, MD
                                       0
                                       0
                                       0
                                      95
Howard County, MD
                                       0
                                       0
                                       0
                                      124
Kent County, MI
                                       0
                                       0
                                       0
                                      330
Wayne County, MI
                                      15
                                      15
                                      645
                                      645
Buchanan County, MO
                                       0
                                       0
                                       0
                                      81
Jackson County, MO
                                       0
                                       0
                                       0
                                      37
Jefferson County, MO
                                       0
                                       0
                                       0
                                      346
St. Louis city, MO
                                       0
                                       0
                                       0
                                      157
St. Louis County, MO
                                       0
                                       0
                                       0
                                      571
Camden County, NJ
                                       0
                                       0
                                      110
                                      110
Union County, NJ
                                       0
                                       0
                                       0
                                      168
New York County, NY
                                       0
                                       0
                                       0
                                      268
Butler County, OH
                                       0
                                       0
                                      571
                                      704
Cuyahoga County, OH
                                      139
                                      139
                                      825
                                      825
Franklin County, OH
                                       0
                                       0
                                       0
                                      96
Hamilton County, OH
                                       0
                                       0
                                       0
                                      439
Jefferson County, OH
                                       0
                                       0
                                      93
                                      93
Lucas County, OH
                                       0
                                       0
                                       0
                                      483
Mahoning County, OH
                                       0
                                       0
                                       0
                                      117
Stark County, OH
                                       0
                                       0
                                       0
                                      644
Summit County, OH
                                       0
                                       0
                                       0
                                      310
Allegheny County, PA
                                      842
                                      994
                                     1,573
                                     1,613
Armstrong County, PA
                                       0
                                       0
                                      142
                                      142
Beaver County, PA
                                       0
                                       0
                                       0
                                      260
Berks County, PA
                                       0
                                       0
                                       0
                                      103
Cambria County, PA
                                       0
                                       0
                                      34
                                      191
Chester County, PA
                                       0
                                       0
                                       0
                                      598
Dauphin County, PA
                                       0
                                       0
                                       0
                                      242
Delaware County, PA
                                       0
                                       0
                                      277
                                      277
Lackawanna County, PA
                                       0
                                       0
                                       0
                                      66
Lancaster County, PA
                                      73
                                      73
                                      805
                                      937
Lebanon County, PA
                                       0
                                       0
                                      44
                                      181
Lehigh County, PA
                                       0
                                       0
                                       0
                                      95
Mercer County, PA
                                       0
                                       0
                                       0
                                      230
Philadelphia County, PA
                                       0
                                       0
                                      524
                                      896
Washington County, PA
                                       0
                                       0
                                       0
                                      242
York County, PA
                                       0
                                       0
                                       0
                                      381
Providence County, RI
                                       0
                                       0
                                       0
                                      195
Davidson County, TN
                                       0
                                       0
                                       0
                                      95
Knox County, TN
                                       0
                                       0
                                       0
                                      410
Berkeley County, WV
                                       0
                                       0
                                       0
                                      124
Brooke County, WV
                                       0
                                       0
                                       0
                                      120
Marshall County, WV
                                       0
                                       0
                                       0
                                      148
Total
                                     1,070
                                     1,222
                                     6,334
                                    19,142


Table 3A-3	Summary of PM2.5 Estimated Emissions Reductions from CoST for the Adjacent Counties in the Northeast (75 counties) for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
County
Adjacent Counties
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Clinton County, IL
Madison County, IL
St. Clair County, IL
                                       0
                                       0
                                       0
                                      122
DuPage County, IL
Cook County, IL
                                       0
                                       0
                                       0
                                      124
Kane County, IL
Cook County, IL
                                       0
                                       0
                                       0
                                      98
Lake County, IL
Cook County, IL
                                       0
                                       0
                                       0
                                      434
McHenry County, IL
Cook County, IL
                                       0
                                       0
                                       0
                                      95
Monroe County, IL
St. Clair County, IL
                                       0
                                       0
                                       0
                                      110
Randolph County, IL
St. Clair County, IL
                                       0
                                       0
                                       0
                                      91
Washington County, IL
St. Clair County, IL
                                       0
                                       0
                                       0
                                      90
Will County, IL
Cook County, IL
                                       0
                                       0
                                       0
                                      476
Boone County, IN
Marion County, IN
                                       0
                                       0
                                       3
                                      75
Clay County, IN
Vigo County, IN
                                       0
                                       0
                                       0
                                      65
Gibson County, IN
Vanderburgh County, IN
                                       0
                                       0
                                       0
                                      29
Hamilton County, IN
Marion County, IN
                                       0
                                       0
                                       8
                                      281
Hancock County, IN
Marion County, IN
                                       0
                                       0
                                       3
                                      77
Hendricks County, IN
Marion County, IN
                                       0
                                       0
                                      17
                                      208
Johnson County, IN
Marion County, IN
                                       0
                                       0
                                       4
                                      168
LaPorte County, IN
St. Joseph County, IN
                                       0
                                       0
                                       0
                                      186
Marshall County, IN
Elkhart County, IN
St. Joseph County, IN
                                       0
                                       0
                                       0
                                      121
Morgan County, IN
Marion County, IN
                                       0
                                       0
                                      12
                                      207
Parke County, IN
Vigo County, IN
                                       0
                                       0
                                       0
                                      30
Posey County, IN
Vanderburgh County, IN
                                       0
                                       0
                                       0
                                      199
Shelby County, IN
Marion County, IN
                                       0
                                       0
                                       3
                                      400
Starke County, IN
St. Joseph County, IN
                                       0
                                       0
                                       0
                                      34
Sullivan County, IN
Vigo County, IN
                                       0
                                       0
                                       0
                                      58
Vermillion County, IN
Vigo County, IN
                                       0
                                       0
                                       0
                                      31
Warrick County, IN
Vanderburgh County, IN
                                       0
                                       0
                                       0
                                      182
Bullitt County, KY
Jefferson County, KY
                                       0
                                       0
                                       0
                                      71
Hardin County, KY
Jefferson County, KY
                                       0
                                       0
                                       0
                                      38
Oldham County, KY
Jefferson County, KY
                                       0
                                       0
                                       0
                                      23
Shelby County, KY
Jefferson County, KY
                                       0
                                       0
                                       0
                                      17
Spencer County, KY
Jefferson County, KY
                                       0
                                       0
                                       0
                                      13
Montgomery County, MD
Howard County, MD
                                       0
                                       0
                                       0
                                       2
Macomb County, MI
Wayne County, MI
                                       0
                                       0
                                      59
                                      409
Monroe County, MI
Wayne County, MI
                                       0
                                       0
                                      240
                                      463
Oakland County, MI
Wayne County, MI
                                       0
                                       0
                                      55
                                      954
Washtenaw County, MI
Wayne County, MI
                                       0
                                       0
                                      53
                                      365
Atlantic County, NJ
Camden County, NJ
                                       0
                                       0
                                       7
                                      98
Burlington County, NJ
Camden County, NJ
                                       0
                                       0
                                      26
                                      183
Essex County, NJ
Union County, NJ
                                       0
                                       0
                                       0
                                      116
Gloucester County, NJ
Camden County, NJ
                                       0
                                       0
                                      27
                                      274
Hudson County, NJ
Union County, NJ
                                       0
                                       0
                                       0
                                      73
Middlesex County, NJ
Union County, NJ
                                       0
                                       0
                                       0
                                      299
Morris County, NJ
Union County, NJ
                                       0
                                       0
                                       0
                                      164
Somerset County, NJ
Union County, NJ
                                       0
                                       0
                                       0
                                      69
Bronx County, NY
New York County, NY
                                       0
                                       0
                                       0
                                      91
Kings County, NY
New York County, NY
                                       0
                                       0
                                       0
                                      215
Queens County, NY
New York County, NY
                                       0
                                       0
                                       0
                                      223
Belmont County, OH
Jefferson County, OH
                                       0
                                       0
                                      81
                                      126
Carroll County, OH
Jefferson County, OH
Stark County, OH
                                       0
                                       0
                                      34
                                      68
Clermont County, OH
Hamilton County, OH
                                       0
                                       0
                                       0
                                      279
Columbiana County, OH
Jefferson County, OH
Mahoning County, OH
Stark County, OH
                                       0
                                       0
                                      144
                                      172
Geauga County, OH
Cuyahoga County, OH
Summit County, OH
                                       0
                                       0
                                       9
                                      256
Harrison County, OH
Jefferson County, OH
                                       0
                                       0
                                      12
                                      109
Lake County, OH
Cuyahoga County, OH
                                       0
                                       0
                                       6
                                      184
Lorain County, OH
Cuyahoga County, OH
                                       0
                                       0
                                      145
                                      301
Medina County, OH
Cuyahoga County, OH
Summit County, OH
                                       0
                                       0
                                       9
                                      340
Montgomery County, OH
Butler County, OH
                                       0
                                       0
                                       0
                                      303
Portage County, OH
Cuyahoga County, OH
Mahoning County, OH
Stark County, OH
Summit County, OH
                                       0
                                       0
                                      15
                                      287
Preble County, OH
Butler County, OH
                                       0
                                       0
                                       0
                                      82
Warren County, OH
Butler County, OH
Hamilton County, OH
                                       0
                                       0
                                       0
                                      366
Bedford County, PA
Cambria County, PA
                                       0
                                       0
                                       0
                                      121
Blair County, PA
Cambria County, PA
                                       0
                                       0
                                       0
                                      365
Bucks County, PA
Lehigh County, PA
Philadelphia County, PA
                                       0
                                       0
                                       0
                                      581
Butler County, PA
Allegheny County, PA
Armstrong County, PA
Beaver County, PA
Mercer County, PA
                                       0
                                       0
                                      34
                                      631
Clarion County, PA
Armstrong County, PA
                                       0
                                       0
                                       4
                                      90
Clearfield County, PA
Cambria County, PA
                                       0
                                       0
                                       0
                                      171
Indiana County, PA
Armstrong County, PA
Cambria County, PA
                                       0
                                       0
                                      55
                                      294
Jefferson County, PA
Armstrong County, PA
                                       0
                                       0
                                       5
                                      260
Montgomery County, PA
Berks County, PA
Chester County, PA
Delaware County, PA
Lehigh County, PA
Philadelphia County, PA
                                       0
                                       0
                                      633
                                      633
Schuylkill County, PA
Berks County, PA
Dauphin County, PA
Lebanon County, PA
Lehigh County, PA
                                       0
                                       0
                                       0
                                      287
Somerset County, PA
Cambria County, PA
                                       0
                                       0
                                       0
                                      204
Westmoreland County, PA
Allegheny County, PA
Armstrong County, PA
Cambria County, PA
Washington County, PA
                                       0
                                       0
                                      37
                                      609
Hancock County, WV
Brooke County, WV
                                       0
                                       0
                                       0
                                      32
Ohio County, WV
Brooke County, WV
Marshall County, WV
                                       0
                                       0
                                       0
                                      96
Wetzel County, WV
Marshall County, WV
                                       0
                                       0
                                       0
                                      45
Total
 
                                       0
                                       0
                                     1,737
                                    15,440



Table 3A-4	Summary of PM2.5 Estimated Emissions Reductions from CoST for the Southeast (35 counties) for Alternative Primary Standard Levels of 10/35g/m[3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
County
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Jefferson County, AL
                                       0
                                       0
                                      671
                                     1,488
Talladega County, AL
                                       0
                                       0
                                       0
                                      131
Pulaski County, AR
                                       0
                                       0
                                       0
                                      777
Union County, AR
                                       0
                                       0
                                       0
                                      66
District of Columbia
                                       0
                                       0
                                       0
                                      140
Bibb County, GA
                                       0
                                       0
                                       0
                                      158
Clayton County, GA
                                       0
                                       0
                                       0
                                      58
Cobb County, GA
                                       0
                                       0
                                       0
                                      42
DeKalb County, GA
                                       0
                                       0
                                       0
                                      34
Dougherty County, GA
                                       0
                                       0
                                       0
                                      481
Floyd County, GA
                                       0
                                       0
                                       0
                                      400
Fulton County, GA
                                       0
                                       0
                                      344
                                      599
Gwinnett County, GA
                                       0
                                       0
                                       0
                                      17
Muscogee County, GA
                                       0
                                       0
                                       0
                                      176
Richmond County, GA
                                       0
                                       0
                                       0
                                      409
Wilkinson County, GA
                                       0
                                       0
                                       0
                                      761
Wyandotte County, KS
                                       0
                                       0
                                       0
                                      90
Caddo Parish, LA
                                       0
                                       0
                                      327
                                      436
East Baton Rouge Parish, LA
                                       0
                                       0
                                       0
                                      531
Iberville Parish, LA
                                       0
                                       0
                                       0
                                      17
St. Bernard Parish, LA
                                       0
                                       0
                                       0
                                      60
West Baton Rouge Parish, LA
                                       0
                                       0
                                       0
                                      393
Hinds County, MS
                                       0
                                       0
                                       0
                                      33
Davidson County, NC
                                       0
                                       0
                                       0
                                      204
Mecklenburg County, NC
                                       0
                                       0
                                       0
                                      91
Wake County, NC
                                       0
                                       0
                                       0
                                      66
Tulsa County, OK
                                       0
                                       0
                                       0
                                      74
Greenville County, SC
                                       0
                                       0
                                       0
                                      98
Cameron County, TX
                                       0
                                       0
                                      148
                                      148
Dallas County, TX
                                       0
                                       0
                                       0
                                      33
El Paso County, TX
                                       0
                                       0
                                      33
                                      240
Harris County, TX
                                      270
                                      270
                                     1,087
                                     1,905
Hidalgo County, TX
                                      205
                                      205
                                      406
                                      406
Nueces County, TX
                                       0
                                       0
                                       0
                                      810
Travis County, TX
                                       0
                                       0
                                      25
                                      842
Total
                                      475
                                      475
                                     3,040
                                    12,212


Table 3A-5	Summary of PM2.5 Estimated Emissions Reductions from CoST for the Adjacent Counties in the Southeast (32 counties) for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
County
Adjacent Counties
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Bartow County, GA
Cobb County, GA
Floyd County, GA
                                       0
                                       0
                                       0
                                      135
Carroll County, GA
Fulton County, GA
                                       0
                                       0
                                       0
                                      154
Chattahoochee County, GA
Muscogee County, GA
                                       0
                                       0
                                       0
                                      37
Chattooga County, GA
Floyd County, GA
                                       0
                                       0
                                       0
                                      116
Cherokee County, GA
Cobb County, GA
Fulton County, GA
                                       0
                                       0
                                       0
                                      151
Coweta County, GA
Fulton County, GA
                                       0
                                       0
                                       0
                                      120
Crawford County, GA
Bibb County, GA
                                       0
                                       0
                                       0
                                      112
Douglas County, GA
Cobb County, GA
Fulton County, GA
                                       0
                                       0
                                       0
                                      71
Fayette County, GA
Clayton County, GA
Fulton County, GA
                                       0
                                       0
                                       0
                                      76
Forsyth County, GA
Fulton County, GA
Gwinnett County, GA
                                       0
                                       0
                                       0
                                      89
Gordon County, GA
Floyd County, GA
                                       0
                                       0
                                       0
                                      123
Harris County, GA
Muscogee County, GA
                                       0
                                       0
                                       0
                                      204
Henry County, GA
Clayton County, GA
DeKalb County, GA
                                       0
                                       0
                                       0
                                      88
Houston County, GA
Bibb County, GA
                                       0
                                       0
                                       0
                                      640
Jones County, GA
Bibb County, GA
Wilkinson County, GA
                                       0
                                       0
                                       0
                                      145
Monroe County, GA
Bibb County, GA
                                       0
                                       0
                                       0
                                      161
Polk County, GA
Floyd County, GA
                                       0
                                       0
                                       0
                                      118
Spalding County, GA
Clayton County, GA
                                       0
                                       0
                                       0
                                      122
Talbot County, GA
Muscogee County, GA
                                       0
                                       0
                                       0
                                      87
Twiggs County, GA
Bibb County, GA
Wilkinson County, GA
                                       0
                                       0
                                       0
                                      180
Walker County, GA
Floyd County, GA
                                       0
                                       0
                                       0
                                      71
Bossier Parish, LA
Caddo Parish, LA
                                       0
                                       0
                                       0
                                      237
De Soto Parish, LA
Caddo Parish, LA
                                       0
                                       0
                                       0
                                      160
East Feliciana Parish, LA
East Baton Rouge Parish, LA
West Baton Rouge Parish, LA
                                       0
                                       0
                                       0
                                      66
Pointe Coupee Parish, LA
Iberville Parish, LA
West Baton Rouge Parish, LA
                                       0
                                       0
                                       0
                                      80
Red River Parish, LA
Caddo Parish, LA
                                       0
                                       0
                                       0
                                     1,001
West Feliciana Parish, LA
West Baton Rouge Parish, LA
                                       0
                                       0
                                       0
                                      121
Brooks County, TX
Hidalgo County, TX
                                       0
                                       0
                                      66
                                      66
Hudspeth County, TX
El Paso County, TX
                                       0
                                       0
                                       0
                                      31
Kenedy County, TX
Hidalgo County, TX
                                       0
                                       0
                                      43
                                      43
Starr County, TX
Hidalgo County, TX
                                       0
                                       0
                                      62
                                      62
Willacy County, TX
Cameron County, TX
Hidalgo County, TX
                                       0
                                       0
                                      22
                                      22
Total
 
                                       0
                                       0
                                      194
                                     4,892
Table 3A-6	Summary of PM2.5 Estimated Emissions Reductions from CoST for the West (36 counties) for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
County
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Maricopa County, AZ
                                       0
                                       0
                                      201
                                      669
Pinal County, AZ
                                       0
                                      164
                                       0
                                      61
Santa Cruz County, AZ
                                       0
                                       0
                                       0
                                      13
Denver County, CO
                                       0
                                       0
                                       0
                                      145
Weld County, CO
                                       0
                                       0
                                       0
                                      47
Benewah County, ID
                                       0
                                      133
                                      133
                                      133
Canyon County, ID
                                       0
                                      115
                                       0
                                      384
Lemhi County, ID
                                       0
                                       0
                                       0
                                       0
Shoshone County, ID
                                       0
                                       0
                                       0
                                       0
Lewis and Clark County, MT
                                       0
                                      87
                                       0
                                       0
Lincoln County, MT
                                      224
                                      224
                                      224
                                      224
Missoula County, MT
                                       0
                                       0
                                      229
                                      697
Ravalli County, MT
                                       0
                                      58
                                       0
                                      31
Silver Bow County, MT
                                       0
                                      25
                                       0
                                      133
Douglas County, NE
                                       0
                                       0
                                       0
                                      19
Sarpy County, NE
                                       0
                                       0
                                       0
                                      28
Dona Ana County, NM
                                       0
                                       0
                                       0
                                      248
Clark County, NV
                                       0
                                       0
                                      94
                                      561
Crook County, OR
                                       0
                                      222
                                       0
                                      126
Harney County, OR
                                       0
                                      49
                                       0
                                      148
Jackson County, OR
                                       0
                                       0
                                      66
                                      533
Klamath County, OR
                                       0
                                      94
                                       0
                                      281
Lake County, OR
                                       0
                                       0
                                       0
                                       0
Lane County, OR
                                       0
                                       0
                                       0
                                      37
Box Elder County, UT
                                       0
                                      149
                                       0
                                       0
Cache County, UT
                                       0
                                      236
                                       0
                                       0
Davis County, UT
                                       0
                                      79
                                       0
                                       0
Salt Lake County, UT
                                       0
                                      162
                                       0
                                       0
Utah County, UT
                                       0
                                      127
                                       0
                                       0
Weber County, UT
                                       0
                                      39
                                       0
                                       0
King County, WA
                                       0
                                       0
                                       0
                                      126
Kittitas County, WA
                                       0
                                       0
                                       0
                                       0
Okanogan County, WA
                                       0
                                      139
                                       0
                                       0
Snohomish County, WA
                                       0
                                      104
                                       0
                                       0
Spokane County, WA
                                       0
                                       0
                                       0
                                      66
Yakima County, WA
                                       0
                                       0
                                       0
                                       0
Total
                                      224
                                     2,206
                                      947
                                     4,711


Table 3A-7	Summary of PM2.5 Estimated Emissions Reductions from CoST for California (26 counties) for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 (tons/year)
County
Air District
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Alameda County, CA
Bay Area AQMD
                                      32
                                      32
                                      349
                                      491
Contra Costa County, CA
Bay Area AQMD
                                       0
                                       0
                                      38
                                      355
Marin County, CA
Bay Area AQMD
                                       0
                                       0
                                       0
                                      45
Napa County, CA
Bay Area AQMD
                                      16
                                      16
                                      33
                                      33
Santa Clara County, CA
Bay Area AQMD
                                       0
                                       0
                                      166
                                      482
Solano County, CA
Bay Area AQMD
                                       0
                                       0
                                       0
                                      150
Butte County, CA
Butte County AQMD
                                       0
                                       0
                                       0
                                      76
Sutter County, CA
Feather River AQMD
                                       0
                                       0
                                       0
                                      191
Imperial County, CA
Imperial County APCD
                                       0
                                       0
                                       0
                                       0
Plumas County, CA
Northern Sierra AQMD
                                       0
                                       0
                                       0
                                       0
Sacramento County, CA
Sacramento Metro AQMD
                                       0
                                      60
                                      79
                                      228
San Diego County, CA
San Diego County APCD
                                       0
                                       0
                                      102
                                      615
Fresno County, CA
San Joaquin Valley APCD
                                      248
                                      248
                                      248
                                      248
Kern County, CA
San Joaquin Valley APCD
                                       0
                                       0
                                       0
                                       0
Kings County, CA
San Joaquin Valley APCD
                                       0
                                       0
                                       0
                                       0
Madera County, CA
San Joaquin Valley APCD
                                      111
                                      111
                                      111
                                      111
Merced County, CA
San Joaquin Valley APCD
                                      101
                                      101
                                      101
                                      101
San Joaquin County, CA
San Joaquin Valley APCD
                                      12
                                      12
                                      168
                                      168
Stanislaus County, CA
San Joaquin Valley APCD
                                      113
                                      113
                                      113
                                      113
Tulare County, CA
San Joaquin Valley APCD
                                       0
                                       0
                                       0
                                       0
San Luis Obispo County, CA
San Luis Obispo County APCD
                                       0
                                       0
                                      128
                                      128
Siskiyou County, CA
Siskiyou County APCD
                                       0
                                      398
                                       0
                                       0
Los Angeles County, CA
South Coast AQMD
                                     1,159
                                     1,159
                                     1,159
                                     1,159
Riverside County, CA
South Coast AQMD
                                       0
                                       0
                                       0
                                       0
San Bernardino County, CA
South Coast AQMD
                                       0
                                       0
                                       0
                                       0
Ventura County, CA
Ventura County APCD
                                       0
                                      229
                                      162
                                      229
Total
 
                                     1,792
                                     2,481
                                     2,958
                                     4,925



Table 3A-8 	Remaining PM2.5 Emissions and Potential Additional Reduction Opportunities
County
Adjacent Counties (NE,SE,W) or Counties in Same Air District (CA) Still Needing Reductions
                                    Sector
                            Annual PM2.5 Emissions
                       Maximum PM2.5 Emissions Reduction
                      Selected PM2.5 Emissions Reductions


                                       
                                       
                                       
                                     12/35
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Cochise County, AZ
Santa Cruz County, AZ
                                    afdust
                                     1,516
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      128
                                      54
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      117
                                      55
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      38
                                       3
                                       -
                                       -
                                       -
                                       -
                                       -
Gila County, AZ
Pinal County, AZ
                                    afdust
                                      900
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      70
                                      30
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      361
                                      240
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      22
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Graham County, AZ
Pinal County, AZ
                                    afdust
                                      718
                                      49
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      38
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Pima County, AZ
Pinal County, AZ
Santa Cruz County, AZ
                                    afdust
                                     3,446
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      739
                                      269
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      79
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      244
                                      25
                                       -
                                       -
                                       -
                                       -
                                       -
Pinal County, AZ
-
                                    afdust
                                     3,385
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      297
                                      156
                                       -
                                       -
                                      156
                                       -
                                      61


                                   ptagfire
                                      19
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      94
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      103
                                       8
                                       -
                                       -
                                       8
                                       -
                                       -
Santa Cruz County, AZ
-
                                    afdust
                                      167
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      47
                                      13
                                       -
                                       -
                                       -
                                       -
                                      13


                                      rwc
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Alameda County, CA
Napa County, CA
Solano County, CA
                                    afdust
                                      543
                                      60
                                       -
                                       -
                                       -
                                       -
                                      60


                                     nonpt
                                      885
                                      134
                                       -
                                       -
                                       -
                                      86
                                      134


                                   ptnonipm
                                      450
                                      208
                                       -
                                      32
                                      32
                                      173
                                      208


                                      rwc
                                      368
                                      90
                                       -
                                       -
                                       -
                                      90
                                      90
Contra Costa County, CA
Alameda County, CA
Napa County, CA
Solano County, CA
                                    afdust
                                      405
                                      47
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      646
                                      82
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                     1,798
                                      999
                                       -
                                       -
                                       -
                                      38
                                      355


                                      rwc
                                      812
                                      169
                                       -
                                       -
                                       -
                                       -
                                       -
Fresno County, CA
Kern County, CA
Kings County, CA
Madera County, CA
Merced County, CA
San Joaquin County, CA
Stanislaus County, CA
Tulare County, CA
                                    afdust
                                     2,277
                                      224
                                       -
                                      224
                                      224
                                      224
                                      224


                                     nonpt
                                      519
                                      81
                                      79
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      36
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      882
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      275
                                      108
                                      82
                                      24
                                      24
                                      24
                                      24


                                      rwc
                                      289
                                      29
                                      29
                                       -
                                       -
                                       -
                                       -
Imperial County, CA
-
                                    afdust
                                     3,596
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      221
                                       9
                                       9
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      198
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      134
                                      80
                                      80
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      18
                                       3
                                       3
                                       -
                                       -
                                       -
                                       -
Kern County, CA
Fresno County, CA
Kings County, CA
Madera County, CA
Merced County, CA
San Joaquin County, CA
Stanislaus County, CA
Tulare County, CA
                                    afdust
                                     1,396
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      823
                                      276
                                      276
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      331
                                      51
                                      51
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      332
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      517
                                      209
                                      209
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      224
                                      27
                                      27
                                       -
                                       -
                                       -
                                       -
Kings County, CA
Fresno County, CA
Kern County, CA
Madera County, CA
Merced County, CA
San Joaquin County, CA
Stanislaus County, CA
Tulare County, CA
                                    afdust
                                      849
                                      30
                                      30
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      57
                                       9
                                       9
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      210
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      69
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      31
                                       4
                                       4
                                       -
                                       -
                                       -
                                       -
Los Angeles County, CA
Riverside County, CA
San Bernardino County, CA
                                    afdust
                                     2,240
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                     5,052
                                      723
                                       0
                                      722
                                      722
                                      722
                                      722


                                   pt_oilgas
                                      18
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                     2,087
                                      638
                                      313
                                      325
                                      325
                                      325
                                      325


                                      rwc
                                      947
                                      112
                                       -
                                      112
                                      112
                                      112
                                      112


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Madera County, CA
Fresno County, CA
Kern County, CA
Kings County, CA
Merced County, CA
San Joaquin County, CA
Stanislaus County, CA
Tulare County, CA
                                    afdust
                                      672
                                      68
                                       -
                                      68
                                      68
                                      68
                                      68


                                     nonpt
                                      197
                                      27
                                       -
                                      27
                                      27
                                      27
                                      27


                                   ptagfire
                                      415
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      52
                                      12
                                       -
                                      12
                                      12
                                      12
                                      12


                                      rwc
                                      52
                                       4
                                       -
                                       4
                                       4
                                       4
                                       4
Marin County, CA
Alameda County, CA
Napa County, CA
Solano County, CA
                                    afdust
                                      168
                                      18
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      144
                                      23
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      74
                                      54
                                       -
                                       -
                                       -
                                       -
                                      45


                                      rwc
                                      220
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
Merced County, CA
Fresno County, CA
Kern County, CA
Kings County, CA
Madera County, CA
San Joaquin County, CA
Stanislaus County, CA
Tulare County, CA
                                    afdust
                                     1,304
                                      73
                                       -
                                      73
                                      73
                                      73
                                      73


                                     nonpt
                                      111
                                      19
                                       -
                                      19
                                      19
                                      19
                                      19


                                   ptagfire
                                      152
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      67
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      114
                                      10
                                       -
                                      10
                                      10
                                      10
                                      10
Napa County, CA
Alameda County, CA
Solano County, CA
                                    afdust
                                      112
                                      10
                                       -
                                       -
                                       -
                                      10
                                      10


                                     nonpt
                                      63
                                       7
                                       -
                                       5
                                       5
                                       7
                                       7


                                   ptagfire
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      37
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      123
                                      16
                                       -
                                      11
                                      11
                                      16
                                      16
Nevada County, CA
Plumas County, CA
                                    afdust
                                      343
                                      44
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      72
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      279
                                      18
                                       -
                                       -
                                       -
                                       -
                                       -
Orange County, CA
Los Angeles County, CA
Riverside County, CA
San Bernardino County, CA
                                    afdust
                                      672
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                     1,862
                                      288
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      200
                                      20
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      305
                                      54
                                       -
                                       -
                                       -
                                       -
                                       -


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Plumas County, CA
-
                                    afdust
                                      483
                                      99
                                      99
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      43
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      326
                                       9
                                       9
                                       -
                                       -
                                       -
                                       -
Riverside County, CA
Los Angeles County, CA
San Bernardino County, CA
                                    afdust
                                     2,589
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      973
                                      137
                                      137
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      34
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      128
                                      21
                                      21
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      468
                                      34
                                      34
                                       -
                                       -
                                       -
                                       -
Sacramento County, CA
-
                                    afdust
                                     1,023
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      713
                                      109
                                       -
                                       -
                                      32
                                      50
                                      109


                                   ptagfire
                                      46
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      92
                                      29
                                       -
                                       -
                                      29
                                      29
                                      29


                                      rwc
                                     1,790
                                      90
                                       -
                                       -
                                       -
                                       -
                                      90
San Bernardino County, CA
Los Angeles County, CA
Riverside County, CA
                                    afdust
                                     2,424
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                     1,094
                                      144
                                      144
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      56
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                     2,642
                                     1,965
                                     1,965
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      470
                                      31
                                      31
                                       -
                                       -
                                       -
                                       -
San Diego County, CA
-
                                    afdust
                                     2,485
                                      194
                                       -
                                       -
                                       -
                                       -
                                      194


                                     nonpt
                                     1,949
                                      371
                                       -
                                       -
                                       -
                                      81
                                      371


                                   ptnonipm
                                      489
                                      12
                                       -
                                       -
                                       -
                                      11
                                      12


                                      rwc
                                      678
                                      39
                                       -
                                       -
                                       -
                                      11
                                      39
San Francisco County, CA
Alameda County, CA
Napa County, CA
Solano County, CA
                                    afdust
                                      108
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      588
                                      107
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      45
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      49
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
San Joaquin County, CA
Fresno County, CA
Kern County, CA
Kings County, CA
Madera County, CA
Merced County, CA
Stanislaus County, CA
Tulare County, CA
                                    afdust
                                     1,110
                                      80
                                       -
                                       -
                                       -
                                      80
                                      80


                                     nonpt
                                      290
                                      40
                                       -
                                       4
                                       4
                                      40
                                      40


                                   ptagfire
                                      126
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      167
                                      19
                                       -
                                       8
                                       8
                                      19
                                      19


                                      rwc
                                      217
                                      28
                                       -
                                       -
                                       -
                                      28
                                      28
San Luis Obispo County, CA
-
                                    afdust
                                      133
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      226
                                      57
                                       -
                                       -
                                       -
                                      57
                                      57


                                   ptagfire
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      42
                                       6
                                       -
                                       -
                                       -
                                       6
                                       6


                                      rwc
                                      475
                                      65
                                       -
                                       -
                                       -
                                      65
                                      65
San Mateo County, CA
Alameda County, CA
Napa County, CA
Solano County, CA
                                    afdust
                                      249
                                      26
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      419
                                      61
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      131
                                      42
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      167
                                      26
                                       -
                                       -
                                       -
                                       -
                                       -
Santa Clara County, CA
Alameda County, CA
Napa County, CA
Solano County, CA
                                    afdust
                                      717
                                      85
                                       -
                                       -
                                       -
                                       -
                                      83


                                     nonpt
                                      945
                                      173
                                       -
                                       -
                                       -
                                      93
                                      173


                                   ptnonipm
                                      244
                                      111
                                       -
                                       -
                                       -
                                      72
                                      103


                                      rwc
                                      614
                                      122
                                       -
                                       -
                                       -
                                       -
                                      122
Sierra County, CA
Plumas County, CA
                                    afdust
                                      240
                                      48
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      35
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Siskiyou County, CA
-
                                    afdust
                                      901
                                      166
                                       -
                                       -
                                      166
                                       -
                                       -


                                     nonpt
                                      480
                                      217
                                       -
                                       -
                                      217
                                       -
                                       -


                                   ptagfire
                                      38
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      217
                                      15
                                       -
                                       -
                                      15
                                       -
                                       -
Solano County, CA
Alameda County, CA
Napa County, CA
                                    afdust
                                      414
                                      34
                                       -
                                       -
                                       -
                                       -
                                      34


                                     nonpt
                                      251
                                      40
                                       -
                                       -
                                       -
                                       -
                                      40


                                   ptagfire
                                      23
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      185
                                      35
                                       -
                                       -
                                       -
                                       -
                                      35


                                      rwc
                                      328
                                      42
                                       -
                                       -
                                       -
                                       -
                                      42


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Sonoma County, CA
Alameda County, CA
Napa County, CA
Solano County, CA
                                    afdust
                                      420
                                      34
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      355
                                      54
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      103
                                      20
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      572
                                      66
                                       -
                                       -
                                       -
                                       -
                                       -
Stanislaus County, CA
Fresno County, CA
Kern County, CA
Kings County, CA
Madera County, CA
Merced County, CA
San Joaquin County, CA
Tulare County, CA
                                    afdust
                                     1,139
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      236
                                      31
                                       -
                                      31
                                      31
                                      31
                                      31


                                   ptagfire
                                      150
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      146
                                      60
                                       -
                                      60
                                      60
                                      60
                                      60


                                      rwc
                                      188
                                      22
                                       -
                                      22
                                      22
                                      22
                                      22
Sutter County, CA
-
                                    afdust
                                      280
                                      25
                                       -
                                       -
                                       -
                                       -
                                      25


                                     nonpt
                                      386
                                      149
                                       -
                                       -
                                       -
                                       -
                                      149


                                   ptagfire
                                      195
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      33
                                       5
                                       -
                                       -
                                       -
                                       -
                                       5


                                      rwc
                                      199
                                      11
                                       -
                                       -
                                       -
                                       -
                                      11
Tulare County, CA
Fresno County, CA
Kern County, CA
Kings County, CA
Madera County, CA
Merced County, CA
San Joaquin County, CA
Stanislaus County, CA
                                    afdust
                                     2,106
                                      137
                                      137
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      222
                                      28
                                      28
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      560
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      96
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      139
                                      13
                                      13
                                       -
                                       -
                                       -
                                       -
Ventura County, CA
-
                                    afdust
                                      529
                                      51
                                       -
                                       -
                                      51
                                       5
                                      51


                                     nonpt
                                      354
                                      63
                                       -
                                       -
                                      63
                                      41
                                      63


                                   pt_oilgas
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      94
                                       7
                                       -
                                       -
                                       7
                                       7
                                       7


                                      rwc
                                      677
                                      108
                                       -
                                       -
                                      108
                                      108
                                      108
Yolo County, CA
Solano County, CA
                                    afdust
                                      808
                                      30
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      335
                                      35
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      66
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      105
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      248
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Yuba County, CA
Sutter County, CA
                                    afdust
                                      177
                                      21
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      78
                                      19
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      47
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      17
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      157
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
Adams County, CO
Denver County, CO
                                    afdust
                                     1,876
                                      65
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      233
                                      57
                                       -
                                       -
                                       -
                                       -
                                       -


                                   np_oilgas
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      21
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      346
                                      112
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      360
                                      36
                                       -
                                       -
                                       -
                                       -
                                       -
Arapahoe County, CO
Denver County, CO
                                    afdust
                                     1,602
                                      115
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      274
                                      63
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      500
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      450
                                      43
                                       -
                                       -
                                       -
                                       -
                                       -
Denver County, CO
-
                                    afdust
                                     1,453
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      389
                                      88
                                       -
                                       -
                                       -
                                       -
                                      88


                                   ptnonipm
                                      204
                                      43
                                       -
                                       -
                                       -
                                       -
                                      43


                                      rwc
                                      177
                                      13
                                       -
                                       -
                                       -
                                       -
                                      13
Jefferson County, CO
Denver County, CO
                                    afdust
                                     1,285
                                      205
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      355
                                      93
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      242
                                      129
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      601
                                      64
                                       -
                                       -
                                       -
                                       -
                                       -
Bartow County, GA
Floyd County, GA
                                    afdust
                                      464
                                      59
                                       -
                                       -
                                       -
                                       -
                                      59


                                     nonpt
                                      147
                                      43
                                       -
                                       -
                                       -
                                       -
                                      43


                                   ptnonipm
                                      44
                                      23
                                       -
                                       -
                                       -
                                       -
                                      23


                                      rwc
                                      93
                                      10
                                       -
                                       -
                                       -
                                       -
                                      10
Bibb County, GA
-
                                    afdust
                                      232
                                      34
                                       -
                                       -
                                       -
                                       -
                                      34


                                     nonpt
                                      150
                                      33
                                       -
                                       -
                                       -
                                       -
                                      33


                                   pt_oilgas
                                      18
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      157
                                      81
                                       -
                                       -
                                       -
                                       -
                                      81


                                      rwc
                                      90
                                       9
                                       -
                                       -
                                       -
                                       -
                                       9


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Carroll County, GA
Fulton County, GA
                                    afdust
                                      590
                                      89
                                       -
                                       -
                                       -
                                       -
                                      89


                                     nonpt
                                      126
                                      43
                                       -
                                       -
                                       -
                                       -
                                      43


                                   ptnonipm
                                      40
                                      11
                                       -
                                       -
                                       -
                                       -
                                      11


                                      rwc
                                      104
                                      11
                                       -
                                       -
                                       -
                                       -
                                      11
Chattahoochee County, GA
Muscogee County, GA
                                    afdust
                                      99
                                      18
                                       -
                                       -
                                       -
                                       -
                                      18


                                     nonpt
                                      26
                                      19
                                       -
                                       -
                                       -
                                       -
                                      19
Chattooga County, GA
Floyd County, GA
                                    afdust
                                      207
                                      34
                                       -
                                       -
                                       -
                                       -
                                      34


                                     nonpt
                                      99
                                      81
                                       -
                                       -
                                       -
                                       -
                                      81


                                   ptnonipm
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      30
                                       1
                                       -
                                       -
                                       -
                                       -
                                       1
Cherokee County, GA
Fulton County, GA
                                    afdust
                                      525
                                      78
                                       -
                                       -
                                       -
                                       -
                                      78


                                     nonpt
                                      181
                                      51
                                       -
                                       -
                                       -
                                       -
                                      51


                                   ptnonipm
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      179
                                      21
                                       -
                                       -
                                       -
                                       -
                                      21
Clayton County, GA
Fulton County, GA
                                    afdust
                                      258
                                      33
                                       -
                                       -
                                       -
                                       -
                                      33


                                     nonpt
                                      88
                                      16
                                       -
                                       -
                                       -
                                       -
                                      16


                                   ptnonipm
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      103
                                       9
                                       -
                                       -
                                       -
                                       -
                                       9
Coweta County, GA
Fulton County, GA
                                    afdust
                                      364
                                      62
                                       -
                                       -
                                       -
                                       -
                                      62


                                     nonpt
                                      128
                                      46
                                       -
                                       -
                                       -
                                       -
                                      46


                                   ptagfire
                                      12
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      110
                                      13
                                       -
                                       -
                                       -
                                       -
                                      13
Crawford County, GA
Bibb County, GA
                                    afdust
                                      141
                                      25
                                       -
                                       -
                                       -
                                       -
                                      25


                                     nonpt
                                      100
                                      88
                                       -
                                       -
                                       -
                                       -
                                      88


                                   ptagfire
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      14
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Douglas County, GA
Fulton County, GA
                                    afdust
                                      235
                                      35
                                       -
                                       -
                                       -
                                       -
                                      35


                                     nonpt
                                      88
                                      25
                                       -
                                       -
                                       -
                                       -
                                      25


                                      rwc
                                      91
                                      10
                                       -
                                       -
                                       -
                                       -
                                      10
Fayette County, GA
Clayton County, GA
Fulton County, GA
                                    afdust
                                      209
                                      29
                                       -
                                       -
                                       -
                                       -
                                      29


                                     nonpt
                                      96
                                      27
                                       -
                                       -
                                       -
                                       -
                                      27


                                   ptnonipm
                                      20
                                      11
                                       -
                                       -
                                       -
                                       -
                                      11


                                      rwc
                                      84
                                      10
                                       -
                                       -
                                       -
                                       -
                                      10


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Floyd County, GA
-
                                    afdust
                                      402
                                      65
                                       -
                                       -
                                       -
                                       -
                                      65


                                     nonpt
                                      109
                                      31
                                       -
                                       -
                                       -
                                       -
                                      31


                                   ptagfire
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      316
                                      294
                                       -
                                       -
                                       -
                                       -
                                      294


                                      rwc
                                      89
                                      10
                                       -
                                       -
                                       -
                                       -
                                      10
Forsyth County, GA
Fulton County, GA
                                    afdust
                                      342
                                      40
                                       -
                                       -
                                       -
                                       -
                                      40


                                     nonpt
                                      127
                                      33
                                       -
                                       -
                                       -
                                       -
                                      33


                                   ptnonipm
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      136
                                      16
                                       -
                                       -
                                       -
                                       -
                                      16
Fulton County, GA
Clayton County, GA
                                    afdust
                                     1,329
                                      159
                                       -
                                       -
                                       -
                                       -
                                      159


                                     nonpt
                                      729
                                      168
                                       -
                                       -
                                       -
                                      150
                                      168


                                   ptnonipm
                                      289
                                      237
                                       -
                                       -
                                       -
                                      157
                                      237


                                      rwc
                                      371
                                      36
                                       -
                                       -
                                       -
                                      36
                                      36
Gordon County, GA
Floyd County, GA
                                    afdust
                                      341
                                      43
                                       -
                                       -
                                       -
                                       -
                                      43


                                     nonpt
                                      123
                                      75
                                       -
                                       -
                                       -
                                       -
                                      75


                                      rwc
                                      54
                                       6
                                       -
                                       -
                                       -
                                       -
                                       6
Harris County, GA
Muscogee County, GA
                                    afdust
                                      304
                                      59
                                       -
                                       -
                                       -
                                       -
                                      59


                                     nonpt
                                      173
                                      140
                                       -
                                       -
                                       -
                                       -
                                      140


                                   pt_oilgas
                                      17
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      47
                                       5
                                       -
                                       -
                                       -
                                       -
                                       5
Henry County, GA
Clayton County, GA
                                    afdust
                                      278
                                      35
                                       -
                                       -
                                       -
                                       -
                                      35


                                     nonpt
                                      130
                                      37
                                       -
                                       -
                                       -
                                       -
                                      37


                                   pt_oilgas
                                      54
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      138
                                      15
                                       -
                                       -
                                       -
                                       -
                                      15
Houston County, GA
Bibb County, GA
                                    afdust
                                      282
                                      38
                                       -
                                       -
                                       -
                                       -
                                      38


                                     nonpt
                                      271
                                      189
                                       -
                                       -
                                       -
                                       -
                                      189


                                   ptagfire
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      460
                                      403
                                       -
                                       -
                                       -
                                       -
                                      403


                                      rwc
                                      111
                                      11
                                       -
                                       -
                                       -
                                       -
                                      11
Jones County, GA
Bibb County, GA
                                    afdust
                                      303
                                      54
                                       -
                                       -
                                       -
                                       -
                                      54


                                     nonpt
                                      111
                                      88
                                       -
                                       -
                                       -
                                       -
                                      88


                                   ptagfire
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      33
                                       3
                                       -
                                       -
                                       -
                                       -
                                       3
Monroe County, GA
Bibb County, GA
                                    afdust
                                      281
                                      51
                                       -
                                       -
                                       -
                                       -
                                      51


                                     nonpt
                                      134
                                      107
                                       -
                                       -
                                       -
                                       -
                                      107


                                   ptagfire
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      33
                                       3
                                       -
                                       -
                                       -
                                       -
                                       3
Muscogee County, GA
-
                                    afdust
                                      206
                                      28
                                       -
                                       -
                                       -
                                       -
                                      28


                                     nonpt
                                      121
                                      38
                                       -
                                       -
                                       -
                                       -
                                      38


                                   ptnonipm
                                      111
                                      99
                                       -
                                       -
                                       -
                                       -
                                      99


                                      rwc
                                      108
                                      11
                                       -
                                       -
                                       -
                                       -
                                      11
Polk County, GA
Floyd County, GA
                                    afdust
                                      218
                                      33
                                       -
                                       -
                                       -
                                       -
                                      33


                                     nonpt
                                      117
                                      81
                                       -
                                       -
                                       -
                                       -
                                      81


                                   ptnonipm
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      45
                                       4
                                       -
                                       -
                                       -
                                       -
                                       4
Spalding County, GA
Clayton County, GA
                                    afdust
                                      176
                                      29
                                       -
                                       -
                                       -
                                       -
                                      29


                                     nonpt
                                      132
                                      88
                                       -
                                       -
                                       -
                                       -
                                      88


                                   ptagfire
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      50
                                       5
                                       -
                                       -
                                       -
                                       -
                                       5
Talbot County, GA
Muscogee County, GA
                                    afdust
                                      138
                                      25
                                       -
                                       -
                                       -
                                       -
                                      25


                                     nonpt
                                      68
                                      62
                                       -
                                       -
                                       -
                                       -
                                      62


                                   ptagfire
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Twiggs County, GA
Bibb County, GA
                                    afdust
                                      208
                                      32
                                       -
                                       -
                                       -
                                       -
                                      32


