August 2010

Regulatory Impact Analysis:

Amendments to the National Emission Standards for Hazardous Air
Pollutants and New Source Performance Standards (NSPS) for the Portland
Cement Manufacturing Industry

Final Report

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards (OAQPS)

Air Benefit and Cost Group

(MD-C439-02)

Research Triangle Park, NC 27711

	

Regulatory Impact Analysis:

Amendments to the National Emission Standards for Hazardous Air
Pollutants and New Source Performance Standards (NSPS) for the Portland
Cement Manufacturing Industry

Final Report

August 2010

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards (OAQPS)

Air Benefit and Cost Group

(MD-C439-02)

Research Triangle Park, NC 27711

Contents

Section	Page

  TOC \o "1-3" \u  1	Introduction	  PAGEREF _Toc265840590 \h  1-1 

1.1	Executive Summary	  PAGEREF _Toc265840591 \h  1-1 

1.2	Organization of this Report	  PAGEREF _Toc265840592 \h  1-3 

2	Industry Profile	  PAGEREF _Toc265840593 \h  2-1 

2.1	The Supply Side	  PAGEREF _Toc265840594 \h  2-1 

2.1.1	Production Process	  PAGEREF _Toc265840595 \h  2-1 

2.1.2	Types of Portland Cement	  PAGEREF _Toc265840596 \h  2-3 

2.1.3	Production Costs	  PAGEREF _Toc265840597 \h  2-3 

2.2	The Demand Side	  PAGEREF _Toc265840598 \h  2-8 

2.3	Industry Organization	  PAGEREF _Toc265840599 \h  2-10 

2.3.1	Market Structure	  PAGEREF _Toc265840600 \h  2-10 

2.3.2	Manufacturing Plants	  PAGEREF _Toc265840601 \h  2-11 

2.3.3	Firm Characteristics	  PAGEREF _Toc265840602 \h  2-16 

2.4	Markets	  PAGEREF _Toc265840603 \h  2-20 

2.4.1	Market Volumes	  PAGEREF _Toc265840604 \h  2-21 

2.4.2	Market Prices	  PAGEREF _Toc265840605 \h  2-22 

2.4.3	Future Projections	  PAGEREF _Toc265840606 \h  2-22 

3	Economic Impact Analysis	  PAGEREF _Toc265840607 \h  3-1 

3.1	Regulatory Program Costs	  PAGEREF _Toc265840608 \h  3-2 

3.2	Partial-Equilibrium Analysis	  PAGEREF _Toc265840609 \h  3-5 

3.2.1	Regional Structure and Baseline Data	  PAGEREF _Toc265840610 \h 
3-6 

3.2.2	Near-Term Cement Plant Production Decisions	  PAGEREF
_Toc265840611 \h  3-6 

3.2.3	Economic Impact Model Results	  PAGEREF _Toc265840612 \h  3-9 

3.3	Other Economic Analyses: Direct Compliance Cost Methods	  PAGEREF
_Toc265840613 \h  3-18 

3.4	Social Cost Estimates	  PAGEREF _Toc265840614 \h  3-19 

3.5	Energy Impacts	  PAGEREF _Toc265840615 \h  3-21 

3.6	Assessment	  PAGEREF _Toc265840616 \h  3-23 

4	Small Business Impact Analysis	  PAGEREF _Toc265840617 \h  4-1 

4.1	Identify Affected Small Entities	  PAGEREF _Toc265840618 \h  4-1 

4.2	Sales and Revenue Test Screening Analysis	  PAGEREF _Toc265840619 \h
 4-1 

4.3	Additional Market Analysis	  PAGEREF _Toc265840620 \h  4-3 

4.4	Assessment	  PAGEREF _Toc265840621 \h  4-3 

5	Air Quality Modeling of Emission Reductions	  PAGEREF _Toc265840622 \h
 5-1 

5.1	Synopsis	  PAGEREF _Toc265840623 \h  5-1 

5.2	Photochemical Model Background	  PAGEREF _Toc265840624 \h  5-1 

5.3	Model Domain and Grid Resolution	  PAGEREF _Toc265840625 \h  5-2 

5.4	Emissions Input Data	  PAGEREF _Toc265840626 \h  5-2 

5.5	Model Results: Air Quality Impacts	  PAGEREF _Toc265840627 \h  5-5 

5.5	Limitations (Uncertainties) Associated with the Air Quality Modeling