                                     nonpt
                                      150
                                      116
                                       -
                                       -
                                       -
                                       -
                                      116


                                   ptagfire
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      59
                                      32
                                       -
                                       -
                                       -
                                       -
                                      32


                                      rwc
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Walker County, GA
Floyd County, GA
                                    afdust
                                      316
                                      41
                                       -
                                       -
                                       -
                                       -
                                      41


                                     nonpt
                                      66
                                      24
                                       -
                                       -
                                       -
                                       -
                                      24


                                   ptagfire
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      68
                                       7
                                       -
                                       -
                                       -
                                       -
                                       7
Benewah County, ID
Shoshone County, ID
                                    afdust
                                      859
                                      131
                                       -
                                       -
                                      131
                                      131
                                      131


                                     nonpt
                                      33
                                       2
                                       -
                                       -
                                       2
                                       2
                                       2


                                   ptnonipm
                                      30
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      21
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Bonner County, ID
Shoshone County, ID
                                    afdust
                                     2,200
                                      424
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      149
                                      49
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      97
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
Butte County, ID
Lemhi County, ID
                                    afdust
                                      689
                                      102
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Clark County, ID
Lemhi County, ID
                                    afdust
                                      299
                                      36
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Clearwater County, ID
Shoshone County, ID
                                    afdust
                                      457
                                      89
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      21
                                       1
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      48
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      22
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Custer County, ID
Lemhi County, ID
                                    afdust
                                      681
                                      108
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      15
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Idaho County, ID
Lemhi County, ID
                                    afdust
                                     1,509
                                      237
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      44
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      138
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      14
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      46
                                       3
                                       -
                                       -
                                       -
                                       -
                                       -
Kootenai County, ID
Benewah County, ID
Shoshone County, ID
                                    afdust
                                     3,418
                                      689
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      501
                                      237
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      90
                                      62
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      150
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -
Latah County, ID
Benewah County, ID
Shoshone County, ID
                                    afdust
                                     1,850
                                      215
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      54
                                      15
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      32
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      78
                                      72
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      37
                                       2
                                       -
                                       -
                                       -
                                       -
                                       -
Lemhi County, ID
-
                                    afdust
                                      728
                                      116
                                      116
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      19
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Shoshone County, ID
Benewah County, ID
                                    afdust
                                      573
                                      96
                                      96
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      24
                                      11
                                      11
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      28
                                       1
                                       1
                                       -
                                       -
                                       -
                                       -
Valley County, ID
Lemhi County, ID
                                    afdust
                                      786
                                      174
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      25
                                      12
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      28
                                       2
                                       -
                                       -
                                       -
                                       -
                                       -
Clinton County, IL
St. Clair County, IL
                                    afdust
                                     1,326
                                      72
                                       -
                                       -
                                       -
                                       -
                                      72


                                     nonpt
                                      92
                                      43
                                       -
                                       -
                                       -
                                       -
                                      43


                                   pt_oilgas
                                      15
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      15
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      52
                                       7
                                       -
                                       -
                                       -
                                       -
                                       7
Monroe County, IL
St. Clair County, IL
                                    afdust
                                      889
                                      68
                                       -
                                       -
                                       -
                                       -
                                      68


                                     nonpt
                                      79
                                      37
                                       -
                                       -
                                       -
                                       -
                                      37


                                      rwc
                                      43
                                       6
                                       -
                                       -
                                       -
                                       -
                                       6


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Randolph County, IL
St. Clair County, IL
                                    afdust
                                      964
                                      49
                                       -
                                       -
                                       -
                                       -
                                      49


                                     nonpt
                                      80
                                      36
                                       -
                                       -
                                       -
                                       -
                                      36


                                   ptnonipm
                                      35
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      43
                                       6
                                       -
                                       -
                                       -
                                       -
                                       6
St. Clair County, IL
-
                                    afdust
                                     3,376
                                      498
                                       -
                                       -
                                       -
                                       -
                                      498


                                     nonpt
                                      218
                                      57
                                       -
                                       -
                                       -
                                       -
                                      57


                                   ptnonipm
                                      120
                                      14
                                       -
                                       -
                                       -
                                       -
                                      14


                                      rwc
                                      107
                                      10
                                       -
                                       -
                                       -
                                       -
                                      10
Washington County, IL
St. Clair County, IL
                                    afdust
                                     1,249
                                      69
                                       -
                                       -
                                       -
                                       -
                                      69


                                     nonpt
                                      45
                                      16
                                       -
                                       -
                                       -
                                       -
                                      16


                                   np_oilgas
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      32
                                       5
                                       -
                                       -
                                       -
                                       -
                                       5
Boone County, IN
Marion County, IN
                                    afdust
                                      448
                                      23
                                       -
                                       -
                                       -
                                       -
                                      23


                                     nonpt
                                      94
                                      47
                                       -
                                       -
                                       -
                                       -
                                      47


                                      rwc
                                      73
                                       5
                                       -
                                       -
                                       -
                                       3
                                       5


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Clay County, IN
Vigo County, IN
                                    afdust
                                      230
                                       9
                                       -
                                       -
                                       -
                                       -
                                       9


                                     nonpt
                                      35
                                      12
                                       -
                                       -
                                       -
                                       -
                                      12


                                   ptnonipm
                                      42
                                      40
                                       -
                                       -
                                       -
                                       -
                                      40


                                      rwc
                                      50
                                       4
                                       -
                                       -
                                       -
                                       -
                                       4
Hamilton County, IN
Marion County, IN
                                    afdust
                                      786
                                      62
                                       -
                                       -
                                       -
                                       -
                                      62


                                     nonpt
                                      350
                                      195
                                       -
                                       -
                                       -
                                       -
                                      195


                                      rwc
                                      275
                                      24
                                       -
                                       -
                                       -
                                       8
                                      24
Hancock County, IN
Marion County, IN
                                    afdust
                                      324
                                      23
                                       -
                                       -
                                       -
                                       -
                                      23


                                     nonpt
                                      86
                                      46
                                       -
                                       -
                                       -
                                       -
                                      46


                                      rwc
                                      92
                                       9
                                       -
                                       -
                                       -
                                       3
                                       9
Hendricks County, IN
Marion County, IN
                                    afdust
                                      426
                                      37
                                       -
                                       -
                                       -
                                       -
                                      37


                                     nonpt
                                      197
                                      115
                                       -
                                       -
                                       -
                                       -
                                      115


                                   ptnonipm
                                      124
                                      40
                                       -
                                       -
                                       -
                                      11
                                      40


                                      rwc
                                      169
                                      15
                                       -
                                       -
                                       -
                                       6
                                      15
Johnson County, IN
Marion County, IN
                                    afdust
                                      396
                                      32
                                       -
                                       -
                                       -
                                       -
                                      32


                                     nonpt
                                      206
                                      123
                                       -
                                       -
                                       -
                                       -
                                      123


                                      rwc
                                      139
                                      13
                                       -
                                       -
                                       -
                                       4
                                      13
LaPorte County, IN
St. Joseph County, IN
                                    afdust
                                      581
                                      46
                                       -
                                       -
                                       -
                                       -
                                      46


                                     nonpt
                                      160
                                      82
                                       -
                                       -
                                       -
                                       -
                                      82


                                   ptnonipm
                                      107
                                      43
                                       -
                                       -
                                       -
                                       -
                                      43


                                      rwc
                                      139
                                      15
                                       -
                                       -
                                       -
                                       -
                                      15
Marion County, IN
-
                                    afdust
                                     1,534
                                      146
                                       -
                                       -
                                       -
                                      146
                                      146


                                     nonpt
                                      521
                                      92
                                       -
                                       -
                                       -
                                      92
                                      92


                                   pt_oilgas
                                      17
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      235
                                      135
                                       -
                                       -
                                       -
                                      135
                                      135


                                      rwc
                                      330
                                      32
                                       -
                                       -
                                       -
                                      32
                                      32
Marshall County, IN
St. Joseph County, IN
                                    afdust
                                      305
                                      18
                                       -
                                       -
                                       -
                                       -
                                      18


                                     nonpt
                                      94
                                      42
                                       -
                                       -
                                       -
                                       -
                                      42


                                   ptnonipm
                                      78
                                      55
                                       -
                                       -
                                       -
                                       -
                                      55


                                      rwc
                                      66
                                       5
                                       -
                                       -
                                       -
                                       -
                                       5
Morgan County, IN
Marion County, IN
                                    afdust
                                      376
                                      28
                                       -
                                       -
                                       -
                                       -
                                      28


                                     nonpt
                                      120
                                      71
                                       -
                                       -
                                       -
                                       -
                                      71


                                   ptnonipm
                                      105
                                      99
                                       -
                                       -
                                       -
                                       8
                                      99


                                      rwc
                                      101
                                       9
                                       -
                                       -
                                       -
                                       4
                                       9
Parke County, IN
Vigo County, IN
                                    afdust
                                      233
                                       8
                                       -
                                       -
                                       -
                                       -
                                       8


                                     nonpt
                                      36
                                      20
                                       -
                                       -
                                       -
                                       -
                                      20


                                      rwc
                                      35
                                       2
                                       -
                                       -
                                       -
                                       -
                                       2
Shelby County, IN
Marion County, IN
                                    afdust
                                      279
                                      15
                                       -
                                       -
                                       -
                                       -
                                      15


                                     nonpt
                                      69
                                      31
                                       -
                                       -
                                       -
                                       -
                                      31


                                   ptnonipm
                                      410
                                      350
                                       -
                                       -
                                       -
                                       -
                                      350


                                      rwc
                                      64
                                       4
                                       -
                                       -
                                       -
                                       3
                                       4
St. Joseph County, IN
-
                                    afdust
                                      531
                                      45
                                       -
                                       -
                                       -
                                       -
                                      45


                                     nonpt
                                      266
                                      116
                                       -
                                       -
                                       -
                                       -
                                      116


                                   ptnonipm
                                      72
                                      18
                                       -
                                       -
                                       -
                                       -
                                      18


                                      rwc
                                      249
                                      26
                                       -
                                       -
                                       -
                                       -
                                      26
Starke County, IN
St. Joseph County, IN
                                    afdust
                                      134
                                       9
                                       -
                                       -
                                       -
                                       -
                                       9


                                     nonpt
                                      46
                                      22
                                       -
                                       -
                                       -
                                       -
                                      22


                                   pt_oilgas
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      43
                                       4
                                       -
                                       -
                                       -
                                       -
                                       4
Sullivan County, IN
Vigo County, IN
                                    afdust
                                      479
                                      12
                                       -
                                       -
                                       -
                                       -
                                      12


                                     nonpt
                                      38
                                      13
                                       -
                                       -
                                       -
                                       -
                                      13


                                   ptnonipm
                                      44
                                      32
                                       -
                                       -
                                       -
                                       -
                                      32


                                      rwc
                                      31
                                       1
                                       -
                                       -
                                       -
                                       -
                                       1
Vermillion County, IN
Vigo County, IN
                                    afdust
                                      167
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      22
                                       7
                                       -
                                       -
                                       -
                                       -
                                       7


                                   ptnonipm
                                      63
                                      22
                                       -
                                       -
                                       -
                                       -
                                      22


                                      rwc
                                      30
                                       1
                                       -
                                       -
                                       -
                                       -
                                       1
Vigo County, IN
-
                                    afdust
                                      314
                                      24
                                       -
                                       -
                                       -
                                       -
                                      24


                                     nonpt
                                      135
                                      65
                                       -
                                       -
                                       -
                                       -
                                      65


                                   ptnonipm
                                      189
                                      106
                                       -
                                       -
                                       -
                                       -
                                      106


                                      rwc
                                      128
                                      12
                                       -
                                       -
                                       -
                                       -
                                      12
Bossier Parish, LA
Caddo Parish, LA
                                    afdust
                                      433
                                      58
                                       -
                                       -
                                       -
                                       -
                                      58


                                     nonpt
                                      423
                                      174
                                       -
                                       -
                                       -
                                       -
                                      174


                                   np_oilgas
                                      46
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      52
                                       5
                                       -
                                       -
                                       -
                                       -
                                       5


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Caddo Parish, LA
-
                                    afdust
                                      970
                                      108
                                       -
                                       -
                                       -
                                      20
                                      108


                                     nonpt
                                      815
                                      196
                                       -
                                       -
                                       -
                                      196
                                      196


                                   np_oilgas
                                      90
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      243
                                      123
                                       -
                                       -
                                       -
                                      102
                                      123


                                      rwc
                                      87
                                       9
                                       -
                                       -
                                       -
                                       9
                                       9
De Soto Parish, LA
Caddo Parish, LA
                                    afdust
                                      444
                                      57
                                       -
                                       -
                                       -
                                       -
                                      57


                                     nonpt
                                      120
                                      38
                                       -
                                       -
                                       -
                                       -
                                      38


                                   np_oilgas
                                      112
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      40
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      439
                                      64
                                       -
                                       -
                                       -
                                       -
                                      64


                                      rwc
                                      15
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
East Feliciana Parish, LA
West Baton Rouge Parish, LA
                                    afdust
                                      281
                                      38
                                       -
                                       -
                                       -
                                       -
                                      38


                                     nonpt
                                      68
                                      29
                                       -
                                       -
                                       -
                                       -
                                      29


                                   pt_oilgas
                                      25
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Pointe Coupee Parish, LA
West Baton Rouge Parish, LA
                                    afdust
                                      553
                                      53
                                       -
                                       -
                                       -
                                       -
                                      53


                                     nonpt
                                      63
                                      19
                                       -
                                       -
                                       -
                                       -
                                      19


                                   pt_oilgas
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      89
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      318
                                       8
                                       -
                                       -
                                       -
                                       -
                                       8


                                      rwc
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Red River Parish, LA
Caddo Parish, LA
                                    afdust
                                      202
                                      22
                                       -
                                       -
                                       -
                                       -
                                      22


                                     nonpt
                                      52
                                      10
                                       -
                                       -
                                       -
                                       -
                                      10


                                   np_oilgas
                                      22
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      41
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      987
                                      970
                                       -
                                       -
                                       -
                                       -
                                      970
West Baton Rouge Parish, LA
-
                                    afdust
                                      255
                                      35
                                       -
                                       -
                                       -
                                       -
                                      35


                                     nonpt
                                      265
                                      68
                                       -
                                       -
                                       -
                                       -
                                      68


                                   pt_oilgas
                                      35
                                       2
                                       -
                                       -
                                       -
                                       -
                                       2


                                   ptagfire
                                      44
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      420
                                      288
                                       -
                                       -
                                       -
                                       -
                                      288


                                      rwc
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
West Feliciana Parish, LA
West Baton Rouge Parish, LA
                                    afdust
                                      196
                                      27
                                       -
                                       -
                                       -
                                       -
                                      27


                                     nonpt
                                      56
                                      24
                                       -
                                       -
                                       -
                                       -
                                      24


                                   ptnonipm
                                      144
                                      70
                                       -
                                       -
                                       -
                                       -
                                      70


                                      rwc
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Macomb County, MI
Wayne County, MI
                                    afdust
                                      689
                                      104
                                       -
                                       -
                                       -
                                       -
                                      104


                                     nonpt
                                     1,338
                                      264
                                       -
                                       -
                                       -
                                      56
                                      264


                                   pt_oilgas
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      120
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      500
                                      42
                                       -
                                       -
                                       -
                                       3
                                      42
Monroe County, MI
Wayne County, MI
                                    afdust
                                      829
                                      112
                                       -
                                       -
                                       -
                                       -
                                      112


                                     nonpt
                                      254
                                      82
                                       -
                                       -
                                       -
                                       -
                                      82


                                   ptnonipm
                                      309
                                      251
                                       -
                                       -
                                       -
                                      233
                                      251


                                      rwc
                                      172
                                      17
                                       -
                                       -
                                       -
                                       7
                                      17
Oakland County, MI
Wayne County, MI
                                    afdust
                                     1,425
                                      176
                                       -
                                       -
                                       -
                                       -
                                      176


                                     nonpt
                                     1,955
                                      691
                                       -
                                       -
                                       -
                                      43
                                      691


                                   ptnonipm
                                      140
                                       5
                                       -
                                       -
                                       -
                                       -
                                       5


                                      rwc
                                      897
                                      82
                                       -
                                       -
                                       -
                                      13
                                      82
Washtenaw County, MI
Wayne County, MI
                                    afdust
                                      784
                                      112
                                       -
                                       -
                                       -
                                       -
                                      112


                                     nonpt
                                      610
                                      222
                                       -
                                       -
                                       -
                                      42
                                      222


                                   pt_oilgas
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      40
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      273
                                      30
                                       -
                                       -
                                       -
                                      10
                                      30
Wayne County, MI
-
                                    afdust
                                      945
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                     1,719
                                      214
                                       -
                                       -
                                       -
                                      214
                                      214


                                   ptnonipm
                                     1,106
                                      376
                                       -
                                      15
                                      15
                                      376
                                      376


                                      rwc
                                      506
                                      55
                                       -
                                       -
                                       -
                                      55
                                      55
St. Louis city, MO
-
                                    afdust
                                      682
                                      55
                                       -
                                       -
                                       -
                                       -
                                      55


                                     nonpt
                                      240
                                      35
                                       -
                                       -
                                       -
                                       -
                                      35


                                   ptnonipm
                                      237
                                      58
                                       -
                                       -
                                       -
                                       -
                                      58


                                      rwc
                                      82
                                       9
                                       -
                                       -
                                       -
                                       -
                                       9
Beaverhead County, MT
Ravalli County, MT
Silver Bow County, MT
                                    afdust
                                     1,211
                                      89
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      17
                                       3
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      19
                                       1
                                       -
                                       -
                                       -
                                       -
                                       -
Broadwater County, MT
Lewis and Clark County, MT
                                    afdust
                                      967
                                      162
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      16
                                       4
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      30
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      16
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Cascade County, MT
Lewis and Clark County, MT
                                    afdust
                                     2,387
                                      331
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      118
                                      39
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      52
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      50
                                      19
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      84
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
Deer Lodge County, MT
Ravalli County, MT
Silver Bow County, MT
                                    afdust
                                      336
                                      58
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      12
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      14
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Flathead County, MT
Lewis and Clark County, MT
Lincoln County, MT
                                    afdust
                                     4,042
                                      760
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      276
                                      109
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      136
                                      71
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      180
                                      21
                                       -
                                       -
                                       -
                                       -
                                       -
Granite County, MT
Ravalli County, MT
                                    afdust
                                      317
                                      37
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Jefferson County, MT
Lewis and Clark County, MT
Silver Bow County, MT
                                    afdust
                                      613
                                      86
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      30
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      138
                                      123
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      31
                                       2
                                       -
                                       -
                                       -
                                       -
                                       -
Lewis and Clark County, MT
-
                                    afdust
                                     1,677
                                      302
                                      252
                                       -
                                      17
                                       -
                                       -


                                     nonpt
                                      138
                                      64
                                       -
                                       -
                                      64
                                       -
                                       -


                                   ptagfire
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      86
                                      10
                                       1
                                       -
                                       5
                                       -
                                       -
Lincoln County, MT
-
                                    afdust
                                     1,023
                                      206
                                       -
                                      206
                                      206
                                      206
                                      206


                                     nonpt
                                      43
                                      12
                                       -
                                      12
                                      12
                                      12
                                      12


                                      rwc
                                      67
                                       7
                                       -
                                       7
                                       7
                                       7
                                       7


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Madison County, MT
Silver Bow County, MT
                                    afdust
                                     1,280
                                      182
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      19
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      83
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      25
                                       2
                                       -
                                       -
                                       -
                                       -
                                       -
Meagher County, MT
Lewis and Clark County, MT
                                    afdust
                                      441
                                      36
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Powell County, MT
Lewis and Clark County, MT
                                    afdust
                                      677
                                      104
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      18
                                       3
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      22
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Ravalli County, MT
-
                                    afdust
                                     1,755
                                      358
                                      301
                                       -
                                      18
                                       -
                                       -


                                     nonpt
                                      100
                                      29
                                       -
                                       -
                                      29
                                       -
                                      26


                                      rwc
                                      94
                                      11
                                       -
                                       -
                                      11
                                       -
                                       6
Sanders County, MT
Lincoln County, MT
                                    afdust
                                      999
                                      190
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      29
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      12
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      43
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
Silver Bow County, MT
-
                                    afdust
                                      461
                                      76
                                       -
                                       -
                                       -
                                       -
                                      76


                                     nonpt
                                      54
                                      19
                                       -
                                       -
                                       -
                                       -
                                      19


                                   ptnonipm
                                      62
                                      34
                                       -
                                       -
                                      25
                                       -
                                      34


                                      rwc
                                      44
                                       5
                                       -
                                       -
                                       -
                                       -
                                       5
Teton County, MT
Lewis and Clark County, MT
                                    afdust
                                     1,188
                                      67
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      13
                                       4
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      221
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      15
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Atlantic County, NJ
Camden County, NJ
                                    afdust
                                      264
                                      48
                                       -
                                       -
                                       -
                                       -
                                      48


                                     nonpt
                                      129
                                      20
                                       -
                                       -
                                       -
                                       -
                                      20


                                   ptnonipm
                                      17
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      262
                                      31
                                       -
                                       -
                                       -
                                       7
                                      31
Burlington County, NJ
Camden County, NJ
                                    afdust
                                      435
                                      70
                                       -
                                       -
                                       -
                                       -
                                      70


                                     nonpt
                                      229
                                      34
                                       -
                                       -
                                       -
                                       -
                                      34


                                   ptnonipm
                                      49
                                      12
                                       -
                                       -
                                       -
                                      12
                                      12


                                      rwc
                                      562
                                      67
                                       -
                                       -
                                       -
                                      13
                                      67
Camden County, NJ
-
                                    afdust
                                      251
                                      37
                                       -
                                       -
                                       -
                                      37
                                      37


                                     nonpt
                                      245
                                      37
                                       -
                                       -
                                       -
                                      37
                                      37


                                   ptnonipm
                                      18
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      240
                                      35
                                       -
                                       -
                                       -
                                      35
                                      35
Essex County, NJ
Union County, NJ
                                    afdust
                                      317
                                      46
                                       -
                                       -
                                       -
                                       -
                                      46


                                     nonpt
                                      388
                                      59
                                       -
                                       -
                                       -
                                       -
                                      59


                                   ptnonipm
                                      35
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      155
                                      10
                                       -
                                       -
                                       -
                                       -
                                      10
Gloucester County, NJ
Camden County, NJ
                                    afdust
                                      250
                                      34
                                       -
                                       -
                                       -
                                       -
                                      34


                                     nonpt
                                      147
                                      22
                                       -
                                       -
                                       -
                                       -
                                      22


                                   ptnonipm
                                      262
                                      185
                                       -
                                       -
                                       -
                                      20
                                      185


                                      rwc
                                      296
                                      33
                                       -
                                       -
                                       -
                                       7
                                      33
Hudson County, NJ
Union County, NJ
                                    afdust
                                      181
                                      24
                                       -
                                       -
                                       -
                                       -
                                      24


                                     nonpt
                                      305
                                      50
                                       -
                                       -
                                       -
                                       -
                                      50


                                   ptnonipm
                                      21
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Middlesex County, NJ
Union County, NJ
                                    afdust
                                      540
                                      78
                                       -
                                       -
                                       -
                                       -
                                      78


                                     nonpt
                                      442
                                      69
                                       -
                                       -
                                       -
                                       -
                                      69


                                   ptnonipm
                                      202
                                      115
                                       -
                                       -
                                       -
                                       -
                                      115


                                      rwc
                                      267
                                      39
                                       -
                                       -
                                       -
                                       -
                                      39
Morris County, NJ
Union County, NJ
                                    afdust
                                      346
                                      52
                                       -
                                       -
                                       -
                                       -
                                      52


                                     nonpt
                                      281
                                      48
                                       -
                                       -
                                       -
                                       -
                                      48


                                   ptnonipm
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      624
                                      64
                                       -
                                       -
                                       -
                                       -
                                      64
Somerset County, NJ
Union County, NJ
                                    afdust
                                      234
                                       7
                                       -
                                       -
                                       -
                                       -
                                       7


                                     nonpt
                                      189
                                      28
                                       -
                                       -
                                       -
                                       -
                                      28


                                   ptnonipm
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      313
                                      34
                                       -
                                       -
                                       -
                                       -
                                      34
Union County, NJ
-
                                    afdust
                                      314
                                      47
                                       -
                                       -
                                       -
                                       -
                                      47


                                     nonpt
                                      282
                                      43
                                       -
                                       -
                                       -
                                       -
                                      43


                                   pt_oilgas
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      246
                                      66
                                       -
                                       -
                                       -
                                       -
                                      66


                                      rwc
                                      100
                                      12
                                       -
                                       -
                                       -
                                       -
                                      12


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Bronx County, NY
New York County, NY
                                    afdust
                                      275
                                      30
                                       -
                                       -
                                       -
                                       -
                                      30


                                     nonpt
                                      476
                                      61
                                       -
                                       -
                                       -
                                       -
                                      61


                                   ptnonipm
                                      17
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Kings County, NY
New York County, NY
                                    afdust
                                      455
                                      55
                                       -
                                       -
                                       -
                                       -
                                      55


                                     nonpt
                                     1,232
                                      160
                                       -
                                       -
                                       -
                                       -
                                      160


                                   ptnonipm
                                      35
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
New York County, NY
-
                                    afdust
                                      996
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                     1,640
                                      261
                                       -
                                       -
                                       -
                                       -
                                      261


                                   ptnonipm
                                      51
                                       7
                                       -
                                       -
                                       -
                                       -
                                       7
Queens County, NY
New York County, NY
                                    afdust
                                      678
                                      70
                                       -
                                       -
                                       -
                                       -
                                      70


                                     nonpt
                                     1,212
                                      153
                                       -
                                       -
                                       -
                                       -
                                      153


                                   ptnonipm
                                      21
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Belmont County, OH
Jefferson County, OH
                                    afdust
                                      488
                                      54
                                       -
                                       -
                                       -
                                      10
                                      54


                                     nonpt
                                      126
                                      59
                                       -
                                       -
                                       -
                                      59
                                      59


                                   np_oilgas
                                      18
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      120
                                      12
                                       -
                                       -
                                       -
                                      12
                                      12
Butler County, OH
Hamilton County, OH
                                    afdust
                                      643
                                      68
                                       -
                                       -
                                       -
                                      21
                                      68


                                     nonpt
                                      376
                                      160
                                       -
                                       -
                                       -
                                      159
                                      160


                                   ptnonipm
                                      627
                                      446
                                       -
                                       -
                                       -
                                      360
                                      446


                                      rwc
                                      350
                                      31
                                       -
                                       -
                                       -
                                      31
                                      31
Carroll County, OH
Jefferson County, OH
                                    afdust
                                      311
                                      35
                                       -
                                       -
                                       -
                                       7
                                      35


                                     nonpt
                                      50
                                      16
                                       -
                                       -
                                       -
                                      15
                                      16


                                   np_oilgas
                                      18
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      28
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      22
                                      13
                                       -
                                       -
                                       -
                                       6
                                      13


                                      rwc
                                      64
                                       5
                                       -
                                       -
                                       -
                                       5
                                       5
Clermont County, OH
Hamilton County, OH
                                    afdust
                                      499
                                      64
                                       -
                                       -
                                       -
                                       -
                                      64


                                     nonpt
                                      329
                                      192
                                       -
                                       -
                                       -
                                       -
                                      192


                                   ptnonipm
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      262
                                      23
                                       -
                                       -
                                       -
                                       -
                                      23


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Columbiana County, OH
Jefferson County, OH
                                    afdust
                                      522
                                      60
                                       -
                                       -
                                       -
                                      31
                                      60


                                     nonpt
                                      194
                                      95
                                       -
                                       -
                                       -
                                      95
                                      95


                                   np_oilgas
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      41
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      181
                                      18
                                       -
                                       -
                                       -
                                      18
                                      18
Cuyahoga County, OH
-
                                    afdust
                                      949
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      986
                                      157
                                       -
                                      40
                                      40
                                      157
                                      157


                                   ptnonipm
                                      948
                                      616
                                       -
                                      96
                                      96
                                      616
                                      616


                                      rwc
                                      457
                                      52
                                       -
                                       3
                                       3
                                      52
                                      52
Geauga County, OH
Cuyahoga County, OH
                                    afdust
                                      567
                                      85
                                       -
                                       -
                                       -
                                       -
                                      85


                                     nonpt
                                      265
                                      151
                                       -
                                       -
                                       -
                                       -
                                      151


                                      rwc
                                      196
                                      20
                                       -
                                       -
                                       -
                                       9
                                      20
Hamilton County, OH
Butler County, OH
                                    afdust
                                     1,192
                                      92
                                       -
                                       -
                                       -
                                       -
                                      92


                                     nonpt
                                      829
                                      295
                                       -
                                       -
                                       -
                                       -
                                      295


                                   ptnonipm
                                      155
                                      11
                                       -
                                       -
                                       -
                                       -
                                      11


                                      rwc
                                      372
                                      41
                                       -
                                       -
                                       -
                                       -
                                      41
Harrison County, OH
Jefferson County, OH
                                    afdust
                                      308
                                      31
                                       -
                                       -
                                       -
                                       -
                                      31


                                     nonpt
                                      34
                                      10
                                       -
                                       -
                                       -
                                      10
                                      10


                                   np_oilgas
                                      16
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      102
                                      55
                                       -
                                       -
                                       -
                                       -
                                      55


                                   ptnonipm
                                      12
                                      12
                                       -
                                       -
                                       -
                                       -
                                      12


                                      rwc
                                      40
                                       2
                                       -
                                       -
                                       -
                                       2
                                       2
Jefferson County, OH
-
                                    afdust
                                      239
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      115
                                      61
                                       -
                                       -
                                       -
                                      61
                                      61


                                   ptnonipm
                                      72
                                      19
                                       -
                                       -
                                       -
                                      19
                                      19


                                      rwc
                                      130
                                      13
                                       -
                                       -
                                       -
                                      13
                                      13
Lake County, OH
Cuyahoga County, OH
                                    afdust
                                      338
                                      33
                                       -
                                       -
                                       -
                                       -
                                      33


                                     nonpt
                                      297
                                      120
                                       -
                                       -
                                       -
                                       -
                                      120


                                   ptnonipm
                                      66
                                       7
                                       -
                                       -
                                       -
                                       -
                                       7


                                      rwc
                                      237
                                      24
                                       -
                                       -
                                       -
                                       6
                                      24


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Lorain County, OH
Cuyahoga County, OH
                                    afdust
                                      644
                                      85
                                       -
                                       -
                                       -
                                       -
                                      85


                                     nonpt
                                      323
                                      155
                                       -
                                       -
                                       -
                                      107
                                      155


                                   ptnonipm
                                      115
                                      27
                                       -
                                       -
                                       -
                                      27
                                      27


                                      rwc
                                      337
                                      34
                                       -
                                       -
                                       -
                                      11
                                      34
Medina County, OH
Cuyahoga County, OH
                                    afdust
                                      692
                                      93
                                       -
                                       -
                                       -
                                       -
                                      93


                                     nonpt
                                      373
                                      221
                                       -
                                       -
                                       -
                                       -
                                      221


                                   ptnonipm
                                      40
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      245
                                      26
                                       -
                                       -
                                       -
                                       9
                                      26
Montgomery County, OH
Butler County, OH
                                    afdust
                                      752
                                      70
                                       -
                                       -
                                       -
                                       -
                                      70


                                     nonpt
                                      515
                                      179
                                       -
                                       -
                                       -
                                       -
                                      179


                                   ptnonipm
                                      44
                                      15
                                       -
                                       -
                                       -
                                       -
                                      15


                                      rwc
                                      426
                                      38
                                       -
                                       -
                                       -
                                       -
                                      38
Portage County, OH
Cuyahoga County, OH
                                    afdust
                                      558
                                      73
                                       -
                                       -
                                       -
                                       -
                                      73


                                     nonpt
                                      296
                                      157
                                       -
                                       -
                                       -
                                       -
                                      157


                                   ptnonipm
                                      121
                                      35
                                       -
                                       -
                                       -
                                       7
                                      35


                                      rwc
                                      216
                                      22
                                       -
                                       -
                                       -
                                       8
                                      22
Preble County, OH
Butler County, OH
                                    afdust
                                      461
                                      46
                                       -
                                       -
                                       -
                                       -
                                      46


                                     nonpt
                                      76
                                      29
                                       -
                                       -
                                       -
                                       -
                                      29


                                   ptnonipm
                                      27
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      72
                                       6
                                       -
                                       -
                                       -
                                       -
                                       6
Warren County, OH
Butler County, OH
Hamilton County, OH
                                    afdust
                                      521
                                      59
                                       -
                                       -
                                       -
                                       -
                                      59


                                     nonpt
                                      446
                                      284
                                       -
                                       -
                                       -
                                       -
                                      284


                                   pt_oilgas
                                      24
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      252
                                      23
                                       -
                                       -
                                       -
                                       -
                                      23
Crook County, OR
Harney County, OR
                                    afdust
                                     1,126
                                      209
                                       -
                                       -
                                      209
                                       -
                                      126


                                     nonpt
                                      28
                                      16
                                       9
                                       -
                                       7
                                       -
                                       -


                                      rwc
                                      92
                                       9
                                       3
                                       -
                                       5
                                       -
                                       -
Deschutes County, OR
Crook County, OR
Harney County, OR
Lake County, OR
                                    afdust
                                     4,882
                                     1,093
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      292
                                      214
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      689
                                      72
                                       -
                                       -
                                       -
                                       -
                                       -


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Grant County, OR
Crook County, OR
Harney County, OR
                                    afdust
                                      679
                                      110
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      23
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      48
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
Harney County, OR
Crook County, OR
Lake County, OR
                                    afdust
                                     1,332
                                      146
                                       -
                                       -
                                      49
                                       -
                                      146


                                     nonpt
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      32
                                       2
                                       -
                                       -
                                       -
                                       -
                                       2
Jefferson County, OR
Crook County, OR
                                    afdust
                                     1,423
                                      300
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      32
                                      17
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      60
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      93
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
Lake County, OR
Harney County, OR
                                    afdust
                                     1,106
                                      141
                                      141
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      11
                                       4
                                       4
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      36
                                       3
                                       3
                                       -
                                       -
                                       -
                                       -
Malheur County, OR
Harney County, OR
                                    afdust
                                     2,371
                                      336
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      30
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      16
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      51
                                      42
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      78
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
Wheeler County, OR
Crook County, OR
                                    afdust
                                      222
                                      34
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Allegheny County, PA
Armstrong County, PA
                                    afdust
                                     1,401
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                     1,865
                                      664
                                       -
                                      664
                                      663
                                      664
                                      664


                                   np_oilgas
                                      19
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                     1,269
                                      864
                                       -
                                      93
                                      246
                                      824
                                      864


                                      rwc
                                      878
                                      85
                                       -
                                      85
                                      85
                                      85
                                      85
Armstrong County, PA
Allegheny County, PA
                                    afdust
                                      279
                                      18
                                       -
                                       -
                                       -
                                      18
                                      18


                                     nonpt
                                      125
                                      49
                                       -
                                       -
                                       -
                                      49
                                      49


                                   np_oilgas
                                      132
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      12
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      80
                                      61
                                       -
                                       -
                                       -
                                      61
                                      61


                                      rwc
                                      130
                                      15
                                       -
                                       -
                                       -
                                      15
                                      15


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Bedford County, PA
Cambria County, PA
                                    afdust
                                      419
                                      28
                                       -
                                       -
                                       -
                                       -
                                      28


                                     nonpt
                                      155
                                      79
                                       -
                                       -
                                       -
                                       -
                                      79


                                   pt_oilgas
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      142
                                      14
                                       -
                                       -
                                       -
                                       -
                                      14
Blair County, PA
Cambria County, PA
                                    afdust
                                      298
                                      19
                                       -
                                       -
                                       -
                                       -
                                      19


                                     nonpt
                                      424
                                      264
                                       -
                                       -
                                       -
                                       -
                                      264


                                   ptnonipm
                                      94
                                      59
                                       -
                                       -
                                       -
                                       -
                                      59


                                      rwc
                                      203
                                      22
                                       -
                                       -
                                       -
                                       -
                                      22
Bucks County, PA
Philadelphia County, PA
                                    afdust
                                      829
                                      68
                                       -
                                       -
                                       -
                                       -
                                      68


                                     nonpt
                                     1,043
                                      401
                                       -
                                       -
                                       -
                                       -
                                      401


                                   ptnonipm
                                      111
                                      65
                                       -
                                       -
                                       -
                                       -
                                      65


                                      rwc
                                      502
                                      47
                                       -
                                       -
                                       -
                                       -
                                      47
Butler County, PA
Allegheny County, PA
Armstrong County, PA
                                    afdust
                                      549
                                      43
                                       -
                                       -
                                       -
                                       -
                                      43


                                     nonpt
                                      695
                                      419
                                       -
                                       -
                                       -
                                       -
                                      419


                                   np_oilgas
                                      42
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      16
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      413
                                      140
                                       -
                                       -
                                       -
                                      24
                                      140


                                      rwc
                                      274
                                      29
                                       -
                                       -
                                       -
                                      10
                                      29
Cambria County, PA
-
                                    afdust
                                      260
                                      27
                                       -
                                       -
                                       -
                                       -
                                      27


                                     nonpt
                                      273
                                      124
                                       -
                                       -
                                       -
                                      34
                                      124


                                   np_oilgas
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      29
                                      13
                                       -
                                       -
                                       -
                                       -
                                      13


                                      rwc
                                      253
                                      27
                                       -
                                       -
                                       -
                                       -
                                      27
Clarion County, PA
Armstrong County, PA
                                    afdust
                                      230
                                      17
                                       -
                                       -
                                       -
                                       -
                                      17


                                     nonpt
                                      114
                                      56
                                       -
                                       -
                                       -
                                       -
                                      56


                                   np_oilgas
                                      43
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      38
                                       7
                                       -
                                       -
                                       -
                                       -
                                       7


                                      rwc
                                      86
                                      10
                                       -
                                       -
                                       -
                                       4
                                      10
Clearfield County, PA
Cambria County, PA
                                    afdust
                                      265
                                      26
                                       -
                                       -
                                       -
                                       -
                                      26


                                     nonpt
                                      197
                                      92
                                       -
                                       -
                                       -
                                       -
                                      92


                                   np_oilgas
                                      62
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      47
                                      35
                                       -
                                       -
                                       -
                                       -
                                      35


                                      rwc
                                      186
                                      19
                                       -
                                       -
                                       -
                                       -
                                      19
Delaware County, PA
Philadelphia County, PA
                                    afdust
                                      388
                                      38
                                       -
                                       -
                                       -
                                      38
                                      38


                                     nonpt
                                      478
                                      58
                                       -
                                       -
                                       -
                                      58
                                      58


                                   ptnonipm
                                      270
                                      165
                                       -
                                       -
                                       -
                                      165
                                      165


                                      rwc
                                      136
                                      17
                                       -
                                       -
                                       -
                                      17
                                      17
Indiana County, PA
Armstrong County, PA
Cambria County, PA
                                    afdust
                                      356
                                      29
                                       -
                                       -
                                       -
                                       -
                                      29


                                     nonpt
                                      206
                                      91
                                       -
                                       -
                                       -
                                       -
                                      91


                                   np_oilgas
                                      163
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      171
                                      158
                                       -
                                       -
                                       -
                                      48
                                      158


                                      rwc
                                      147
                                      16
                                       -
                                       -
                                       -
                                       6
                                      16
Jefferson County, PA
Armstrong County, PA
                                    afdust
                                      226
                                      16
                                       -
                                       -
                                       -
                                       -
                                      16


                                     nonpt
                                      133
                                      55
                                       -
                                       -
                                       -
                                       -
                                      55


                                   np_oilgas
                                      73
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      192
                                      177
                                       -
                                       -
                                       -
                                       -
                                      177


                                      rwc
                                      99
                                      11
                                       -
                                       -
                                       -
                                       5
                                      11
Lancaster County, PA
Lebanon County, PA
                                    afdust
                                     1,871
                                      95
                                       -
                                       -
                                       -
                                       -
                                      95


                                     nonpt
                                     1,310
                                      530
                                       -
                                       1
                                       1
                                      529
                                      530


                                   pt_oilgas
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      494
                                      272
                                       -
                                      58
                                      58
                                      235
                                      272


                                      rwc
                                      419
                                      41
                                       -
                                      15
                                      15
                                      41
                                      41
Lebanon County, PA
Lancaster County, PA
                                    afdust
                                      441
                                      31
                                       -
                                       -
                                       -
                                       -
                                      31


                                     nonpt
                                      310
                                      135
                                       -
                                       -
                                       -
                                      34
                                      135


                                   ptnonipm
                                      28
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      152
                                      15
                                       -
                                       -
                                       -
                                      10
                                      15
Montgomery County, PA
Delaware County, PA
Philadelphia County, PA
                                    afdust
                                     1,057
                                      75
                                       -
                                       -
                                       -
                                      75
                                      75


                                     nonpt
                                     1,352
                                      377
                                       -
                                       -
                                       -
                                      377
                                      377


                                   pt_oilgas
                                      11
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      328
                                      143
                                       -
                                       -
                                       -
                                      143
                                      143


                                      rwc
                                      433
                                      38
                                       -
                                       -
                                       -
                                      38
                                      38
Philadelphia County, PA
Delaware County, PA
                                    afdust
                                      633
                                      57
                                       -
                                       -
                                       -
                                       -
                                      57


                                     nonpt
                                     1,098
                                      162
                                       -
                                       -
                                       -
                                       -
                                      162


                                   ptnonipm
                                      988
                                      674
                                       -
                                       -
                                       -
                                      524
                                      674


                                      rwc
                                      42
                                       4
                                       -
                                       -
                                       -
                                       -
                                       4


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Schuylkill County, PA
Lebanon County, PA
                                    afdust
                                      430
                                      38
                                       -
                                       -
                                       -
                                       -
                                      38


                                     nonpt
                                      427
                                      214
                                       -
                                       -
                                       -
                                       -
                                      214


                                   ptnonipm
                                      104
                                      10
                                       -
                                       -
                                       -
                                       -
                                      10


                                      rwc
                                      255
                                      25
                                       -
                                       -
                                       -
                                       -
                                      25
Somerset County, PA
Cambria County, PA
                                    afdust
                                      479
                                      25
                                       -
                                       -
                                       -
                                       -
                                      25


                                     nonpt
                                      257
                                      146
                                       -
                                       -
                                       -
                                       -
                                      146


                                   ptnonipm
                                      89
                                      15
                                       -
                                       -
                                       -
                                       -
                                      15


                                      rwc
                                      173
                                      17
                                       -
                                       -
                                       -
                                       -
                                      17
Westmoreland County, PA
Allegheny County, PA
Armstrong County, PA
Cambria County, PA
                                    afdust
                                      640
                                      58
                                       -
                                       -
                                       -
                                       -
                                      58


                                     nonpt
                                      765
                                      356
                                       -
                                       -
                                       -
                                       -
                                      356


                                   np_oilgas
                                      88
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      33
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      228
                                      135
                                       -
                                       -
                                       -
                                      18
                                      135


                                      rwc
                                      561
                                      60
                                       -
                                       -
                                       -
                                      20
                                      60
Brooks County, TX
Hidalgo County, TX
                                    afdust
                                      467
                                      66
                                       -
                                       -
                                       -
                                      66
                                      66


                                   np_oilgas
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Cameron County, TX
Hidalgo County, TX
                                    afdust
                                      910
                                      83
                                       -
                                       -
                                       -
                                      83
                                      83


                                     nonpt
                                      200
                                      63
                                       -
                                       -
                                       -
                                      63
                                      63


                                   ptagfire
                                      94
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      26
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      36
                                       2
                                       -
                                       -
                                       -
                                       2
                                       2
El Paso County, TX
-
                                    afdust
                                     1,592
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      442
                                      169
                                       -
                                       -
                                       -
                                      10
                                      169


                                   pt_oilgas
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      234
                                      65
                                       -
                                       -
                                       -
                                      21
                                      65


                                      rwc
                                      60
                                       6
                                       -
                                       -
                                       -
                                       1
                                       6
Hidalgo County, TX
Cameron County, TX
                                    afdust
                                     1,758
                                      170
                                       -
                                      22
                                      22
                                      170
                                      170


                                     nonpt
                                      430
                                      156
                                       -
                                      156
                                      156
                                      156
                                      156


                                   np_oilgas
                                      30
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      128
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      117
                                      74
                                       -
                                      21
                                      21
                                      74
                                      74


                                      rwc
                                      60
                                       6
                                       -
                                       6
                                       6
                                       6
                                       6


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Hudspeth County, TX
El Paso County, TX
                                    afdust
                                      245
                                      31
                                       -
                                       -
                                       -
                                       -
                                      31


                                   pt_oilgas
                                      19
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Kenedy County, TX
Hidalgo County, TX
                                    afdust
                                      269
                                      43
                                       -
                                       -
                                       -
                                      43
                                      43
Starr County, TX
Hidalgo County, TX
                                    afdust
                                      474
                                      47
                                       -
                                       -
                                       -
                                      47
                                      47


                                     nonpt
                                      47
                                      16
                                       -
                                       -
                                       -
                                      16
                                      16


                                   np_oilgas
                                      28
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Willacy County, TX
Cameron County, TX
Hidalgo County, TX
                                    afdust
                                      355
                                      22
                                       -
                                       -
                                       -
                                      22
                                      22


                                     nonpt
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      32
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Cache County, UT
Weber County, UT
                                    afdust
                                     1,603
                                      225
                                       -
                                       -
                                      225
                                       -
                                       -


                                     nonpt
                                      53
                                       9
                                       -
                                       -
                                       9
                                       -
                                       -


                                      rwc
                                      26
                                       2
                                       -
                                       -
                                       2
                                       -
                                       -
Davis County, UT
Salt Lake County, UT
Weber County, UT
                                    afdust
                                      455
                                      43
                                       -
                                       -
                                      43
                                       -
                                       -


                                     nonpt
                                      125
                                      23
                                       -
                                       -
                                      23
                                       -
                                       -


                                   ptnonipm
                                      95
                                       9
                                       -
                                       -
                                       9
                                       -
                                       -