  PAGEREF _Toc265840627 \h  5-5 

6	Benefits of Emissions Reductions	  PAGEREF _Toc265840628 \h  6-1 

6.1	Synopsis	  PAGEREF _Toc265840629 \h  6-1 

6.2	Calculation of PM2.5 Human Health Benefits	  PAGEREF _Toc265840630
\h  6-2 

6.2.1	Methodology Improvements since Proposal	  PAGEREF _Toc265840631 \h
 6-2 

6.2.2	Benefits Analysis Approach	  PAGEREF _Toc265840632 \h  6-3 

6.2.3	Health Impact Analysis (HIA)	  PAGEREF _Toc265840633 \h  6-4 

6.2.4	Estimating PM2.5-related premature mortality	  PAGEREF
_Toc265840634 \h  6-8 

6.2.5	Economic valuation of health impacts	  PAGEREF _Toc265840635 \h 
6-11 

6.3	Health Benefits Results	  PAGEREF _Toc265840636 \h  6-13 

6.4	Energy Disbenefits	  PAGEREF _Toc265840637 \h  6-20 

6.4.1	PM2.5 Disbenefits	  PAGEREF _Toc265840638 \h  6-25 

6.4.2	Social Cost of Carbon and Greenhouse Gas Disbenefits	  PAGEREF
_Toc265840639 \h  6-27 

6.4.2	Total Disbenefits	  PAGEREF _Toc265840639 \h  6-27 

6.5	Unquantified or Nonmonetized Benefits	  PAGEREF _Toc265840637 \h 
6-20 

6.5.1	Other SO2 and PM Benefits	  PAGEREF _Toc265840638 \h  6-25 

6.5.2	HAP Benefits	  PAGEREF _Toc265840639 \h  6-27 

6.6	Limitations and Uncertainties	  PAGEREF _Toc265840640 \h  6-41 

6.6.1	Monte Carlo Analysis	  PAGEREF _Toc265840641 \h  6-41 

6.6.2	Alternate Concentration-Response Functions for PM Mortality	 
PAGEREF _Toc265840642 \h  6-42 

6.6.3	LML Assessment	  PAGEREF _Toc265840643 \h  6-42 

6.6.4	Qualitative Assessment of Uncertainty and Other Analysis
Limitations	  PAGEREF _Toc265840644 \h  6-43 

6.7	Comparison of Benefits and Costs	  PAGEREF _Toc265840645 \h  6-44 

7	References	  PAGEREF _Toc265840646 \h  7-1 

 Appendixes

A	Short-Run Regional Portland Cement Economic Model	A-1

B	The Cement Plant’s Production Decision: A Mathematical
Representation	B-1

C	Social Cost Methodology	C-1

D	Summary of Expert Opinions on the Existence of a Threshold in the
Concentration-Response Function for PM2.5-related Mortality	D-1

List of Figures

Number	Page

  TOC \t "Figure Title,5"  2-1.	Simplified Flow Sheet of Clinker and
Cement Manufacture	  PAGEREF _Toc265841183 \h  2-2 

2-2.	Labor Costs per Metric Ton of Cement ($2005)	  PAGEREF
_Toc265841184 \h  2-7 

2-3.	Distribution of Energy Consumption	  PAGEREF _Toc265841185 \h  2-8 

2-4.	End Uses of Cement: 1975 to 2003	  PAGEREF _Toc265841186 \h  2-9 

2-5.	Producer Price Indices for Competitive Building Materials: 2003 to
2008	  PAGEREF _Toc265841187 \h  2-10 

2-6.	Distribution of Cement Kilns in the United States	  PAGEREF
_Toc265841188 \h  2-13 

2-7.	Historical U.S. Cement Price	  PAGEREF _Toc265841189 \h  2-23 

2-8.	Deviation from National Average Cement Price per Metric Ton by
Region: 2005	  PAGEREF _Toc265841190 \h  2-24 

3-1.	Range of Per-Ton Total Annualized Compliance Costs (2005$)	 
PAGEREF _Toc265841191 \h  3-4 

5-1.	Map of the Photochemical Modeling Domain	  PAGEREF _Toc265841192 \h
 5-3 

6-1.	Total Monetized Benefits for the Final Cement NESHAP and NSPS in
2013	  PAGEREF _Toc265841193 \h  6-2 

6-2.	Illustration of BenMAP Approach	  PAGEREF _Toc265841194 \h  6-5 

6-3.	Data inputs and outputs for the BenMAP model	  PAGEREF
_Toc265841195 \h  6-6 