                                      rwc
                                      67
                                       5
                                       -
                                       -
                                       5
                                       -
                                       -
Morgan County, UT
Davis County, UT
Salt Lake County, UT
Weber County, UT
                                    afdust
                                      201
                                      32
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      26
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Rich County, UT
Cache County, UT
Weber County, UT
                                    afdust
                                      345
                                      29
                                       -
                                       -
                                       -
                                       -
                                       -
Salt Lake County, UT
Davis County, UT
                                    afdust
                                     1,649
                                      83
                                       -
                                       -
                                      83
                                       -
                                       -


                                     nonpt
                                      445
                                      84
                                      22
                                       -
                                      12
                                       -
                                       -


                                   ptnonipm
                                      789
                                      263
                                      206
                                       -
                                      57
                                       -
                                       -


                                      rwc
                                      234
                                      14
                                       2
                                       -
                                      10
                                       -
                                       -
Summit County, UT
Salt Lake County, UT
                                    afdust
                                      635
                                      92
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      40
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      61
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      12
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Tooele County, UT
Davis County, UT
Salt Lake County, UT
Weber County, UT
                                    afdust
                                      641
                                      104
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      26
                                       2
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      773
                                      42
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      15
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Wasatch County, UT
Salt Lake County, UT
                                    afdust
                                      756
                                      144
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      15
                                       2
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -
Weber County, UT
Cache County, UT
Davis County, UT
                                    afdust
                                      557
                                      19
                                       -
                                       -
                                      19
                                       -
                                       -


                                     nonpt
                                      91
                                      15
                                       -
                                       -
                                      15
                                       -
                                       -


                                   ptnonipm
                                      65
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      59
                                       5
                                       -
                                       -
                                       5
                                       -
                                       -
Benton County, WA
Yakima County, WA
                                    afdust
                                     1,539
                                      63
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      139
                                      24
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      71
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      108
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
Chelan County, WA
Kittitas County, WA
Okanogan County, WA
                                    afdust
                                      329
                                      44
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      81
                                      14
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      26
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      227
                                      27
                                       -
                                       -
                                       -
                                       -
                                       -
Douglas County, WA
Kittitas County, WA
Okanogan County, WA
                                    afdust
                                     2,049
                                      186
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      23
                                       2
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      12
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      84
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
Ferry County, WA
Okanogan County, WA
                                    afdust
                                      397
                                      63
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      41
                                       5
                                       -
                                       -
                                       -
                                       -
                                       -
Grant County, WA
Kittitas County, WA
Okanogan County, WA
Yakima County, WA
                                    afdust
                                     3,242
                                      169
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      78
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      264
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      68
                                      43
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      109
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Kittitas County, WA
Yakima County, WA
                                    afdust
                                      472
                                      47
                                      47
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      51
                                       8
                                       8
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                       9
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      113
                                      13
                                      13
                                       -
                                       -
                                       -
                                       -
Klickitat County, WA
Yakima County, WA
                                    afdust
                                      568
                                      54
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      20
                                       1
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      14
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      44
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      55
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
Lewis County, WA
Yakima County, WA
                                    afdust
                                      550
                                      65
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      82
                                      15
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      94
                                      20
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      226
                                      24
                                       -
                                       -
                                       -
                                       -
                                       -
Lincoln County, WA
Okanogan County, WA
                                    afdust
                                     2,537
                                      130
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      10
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      40
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      26
                                       3
                                       -
                                       -
                                       -
                                       -
                                       -
Okanogan County, WA
-
                                    afdust
                                      771
                                      113
                                       -
                                       -
                                      113
                                       -
                                       -


                                     nonpt
                                      42
                                       9
                                       -
                                       -
                                       9
                                       -
                                       -


                                   ptagfire
                                      12
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      154
                                      17
                                       -
                                       -
                                      17
                                       -
                                       -
Pierce County, WA
Kittitas County, WA
Yakima County, WA
                                    afdust
                                     1,540
                                       7
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      574
                                      103
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      200
                                      99
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                     1,047
                                      93
                                       -
                                       -
                                       -
                                       -
                                       -
Skagit County, WA
Okanogan County, WA
                                    afdust
                                      626
                                      67
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      131
                                      24
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      252
                                      137
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      237
                                      26
                                       -
                                       -
                                       -
                                       -
                                       -
Skamania County, WA
Yakima County, WA
                                    afdust
                                      122
                                      18
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      57
                                       6
                                       -
                                       -
                                       -
                                       -
                                       -


                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
Whatcom County, WA
Okanogan County, WA
                                    afdust
                                      874
                                      69
                                       -
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      227
                                      48
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      851
                                      762
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      365
                                      35
                                       -
                                       -
                                       -
                                       -
                                       -
Yakima County, WA
Kittitas County, WA
                                    afdust
                                     1,845
                                      100
                                      100
                                       -
                                       -
                                       -
                                       -


                                     nonpt
                                      193
                                      41
                                      41
                                       -
                                       -
                                       -
                                       -


                                   ptagfire
                                      177
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      345
                                      39
                                      39
                                       -
                                       -
                                       -
                                       -
Brooke County, WV
-
                                    afdust
                                      90
                                      12
                                       -
                                       -
                                       -
                                       -
                                      12


                                     nonpt
                                      44
                                      19
                                       -
                                       -
                                       -
                                       -
                                      19


                                   ptnonipm
                                      127
                                      76
                                       -
                                       -
                                       -
                                       -
                                      76


                                      rwc
                                      103
                                      13
                                       -
                                       -
                                       -
                                       -
                                      13
Hancock County, WV
Brooke County, WV
                                    afdust
                                      58
                                       8
                                       -
                                       -
                                       -
                                       -
                                       8


                                     nonpt
                                      51
                                       9
                                       -
                                       -
                                       -
                                       -
                                       9


                                   ptnonipm
                                      32
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      122
                                      15
                                       -
                                       -
                                       -
                                       -
                                      15
Marshall County, WV
-
                                    afdust
                                      179
                                      24
                                       -
                                       -
                                       -
                                       -
                                      24


                                     nonpt
                                      46
                                      13
                                       -
                                       -
                                       -
                                       -
                                      13


                                   np_oilgas
                                      14
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                      13
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   ptnonipm
                                      109
                                      92
                                       -
                                       -
                                       -
                                       -
                                      92


                                      rwc
                                      158
                                      19
                                       -
                                       -
                                       -
                                       -
                                      19
Ohio County, WV
Brooke County, WV
Marshall County, WV
                                    afdust
                                      238
                                      40
                                       -
                                       -
                                       -
                                       -
                                      40


                                     nonpt
                                      97
                                      38
                                       -
                                       -
                                       -
                                       -
                                      38


                                   np_oilgas
                                      26
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      143
                                      18
                                       -
                                       -
                                       -
                                       -
                                      18
Wetzel County, WV
Marshall County, WV
                                    afdust
                                      77
                                      12
                                       -
                                       -
                                       -
                                       -
                                      12


                                     nonpt
                                      41
                                      22
                                       -
                                       -
                                       -
                                       -
                                      22


                                   np_oilgas
                                      35
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                   pt_oilgas
                                       8
                                       -
                                       -
                                       -
                                       -
                                       -
                                       -


                                      rwc
                                      82
                                      11
                                       -
                                       -
                                       -
                                       -
                                      11

ENGINEERING COST ANALYSIS AND QUALITATIVE DISCUSSION OF SOCIAL COSTS
 Overview
This chapter provides estimates of the engineering costs of the illustrative control strategies identified in Chapter 3 for the proposed annual and current 24-hour alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/m[3], as well as the following two more stringent alternative standard levels: 8/35 g/ - m - [3] -  and 10/30 g/ - m - [3] - . Because the EPA is proposing that the current secondary PM standards be retained, we did not evaluate alternative secondary standard levels in this RIA. The chapter summarizes the methods, tools, and data sources used to estimate the engineering costs presented. As discussed in Chapter 3, for the alternative standards analyzed we applied control measures to sources in the following emissions inventory sectors: non-electric generating unit (non-EGU) point, oil and gas point, non-point (area), residential wood combustion, and area fugitive dust.
The estimated costs for the alternative standard levels are a function of (i) assumptions used in the analysis, including assumptions about which areas will require emissions controls and the sources and controls available in those areas; (ii) the level of sufficient, detailed information on emissions sources and control measures needed to estimate engineering costs; and (iii) the future year baseline emissions from which the emissions reductions are measured.
For the proposed alternative standard level of 10/35 g/ - m - [3] - , because 15 of the 24 counties that need emissions reductions are counties in California, the majority of the estimated costs are incurred in California. In addition, as the alternative standard levels become more stringent, more counties in the northeast and southeast need emissions reductions. As additional controls are applied in those areas (and less so in the west and California because availability of additional controls is limited), those areas account for a relatively higher proportion of estimated costs. For example, for alternative standard levels of 9/35 g/ - m - [3] -  and 8/35 g/ - m - [3] - , more controls are available to apply in the northeast and their adjacent counties and the southeast and their adjacent counties. The estimated costs for those areas are higher than the estimated costs for the west and California. Note that in the northeast and southeast we identified control measures and associated emissions reductions from adjacent counties and used a ppb/ton PM2.5 air quality ratio that was four times less responsive than the ratio used when applying in-county emissions reductions (i.e., applied four tons of PM2.5 emissions reductions from an adjacent county for one ton of emissions reduction needed in a given county); the cost of the additional reductions from adjacent counties also contributes to the higher proportion of the estimated costs. Lastly, for the more stringent alternative standard level of 8/35 g/m[3], across all areas the largest share of estimated costs is from controls for area fugitive dust emissions. 
The remainder of the chapter is organized as follows. Section 4.1 presents the engineering costs associated with the application of controls identified in EPA's national-scale analysis. Section 4.2 provides a discussion of the uncertainties and limitations associated with the engineering cost estimates. Section 4.3 includes a qualitative discussion on social costs. Section 4.4 includes references.
Estimating Engineering Costs
The engineering costs described in this chapter generally include the costs of purchasing, installing, operating, and maintaining the control technologies applied. The costs associated with monitoring, testing, reporting, and recordkeeping for potentially affected sources are not included in the annualized cost estimates. These cost estimates are presented for 2032 but reflect the annual cost that is expected to be incurred each year over a longer time horizon. We calculate the present value of these annual costs over 20 years in Chapter 8 using 3 and 7 percent discount rates.
This analysis focuses on emissions reductions needed for the proposed and more stringent alternative standard levels. As discussed in this analysis, the control technologies and strategies selected for analysis were from information available in EPA's control measures database; these control strategies illustrate one way in which nonattainment areas could work toward meeting a revised standard. There are many ways to construct and evaluate potential control programs for a revised standard, and the EPA anticipates that state and local governments will consider programs best suited for local conditions. 
The EPA understands that some states will incur costs both designing State Implementation Plans (SIPs) and implementing new control strategies to meet a revised standard. However, the EPA does not know what specific actions states will take to design their SIPs to meet a revised standard. Therefore, we do not present estimated costs that government agencies may incur for managing the requirement or implementing these (or other) control strategies. 
Methods, Tools, and Data
The EPA uses the Control Strategy Tool (CoST) (U.S. EPA, 2019) to estimate engineering control costs. CoST models emissions reductions and control costs associated with the application of control technologies or measures by matching the controls in the control measures database (CMDB) to emissions sources in the future year projected emissions inventory by source classification code (SCC).[,] CoST was used in two ways in the analysis. First, CoST was used to identify controls and related potential PM2.5 emissions reductions in counties projected to exceed the proposed and more stringent alternative annual and 24-hour standard levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in the analytical baseline (see Chapter 3, Section 3.2.1 for a discussion of the counties and areas). Second, CoST was used to estimate the control costs for the measures identified. As indicated in Chapter 3, Section 3.2.2., for the control strategy analyses in this RIA, to maximize the number of emissions sources included we applied controls to emissions sources with greater than 5 tons per year of PM2.5 emissions at a marginal cost threshold of up to a $160,000/ton.
CoST calculates engineering costs using one of two different methods: (1) an equation that incorporates key operating unit information, such as unit design capacity or stack flow rate, or (2) an average annualized cost-per-ton factor multiplied by the total tons of reduction of a pollutant. Most control cost information within CoST was developed based on the cost-per-ton approach because (1) parameters used in the engineering equations are not readily available or broadly representative across emissions sources within the emissions inventory and (2) estimating engineering costs using an equation requires data from the emissions inventory, which may not be available. The cost equations used in CoST estimate annual, capital and/or operating and maintenance (O&M) costs and are used primarily for some larger emissions sources such as industrial, commercial, and institutional (ICI) boilers, glass manufacturing furnaces, and cement kilns. 
CoST gets key operating unit information from the emissions inventory data submitted by state, local, and tribal air agencies (S/L/T), including detailed information by source on emissions, installed control devices, and control device efficiency. Much of this underlying emissions inventory data serves as key inputs into CoST and the control strategy analyses. The information on whether a source is currently controlled, by what control device, and control device efficiency is required under the Air Emissions Reporting Rule (AERR) used to collect the emissions inventory data. However, control information may not be fully reported by S/L/T agencies and would not be available for purposes of the control strategy analyses, introducing the possibility that CoST applies controls to already controlled emissions sources.
When sufficient information is available to estimate control costs using equations, the capital costs of the control equipment must be annualized. Capital costs are converted to annual costs using the capital recovery factor (CRF). The engineering cost analysis uses the equivalent uniform annual costs (EUAC) method, in which annualized costs are calculated based on the equipment life for the control measure and the interest rate incorporated into the CRF. Annualized costs represent an equal stream of yearly costs over the period the control technology is expected to operate. For more information on the EUAC method, refer to the EPA Air Pollution Control Cost Manual (U.S. EPA, 2017a).
Cost Estimates for the Control Strategies
In this section, we provide engineering cost estimates for the control technologies and measures presented in Chapter 3 that include control technologies for non-EGU point sources, oil and gas point, non-point (area) sources, residential wood combustion sources, and area fugitive dust emissions. The cost estimates presented in Table 4-1 through Table 4-5 reflect the engineering costs annualized at 7 percent, to the extent possible. When calculating the annualized costs we would like to use the interest rates faced by firms; however, we do not know what those rates are. As such we use 7 percent as a conservative estimate.
By area, Table 4-1 includes a summary of estimated control costs from control applications for the alternative standard levels analyzed. Tables 4A-1 through 4A-6 in Appendix 4A include detailed information on estimated costs by area and by county. 
Table 4-1	By Area, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/m - [3] - , and 8/35 g/ - m - [3] -  for 2032 (millions of 2017$)
Area
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
                                     $7.3
                                     $12.8
                                    $183.5
                                    $560.2
Northeast (Adjacent Counties)
                                      $0
                                      $0
                                     $22.3
                                    $539.7
Southeast
                                     $4.1
                                     $4.1
                                     $50.4
                                    $250.6
Southeast (Adjacent Counties)
                                      $0
                                      $0
                                     $18.2
                                    $186.5
West
                                     $19.0
                                    $150.0
                                     $34.2
                                    $121.8
CA
                                     $64.1
                                     $90.4
                                     $84.7
                                    $162.9
Total
                                     $94.5
                                    $257.2
                                    $393.3
                                   $1,821.7

For the proposed alternative standard level of 10/35 g/ - m - [3] - , the majority of the estimated costs are incurred in California because 15 of the 24 counties that need emissions reductions are located in California. Looking at the more stringent alternative standard level of 10/30 g/m - [3] -  in the west, an additional 20 counties need emissions reductions, and the estimated costs increase significantly; estimated costs for the proposed alternative standard level of 9/35 g/ - m - [3] -  -  are higher than for 10/35 g/ - m - [3] -  -  but lower than for 10/30 g/ - m - [3] -  -  in this area. For alternative standard levels of 9/35 g/ - m - [3] -  and 8/35 g/m - [3] - , more controls are available to apply in the northeast and the southeast as compared to in California and the west. Therefore, the estimated costs for the northeast and the southeast are significantly higher for 9/35 g/ - m - [3] -  and 8/35 g/m - [3]. See Tables 3A.2 through 3A.7 for more details on emissions reductions available by area and county.
As discussed in Chapter 3, in the northeast and southeast when we applied the emissions reductions from adjacent counties, we applied a ratio of 4:1. That is, it is assumed that four tons of PM2.5 emissions reductions from an adjacent county are needed to produce the equivalent air quality change of one ton of emissions reduction if it had occurred within the county needing the reduction. Application of this ratio contributes to the higher cost estimates for alternative standard levels of 9/35 g/ - m - [3] -  and 8/35 g/m - [3] - .  Naturally, it is anticipated that states will first attempt to find emissions reductions within the counties that actually need the reductions. To the extent that states are able to identify control opportunities within those counties beyond the reductions identified by CoST, the need for reductions from adjacent counties will be reduced. Also, depending on local air quality factors, the resulting air quality impact may be greater than a 4:1 ratio suggests. As a result, the estimate of costs for adjacent counties may be an overestimate. 
By emissions inventory sector, Table 4-2 includes a summary of the estimated costs from control applications for the alternative standard levels analyzed. For all of the alternative standard levels analyzed, controls for area fugitive dust emissions comprise the largest share of the estimated costs, ranging from 49 to 81 percent of the cost estimates. Non-EGU point and non-point (area) controls represent the next largest shares of the cost estimates. 
By area and by emissions inventory sector, Table 4-3 includes a summary of the estimated costs from control applications for the alternative standard levels analyzed. For the more stringent alternative standard level of 8/35 g/m[3] across all areas the largest share of estimated costs is from controls for area fugitive dust emissions. In addition, as the alternative standard levels become more stringent, more counties in the northeast and southeast need emissions reductions and controls are applied in those areas (and less so in the west and California because availability of additional controls is limited), resulting in a relatively higher proportion of estimated costs for those areas.
Table 4-2	By Emissions Inventory Sector, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  for 2032 (millions of 2017$) 
Sector
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Non-EGU Point
                                     $10.2
                                     $21.2
                                    $144.3
                                    $423.7
Oil & Gas Point
                                      $0
                                      $0
                                      $0
                                     $5.0
Non-Point (Area)
                                     $15.8
                                     $21.4
                                     $46.3
                                    $189.2
Residential Wood Combustion
                                     $3.1
                                     $5.6
                                     $11.3
                                     $36.7
Area Source Fugitive Dust
                                     $65.4
                                    $209.1
                                    $191.5
                                   $1,167.0
Total
                                     $94.5
                                    $257.2
                                    $393.3
                                   $1,821.7

Table 4-3	By Area and by Emissions Inventory Sector, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  for 2032 (millions of 2017$) 
Area
Sector
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Northeast
Non-EGU Point
                                     $1.7
                                     $7.3
                                    $125.0
                                    $232.8

Non-Point (Area)
                                     $4.6
                                     $4.6
                                     $16.8
                                     $56.4

Residential Wood Combustion
                                     $1.0
                                     $1.0
                                     $4.1
                                     $10.5

Area Source Fugitive Dust
                                      $0
                                      $0
                                     $37.7
                                    $260.5
Northeast (Adjacent Counties)
Non-EGU Point
                                      $0
                                      $0
                                     $4.0
                                     $65.3

Oil & Gas Point
                                      $0
                                      $0
                                      $0
                                     $5.0

Non-Point (Area)
                                      $0
                                      $0
                                     $4.4
                                     $50.5

Residential Wood Combustion
                                      $0
                                      $0
                                     $0.8
                                     $10.6

Area Source Fugitive Dust
                                      $0
                                      $0
                                     $13.1
                                    $408.4
Southeast
Non-EGU Point
                                     $1.2
                                     $1.2
                                     $6.2
                                     $81.4

Oil & Gas Point
                                      $0
                                      $0
                                      $0
                                     $0.02

Non-Point (Area)
                                     $2.0
                                     $2.0
                                     $10.1
                                     $37.7

Residential Wood Combustion
                                     $0.3
                                     $0.3
                                     $0.6
                                     $2.4

Area Source Fugitive Dust
                                     $0.7
                                     $0.7
                                     $33.6
                                    $129.0
Southeast (Adjacent Counties)
Non-EGU Point
                                      $0
                                      $0
                                      $0
                                     $17.9

Non-Point (Area)
                                      $0
                                      $0
                                     $0.1
                                     $10.0

Residential Wood Combustion
                                      $0
                                      $0
                                      $0
                                     $1.4

Area Source Fugitive Dust
                                      $0
                                      $0
                                     $18.1
                                    $157.3
West
Non-EGU Point
                                      $0
                                     $5.4
                                     $0.6
                                     $11.9

Non-Point (Area)
                                     $0.06
                                     $3.6
                                     $2.1
                                     $13.4

Residential Wood Combustion
                                     $0.03
                                     $1.1
                                     $0.4
                                     $2.8

Area Source Fugitive Dust
                                     $19.0
                                    $139.9
                                     $31.0
                                     $93.7
CA
Non-EGU Point
                                     $7.3
                                     $7.3
                                     $8.4
                                     $14.5

Non-Point (Area)
                                     $9.2
                                     $11.2
                                     $12.8
                                     $21.2

Residential Wood Combustion
                                     $1.9
                                     $3.3
                                     $5.5
                                     $9.0

Area Source Fugitive Dust
                                     $45.8
                                     $68.5
                                     $58.0
                                    $118.2
Total
 
                                     $94.5
                                    $257.2
                                    $393.3
                                   $1,821.7

By control technology, Table 4-4 includes a summary of the estimated costs from control applications for the alternative standard levels analyzed. Across all of the alternative standard levels analyzed, the control technologies that comprise more than 80 percent of the cost estimates include Pave Existing Shoulders at 25% rule penetration (RP) (area fugitive dust inventory sector), Pave Unpaved Roads at 25% RP (area fugitive dust inventory sector), Fabric Filter-All Types (non-EGU point inventory sector), and Electrostatic Precipitator at 25% RP (non-point (area) inventory sector).
By emissions inventory sector and by control technology, Table 4-5 includes a summary of the cost estimates. Across all of the alternative standard levels analyzed, for the non-EGU point sector, the application of Fabric Filter-All Types results in the highest portion of estimated costs for that inventory sector; for the non-point (area) sector, the application of Electrostatic Precipitator at 25% RP and Substitute Chipping for Burning result in the highest portion of estimated costs for that inventory sector; for the residential wood combustion sector, the application of Convert to Gas Logs at 25% RP results in the highest portion of estimated costs for that inventory sector; and for the area fugitive dust sector, the application of Pave Existing Shoulders at 25% and Pave Unpaved Roads at 25% result in the highest portion of estimated costs for that inventory sector.

Table 4-4	By Control Technology, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  for 2032 (millions of 2017$)
Control Technology
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Add-on Scrubber at 25% RP
                                     $0.06
                                     $0.06
                                      $0
                                      $0
Annual tune-up at 10% RP
                                      $0
                                     $0.01
                                     $0.01
                                     $0.01
Annual tune-up at 25% RP
                                     $0.6
                                     $0.7
                                     $3.4
                                     $12.0
Biennial tune-up at 10% RP
                                     $0.0
                                     $0.0
                                     $0.0
                                     $0.3
Biennial tune-up at 25% RP
                                     $0.1
                                     $0.3
                                     $0.3
                                     $2.0
Catalytic oxidizers at 25% RP
                                     $0.3
                                     $0.4
                                     $1.1
                                     $1.4
Chemical Stabilizer at 10% RP
                                     $0.7
                                     $2.2
                                     $1.3
                                     $46.8
Chemical Stabilizer at 25% RP
                                      $0
                                      $0
                                     $1.6
                                     $49.8
Convert to Gas Logs at 25% RP
                                     $2.6
                                     $4.4
                                     $9.5
                                     $29.0
Dust Suppressants at 10% RP
                                      $0
                                      $0
                                      $0
                                     $0.02
Dust Suppressants at 25% RP
                                      $0
                                      $0
                                      $0
                                     $5.4
Electrostatic Precipitator-All Types
                                     $0.4
                                      $0
                                     $0.4
                                     $0.7
Electrostatic Precipitator at 10% RP
                                      $0
                                      $0
                                     $0.1
                                     $0.01
Electrostatic Precipitator at 25% RP
                                     $10.7
                                     $13.1
                                     $20.4
                                     $80.6
EPA-certified wood stove at 10% RP
                                      $0
                                      $0
                                      $0
                                     $0.01
EPA Phase 2 Qualified Units at 10% RP
                                      $0
                                      $0
                                     $0.2
                                     $0.03
EPA Phase 2 Qualified Units at 25% RP
                                     $0.2
                                     $0.2
                                      $0
                                     $0.7
Fabric Filter-All Types
                                     $9.0
                                     $18.9
                                    $129.1
                                    $397.2
HEPA filters at 10% RP
                                     $0.01
                                     $0.01
                                     $0.01
                                     $0.02
HEPA filters at 25% RP
                                     $0.02
                                      $0
                                     $0.09
                                     $0.4
Install Cleaner Hydronic Heaters at 10% RP
                                      $0
                                     $0.0
                                      $0
                                      $0
Install Cleaner Hydronic Heaters at 25% RP
                                     $0.02
                                     $0.03
                                     $0.2
                                     $0.7
Install new drift eliminator at 10% RP
                                      $0
                                      $0
                                     $0.02
                                     $0.01
Install new drift eliminator at 25% RP
                                     $0.5
                                     $0.5
                                     $0.6
                                     $1.3
Install Retrofit Devices at 10% RP
                                      $0
                                      $0
                                     $0.1
                                     $0.06
Install Retrofit Devices at 25% RP
                                     $0.1
                                     $0.1
                                      $0
                                     $0.08
New gas stove or gas logs at 10% RP
                                     $0.03
                                     $0.4
                                     $0.4
                                     $0.7
New gas stove or gas logs at 25% RP
                                     $0.2
                                     $0.5
                                     $0.9
                                     $5.4
Pave existing shoulders at 10% RP
                                      $0
                                      $0
                                      $0
                                     $7.6
Pave existing shoulders at 25% RP
                                     $31.1
                                     $95.0
                                    $119.6
                                    $755.0
Pave Unpaved Roads at 25% RP
                                     $33.7
                                    $111.8
                                     $69.0
                                    $302.5
Smokeless Broiler at 10% RP
                                     $0.4
                                     $0.6
                                     $1.1
                                     $0.3
Smokeless Broiler at 25% RP
                                      $0
                                      $0
                                     $3.1
                                     $1.3
Substitute chipping for burning
                                     $3.5
                                     $6.1
                                     $16.6
                                     $90.6
Venturi Scrubber
                                     $0.3
                                     $1.7
                                     $14.3
                                     $29.7
Total
                                     $94.5
                                    $257.2
                                    $393.3
                                   $1,821.7
Note - The 10% RP and 25% RP indicate the rule penetration percent, or the percent of the non-point (area), residential wood combustion, or area fugitive dust inventory emissions that the control measure is applied to at a specified percent control efficiency.

Table 4-5	By Emissions Inventory Sector and Control Technology, Summary of Annualized Control Costs for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  for 2032 (millions of 2017$)
Inventory Sector
Control Technology
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Non-EGU Point
Electrostatic Precipitator-All Types
                                     $0.4
                                      $0
                                     $0.4
                                     $0.7

Fabric Filter-All Types
                                     $9.0
                                     $18.9
                                    $129.0
                                    $392.0

Install new drift eliminator at 10% RP
                                      $0
                                      $0
                                     $0.02
                                     $0.01

Install new drift eliminator at 25% RP
                                     $0.5
                                     $0.5
                                     $0.6
                                     $1.3

Venturi Scrubber
                                     $0.3
                                     $1.7
                                     $14.3
                                     $29.7
Oil & Gas Point
Fabric Filter-All Types
                                      $0
                                      $0
                                      $0
                                     $5.0

Install new drift eliminator at 25% RP
                                      $0
                                      $0
                                      $0
                                     $0.02
Non-Point (Area)
Add-on Scrubber at 25% RP
                                     $0.06
                                     $0.06
                                      $0
                                      $0

Annual tune-up at 10% RP
                                      $0
                                     $0.01
                                     $0.01
                                     $0.01

Annual tune-up at 25% RP
                                     $0.6
                                     $0.7
                                     $3.4
                                     $12.0

Biennial tune-up at 10% RP
                                     $0.0
                                     $0.0
                                     $0.0
                                     $0.3

Biennial tune-up at 25% RP
                                     $0.1
                                     $0.3
                                     $0.3
                                     $2.0

Catalytic oxidizers at 25% RP
                                     $0.3
                                     $0.4
                                     $1.1
                                     $1.4

Electrostatic Precipitator at 10% RP
                                      $0
                                      $0
                                     $0.1
                                     $0.01

Electrostatic Precipitator at 25% RP
                                     $10.7
                                     $13.1
                                     $20.4
                                     $80.6

Fabric Filter-All Types
                                      $0
                                      $0
                                     $0.09
                                     $0.2

HEPA filters at 10% RP
                                     $0.01
                                     $0.01
                                     $0.01
                                     $0.02

HEPA filters at 25% RP
                                     $0.02
                                      $0
                                     $0.09
                                     $0.4

Smokeless Broiler at 10% RP
                                     $0.4
                                     $0.6
                                     $1.1
                                     $0.3

Smokeless Broiler at 25% RP
                                      $0
                                      $0
                                     $3.1
                                     $1.3

Substitute chipping for burning
                                     $3.5
                                     $6.1
                                     $16.6
                                     $90.6
Residential Wood Combustion
Convert to Gas Logs at 25% RP
                                     $2.6
                                     $4.4
                                     $9.5
                                     $29.0

EPA-certified wood stove at 10% RP
                                      $0
                                      $0
                                      $0
                                     $0.01

EPA Phase 2 Qualified Units at 10% RP
                                      $0
                                      $0
                                     $0.2
                                     $0.03

EPA Phase 2 Qualified Units at 25% RP
                                     $0.2
                                     $0.2
                                      $0
                                     $0.7

Install Cleaner Hydronic Heaters at 10% RP
                                      $0
                                     $0.0
                                      $0
                                      $0

Install Cleaner Hydronic Heaters at 25% RP
                                     $0.02
                                     $0.03
                                     $0.2
                                     $0.7

Install Retrofit Devices at 10% RP
                                      $0
                                      $0
                                     $0.1
                                     $0.06

Install Retrofit Devices at 25% RP
                                     $0.1
                                     $0.1
                                      $0
                                     $0.08

New gas stove or gas logs at 10% RP
                                     $0.03
                                     $0.4
                                     $0.4
                                     $0.7

New gas stove or gas logs at 25% RP
                                     $0.2
                                     $0.5
                                     $0.9
                                     $5.4
Area Source Fugitive Dust
Chemical Stabilizer at 10% RP
                                     $0.7
                                     $2.2
                                     $1.3
                                     $46.8

Chemical Stabilizer at 25% RP
                                      $0
                                      $0
                                     $1.6
                                     $49.8

Dust Suppressants at 10% RP
                                      $0
                                      $0
                                      $0
                                     $0.02

Dust Suppressants at 25% RP
                                      $0
                                      $0
                                      $0
                                     $5.4

Pave existing shoulders at 10% RP
                                      $0
                                      $0
                                      $0
                                     $7.6

Pave existing shoulders at 25% RP
                                     $31.1
                                     $95.0
                                    $119.6
                                    $755.0

Pave Unpaved Roads at 25% RP
                                     $33.7
                                    $111.8
                                     $69.0
                                    $302.5
Total
 
                                     $94.5
                                    $257.2
                                    $393.3
                                   $1,821.7
Note - The 10% RP and 25% RP indicate the rule penetration percent, or the percent of the non-point (area), residential wood combustion, or area fugitive dust inventory emissions that the control measure is applied to at a specified percent control efficiency.

     As discussed in Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6, for the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] there are remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California - ; the areas include a county in Pennsylvania affected by local sources, 3 counties in border areas, 5 counties in small western mountain valleys, and 13 counties in California's air basins and districts. The characteristics of the air quality challenges for these areas include features of local source-to-monitor impacts, cross-border transport, effects of complex terrain in the west and California, and identifying wildfire influence on projected PM2.5 DVs that could qualify for exclusion as atypical, extreme, or unrepresentative events (USEPA, 2019b). To the extent that state and local areas are able to find alternative lower-cost approaches to reducing emissions, the annualized control costs above may be overestimated. To the extent that additional PM2.5 emissions reductions are required that were not identified in our analysis of these areas, the annualized control costs above may be underestimated. 
Limitations and Uncertainties in Engineering Cost Estimates 
The EPA acknowledges several important limitations of this analysis, which include the following:
 Exclusions from the cost analysis: As mentioned above, recordkeeping, reporting, testing and monitoring costs are not included. The costs some states will incur both designing SIPs and implementing new control strategies to meet a revised standard are also not included. 
 Cost and effectiveness of control measures: We are not able to account for regional or local variation in capital and annual cost items such as energy, labor, or materials. Our estimates of control measure costs may over- or under-estimate the costs depending on how the difficulty of actual retrofitting and equipment life compares with our control assumptions. In addition, our estimates of control efficiencies for the controls assume that the control devices are properly installed and maintained. Further, our estimates of control efficiencies do not account for differences in individual applications as we use a single value for each control that does not account for differences in individual applications  -  sometimes a control operates more or less effectively than the specified efficiency. There is also variability in scale of application that is difficult to reflect for small area sources of emissions.
 Interest rate: Because we obtain control cost data from many sources, we are not always able to obtain consistent data across original data sources. If disaggregated control cost data is available (i.e., where capital, equipment life value, and O&M costs are separated out) we can calculate costs using a specified percent interest rate. The EPA may not know the interest rates used to calculate costs if disaggregated control cost data is unavailable (i.e., where we only have a $/ton value and where capital, equipment life value, and O&M costs are not separated out). In general, we have some disaggregated data available for non-EGU point source controls, but we do not have any disaggregated control cost data for non-point (area) source controls. 
 Differences between ex ante and ex post compliance cost estimates: In comparing regulatory cost estimates before and after regulation, ex ante cost estimate predictions may differ from actual costs. Harrington et al. (2000) surveyed the predicted and actual costs of 28 federal and state rules, including 21 issued by the U.S. Environmental Protection Agency and the Occupational Safety and Health Administration (OSHA). In 14 of the 28 rules, predicted total costs were overestimated, while analysts underestimated costs in three of the remaining rules. In EPA rules where per-unit costs were specifically evaluated, costs of regulations were overestimated in five cases, underestimated in four cases, and accurately estimated in four cases (Harrington et al., 2000). The collection of literature regarding the accuracy of cost estimates seems to reflect these splits. A recent EPA report, the "Retrospective Study of the Costs of EPA Regulations" that examined the compliance costs of five EPA regulations in four case studies, found that several of the case studies suggested that cost estimates were over-estimated ex ante and did not find the evidence to be conclusive. The EPA stated in the report that the small number of regulatory actions covered, as well as significant data and analytical challenges associated with the case studies limited the certainty of this conclusion (U.S. EPA, 2014).
Social Costs
As discussed in EPA's Guidelines for Preparing Economic Analyses, social costs are the total economic burden of a regulatory action (U.S. EPA, 2010). This burden is the sum of all opportunity costs incurred due to the regulatory action, where an opportunity cost is the value lost to society of any goods and services that will not be produced and consumed as a result of reallocating some resources toward pollution mitigation. Estimates of social costs may be compared to the social benefits expected as a result of a regulation to assess its net impact on society.
Computable General Equilibrium (CGE) models are analytical tools that can be used to evaluate the broad impacts of a regulatory action and are therefore often used to estimate social costs. While this section includes a qualitative discussion of social costs and economic impact modeling, CGE modeling was not conducted for this analysis because EPA's current CGE model, discussed later in this section, does not have the resolution needed to accurately model the emissions inventory sectors being controlled (e.g., area fugitive dust inventory sector, residential wood combustion inventory sector). However, the EPA continues to be committed to the use of CGE models to evaluate the economy-wide effects of its regulations.
Economic impacts focus on the behavioral response to the costs imposed by a policy being analyzed. The responses typically analyzed are market changes in prices, quantities produced and purchased, changes in international trade, changes in profitability, facility closures, and employment. Sometimes these behavioral changes can be used to estimate social costs if there is indication that the social costs differ from the estimate of control costs because behavioral change results in other ways of meeting the requirements (e.g., facilities choosing to reduce emissions by producing less rather than adding pollution control devices). 
      Changes in production in a directly regulated sector may have indirect effects on a myriad of other markets when output from that is used as an input in the production of many other goods. It may also affect upstream industries that supply goods and services to the sector, along with labor and capital markets, as these suppliers alter production processes in response to changes in factor prices. In addition, households may change their demand for particular goods and services due to changes in the price of those goods.
      When new regulatory requirements are expected to result in effects outside of regulated and closely related sectors, a key challenge is determining whether they are of sufficient magnitude to warrant explicit evaluation (Hahn and Hird 1990). It is not possible to estimate the magnitude and direction of all of these potential effects outside of the regulated sector(s) without an economy-wide modeling approach. For example, studies of air pollution regulations for the power sector have found that the social costs and benefits may be greater or lower than when secondary market impacts are taken into account, and that the direction of the estimates may depend on the form of the regulation (e.g., Goulder et al. 1999, Williams 2002, Goulder et al. 2016). 
The alternative standard levels analyzed are anticipated to impact multiple markets in many places over time. CGE models are one possible tool for evaluating the impacts of a regulation on the broader economy because this class of models explicitly captures interactions between markets across the entire economy. While a CGE model captures the effects of behavioral responses on the part of consumers or other producers to changes in price that are missed by an engineering estimate of compliance costs, most CGE models do not model the environmental externality or the benefits that accrue to society from mitigating the externality. When benefits from a regulation are expected to be substantial, social cost cannot be interpreted as a complete characterization of economic welfare. To the extent that the benefits affect behavioral responses in markets, the social cost measure may also be potentially biased.
 A CGE-based approach to cost estimation concurrently considers the effect of a regulation across all sectors in the economy. It is structured around the assumption that, for some discrete period of time, an economy can be characterized by a set of equilibrium conditions in which supply equals demand in all markets. When the imposition of a regulation alters conditions in one market, a general equilibrium approach will determine a new set of prices for all markets that will return the economy to equilibrium. These prices in turn determine the outputs and consumption of goods and services in the new equilibrium. In addition, a new set of prices and demands for the factors of production (labor, capital, and land), the returns to which compose the income of businesses and households, will be determined in general equilibrium. The social cost of the regulation can then be estimated by comparing the value of variables in the pre-regulation "baseline" equilibrium with those in the post-regulation, simulated equilibrium.
In 2015, the EPA established a Science Advisory Board (SAB) panel to consider the technical merits and challenges of using economy-wide models to evaluate costs, benefits, and economic impacts in regulatory development. In its final report (U.S. EPA, 2017b), the SAB recommended that the EPA begin to integrate CGE modeling into regulatory analysis to offer a more comprehensive assessment of the effects of air regulations. The SAB noted that CGE models can provide insight into the likely social costs of a regulation even when they do not include a characterization of the likely social benefits of the regulation. CGE models may also offer insights into the ways costs are distributed across regions, industry sectors, or households.
The SAB also noted that the case for using CGE models to evaluate a regulation's effects is strongest when the industry sector has strong linkages to the rest of the economy. The report also noted that the extent to which CGE models add value to the analysis depends on data availability. CGE models provide aggregated representations of the entire economy and are designed to capture substitution possibilities between production, consumption, and trade; interactions between economic sectors; and interactions between a policy shock and pre-existing distortions, such as taxes. However, one also needs to adequately represent a regulation in the model to estimate its effects. 
In response to the SAB's recommendations, the EPA built a new CGE model called SAGE. A second SAB panel performed a peer review of SAGE, and the review concluded in 2020. While the EPA now has a peer-reviewed CGE model for analyzing the potential economy-wide effects of regulations, we did not use the model in the RIA for this proposal, but the EPA continue to be committed to the use of CGE models to evaluate the economy-wide effects of its regulations.
Lastly, the EPA included specific types of health benefits in a CGE model for the prospective analysis, The Benefits and Costs of the Clean Air Act from 1990 to 2020 (EPA 2011), and demonstrated the importance of their inclusion when evaluating the economic welfare effects of policy. However, while the external Council on Clean Air Compliance Analysis (Council) peer review of this the EPA report (Hammitt, 2010) stated that inclusion of benefits in an economy-wide model, specifically adapted for use in that study, "represent[ed] a significant step forward in benefit-cost analysis", serious technical challenges remain when attempting to evaluate the benefits and costs of potential regulatory actions using economy-wide models.