6-4.	Breakdown of Monetized PM2.5 Health Benefits using Mortality
Function from Pope et al. (2002)	  PAGEREF _Toc265841196 \h  6-16 

6-5.	Total Monetized PM2.5 Benefits for the Final Cement NESHAP and NSPS
in 2013	  PAGEREF _Toc265841197 \h  6-18 

6-6.	Percentage of Total PM-Related Mortalities Avoided by Baseline Air
Quality Level for Final Portland Cement NESHAP and NSPS	  PAGEREF
_Toc265841198 \h  6-19 

6-7.	Cumulative Percentage of Total PM-related Mortalities Avoided by
Baseline Air Quality Level for Final Portland Cement NESHAP and NSPS	 
PAGEREF _Toc265841199 \h  6-20 

6-8.	Estimated County Level Carcinogenic Risk from HAP exposure from
outdoor sources (NATA, 2002)	  PAGEREF _Toc265841200 \h  6-28 

6-9.	Estimated County Level Noncancer (Respiratory) Risk from HAP
exposure from outdoor sources (NATA, 2002)	  PAGEREF _Toc265841201 \h 
6-29 

6-10.	Reductions in Total Mercury Deposition (µg/m2) in the Eastern
U.S.	  PAGEREF _Toc265841202 \h  6-33 

6-11.	Reductions in Total Mercury Deposition (µg/m2) in the Western
U.S.	  PAGEREF _Toc265841203 \h  6-34 

6-12.	Net Benefits for the Final Portland Cement NESHAP and NSPS at 3%
Discount Rate	  PAGEREF _Toc265841204 \h  6-47 

6-13.	Net Benefits for the Final Portland Cement NESHAP and NSPS at 7%
Discount Rate	  PAGEREF _Toc265841205 \h  6-48  

List of Tables

Number	Page

  TOC \t "Table Title,5"  1-1.	Summary of the Monetized Benefits, Social
Costs, and Net Benefits for the Final Portland Cement NESHAP in 2013
(millions of 2005$)	  PAGEREF _Toc265841303 \h  1-4 

2-1.	Portland Cement Shipped from Plants in the United States to
Domestic Customers, by Type	  PAGEREF _Toc265841304 \h  2-4 

2-2.	Raw Material Input Ratios for the U.S. Cement Industry: 2000 to
2005	  PAGEREF _Toc265841305 \h  2-5 

2-3.	Raw Material Costs by Market and State: 2005	  PAGEREF
_Toc265841306 \h  2-6 

2-4.	Labor Productivity Measures for the U.S. Cement Industry by Process
Type: 2000 to 2005 (employee hours per metric ton)	  PAGEREF
_Toc265841307 \h  2-6 

2-5.	Energy Consumption by Type of U.S. Cement Plant (million BTU per
metric ton)	  PAGEREF _Toc265841308 \h  2-8 

2-6.	Number of Kilns and Clinker Capacity by State: 2005	  PAGEREF
_Toc265841309 \h  2-12 

2-7.	Number of Kilns and Clinker Capacity by Age and Process Type	 
PAGEREF _Toc265841310 \h  2-14 

2-8.	Clinker Capacity, Production, and Capacity Utilization in the
United States: 2000 to 2005	  PAGEREF _Toc265841311 \h  2-15 

2-9.	Capacity Utilization Rates by State: 2005	  PAGEREF _Toc265841312
\h  2-17 

2-10.	Cement Manufacturing Employment (NAICS 327310): 2000 to 2005	 
PAGEREF _Toc265841313 \h  2-18 

2-11.	Ultimate Parent Company Summary Data: 2005	  PAGEREF _Toc265841314
\h  2-19 

2-12.	Historical U.S. Cement Statistics (106 metric tons)	  PAGEREF
_Toc265841315 \h  2-21 

2-13.	U.S. Cement Trade Data: 2000 to 2007	  PAGEREF _Toc265841316 \h 
2-22 

3-1.	Summary of Direct Total Annualized Compliance Costs (million,
2005$)	  PAGEREF _Toc265841317 \h  3-3 

3-2.	Range of Per-ton Total Annualized Compliance Costs by State (2005$)
  PAGEREF _Toc265841318 \h  3-5 

3-3.	Portland Cement Prices by Market ($/metric tons): 2005	  PAGEREF
_Toc265841319 \h  3-7 

3-4.	Portland Cement Markets (106 metric tons): 2005	  PAGEREF
_Toc265841320 \h  3-8 

3-5.	National-Level Market Impacts: 2005	  PAGEREF _Toc265841321 \h  3-9


3-6.	Regional Compliance Costs and Market Price Changes ($/metric ton of
cement): 2005	  PAGEREF _Toc265841322 \h  3-10 

3-7.	Summary of Regional Market Impacts: 2005	  PAGEREF _Toc265841323 \h
 3-12 

3-8.	Distribution of Industry Impacts: 2005	  PAGEREF _Toc265841324 \h 
3-13 

3-9.	Cement Plants with Significant Utilization Changes: 2005	  PAGEREF
_Toc265841325 \h  3-14 

3-10.	Job Losses/Gains Associated with the Final Rule	  PAGEREF
_Toc265841326 \h  3-16 

3-11.	Distribution of Social Costs ($106): 2005	  PAGEREF _Toc265841327
\h  3-20 

3-12.	U.S. Cement Sector Energy Consumption (Trillion BTUs): 2013	 
PAGEREF _Toc265841328 \h  3-23 

4-1.	Small Entity Analysis	  PAGEREF _Toc265841329 \h  4-2 

5-1.	Geographic Elements of Domains Used in Photochemical Modeling	 
PAGEREF _Toc265841330 \h  5-3 

5-2.	Cement Kiln Emissions in 2005 Base and Estimated Future Year (2013)
in tons per year	  PAGEREF _Toc265841330 \h  5-3 

6-1.	Human Health and Welfare Effects of Pollutants Affected	  PAGEREF
_Toc265841331 \h  6-7 

6-2.	Summary of Monetized Benefits Estimates for Final Cement NESHAP and
NSPS in 2013 (millions of 2005$)	  PAGEREF _Toc265841332 \h  6-14 

6-3.	Summary of Reductions in Health Incidences and Monetized Benefits
from PM2.5 Benefits for the Final Cement NESHAP and NSPS in 2013 (95th
percentile confidence interval)	  PAGEREF _Toc265841333 \h  6-15 

6-4.	Comparison of Monetized Benefits and Emission Reductions for Final
Cement NESHAP and NSPS in 2013 (2005$)	  PAGEREF _Toc265841334 \h  6-17 

6-5.	Summary of Monetized PM2.5 Energy Disbenefits for the Final
Portland Cement NSPS and NESHAP in 2013 (2005$)	  PAGEREF _Toc265841334
\h  6-17 

6-6.	Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2013	 
PAGEREF _Toc265841334 \h  6-17 

6-7.	Monetized Disbenefits of CO2 Emission Increases in 2013	  PAGEREF
_Toc265841334 \h  6-17 

6-8.	Summary of the Monetized Benefits, Social Costs, and Net Benefits
for the final Portland Cement NESHAP in 2013 (millions of 2005$)	 
PAGEREF _Toc265841335 \h  6-46 

 

Introduction

The U.S. Environmental Protection Agency (EPA) is finalizing amendments
to the National Emission Standards for Hazardous Air Pollutants (NESHAP)
from the Portland cement manufacturing industry and New Source
Performance Standards (NSPS) for Portland cement plants. The final
amendments to the NESHAP add or revise, as applicable, emission limits
for mercury (Hg), total hydrocarbons (THC), and particulate matter (PM)
from kilns located at a major or an area sources, and hydrochloric acid
(HCl) from kilns and located at major sources. EPA is also adopting
separate standards for these pollutants that apply during startup,
shutdown, and operating modes. Finally, EPA is adopting performance
specifications for use of Hg continuous emission monitors (CEMS) and
updating recordkeeping and testing requirements. The final amendments to
the NSPS add or revise, as applicable, emission limits for particulate
matter (PM), opacity, nitrogen oxides (NOx), and sulfur dioxide (SO2)
for facilities that commence construction, modification, or
reconstruction after June 16, 2008. The final rule also includes
additional testing and monitoring requirements for affected sources. As
part of the regulatory process, EPA is required to develop a regulatory
impact analysis (RIA). The RIA includes an economic impact analysis
(EIA) and a small entity impacts analysis and documents the RIA methods
and results. 

Executive Summary

The key results of the RIA are as follows:

Options Analyzed: EPA’s analysis focuses on the results of the final
NESHAP and NSPS. We also present additional information on different
combinations of the regulatory programs to help stakeholders better
understand the size and scope of each. These include

final NSPS only,

final NESHAP only, and

alternative: more stringent NSPS and final NESHAP. 