References 
Goulder, L., I. Parry, R. Williams, and D. Burtraw (1999). The cost-effectiveness of alternative instruments for environmental protection in a second-best setting. Journal of Public Economics, 72(3): 329-360. 144
Goulder, L., M. Hafstead, and R. Williams III (2016). General equilibrium impacts of a federal clean energy standard. American Economic Journal  -  Economic Policy 8(2): 186 - 218.
Hahn, R., and J. Hird (1990). The costs and benefits of regulation: review and synthesis. Yale Journal of Regulation 8: 233-278.
Hammitt, J.K. (2010). Review of the final integrated report for the second section 812 prospective study of the benefits and costs of the clean air act. Available at: https://council.epa.gov/ords/sab/apex_util.get_blob?s=3667362893551&a=104&c=45494121954152467&p=18&k1=946&k2=&ck=7rX81JLu6VlfAmvI_4bpsKFHvLUkoKf4dqoGFPV89m2l6Y5SNmFm10LePlqgIx1BH1u8weBKnW7yvfsOXfV25Q&rt=IR.
Harrington, W., R.D. Morgenstern, and P. Nelson (2000). On the accuracy of regulatory cost estimates. Journal of Policy Analysis and Management 19, 297-322.
U.S. EPA (2010). EPA Guidelines for Preparing Economic Analyses. Available at: https://www.epa.gov/sites/default/files/2017-08/documents/ee-0568-50.pdf.
U.S. EPA (2011). The Benefits and Costs of the Clean Air Act from 1990 to 2020. Final Report. Office of Air and Radiation, Washington, DC. Available at: https://www.epa.gov/sites/default/files/2015-07/documents/fullreport_rev_a.pdf.
U.S. EPA (2014). Retrospective Study of the Costs of EPA Regulations: A Report of Four Case Studies. Available at http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0575.pdf/$file/EE-0575.pdf.
U.S. EPA (2017a). EPA Air Pollution Control Cost Manual, Section 1, Chapter 2. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at https://www.epa.gov/sites/default/files/2017-12/documents/epaccmcostestimationmethodchapter_7thedition_2017.pdf.
U.S. EPA (2017b). SAB Advice on the Use of Economy-Wide Models in Evaluating the Social Costs, Benefits, and Economic Impacts of Air Regulations. EPA-SAB-17-012.
U.S. EPA (2019). CoST v3.7 User's Guide. Office of Air Quality Planning and Standards, Research Triangle Park, NC. November 2019. Available at: https://www.cmascenter.org/help/documentation.cfm?model=cost&version=3.7.
Williams III, R. (2002). Environmental tax interactions when pollution affects health or productivity. Journal of Environmental Economics and Management 44(2): 261-270.
 APPENDIX 4A: ENGINEERING COST ANALYSIS
 Overview
Chapter 4 describes the engineering cost analysis approach that EPA used to analyze the following alternative annual and 24-hour standard levels in this regulatory impact analysis (RIA) -- 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] - . This Appendix contains more detailed information about the estimated costs from application of controls by area and by county for the northeast and their adjacent counties, the southeast and their adjacent counties, the west, and California.
 4A.1	Estimated Costs by County for Alternative Standard Levels
The  cost estimates presented in Table 4A-1 through Table 4A-6 reflect the engineering costs annualized at 7 percent, to the extent possible. When calculating the annualized costs we would like to use the interest rates faced by firms; however, we do not know what those rates are. As such we use 7 percent as a conservative estimate.
Table 4A-1 and Table 4A-2 present the cost estimates for the northeast counties and their adjacent counties. Table 4A-3 and Table 4A-4 present the cost estimates for the northeast counties and their adjacent counties. Table 4A-5 presents the cost estimates for the counties in the west, and Table 4A-6 presents the cost estimates for the counties in California, organized by air district.
Table 4A-1	Summary of Estimated Annual Control Costs for the Northeast (57 counties) for Alternative Primary Standard Levels of 10/35 g/m[3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/m[3] for 2032 (millions of 2017$)
County
                                     10/35
                                     10/30
                                     9/35
                                     8/35
New Castle County, DE
                                      $0
                                      $0
                                      $0
                                     $0.8
Cook County, IL
                                      $0
                                      $0
                                     $2.1
                                     $13.7
Madison County, IL
                                      $0
                                      $0
                                      $0
                                     $23.8
St. Clair County, IL
                                      $0
                                      $0
                                      $0
                                     $48.8
Allen County, IN
                                      $0
                                      $0
                                      $0
                                     $0.2
Clark County, IN
                                      $0
                                      $0
                                      $0
                                     $1.9
Elkhart County, IN
                                      $0
                                      $0
                                      $0
                                     $9.7
Floyd County, IN
                                      $0
                                      $0
                                      $0
                                     $0.2
Lake County, IN
                                      $0
                                      $0
                                      $0
                                     $0.7
Marion County, IN
                                      $0
                                      $0
                                     $31.1
                                     $31.1
St. Joseph County, IN
                                      $0
                                      $0
                                      $0
                                     $10.0
Vanderburgh County, IN
                                      $0
                                      $0
                                      $0
                                     $7.4
Vigo County, IN
                                      $0
                                      $0
                                      $0
                                     $6.6
Jefferson County, KY
                                      $0
                                      $0
                                      $0
                                     $22.0
Baltimore city, MD
                                      $0
                                      $0
                                      $0
                                     $0.3
Howard County, MD
                                      $0
                                      $0
                                      $0
                                     $10.0
Kent County, MI
                                      $0
                                      $0
                                      $0
                                     $1.3
Wayne County, MI
                                     $0.02
                                     $0.02
                                     $15.1
                                     $15.1
Buchanan County, MO
                                      $0
                                      $0
                                      $0
                                     $0.9
Jackson County, MO
                                      $0
                                      $0
                                      $0
                                     $0.07
Jefferson County, MO
                                      $0
                                      $0
                                      $0
                                     $1.4
St. Louis city, MO
                                      $0
                                      $0
                                      $0
                                     $10.5
St. Louis County, MO
                                      $0
                                      $0
                                      $0
                                     $11.0
Camden County, NJ
                                      $0
                                      $0
                                     $6.6
                                     $6.6
Union County, NJ
                                      $0
                                      $0
                                      $0
                                     $8.3
New York County, NY
                                      $0
                                      $0
                                      $0
                                     $4.0
Butler County, OH
                                      $0
                                      $0
                                     $13.3
                                     $31.8
Cuyahoga County, OH
                                     $0.4
                                     $0.4
                                     $23.5
                                     $23.5
Franklin County, OH
                                      $0
                                      $0
                                      $0
                                     $0.5
Hamilton County, OH
                                      $0
                                      $0
                                      $0
                                     $16.9
Jefferson County, OH
                                      $0
                                      $0
                                     $1.0
                                     $1.0
Lucas County, OH
                                      $0
                                      $0
                                      $0
                                     $11.5
Mahoning County, OH
                                      $0
                                      $0
                                      $0
                                     $0.6
Stark County, OH
                                      $0
                                      $0
                                      $0
                                     $18.4
Summit County, OH
                                      $0
                                      $0
                                      $0
                                     $11.5
Allegheny County, PA
                                     $6.8
                                     $12.3
                                     $60.3
                                     $65.8
Armstrong County, PA
                                      $0
                                      $0
                                     $4.1
                                     $4.1
Beaver County, PA
                                      $0
                                      $0
                                      $0
                                     $7.5
Berks County, PA
                                      $0
                                      $0
                                      $0
                                     $0.4
Cambria County, PA
                                      $0
                                      $0
                                     $0.2
                                     $5.5
Chester County, PA
                                      $0
                                      $0
                                      $0
                                     $17.1
Dauphin County, PA
                                      $0
                                      $0
                                      $0
                                     $1.4
Delaware County, PA
                                      $0
                                      $0
                                     $15.8
                                     $15.8
Lackawanna County, PA
                                      $0
                                      $0
                                      $0
                                     $0.08
Lancaster County, PA
                                     $0.08
                                     $0.08
                                     $8.1
                                     $27.2
Lebanon County, PA
                                      $0
                                      $0
                                     $0.2
                                     $5.6
Lehigh County, PA
                                      $0
                                      $0
                                      $0
                                     $0.4
Mercer County, PA
                                      $0
                                      $0
                                      $0
                                     $6.3
Philadelphia County, PA
                                      $0
                                      $0
                                     $2.2
                                     $22.5
Washington County, PA
                                      $0
                                      $0
                                      $0
                                     $1.2
York County, PA
                                      $0
                                      $0
                                      $0
                                     $1.6
Providence County, RI
                                      $0
                                      $0
                                      $0
                                     $1.0
Davidson County, TN
                                      $0
                                      $0
                                      $0
                                     $0.4
Knox County, TN
                                      $0
                                      $0
                                      $0
                                     $1.3
Berkeley County, WV
                                      $0
                                      $0
                                      $0
                                     $0.5
Brooke County, WV
                                      $0
                                      $0
                                      $0
                                     $6.4
Marshall County, WV
                                      $0
                                      $0
                                      $0
                                     $6.0
Total
                                     $7.3
                                     $12.8
                                    $183.5
                                    $560.2

Table 4A-2	Summary of Estimated Annual Control Costs for Adjacent Counties in the Northeast (75 counties) for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/m[3] for 2032 (millions of 2017$)
County
Adjacent Counties
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Clinton County, IL
Madison County, IL
St. Clair County, IL
                                      $0
                                      $0
                                      $0
                                     $7.1
DuPage County, IL
Cook County, IL
                                      $0
                                      $0
                                      $0
                                     $1.5
Kane County, IL
Cook County, IL
                                      $0
                                      $0
                                      $0
                                     $1.1
Lake County, IL
Cook County, IL
                                      $0
                                      $0
                                      $0
                                     $11.3
McHenry County, IL
Cook County, IL
                                      $0
                                      $0
                                      $0
                                     $0.9
Monroe County, IL
St. Clair County, IL
                                      $0
                                      $0
                                      $0
                                     $6.7
Randolph County, IL
St. Clair County, IL
                                      $0
                                      $0
                                      $0
                                     $4.8
Washington County, IL
St. Clair County, IL
                                      $0
                                      $0
                                      $0
                                     $6.7
Will County, IL
Cook County, IL
                                      $0
                                      $0
                                      $0
                                     $11.7
Boone County, IN
Marion County, IN
                                      $0
                                      $0
                                     $0.0
                                     $3.9
Clay County, IN
Vigo County, IN
                                      $0
                                      $0
                                      $0
                                     $2.2
Gibson County, IN
Vanderburgh County, IN
                                      $0
                                      $0
                                      $0
                                     $0.2
Hamilton County, IN
Marion County, IN
                                      $0
                                      $0
                                     $0.01
                                     $11.1
Hancock County, IN
Marion County, IN
                                      $0
                                      $0
                                     $0.0
                                     $3.8
Hendricks County, IN
Marion County, IN
                                      $0
                                      $0
                                     $0.02
                                     $10.3
Johnson County, IN
Marion County, IN
                                      $0
                                      $0
                                     $0.0
                                     $5.8
LaPorte County, IN
St. Joseph County, IN
                                      $0
                                      $0
                                      $0
                                     $8.9
Marshall County, IN
Elkhart County, IN
St. Joseph County, IN
                                      $0
                                      $0
                                      $0
                                     $5.8
Morgan County, IN
Marion County, IN
                                      $0
                                      $0
                                     $0.01
                                     $7.4
Parke County, IN
Vigo County, IN
                                      $0
                                      $0
                                      $0
                                     $1.3
Posey County, IN
Vanderburgh County, IN
                                      $0
                                      $0
                                      $0
                                     $3.9
Shelby County, IN
Marion County, IN
                                      $0
                                      $0
                                     $0.0
                                     $10.1
Starke County, IN
St. Joseph County, IN
                                      $0
                                      $0
                                      $0
                                     $1.5
Sullivan County, IN
Vigo County, IN
                                      $0
                                      $0
                                      $0
                                     $2.8
Vermillion County, IN
Vigo County, IN
                                      $0
                                      $0
                                      $0
                                     $0.06
Warrick County, IN
Vanderburgh County, IN
                                      $0
                                      $0
                                      $0
                                     $4.8
Bullitt County, KY
Jefferson County, KY
                                      $0
                                      $0
                                      $0
                                     $0.08
Hardin County, KY
Jefferson County, KY
                                      $0
                                      $0
                                      $0
                                     $0.1
Oldham County, KY
Jefferson County, KY
                                      $0
                                      $0
                                      $0
                                     $0.06
Shelby County, KY
Jefferson County, KY
                                      $0
                                      $0
                                      $0
                                     $0.07
Spencer County, KY
Jefferson County, KY
                                      $0
                                      $0
                                      $0
                                     $0.06
Montgomery County, MD
Howard County, MD
                                      $0
                                      $0
                                      $0
                                     $0.0
Macomb County, MI
Wayne County, MI
                                      $0
                                      $0
                                     $0.3
                                     $15.8
Monroe County, MI
Wayne County, MI
                                      $0
                                      $0
                                     $0.9
                                     $14.4
Oakland County, MI
Wayne County, MI
                                      $0
                                      $0
                                     $0.2
                                     $30.5
Washtenaw County, MI
Wayne County, MI
                                      $0
                                      $0
                                     $0.2
                                     $14.9
Atlantic County, NJ
Camden County, NJ
                                      $0
                                      $0
                                     $0.01
                                     $6.4
Burlington County, NJ
Camden County, NJ
                                      $0
                                      $0
                                     $0.02
                                     $10.3
Essex County, NJ
Union County, NJ
                                      $0
                                      $0
                                      $0
                                     $8.0
Gloucester County, NJ
Camden County, NJ
                                      $0
                                      $0
                                     $0.03
                                     $8.1
Hudson County, NJ
Union County, NJ
                                      $0
                                      $0
                                      $0
                                     $4.3
Middlesex County, NJ
Union County, NJ
                                      $0
                                      $0
                                      $0
                                     $13.5
Morris County, NJ
Union County, NJ
                                      $0
                                      $0
                                      $0
                                     $8.7
Somerset County, NJ
Union County, NJ
                                      $0
                                      $0
                                      $0
                                     $1.5
Bronx County, NY
New York County, NY
                                      $0
                                      $0
                                      $0
                                     $5.3
Kings County, NY
New York County, NY
                                      $0
                                      $0
                                      $0
                                     $10.4
Queens County, NY
New York County, NY
                                      $0
                                      $0
                                      $0
                                     $12.6
Belmont County, OH
Jefferson County, OH
                                      $0
                                      $0
                                     $0.7
                                     $7.1
Carroll County, OH
Jefferson County, OH
Stark County, OH
                                      $0
                                      $0
                                     $0.3
                                     $4.7
Clermont County, OH
Hamilton County, OH
                                      $0
                                      $0
                                      $0
                                     $9.2
Columbiana County, OH
Jefferson County, OH
Mahoning County, OH
Stark County, OH
                                      $0
                                      $0
                                     $1.6
                                     $7.6
Geauga County, OH
Cuyahoga County, OH
Summit County, OH
                                      $0
                                      $0
                                     $0.01
                                     $10.8
Harrison County, OH
Jefferson County, OH
                                      $0
                                      $0
                                     $0.05
                                     $9.1
Lake County, OH
Cuyahoga County, OH
                                      $0
                                      $0
                                     $0.01
                                     $6.0
Lorain County, OH
Cuyahoga County, OH
                                      $0
                                      $0
                                     $0.6
                                     $11.8
Medina County, OH
Cuyahoga County, OH
Summit County, OH
                                      $0
                                      $0
                                     $0.01
                                     $13.0
Montgomery County, OH
Butler County, OH
                                      $0
                                      $0
                                      $0
                                     $14.2
Portage County, OH
Cuyahoga County, OH
Mahoning County, OH
Stark County, OH
Summit County, OH
                                      $0
                                      $0
                                     $0.04
                                     $11.7
Preble County, OH
Butler County, OH
                                      $0
                                      $0
                                      $0
                                     $5.9
Warren County, OH
Butler County, OH
Hamilton County, OH
                                      $0
                                      $0
                                      $0
                                     $9.6
Bedford County, PA
Cambria County, PA
                                      $0
                                      $0
                                      $0
                                     $4.9
Blair County, PA
Cambria County, PA
                                      $0
                                      $0
                                      $0
                                     $8.3
Bucks County, PA
Lehigh County, PA
Philadelphia County, PA
                                      $0
                                      $0
                                      $0
                                     $14.1
Butler County, PA
Allegheny County, PA
Armstrong County, PA
Beaver County, PA
Mercer County, PA
                                      $0
                                      $0
                                     $0.03
                                     $14.5
Clarion County, PA
Armstrong County, PA
                                      $0
                                      $0
                                     $0.0
                                     $3.4
Clearfield County, PA
Cambria County, PA
                                      $0
                                      $0
                                      $0
                                     $5.6
Indiana County, PA
Armstrong County, PA
Cambria County, PA
                                      $0
                                      $0
                                     $0.06
                                     $6.8
Jefferson County, PA
Armstrong County, PA
                                      $0
                                      $0
                                     $0.0
                                     $6.9
Montgomery County, PA
Berks County, PA
Chester County, PA
Delaware County, PA
Lehigh County, PA
Philadelphia County, PA
                                      $0
                                      $0
                                     $17.2
                                     $17.2
Schuylkill County, PA
Berks County, PA
Dauphin County, PA
Lebanon County, PA
Lehigh County, PA
                                      $0
                                      $0
                                      $0
                                     $7.2
Somerset County, PA
Cambria County, PA
                                      $0
                                      $0
                                      $0
                                     $5.2
Westmoreland County, PA
Allegheny County, PA
Armstrong County, PA
Cambria County, PA
Washington County, PA
                                      $0
                                      $0
                                     $0.03
                                     $17.4
Hancock County, WV
Brooke County, WV
                                      $0
                                      $0
                                      $0
                                     $0.9
Ohio County, WV
Brooke County, WV
Marshall County, WV
                                      $0
                                      $0
                                      $0
                                     $4.4
Wetzel County, WV
Marshall County, WV
                                      $0
                                      $0
                                      $0
                                     $1.4
Total
 
                                      $0
                                      $0
                                     $22.3
                                    $539.7

Table 4A-3	Summary of Estimated Annual Control Costs for the Southeast (35 counties) for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/m[3] for 2032 (millions of 2017$)
County
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Jefferson County, AL
                                      $0
                                      $0
                                     $0.7
                                     $4.5
Talladega County, AL
                                      $0
                                      $0
                                      $0
                                     $0.3
Pulaski County, AR
                                      $0
                                      $0
                                      $0
                                     $12.8
Union County, AR
                                      $0
                                      $0
                                      $0
                                     $0.3
District of Columbia
                                      $0
                                      $0
                                      $0
                                     $7.1
Bibb County, GA
                                      $0
                                      $0
                                      $0
                                     $7.8
Clayton County, GA
                                      $0
                                      $0
                                      $0
                                     $5.4
Cobb County, GA
                                      $0
                                      $0
                                      $0
                                     $0.3
DeKalb County, GA
                                      $0
                                      $0
                                      $0
                                     $0.3
Dougherty County, GA
                                      $0
                                      $0
                                      $0
                                     $2.4
Floyd County, GA
                                      $0
                                      $0
                                      $0
                                     $15.4
Fulton County, GA
                                      $0
                                      $0
                                     $3.1
                                     $29.9
Gwinnett County, GA
                                      $0
                                      $0
                                      $0
                                     $0.1
Muscogee County, GA
                                      $0
                                      $0
                                      $0
                                     $8.5
Richmond County, GA
                                      $0
                                      $0
                                      $0
                                     $5.6
Wilkinson County, GA
                                      $0
                                      $0
                                      $0
                                     $14.0
Wyandotte County, KS
                                      $0
                                      $0
                                      $0
                                     $0.2
Caddo Parish, LA
                                      $0
                                      $0
                                     $2.9
                                     $16.7
East Baton Rouge Parish, LA
                                      $0
                                      $0
                                      $0
                                     $2.9
Iberville Parish, LA
                                      $0
                                      $0
                                      $0
                                     $0.02
St. Bernard Parish, LA
                                      $0
                                      $0
                                      $0
                                     $0.9
West Baton Rouge Parish, LA
                                      $0
                                      $0
                                      $0
                                     $11.7
Hinds County, MS
                                      $0
                                      $0
                                      $0
                                     $0.2
Davidson County, NC
                                      $0
                                      $0
                                      $0
                                     $3.3
Mecklenburg County, NC
                                      $0
                                      $0
                                      $0
                                     $0.5
Wake County, NC
                                      $0
                                      $0
                                      $0
                                     $0.3
Tulsa County, OK
                                      $0
                                      $0
                                      $0
                                     $0.4
Greenville County, SC
                                      $0
                                      $0
                                      $0
                                     $0.6
Cameron County, TX
                                      $0
                                      $0
                                     $11.2
                                     $11.2
Dallas County, TX
                                      $0
                                      $0
                                      $0
                                     $0.2
El Paso County, TX
                                      $0
                                      $0
                                     $0.2
                                     $4.7
Harris County, TX
                                     $1.4
                                     $1.4
                                     $5.5
                                     $25.0
Hidalgo County, TX
                                     $2.7
                                     $2.7
                                     $26.6
                                     $26.6
Nueces County, TX
                                      $0
                                      $0
                                      $0
                                     $25.4
Travis County, TX
                                      $0
                                      $0
                                     $0.2
                                     $4.9
Total
                                     $4.1
                                     $4.1
                                     $50.4
                                    $250.6

Table 4A-4	Summary of Estimated Annual Control Costs for Adjacent Counties in the Southeast (32 counties) for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/m[3] for 2032 (millions of 2017$)
County
Adjacent Counties
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Bartow County, GA
Cobb County, GA
Floyd County, GA
                                      $0
                                      $0
                                      $0
                                     $8.4
Carroll County, GA
Fulton County, GA
                                      $0
                                      $0
                                      $0
                                     $11.1
Chattahoochee County, GA
Muscogee County, GA
                                      $0
                                      $0
                                      $0
                                     $1.9
Chattooga County, GA
Floyd County, GA
                                      $0
                                      $0
                                      $0
                                     $4.0
Cherokee County, GA
Cobb County, GA
Fulton County, GA
                                      $0
                                      $0
                                      $0
                                     $10.3
Coweta County, GA
Fulton County, GA
                                      $0
                                      $0
                                      $0
                                     $7.8
Crawford County, GA
Bibb County, GA
                                      $0
                                      $0
                                      $0
                                     $3.0
Douglas County, GA
Cobb County, GA
Fulton County, GA
                                      $0
                                      $0
                                      $0
                                     $5.0
Fayette County, GA
Clayton County, GA
Fulton County, GA
                                      $0
                                      $0
                                      $0
                                     $4.1
Forsyth County, GA
Fulton County, GA
Gwinnett County, GA
                                      $0
                                      $0
                                      $0
                                     $6.8
Gordon County, GA
Floyd County, GA
                                      $0
                                      $0
                                      $0
                                     $5.3
Harris County, GA
Muscogee County, GA
                                      $0
                                      $0
                                      $0
                                     $7.0
Henry County, GA
Clayton County, GA
DeKalb County, GA
                                      $0
                                      $0
                                      $0
                                     $5.9
Houston County, GA
Bibb County, GA
                                      $0
                                      $0
                                      $0
                                     $11.0
Jones County, GA
Bibb County, GA
Wilkinson County, GA
                                      $0
                                      $0
                                      $0
                                     $6.1
Monroe County, GA
Bibb County, GA
                                      $0
                                      $0
                                      $0
                                     $6.1
Polk County, GA
Floyd County, GA
                                      $0
                                      $0
                                      $0
                                     $4.1
Spalding County, GA
Clayton County, GA
                                      $0
                                      $0
                                      $0
                                     $4.0
Talbot County, GA
Muscogee County, GA
                                      $0
                                      $0
                                      $0
                                     $2.9
Twiggs County, GA
Bibb County, GA
Wilkinson County, GA
                                      $0
                                      $0
                                      $0
                                     $5.9
Walker County, GA
Floyd County, GA
                                      $0
                                      $0
                                      $0
                                     $4.8
Bossier Parish, LA
Caddo Parish, LA
                                      $0
                                      $0
                                      $0
                                     $8.0
De Soto Parish, LA
Caddo Parish, LA
                                      $0
                                      $0
                                      $0
                                     $8.2
East Feliciana Parish, LA
East Baton Rouge Parish, LA
West Baton Rouge Parish, LA
                                      $0
                                      $0
                                      $0
                                     $4.2
Pointe Coupee Parish, LA
Iberville Parish, LA
West Baton Rouge Parish, LA
                                      $0
                                      $0
                                      $0
                                     $5.9
Red River Parish, LA
Caddo Parish, LA
                                      $0
                                      $0
                                      $0
                                     $5.4
West Feliciana Parish, LA
West Baton Rouge Parish, LA
                                      $0
                                      $0
                                      $0
                                     $7.8
Brooks County, TX
Hidalgo County, TX
                                      $0
                                      $0
                                     $6.6
                                     $6.6
Hudspeth County, TX
El Paso County, TX
                                      $0
                                      $0
                                      $0
                                     $3.3
Kenedy County, TX
Hidalgo County, TX
                                      $0
                                      $0
                                     $4.3
                                     $4.3
Starr County, TX
Hidalgo County, TX
                                      $0
                                      $0
                                     $5.1
                                     $5.1
Willacy County, TX
Cameron County, TX
Hidalgo County, TX
                                      $0
                                      $0
                                     $2.3
                                     $2.3
Total
 
                                      $0
                                      $0
                                     $18.2
                                    $186.5

Table 4A-5	Summary of Estimated Annual Control Costs for the West (36 counties) for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/m[3] for 2032 (millions of 2017$)
County
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Maricopa County, AZ
                                      $0
                                      $0
                                     $1.4
                                     $7.4
Pinal County, AZ
                                      $0
                                     $1.0
                                      $0
                                     $0.3
Santa Cruz County, AZ
                                      $0
                                      $0
                                      $0
                                     $0.09
Denver County, CO
                                      $0
                                      $0
                                      $0
                                     $2.2
Weld County, CO
                                      $0
                                      $0
                                      $0
                                     $0.05
Benewah County, ID
                                      $0
                                     $12.1
                                     $12.1
                                     $12.1
Canyon County, ID
                                      $0
                                     $1.1
                                      $0
                                     $9.7
Lemhi County, ID
                                      $0
                                      $0
                                      $0
                                      $0
Shoshone County, ID
                                      $0
                                      $0
                                      $0
                                      $0
Lewis and Clark County, MT
                                      $0
                                     $3.1
                                      $0
                                      $0
Lincoln County, MT
                                     $19.0
                                     $19.0
                                     $19.0
                                     $19.0
Missoula County, MT
                                      $0
                                      $0
                                     $1.1
                                     $15.8
Ravalli County, MT
                                      $0
                                     $3.0
                                      $0
                                     $0.1
Silver Bow County, MT
                                      $0
                                     $0.03
                                      $0
                                     $7.8
Douglas County, NE
                                      $0
                                      $0
                                      $0
                                     $0.02
Sarpy County, NE
                                      $0
                                      $0
                                      $0
                                     $0.1
Dona Ana County, NM
                                      $0
                                      $0
                                      $0
                                     $6.9
Clark County, NV
                                      $0
                                      $0
                                     $0.3
                                     $5.5
Crook County, OR
                                      $0
                                     $19.3
                                      $0
                                     $5.4
Harney County, OR
                                      $0
                                     $1.5
                                      $0
                                     $13.4
Jackson County, OR
                                      $0
                                      $0
                                     $0.3
                                     $8.4
Klamath County, OR
                                      $0
                                     $0.5
                                      $0
                                     $6.2
Lake County, OR
                                      $0
                                      $0
                                      $0
                                      $0
Lane County, OR
                                      $0
                                      $0
                                      $0
                                     $0.04
Box Elder County, UT
                                      $0
                                     $14.6
                                      $0
                                      $0
Cache County, UT
                                      $0
                                     $22.0
                                      $0
                                      $0
Davis County, UT
                                      $0
                                     $7.1
                                      $0
                                      $0
Salt Lake County, UT
                                      $0
                                     $16.0
                                      $0
                                      $0
Utah County, UT
                                      $0
                                     $12.3
                                      $0
                                      $0
Weber County, UT
                                      $0
                                     $3.2
                                      $0
                                      $0
King County, WA
                                      $0
                                      $0
                                      $0
                                     $0.8
Kittitas County, WA
                                      $0
                                      $0
                                      $0
                                      $0
Okanogan County, WA
                                      $0
                                     $13.3
                                      $0
                                      $0
Snohomish County, WA
                                      $0
                                     $0.7
                                      $0
                                      $0
Spokane County, WA
                                      $0
                                      $0
                                      $0
                                     $0.4
Yakima County, WA
                                      $0
                                      $0
                                      $0
                                      $0
Total
                                     $19.0
                                    $150.0
                                     $34.2
                                    $121.8

Table 4A-6	Summary of Estimated Annual Control Costs for California (26 counties) for Alternative Primary Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/m[3] for 2032 (millions of 2017$)
County
Air District
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Alameda County, CA
Bay Area AQMD
                                     $0.03
                                     $0.03
                                     $2.0
                                     $14.5
Contra Costa County, CA
Bay Area AQMD
                                      $0
                                      $0
                                     $0.04
                                     $0.4
Marin County, CA
Bay Area AQMD
                                      $0
                                      $0
                                      $0
                                     $0.05
Napa County, CA
Bay Area AQMD
                                     $0.2
                                     $0.2
                                     $1.7
                                     $1.7
Santa Clara County, CA
Bay Area AQMD
                                      $0
                                      $0
                                     $1.0
                                     $16.8
Solano County, CA
Bay Area AQMD
                                      $0
                                      $0
                                      $0
                                     $6.7
Butte County, CA
Butte County AQMD
                                      $0
                                      $0
                                      $0
                                     $0.3
Sutter County, CA
Feather River AQMD
                                      $0
                                      $0
                                      $0
                                     $3.6
Imperial County, CA
Imperial County APCD
                                      $0
                                      $0
                                      $0
                                      $0
Plumas County, CA
Northern Sierra AQMD
                                      $0
                                      $0
                                      $0
                                      $0
Sacramento County, CA
Sacramento Metro AQMD
                                      $0
                                     $0.2
                                     $0.4
                                     $2.3
San Diego County, CA
San Diego County APCD
                                      $0
                                      $0
                                     $0.7
                                     $30.3
Fresno County, CA
San Joaquin Valley APCD
                                     $30.1
                                     $30.1
                                     $30.1
                                     $30.1
Kern County, CA
San Joaquin Valley APCD
                                      $0
                                      $0
                                      $0
                                      $0
Kings County, CA
San Joaquin Valley APCD
                                      $0
                                      $0
                                      $0
                                      $0
Madera County, CA
San Joaquin Valley APCD
                                     $9.1
                                     $9.1
                                     $9.1
                                     $9.1
Merced County, CA
San Joaquin Valley APCD
                                     $9.0
                                     $9.0
                                     $9.0
                                     $9.0
San Joaquin County, CA
San Joaquin Valley APCD
                                     $0.03
                                     $0.03
                                     $11.9
                                     $11.9
Stanislaus County, CA
San Joaquin Valley APCD
                                     $2.9
                                     $2.9
                                     $2.9
                                     $2.9
Tulare County, CA
San Joaquin Valley APCD
                                      $0
                                      $0
                                      $0
                                      $0
San Luis Obispo County, CA
San Luis Obispo County APCD
                                      $0
                                      $0
                                     $1.2
                                     $1.2
Siskiyou County, CA
Siskiyou County APCD
                                      $0
                                     $16.9
                                      $0
                                      $0
Los Angeles County, CA
South Coast AQMD
                                     $12.9
                                     $12.9
                                     $12.9
                                     $12.9
Riverside County, CA
South Coast AQMD
                                      $0
                                      $0
                                      $0
                                      $0
San Bernardino County, CA
South Coast AQMD
                                      $0
                                      $0
                                      $0
                                      $0
Ventura County, CA
Ventura County APCD
                                      $0
                                     $9.1
                                     $1.8
                                     $9.1
Total
 
                                     $64.1
                                     $90.4
                                     $84.7
                                    $162.9

BENEFITS ANALYSIS APPROACH AND RESULTS
 Overview
This chapter presents the estimated human health-related and welfare benefits of meeting the proposed National Ambient Air Quality Standards (NAAQS) for particulate matter (PM). In this Regulatory Impact Analysis (RIA), we are analyzing the proposed annual and current 24-hour alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/m[3], as well as the following two more stringent alternative standard levels: (1) an alternative annual standard level of 8 g/ - m - [3] -  in combination with the current 24-hour standard (i.e., 8/35 g/ - m - [3] - ), and (2) an alternative 24-hour standard level of 30 g/ - m - [3] in combination with the proposed annual standard level of 10 g/ - m - [3] -  (i.e., 10/30 g/ - m - [3] - ). We quantify the number and economic value of the estimated avoided premature deaths and illnesses attributable to applying hypothetical national control strategies for the more stringent annual PM2.5 NAAQS standards with a sensitivity analysis for a more stringent 24-hour standard that reduces fine particulate matter (PM2.5) concentrations in 2032. Reducing directly emitted PM2.5 and PM2.5 precursor emissions would also improve environmental quality (U.S. EPA, 2019c, U.S. EPA, 2022a) and reduce the ecological effects of nitrogen and sulfur deposition. Because the EPA is proposing that the current secondary PM NAAQS standards be retained, we did not evaluate alternative secondary standard levels in this RIA, or any visibility-, climate change-, or materials-damage-related benefits of the proposed rule (Cox, 2019, U.S. EPA, 2019c).
The analysis in this chapter aims to characterize the benefits of the air quality changes resulting from the implementation of revised PM standard levels by answering two key questions:
 What is the estimated number and geographic distribution of avoided PM2.5-attributable premature deaths and illnesses expected to result from applying hypothetical national control strategies for a more stringent PM2.5 NAAQS? This chapter presents these results. As discussed in Chapter 3, Section 3.2.5, the estimated PM2.5 emissions reductions from control applications do not fully account for all the emissions reductions needed to reach the proposed and more stringent alternative standard levels in some counties in the northeast, southeast, west, and California. In Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6, we discuss the remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California for the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - .
 What is the estimated number and geographic distribution of avoided PM2.5-attributable premature deaths and illnesses expected to result if we assume that areas identify all of the controls needed for compliance with the proposed and alternative PM2.5 NAAQS? Appendix 5A presents these results.
 What is the estimated economic value of these avoided impacts?
To answer these questions we perform a human health benefits analysis (NRC, 2002). Starting first with the Integrated Science Assessment (ISA) for Particulate Matter (U.S. EPA, 2019b) and the Supplement to the ISA for Particulate Matter (U.S. EPA, 2022a), we identify the human health effects associated with ambient particles, which include premature death and a variety of morbidity effects associated with acute (hours-long) and chronic (months- or years- long) exposures. Table 5-2 summarizes human health categories monetized and reflected in the total value of the benefits reported and those categories not monetized due to limited data or resources. The list of benefits categories is neither exhaustive nor completely quantified. We excluded effects not identified as having a causal or likely to be causal relationship with the affected pollutants in the most recent PM ISA (U.S. EPA, 2019b,U.S. EPA, 2022a). In a Technical Support Document (TSD) accompanying this RIA we specify in detail our approach for identifying, selecting, and parametrizing concentration-response relationships and economic unit values to support this benefits analysis. Below in Section 5.1 we summarize this information for readers, describing how we updated our methods for quantifying the number and value of PM-related benefits to reflect the information reported in the PM ISA and supplement to the PM ISA. 
This chapter contains a subset of the estimated health benefits  of the proposed and alternative PM2.5 standard levels in 2032 that EPA was able to quantify, given available resources and methods. This benefits analysis relies on an array of data inputs -- including air quality modeling, health impact functions and valuation estimates -- which are themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in this analysis. We employ several techniques to characterize this uncertainty, which are described in detail in section 5.5. 
As described in Chapter 1, the analytical objectives of the NAAQS RIA are unique as compared to other RIAs, such as the recent Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2020c). The NAAQS RIAs illustrate the potential costs and benefits of attaining one or more revised air quality standard(s) nationwide; these estimated costs and benefits are estimated after we first assume the current standards have been attained. In this RIA, we illustrate the potential costs and benefits for the proposed and more stringent alternative standard levels nationwide. The NAAQS RIAs hypothesize the control strategies that States may choose to enact when implementing a revised NAAQS, but they cannot do so with perfect foresight; individual states will formulate air quality management plans whose mix of emissions controls may differ substantially from those we simulate here. Hence, NAAQS RIAs are illustrative. The benefits and costs estimated in a NAAQS RIA are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and emissions reductions. By contrast, EPA is generally confident in the emissions projected to be reduced from rules affecting specific and well-characterized sources -- such as mobile and Electric Generating Units (U.S. EPA, 2019a). Hence, the emissions reduced by final rules affecting such sources are accounted for when simulating attainment with alternative NAAQS. 
In the following sections of this chapter, we estimate health benefits occurring as an increment to a 2032 baseline in which the nation fully attains the current primary PM2.5 standards (i.e., an annual standard of 12 ug/m[3] and a 24-hour standard of 35 ug/m[3], hereafter referred to as "12/35"). This baseline accounts for: (1) promulgated regulations (Chapter 1, Section 1.3.); and (2) any additional illustrative emissions reductions needed to simulate attainment with 12/35 (Chapter 3, Section 3.1). As above, the baseline for the benefits does not include potential disbenefits associated with increases in wildfires or the increased prescribed burns required in recent statutes. We project PM2.5 levels in 2032 in certain areas would exceed 10/35, 10/30, 9/35 and 8/35, even after illustrative controls applied to simulate attainment with 12/35 and estimate emissions reductions needed to attain the alternative standard levels (Chapter 3, Table 3-2). Table 5-1 summarizes the total national monetized benefits resulting from applying the control strategies in 2032. Since the analyses in the RIA are national-level assessments and the ambient air quality issues are complex and local in nature, we do not currently have sufficiently detailed local information for the areas being analyzed, including local inventory information on emissions sources, higher resolution air quality modeling, and local information on emissions controls to estimate the control measures or strategies that might result in meeting the range of revised annual and 24-hour alternative standard levels in the proposal. 
Whereas the main analysis in this chapter presents the benefits of the applied control strategies for the standards levels (Table 5-5through Table 5-9), in Appendix 5A, we present the potential health and monetized benefits of full compliance with the alternative standard levels; the tables in Appendix 5A present potential health benefits regardless of whether the technology or control measures to achieve them is currently available or whether an agency submits information on cross-border transport or wildfire influence on projected PM2.5 DVs that could potentially qualify for exclusion as atypical, extreme, or unrepresentative events, potentially affecting the amount of any additional control needed. The estimates reflect the value of the avoided PM2.5-attributable deaths and the value of morbidity impacts, including, for example, hospital admissions and emergency department visits for cardiovascular and respiratory health issues.
Table 5-1	Estimated Monetized Benefits of the Applied Control Strategies for the Proposed and Alternative Combinations of Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)
                               Benefits Estimate
                 10 ug/m[3] annual &
35 ug/m[3] 24-hour
                 10 ug/m[3] annual &
30 ug/m[3] 24-hour
                  9 ug/m[3] annual &
35 ug/m[3] 24-hour
                  8 ug/m[3] annual &
35 ug/m[3] 24-hour
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Pope III et al., 2019
  3% discount rate
                                    $17 + B
                                    $20 + B
                                    $43 + B
                                    $95 + B
  7% discount rate
                                    $16 + B
                                    $18 + B
                                    $39 + B
                                    $86 + B
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Wu et al., 2020
  3% discount rate
                                   $8.5 + B
                                   $9.6 + B
                                    $21 + B
                                    $46 + B
  7% discount rate
                                   $7.6 + B
                                   $8.6 + B
                                    $19 + B
                                    $41 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and welfare benefits.

 Because the method used in this analysis to simulate the control strategies does not also simulate changes in ambient concentrations of other pollutants, we were not able to quantify the additional benefits associated with reduced exposure to other pollutants. We also did not estimate the additional benefits from improvements in welfare effects, such as climate effects, ecosystem effects, and visibility (Cox, 2019, U.S. EPA, 2019c). With regard to potential climate benefits, we note that because the available evidence suggests direct PM control measures will be most effective in reducing ambient PM2.5 concentrations, and because we lack information on the CO2-related emissions changes that may result from such measures, we do not quantitatively estimate CO2-related climate benefits in this RIA. 
Updated Methodology Presented in the RIA
In 2021, EPA published a TSD titled "Estimating PM2.5- and Ozone-Attributable Health Benefits" that accompanied the RIA for the Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2021). As noted above, that TSD described the EPA's approach for quantifying the number and value of air pollution-related premature deaths and illnesses. Since publishing the Revised Cross-State Air Pollution Rule Update TSD, the EPA released a Supplement to the PM ISA (U.S. EPA, 2022a). EPA evaluated the new evidence reported in the Supplement to the PM ISA and revised the TSD accordingly; this process is described in detail within the TSD. The updated TSD will be published as a new document alongside this RIA. Key changes from the most recent version of the TSD are summarized below: 
 Incorporated alternative long-term exposure mortality studies. We selected a hazard ratio from an analysis of the National Health Interview Survey (NHIS) (Pope III et al., 2019). Compared to the American Cancer Society study it replaces (Turner et al., 2016), the NHIS cohort reflects more recent years of PM2.5 concentrations and produces a larger number of estimated PM-attributable deaths. We also selected a hazard ratio from an extended analysis of the Medicare cohort (Wu et al., 2020). Compared to the study it replaces (Di et al., 2017), the Wu et al., 2020 analysis includes additional, and more current, years of PM2.5 concentrations and more person-time; this newer study produces a similar number of estimated PM-attributable deaths. We elaborate on our rationale for these choices in section 5.3.3.1 of the TSD.  
 Altered our approach for estimating counts of Acute Myocardial Infarctions. We selected a risk estimate from an analysis of the Medicare cohort (Wei et al., 2019), in which the authors performed a case-crossover analysis of over 95 million Medicare inpatient hospital claims from 2000-2012. The risk estimate from this study replaces a pooled estimate of single- and multi-city studies that accounted for a smaller population, more limited geographic coverage and less recent PM2.5 concentrations; that latter approach yielded a range of estimated non-fatal heart attacks whose upper bound was significantly larger than the estimate reported in this RIA. 
Human Health Benefits Analysis Methods
We estimate the quantity and economic value of air pollution-related effects using a "damage-function." This approach quantifies counts of air pollution-attributable cases of adverse health outcomes and assigns dollar values to those counts, while assuming that each outcome is independent of one another. We construct this damage function by adapting primary research -- specifically, air pollution epidemiology studies and economic value studies -- from similar contexts. This approach is sometimes referred to as "benefits transfer." Below we describe the procedure we follow for: (1) selecting air pollution health endpoints to quantify; (2) calculating counts of air pollution effects using a health impact function; (3) calculating the economic value of the health impacts. 
Selecting Air Pollution Health Endpoints to Quantify
As a first step in quantifying PM2.5-related human health impacts, the Agency consults the most recent PM ISA and the Supplement to the ISA for Particulate Matter (U.S. EPA, 2019b, U.S. EPA, 2022a). This document synthesizes the toxicological, clinical and epidemiological evidence to determine whether PM is causally related to an array of adverse human health outcomes associated with either acute (i.e., hours or days-long) or chronic (i.e., years-long) exposure; for each outcome, the ISA reports this relationship to be causal, likely to be causal, suggestive of a causal relationship, inadequate to infer a causal relationship or not likely to be a causal relationship. Historically, the Agency estimates the incidence of air pollution effects for those health endpoints that the ISA classified as either causal or likely-to-be-causal.
Consistent with economic theory, the willingness-to-pay (WTP) for reductions in exposure to environmental hazard will depend on the expected impact of those reductions on human health and other outcomes. All else equal, WTP is expected to be higher when there is stronger evidence of a causal relationship between exposure to the contaminant and changes in a health outcome (McGartland et al., 2017). For example, in the case where there is no evidence of a potential relationship the WTP would be expected to be zero and the effect should be excluded from the analysis. Alternatively, when there is some evidence of a relationship between exposure and the health outcome, but that evidence is insufficient to definitively conclude that there is a causal relationship, individuals may have a positive WTP for a reduction in exposure to that hazard (U.S. EPA-SAB, 2020; Kivi and Shogren, 2010). Lastly, the WTP for reductions in exposure to pollutants with strong evidence of a relationship between exposure and effect are likely positive and larger than for endpoints where evidence is weak, all else equal. Unfortunately, the economic literature currently lacks a settled approach for accounting for how WTP may vary with uncertainty about causal relationships.
Given this challenge, the Agency draws its assessment of the strength of evidence on the relationship between exposure to PM2.5 and potential health endpoints from the ISAs that are developed for the NAAQS process as discussed above. The focus on categories identified as having a "causal" or "likely to be causal" relationship with the pollutant of interest is to estimate the pollutant-attributable human health benefits in which we are most confident. All else equal, this approach may underestimate the benefits of PM2.5 exposure reductions as individuals may be willing to pay to avoid specific risks where the evidence is insufficient to conclude they are "likely to be caus[ed]" by exposure to these pollutants.[6] At the same time, WTP may be lower for those health outcomes for which causality has not been definitively established. This approach treats relationships with ISA causality determinations of "likely to be causal" as if they were known to be causal, and therefore benefits could be overestimated. Table 5-2 reports the effects we quantified and those we did not quantify in this RIA. The list of benefit categories not quantified is not exhaustive. The table below omits welfare effects such as acidification and nutrient enrichment. 
Table 5-2	Human Health Effects of Pollutants Potentially Affected by Attainment of the Primary PM2.5 NAAQS 
                                   Pollutant
                                 Effect (age)
                               Effect Quantified
                               Effect Monetized
                               More Information
                                     PM2.5
                                       
Adult premature mortality based on cohort study estimates (>17 or >64)
                                      
                                      
PM ISA
                                       
Infant mortality (<1)
                                      
                                      
PM ISA
                                       
Non-fatal heart attacks (>18)
                                      
                                      
PM ISA
                                       
Hospital admissions - cardiovascular (all)
                                      
                                      
PM ISA
                                       
Hospital admissions - respiratory (<19 and >64)
                                      
                                      
PM ISA
                                       
Hospital admissions - Alzheimer's disease (>64)[2]
                                      
                                      
PM ISA
                                       
Hospital admissions - Parkinson's disease (>64) 2
                                      
                                      
PM ISA
                                       
Emergency department visits  -  cardiovascular (all)
                                      
                                      
PM ISA
                                       
Emergency department visits  -  respiratory (all)
                                      
                                      
PM ISA
                                       
Emergency hospital admissions (>65)
                                      
                                      
PM ISA
                                       
Non-fatal lung cancer (>29)[2]
                                      
                                      
PM ISA
                                       
Out-of-hospital cardiac arrest (all)[2]
                                      
                                       -- 
PM ISA
                                       
Stroke incidence (50-79)[2]
                                      
                                      
PM ISA
                                       
New onset asthma (<12)[2]
                                      
                                      
PM ISA
                                       

                                       
                                       

                                       

                                       
                                       

                                       
Exacerbated asthma  -  albuterol inhaler use (asthmatics, 6-13)
                                      
                                      
PM ISA
                                       
Lost work days (18-64)
                                      
                                      
PM ISA
                                       
Minor restricted-activity days (18-64)
                                      
                                       -- 
PM ISA
                                       
Other cardiovascular effects (e.g., doctor's visits, prescription medication)
                                       -- 
 -- 
PM ISA[1]
                                       
Other respiratory effects (e.g., pulmonary function, other ages)
 -- 
 -- 
PM ISA[1]
                                       
Other cancer effects (e.g., mutagenicity, genotoxicity)
 -- 
 -- 
PM ISA[1]
                                       
Other nervous system effects (e.g., dementia)
 -- 
 -- 
PM ISA[1]
                                       
Metabolic effects (e.g., diabetes, metabolic syndrome)
 -- 
 -- 
PM ISA[1]
                                       
Reproductive and developmental effects (e.g., low birth weight, pre-term births)
 -- 
 -- 
PM ISA[1]
1 We assess these benefits qualitatively due to epidemiological or economic data limitations.
[2] Quantified endpoints have been added since the 2021 version of the Estimating PM2.5- and Ozone-Attributable Health Benefits TSD.  Full details of the updates can be found in the TSD published alongside this RIA.

Calculating Counts of Air Pollution Effects Using the Health Impact Function
We use the environmental Benefits Mapping and Analysis Program -- Community Edition (BenMAP-CE) software program to quantify counts of premature deaths and illnesses attributable to photochemical modeled changes in annual mean PM2.5 for the year 2032 using a health impact function (Sacks et al., 2018). A health impact function combines information regarding: the concentration-response relationship between air quality changes and the risk of a given adverse outcome; the population exposed to the air quality change; the baseline rate of death or disease in that population; and, the air pollution concentration to which the population is exposed.
The following provides an example of a PM2.5 mortality risk health impact function. We estimate counts of PM2.5-related total deaths (yij) during each year i (i=2032) among adults aged 18 and older (a) in each county in the contiguous U.S. j (j=1,...,J where J is the total number of counties) as
yij= Σa yija
yija = moija x(e[β∙∆C]ij-1) x Pija,    Eq[1]
where moija is the baseline total mortality rate for adults aged a=18-99 in county j in year i stratified in 10-year age groups, β is the risk coefficient for total mortality for adults associated with annual average PM2.5 exposure, Cij is the annual mean PM2.5 concentration in county j in year i, and Pija is the number of county adult residents aged a=18-99 in county j in year i stratified into 5-year age groups.
To assess economic value in a damage-function framework, the changes in environmental quality must be translated into effects on people or on the things that people value. In some cases, the changes in environmental quality can be directly valued. In other cases, such as for changes in ozone and PM, a health and welfare impact analysis must first be conducted to convert air quality changes into effects that can be assigned dollar values. For the purposes of this RIA, the health impacts analysis is limited to those health effects that are directly and specifically linked to PM2.5.
We note at the outset that EPA rarely has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory analyses. Thus, similar to Künzli et al., 2000 and other, more recent health impact analyses, our estimates are based on the best available methods of benefits transfer. 
Calculating the Economic Valuation of Health Impacts
After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure is therefore ex ante WTP for changes in risk. However, epidemiological studies generally provide estimates of the relative risks of a particular health effect avoided due to a reduction in air pollution. A convenient way to use these data in a consistent framework is to convert probabilities to units of avoided statistical incidences. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a regulation reduces the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 change in risk). The same type of calculation can produce values for statistical incidences of other health endpoints.
For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we instead use the cost of treating or mitigating the effect to economically value the health impact. For example, for the valuation of hospital admissions we use the avoided medical costs as an estimate of the value of avoiding the health effects causing the admission. These cost-of-illness (COI) estimates generally (although not in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect.
Benefits Analysis Data Inputs
In Figure 5-1, we summarize the key data inputs to the health impact and economic valuation estimates, which were calculated using BenMAP-CE model version 1.5.1 (Sacks et al., 2018). In the sections below we summarize the data sources for each of these inputs, including demographic projections, incidence and prevalence rates, effect coefficients, and economic valuation. We indicate where we have updated key data inputs since the benefits analysis conducted for the Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2020c).
                Modeled Baseline and Post-Control Ambient PM2.5


                            PM2.5 Health Functions

                         Economic Valuation Functions

                          2032 Population Projections

                     PM2.5 Incremental Air Quality Change

                         PM2.5-Related Health Impacts

Woods & Poole Population Projections

                   Background Incidence and Prevalence Rates

                       Monetized PM2.5-related Benefits

Blue identifies a user-selected input within the BenMAP model
Green identifies a data input generated outside of the BenMAP model

                            Census Population Data

Modeled Baseline and Post-Control Ambient PM2.5


                            PM2.5 Health Functions

                         Economic Valuation Functions

                          2032 Population Projections

                     PM2.5 Incremental Air Quality Change

                         PM2.5-Related Health Impacts

Woods & Poole Population Projections

                   Background Incidence and Prevalence Rates

                       Monetized PM2.5-related Benefits

Blue identifies a user-selected input within the BenMAP model
Green identifies a data input generated outside of the BenMAP model

                            Census Population Data

Figure 5-1	Data Inputs and Outputs for the BenMAP-CE Model
Demographic Data
Quantified and monetized human health impacts depend on the demographic characteristics of the population, including age, location, and income. We use projections based on economic forecasting models developed by Woods & Poole, Inc. (Woods & Poole, 2015). The Woods & Poole database contains county-level projections of population by age, sex, and race out to 2060, relative to a baseline using the 2010 Census data. Projections in each county are determined simultaneously with every other county in the U.S. to consider patterns of economic growth and migration. The sum of growth in county-level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates (Hollmann et al., 2000). According to Woods & Poole, linking county-level growth projections together and constraining the projected population to a national-level total growth avoids potential errors introduced by forecasting each county independently (for example, the projected sum of county-level populations cannot exceed the national total). County projections are developed in a four-stage process:
 First, national-level variables such as income, employment, and populations are forecasted.
 Second, employment projections are made for 179 economic areas defined by the Bureau of Economic Analysis (U.S. BEA, 2004), using an "export-base" approach, which relies on linking industrial-sector production of non-locally consumed production items, such as outputs from mining, agriculture, and manufacturing with the national economy. The export-based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector.
 Third, population is projected for each economic area based on net migration rates derived from employment opportunities and following a cohort-component method based on fertility and mortality in each area.
 Fourth, employment and population projections are repeated for counties, using the economic region totals as bounds. The age, sex, and race distributions for each region or county are determined by aging the population by single year by sex and race for each year through 2060 based on historical rates of mortality, fertility, and migration.
Baseline Incidence and Prevalence Estimates
Epidemiological studies of the association between pollution levels and adverse health effects generally provide a direct estimate of the relationship of air quality changes to the relative risk of a health effect, rather than estimating the absolute number of avoided cases. For example, a typical result might be that a 5 ug/m[3] decrease in daily PM2.5 levels is associated with a decrease in hospital admissions of 3%. A baseline incidence rate, necessary to convert this relative change into a number of cases, is the estimate of the number of cases of the health effect per year in the assessment location, as it corresponds to baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population number. For example, if the baseline incidence rate is the number of cases per year per million people, that number must be multiplied by the millions of people in the total population.
Table 12 from the TSD (reproduced below as Table 5-3) summarizes the sources of baseline incidence rates and reports average incidence rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, we used age-specific rates where available. We applied concentration-response functions to individual age groups and then summed over the relevant age range to provide an estimate of total population benefits. National-level incidence rates were used for most morbidity endpoints, whereas county-level data are available for premature mortality. Whenever possible, the national rates used are national averages, because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence at the national level. 



Table 5-3	Baseline Incidence Rates for Use in Impact Functions
                                   Endpoint
                                   Parameter
                                     Rates
                                       
                                       
                                     Value
                                    Source
Mortality[1]
Daily or annual projected incidence to 2060 in 5-year increments (0--99)
Age-, cause-, race-, and county-stratified rates
CDC WONDER (2012 - 2014)
U.S. Census Bureau, 2012
Hospitalizations[2]
Daily incidence rates for all ages
Age-, region/state/county-, and cause- stratified rates
2011-2014 HCUP data files and data requested from and supplied by individual states
Emergency Department Visits[2]
Daily emergency department visit incidence rates for all ages
Age-, region-, state-, county-, and cause- stratified rates
2011-2014 HCUP data files and data requested from and supplied by individual states
Nonfatal Acute Myocardial Infarction
Daily nonfatal AMI incidence rate per person aged 18-99
Age-, region-, state-, and county- stratified rates
AHRQ, 2016 
Asthma Symptoms
Daily incidence among asthmatic children

Wheeze (ages 5-12)
Cough (ages 5-12)
Shortness of breath (ages 5-12)
Albuterol use (ages 6-13)
Age- and race- stratified rates


2.2 puffs per day
Ostro et al., 2001



Rabinovitch et al., 2006
Asthma Onset
Annual incidence 
0 - 4
5 - 11
12 - 17
0.0234
0.0111
0.0044
Winer et al., 2012
Alzheimer's Disease
Daily incidence rates for all ages
Age-, region-, state-, and county- stratified rates

2011-2014 HCUP data files
Parkinson's Disease
Annual incidence 
18 - 44
45 - 64
65 - 84
85 - 99
0.0000011
0.0000366
0.0002001
0.0002483
HCUPnet
Allergic Rhinitis
Respondents aged 3-17 experiencing allergic rhinitis/hay fever symptoms within the year prior to the survey
0.192
Parker et al., 2009
Cardiac Arrest
Daily nonfatal incidence rates
0 - 17
18 - 39
40 - 64
65 - 99
0.00000002
0.00000009
0.00000056
0.00000133
Ensor et al., 2013, Rosenthal et al., 2008, Silverman et al., 2010
Lung Cancer
Annual nonfatal incidence
25 - 34
35 - 44
45 - 54
55 - 64
65 - 74
75 - 84
95 - 99
0.000001746
0.000014919
0.000067463
0.000208053
0.000052370
0.000576950
0.000557130
SEER, 2015 and Gharibvand et al., 2017
Stroke
Annual nonfatal incidence in ages 65-99
0.00446
Kloog et al., 2012
Work Loss Days
Daily incidence rate per person (18 - 64)
Aged 18 - 24
Aged 25 - 44
Aged 45 - 64
0.00540
0.00678
0.00492
Adams et al., 1999, Table 41; U.S. Census Bureau, 2000
School Loss Days
Rate per person per year, assuming 180 school days per year
9.9
Adams et al., 1999, Table 47
Minor Restricted-Activity Days
Daily MRAD incidence rate per person (18-64)
0.02137
Ostro and Rothschild, 1989, p. 243
CDC-Centers for Disease Control; NHS-National Health Interview Survey. Detailed references associated with this table are located in the TSD.
[1]Mortality rates are only available in 5-year increments. The Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level hospital and emergency department discharges for a variety of International Classification of Diseases (ICD) codes (AHRQ, 2016). 
[2]Baseline incidence rates now include corrections from the states of Indiana and Montana.

We projected mortality rates such that future mortality rates are consistent with our projections of population growth (U.S. EPA, 2018). To perform this calculation, we began first with an average of 2007-2016 cause-specific mortality rates. Using Census Bureau projected national-level annual mortality rates stratified by age range, we projected these mortality rates to 2060 in 5-year increments (U.S. Census Bureau, 2009, U.S. EPA, 2018). Further information regarding this procedure may be found in the TSD for this RIA and the appendices to the BenMAP user manual (U.S. EPA, 2022b).
The baseline incidence rates for hospital admissions and emergency department visits reflect the revised rates first applied in the Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2021). In addition, we revised the baseline incidence rates for acute myocardial infarction. These revised rates are more recent (AHRQ, 2016) than the rates they replace and more accurately represent the rates at which populations of different ages, and in different locations, visit the hospital and emergency department for air pollution-related illnesses. Lastly, these rates reflect unscheduled hospital admissions only, which represents a conservative assumption that most air pollution-related visits are likely to be unscheduled. If air pollution-related hospital admissions are scheduled, this assumption would underestimate these benefits.
Effect Coefficients
Our approach for selecting and parametrizing effect coefficients for the benefits analysis is described fully in the TSD accompanying this RIA. Because of the substantial economic value associated with estimated counts of PM2.5-attributable deaths, we describe our rationale for selecting among long-term exposure epidemiologic studies below; a detailed description of all remaining endpoints may be found in the TSD. 
PM2.5 Premature Mortality Effect Coefficients for Adults
A substantial body of published scientific literature documents the association between PM2.5 concentrations and the risk of premature death (U.S. EPA, 2019b U.S. EPA, 2022a). This body of literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA, completed as part of this review of the PM standards and reviewed by the Clean Air Scientific Advisory Committee (CASAC) (Sheppard, 2022), concluded that there is a causal relationship between mortality and both long-term and short-term exposure to PM2.5 based on the full body of scientific evidence (U.S. EPA, 2019b U.S. EPA, 2022a). The size of the mortality effect estimates from epidemiologic studies, the serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reduction the most significant health endpoint quantified in this analysis. EPA selects Hazard Ratios from cohort studies to estimate counts of PM-related premature death, following a systematic approach detailed in the TSD accompanying this RIA that is generally consistent with previous RIAs (e.g., U.S. EPA, 2011a, U.S. EPA, 2011b, U.S. EPA, 2011c, U.S. EPA, 2012a, U.S. EPA, 2012b, U.S. EPA, 2015a, U.S. EPA, 2019a). 
As premature mortality typically constitutes the vast majority of monetized benefits in a PM2.5 benefits assessment, quantifying effects using risk estimates reported from multiple long-term exposure studies using different cohorts helps account for uncertainty in the estimated number of PM-related premature deaths. Below we summarize the three identified studies and hazard ratios and then describe our rationale for quantifying premature PM-attributable deaths using two of these studies.
Wu et al., 2020 evaluated the relationship between long-term PM2.5 exposure and all-cause mortality in more than 68.5 million Medicare enrollees (over the age of 64), using Medicare claims data from 2000-2016 representing over 573 million person-years of follow up and over 27 million deaths. This cohort included over 20% of the U.S. population and was, at the time of publishing, the largest air pollution study cohort to date. The authors modeled PM2.5 exposure at a 1-km[2] grid resolution using a hybrid ensemble-based prediction model that combined three machine learning models and relied on satellite data, land-use information, weather variables, chemical transport model simulation outputs, and monitor data. Wu et al., 2020 fit five different statistical models: a Cox proportional hazards model, a Poisson regression model, and three causal inference approaches (GPS estimation, GPS matching, and GPS weighting). All five statistical approaches provided consistent results; we report the results of the Cox proportional hazards model here. The authors adjusted for numerous individual-level and community-level confounders, and sensitivity analyses suggest that the results are robust to unmeasured confounding bias. In a single-pollutant model, the coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.066) and 95% confidence interval (1.058-1.074) associated with a change in annual mean PM2.5 exposure of 10.0 ug/m[3] (Wu et al., 2020, Table S3, Main analysis, 2000-2016 Cohort, Cox PH). We use a risk estimate from this study in place of the risk estimate from Di et al., 2017. These two epidemiologic studies share many attributes, including the Medicare cohort and statistical model used to characterize population exposure to PM2.5. As compared to Di et al., 2017, Wu et al., 2020 includes a longer follow-up period and reflects more recent PM2.5 concentrations.  
Pope III et al., 2019 examined the relationship between long-term PM2.5 exposure and all-cause mortality in a cohort of 1,599,329 U.S. adults (aged 18-84 years) who were interviewed in the National Health Interview Surveys (NHIS) between 1986 and 2014 and linked to the National Death Index (NDI) through 2015. The authors also constructed a sub-cohort of 635,539 adults from the full cohort for whom body mass index (BMI) and smoking status data were available. The authors employed a hybrid modeling technique to estimate annual-average PM2.5 concentrations derived from regulatory monitoring data and constructed in a universal kriging framework using geographic variables including land use, population, and satellite estimates. Pope III et al., 2019 assigned annual-average PM2.5 exposure from 1999-2015 to each individual by census tract and used complex (accounting for NHIS's sample design) and simple Cox proportional hazards models for the full cohort and the sub-cohort. We select the Hazard Ratio calculated using the complex model for the sub-cohort, which controls for individual-level covariates including age, sex, race-ethnicity, inflation-adjusted income, education level, marital status, rural versus urban, region, survey year, BMI, and smoking status. In a single-pollutant model, the coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.12) and 95% confidence interval (1.08-1.15) associated with a change in annual mean PM2.5 exposure of 10.0 ug/m[3] (Pope III et al., 2019, Table 2, Subcohort). This study exhibits two key strengths that makes it particularly well suited for a benefits analysis: (1) it includes a long follow-up period with recent (and thus relatively low) PM2.5 concentrations; (2) the NHIS cohort is representative of the U.S. population, especially with respect to the distribution of individuals by race, ethnicity, income, and education.
     EPA has historically used estimated Hazard Ratios from extended analyses of the ACS cohort (Pope et al., 1995, Pope III et al., 2002, Krewski et al, 2009) to estimate PM-related risk of premature death. More recent ACS analyses (Pope et al., 2015, Turner et al., 2016):
 extended the follow-up period of the ACS CSP-II to 22 years (1982-2004), 
 evaluated 669,046 participants over 12,662,562 person-years of follow up and 237,201 observed deaths, and
 applied a more advanced exposure estimation approach than had previously been used when analyzing the ACS cohort, combining the geostatistical Bayesian Maximum Entropy framework with national-level land use regression models. 
The total mortality hazard ratio best estimating risk from these ACS cohort studies was based on a random-effects Cox proportional hazard model incorporating multiple individual and ecological covariates (relative risk =1.06, 95% confidence intervals 1.04 - 1.08 per 10ug/m3 increase in PM2.5) from Turner et al., 2016. The relative risk estimate is identical to a risk estimate drawn from earlier ACS analysis of all-cause long-term exposure PM2.5-attributable mortality (Krewski et al., 2009). However, as the ACS hazard ratio is quite similar to the Medicare estimate of (1.066, 1.058-1.074), especially when considering the broader age range (>29 vs >64), only the Wu et all., 2020 and Pope III et al., 2019 are included in the main benefits assessments, with Wu et al., 2020 representing results from both the Medicare and ACS cohorts. 
Unquantified Human Health Benefits
Although we have quantified many of the health benefits associated with reducing exposure to PM2.5, as shown in Table 5-2, we are unable to quantify the health benefits of implementing the illustrative control strategies described in Chapter 3 associated with reducing ozone exposure, SO2 exposure, or NO2 exposure. This is because we focused on reducing direct PM emissions and do not have air quality modeling data for these pollutants. Although we used air quality surfaces that reflect applying the control strategies for the impact of each alternative combination of standard levels on ambient levels of PM2.5, this method does not simulate how the illustrative emissions reductions would affect ambient levels of ozone, SO2, or NO2. Below we provide a qualitative description of these health benefits. In general, previous analyses have shown that the monetized value of these additional health benefits is much smaller than PM2.5-related benefits (U.S. EPA, 2010, U.S. EPA, 2015a). The extent to which ozone, SO2, and/or NOx would be reduced would depend on the specific control strategies used to reduce PM2.5 in a given area.
Exposure to ambient ozone is associated with human health effects, including respiratory and metabolic morbidity (U.S. EPA, 2020a). Epidemiological researchers have associated ozone exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies (U.S. EPA, 2020a). When adequate data and resources are available, EPA generally quantifies several health effects associated with exposure to ozone (e.g., U.S. EPA, 2014b, U.S. EPA, 2015a). These health effects include respiratory morbidity such as asthma attacks, hospital admissions, emergency department visits, and school loss days. The scientific literature suggests that exposure to ozone is also associated with chronic respiratory damage and premature aging of the lungs, but EPA has not quantified these effects in benefits analyses previously.
Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Sulfur Dioxide -- Health Criteria (SO2 ISA) concluded that there is a causal relationship between respiratory health effects and short-term exposure to SO2 (U.S. EPA, 2017). The immediate effect of SO2 on the respiratory system in humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely resulting from preexisting inflammation associated with this disease. A clear concentration-response relationship has been demonstrated in laboratory studies following exposures to SO2, both in terms of increasing severity of effect and percentage of asthmatics adversely affected. Based on our review of this information, we identified three short-term morbidity endpoints that the SO2 ISA identified as a "causal relationship": asthma exacerbation, respiratory-related emergency department visits, and respiratory-related hospitalizations. The differing evidence and associated strength of the evidence for these different effects is described in detail in the SO2 ISA (U.S. EPA, 2017). The SO2 ISA also concluded that the relationship between short-term SO2 exposure and premature mortality was "suggestive of a causal relationship" because it is difficult to attribute the mortality risk effects to SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a relationship between SO2 exposure and mortality, the number of studies was limited. Because we focused on reducing primary PM emissions, we did not quantify these benefits.
Epidemiological researchers have associated NO2 exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies, as described in the Integrated Science Assessment for Oxides of Nitrogen -- Health Criteria (NO2 ISA) (U.S. EPA, 2016). The NO2 ISA provides a comprehensive review of the current evidence of health and environmental effects of NO2. The NO2 ISA concluded that "evidence for asthma attacks supports a causal relationship between short-term NO2 exposure and respiratory effects," and "evidence for development of asthma supports a likely to be causal relationship between long-term NO2 exposure and respiratory effects." These are stronger conclusions than those determined in the 2008 NO2 ISA (U.S. EPA, 2008).These epidemiologic and experimental studies encompass a number of endpoints including emergency department visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. These are stronger conclusions than those determined in the 2008 NO2 ISA (U.S. EPA, 2008). These epidemiologic and experimental studies encompass a number of endpoints including emergency department visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. Effect estimates from epidemiologic studies conducted in the United States and Canada generally indicate a 2 - 20% increase in risks for ED visits and hospital admissions and higher risks for respiratory symptoms. The NO2 ISA concluded that the relationship between short-term NO2 exposure and premature mortality was "suggestive but not sufficient to infer a causal relationship" because it is difficult to attribute the mortality risk effects to NO2 alone. Although the NO2 ISA stated that studies consistently reported a relationship between NO2 exposure and mortality, the effect was generally smaller than that for other pollutants such as PM. Because we focused on reducing primary PM emissions, we did not quantify these benefits.
Illustrative controls to meet the alternative standard levels are expected to reduce PM2.5 emissions from fossil fuel and wood combustion, as well as industrial processes, and consequentially is expected to lead to reduced Hazardous Air Pollutant (HAP) emissions. HAP emissions from EGUs and other industrial sources may contribute to increased cancer risks and other serious health effects,  including damage to the immune system, as well as neurological, reproductive (e.g., reduced fertility), developmental, respiratory and other health problems. These public health implications of exposure to HAPs can be particularly pronounced for segments of the population that are especially vulnerable to some of these effects (e.g., children are especially vulnerable to neurological effects because their brains are still developing). Some HAPs can also detrimentally affect ecosystems used for recreational and commercial purposes.
Unquantified Welfare Benefits
      The Clean Air Act definition of welfare effects includes, but is not limited to, effects on soils, water, wildlife, vegetation, visibility, weather, and climate, as well as effects on man-made materials, economic values, and personal comfort and well-being. Detailed information regarding the ecological effects of nitrogen and sulfur deposition is available in the Integrated Science Assessment for Oxides of Nitrogen, Oxides of Sulfur, and Particulate Matter-- Ecological Criteria (ISA) (U.S. EPA, 2020b). 
      Particulate matter (PM) is composed of some or all of the following components: nitrate (NO3−), sulfate (SO42−), ammonium (NH4+), metals, minerals (dust), and organic and elemental carbon. Nitrate, sulfate, and ammonium contribute to nitrogen (N) and sulfur (S) deposition, which causes substantial ecological effects. The ecological effects of deposition are grouped into three main categories: acidification, N enrichment/N driven eutrophication, and S enrichment. Ecological effects are further subdivided into terrestrial, wetland, freshwater, and estuarine/near-coastal ecosystems. These ecosystems and effects are linked by the connectivity of terrestrial and aquatic habitats through biogeochemical pathways of N and S.
      In the ISA, information on ecological effects from controlled exposure, field addition, ambient deposition, and toxicological studies, among others, are integrated to form conclusions about the causal relationships between NOy, SOx, and PM and ecological effects. A consistent and transparent framework (U.S. EPA, 2015b, Table II) is applied to classify the ecological effect evidence according to a five-level hierarchy: 
      1. Causal relationship
      2. Likely to be a causal relationship
      3. Suggestive of, but not sufficient to infer, a causal relationship
      4. Inadequate to infer a causal relationship
      5. Not likely to be a causal relationship
      Table 5-4 summarizes the causal determinations for relationships between N and S deposition and ecological effects. Though not quantified in this RIA, it is reasonable to infer that reducing fine particle levels by controlling emissions of NOx and SOx will yield the ecological benefits detailed below. 
Table 5-4 	Causal Determinations Identified in Integrated Science Assessment for Oxides of Nitrogen, Oxides of Sulfur, and Particulate Matter-- Ecological Criteria 2020b
                                Effect Category
                             Causal Determination
N and acidifying deposition to terrestrial ecosystems

N and S deposition and alteration of soil biogeochemistry in terrestrial ecosystems 
Section IS.5.1 and Appendix 4.1 

                              Causal relationship
N deposition and the alteration of the physiology and growth of terrestrial organisms and the productivity of terrestrial ecosystems
Section IS.5.2 and Appendix 6.6.1
                              Causal relationship
N deposition and the alteration of species richness, community composition, and biodiversity in terrestrial ecosystems
Section IS.5.2 and Appendix 6.6.2
                              Causal relationship
Acidifying N and S deposition and the alteration of the physiology and growth of terrestrial organisms and the productivity of terrestrial ecosystems
Section IS.5.3 and Appendix 5.7.1
                              Causal relationship
Acidifying N and S deposition and the alteration of species richness, community composition, and biodiversity in terrestrial ecosystems
Section IS.5.3 and Appendix 5.7.2
                              Causal relationship
N and acidifying deposition to freshwater ecosystems
                                       
N and S deposition and alteration of freshwater biogeochemistry
Section IS.6.1 and Appendix 7.1.7
                              Causal relationship
Acidifying N and S deposition and changes in biota, including physiological impairment and alteration of species richness, community composition, and biodiversity in freshwater ecosystems
Section IS.6.3 and Appendix 8.6
                              Causal relationship
N deposition and changes in biota, including altered growth and productivity, species richness, community composition, and biodiversity due to N enrichment in freshwater ecosystems
Section IS.6.2 and Appendix 9.6
                              Causal relationship
N deposition to estuarine ecosystems
                                       
N deposition and alteration of biogeochemistry in estuarine and near-coastal marine systems
Section IS.7.1 and Appendix 7.2.10
                              Causal relationship
N deposition and changes in biota, including altered growth, total primary production, total algal community biomass, species richness, community composition, and biodiversity due to N enrichment in estuarine environments
Section IS.7.2 and Appendix 10.7
                              Causal relationship
N deposition to wetland ecosystems
                                       
N deposition and the alteration of biogeochemical cycling in wetlands
Section IS.8.1 and Appendix 11.10
                              Causal relationship
N deposition and the alteration of growth and productivity, species physiology, species richness, community composition, and biodiversity in wetlands
Section IS.8.2 and Appendix 11.10
                              Causal relationship
S deposition to wetland and freshwater ecosystems
                                       
S deposition and the alteration of mercury methylation in surface water, sediment, and soils in wetland and freshwater ecosystems
Section IS.9.1 and Appendix 12.7
                              Causal relationship
S deposition and changes in biota due to sulfide phytotoxicity, including alteration of growth and productivity, species physiology, species richness, community composition, and biodiversity in wetland and freshwater ecosystems
Section IS.9.2 and Appendix 12.7
                              Causal relationship
      
      
Visibility Impairment Benefits
Reducing PM2.5 would improve levels of visibility in the U.S. because suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people's enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they engage in recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern U.S. and particulate nitrate is an important contributor to light extinction in California and the upper Midwestern U.S., particularly during winter (U.S. EPA, 2009). Previous analyses (U.S. EPA, 2011d) show that visibility benefits can be a significant welfare benefit category. Without air quality modeling, we are unable to estimate visibility-related benefits, and we are also unable to determine whether the emission reductions associated with the proposal would be likely to have a significant impact on visibility in urban areas or Class I areas.  
Climate Effects of PM2.5
In the climate section of Chapter 5 of the 2020 PM2.5 Primary NAAQS Policy Assessment it states "Thus, as in the last review, the data remain insufficient to conduct quantitative analyses for PM effects on climate in the current review." (U.S. EPA, 2020d) Pollutants that affect the energy balance of the earth are referred to as climate forcers. A pollutant that increases the amount of energy in the Earth's climate system is said to exert "positive radiative forcing," which leads to warming and climate change. In contrast, a pollutant that exerts negative radiative forcing reduces the amount of energy in the Earth's system and leads to cooling.
Atmospheric particles influence climate in multiple ways: directly absorbing light, scattering light, changing the reflectivity ("albedo") of snow and ice through deposition, and interacting with clouds. Depending on the particle's composition, the timing of emissions, and where it is in the atmosphere determine if it contributes to cooling or warming. The short atmospheric lifetime of particles, lasting from days to weeks, and the mechanisms by which particles affect climate, distinguish it from long‐lived greenhouse gases like CO2. This means that actions taken to reduce PM2.5 will have near term effects on climate change. The Intergovernmental Panel on Climate Change Sixth Assessment Report concludes that for forcers with short lifetimes, "the response in surface temperature occurs strongly, as soon as a sustained change in emissions is implemented" (Naik et al., 2021). The potential to affect near-term climate change and the rate of climate change with policies to address these emissions is gaining attention nationally and internationally (e.g., Black Carbon Report to Congress, Arctic Council, Climate and Clean Air Coalition, and Convention on Long-Range Transboundary Air Pollution of the United Nations Economic Commission for Europe). Recent reports have concluded that short-lived compounds play a prominent role in keeping global warming below 1.5° C (IPCC, 2018), and are especially important in the rapidly warming Arctic (AMAP, 2021). While reducing long-lived GHGs such as CO2 is necessary to protect against long-term climate change, reducing short-lived forcers and would slow the rate of climate change within the first half of this century (UNEP, 2011).
Climate Effects of Carbonaceous Particles
The illustrative control strategies are focused on emissions sources that are significant sources of carbonaceous particles, including black carbon and organic carbon. Black Carbon (BC), also called soot, is the most strongly light‐absorbing component of PM2.5, and is formed by incomplete combustion of fossil fuels, biofuels, and biomass. Another contributor to carbonaceous particles is organic carbon (OC), which in addition to carbon are also composed of oxygen and hydrogen. Organic carbon particles can be directly emitted from the same sources as black carbon or formed in the atmosphere from chemical reactions. They can be light-absorbing, but most have a larger light-scattering component. 
Both BC and organic carbon in the atmosphere influence climate in multiple ways: directly absorbing or reflecting light, modifying the rate of vertical mixing, and interacting with clouds. Light-absorbing particles also have an additional climate effect when deposited on snow and ice. These particles darken the surface and decrease albedo, thereby increasing absorption and accelerating melting (Hock et al., 2019; Meredith et al., 2019). Regional climate impacts of BC are highly variable, and sensitive regions such as the Arctic are particularly vulnerable to the warming and melting effects of BC. Snow and ice cover in the western U.S. has also been affected by BC. Specifically, deposition of BC on mountain glaciers and snowpacks produces a positive snow and ice albedo effect, contributing to the melting of snowpack earlier in the spring and reducing the amount of snowmelt that normally would occur later in the spring and summer (Hadley et al. 2010). This has implications for freshwater resources in regions of the U.S. dependent on snow‐fed or glacier‐fed water systems. In the Sierra Nevada mountain range, Hadley et al. (2010) found BC at different depths in the snowpack, deposited over the winter months by snowfall. In the spring, the continuous uncovering of the BC contributed to the early melt. A model capturing the effects of soot on snow in the western U.S. shows significant decreases in snowpack between December and May (Qian et al., 2009). Snow water equivalent (the amount of water that would be produced by melting all the snow) is reduced 2‐50 millimeters (mm) in mountainous areas, particularly over the Central Rockies, Sierra Nevadas, and western Canada. A study found that biomass burning emissions in Alaska and the Rocky Mountain region during the summer can enhance snowmelt (McKenzie Skiles et al 2018). Light-absorbing particles and especially BC can have an additional warming effect when deposited on snow and ice, and this effect is highly seasonal and regional.  
Relative to greenhouse gases, the net effect of carbonaceous particles is both more regionally variable and more uncertain (Naik et al., 2021). Particles have a relatively short lifetime in the atmosphere, leading to spatial concentration differences, while greenhouse gases are more well mixed and have less global variability. The amount of light absorption by particles depends on the season, with different effects in the summer and winter. Lastly, even light-absorbing particles can also contribute to cooling (e.g., by shading the surface).
Climate Effects: Summary and Conclusions
The net climate change effect of carbonaceous aerosols in the illustrative control strategies depends on the location, timing, and type of the emissions controls. As described above, the black carbon emissions are more likely to contribute to warming and organic aerosols more likely to contribute to cooling. Emissions sources with larger amounts of light-absorbing aerosols, like diesel vehicles, or with emissions near snow or the Arctic, like residential wood combustion, are more likely to contribute to warming (Bond et al., 2013). 
However, assessing the net effect is beyond the scope of this RIA and requires climate atmospheric modeling that has not been undertaken. Furthermore, there are uncertainties relevant to the assessment of the net climate change effects of PM2.5, especially at a regional scale (U.S. EPA, 2019b). Strategies that could be implemented by State and Local governments that would likely provide climate change mitigation benefits include prioritizing (i) emissions control actions that also achieve emissions reductions for warming agents like carbon dioxide, methane, and ozone precursors (carbon monoxide and volatile organic compounds), and (ii) sources of light-absorbing carbonaceous aerosols, especially diesel engines and residential wood combustion.
Economic Valuation Estimates
To directly compare benefits estimates associated with a rulemaking to cost estimates, the number of instances of each air pollution-attributable health impact must be converted to a monetary value. This requires a valuation estimate for each unique health endpoint, and potentially also discounting if the benefits are expected to accrue over more than a single year, as recommended by the Guidelines for Preparing Economic Analyses (U.S. EPA, 2014a). 
Characterizing Uncertainty
In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty. This analysis is no exception. The TSD accompanying this RIA details our approach to characterizing uncertainty in both quantitative and qualitative terms. That TSD describes the sources of uncertainty associated with key input parameters including emissions inventories, air quality data from models (with their associated parameters and inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing benefits, and assumptions regarding the future state of the country (i.e., regulations, technology, and human behavior). Each of these inputs is uncertain and affects the size and distribution of the estimated benefits. When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benefits.
To characterize uncertainty and variability into this assessment, we incorporate three quantitative analyses described below and in greater detail within the TSD (Section 7.1): 
1.	A Monte Carlo assessment that accounts for random sampling error and between study variability in the epidemiological and economic valuation studies;
2.	The quantification of PM-related mortality using alternative PM2.5 mortality effect estimates drawn from two long-term cohort studies; and
3. 	Presentation of 95[th] percentile confidence interval around each risk estimate. 

Quantitative characterization of other sources of PM2.5 uncertainties are discussed only in Section 7.1 of the TSD:
 For adult all-cause mortality:
 The distributions of air quality concentrations experienced by the original cohort population (TSD Section 7.1.2.1);
 Methods of estimating and assigning exposures in epidemiologic studies (TSD Section 7.1.2.2);
 Confounding by ozone (TSD Section 7.1.2.3); and
 The statistical technique used to generate hazard ratios in the epidemiologic study (TSD Section 7.1.2.4).
Plausible alternative risk estimates for asthma onset in children (TSD Section 7.1.3), cardiovascular hospital admissions (TSD Section 7.1.4,), and respiratory hospital admissions (TSD Section 7.1.5);
Effect modification of PM2.5-attributable health effects in at-risk populations (TSD Section 7.1.6).
Quantitative consideration of baseline incidence rates and economic valuation estimates are provided in Section 7.3 and 7.4 of the TSD, respectively. Qualitative discussions of various sources of uncertainty can be found in Section 7.5 of the TSD.
Monte Carlo Assessment
Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the concentration response functions from epidemiological studies and random effects modeling to characterize both sampling error and variability across the economic valuation functions. The Monte Carlo simulation in the BenMAP-CE software randomly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to generate confidence intervals around the estimated health impact and monetized benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates for endpoints estimated using a single study. For endpoints estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and the variance across studies. The confidence intervals around the monetized benefits incorporate the epidemiology standard errors as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed, and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates. 
Sources of Uncertainty Treated Qualitatively
Although we strive to incorporate as many quantitative assessments of uncertainty as possible, there are several aspects we are only able to address qualitatively. These attributes are summarized below and described more fully in the TSD. 
Key assumptions underlying the estimates for premature mortality, which account for over 98% of the total monetized benefits in this analysis, include the following:
 We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The PM ISA, which was reviewed by CASAC, concluded that "across exposure durations and health effects categories ... the evidence does not indicate that any one source or component is consistently more strongly related with health effects than PM2.5 mass" (U.S. EPA, 2019b).
 We assume that the health impact function for fine particles is log-linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM2.5, including both regions that are in attainment with the fine particle standard and those that do not meet the standard down to the lowest modeled concentrations. The PM ISA concluded that "the majority of evidence continues to indicate a linear, no-threshold concentration-response relationship for long-term exposure to PM2.5 and total (nonaccidental) mortality" U.S. EPA, 2019b . 
 We assume that there is a "cessation" lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM - 2.5 exposures occur in a distributed fashion over the 20 years following exposure based on the advice of the SAB-HES (Cameron, 2004), which affects the valuation of mortality benefits at different discount rates. Similarly, we assume there is a cessation lag between the change in PM exposures and both the development and diagnosis of lung cancer.
Benefits Results
Benefits of the Applied Control Strategies for the Alternative Combinations of Primary PM2.5 Standard Levels
Applying the impact and valuation functions described previously in this chapter to the estimated changes in PM2.5 yields estimates of the changes in physical damages (e.g., premature mortalities, cases of hospital admissions and emergency department visits) and the associated monetary values for those changes. Not all known PM health effects could be quantified or monetized.
We present two sets of tables.  -  one set in this chapter and one set in Appendix 5A. First, Table 5-5 through Table 5-9 present benefits associated with the illustrative control strategies identified in Chapter 3. More specifically, for the proposed alternative standard level of 9/35 g/m[3], for the northeast we were able to identify approximately 97 percent of the reductions needed. For the southeast we were able to identify approximately 76 percent of the reductions needed. For the west, we were able to identify approximately 31 percent of the reductions needed, and for California the percentage is approximately 17 percent.   As such, these tables present the benefits associated with the illustrative control strategies and reflect the remaining air quality challenges (discussed in Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6).
Second, Table 5A-1 through 5A-5 in Appendix 5A present the potential benefits associated with fully meeting the proposed and alternative standards. 
 Table 5-5 through Table 5-9 present the benefits results of applying the control strategies for the proposed annual and current 24-hour alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/m[3], as well as the following two more stringent alternative standard levels: (1) an alternative annual standard level of 8 g/ - m - [3] -  in combination with the current 24-hour standard (i.e., 8/35 g/ - m - [3] - ), and (2) an alternative 24-hour standard level of 30 g/ - m - [3] in combination with the proposed annual standard level of 10 g/ - m - [3] -  (i.e., 10/30 g/ - m - [3] - ).
Table 5-5 presents the estimated avoided incidences of PM-related illnesses and premature mortality resulting from the control strategies applied to each of the alternative standard levels in 2032. Table 5-6 and Table 5-7 present the monetized valuation benefits (discounted at a 3% and 7% discount rate, respectively) of the avoided health outcomes presented in Table 5-5.
Table 5-8 and Table 5-9 present a summary of the monetized benefits associated with each of the alternative standard levels, both nationally and by region. The regional monetized benefits in Table 5-8 are presented in four regions: California (CA), the Northeastern (NE) states, the Southeastern (SE) states, and the Western (W) states. For Table 5-8 and Table 5-9, the monetized value of unquantified effects is represented by adding an unknown "B" to the aggregate total. This B represents both uncertainty and a bias in this analysis, as it reflects health and welfare benefits that we are unable to quantify.
For a more detailed description of the geographic distribution of the emissions reductions needed for each of the alternative standard levels, see the discussion in Chapter 3, Section 3.2.5. The estimated PM2.5 emissions reductions from control applications do not result in all counties in the northeast, southeast, west, and California meeting the proposed and more stringent alternative standard levels. For the proposed alternative standard level of 10/35 g/m[3] -  - , the northeast and southeast have sufficient estimated emissions reductions to reach attainment. For the west, the estimated emissions reductions are approximately 27 percent of the total needed to reach attainment, and for California the estimated emissions reductions are approximately 18 percent of the total needed to reach attainment. 

Table 5-5 	Estimated Avoided PM-Related Premature Mortalities and Illnesses of the Applied Control Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (95% Confidence Interval)
Avoided Mortality[a]
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope III et al., 2019 (adult mortality ages 18-99 years)
                                     1,700
                               (1,200 to 2,100)
                                     1,900
                               (1,400 to 2,400)
                                     4,200
                               (3,000 to 5,300)
                                     9,200
                               (6,600 to 12,000)
Wu et al., 2020 (adult mortality ages 65-99 years)
                                      810
                                 (710 to 900)
                                      920
                                (810 to 1,000)
                                     2,000
                               (1,800 to 2,200)
                                     4,400
                               (3,900 to 4,900)
Woodruff et al., 2008 (infant mortality)
                                      1.6
                                (-0.99 to 4.0)
                                      1.8
                                 (-1.1 to 4.6)
                                      4.7
                                 (-3.0 to 12)
                                      11
                                 (-6.9 to 28)
Avoided Morbidity 
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
                                      140
                                 (100 to 170)
                                      150
                                 (110 to 190)
                                      310
                                 (230 to 400)
                                      660
                                 (480 to 840)
Hospital admissions -- respiratory
                                      93
                                  (31 to 150)
                                      100
                                  (35 to 170)
                                      210
                                  (74 to 350)
                                      460
                                 (160 to 740)
ED visits--cardiovascular
                                      260
                                 (-100 to 610)
                                      290
                                 (-110 to 670)
                                      630
                                (-240 to 1,500)
                                     1,400
                                (-530 to 3,200)
ED visits -- respiratory
                                      490
                                 (95 to 1,000)
                                      530
                                (100 to 1,100)
                                     1,200
                                (240 to 2,600)
                                     2,700
                                (540 to 5,700)
Acute Myocardial Infarction
                                      29
                                  (5.9 to 17)
                                      32
                                  (19 to 45)
                                      67
                                  (39 to 94)
                                      140
                                  (83 to 200)
Cardiac arrest
                                      15
                                 (-5.9 to 33)
                                      16
                                 (-6.6 to 37)
                                      34
                                  (-14 to 76)
                                      72
                                 (-29 to 160)
Hospital admissions-- Alzheimer's Disease
                                      360
                                 (270 to 440)
                                      390
                                 (300 to 480)
                                      850
                                (640 to 1,000)
                                     1,900
                               (1,500 to 2,400)
Hospital admissions-- Parkinson's Disease
                                      48
                                  (25 to 70)
                                      54
                                  (28 to 79)
                                      120
                                  (63 to 180)
                                      270
                                 (140 to 390)
Stroke
                                      55
                                  (14 to 94)
                                      61
                                  (16 to 110)
                                      130
                                  (33 to 220)
                                      270
                                  (71 to 470)
Lung cancer
                                      65
                                  (20 to 110)
                                      73
                                  (22 to 120)
                                      150
                                  (46 to 250)
                                      320
                                  (99 to 530)
Hay Fever/Rhinitis
                                    15,000
                               (3,500 to 25,000)
                                    16,000
                               (4,000 to 28,000)
                                    35,000
                               (8,500 to 60,000)
                                    75,000
                              (18,000 to 130,000)
Asthma Onset
                                     2,200
                               (2,100 to 2,300)
                                     2,500
                               (2,400 to 2,600)
                                     5,400
                               (5,100 to 5,600)
                                    11,000
                              (11,000 to 12,000)
Asthma symptoms  -  Albuterol use
                                    310,000
                             (-150,000 to 750,000)
                                    350,000
                             (-170,000 to 850,000)
                                    740,000
                            (-360,000 to 1,800,000)
                                   1,600,000
                            (-780,000 to 3,900,000)
Lost work days
                                    110,000
                              (97,000 to 130,000)
                                    130,000
                             (110,000 to 150,000)
                                    270,000
                             (230,000 to 310,000)
                                    580,000
                             (490,000 to 660,000)
Minor restricted-activity days[d,f]
                                    680,000
                             (550,000 to 800,000)
                                    750,000
                             (610,000 to 890,000)
                                   1,600,000
                           (1,300,000 to 1,900,000)
                                   3,400,000
                           (2,700,000 to 4,000,000)
Note: Values rounded to two significant figures. 
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-term exposure to PM2.5.  These values should not be added to one another.

Table 5-6 	Monetized PM-Related Premature Mortalities and Illnesses of the Applied Control Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 3% discount rate; 95% Confidence Interval)
Avoided Mortality[a]
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope III et al., 2019 (adult mortality ages 18-99 years)
                                    17,000
                               (1,600 to 47,000)
                                    20,000
                               (1,800 to 53,000)
                                    43,000
                              (3,900 to 120,000)
                                    94,000
                              (8,600 to 260,000)
Wu et al., 2020 (adult mortality ages 65-99 years)
                                     8,300
                                (770 to 22,000)
                                     9,400
                                (870 to 25,000)
                                    20,000
                               (1,900 to 54,000)
                                    45,000
                              (4,200 to 120,000)
Woodruff et al., 2008 (infant mortality)
                                      18
                                 (-9.9 to 70)
                                      20
                                  (-11 to 80)
                                      53
                                 (-30 to 210)
                                      120
                                 (-69 to 490)
Avoided Morbidity 
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
                                      2.3
                                 (1.7 to 2.9)
                                      2.5
                                 (1.8 to 3.2)
                                      5.2
                                 (3.7 to 6.5)
                                      11
                                  (7.9 to 14)
Hospital admissions -- respiratory
                                      1.6
                                 (0.35 to 2.7)
                                      1.7
                                 (0.39 to 3.0)
                                      3.6
                                 (0.81 to 6.2)
                                      7.6
                                  (1.7 to 13)
ED visits--cardiovascular
                                     0.32
                                (-0.12 to 0.75)
                                     0.35
                                (-0.14 to 0.83)
                                     0.78
                                 (-0.3 to 1.8)
                                      1.7
                                 (-0.65 to 4)
ED visits -- respiratory
                                     0.45
                                (0.089 to 0.94)
                                      0.5
                                 (0.098 to 1)
                                      1.2
                                 (0.23 to 2.4)
                                      2.6
                                 (0.5 to 5.3)
Acute Myocardial Infarction
                                      1.5
                                 (0.88 to 2.1)
                                      1.7
                                 (0.97 to 2.4)
                                      3.5
                                 (2.0 to 4.9)
                                      7.4
                                  (4.3 to 10)
Cardiac arrest
                                     0.55
                                (-0.23 to 1.3)
                                     0.62
                                (-0.25 to 1.4)
                                      1.3
                                (-0.52 to 2.9)
                                      2.7
                                 (-1.1 to 6.2)
Hospital admissions-- Alzheimer's Disease
                                      4.6
                                 (3.5 to 5.7)
                                       5
                                 (3.8 to 6.2)
                                      11
                                  (8.3 to 13)
                                      25
                                  (19 to 31)
Hospital admissions-- Parkinson's Disease
                                     0.66
                                (0.34 to 0.96)
                                     0.74
                                 (0.38 to 1.1)
                                      1.7
                                 (0.86 to 2.4)
                                      3.7
                                 (1.9 to 5.3)
Stroke
                                       2
                                 (0.51 to 3.4)
                                      2.2
                                 (0.58 to 3.8)
                                      4.6
                                 (1.2 to 7.8)
                                      9.9
                                  (2.6 to 17)
Lung cancer
                                       1
                                 (0.31 to 1.7)
                                      1.1
                                 (0.35 to 1.9)
                                      2.3
                                 (0.71 to 3.8)
                                      4.9
                                 (1.5 to 8.1)
Hay Fever/Rhinitis
                                      9.3
                                  (2.3 to 16)
                                      11
                                  (2.5 to 18)
                                      22
                                  (5.4 to 38)
                                      48
                                  (12 to 82)
Asthma Onset
                                      100
                                  (98 to 110)
                                      120
                                 (110 to 130)
                                      250
                                 (240 to 270)
                                      540
                                 (510 to 570)
Asthma symptoms  -  Albuterol use
                                     0.11
                               (-0.055 to 0.28)
                                     0.13
                               (-0.062 to 0.31)
                                     0.27
                                (-0.13 to 0.66)
                                     0.59
                                (-0.29 to 1.4)
Lost work days
                                      21
                                  (17 to 24)
                                      23
                                  (19 to 26)
                                      48
                                  (41 to 56)
                                      100
                                  (88 to 120)
Minor restricted-activity days
                                 53
(28 to 80)
                                      59
                                  (31 to 89)
                                      120
                                  (64 to 190)
                                      260
                                 (140 to 400)
Note: Values rounded to two significant figures.
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-term exposure to PM2.5.  These values should not be added to one another.



 
Table 5-7 	Monetized PM-Related Premature Mortalities and Illnesses of the Applied Control Strategies for the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 7% discount rate; 95% Confidence Interval)
Avoided Mortality[a]
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope III et al., 2019 (adult mortality ages 18-99 years)
                                    16,000
                               (1,400 to 42,000)
                                    18,000
                               (1,600 to 47,000)
                                    38,000
                              (3,500 to 100,000)
                                    85,000
                              (7,700 to 230,000)
Wu et al., 2020 (adult mortality ages 65-99 years)
                                     7,500
                                (690 to 20,000)
                                     8,500
                                (780 to 22,000)
                                    18,000
                               (1,700 to 49,000)
                                    41,000
                              (3,800 to 110,000)
Woodruff et al., 2008 (infant mortality)
                                      18
                                 (-9.9 to 70)
                                      20
                                  (-11 to 80)
                                      53
                                 (-30 to 210)
                                      120
                                 (-69 to 490)
Avoided Morbidity 
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
                                      2.3
                                 (1.7 to 2.9)
                                      2.5
                                 (1.8 to 3.2)
                                      5.2
                                 (3.7 to 6.5)
                                      11
                                  (7.9 to 14)
Hospital admissions -- respiratory
                                      1.6
                                 (0.35 to 2.7)
                                      1.7
                                 (0.39 to 3.0)
                                      3.6
                                 (0.81 to 6.2)
                                      7.6
                                  (1.7 to 13)
ED visits--cardiovascular
                                     0.32
                                (-0.12 to 0.75)
                                     0.35
                                (-0.14 to 0.83)
                                     0.78
                                 (-0.3 to 1.8)
                                      1.7
                                 (-0.65 to 4)
ED visits -- respiratory
                                     0.45
                                (0.089 to 0.94)
                                      0.5
                                 (0.098 to 1)
                                      1.2
                                 (0.23 to 2.4)
                                      2.6
                                 (0.5 to 5.3)
Acute Myocardial Infarction
                                      1.5
                                 (0.86 to 2.1)
                                      1.6
                                 (0.97 to 2.4)
                                      3.4
                                 (2.0 to 4.8)
                                      7.3
                                  (4.2 to 10)
Cardiac arrest
                                     0.55
                                (-0.22 to 1.2)
                                     0.61
                                (-0.25 to 1.4)
                                      1.3
                                (-0.51 to 2.8)
                                      2.7
                                 (-1.1 to 6.1)
Hospital admissions-- Alzheimer's Disease
                                      4.6
                                 (3.5 to 5.7)
                                       5
                                 (3.8 to 6.2)
                                      11
                                  (8.3 to 13)
                                      25
                                  (19 to 31)
Hospital admissions-- Parkinson's Disease
                                     0.66
                                (0.34 to 0.96)
                                     0.74
                                 (0.38 to 1.1)
                                      1.7
                                 (0.86 to 2.4)
                                      3.7
                                 (1.9 to 5.3)
Stroke
                                       2
                                 (0.51 to 3.4)
                                      2.2
                                 (0.58 to 3.8)
                                      4.6
                                 (1.2 to 7.8)
                                      9.9
                                  (2.6 to 17)
Lung cancer
                                     0.72
                                 (0.22 to 1.2)
                                      0.8
                                 (0.25 to 1.3)
                                      1.6
                                 (0.5 to 2.7)
                                      3.4
                                 (1.1 to 5.7)
Hay Fever/Rhinitis
                                      9.3
                                  (2.3 to 16)
                                      11
                                  (2.5 to 18)
                                      22
                                  (5.4 to 38)
                                      48
                                  (12 to 82)
Asthma Onset
                                      65
                                  (60 to 69)
                                      73
                                  (68 to 78)
                                      160
                                 (150 to 170)
                                      340
                                 (310 to 360)
Asthma symptoms  -  Albuterol use
                                     0.11
                               (-0.055 to 0.28)
                                     0.13
                               (-0.062 to 0.31)
                                     0.27
                                (-0.13 to 0.66)
                                     0.59
                                (-0.29 to 1.4)
Lost work days
                                      21
                                  (17 to 24)
                                      23
                                  (19 to 26)
                                      48
                                  (41 to 56)
                                      100
                                  (88 to 120)
Minor restricted-activity days
                                 53
(28 to 80)
                                      59
                                  (31 to 89)
                                      120
                                  (64 to 190)
                                      260
                                 (140 to 400)
Note: Values rounded to two significant figures. 
a Reported here are two alternative estimates of the number of premature deaths among adults due to long-term exposure to PM2.5.  These values should not be added to one another.


Table 5-8	Estimated Monetized Benefits of the Applied Control Strategies for the Proposed and More Stringent Alternative Combinations of Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)
                               Benefits Estimate
                 10 ug/m[3] annual &
35 ug/m[3] 24-hour
                 10 ug/m[3] annual &
30 ug/m[3] 24-hour
                  9 ug/m[3] annual &
35 ug/m[3] 24-hour
                  8 ug/m[3] annual &
35 ug/m[3] 24-hour
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Pope III et al., 2019
  3% discount rate
                                    $17 + B
                                    $20 + B
                                    $43 + B
                                    $95 + B
  7% discount rate
                                    $16 + B
                                    $18 + B
                                    $39 + B
                                    $86 + B
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Wu et al., 2020
  3% discount rate
                                   $8.5 + B
                                   $9.6 + B
                                    $21 + B
                                    $46 + B
  7% discount rate
                                   $7.6 + B
                                   $8.6 + B
                                    $19 + B
                                    $41 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not all possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and welfare benefits.

Table 5-9 is a summary of the monetized benefits associated with applying the control strategies for each of the alternative standard levels by four regions: California, the Northeast, the Southeast, and the West. The monetized benefits differ regionally and by each alternative standard level. For the proposed alternative standard level of 10/35 g/ - m - [3] - , because 15 of the 24 counties that need emissions reductions are counties in California, the majority of the benefits are incurred in California (Table 5-9). For California, we were able to identify approximately 18 percent of the reductions needed. In addition, as the alternative standard levels become more stringent, more counties in the northeast and southeast need emissions reductions. As additional controls are applied in those areas, those areas account for a relatively higher proportion of the benefits. For example, for alternative standard levels of 9/35 g/ - m - [3] -  and 8/35 g/ - m - [3] - , more controls are available to apply in the northeast and their adjacent counties and the southeast and their adjacent counties. The benefits for those areas are higher than the costs for the west and California. 
Table 5-9	Estimated Monetized Benefits by Region of the Applied Control Strategies for the Proposed and More Stringent Alternative Combinations of Primary PM2.5 Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)
                               Benefits Estimate
                                    Region
                                 10 ug/m[3] 
                       annual &
35 ug/m[3] 24-hour
                                 10 ug/m[3] 
                       annual &
30 ug/m[3] 24-hour
                                  9 ug/m[3] 
                       annual &
35 ug/m[3] 24-hour
                  8 ug/m[3] annual &
35 ug/m[3] 24-hour
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Pope III et al., 2019
  3% discount rate
                                  California
                                    $13 + B
                                    $14 + B
                                    $17 + B
                                    $23 + B
  
                                   Northeast
                                   $2.3 + B
                                   $2.6 + B
                                    $15 + B
                                    $40 + B
  
                                   Southeast
                                   $1.8 + B
                                   $1.8 + B
                                   $8.8 + B
                                    $22 + B
  
                                     West
                                  $0.018 + B
                                   $1.1 + B
                                   $2.2 + B
                                    $11 + B
  7% discount rate
                                  California
                                    $12 + B
                                    $13 + B
                                    $16 + B
                                    $21 + B
  
                                   Northeast
                                    $2 + B
                                   $2.3 + B
                                    $13 + B
                                    $36 + B
  
                                   Southeast
                                   $1.6 + B
                                   $1.6 + B
                                   $7.9 + B
                                    $20 + B
  
                                     West
                                  $0.016 + B
                                    $1 + B
                                    $2 + B
                                   $9.5 + B
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Wu et al., 2020
  3% discount rate
                                  California
                                   $6.5 + B
                                   $6.9 + B
                                   $8.4 + B
                                    $11 + B
  
                                   Northeast
                                   $1.1 + B
                                   $1.3 + B
                                   $7.3 + B
                                    $19 + B
  
                                   Southeast
                                   $0.84 + B
                                   $0.84 + B
                                   $4.1 + B
                                    $10 + B
  
                                     West
                                  $0.0092 + B
                                   $0.56 + B
                                   $1.1 + B
                                   $5.1 + B
  7% discount rate
                                  California
                                   $5.8 + B
                                   $6.2 + B
                                   $7.5 + B
                                    $10 + B
  
                                   Northeast
                                    $1 + B
                                   $1.2 + B
                                   $6.6 + B
                                    $17 + B
  
                                   Southeast
                                   $0.75 + B
                                   $0.75 + B
                                   $3.6 + B
                                   $9.2 + B
  
                                     West
                                  $0.0082 + B
                                   $0.5 + B
                                   $0.97 + B
                                   $4.6 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and welfare benefits.

Discussion
      The estimated benefits to human health and the environment of the alternative PM2.5 daily and annual standard levels are substantial. We estimate that by 2032 the emissions reduced by the applied control strategies for the proposed annual primary standards would decrease the number of PM2.5-related premature deaths and illnesses. The emissions reduction strategies will also yield significant welfare benefits (see Section 5.3.5), though this RIA does not quantify those endpoints. 
Inherent to any complex analysis quantifying the benefits of improved air quality, such as this one, are multiple sources of uncertainty. Some of these we characterized through our use of Monte Carlo techniques to sample the statistical error reported in the epidemiologic and economic studies supplying concentration-response parameters and economic unit values. Other key sources of uncertainty that affect the size and distribution of the estimated benefits -- including projected atmospheric conditions and source-level emissions, projected baseline rates of illness and disease, incomes and expected advances in healthcare -- remain unquantified. When evaluated within the context of these uncertainties, the estimated health impacts and monetized benefits in this RIA provide important information regarding the public health benefits associated with a revised PM NAAQS. 
There are two important differences worth noting in the design and analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2020c). First, the NAAQS RIAs illustrate the potential costs and benefits of a revised air quality standard nationwide based on an array of emission reduction strategies for different sources. Second, those costs and benefits are calculated incremental to implementation  of existing regulations as well as additional controls applied to reach the current standards and create the analytical baseline for the analysis. In short, NAAQS RIAs hypothesize, but do not predict, the strategies that States may follow to reduce emissions when implementing previous and revised NAAQS options. Setting a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs illustrate potential benefits and costs; these estimated values cannot be added, or directly compared, to the costs and benefits of regulations that require specific emissions control strategies to be implemented. 
This latter type of regulatory action -- often referred to as an implementation rule -- reduces emissions for specific, well-characterized sources (see: Revised Cross-State Air Pollution Rule Update (U.S. EPA, 2020c)). In general, the EPA is more confident in the magnitude and location of the emissions reductions for these implementation rules. As such, emissions reductions achieved under promulgated implementation rules such as the RCU have been reflected in the baseline of this NAAQS analysis. For this reason, the benefits estimated in this RIA and all other NAAQS RIAs should not be added to the benefits estimated for implementation rules. 
In setting the NAAQS, the EPA accounts for the variability in PM2.5 concentrations over space and time. While the standard is designed to limit concentrations at the highest monitor in an area, EPA acknowledges that emissions controls implemented to meet the standard at the highest monitor will simultaneously result in lower PM2.5 concentrations in neighboring areas. In fact, the Policy Assessment for the Review of the National Ambient Air Quality Standards for Particulate Matter (U.S. EPA, 2022c) shows how different standard levels would affect the distribution of PM2.5 concentrations, as well as people's risk, across urban areas. For this reason, it is inappropriate to use the NAAQS level as a bright line for health effects. 
The NAAQS are not set at levels that eliminate the risk of air pollution completely. Instead, the Administrator sets the NAAQS at a level requisite to protect public health with an adequate margin of safety, taking into consideration effects on susceptible populations based on the scientific literature. The risk analysis prepared in support of this PM NAAQS reported risks below these levels, while acknowledging that the confidence in those effect estimates is higher at levels closer to the standard (U.S. EPA, 2022c). While benefits occurring below the standard may be somewhat more uncertain than those occurring above the standard, the EPA considers these to be legitimate components of the total benefits estimate. Though there are greater uncertainties at lower PM2.5 concentrations, there is no evidence of a threshold in PM2.5-related health effects in the epidemiology literature. Given that the epidemiological literature in most cases has not provided estimates based on threshold models, there would be additional uncertainties imposed by assuming thresholds or other non-linear concentration response functions for the purposes of benefits analysis. 
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U.S. EPA (2020b). Integrated Science Assessment (ISA) for Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter Ecological Criteria. U.S. Environmental Protection Agency. Washington, DC. Office of Research and Development. EPA/600/R-20/278. Available at: https://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=349473.
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 APPENDIX 5A: BENEFITS OF THE PROPOSED AND ALTERNATIVE STANDARD LEVELS
 Overview
      In this Appendix, we estimate the potential health benefits resulting from identifying controls and emissions reductions to comply with the proposed and alternative standard levels, incremental to a 2032 baseline in which the nation fully attains the current primary PM2.5 standards (i.e., an annual standard of 12 ug/m[3] and a 24-hour standard of 35 ug/m[3]). In contrast the main analysis in Chapter 5, we present the national health impacts and monetized benefits resulting only from the applied control strategies identified in Chapter 3 for each of the alternative PM2.5 standard levels in 2032. After applying the control strategies for the main analysis, we estimated that PM2.5 emissions reductions would still be needed in certain areas to meet the 10/35, 10/30, 9/35 and 8/35 alternative standard levels. Additional information on estimating the emission reductions needed to meet each of the alternative standards is available in section 2A.3.4.2 of Appendix 2A. Also, additional information on the emissions reductions still needed is available in Chapter 3, Section 3.2.5. Lastly, Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6 discuss the remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California that may still need emissions reductions. These challenges limit our ability to characterize how standard levels might be met given highly local influences that require more specific information beyond what is available for this type of national analysis. In this Appendix, we assume the remaining emissions reductions are identified to meet the proposed and more stringent alternative standard levels, and we present the resulting health and monetized benefits below. To the extent that the additional PM2.5 emissions reductions are not achieved, the health benefits reported below may be overestimated.
      For this appendix, the annual-mean PM2.5 concentration fields where existing and alternative NAAQS standard levels are just met were developed to estimate the emission changes resulting from fully meeting each of the proposed and more stringent alternative standard levels. Using the methods described in Chapter 5 of this RIA and the "Technical Support Document (TSD) for the PM2.5 NAAQS Proposal: Estimating PM2.5- and Ozone-Attributable Health Benefits" that will be published with this RIA, we estimate health benefits from achieving the proposed and more stringent alternative standard levels occurring as an increment to a 12/35 baseline. These benefits reflect the value of the avoided PM2.5-attributable deaths and the value of avoided morbidity impacts, including, for example, hospital admissions and emergency department visits for cardiovascular and respiratory health issues.
 5A.1	Benefits of the Proposed and More Stringent Alternative Standard Levels of Primary PM2.5 Standards
Applying the impact and valuation functions described in Chapter 5 and the TSD to the projected changes in PM2.5 yields estimates of the changes in physical damages (e.g., premature mortalities, cases of hospital admissions and emergency department visits) and the associated monetary values for those changes. Not all known PM health effects could be quantified or monetized. Tables 5A-1 through 5A-5 present the benefits results for the proposed and more stringent alternative annual primary PM2.5 standard levels. Table 5A-1 presents the estimated avoided incidences of PM-related illnesses and premature mortality for achieving each alternative standard level in 2032. Tables 5A-2 and 5A-3 present the monetized valuation benefits of the avoided morbidity and premature mortality (at a 3% and 7% discount rate respectively) of the health outcomes in Table 5A-1 for each alternative standard level in 2032.
Tables 5A-4 and 5A-5 present a summary of the monetized benefits nationally and by region of achieving the alternative standard levels. The regional monetized benefits in Table 5A-5 are presented in four regions: California, the Northeast, the Southeast, and the West. For Tables 5A-4 and 5A-5, the monetized value of unquantified effects is represented by adding an unknown "B" to the aggregate total. The estimate of total monetized health benefits is thus equal to the subset of monetized PM-related health benefits plus B, the sum of the non-monetized health and welfare benefits; this B represents both uncertainty and a bias in this analysis, as it reflects those benefits categories that we are unable to quantify in this analysis.
Table 5A-1 	Estimated Avoided PM-Related Premature Mortalities and Illnesses of Meeting the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (95% Confidence Interval)
Avoided Mortality
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope et al. (adult mortality ages 18-99 years)
                                     3,200
                               (2,300 to 4,100)
                                     3,800
                               (2,700 to 4,800)
                                     7,300
                               (5,200 to 9,300)
                                    15,000
                              (11,000 to 20,000)
Wu et al. (adult mortality ages 65-99 years)
                                     1,500
                               (1,300 to 1,700)
                                     1,800
                               (1,600 to 2,000)
                                     3,500
                               (3,100 to 3,900)
                                     7,400
                               (6,500 to 8,200)
Woodruff et al. (infant mortality)
                                      3.4
                                 (-2.1 to 8.6)
                                      3.9
                                 (-2.5 to 10)
                                      8.3
                                 (-5.2 to 21)
                                      18
                                  (-11 to 45)
Avoided Morbidity 
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
                                      260
                                 (190 to 330)
                                      300
                                 (220 to 380)
                                      570
                                 (410 to 720)
                                     1,200
                                (840 to 1,500)
Hospital admissions -- respiratory
                                      180
                                  (64 to 300)
                                      210
                                  (72 to 330)
                                      400
                                 (140 to 650)
                                      810
                                (280 to 1,300)
ED visits--cardiovascular
                                      500
                                (-190 to 1,200)
                                      570
                                (-220 to 1,300)
                                     1,100
                                (-430 to 2,600)
                                     2,300
                                (-900 to 5,500)
ED visits -- respiratory
                                      990
                                (200 to 2,100)
                                     1,100
                                (220 to 2,300)
                                     2,300
                                (450 to 4,700)
                                     4,700
                                (920 to 9,800)
Acute Myocardial Infarction
                                      57
                                  (33 to 80)
                                      65
                                  (38 to 91)
                                      120
                                  (72 to 170)
                                      250
                                 (150 to 350)
Cardiac arrest
                                      28
                                  (-11 to 63)
                                      32
                                  (-13 to 73)
                                      61
                                 (-25 to 140)
                                      130
                                 (-51 to 280)
Hospital admissions-- Alzheimer's Disease
                                      610
                                 (470 to 740)
                                      690
                                 (520 to 840)
                                     1,400
                               (1,000 to 1,700)
                                     3,000
                               (2,300 to 3,600)
Hospital admissions-- Parkinson's Disease
                                      87
                                  (45 to 120)
                                      100
                                  (53 to 150)
                                      200
                                 (100 to 290)
                                      430
                                 (220 to 610)
Stroke
                                      100
                                  (27 to 180)
                                      120
                                  (31 to 210)
                                      230
                                  (59 to 390)
                                      470
                                 (120 to 810)
Lung cancer
                                      120
                                  (38 to 200)
                                      140
                                  (44 to 230)
                                      270
                                  (83 to 440)
                                      550
                                 (170 to 890)
Hay Fever/Rhinitis
                                    30,000
                               (7,400 to 52,000)
                                    35,000
                               (8,500 to 60,000)
                                    66,000
                              (16,000 to 110,000)
                                    130,000
                              (33,000 to 230,000)
Asthma Onset
                                     4,600
                               (4,400 to 4,800)
                                     5,300
                               (5,100 to 5,500)
                                    10,000
                               (9,700 to 10,000)
                                    20,000
                              (19,000 to 21,000)
Asthma symptoms  -  Albuterol use
                                    650,000
                            (-320,000 to 1,600,000)
                                    750,000
                            (-360,000 to 1,800,000)
                                   1,400,000
                            (-690,000 to 3,400,000)
                                   2,900,000
                           (-1,400,000 to 7,000,000)
Lost work days
                                    230,000
                             (190,000 to 260,000)
                                    260,000
                             (220,000 to 300,000)
                                    500,000
                             (420,000 to 570,000)
                                   1,000,000
                            (850,000 to 1,200,000)
Minor restricted-activity days
                                   1,300,000
                           (1,100,000 to 1,600,000)
                                   1,500,000
                           (1,200,000 to 1,800,000)
                                   2,900,000
                           (2,400,000 to 3,400,000)
                                   5,900,000
                           (4,800,000 to 7,000,000)
Note: Values rounded to two significant figures.

Table 5A-2 	Monetized Avoided PM-Related Premature Mortalities and Illnesses of Meeting the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 3% discount rate; 95% Confidence Interval)
Avoided Mortality
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope et al. (adult mortality ages 18-99 years)
                                    33,000
                               (3,000 to 89,000)
                                    39,000
                              (3,500 to 100,000)
                                    75,000
                              (6,800 to 200,000)
                                    160,000
                              (14,000 to 430,000)
Wu et al. (adult mortality ages 65-99 years)
                                    16,000
                               (1,400 to 41,000)
                                    18,000
                               (1,700 to 49,000)
                                    36,000
                               (3,300 to 94,000)
                                    76,000
                              (7,000 to 200,000)
Woodruff et al. (infant mortality)
                                      38
                                 (-21 to 150)
                                      44
                                 (-25 to 180)
                                      94
                                 (-52 to 370)
                                      200
                                 (-110 to 800)
Avoided Morbidity 
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
	4.3
(3.1 to 5.4)
4.9
(3.5 to 6.2)
9.3
(6.8 to 12)
19
(14 to 24)
Hospital admissions -- respiratory
3.0
(0.70 to 5.3)
3.4
(0.79 to 5.9)
6.6
(1.5 to 11)
13
(3.1 to 23)
ED visits--cardiovascular
0.62
(-0.24 to 1.4)
0.7
(-0.27 to 1.6)
1.4
(-0.54 to 3.2)
2.9
(-1.1 to 6.7)
ED visits -- respiratory
0.92
(0.18 to 1.9)
1
(0.2 to 2.2)
2.1
(0.42 to 4.4)
4.4
(0.86 to 9.1)
Acute Myocardial Infarction
3.0
(1.7 to 4.1)
3.4
(2.0 to 4.7)
6.4
(3.7 to 9.0)
13
(7.6 to 18)
Cardiac arrest
1.1
(-0.43 to 2.4)
1.2
(-0.5 to 2.8)
2.3
(-0.95 to 5.2)
4.8
(-2 to 11)
Hospital admissions-- Alzheimer's Disease
7.8
(6 to 9.5)
8.8
(6.7 to 11)
18
(13 to 21)
38
(29 to 46)
Hospital admissions-- Parkinson's Disease
1.2
(0.62 to 1.7)
1.4
(0.72 to 2)
2.7
(0.86 to 2.4)
5.8
(3.1 to 8.3)
Stroke
3.7
(0.97 to 6.4)
4.4
(1.1 to 7.5)
8.3
(2.1 to 14)
17
(4.4 to 29)
Lung cancer
1.9
(0.59 to 3.1)
2.2
(0.68 to 3.6)
4.1
(1.3 to 6.7)
8.4
(2.6 to 14)
Hay Fever/Rhinitis
19
(4.7 to 33)
22
(5.4 to 38)
42
(10 to 73)
85
(21 to 150)
Asthma Onset
220
(200 to 230)
250
(230 to 260)
470
(440 to 500)
950
(890 to 1,000)
Asthma symptoms  -  Albuterol use
0.24
(-0.12 to 0.58)
0.27
(-0.13 to 0.67)
0.52 
(-0.25 to 1.3)
1.1
(-0.51 to 2.6)
Lost work days
41
                                  (35 to 47)
47
                                  (40 to 54)
90
                                  (76 to 100)
180
                                 (150 to 210)
Minor restricted-activity days
100
(55 to 160)
120
(63 to 180)
230
(120 to 350)
460
(240 to 700)
Note: Values rounded to two significant figures.



Table 5A-3 	Monetized Avoided PM-Related Premature Mortalities and Illnesses of Meeting the Proposed and More Stringent Alternative Primary PM2.5 Standard Levels for 2032 (Millions of 2017$, 7% discount rate; 95% Confidence Interval)
Avoided Mortality
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Pope et al. (adult mortality ages 18-99 years)
                                    30,000
                               (2,700 to 80,000)
                                    35,000
                               (3,100 to 94,000)
                                    67,000
                              (6,100 to 180,000)
                                    140,000
                              (13,000 to 380,000)
Wu et al. (adult mortality ages 65-99 years)
                                    14,000
                               (1,300 to 37,000)
                                    17,000
                               (1,500 to 44,000)
                                    32,000
                               (3,000 to 85,000)
                                    68,000
                              (6,300 to 180,000)
Woodruff et al. (infant mortality)
                                      38
                                 (-21 to 150)
                                      44
                                 (-25 to 180)
                                      94
                                 (-52 to 370)
                                      200
                                 (-110 to 800)
Avoided Morbidity 
                                     10/35
                                     10/30
                                     9/35
                                     8/35
Hospital admissions -- cardiovascular (age > 18)
                                      4.3
                                 (3.1 to 5.4)
                                      4.9
                                 (3.5 to 6.2)
                                      9.3
                                  (6.8 to 12)
                                      19
                                  (14 to 24)
Hospital admissions -- respiratory
                                      3.0
                                 (0.70 to 5.3)
                                      3.4
                                 (0.79 to 5.9)
                                      6.6
                                  (1.5 to 11)
                                      13
                                  (3.1 to 23)
ED visits--cardiovascular
                                     0.62
                                (-0.24 to 1.4)
                                      0.7
                                (-0.27 to 1.6)
                                      1.4
                                (-0.54 to 3.2)
                                      2.9
                                 (-1.1 to 6.7)
ED visits -- respiratory
                                     0.92
                                 (0.18 to 1.9)
                                       1
                                 (0.2 to 2.2)
                                      2.1
                                 (0.42 to 4.4)
                                      4.4
                                 (0.86 to 9.1)
Acute Myocardial Infarction
                                      2.9
                                 (1.7 to 4.0)
                                      3.3
                                 (1.9 to 4.6)
                                      6.3
                                 (3.6 to 8.8)
                                      13
                                  (7.4 to 18)
Cardiac arrest
                                       1
                                (-0.43 to 2.4)
                                      1.2
                                 (-0.5 to 2.7)
                                      2.3
                                (-0.94 to 5.2)
                                      4.7
                                 (-1.9 to 11)
Hospital admissions-- Alzheimer's Disease
                                      7.8
                                  (6 to 9.5)
                                      8.8
                                  (6.7 to 11)
                                      18
                                  (13 to 21)
                                      38
                                  (29 to 46)
Hospital admissions-- Parkinson's Disease
                                      1.2
                                 (0.62 to 1.7)
                                      1.4
                                  (0.72 to 2)
                                      2.7
                                 (1.4 to 3.9)
                                      5.8
                                 (3.1 to 8.3)
Stroke
                                      3.7
                                 (0.97 to 6.4)
                                      4.4
                                 (1.1 to 7.5)
                                      8.3
                                  (2.1 to 14)
                                      17
                                  (4.4 to 29)
Lung cancer
                                      1.3
                                 (0.41 to 2.2)
                                      1.5
                                 (0.48 to 2.5)
                                      2.9
                                 (0.9 to 4.7)
                                      5.9
                                 (1.8 to 9.6)
Hay Fever/Rhinitis
                                      19
                                  (4.7 to 33)
                                      22
                                  (5.4 to 38)
                                      42
                                  (10 to 73)
                                      85
                                  (21 to 150)
Asthma Onset
                                      130
                                 (130 to 140)
                                      160
                                 (140 to 160)
                                      290
                                 (270 to 310)
                                      590
                                 (550 to 630)
Asthma symptoms  -  Albuterol use
                                     0.24
                                (-0.12 to 0.58)
                                     0.27
                                (-0.13 to 0.67)
                                     0.52 
                                (-0.25 to 1.3)
                                      1.1
                                (-0.51 to 2.6)
Lost work days
                                      41
                                  (35 to 47)
                                      47
                                  (40 to 54)
                                      90
                                  (76 to 100)
                                      180
                                 (150 to 210)
Minor restricted-activity days
                                      100
                                  (55 to 160)
                                      120
                                  (63 to 180)
                                      230
                                 (120 to 350)
                                      460
                                 (240 to 700)
 Note: Values rounded to two significant figures.



Table 5A-4	Total Estimated Monetized Benefits of Meeting the Proposed and More Stringent Alternative Primary Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)
                               Benefits Estimate
                 10 ug/m[3] annual &
35 ug/m[3] 24-hour
                 10 ug/m[3] annual &
30 ug/m[3] 24-hour
                  9 ug/m[3] annual &
35 ug/m[3] 24-hour
                  8 ug/m[3] annual &
35 ug/m[3] 24-hour
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Pope (2019)
  3% discount rate
                                    $33 + B
                                    $39 + B
                                    $76 + B
                                   $160 + B
  7% discount rate
                                    $30 + B
                                    $35 + B
                                    $68 + B
                                    $140+ B
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Wu et al. (2020)
  3% discount rate
                                    $16 + B
                                    $19 + B
                                    $36 + B
                                    $77 + B
  7% discount rate
                                    $14 + B
                                    $17 + B
                                    $33 + B
                                    $69 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and welfare benefits.


















Table 5A-5	Total Estimated Monetized Benefits by Region of Meeting the Proposed and More Stringent Alternative Primary Standard Levels in 2032, Incremental to Attainment of 12/35 (billions of 2017$)
                               Benefits Estimate
                                    Region
                                 10 ug/m[3] 
                       annual &
35 ug/m[3] 24-hour
                                 10 ug/m[3] 
                       annual &
30 ug/m[3] 24-hour
                                  9 ug/m[3] 
                       annual &
35 ug/m[3] 24-hour
                  8 ug/m[3] annual &
35 ug/m[3] 24-hour
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Pope (2019)
  3% discount rate
                                  California
                                    $29 + B
                                    $32 + B
                                    $49 + B
                                    $76 + B
  
                                   Northeast
                                   $2.3 + B
                                   $2.6 + B
                                    $15 + B
                                    $46 + B
  
                                   Southeast
                                   $1.8 + B
                                   $1.8 + B
                                   $9.6 + B
                                    $26 + B
  
                                     West
                                  $0.086 + B
                                   $2.8 + B
                                   $2.4 + B
                                    $12 + B
  7% discount rate
                                  California
                                    $26 + B
                                    $28 + B
                                    $44 + B
                                    $68 + B
  
                                   Northeast
                                    $2 + B
                                   $2.3 + B
                                    $13 + B
                                    $41 + B
  
                                   Southeast
                                   $1.6 + B
                                   $1.6 + B
                                   $8.6 + B
                                    $23 + B
  
                                     West
                                  $0.077 + B
                                   $2.6 + B
                                   $2.2 + B
                                    $11 + B
Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from Wu et al. (2020) 
  3% discount rate
                                  California
                                    $14 + B
                                    $15 + B
                                    $24 + B
                                    $37 + B
  
                                   Northeast
                                   $1.1 + B
                                   $1.3 + B
                                   $7.2 + B
                                    $23 + B
  
                                   Southeast
                                   $0.84 + B
                                   $0.84 + B
                                   $4.4 + B
                                    $12 + B
  
                                     West
                                  $0.044 + B
                                   $1.4 + B
                                   $1.2 + B
                                   $5.9 + B
  7% discount rate
                                  California
                                    $13 + B
                                    $14 + B
                                    $21 + B
                                    $33 + B
  
                                   Northeast
                                    $1 + B
                                   $1.2 + B
                                   $6.4 + B
                                    $20 + B
  
                                   Southeast
                                   $0.75 + B
                                   $0.75 + B
                                    $4 + B
                                    $11 + B
  
                                     West
                                   $0.04 + B
                                   $1.3 + B
                                   $1.1 + B
                                   $5.3 + B
Note: Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not all possible to quantify all benefits due to data limitations in this analysis. "B" is the sum of all unquantified health and welfare benefits.



 5A.2		References
Pope III, CA, Lefler, JS, Ezzati, M, Higbee, JD, Marshall, JD, Kim, S-Y, Bechle, M, Gilliat, KS, Vernon, SE and Robinson, AL (2019). Mortality risk and fine particulate air pollution in a large, representative cohort of US adults. Environmental health perspectives 127(7): 077007.
Wu, X, Braun, D, Schwartz, J, Kioumourtzoglou, M and Dominici, F (2020). Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly. Science advances 6(29): eaba5692.
Woodruff, TJ, Darrow, LA and Parker, JD (2008). Air pollution and postneonatal infant mortality in the United States, 1999 - 2002. Environmental Health Perspectives 116(1): 110-115.



  -  -  - ENVIRONMENTAL JUSTICE
 Introduction
Executive Order 12898 directs the EPA to "achiev[e] environmental justice (EJ) by identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects" (59 FR 7629, February 16, 1994), termed disproportionate impacts in this chapter. Additionally, Executive Order 13985 was signed to advance racial equity and support underserved communities through Federal government actions (86 FR 7009, January 20, 2021). The EPA defines EJ as the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies. The EPA further defines the term fair treatment to mean that "no group of people should bear a disproportionate burden of environmental harms and risks, including those resulting from the negative environmental consequences of industrial, governmental, and commercial operations or programs and policies". Meaningful involvement means that: (1) potentially affected populations have an appropriate opportunity to participate in decisions about a proposed activity that will affect their environment and/or health; (2) the public's contribution can influence the regulatory Agency's decision; (3) the concerns of all participants involved will be considered in the decision-making process; and (4) the rule-writers and decision-makers seek out and facilitate the involvement of those potentially affected.
The term "disproportionate impacts" refers to differences in impacts or risks that are extensive enough that they may merit Agency action. In general, the determination of whether a disproportionate impact exists is ultimately a policy judgment which, while informed by analysis, is the responsibility of the decision-maker. The terms "difference" or "differential" indicate an analytically discernible distinction in impacts or risks across population groups. It is the role of the analyst to assess and present differences in anticipated impacts across population groups of concern for both the baseline and proposed regulatory options, using the best available information (both quantitative and qualitative) to inform the decision-maker and the public.
A regulatory action may involve potential EJ concerns if it could: (1) create new disproportionate impacts on minority populations, low-income populations, and/or Indigenous peoples; (2) exacerbate existing disproportionate impacts on minority populations, low-income populations, and/or Indigenous peoples; or (3) present opportunities to address existing disproportionate impacts on minority populations, low-income populations, and/or Indigenous peoples through the action under development.
The Presidential Memorandum on Modernizing Regulatory Review (86 FR 7223; January 20, 2021) calls for procedures to "take into account the distributional consequences of regulations, including as part of a quantitative or qualitative analysis of the costs and benefits of regulations, to ensure that regulatory initiatives appropriately benefit, and do not inappropriately burden disadvantaged, vulnerable, or marginalized communities." Under Executive Order 13563, federal agencies may consider equity, human dignity, fairness, and distributional considerations, where appropriate and permitted by law. For purposes of analyzing regulatory impacts, the EPA relies upon its June 2016 "Technical Guidance for Assessing Environmental Justice in Regulatory Analysis," which provides recommendations that encourage analysts to conduct the highest quality analysis feasible, recognizing that data limitations, time, resource constraints, and analytical challenges will vary by media and circumstance.
A reasonable starting point for assessing the need for a more detailed EJ analysis is to review the available evidence from the published literature and from community input on what factors may make population groups of concern more vulnerable to adverse effects (e.g., underlying risk factors that may contribute to higher exposures and/or impacts). It is also important to evaluate the data and methods available for conducting an EJ analysis. EJ analyses can be grouped into two types, both of which are informative, but not always feasible for a given rulemaking:
          Baseline: Describes the current (pre-control) distribution of exposures and risk, identifying potential disparities.
          Policy: Describes the distribution of exposures and risk after the regulatory option(s) have been applied (post-control), identifying how potential disparities change in response to the rulemaking.
EPA's 2016 Technical Guidance does not prescribe or recommend a specific approach or methodology for conducting EJ analyses, though a key consideration is consistency with the assumptions underlying other parts of the regulatory analysis when evaluating the baseline and regulatory options.
Analyzing EJ Impacts in This Proposal
In addition to the benefits assessment (Chapter 5), the EPA considers potential EJ concerns of this proposed rulemaking.  A potential EJ concern is defined as "the actual or potential lack of fair treatment or meaningful involvement of minority populations, low-income populations, tribes, and indigenous peoples in the development, implementation and enforcement of environmental laws, regulations and policies" (U.S. EPA, 2015). For analytical purposes, this concept refers more specifically to "disproportionate impacts on minority populations, low-income populations, and/or indigenous peoples that may exist prior to or that may be created by the proposed regulatory action" (U.S. EPA, 2015). Although EJ concerns for each rulemaking are unique and should be considered on a case-by-case basis, the EPA's EJ Technical Guidance (U.S. EPA, 2015) states that "[t]he analysis of potential EJ concerns for regulatory actions should address three questions: 
 Are there potential EJ concerns associated with environmental stressors affected by the regulatory action for population groups of concern in the baseline? 
 Are there potential EJ concerns associated with environmental stressors affected by the regulatory action for population groups of concern for the regulatory option(s) under consideration? 
 For the regulatory option(s) under consideration, are potential EJ concerns created [, exacerbated,] or mitigated compared to the baseline?" 
To address these questions, the EPA developed an analytical approach that considers the purpose and specifics of this proposed rulemaking, as well as the nature of known and potential exposures and health impacts. The purpose of this Regulatory Impact Analysis (RIA) is to provide estimates of the potential costs and benefits of the illustrative national control strategies in 2032 for the alternative standard levels analyzed.  The alternative standard levels evaluated in the RIA are more stringent than the current standards. This means that in reducing emissions to reach lower standard levels, some areas above or near the current standards are expected to experience greater air quality improvements, and thus health improvements, than other areas already at or below lower alternative standard levels. As differences in both exposure and susceptibility (i.e., intrinsic individual risk factors) contribute to environmental impacts, the analytical approach used here first determines whether exposure (Section 6.2) and health effect (Section 6.3) disparities exist under the baseline scenario. The approach then evaluates if and how disparities are impacted when illustrative emissions control strategies are analyzed. Both the exposure and health effects analyses were developed using available scientific evidence from the current PM NAAQS reconsideration, for the future year 2032, and are associated with various uncertainties. Consistent with the methods the EPA uses to fully characterize the benefits of a regulatory action, these EJ analyses evaluate the full set of exposure and health outcome distributions resulting from this proposed action at the national scale. Recognizing, however, that only some areas of the U.S. are projected to exceed the proposed alternative standard levels, the EPA conducted a case study analysis to further examine the impacts of this proposed action on populations living in areas with the highest exposures and health risks in the baseline. By focusing on locations that are projected to exceed one of the analytical alternatives examined, this case study analysis considers the magnitude of exposure and health effect disparities across the smaller geographical scale where the impacts of alternative standard levels are expected (Section 6.4).
The EJ exposure assessment portion of the analysis focuses on associating ambient PM2.5 concentrations with various demographic variables. Because this type of analysis requires less a priori information, we were able to include a broad array of demographic characteristics. Estimating actual health outcomes modified by demographic population requires additional scientific information, which constrained the scope of the second portion of the assessment. We focused the EJ health effects analysis on populations and health outcomes with the strongest scientific support (U.S. EPA, 2019, U.S. EPA, 2020, U.S. EPA, 2022a). However, the EJ health effects analysis does not include information about differences in other factors that could affect the likelihood of adverse impacts (e.g., access to health care, BMI, etc.) across groups, due to limitations on the underlying data. Both the EJ exposure and health effects analyses are subject to uncertainties related to input parameters and assumptions. For example, both analyses focus on annual PM2.5 concentrations and do not evaluate whether concentrations experienced by different groups persist across the distribution of daily PM2.5 exposures. Additionally, the EJ health effects analysis is subject to additional uncertainties related to concentration-response relationships and baseline incidence data.
Since NAAQS RIAs are national-level assessments and air quality issues are complex and local in nature, the RIA presents costs and benefits of PM2.5 emission reductions associated with illustrative control strategies. Correspondingly, the main EJ analyses in this chapter also evaluates implications of air quality surfaces associated with the illustrative emission control strategies for both current (i.e., baseline) and alternative standard levels. However, the illustrative control strategies do not result in all counties identifying emissions reductions needed to meet either the current or more stringent alternative standard levels (Chapters 3). As such, the appendix to this chapter provides EJ implications of air quality scenarios associated with meeting the standards (labelled in some Section 6.6 figures as "Standards") and allows for direct comparison with results associated with the illustrative emissions control strategies (labelled in some Section 6.6 figures as "Controls"). 
Complex analyses using estimated parameters and inputs from numerous models are likely to include multiple sources of uncertainty. As this analysis is based on the same PM2.5 spatial fields as the benefits assessment (Appendix 2A), it is subject to similar types of uncertainty (Chapter 5, Section 5.4). A particularly germane limitation is the illustrative nature of the emission reductions in NAAQS RIAs; as a result, the EJ analyses in this chapter illustrate the estimated EJ impacts of the illustrative control strategies and may not reflect state-level implementation decisions. Relatedly, while proximity analyses can sometimes provide limited EJ information regarding the demographics of populations living near emissions sources, in this case state-level implementation decisions are unknown. Therefore, proximity analyses of populations living near individual sources that could potentially install controls would be highly uncertain and were not conducted in this EJ assessment. However, the EJ exposure and health analyses included in this chapter provide more relevant and high-confidence information than a proximity analysis, since these analyses relate actual PM2.5 concentrations (not just emissions) to various demographic populations. 
As with all EJ analyses, data limitations make it quite possible that there exist additional disparities unidentified in this analysis. This is especially relevant for potential EJ characteristics and more granular spatial resolutions that were not evaluated. For example, results are provided here at national- and county-levels, potentially masking tract- or block-level EJ impacts. Additional uncertainties are briefly discussed in the summary of this analysis (Section 6.5).

EJ Analysis of Exposures Under Current Standard and Alternative Standard Levels
This EJ PM2.5 exposure  analysis aims to evaluate the potential for EJ concerns related to PM2.5 exposures among potentially vulnerable populations from three perspectives, which correspond to the three EJ questions listed in Section 6.1. Specifically, the following questions are addressed:
 Are there disproportionate PM2.5 exposures under baseline/current PM NAAQS standard levels (question 1)?
 Are there disproportionate PM2.5 health effects under illustrative alternative PM NAAQS standard levels (question 2)?
 Are PM2.5 exposure disparities created, exacerbated, or mitigated under illustrative alternative PM NAAQS standard levels as compared to the baseline (question 3)?
Population variables considered in this EJ exposure assessment include race/ethnicity, poverty status, educational attainment, age, and sex (Table 6-1). The results presented below reflect the control strategies described in Chapter 3.

Table 6-1 	Populations Included in the PM2.5 Exposure Analysis
                                  Population
                                    Groups
                                   Ethnicity
                            Hispanic; Non-Hispanic
                                     Race
                     Asian; American Indian; Black; White
                            Educational Attainment
               High school degree or more; No high school degree
                                Poverty Status
                Above the poverty line; Below the poverty line
                                      Age
             Children (0-17); Adults (18-64); Older Adults (65-99)
                                      Sex
                                 Female; Male

Total Exposure
We begin by considering the first two questions from EPA's EJ Technical Guidance (i.e., are there potential EJ concerns 1) in the baseline, and 2) for the regulatory option(s) under consideration) with respect to PM2.5 exposures. Estimated exposures as measured by the projected national and regional ambient PM2.5 concentrations experienced by various demographic populations for the current standards or alternative standard levels analyzed are provided in Sections 6.2.1.1 and 6.2.1.2, respectively. Information regarding identified emissions controls, as well as areas where air quality has been adjusted, is available in Chapters 2 and 3.
National
As NAAQS are national rules, we begin by evaluating annual average PM2.5 concentrations in absolute terms projected to be experienced by various demographic groups that may be of EJ concern, averaged across the contiguous US (national). Figure 6-1 shows the national average annual PM2.5 concentrations associated with the control strategy baseline scenario for the current annual standard of 12 ug/m[3] and current 24-hour standard of 35 ug/m[3] (12/35) as a heat map, with higher estimated annual PM2.5 concentrations shown in darker shades of blue. Populations with potential EJ concerns can be compared to the reference/overall population and/or other populations (i.e., White, Non-Hispanic, above the poverty line, more educated, and adults 18-64). On average, Asians, Blacks, Hispanics, and those over 25 without a high school education live in areas with higher annual PM2.5 concentrations than the reference population, with Hispanic and Asian populations experiencing the highest relative concentrations. The most substantial discrepancy in national average annual PM2.5 exposures is noted between Hispanic populations and non-Hispanic populations. It is noteworthy that the national average annual exposures for all demographic groups are well below the current annual NAAQS.

                                       
Figure 6-1	Heat Map of National Average Annual PM2.5 Concentrations (ug/m[3]) by Demographic for Current and Alternative PM NAAQS Levels (10/35, 10/30, 9/35, and 8/35) After Application of Controls
	
Figure 6-1 also shows the national average total PM2.5 concentrations associated with control strategies applied for the potential alternative annual and 24-hour standard levels: 10/35, 10/30, 9/35, and 8/35. Although average concentrations under 10/35 and 10/30 are similar, most demographic groups are projected to experience greater annual PM2.5 concentration reductions after implementing the illustrative control strategies for lower alternative annual standard levels. However, after implementing the illustrative control strategies associated with all alternative standard levels evaluated, Asians, Blacks, Hispanics, those over 25 without a high school education, and those under the poverty level live in areas with higher projected annual PM2.5 concentrations than the reference population, again with Hispanic and Asian populations experiencing the highest average concentrations. This suggests that while emissions reductions associated with more stringent standard levels will result in air quality improvements across the board, disparities seen in the baseline likely remain, at least when considering the average national exposure levels by demographic group. These annual average exposures are also well below the current standards and all alternative standard levels evaluated.
While average PM2.5 concentrations can provide some insight when comparing population impacts, information on the full distribution of concentrations affords a more comprehensive understanding. This is because both demographic groups and ambient concentrations are unevenly distributed, meaning that average exposures may mask important disparities that occur on a more localized basis. To evaluate how the distribution of annual exposures varies within and across demographic groups at the county level, we plot the full array of exposures (including very high and very low exposures) projected to be experienced by different subpopulations. Distributional figures present the running sum of each population, converted to a percentage, on the y-axes (i.e., cumulative percent). Conversion of each total population to a percent of the total permits direct comparison of annual PM2.5 exposures across demographic populations with different absolute numbers. The x-axes show annual PM2.5 concentrations (ug/m[3]) from low to high. For Figure 6-2, PM2.5 concentrations are county-level averages from all counties in the contiguous U.S. In other words, plots compare the running sum of each population against increasing annual PM2.5 concentrations.  
Information on the distribution of county-level PM2.5 concentrations associated with the illustrative control strategies associated with the current and alternative PM standard levels across and within populations can be found in Figure 6-2. The reference population in the top row shows that emissions reductions associated with the current or alternative standard levels yields a fairly smooth S-curve, with the majority of the population experiencing annual PM2.5 concentrations between 4 and 10 ug/m[3] under air quality scenarios associated with the control strategies for current standards (12/35). Lower PM2.5 concentrations remain similar across lower alternative standard levels, while higher concentrations are reduced.
To evaluate differential exposures, populations of potential EJ concern are shown with a colored line and can be compared to the respective reference population shown with a black line. Colored lines to the right of a black line suggest that the potential EJ population is experiencing disproportionately higher PM2.5 concentrations. The greatest disproportionate exposures are observed when considering ethnicity. The Hispanic population (dark orange) is predicted to experience higher PM2.5 concentrations than the non-Hispanic population (black) across a large portion of the exposure distribution. This difference is approximately 1 ug/m[3] at all concentrations above 6 ug/m[3]. 
Similarly, when considering race across the various standard levels evaluated, portions of the Asian (bright orange) and Black (blue) populations live in areas with higher PM2.5 concentrations than the White (black) population, and portions of the American Indian (light orange) population live in areas with lower PM2.5 concentrations. Interestingly, Black and White population exposures are very similar at concentrations above about 8 ug/m[3] under air quality scenarios associated with controls for 12/35 and about 7.5 ug/m[3] air quality scenarios associated with controls for 8/35. This could suggest that exposure disparities in the Black population occur in rural areas with lower PM2.5 concentrations. The Asian population experiences higher PM2.5 concentrations across a larger portion of the distribution, but higher exposures become more similar to the White distribution at lower alternative PM standard levels. Those living below the poverty level, those over 25 without a high school diploma, and the two sexes experience virtually identical distributions of exposure of all standard levels. 


                                       
Figure 6-2	National Distributions of Annual PM2.5 Concentrations by Demographic for Current and Alternative PM NAAQS Levels After Application of Controls

Regional
As both emissions changes and overrepresentation of people/communities of color (POC/COC) vary with respect to location, we also parse the aggregated and distributional absolute PM2.5 concentration by geographic region (southeast [SE], northeast [NE], west [W], and California [CA]) (Figure 6-3 and Figure 6-4).[,] Across all current and alternative standard levels, average annual reference PM2.5 concentrations are highest in CA, followed by the SE and NE, and are lowest in the W (Figure 6-3). Comparing populations of potential EJ concern with their respective references within each region, disparities are observed in all four regions, though not all for the same demographic populations. 
Regarding racial and ethnic disparities, annual PM2.5 concentrations for Black populations are substantially higher in the NE across the full distribution, but only slightly higher in the W and in CA. Also, concentrations for Black populations are slightly higher than concentrations for White populations only in the lowest ~50 percent of the populations in the SE. PM2.5 concentrations among Hispanics are higher than concentrations for Non-Hispanic populations in all four regions, although disparities are largest at higher PM2.5 concentrations in CA and smallest at lower PM2.5 concentrations in the NE. Total PM2.5 concentrations for Asian populations in the NE and SE are higher than the reference PM2.5 concentrations, but similar in the W and CA.
People living below the poverty level and people over 25 without a high school diploma experience similar annual PM2.5 concentrations to those above the poverty line and with a high school diploma in the NE, SE, and W, but experience higher PM2.5 concentrations in CA under controls associated with the current standards (12/35). Older adults (65-99) experience slightly lower PM2.5 concentrations associated with the illustrative control strategies for the more stringent alternative standard levels in all regions. Children experience higher annual PM2.5 concentrations in some areas in the W.

                                       
Figure 6-3	Heat Map of Regional Average Annual PM2.5 Concentrations (ug/m[3]) by Demographic for Current (12/35) and Alternative PM NAAQS Levels (10/35, 10/30, 9/35, and 8/35) After Application of Controls
                                       
Figure 6-4	Regional Distributions of Annual PM2.5 Concentrations by Demographic for Current and Alternative PM NAAQS Levels After Application of Controls
Exposure Changes
In addition to evaluating total/absolute exposures under control strategies associated with current/baseline and potential alternative standard levels (Section 6.2), we evaluate the extent to which exposures change for each demographic population, to compare improvements in air quality among populations. This begins to address the third question from EPA's EJ Technical Guidance: how disparities observed between demographic groups in the baseline scenario (12/35) are impacted (e.g., exacerbated/mitigated) under alternative standard levels. The national and regional changes in PM2.5 concentrations experienced by different demographic populations for the current and alternative standard levels are provided in Sections 6.2.2.1 and 6.2.2.2, respectively.
National
First, we consider how average exposures change across different demographic groups at the national level. Figure 6-5 shows the average PM2.5 concentration reduction and Figure 6-6 shows the distributions of county-level PM2.5 concentration exposure reductions for each population when moving from the current standard to alternative standard levels. The magnitude of these numbers is quite small because they are national averages and include individuals residing in 12km x 12km gridded areas not predicted to experience PM2.5 concentration reductions. For example, Figure 6-6 shows that only ~15% of the non-Hispanic population will experience PM2.5 concentration reductions when moving from the baseline of control strategies associated with the current standards to control strategies associated with the alternative standard levels of 10/35, whereas ~30% of the Hispanic population will experience PM2.5 concentration reductions under air quality scenarios associated with the same control strategies. Figure 6-6 also shows that greater reductions are expected in the ~30% of the Hispanic population projected to experience PM2.5 concentration reductions than the ~15% of the non-Hispanic population projected to experience PM2.5 concentration reductions. Together, these differences lead to an estimated four-fold greater reduction in average PM2.5 concentrations when moving from the baseline of air quality associated with control strategies for the current standards of 12/35 to control strategies associated with the proposed alternative standard level of 10/35 (12/35-10/35) in Figure 6-5. Colored lines again represent potential populations of EJ concern and black lines the respective reference population; however, in these figures, colored lines to the right of the black line now indicate greater relative air quality improvements. 
In general, populations with higher total PM2.5 exposures (Section 6.2.1) are also expected to see the greatest reductions in average PM2.5 concentrations under the alternative standard levels. On average nationwide, Asians, Hispanics, and those over 25 without a high school diploma are predicted to experience substantially greater PM2.5 concentration reductions under air quality scenarios associated with control strategies for all alternative standard levels as compared to the reference population. Black populations may experience slightly smaller PM2.5 concentration reductions for alternative standard levels of 12/35-10/35 and 12/35-10/30 as compared to either the reference/overall population or other populations (Asian, Hispanic, and those over 25 without a high school diploma), but that disparity is smaller for control strategies associated with 12/35-9/35 or 12/35-8/35, and in fact average PM2.5 concentration improvements are on par or slightly greater than in the reference population for these more stringent alternative standard levels. 
                                       
Figure 6-5	Heat Map of National Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

                                       
Figure 6-6	National Distributions of Annual PM2.5 Concentration Reductions for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Regional
Next, we consider how average exposures change across different demographic groups at the regional level. Information on average and distributional exposure changes by region when moving from control strategies associated with the current standard to control strategies associated with alternative standard levels are available in Figure 6-7 and Figure 6-8, respectively. Similar to the average annual PM2.5 concentrations going from highest in CA, followed by the SE and NE, and being lowest in the W (Section 6.2.1.2), average PM2.5 concentration reductions also follow the same order. Comparing how these reductions affect populations of potential EJ concern with each region, we note that there are differences across regions in terms of which demographic populations benefit the most (or least), particularly for 12/35-9/35 or 12/35-8/35. 
Going through each region, the largest regional PM2.5 concentration reductions occur in CA, where Blacks, Hispanics, those below the poverty line, and those less educated are expected to experience greater PM2.5 concentration reductions when moving from the baseline to alternative standard levels. In the SE, there are greater PM2.5 concentration reductions for Asians, Hispanics, and those less educated under all alternative standard levels. Asian and Black populations in CA experience greater PM2.5 concentration reductions when moving from 12/35-8/35. In the NE for 12/35-9/35 and 12/35-8/35 there are greater PM2.5 concentration reductions for Blacks, and slightly greater PM2.5 concentration reductions for Asians. This is similar to the W, where Blacks, Hispanics, and those less educated are predicted to see greater PM2.5 concentration reductions for 12/35-9/35 and 12/35-8/35. 

                                       
Figure 6-7	Heat Map of Regional Reductions in PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

                                       
Figure 6-8	Regional Distributions of Total PM2.5 for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Proportional Changes in Exposure
To put the changes in exposure discussed in section 6.2.2 in perspective, especially in light of the disparities in the exposure baseline across population groups as discussed in section 6.2.1, it helps to consider whether the absolute changes represent equivalent (proportional) reductions in exposure. In some cases, moving to more stringent control strategies could both reduce total average exposures and reduce disparities in exposure across groups. However, it can be difficult to determine the relative proportionality of changes in PM2.5 concentrations for demographic populations using just the absolute exposure changes when moving from the current standard to a potential alternative standard level, like those shown in section 6.2.2. 
In this section, the proportionality of PM2.5 concentration changes when moving from the current (baseline) to alternative standard levels under air quality scenarios associated with the illustrative emission control strategies is directly calculated. To compare air quality improvements on a percentage basis, first exposures under the current standard are divided by exposures under the alternative standard levels at the national and regional levels. Those results are then subtracted from 1 to get the remainder, and then multiplied by 100 to get the percent change. For example, if the average annual PM2.5 concentration in population A is 7 under control strategies associated with the current standard and 6 under an alternative standard level, the proportional change would be (1-(6/7)) x 100 = (1-0.857) x 100 = 0.143 x 100 = 14.3%. If the average annual PM2.5 concentration in population B is 6 under the current standard and 5 under an alternative standard level, the proportional change would be (1-(5/6)) x 100 = (1-0.833) x 100 = 0.167 x 100 = 16.7%. Therefore, even though the absolute reduction is equivalent, population B would experience a proportionally larger reduction under controls strategies associated with the alternate standard level because the starting concentration was lower. As average PM2.5 concentrations have been representative of the distributions, for simplicity we only present the average proportional reduction for each population and scenario, at the national and regional levels (6.2.3.1 and 6.2.3.2).

National
Nationally, alternative PM standard levels associated with control strategies reduce the average PM2.5 exposure concentrations experienced by the reference population by an increasing percentage as the alternative standards are lowered, with a 0.7% improvement for 12/35-10/35 and a 3.8% improvement for 12/35-8/35 (Figure 6-9). Non-Hispanics experience slightly smaller proportional reductions, 0.5% for 12/35-10/35 and 3.4% for 12/35-8/35. Hispanics and Asian populations are predicted to experience the proportionally largest reductions in PM2.5 concentrations under all alternative standard levels evaluated, followed by those less educated. Black populations experience smaller proportional PM2.5 concentration improvements than Whites when moving from 12/35-10/35 or 12/35-10/30, but greater proportional PM2.5 concentration improvements than Whites when moving from 12/35-9/35 or 12/35-8/35. This is likely due to the fact that gaps between the PM2.5 concentrations experienced by Black populations vs. those experienced by White populations in the baseline is greater at lower ambient PM2.5 concentrations (Figure 6-2, Figure 6-4, Figure 6-6, and Figure 6-8), with Black populations experiencing higher PM2.5 levels relative to Whites throughout the distribution but particularly at lower ambient concentrations. This leads to proportionally greater improvements for Black populations (i.e., a narrowing of disparities as compared to White populations) at lower alternative PM2.5 standards. Native Americans are estimated to experience the opposite, with slightly greater proportional PM2.5 concentration improvements than Whites when moving from 12/35-10/35 or 12/35-10/30, and smaller proportional PM2.5 concentration improvements than Whites when moving from 12/35-9/35 or 12/35-8/35. Older adults are estimated to experience proportionally smaller reductions in PM2.5 concentrations under all alternative standard levels evaluated; however older adults experience lower PM2.5 concentrations under air quality scenarios associated with control strategies for the baseline and all alternative NAAQS (Figure 6-1 through Figure 6-8).
                                       
Figure 6-9	Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Regional
Regionally the greatest proportional reductions are estimated for CA when moving from the current to all alternative standards under air quality associated with the illustrative emission control strategies (Figure 6-10). Like the national analysis, percent reductions get larger as alternative standard levels decrease. In addition to trends observed at the national level (Section 6.2.3.1), there are notable proportional reductions of PM2.5 concentrations for Hispanic populations in CA, the SE, and the W, as well as for Asian populations in the SE and CA for all alternative standard levels and in the W for 12/35-8/35. 

                                       
Figure 6-10	Heat Map of Regional Percent Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) for Demographic Groups When Moving from Current (12/35) to Alternative PM NAAQS Level (10/35, 10/30, 9/35, and 8/35 After Application of Controls

EJ Analysis of Health Effects under Current Standards and Alternative Standard Levels
In addition to comparing PM2.5 concentrations for potential demographic populations of concern in the EJ exposure analysis (Section 6.2.1), we conducted an EJ analysis of health effects. This analysis aims to evaluate the potential for EJ concerns related to PM2.5 health outcomes among populations potentially at increased risk of or to PM2.5 exposures from three perspectives, which correspond to the three EJ questions listed in Section 6.1. Specifically, the following questions are addressed:
 Are there disproportionate PM2.5 health effects (e.g., mortality) under baseline/current PM NAAQS standard levels (question 1)?
 Are there disproportionate PM2.5 health effects under illustrative alternative PM NAAQS standard levels (question 2)?
 Are disparities in PM2.5 health effects created, exacerbated, or mitigated under illustrative alternative PM NAAQS standard levels as compared to the baseline (question 3)?
There is considerable scientific evidence that specific populations and lifestages are at increased risk of PM2.5-related health effects (Section 1.5.5 and Chapter 12 of  U.S. EPA, 2019). Factors that may contribute to increased risk of PM2.5-related health effects include lifestage (e.g., children), pre-existing diseases (e.g., cardiovascular disease and respiratory disease), race/ethnicity, and socioeconomic status. Of these factors, the ISA found "adequate evidence" indicating that children and some races are at increased risk of PM2.5-related health effects, in part due to disparities in exposure. However, we lack associated epidemiologic information that would enable us to conduct a health effects analysis for children.
Therefore, due to the limited availability of both new scientific evidence in this NAAQS review and input information (U.S. EPA, 2019, U.S. EPA, 2022a), the one health endpoint for which we evaluate EJ implications is premature mortality. The PM ISA and PM ISA Supplement provided evidence that there are consistent racial and ethnic disparities in PM2.5 exposure across the U.S., particularly for Black/African Americans, as compared to non-Hispanic White populations. Additionally, some studies provided evidence of increased PM2.5-related mortality and other health effects from long-term exposure to PM2.5 among Black populations. Taken together, the 2019 PM ISA concluded that the evidence was adequate to conclude that race and ethnicity modify PM2.5-related risk, and that non-White individuals, particularly Black individuals, are at increased risk for PM2.5-related health effects, in part due to disparities in exposure (U.S. EPA, 2019, U.S. EPA, 2022a). 
As such, this EJ health analysis assesses long-term PM2.5-attributable mortality rates stratified by racial and ethnic demographic populations.[,] Mortality is presented as a rate per 100,000 (100k) individuals to permit direct comparisons between population demographics with different total population counts. Additional information on the concentration-response functions and baseline incidence rates used as input information in this health EJ analysis can be found in Section 6.6.1.2 and Appendix C of the draft PM Policy Assessment (U.S. EPA, 2021).
Total Mortality Rates 
National and regional relative disparities between the demographic-specific mortality rates under air quality scenarios associated with control strategies for the current and potential alternative lower standard levels are provided in Sections 6.3.1.1 and 6.3.1.2, respectively.
National
Figure 6-11 and Figure 6-12 show the national averages and distributions of estimated mortality rates per 100k individuals for each demographic population over the age of 64. These estimates are calculated using various inputs, including air quality changes, concentration-response functions, and baseline incidence. The greater magnitude concentration-response relationship between exposure and mortality for the Black population of older adults found by Di et al., 2017 results in estimated higher mortality rates in Blacks. Higher estimated average PM2.5 concentrations among Hispanics, as discussed in the previous sections, leads to larger mortality rates in Hispanics than in non-Hispanics even though the baseline incidence rate in Hispanics is slightly lower than the overall rate (U.S. EPA, 2021, Appendix C).

                                       
Figure 6-11	Heat Map of National Average Annual Total Mortality Rates (per 100K) for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls

                                       
Figure 6-12	National Distributions of Total Annual Mortality Rates for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls

Regional
Regionally, the highest mortality rates for reference populations are in CA under air quality scenarios associated with control strategies for both current and alternative PM standard levels, followed by the NE, SE, and then the W (Figure 6-13 and Figure 6-14). Total mortality rates in the reference populations decrease slightly under alternative standard levels in all regions, and the most in CA. Within each of the four regions, average and distributional mortality rates are highest among Blacks and lowest among Asians, although there are differences in the ordinality of other races and ethnicities across regions. Interestingly, the distribution of Hispanic mortality rates in the SE suggests there may be a subset of locations in which Hispanics have higher baseline incidence rates, as the PM2.5 concentration differentials between Hispanic and non-Hispanic populations remained fairly constant across PM2.5 concentration distributions (Figure 6-4).
                                       
Figure 6-13	Heat Map of Regional Average Annual Total Mortality Rates (per 100K) for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls

                                       
Figure 6-14	Regional Distributions of Total Annual Mortality Rates for Demographic Groups for Current and Alternative PM NAAQS Levels After Application of Controls

Mortality Rate Changes
National and regional relative changes in disparities between the demographic-specific mortality rates when moving from air quality associated with control strategies for the current to alternative standard levels are provided in Sections 6.3.2.1 and 6.3.2.2, respectively.
National
Nationally, the rate of PM2.5-attributable mortality is estimated to decrease for all races and ethnicities when moving from current alternative standard levels, and more so under lower alternate standard levels (Figure 6-15 and Figure 6-16). In addition, reductions in mortality rates are larger for all other races as compared to Whites, and for Hispanics as compared to non-Hispanics. 
                                       
Figure 6-15	Heat Map of National Average Annual Mortality Rate Reductions (per 100k) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

                                       
Figure 6-16	National Distributions of Annual Mortality Rate Reductions for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

Regional
Of the four regions, the largest mortality rate reductions for the greatest percent of each population are estimated in CA when moving from the current to alternative standard levels (Figure 6-17 and Figure 6-18). Reductions are smaller in the other three regions, although reductions become more substantial in the other three regions for 12/35-9/35 or 12/35-8/35. When comparing across race and ethnicities, Blacks are predicted to see the largest mortality rate reductions and Whites are predicted to see the smallest rate reductions.
                                       
Figure 6-17	Heat Map of Regional Average Annual Mortality Rate Reductions (per 100k) for Demographic Groups When Moving from Current and Alternative PM NAAQS Levels After Application of Controls
                                       
Figure 6-18	Regional Distributions of Annual Mortality Rate Reductions for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls


Proportional Changes in Mortality Rates
The proportional change in mortality rate for different demographic groups when moving from current to alternative PM2.5 standard levels associated with the illustrate control strategies is calculated in the same way we estimated proportional changes in PM2.5 concentrations in Section 6.2.3. Briefly, the mortality rate under the alternative standard level is divided by the mortality rate under the current standard, then subtracted from 1, and multiplied by 100 to get a percent. As the average mortality rates have been representative of the distributions, for simplicity we again only present the average proportional change for each population and scenario, at the national and regional levels (6.3.3.1 and 6.3.3.2).

National
Hispanics and Asians are estimated to experience proportionally larger reductions in mortality rates when moving from the current to alternative standard levels associated with control strategies, with the percent relative improvement increasing as standards are lowered (Figure 6-19).
                                       
Figure 6-19	Heat Map of National Average Percent Mortality Rate Reductions (per 100k People) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls


Regional
Hispanics and Asians are estimated to experience proportionally larger reductions in mortality rates when moving from current standard to alternative standard levels associated with control strategies in the SE and CA. Blacks experience proportionally larger reductions in mortality rates for 12/35-9/35 and 12/35-8/35.

                                       
Figure 6-20	Heat Map of Regional Average Percent Mortality Rate Reductions (per 100k) for Demographic Groups When Moving from Current to Alternative PM NAAQS Levels After Application of Controls

EJ Case Study of Exposure and Health Effects in Impacted Areas 
The analyses presented above in sections 6.2 and 6.3 encompass the entire contiguous U.S., including areas that already meet potential alternative standards. Such areas would not be required to reduce emissions to meet the proposed more stringent standards, and therefore PM2.5 concentrations in these areas would not be expected to change as a result of EPA adopting more stringent PM2.5 standard level(s). Including such areas in the analysis reduces the resulting average exposure and mortality rate change estimates and potentially masks proportionally greater changes (i.e., reductions) in exposure and health impacts in areas that are projected to exceed the proposed alternative standards in the baseline. Areas that exceed the proposed alternative standards can be expected to experience the greatest PM2.5 concentration changes following the application of control strategies. Therefore, in addition to analyses of the whole contiguous U.S. (Sections 6.2 and 6.3), here we perform an EJ case study focusing on areas that are predicted to experience PM2.5 concentration changes when moving from the current standard of 12/35 to the alternative standard 9/35 under the emission control scenario described in Chapter 3. 
This case study is intended to illustrate how changes in higher concentration areas compare to changes at the national scale; for purposes of this illustration, we focus on the single lower alternative standard of 9/35. The specific areas in which PM2.5 concentrations change when moving to a lower standard differ with each alternative lower standard, with the number of areas increasing as the standard lowers. As such, fewer areas would be included if we analyzed 10/35 or 10/30, and additional areas would be included if we analyzed 8/35. Also, the case study analysis is limited to the assessment of average PM2.5 exposures and risks and does not include all of the distributional information presented in the national analysis above. It is important to note that some of the limitations and caveats that affect the national scale analysis become even more relevant to this case study analysis. For example, 12 km grid scale air quality information may not be sufficiently resolved to detect hyperlocal differences in population exposures; this limitation becomes more important as we try to dial in on changes in exposure and risk in the considerably smaller areas included in the case study. Finally, the illustrative nature of the emission control strategy leading to emissions reductions in this NAAQS RIA may lead to increased uncertainties when looking only at areas in which PM2.5 concentrations are predicted to change, as PM2.5 concentrations in this analysis may not reflect state-level implementation decisions.
The subset of areas in which PM2.5 concentrations are predicted to change when moving from 12/35 to 9/35 are colored blue in Figure 6-21. The subset of areas constitutes approximately 5% of the area across the contiguous U.S. and just over a quarter of the population. Information regarding the other ~95% of areas, which are projected to already meet a standard of 9/35 and therefore are not projected to experience a change in PM2.5 concentrations under this more stringent standard, is also provided in certain figures for context.
                                       
Figure 6-21	Map of Areas in which PM2.5 Concentrations Change when Moving from 12/35 to 9/35 After Application of Controls

Exposures 
Average annual PM2.5 concentrations and concentration changes for the various demographic populations analyzed are presented for the subset of areas in Figure 6-22. Columns labelled `12/35 (Subset)' and `9/35 (Subset)' provide average PM2.5 concentrations experienced by populations residing in the subset of ~5% of areas (~25% of people) where PM2.5 concentration changes when moving from 12/35 to 9/35. The far-right column labeled `No PM Changes' provides the average PM2.5 concentrations experienced by populations residing in the other ~95% of areas (~75% of people) that do not experience a change in PM2.5 concentration under the more stringent standard of 9/35. The column labelled `12/35-9/35 (Subset)' also shows the average PM2.5 concentration reduction afforded to each population residing in the subset of areas where concentrations change, when moving from 12/35 to 9/35. 
Comparing these averages to national-level estimates (Figure 6-1), we note that as expected, we observe higher average baseline exposures in areas where air quality changes, but the overall pattern of exposure across groups is fairly similar to the national pattern. Like Figure 6-1, Figure 6-22 shows that the most substantial disparity in average annual PM2.5 exposures occurs between Hispanic populations and non-Hispanic populations. Further, in comparing the subset of areas where air quality changes to areas where it does not change, we note that average exposures in the subset of areas where air quality does change are at least 1 ug/m[3] higher than averages in the areas where air quality does not change under both the baseline and 9/35 scenarios. In addition, disparities are pronounced among certain demographics (e.g., the average baseline exposure among Hispanics living in areas where air quality does change is almost 2 ug/m[3] higher than exposures among Hispanics in areas that already meet 9/35).  Similarly, the average air quality improvements experienced by populations living in areas where air quality does change are 2-4 times larger than when such changes are averaged over the entire contiguous U.S. (Figure 6-5).
                                        
Figure 6-22	Heat Map of National Average Annual PM2.5 Concentrations and Concentration Changes (ug/m[3]) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of Areas that Do and Do Not Experience Changes in Air Quality When Moving from 12/35 to 9/35

Average exposures of the subset of areas where air quality changes in each of the four regions analyzed show similar results, with larger average annual PM2.5 concentrations and concentration reductions for this subset of areas in all regions (Figure 6-23). In the subset of areas where air quality does change moving from 12/35 to 9/35, absolute concentration reductions are more similar across the regions than when all areas are included as in Sections 6.2 and 6.3, with the largest reductions predicted in the SE, followed by CA, the NE, and the W. We note that this is tied to the control strategy, which identified different available measures in each region. 
                                        
Figure 6-23	Heat Map of Regional Average Annual PM2.5 Concentrations and Concentration Changes (ug/m[3]) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of Areas that Do and Do Not Experience Changes in Air Quality When Moving from 12/35 to 9/35

While absolute exposure and exposure reduction estimates are necessary foundational information, the proportionality of the reductions more clearly answers question 3 of the EJ Technical Guidance (U.S. EPA, 2015). Proportional exposure reductions (i.e., the percent change in PM2.5 concentrations when moving from 12/35 to 9/35 divided by the total exposure under 12/35) for the national and regional subset of areas in which PM2.5 concentrations changed when moving from 12/35 to 9/35 are shown in Figure 6-24. As expected, proportional reductions are also greater than the national proportions (Figure 6-9 and Figure 6-10). Nationally, all populations with exposures higher than the overall reference (i.e., Hispanic, Asian, Black, and those less educated) are predicted to have larger proportional exposure decreases than the reference population. CA reflects the national trend, although there are variations in the NE, SE, and W. For example, ethnic exposure disparities in the NE, Black exposure disparities in the SE, and educational attainment disparities in the W are not proportionally mitigated in the subset of areas with air quality improvements when moving from the current standard to 9/35. However, it is also important to note that Hispanics are underrepresented in the NE, and population counts are lowest in the W.
                                       
Figure 6-24	Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations for Demographic Groups in the Subset of Areas in which PM2.5 Concentrations Change When Moving from 12/35 to 9/35 

Mortality Rates 
Although the mitigation of exposure disparities is predicted for all demographic groups at the national level and most demographic groups at the regional scale in areas in which PM2.5 concentrations are expected to change in moving from 12/35 to 9/35, it is also important to translate exposure disparities into health disparities when feasible, acknowledging that additional uncertainties are associated with estimating population-stratified health effects. To exemplify the potential importance of stratifying health impacts within various demographic of potential EJ concern, when employing the Di et al., 2017 population-stratified mortality hazard ratios (Table 6-2), the same PM2.5 exposure reduction will reduce the hazard of mortality ~3-fold more in Black populations than in White populations. Therefore, we also separate mortality rate impacts in the subset of areas where PM2.5 concentrations are expected to change when moving from 12/35 to 9/35 from areas that are not associated with PM2.5 concentration changes.
Average national annual mortality rates and mortality rate changes for the various demographic populations analyzed are presented in Figure 6-25. Similar to average PM2.5 concentrations (Figure 6-22), average mortality rates in the subset of areas where air quality changes are higher, and averages in the areas where air quality does not change are lower than in the analysis of all areas (Figure 6-11 and Figure 6-13). The mortality rate reductions are also 2-5 times larger (Figure 6-15 and Figure 6-17).
                                       
Figure 6-25	Heat Map of National Average Annual Total Mortality Rates and Mortality Rate Reductions (per 100K) by Demographic for 12/35, 9/35, and 12/35-9/35 in the Subset of Areas that Do and Do Not Experience Changes in Air Quality when Moving from 12/35 to 9/35

In the subset of areas in which PM2.5 air quality changes in moving from 12/35 to 9/35, absolute mortality rate reductions are larger and also more similar across the regions than when all areas are included as in Sections 6.2 and 6.3 (Figure 6-26). 


Figure 6-26	Heat Map of Regional Average Annual Total Mortality Rates and Mortality Rate Reductions (per 100K) by Demographic for 12/35 9/35, and 12/35-9/35, in the Subset of Areas that Do and Do Not Change When Moving from 12/35-9/35

Proportionally, mortality rate reductions associated with the change between the 12/35 and 9/35 scenarios are greatest for Black and Hispanic populations, helping to alleviate some of the disparities in the baseline (Figure 6-27). While mortality rate disparities for Blacks are predicted to be reduced in each region, impacts on disparities for Hispanics vary by region, with the greatest percent reduction in CA and the W. In comparing these reductions to the overall reductions in mortality rates nationally (Figure 6-19 and Figure 6-20), we note that the percent reductions are larger in the areas in which air quality changes when moving from 12/35 to 9/35, and that the pattern of results also varies somewhat by region (e.g., the greatest proportional rate reductions are seen among Asians in the SE, as compared to Blacks and Asians in CA in the analysis of all areas).
                                       
Figure 6-27	Heat Map of National and Regional Percent Reductions in Average Annual Total Mortality Rates (per 100K) by Demographic in the Subset of Areas in which PM2.5 Concentrations Change When Moving from 12/35-9/35
Summary
For this proposal, we quantitatively evaluate the potential for disparities in PM2.5 concentrations and mortality effects across different demographic populations for the current (12/35; baseline) and potential alternative PM2.5 NAAQS levels (10/35, 10/30, 9/35, and 8/35) under air quality scenarios associated with illustrative emission control strategies. Specifically, we provide information on totals, changes, and proportional changes in 1) annual average PM2.5 concentrations and 2) premature mortality as rates per 100,000 individuals across and within various demographic populations. Each type of analysis has strengths and weaknesses, but when taken together, can respond to the three questions from EPA's EJ Technical Guidance. Total concentration/mortality rate analyses provide information on absolute PM2.5 concentrations and mortality rates; however, it can be difficult to compare improvements in air quality/mortality rates among populations from total information. In contrast, comparing changes in concentration/mortality rates provides information on how improvements compare across and within populations, but does not provide information on which populations experience higher total concentration/mortality rates. Proportional changes are provided as a percent of the total concentration/mortality rate information, so although they are similar to absolute changes, they are more closely related to total concentration/mortality rate information.
EJ analyses were performed both at national and regional scales, as geography is relevant both to PM NAAQS attainment and population demographics. We also conducted a case study to examine the subset of areas in which air quality is projected to change after the application of controls outlined in Chapter 3 to illustrate how air quality improvements in the areas with the highest starting concentrations might be distributed demographically. For all of these analyses, we note that the results should be considered illustrative only, Further, as with all EJ analyses, data limitations make it possible that disparities may exist that our analysis did not identify. This is especially relevant for potential EJ characteristics, environmental impacts, and more granular spatial resolutions that were not evaluated. We note again that this analysis is based on air quality modeling conducted on a 12 by 12 km grid scale, which may mask more local disparities in exposure and risk. Additionally, EJ concerns for each rulemaking are unique and should be considered on a case-by-case basis.
Whereas all populations experience reductions in PM2.5 concentrations and health effects at lower PM standard levels, to make conclusions regarding EJ impacts of this proposed rule we refer back to the three questions that EPA's EJ Technical Guidance (U.S. EPA, 2015) states should be addressed, which for purposes of the PM NAAQS RIA EJ analysis are: 
 Are there disproportionate PM2.5 exposures/health effects under baseline/current PM NAAQS standard levels?
 Are there disproportionate PM2.5 exposures/health effects under illustrative alternative PM NAAQS standard levels?
 Are PM2.5 exposure/health effect disparities created, exacerbated, or mitigated under illustrative alternative PM NAAQS standard levels as compared to the baseline?
Considering results of both the EJ exposure analysis (Section 6.2) and the EJ health effects analysis (Section 6.3), responses to the above three questions can be summarized as:
 Disparities in the baseline: Under air quality scenarios associated with control strategies for the baseline (12/35) PM NAAQS scenario, some populations are predicted to experience disproportionately higher annual PM2.5 concentrations nationally than the reference (overall) population, both in terms of aggregated average concentrations and across the distribution of air quality (Figure 6-1 and Figure 6-2). Specifically, Hispanics, Asians, Blacks, and those less educated (no high school) have higher national annual concentrations, on average and across the distributions, than both the overall reference population or other populations (e.g., non-Hispanic, White, and more educated). In particular, the Hispanic population is estimated to experience the highest concentrations, both on average and across PM2.5 concentration distributions, of all demographic groups analyzed. These disproportionalities are also observed at the regional level, though to different extents, as Asian concentrations in the W and CA are similar to the reference group, and those less educated are exposed to higher PM2.5 concentrations only in CA (Figure 6-3 and Figure 6-4). Similar, but magnified, trends are observed when evaluating only the areas in which air quality improvements are predicted.
      In terms of health effects, some demographic populations are also predicted to experience disproportionately higher rates of premature mortality than reference populations (Figure 6-11 through Figure 6-14). Black populations are estimated to have the highest national and regional mortality rates, both on average and across population distributions. This may be partly due to higher PM2.5 concentrations for this population, which could contribute to the higher magnitude concentration-response relationship between exposure concentrations and premature mortality (Di et al., 2017), as well as other underlying health factors which may increase susceptibility to adverse outcomes among Black populations. Hispanic mortality rates are disproportionately higher in the SE, W, and CA. Higher mortality rates are predicted for Asians and American Indians in CA and for American Indians in the SE. Similar, but larger, trends are also observed when evaluating only the areas in which air quality improvements are predicted.
 Disparities under alternative policy options:  While more stringent control strategies reduce PM2.5 concentrations and mortality rates across all demographic groups, disparities seen in the baseline are also reflected in the policy options under consideration. Specifically, disproportionately higher PM2.5 concentrations and health effects remain for some populations estimated under air quality scenarios associated with the illustrative control strategies (10/35, 10/30, 9/35, and 8/35) (Figure 6-1 through Figure 6-4 and Figure 6-11 through Figure 6-14). Nationally and regionally, these patterns and the populations affected are similar to those seen in the baseline. and larger when considering only the subset of areas in which air quality improvements are expected.
 Relative impact of alternative policy options on disparities in the baseline: For most populations assessed, PM2.5 concentration disparities are mitigated in the illustrative air quality scenarios associated with control strategies for more stringent PM2.5 control strategies (10/35, 10/30, 9/35, and 8/35) as compared to the baseline (12/35), to differing degrees (Figure 6-1 through Figure 6-10). This conclusion is strengthened when restricting analyses to areas in which PM2.5 concentrations are predicted to decrease (Figure 6-29 through Figure 6-34). More specifically, increasing portions of certain populations of potential EJ concern are expected to experience greater PM2.5 concentration reductions as the control strategies become more stringent (Figure 6-6). At the national scale, Hispanics, Asians, and those less educated are estimated to see greater proportional reductions in PM2.5 concentrations than reference populations under all lower standard levels evaluated, with proportional reductions increasing as the standard levels decrease. However, concentrations in the Black population are estimated to proportionally decrease on par with reference concentrations. Average concentration reductions were also similar across Black and White populations when the spatial scale of the analysis was limited to those areas affected by the illustrative control strategies. Considering the four geographic regions, proportionally greater reductions in PM2.5 concentrations experienced by Asian, Hispanic, and less educated populations are most notable in the SE and CA, whereas PM2.5 concentration reductions among Black populations tend to be proportionally larger than among the reference population in CA, W, and the NE, especially under lower standard levels. Due to the higher prevalence of Black populations in the SE, the lack of proportional concentration reductions in that region may mask increased concentration reductions in other regions at the national level.  
      In general, more stringent control strategies are also associated with reductions in mortality rate disparities. Specifically, the analysis shows that as the PM2.5 control strategies become increasingly stringent, differences in mortality rates across demographic groups decline, particularly for the lowest alternatives evaluated (12/35-9/35 and 12/35-8/35). Similar to the estimated changes in PM2.5 concentrations following reductions in PM2.5 concentrations under alternative standards, disparities in PM2.5 mortality rates across demographic groups are mitigated nationally for Hispanics in all the alternative PM standard levels (10/35, 10/30, 9/35, and 8/35) as compared to the baseline (Figure 6-19). Nationally, Black populations are predicted to experience proportionally similar mortality rate reductions to White populations under control strategies associated with 12/35-10/35 or 12/35-10/30, but greater reductions in mortality rates than White populations under control strategies associated with 12/35-9/35 or 12/35-8/35. While Asians are estimated to experience the greatest proportional mortality rate reductions of the races/ethnicities analyzed, they are predicted to initially experience disproportionally lower mortality rates under the baseline scenario. When the spatial scale of the analysis was limited to those areas affected by the illustrative control strategies for 9/35, Asian, Black and Hispanics experienced the greatest reduction in mortality rates nationally and in most regions.
      

Summary of Counties by Bin that Still Need Emissions Reductions for Proposed Alternative Primary Standard Levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - 
Bin
Area
Counties[a] for
10/35 mg/ - m - [3]
Additional Counties[a] for 
9/35 mg/ - m - [3] - 
Delaware County, Pennsylvania
Northeast
--
Delaware County, PA
Border Areas
Southeast
--
Cameron County, TX
Hidalgo County, TX

California
Imperial County, CA
--
Small Mountain Valleys
West
Plumas County, CA
Lemhi County, ID
Shoshone County, ID
Lincoln County, MT
Benewah County, ID
California Areas

Fresno County, CA (SJVAPCD)
Kern County, CA (SJVAPCD)
Kings County, CA (SJVAPCD)
Los Angeles County, CA (SCAQMD)
Madera County, CA (SJVAPCD)
Merced County, CA (SJVAPCD)
Riverside County, CA (SCAQMD)
San Bernardino County, CA (SCAQMD)
Stanislaus County, CA (SJVAPCD)
Tulare County, CA (SJVAPCD)
Napa County, CA (BAAQMD)
San Joaquin County, CA (SJVAPCD)
San Luis Obispo County, CA
Note: For California counties that are part of multi-county air districts, the relevant district is indicated in parentheses; BAAQMD = Bay Area Air Quality Management District, SCAQMD = South Coast Air Quality Management District, and SJVAPCD= San Joaquin Valley Air Pollution Control District.
[a] The following counties have no identified PM2.5 emissions reductions because available controls were applied for the current standard of 12/35 g/m[3] and additional controls were not available: Imperial, Kern, Kings, Lemhi, Plumas, Riverside, San Bernardino, Shoshone, and Tulare.
      
      In particular, the Hispanic population is estimated to experience the highest exposures, both on average and across PM2.5 concentration distributions, of all demographic groups analyzed. These disproportionalities are also observed at the regional level, though to different extents.
Environmental Justice Appendix
Input Information
EJ Exposure Analysis Input Data
In Appendix 2A, the exposure assessment involves demographic population data projected out to the future year 2032. We use population projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods & Poole, 2015). The Woods and Poole database contains county-level projections of population by age, sex, and race out to 2060, relative to a baseline using the 2010 Census data. Projections in each county are determined simultaneously with every other county in the U.S to consider patterns of economic growth and migration. The sum of growth in county-level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates (Hollmann et al., 2000). According to Woods and Poole, linking county-level growth projections together and constraining to a national-level total growth avoids potential errors introduced by forecasting each county independently (Woods & Poole, 2015).
EJ Health Effects Analysis Input Data
The health assessment requires input data in addition to the information used in the exposure assessment (Section 6.6.1.1). As such, there are additional uncertainties, albeit similar to the benefits assessment results (Chapter 5). We evaluated the available studies and concentration-response functions to determine if sufficient information exists for use in a quantitative analysis and to determine which study or studies best characterizes at-risk nonwhite populations across the U.S. Of the available studies, Di et al., 2017 was a nationwide study, evaluated the largest study size over one of the most recent time spans, used a sophisticated exposure estimation technique, and provided sufficient information to apply risk models quantifying increased risks to the following nonwhite groups: Black, Asian, Native American, and Hispanic populations. Although Di et al., 2017 effect estimates were derived from a cohort aged 65 and older and the study did not provide a non-Hispanic concentration-response function, it was identified as best characterizing populations potentially at increased risk of long-term exposure and all-cause mortality. Health impact functions, including beta parameters and standard errors (SE), were developed for each at-risk population demographic described by Di et al., 2017 and are provided in Table 6-2.
Table 6-2	Hazard Ratios, Beta Coefficients, and Standard Errors (SE) from Di et al., 2017
                            Demographic Population
          Risk of Death Associated with a 10 ug/m3 Increase in PM2.5
                             Beta Coefficient (SE)
                                     White
                             1.063 (1.060, 1.065)
                                0.0061 (0.0001)
                                      All
                             1.073 (1.071, 1.075)
                                0.0070 (0.0001)
                                   Hispanic
                             1.116 (1.100, 1.133)
                                0.0110 (0.0008)
                                     Black
                             1.208 (1.199, 1.217)
                                0.0189 (0.0004)
                                     Asian
                             1.096 (1.075, 1.117)
                                0.0092 (0.0010)
                                Native American
                             1.100 (1.060, 1.140)
                                0.0095 (0.0019)

Concentration-response functions stratified by race and ethnicity were only available for ages greater than 64. While BenMAP-CE includes population information for 5-year age spans up to 84 and Di et al., 2017 provides stratified concentration-response functions for 10-year age spans (65-74, 75-84, and 85-99), the stratified concentration-response functions for 10-year age spans were not also stratified by race or ethnicity. Therefore, this analysis only evaluated a single age range group of 65-99 years.
BenMAP-CE includes baseline incidence rates at the most geographically- and age-specific levels available for each health endpoint assessed. For many locations within the U.S., these data are resolved at the county- or state-level, providing a better characterization of the geographic distribution of mortality rates than the national-level rates. Race- and ethnicity-stratified baseline incidence rates from 2007-2016 Census data were recently improved for the all-cause mortality health endpoint, by adding the geographic level option of rural/urban state between county-level and state-level. Both overall and race/ethnicity-stratified baseline rates are used in this analysis of EJ health impacts analysis. 
To estimate race-stratified and age-stratified incidence rates at the county level, we downloaded all-cause and respiratory mortality data from 2007 to 2016 from the CDC WONDER mortality database. Race-stratified incidence rates were calculated for the following age groups: < 1 year, 1-4 years, 5-14 years, 15-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, 65-74 years, 75-84 years, and 85+ years. To address the frequent county-level data suppression for race-specific death counts, we stratified the county-level data into two broad race categories, White and Non-White populations. In a later step, we stratified the non-White incidence rates by race (Black, Asian, Native American) using the relative magnitudes of incidence values by race at the regional level, described in more detail below. 
We followed methods outlined in Section D.1.1 of the BenMAP User Manual with one notable difference in methodology; we included an intermediate spatial scale between county and state for imputation purposes. We designated urban and rural counties within each state using CDC WONDER and, where possible, imputed missing data using the state-urban and state-rural classifications before relying on broader statewide data. We followed methods for dealing with suppressed and unreliable data at each spatial scale as described in Section D.1.1.
A pooled non-White incidence rate masks important differences in mortality risks by race. To estimate county-level mortality rates by individual race (Black, Asian, Native American), we applied regional race-specific incidence relationships to the county-level pooled non-White incidence rates. We calculated a weighted average of race-specific incidence rates using regional incidence rates for each region/age/race group normalized to one reference population (the Asian race group) and county population proportions based on race-specific county populations from CDC WONDER where available. In cases of population suppression across two or more races per county, we replaced all three race-specific population proportions derived from CDC WONDER with population proportions derived from 2010 Census data in BenMAP-CE (e.g., 50 percent Black, 30 percent Asian, 20 percent Native American).
To estimate ethnicity-stratified and age-stratified incidence rates at the county level, we downloaded all-cause and respiratory mortality data from 2007 to 2016 from the CDC WONDER mortality database. Ethnicity-stratified incidence rates were calculated for the following age groups: < 1 year, 1-4 years, 5-14 years, 15-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, 65-74 years, 75-84 years, and 85+ years. We stratified county-level data by Hispanic origin (Hispanic and non-Hispanic). We followed the methods outlined in Section D.1.1 to deal with suppressed and unreliable data. We also included an intermediate spatial scale between county and state designating urban and rural counties for imputation purposes, described in detail in Section D.1.3 of the BenMAP User Manual. 
EJ Analysis of Total Exposures Associated with Meeting the Standards
In addition to air quality surfaces associated with the illustrative emission control strategies evaluated in the main EJ chapter, PM2.5 air quality surfaces associated with meeting the current and alternative standard levels were also developed. Air quality associated with meeting the standards was based on assumptions that emission controls could be identified to meet the required emission amounts (Appendix 2A). Results for both air quality scenarios are included in this appendix, to allow for direct comparisons. In general, for populations experiencing higher baseline PM2.5 concentrations and mortality rates, air quality scenarios associated with meeting the standards reduce disparities more so than air quality scenarios associated with the control strategies, especially for Hispanics populations in CA.
National and regional PM2.5 concentrations by demographic populations for air quality scenarios associated with both the control strategies and meeting the standards are provided in Sections 6.6.2.1 and 6.6.2.2, respectively.
National
At the national level, air quality scenarios associated with meeting the standards led to similar and/or slightly lower PM2.5 concentrations under the current and lower alternative standard levels than air quality scenarios associated with control strategies (Figure 6-28 and Figure 6-29). This may narrow disproportionate PM2.5 concentrations for certain populations, such as Hispanics, under air quality associated with more stringent alternative standard levels.
                                       
Figure 6-28	Heat Map of National Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies (Controls) or with Meeting the Standards (Standards) by Demographic for Current (12/35) and Alternative PM NAAQS Levels (10/35, 10/30, 9/35, and 8/35) 

                                       
Figure 6-29	National Distributions of Annual PM2.5 Concentrations Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels
Regional
Regionally, air quality scenarios associated with meeting the standards also led to similar or slightly lower PM2.5 concentrations as air quality scenarios associated with the current standards for more stringent standard levels, except for in CA, where air quality associated with the standards resulted in substantially lower PM2.5 concentrations (Figure 6-30 and Figure 6-31). 

                                       
Figure 6-30	Heat Map of Regional Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels

                                       
Figure 6-31	Regional Distributions of Annual PM2.5 Concentrations Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels

EJ Analysis of Exposure Changes Associated with Meeting the Standards 
National and regional changes in PM2.5 concentrations for demographic populations when moving from current to more stringent alternative standard levels for air quality scenarios associated with meeting the standards, and the ability to compare them with air quality scenarios associated with the illustrative emission control strategies, are provided in Sections 6.2.3.1 and 6.2.3.2, respectively.
National
Nationally, PM2.5 concentration reductions for air quality scenarios associated with the illustrative emission control strategies are estimated to be similar or slightly greater than PM2.5 concentration reductions for air quality scenarios associated with meeting the standards when moving from current to more stringent standard levels (Figure 6-32 and Figure 6-33). 
                                       
Figure 6-32	Heat Map of National Average Annual Reductions in PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels

                                       
Figure 6-33	National Distributions of Annual Reductions in PM2.5 Concentrations Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels


Regional
Regionally, air quality scenarios associated with meeting the standards led to similar PM2.5 concentration changes as air quality scenarios associated with control strategies under more stringent alternative standard levels in the NE, SE, and W (Figure 6-34 and Figure 6-35).[2][3] In CA, PM2.5 concentration reductions were substantially greater under air quality scenarios associated with meeting the standards.


                                       
Figure 6-34	Heat Map of National Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels

                                       
Figure 6-35	National Distributions of Reductions in Annual PM2.5 Concentrations Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels

           Proportionality of Exposure Changes Associated with Meeting the Standards
The proportionality of national and regional changes in demographic-specific PM2.5 concentrations when moving from air quality scenarios associated with meeting the standards, as opposed to air quality scenarios associated with control strategies, when moving from current to more stringent alternative standard levels are provided in Sections 6.6.4.1 and 6.6.4.2, respectively.
National
Nationally, air quality scenarios associated with meeting the standards proportionally reduce PM2.5 concentrations in the reference population by a larger amount than air quality scenarios associated with the illustrative control strategies as alternative standard levels are lowered (Figure 6-36).  
                                       
Figure 6-36	Heat Map of National Percent Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving From Current to Alternative PM NAAQS Levels


Regional
Dividing the country into the four regions shows that air quality associated with meeting the standards in California would lead to substantially greater proportional PM2.5 concentration reductions under all scenarios evaluated (Figure 6-37). Also, differences between air quality scenarios associated control strategies versus meeting the standards are greater when moving to lower alternative standard levels.
 However, as pointed out earlier, since the California counties shown in Table xx are out of attainment with the 2009 and 2012 standards, they are unlikely to come near meeting the ambient levels necessary to meet the lower standard upon which EPA is requesting comments.  Figure 6-47 shows that the larger increases in protection from the 8 ug/m3 are the result of bringing levels down in parts of the country other than California, which accounts for the extra benefits afforded to all race/ethnicity groups, with slightly higher proportion increases for blacks.

                                       
Figure 6-37	Heat Map of Regional Percent Reductions in Average Annual PM2.5 Concentrations (ug/m[3]) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving From Current to Alternative PM NAAQS Levels

EJ Analysis of Total Mortality Rates Associated with Meeting the Standards 
National and regional total demographic-specific mortality rates for both air quality scenarios associated with control strategies and meeting the standards are provided in Sections 6.6.5.1 and 6.6.5.2, respectively.
National
Using concentration-response relationships derived from Di et al., 2017, the older (>64 years) Black population is estimated to have the highest mortality rates per 100k of all races and ethnicities evaluated. This is the case under air quality scenarios associated with either the illustrative emission control scenarios or under air quality scenarios associated with meeting the standards for current and alternative standard levels (Figure 6-38 and Figure 6-39). Older Hispanics and older American Indians are also predicted to have a higher rate of mortality than older non-Hispanics and older Whites, respectively, under all air quality scenarios evaluated.
                                       
Figure 6-38	Heat Map of National Average Annual Total Mortality Rates (per 100K People) Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels
                                       
Figure 6-39	National Distributions of Total Mortality Rates Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels

                      Regional
Similar to PM2.5 concentrations, regional average mortality rates are lowest in the W and highest in CA (Figure 6-40). Black populations are estimated to have the highest mortality rates in all regions. Hispanic mortality rates are lower in the NE and higher in the other three regions.
                                       
Figure 6-40	Heat Map of Regional Average Annual Total Mortality Rates (per 100K People) Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels

                                       
Figure 6-41	Regional Distributions of Total Mortality Rates Associated Either with Control Strategies or with Meeting the Standards by Demographic for Current and Alternative PM NAAQS Levels




EJ Analysis of Mortality Rate Change Associated with Meeting the Standards
National and regional changes in demographic-specific mortality rates when moving from current to alternate standard levels under air quality surfaces associated with either control strategies or meeting the standards levels are provided in Sections 6.6.6.1 and 6.6.6.2, respectively.
National
Nationally, mortality rate reductions are larger for Asians and Hispanics under air quality associated with the standards, as compared to air quality associated with the illustrative emission control strategies (Figure 6-42 and Figure 6-43). Mortality rate reductions increase in absolute terms for Black as alternative standard levels become more stringent. 

                                       
Figure 6-42	Heat Map of National Average Annual Total Mortality Rate Reductions (per 100K People) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels

                                       
Figure 6-43	National Distributions of Annual Total Mortality Rate Reductions Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels

Regional
Absolute mortality rate reductions per 100k individuals are most notable in CA and for Hispanic, Asian, and Black populations under full attainment scenarios at lower alternative standards (Figure 6-44 and Figure 6-45). However, as pointed out earlier, since several California counties are out of attainment with the current standards, thus are unlikely to achieve ambient levels necessary to meet alternatives standard levels upon which we are requesting comments.
. 


                                       
Figure 6-44	Heat Map of Regional Average Annual Total Mortality Rate Reductions (per 100K People) Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels
                                       
Figure 6-45	Regional Distributions of Average Annual Total Mortality Rate Reductions Associated Either with Control Strategies or with Meeting the Standards by Demographic for When Moving from Current to Alternative PM NAAQS Levels

Proportionality of Mortality Rate Changes Associated with Meeting the Standards
The proportionality of national and regional changes in demographic-specific mortality rates when moving from current to more stringent alternative standard levels under air quality scenarios associated with control strategies and with meeting the standards are provided in Sections 6.6.7.1 and 6.6.7.2, respectively.
National
Proportional reductions when moving to more stringent alternative PM NAAQS reduce mortality rate disparities for Hispanics under all air quality scenarios evaluated at the national scale.  Proportional reductions when moving to more stringent alternative standards reduce mortality rate disparities at the national level for Blacks are similar to Whites for 12/35-10/35 and 10/30, but larger than Whites for 12/35-9/35 and 12/35-8/35 (Figure 6-46). 
                                       
Figure 6-46	Heat Map of National Percent Changes in Average Mortality Rate Reductions Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels


Regional
Proportional changes also demonstrate that mortality rates disparities are expected to be reduced for Hispanics and Blacks in California, especially under more stringent alternative standard levels and under air quality scenarios associated with meeting the standards (Figure 6-47). However, as pointed out earlier, since several California counties are out of attainment with the current standards, thus are unlikely to achieve ambient levels necessary to meet alternatives standard levels upon which we are requesting comments.
 


                                       
Figure 6-47	Heat Map of Regional Percent Reductions in Average Mortality Rate Reductions Associated Either with Control Strategies or with Meeting the Standards by Demographic When Moving from Current to Alternative PM NAAQS Levels

References 
Di, Q, Wang, Y, Zanobetti, A, Wang, Y, Koutrakis, P, Choirat, C, Dominici, F and Schwartz, JD (2017). Air pollution and mortality in the Medicare population. New England Journal of Medicine 376(26): 2513-2522.
Hollmann, F, Mulder, T and Kallan, JJW, DC: US Bureau of the Census (2000). Methodology and assumptions for the population projections of the United States: 1999 to 2100 (Population Division Working Paper No. 38).  338.
U.S. EPA (2015). Guidance on Considering Environmental Justice During the Development of Regulatory Actions. https://www.epa.gov/sites/default/files/2016-06/documents/ejtg_5_6_16_v5.1.pdf.
U.S. EPA (2019). Integrated Science Assessment (ISA) for Particulate Matter (Final Report). U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment. Research Triangle Park, NC. U.S. EPA. EPA/600/R-19/188. December 2019. Available at: https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-assessments-current-review.
U.S. EPA (2020). Policy Assessment for the Review of the National Ambient Air Quality Standards for Particulate Matter. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-452/R-20-002. January 2020. Available at: https://www.epa.gov/naaqs/particulate-matter-pm-standards-policy-assessments-current-review-0.
U.S. EPA (2021). Draft Policy Assessment for the Reconsideration of the National Ambient Air Quality Standards for Particulate Matter. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-452/P-21-001. October 2021. Available at: https://www.epa.gov/system/files/documents/2021-10/draft-policy-assessment-for-the-reconsideration-of-the-pm-naaqs_october-2021_0.pdf.
U.S. EPA (2022a). Supplement to the 2019 Integrated Science Assessment for Particulate Matter (Final Report). U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment. Research Triangle Park, NC. U.S. EPA. EPA/600/R-22/028. May 2022. Available at: https://www.epa.gov/isa/integrated-science-assessment-isa-particulate-matter.
Woods & Poole (2015). Complete Demographic Database.


LABOR IMPACTS

 Overview
This chapter discusses baseline employment in some of the industries potentially affected by this proposal. As economic activity shifts in response to a regulation, typically there will be a mix of declines and gains in employment in different parts of the economy over time and across regions. To present a complete picture, an employment impact analysis will describe the potential positive and negative changes in employment levels. Significant challenges arise however when trying to evaluate the employment effects due to an environmental regulation and separate them from employment effects due to a wide variety of other concurrent economic changes, including such important macroeconomic events as the coronavirus pandemic, or the state of the macroeconomy generally. Despite these challenges, the economics literature provides a constructive framework and empirical evidence that sheds light on the labor impacts of environmental regulation. To simplify, we focus on potential impacts on labor demand related to compliance behavior. Environmental regulation may also have important effects on labor supply through changes in worker health and productivity (Graff Zivin and Neidell, 2018).
Labor Impacts
Economic theory of labor demand indicates that employers affected by environmental regulation may increase their demand for some types of labor, decrease demand for other types, or for still other types, not change it at all (Morgenstern et al., 2002, Deschênes, 2018, Berman and Bui, 2001). To study labor demand impacts empirically, a growing literature has compared employment levels at facilities subject to an environmental regulation to employment levels at similar facilities not subject to that environmental regulation; some studies find no employment effects, and others find significant differences. For example, see (Berman and Bui 2001), (Greenstone 2002), (Ferris, Shadbegian and Wolverton 2014), and (Curtis 2018, 2020).
A variety of conditions can affect employment impacts of environmental regulation, including baseline labor market conditions and employer and worker characteristics such as occupation and industry. This baseline labor analysis is illustrative and focused on potential labor impacts in the emissions inventory sectors and industries that may apply control technologies, as identified in Chapter 3. We present information on baseline characteristics of labor markets in the affected emissions inventory sectors: non-electric generating unit (non-EGU) point, oil and gas point, non-point (area), residential wood combustion, and area fugitive dust. Baseline information presented includes employment levels, recent trends in employment, and labor intensity of production. We do not have detailed information on the industries that may require pollution controls, and in which states they may be required. Thus, the presentation of nationwide baseline information is merely suggestive of employment conditions in the industries that might be affected.  
Table 7-1 presents baseline employment for industries that fall into the emissions inventory sectors of non-EGU point, oil and gas point, residential wood combustion, and area fugitive dust. The table shows national employment levels in 2020 and the percent change in employment over the ten years from 2011 to 2020 for the industries and North American Industry Classification System (NAICS) codes identified as potentially affected industries under each emissions inventory sector. Non-EGU point sources include emissions units in the cement and concrete product manufacturing, basic chemical manufacturing, pulp, paper, and paperboard mills, iron and steel mills and ferroalloy manufacturing, non-ferrous metals production and processing, petroleum and coal products manufacturing, and mining industries. The oil and gas point emissions inventory sector includes oil and gas extraction. The residential wood combustion emissions inventory sector reflects HVAC and commercial refrigeration equipment manufacturing, and hardware, and plumbing and heating equipment supplies merchant wholesalers as both of those industries include establishments engaged in manufacturing and repairing heating equipment, including wood stoves, fireplaces, and wood furnaces. Because potential control measures that could reduce fugitive road dust are to apply asphalt or concrete to roadbeds or roadsides, we included asphalt paving, roofing, and saturated materials under the area fugitive dust emissions inventory sector.  
Table 7-1  	Baseline Industry Employment 
Potentially Affected Industries by Emissions Inventory Sector and by Industry
NAICS
                        Employment in 2020 (thousands)
                    Percent Change in Employment 2011-2020
Non-EGU Point
Cement and Concrete Product Manufacturing  
                                     3273
                                     194.5
                                      18
Basic Chemical Manufacturing
                                     3251
                                     150.1
                                       5
Pulp, Paper, and Paperboard Mills
                                     3221
                                     92.6
                                      -15
Iron and Steel Mills and Ferroalloy Manufacturing
                                     3311
                                     83.2
                                      -10
Non-ferrous Metal (except Aluminum) Production and Processing
                                     3314
                                     58.2
                                      -6
Petroleum and Coal Products Manufacturing
                                     3241
                                     106.5
                                      -5
Mining (except Oil and Gas)
                                      212
                                     179.4
                                      -19
Oil and Gas Point
Oil and Gas Extraction
                                     2111
                                     138.6
                                      -20
Residential Wood Combustion
Ventilation, Heating, Air Conditioning and Commercial Refrigeration Equipment Manufacturing
                                     3334
                                     134.4
                                       3
Hardware, and Plumbing and Heating Equipment Supplies Merchant Wholesalers
                                     4237
                                     280.2
                                      18
Area Fugitive Dust
Asphalt Paving, Roofing, and Saturated Materials Manufacturing
                                     32412
                                    N/A[a]
                                      N/A
Note: NAICS is North American Industry Classification System. The source of the information is the U.S. Bureau of Labor Statistics and is available at https://www.bls.gov/emp/data/industry-out-and-emp.htm. 
a N/A  -  not available. The U.S. Bureau of Labor Statistics only provides information at the 4-digit NAICS code. By Standard Industrial Classification (SIC) code, we located information on employment for paving, surfacing and tamping equipment operators (47-2071), which is briefly discussed below. 

Cement and concrete product manufacturing, hardware and plumbing and heating equipment supplies merchant wholesalers, and mining are the largest industries in terms of number people employed. The basic chemical manufacturing and oil and gas extraction industries also have high employment. Each of the industries has had different trends in employment over the past decade. Cement and concrete product manufacturing and hardware and plumbing and heating equipment supplies merchant wholesalers have had sizable increases in employment over the past decade, while pulp, paper, and paperboard mills, oil and gas extraction, and mining have experienced a decline in employment over the last decade.  
Under the area fugitive dust emissions inventory sector, potential control measures that could reduce fugitive road dust are to apply asphalt or concrete to roadbeds or roadsides, i.e., shoulders. Associated with these control measures, the overall employment for paving, surfacing and tamping equipment operators in 2021 was 44,200. The industry with the highest concentration of employment in paving, surfacing and tamping equipment operators is highway, street and bridge construction which employs 16,410 workers. Texas, California, New York, Illinois, and Florida are the states with the highest employment level in paving, surfacing and tamping equipment operators. 
Understanding the relative use of labor and capital in potentially affected industries can shed light on potential labor impacts. Many of these manufacturing industries are capital intensive. We rely on three public sources to get a range of estimates of employment per output by industry. Two of the public sources are provided by the U.S. Census Bureau: the Economic Census (EC) and the Annual Survey of Manufacturers (ASM). The EC is conducted every 5 years and was most recently conducted in 2017. The ASM is an annual subset of the EC and is based on a sample of establishments. The latest set of data from the ASM is from 2020. Both sets of U.S. Census Bureau data provide detailed industry data, providing estimates at the 4-digit NAICS level. The data sets provide separate estimates of the number of employees and the value of shipments at the 4-digit NAICS, which we convert to a ratio in this analysis. The third public source that gives an estimate of employment per output by industry is the U.S. Bureau of Labor Statistics (BLS). Table 7-2 provides estimates of employment per $1 million of products sold by the industry for each data source in 2017$. While the ratios are not the same, they are similar across time for each data source. Cement and concrete products manufacturing and ventilation, heating, air conditioning and commercial refrigeration equipment manufacturing appear to be the most labor-intensive industries.
Table 7-2  	Employment per $1 Million Output (2017$) by Industry (4-digit NAICS) 
                                       
                              Source of Estimate
Emissions Inventory Sector and Industry Sector
                                      BLS
                                Economic Census
                                  ASM (2020)
Non-EGU Point
Cement and Concrete Product Manufacturing
                                     3.39
                                     2.92
                                     2.88
Basic Chemical Manufacturing
                                     0.57
                                     0.68
                                     0.85
Pulp, and Paper, and Paperboard Mills
                                     1.18
                                     1.24
                                     1.41
Iron and Steel Mills and Ferroalloy Manufacturing
                                     0.97
                                     0.97
                                     1.14
Non-ferrous Metals (except Aluminum) Production and Processing
                                     1.33
                                     1.21
                                     1.25
Petroleum and Coal Products Manufacturing
                                      N/A
                                     0.20
                                     0.31
Mining (except Oil and Gas)
                                      N/A
                                     2.02
                                      N/A
Oil and Gas Point
Oil and Gas Extraction
                                      N/A
                                     0.54
                                      N/A
Residential Wood Combustion
Ventilation, Heating, Air Conditioning and Commercial Refrigeration Equipment Manufacturing
                                     2.84
                                     3.04
                                     3.38
Hardware, and Plumbing and Heating Equipment Supplies Merchant Wholesalers
                                      N/A
                                     1.39
                                      N/A
Area Fugitive Dust
Asphalt Paving, Roofing, and Saturated Materials Manufacturing
                                      N/A
                                     1.12
                                     1.28
Note: N/A  -  not available. The source of the information is the U.S. Bureau of Labor Statistics: BLS and is available at https://www.bls.gov/emp/data/industry-out-and-emp.htm.

In general, there are significant challenges when trying to evaluate the employment effects due to an environmental regulation. Employment effects must be evaluated in light of a wide variety of dynamic economic and social factors that also influence employment in the U.S. economy. In addition to these challenges, the EPA does not have detailed information on the industries that may require pollution controls for this proposal. Thus, the EPA did not estimate potential employment impacts associated with this proposal. However, to provide information about baseline conditions in relevant employment markets that might experience incremental impacts, this chapter presented employment levels, trends, and labor intensities of production in potentially affected industries. 
References 
Berman, E. and L. T. M. Bui (2001). Environmental regulation and labor demand: evidence from the South Coast Air Basin. Journal of Public Economics. 79(2): 265-295.
Curtis, E. M. (2018). Who loses under cap-and-trade programs? The labor market effects of the NOx budget trading program. Review of Economics and Statistics 100 (1): 151 - 66. 
Curtis, E.M. (2020). Reevaluating the ozone nonattainment standards: evidence from the 2004 expansion. Journal of Environmental Economics and Management, 99: 102261.
Deschênes, O. (2018). Environmental regulations and labor markets. IZA World of Labor: 22: 1-10; 
Ferris, A. E., R. Shadbegian, A. Wolverton (2014). The effect of environmental regulation on power sector employment: phase I of the Title IV SO2 trading program. Journal of the Association of Environmental and Resource Economics 1(4): 521-553.
Graff Zivin, J. and M. Neidell (2018). Air pollution's hidden impacts. Science. 359(6371). 39-40. 
Greenstone, M. (2002). The impacts of environmental regulations on industrial activity: evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures. Journal of Political Economy 110(6): 1175-1219.
Harrington, W., R.D. Morgenstern, and P. Nelson (2000). On the accuracy of regulatory cost estimates. Journal of Policy Analysis and Management 19, 297-322.
Morgenstern, R.D., W.A. Pizer, and J. Shih (2002). Jobs versus the environment: an industry- level perspective. Journal of Environmental Economics and Management 43: 412-436.
 


COMPARISON OF BENEFITS AND COSTS
 Overview
As discussed in Chapter 1, the Agency is proposing to revise the current annual PM2.5 standard to a level within the range of 9-10 g/m[3] and is soliciting comment on an alternative annual standard level down to 8 g/m[3] and a level up to 11 g/m[3]. The Agency is also proposing to retain the current 24-hour standard of 35 g/m[3] and is soliciting comment on an alternative 24-hour standard level of 30 g/m[3]. OMB Circular A-4 requires analysis of one potential alternative standard level more stringent than the proposed standard and one less stringent than the proposed standard. In this Regulatory Impact Analysis (RIA), we are analyzing the proposed annual and current 24-hour alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/m[3], as well as the following two more stringent alternative standard levels: (1) an alternative annual standard level of 8 g/ - m - [3] -  in combination with the current 24-hour standard (i.e., 8/35 g/ - m - [3] - ), and (2) an alternative 24-hour standard level of 30 g/ - m - [3] in combination with the proposed annual standard level of 10 g/ - m - [3] -  (i.e., 10/30 g/ - m - [3] - ). Because the EPA is proposing that the current secondary PM standards be retained, we did not evaluate alternative secondary standard levels in this RIA. The docket for the proposed rulemaking is EPA-HQ-OAR-2015-0072.
      The analyses in this RIA rely on national-level data (emissions inventory and control measure information) for use in national-level assessments (air quality modeling, control strategies, environmental justice, and benefits estimation). However, the ambient air quality issues being analyzed are highly complex and local in nature, and the results of these national-level assessments therefore contain uncertainty. It is beyond the scope of this RIA to develop detailed local information for the areas being analyzed, including populating the local emissions inventory information, obtaining local information to increase the resolution of the air quality modeling, and obtaining local information on emissions controls, all of which would reduce some of the uncertainty in these national-level assessments. For example, having more refined data would be ideal for agricultural dust and burning, prescribed burning, and non-point (area) sources due to their large contribution to primary PM2.5 emissions and the limited availability of emissions controls. The estimated benefits and costs associated with applying emissions controls are incremental to a baseline of attaining the current primary annual and 24-hour PM2.5 standards of 12/35 g/ - m - [3] -  in ambient air and incorporate air quality improvements achieved through the projected implementation of existing regulations. 
Results
The EPA prepared an illustrative control strategy analysis to estimate the costs and human health benefits associated with the control strategies applied toward reaching the proposed and more stringent alternative PM2.5 standard levels. The control strategies presented in this RIA are an illustration of one possible set of control strategies states might choose to implement toward meeting the proposed standard levels. States, not the EPA, will implement the proposed NAAQS and will ultimately determine appropriate emissions control strategies and measures. This section summarizes the results of the analyses.
As shown in Chapter 4, the estimated costs associated with the control strategies for the proposed alternative standard levels are approximately $95 million for the proposed alternative standard level of 10/35 g/m3 and $390 million for the proposed alternative standard level of 9/35 g/m3 in 2032 (2017$, 7 percent interest rate). As shown in Chapter 5, the estimated monetized benefits associated with these control strategies for the proposed alternative standard levels are approximately $7.6 billion and $16 billion for the proposed alternative standard level of 10/35 g/m3 and $19 billion and $39 billion for the proposed alternative standard level of 9/35 g/m3 in 2032 (2017$, based on a real discount rate of 7 percent). The benefits are associated with two point estimates from two different epidemiologic studies discussed in more detail in Chapter 5, Section 5.3.3. It is expected that some costs and benefits will begin occurring before 2032, as states begin implementing control measures to attain earlier or to show progress towards attainment. 
As discussed Chapter 3, Section 3.2.5, the estimated PM2.5 emissions reductions from control applications do not fully account for all the emissions reductions needed to reach the proposed and more stringent alternative standard levels in some counties in the northeast, southeast, west, and California. In Chapter 2, Section 2.4 and Chapter 3, Section 3.2.6, we discuss the remaining air quality challenges for areas in the northeast and southeast, as well as in the west and California for the proposed alternative standard levels of 10/35 g/ - m - [3] -  and 9/35 g/ - m - [3] - . The EPA calculates the monetized net benefits of the proposed alternative standard levels by subtracting the estimated monetized compliance costs from the estimated monetized benefits in 2032. These estimates do not fully account for all of the emissions reductions needed to reach the proposed and more stringent alternative standard levels. In 2032, the monetized net benefits of the proposed alternative standard level of 10/35 g/m3 are approximately $8.4 billion and $17 billion using a 3 percent real discount rate for the benefits estimates and the monetized net benefits of the proposed alternative standard level of 9/35 g/m3 are approximately $20 billion and $43 billion using a 3 percent real discount rate for the benefits estimates (in 2017$). The benefits are associated with two point estimates from two different epidemiologic studies discussed in more detail in Chapter 5, Section 5.3.3. Table 8-1 presents a summary of these impacts for the proposed alternative standard levels and the more stringent alternative standard levels for 2032. 
Table 8-1	Estimated Monetized Benefits, Costs, and Net Benefits of the Control Strategies Applied Toward Primary Alternative Standard Levels of 10/35 g/ - m - [3] - , 10/30 g/ - m - [3] - , 9/35 g/ - m - [3] - , and 8/35 g/ - m - [3] -  in 2032 for the U.S. (millions of 2017$)
                                       
                                     10/35
                                     10/30
                                     9/35
                                     8/35
                                  Benefits[a]
                              $8,500 and $17,000
                              $9,600 and $20,000
                              $21,000 and $43,000
                              $46,000 and $95,000
                                   Costs[b]
                                      $95
                                     $260
                                     $390
                                    $1,800
                                 Net Benefits
                              $8,400 and $17,000
                              $9,300 and $19,000
                              $20,000 and $43,000
                              $44,000 and $93,000
Notes: Rows may not appear to add correctly due to rounding. We focus results to provide a snapshot of costs and benefits in 2032, using the best available information to approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
a We assume that there is a cessation lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion over the 20 years following exposure, which affects the valuation of mortality benefits at different discount rates. Similarly, we assume there is a cessation lag between the change in PM exposures and both the development and diagnosis of lung cancer. The benefits are associated with two point estimates from two different epidemiologic studies, and we present the benefits calculated at a real discount rate of 3 percent. The benefits exclude additional health and welfare benefits that could not be quantified (see Chapter 5, Sections 5.3.4 and 5.3.5).
b The costs are annualized using a 7 percent interest rate.

As part of fulfilling analytical guidance with respect to E.O. 12866, the EPA presents estimates of the present value (PV) of the monetized benefits and costs over the twenty-year period 2032 to 2051. To calculate the present value of the social net benefits of the proposed alternative standard levels, annual benefits and costs are discounted to 2022 at 3 percent and 7 percent discount rates as directed by OMB's Circular A-4. The EPA also presents the equivalent annualized value (EAV), which represents a flow of constant annual values that, had they occurred in each year from 2032 to 2051, would yield a sum equivalent to the PV. The EAV represents the value of a typical cost or benefit for each year of the analysis, in contrast to the 2032-specific estimates.
For the twenty-year period of 2032 to 2051, for the proposed alternative standard level of 10/35 g/m3 the PV of the costs, in 2017$ and discounted to 2022, is $1.1 billion when using a 3 percent discount rate and $540 million when using a 7 percent discount rate. The EAV is $72 million per year when using a 3 percent discount rate and $51 million when using a 7 percent discount rate. For the twenty-year period of 2032 to 2051, for the proposed alternative standard level of 9/35 g/m3 the PV of the costs, in 2017$ and discounted to 2022, is $4.5 billion when using a 3 percent discount rate and $2.3 billion when using a 7 percent discount rate. The EAV is $300 million per year when using a 3 percent discount rate and $210 million when using a 7 percent discount rate. The costs in PV and EAV terms for the proposed alternative standard levels can be found in Table 8-2 and Table 8-3. 

Table 8-2	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs of the Control Strategies Applied Toward the Primary Alternative Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3] 8/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022, 3 percent discount rate)
                                     Year
                                     10/35
                                     10/30
                                     9/35
                                     8/35
                                     2032
                                      $70
                                     $190
                                     $290
                                    $1,400
                                     2033
                                      $68
                                     $190
                                     $280
                                    $1,300
                                     2034
                                      $66
                                     $180
                                     $280
                                    $1,300
                                     2035
                                      $64
                                     $180
                                     $270
                                    $1,200
                                     2036
                                      $63
                                     $170
                                     $260
                                    $1,200
                                     2037
                                      $61
                                     $170
                                     $250
                                    $1,200
                                     2038
                                      $59
                                     $160
                                     $250
                                    $1,100
                                     2039
                                      $57
                                     $160
                                     $240
                                    $1,100
                                     2040
                                      $56
                                     $150
                                     $230
                                    $1,100
                                     2041
                                      $54
                                     $150
                                     $220
                                    $1,000
                                     2042
                                      $52
                                     $140
                                     $220
                                    $1,000
                                     2043
                                      $51
                                     $140
                                     $210
                                     $980
                                     2044
                                      $49
                                     $130
                                     $210
                                     $950
                                     2045
                                      $48
                                     $130
                                     $200
                                     $920
                                     2046
                                      $47
                                     $130
                                     $190
                                     $900
                                     2047
                                      $45
                                     $120
                                     $190
                                     $870
                                     2048
                                      $44
                                     $120
                                     $180
                                     $840
                                     2049
                                      $43
                                     $120
                                     $180
                                     $820
                                     2050
                                      $41
                                     $110
                                     $170
                                     $800
                                     2051
                                      $40
                                     $110
                                     $170
                                     $770
                                 Present Value
                                    $1,100
                                    $2,900
                                    $4,500
                                    $21,000
                          Equivalent Annualized Value
                                      $72
                                     $200
                                     $300
                                    $1,400


Table 8-3	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs of the Control Strategies Applied Toward the Primary Alternative Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3] 8/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022, 7 percent discount rate)
                                     Year
                                     10/35
                                     10/30
                                     9/35
                                     8/35
                                     2032
                                      $48
                                     $130
                                     $200
                                     $930
                                     2033
                                      $45
                                     $120
                                     $190
                                     $870
                                     2034
                                      $42
                                     $110
                                     $170
                                     $810
                                     2035
                                      $39
                                     $110
                                     $160
                                     $760
                                     2036
                                      $37
                                     $100
                                     $150
                                     $710
                                     2037
                                      $34
                                      $93
                                     $140
                                     $660
                                     2038
                                      $32
                                      $87
                                     $130
                                     $620
                                     2039
                                      $30
                                      $81
                                     $120
                                     $580
                                     2040
                                      $28
                                      $76
                                     $120
                                     $540
                                     2041
                                      $26
                                      $71
                                     $110
                                     $500
                                     2042
                                      $24
                                      $66
                                     $100
                                     $470
                                     2043
                                      $23
                                      $62
                                      $95
                                     $440
                                     2044
                                      $21
                                      $58
                                      $89
                                     $410
                                     2045
                                      $20
                                      $54
                                      $83
                                     $380
                                     2046
                                      $19
                                      $51
                                      $78
                                     $360
                                     2047
                                      $17
                                      $47
                                      $72
                                     $340
                                     2048
                                      $16
                                      $44
                                      $68
                                     $310
                                     2049
                                      $15
                                      $41
                                      $63
                                     $290
                                     2050
                                      $14
                                      $39
                                      $59
                                     $270
                                     2051
                                      $13
                                      $36
                                      $55
                                     $260
                                 Present Value
                                     $540
                                    $1,500
                                    $2,300
                                    $10,000
                          Equivalent Annualized Value
                                      $51
                                     $140
                                     $210
                                     $990

For the twenty-year period of 2032 to 2051, for the proposed alternative standard level of 10/35 g/m3 the PV of the benefits, in 2017$ and discounted to 2022, is $200 billion when using a 3 percent discount rate and $91 billion when using a 7 percent discount rate. The EAV is $13 billion per year when using a 3 percent discount rate and $8.5 billion when using a 7 percent discount rate. For the twenty-year period of 2032 to 2051, for the proposed alternative standard level of 9/35 g/m3 the PV of the benefits, in 2017$ and discounted to 2022, is $490 billion when using a 3 percent discount rate and $220billion when using a 7 percent discount rate. The EAV is $33 billion per year when using a 3 percent discount rate and $21 billion when using a 7 percent discount rate. The benefits in PV and EAV terms for the proposed alternative standard levels can be found in Table 8-4 and Table 8-5. 
Table 8-4	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Benefits of the Control Strategies Applied Toward the Primary Alternative Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3] 8/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022, 3 percent discount rate)
                                     Year
                                     10/35
                                     10/30
                                     9/35
                                     8/35
                                     2032
                                    $13,000
                                    $15,000
                                    $32,000
                                    $71,000
                                     2033
                                    $13,000
                                    $14,000
                                    $31,000
                                    $69,000
                                     2034
                                    $12,000
                                    $14,000
                                    $30,000
                                    $67,000
                                     2035
                                    $12,000
                                    $13,000
                                    $29,000
                                    $65,000
                                     2036
                                    $12,000
                                    $13,000
                                    $29,000
                                    $63,000
                                     2037
                                    $11,000
                                    $13,000
                                    $28,000
                                    $61,000
                                     2038
                                    $11,000
                                    $12,000
                                    $27,000
                                    $59,000
                                     2039
                                    $11,000
                                    $12,000
                                    $26,000
                                    $58,000
                                     2040
                                    $10,000
                                    $12,000
                                    $25,000
                                    $56,000
                                     2041
                                    $9,900
                                    $11,000
                                    $25,000
                                    $54,000
                                     2042
                                    $9,700
                                    $11,000
                                    $24,000
                                    $53,000
                                     2043
                                    $9,400
                                    $11,000
                                    $23,000
                                    $51,000
                                     2044
                                    $9,100
                                    $10,000
                                    $23,000
                                    $50,000
                                     2045
                                    $8,800
                                    $10,000
                                    $22,000
                                    $48,000
                                     2046
                                    $8,600
                                    $9,700
                                    $21,000
                                    $47,000
                                     2047
                                    $8,300
                                    $9,400
                                    $21,000
                                    $46,000
                                     2048
                                    $8,100
                                    $9,100
                                    $20,000
                                    $44,000
                                     2049
                                    $7,900
                                    $8,900
                                    $19,000
                                    $43,000
                                     2050
                                    $7,600
                                    $8,600
                                    $19,000
                                    $42,000
                                     2051
                                    $7,400
                                    $8,400
                                    $18,000
                                    $40,000
                                 Present Value
                                   $200,000
                                   $220,000
                                   $490,000
                                  $1,100,000
                          Equivalent Annualized Value
                                    $13,000
                                    $15,000
                                    $33,000
                                    $73,000


Table 8-5	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Benefits of the Control Strategies Applied Toward the Primary Alternative Standard Levels of 10/35 g/m[3], 10/30 g/m[3], 9/35 g/m[3] 8/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022, 7 percent discount rate)
                                     Year
                                     10/35
                                     10/30
                                     9/35
                                     8/35
                                     2032
                                    $8,000
                                    $9,000
                                    $20,000
                                    $44,000
                                     2033
                                    $7,500
                                    $8,400
                                    $18,000
                                    $41,000
                                     2034
                                    $7,000
                                    $7,900
                                    $17,000
                                    $38,000
                                     2035
                                    $6,500
                                    $7,400
                                    $16,000
                                    $36,000
                                     2036
                                    $6,100
                                    $6,900
                                    $15,000
                                    $33,000
                                     2037
                                    $5,700
                                    $6,400
                                    $14,000
                                    $31,000
                                     2038
                                    $5,300
                                    $6,000
                                    $13,000
                                    $29,000
                                     2039
                                    $5,000
                                    $5,600
                                    $12,000
                                    $27,000
                                     2040
                                    $4,600
                                    $5,200
                                    $11,000
                                    $25,000
                                     2041
                                    $4,300
                                    $4,900
                                    $11,000
                                    $24,000
                                     2042
                                    $4,100
                                    $4,600
                                    $10,000
                                    $22,000
                                     2043
                                    $3,800
                                    $4,300
                                    $9,400
                                    $21,000
                                     2044
                                    $3,500
                                    $4,000
                                    $8,800
                                    $19,000
                                     2045
                                    $3,300
                                    $3,700
                                    $8,200
                                    $18,000
                                     2046
                                    $3,100
                                    $3,500
                                    $7,700
                                    $17,000
                                     2047
                                    $2,900
                                    $3,300
                                    $7,200
                                    $16,000
                                     2048
                                    $2,700
                                    $3,100
                                    $6,700
                                    $15,000
                                     2049
                                    $2,500
                                    $2,900
                                    $6,300
                                    $14,000
                                     2050
                                    $2,400
                                    $2,700
                                    $5,800
                                    $13,000
                                     2051
                                    $2,200
                                    $2,500
                                    $5,500
                                    $12,000
                                 Present Value
                                    $91,000
                                   $100,000
                                   $220,000
                                   $490,000
                          Equivalent Annualized Value
                                    $8,500
                                    $9,600
                                    $21,000
                                    $47,000

For the twenty-year period of 2032 to 2051, for the proposed alternative standard level of 10/35 g/m3 the PV of the net benefits, in 2017$ and discounted to 2022, is $200 billion when using a 3 percent discount rate and $90 billion when using a 7 percent discount rate. The EAV is $13 billion per year when using a 3 percent discount rate and $8.5 billion when using a 7 percent discount rate. For the twenty-year period of 2032 to 2051, for the proposed alternative standard level of 9/35 g/m3 the PV of the net benefits, in 2017$ and discounted to 2022, is $490 billion when using a 3 percent discount rate and $220 billion when using a 7 percent discount rate. The EAV is $33 billion per year when using a 3 percent discount rate and $21 billion when using a 7 percent discount rate. The comparison of benefits and costs in PV and EAV terms for the proposed alternative standard levels can be found in Table 8-6 and Table 8-7. Estimates in the tables are presented as rounded values.   
Table 8-6	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies Applied Toward the Proposed Primary Alternative Standard Level of 10/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount rates)
                                       
                                  Benefits[a]
                                   Costs[b]
                                 Net Benefits
                                     Year
                                      3%
                                      7%
                                      3%
                                      7%
                                      3%
                                      7%
                                     2032
                                    $13,000
                                    $8,000
                                      $70
                                      $48
                                    $13,000
                                    $7,900
                                     2033
                                    $13,000
                                    $7,500
                                      $68
                                      $45
                                    $13,000
                                    $7,400
                                     2034
                                    $12,000
                                    $7,000
                                      $66
                                      $42
                                    $12,000
                                    $6,900
                                     2035
                                    $12,000
                                    $6,500
                                      $64
                                      $39
                                    $12,000
                                    $6,500
                                     2036
                                    $12,000
                                    $6,100
                                      $63
                                      $37
                                    $11,000
                                    $6,100
                                     2037
                                    $11,000
                                    $5,700
                                      $61
                                      $34
                                    $11,000
                                    $5,700
                                     2038
                                    $11,000
                                    $5,300
                                      $59
                                      $32
                                    $11,000
                                    $5,300
                                     2039
                                    $11,000
                                    $5,000
                                      $57
                                      $30
                                    $10,000
                                    $4,900
                                     2040
                                    $10,000
                                    $4,600
                                      $56
                                      $28
                                    $10,000
                                    $4,600
                                     2041
                                    $9,900
                                    $4,300
                                      $54
                                      $26
                                    $9,900
                                    $4,300
                                     2042
                                    $9,700
                                    $4,100
                                      $52
                                      $24
                                    $9,600
                                    $4,000
                                     2043
                                    $9,400
                                    $3,800
                                      $51
                                      $23
                                    $9,300
                                    $3,800
                                     2044
                                    $9,100
                                    $3,500
                                      $49
                                      $21
                                    $9,100
                                    $3,500
                                     2045
                                    $8,800
                                    $3,300
                                      $48
                                      $20
                                    $8,800
                                    $3,300
                                     2046
                                    $8,600
                                    $3,100
                                      $47
                                      $19
                                    $8,500
                                    $3,100
                                     2047
                                    $8,300
                                    $2,900
                                      $45
                                      $17
                                    $8,300
                                    $2,900
                                     2048
                                    $8,100
                                    $2,700
                                      $44
                                      $16
                                    $8,000
                                    $2,700
                                     2049
                                    $7,900
                                    $2,500
                                      $43
                                      $15
                                    $7,800
                                    $2,500
                                     2050
                                    $7,600
                                    $2,400
                                      $41
                                      $14
                                    $7,600
                                    $2,300
                                     2051
                                    $7,400
                                    $2,200
                                      $40
                                      $13
                                    $7,400
                                    $2,200
                                 Present Value
                                   $200,000
                                    $91,000
                                    $1,100
                                     $540
                                   $200,000
                                    $90,000
                          Equivalent Annualized Value
                                    $13,000
                                    $8,500
                                      $72
                                      $51
                                    $13,000
                                    $8,500
Notes: Rows may not appear to add correctly due to rounding. The annualized present value of costs and benefits are calculated over a 20-year period from 2032 to 2051. 
[a] The benefits values use the larger of the two avoided premature deaths estimates presented in Chapter 5, Table 5-7, and are discounted at a rate of 3 percent over the SAB-recommended 20-year segmented lag. The benefits exclude additional health and welfare benefits that could not be quantified (see Chapter 5, Sections 5.3.4 and 5.3.5).
[b] The costs are annualized using a 7 percent interest rate.
Table 8-7	Summary of Present Values and Equivalent Annualized Values for Estimated Monetized Compliance Costs, Benefits, and Net Benefits of the Control Strategies Applied Toward the Proposed Primary Alternative Standard Level of 9/35 g/m[3] (millions of 2017$, 2032-2051, discounted to 2022 using 3 and 7 percent discount rates)
                                       
                                  Benefits[a]
                                   Costs[b]
                                 Net Benefits
                                     Year
                                      3%
                                      7%
                                      3%
                                      7%
                                      3%
                                      7%
                                     2032
                                    $32,000
                                    $20,000
                                     $290
                                     $200
                                    $32,000
                                    $20,000
                                     2033
                                    $31,000
                                    $18,000
                                     $280
                                     $190
                                    $31,000
                                    $18,000
                                     2034
                                    $30,000
                                    $17,000
                                     $280
                                     $170
                                    $30,000
                                    $17,000
                                     2035
                                    $29,000
                                    $16,000
                                     $270
                                     $160
                                    $29,000
                                    $16,000
                                     2036
                                    $29,000
                                    $15,000
                                     $260
                                     $150
                                    $28,000
                                    $15,000
                                     2037
                                    $28,000
                                    $14,000
                                     $250
                                     $140
                                    $27,000
                                    $14,000
                                     2038
                                    $27,000
                                    $13,000
                                     $250
                                     $130
                                    $27,000
                                    $13,000
                                     2039
                                    $26,000
                                    $12,000
                                     $240
                                     $120
                                    $26,000
                                    $12,000
                                     2040
                                    $25,000
                                    $11,000
                                     $230
                                     $120
                                    $25,000
                                    $11,000
                                     2041
                                    $25,000
                                    $11,000
                                     $220
                                     $110
                                    $24,000
                                    $11,000
                                     2042
                                    $24,000
                                    $10,000
                                     $220
                                     $100
                                    $24,000
                                    $9,900
                                     2043
                                    $23,000
                                    $9,400
                                     $210
                                      $95
                                    $23,000
                                    $9,300
                                     2044
                                    $23,000
                                    $8,800
                                     $210
                                      $89
                                    $22,000
                                    $8,700
                                     2045
                                    $22,000
                                    $8,200
                                     $200
                                      $83
                                    $22,000
                                    $8,100
                                     2046
                                    $21,000
                                    $7,700
                                     $190
                                      $78
                                    $21,000
                                    $7,600
                                     2047
                                    $21,000
                                    $7,200
                                     $190
                                      $72
                                    $20,000
                                    $7,100
                                     2048
                                    $20,000
                                    $6,700
                                     $180
                                      $68
                                    $20,000
                                    $6,600
                                     2049
                                    $19,000
                                    $6,300
                                     $180
                                      $63
                                    $19,000
                                    $6,200
                                     2050
                                    $19,000
                                    $5,800
                                     $170
                                      $59
                                    $19,000
                                    $5,800
                                     2051
                                    $18,000
                                    $5,500
                                     $170
                                      $55
                                    $18,000
                                    $5,400
                                 Present Value
                                   $490,000
                                   $220,000
                                    $4,500
                                    $2,300
                                   $490,000
                                   $220,000
                          Equivalent Annualized Value
                                    $33,000
                                    $21,000
                                     $300
                                     $210
                                    $33,000
                                    $21,000
Notes: Rows may not appear to add correctly due to rounding. The annualized present value of costs and benefits are calculated over a 20-year period from 2032 to 2051.
[a] The benefits values use the larger of the two avoided premature deaths estimates presented in Chapter 5, Table 5-7, and are discounted at a rate of 3 percent over the SAB-recommended 20-year segmented lag. The benefits exclude additional health and welfare benefits that could not be quantified (see Chapter 5, Sections 5.3.4 and 5.3.5).
[b] The costs are annualized using a 7 percent interest rate.

Limitations of Present Value Estimates
The net present value (NPV) estimates presented reflect the costs and benefits associated with the illustrative control strategies; as discussed in Chapter 3, Section 3.2.5, some areas still need emissions reductions after control applications for the alternative standards analyzed. Additionally, there are methodological complexities associated with calculating the NPV of a stream of costs and benefits for national ambient air quality standards. The estimated NPV can better characterize the stream of benefits and costs over a multi-year period; however, calculating the PV of improved air quality is generally quite data-intensive and costly. While NPV analysis allows evaluation of alternatives by summing the present value of all future costs and benefits, insights into how costs will occur over time are limited by underlying assumptions and data. Calculating a PV of the stream of future benefits also poses special challenges, which we describe below. Further, the results are sensitive to assumptions regarding the time period over which the stream of benefits is discounted.  
To estimate engineering costs, the EPA employs the equivalent uniform annual cost (EUAC) method, which annualizes costs over varying lifetimes of control measures applied in the analysis. Using the EUAC method results in a stream of annualized costs that is equal for each year over the lifetime of control measures, resulting in a value similar to the value associated with an amortized mortgage or other loan payment. Control equipment is often purchased by incurring debt rather than through a single up-front payment. Recognizing this led the EPA to estimate costs using the EUAC method instead of a method that mimics firms paying up front for the future costs of installation, maintenance, and operation of pollution control devices.  
Further, because we do not know when a facility will stop using a control measure or change to another measure based on economic or other reasons, the EPA assumes the control equipment and measures applied in the illustrative control strategies remain in service for their full useful life. As a result, the annualized cost of controls in a single future year is the same throughout the lifetimes of control measures analyzed, allowing the EPA to compare the annualized control costs with the benefits in a single year for consistent comparison.  
The theoretically appropriate approach for characterizing the PV of benefits is the life table approach. The life table, or dynamic population, approach explicitly models the year-to-year influence of air pollution on baseline mortality risk, population growth and the birth rate -- typically for each year over the course of a 50-to-100 year period (U.S. EPA SAB, 2010; Miller, 2003). In contrast to the pulse approach that is employed in this analysis, a life table models these variables endogenously by following a population cohort over time. For example, a life table will "pass" the air pollution-modified baseline death rate and population from year to year; impacts estimated in year 50 will account for the influence of air pollution on death rates and population growth in the preceding 49 years. 
Calculating year-to-year changes in mortality risk in a life table requires some estimate of the annual change in air quality levels. It is both impractical to model air quality levels for each year and challenging to account for changes in federal, state, and local policies that will affect the annual level and distribution of pollutants. For each of these reasons the EPA does not always report the PV of benefits for air rules but has instead pursued a pulse approach. 
References 
Miller BG (2003). Life table methods for quantitative impact assessments in chronic mortality. Journal of Epidemiology & Community Health, 57(3):200 - 206.
U.S. EPA Science Advisory Board (2010). Review of EPA's DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act. Washington, DC.


United States
Environmental Protection
Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Research Triangle Park, NC
Publication No. EPA-452/P-22-001
August 2022