The rest of this summary addresses the results of analyzing the final
NESHAP and NSPS.

Engineering Cost Analysis: EPA estimates that total annualized costs
with the final NESHAP and NSPS will be $466 million (2005$).

Market Analysis: The partial-equilibrium economic model suggests the
average national price for Portland cement could be 5% higher with the
NESHAP, or $4.50 per metric ton, while annual domestic production may
fall by 11%, or 10 million tons per year. Because of higher domestic
prices, imports rise by 10%, or 3 million metric tons per year. 

Industry Analysis: Net industry operating profits fall by $241 million;
EPA also identified 10 domestic plants with negative operating profits
and significant utilization changes that could temporarily idle until
market demand conditions improve. The plants have unit compliance costs
close to $8 per ton of clinker capacity and $116 million total change in
operating profits. Since these plants account for approximately 8% of
domestic capacity, a decision to permanently shut down these plants
would reduce domestic supply and could lead to additional projected
market price increases and reductions in pollution control costs. 

Employment Changes: EPA uses two methods for estimating employment
impacts.   A simplistic, limited assessment narrowly focused on output
changes in the Portland cement industry indicates that the final
rule’s gross impact on employment is 1,500 job losses.  However, this
approach inherently overstates job losses, as it is based on the
assumption that employment is proportional to output, and because it
ignores offsetting general equilibrium and other effects as discussed in
detail in Chapter 3.  A more sophisticated analytical approach that
includes other types of employment effects estimates changes in net
employment could range from a loss of 600 to a net gain of 1,300 jobs. 

Social Cost Analysis: The estimated social cost is $926 to $950 million
(2005$). The range represents the estimated difference in surplus if ten
facilities with low estimated post regulation capacity utilization
choose to idle or close rather than operate at a low capacity
utilization. The social cost estimates are significantly higher than the
engineering analysis estimates, which estimated annualized costs of $466
 million. This is a direct consequence of EPA’s assumptions about
existing market structure discussed extensively in previous cement
industry rulemakings and Section 2, Appendix A, and Appendix B of this
RIA. Under baseline conditions without regulation, the existing domestic
cement plants are assumed to choose a production level that is less than
the level produced under perfect competition. As a result, a preexisting
market distortion exists in the markets covered by the final rule (i.e.,
the observed baseline market price is higher than the [unobserved]
market price that a model of perfect competition would predict). The
imposition of additional regulatory costs tends to widen the gap between
price and marginal cost in these markets and contributes to additional
social costs. 

Energy Impacts: EPA concludes that the rule when implemented will not
have a significant adverse effect on the supply, distribution, or use of
energy. The cement industry accounts for less than 0.4% of the U.S.
total energy use. EPA estimates the additional add-on controls may
increase national electrical demand by 780 million kWh per year and the
natural gas use to be 1.0 million MMBTU per year for existing kilns. For
new kilns, assuming that of the 16 new kilns to start up by 2013  all
16will add alkaline scrubbers and ACI systems, the electrical demand is
estimated to be 199 million kWh per year. This is less than 0.1% of AEO
2010 forecasts of total electricity and natural gas consumption. 

Small Business Analysis: Only 4 of the over 40 cement parent companies
are small entities. EPA performed a screening analysis for impacts on
the 4 small entities by comparing compliance costs to average company
revenues. EPA’s analysis found that the ratio of compliance cost to
company revenue falls below 1% for two of the four small entities
(includes a Tribal government). Two small entities would have an
annualized cost of between 1% and 3% of sales. No small businesses would
have an annualized cost greater than 3% of sales. 

Benefits Analysis: In the year of full implementation (2013), EPA
estimates that the total monetized benefits of the final NESHAP and NSPS
are $7.4 billion to $18 billion and $6.7 billion to $16 billion, at 3%
and 7% discount rates, respectively (Table 1-1). All estimates are in
2005 dollars for the year 2013. Using alternate relationships between
PM2.5 and premature mortality supplied by experts, higher and lower
benefits estimates are plausible, but most of the expert-based estimates
fall between these estimates. Due to data, methodology, and resource
limitations, the benefits from reducing other air pollutants have not
been monetized in this analysis, including reducing 4,400 tons of NOx,
5,200 tons of organic hazardous air pollutants (HAPs), 5,900 tons of
HCl, and 16,400 pounds of Hg each year. In addition, ecosystem benefits
and visibility benefits have not been monetized in this analysis.  These
estimates include the energy disbenefits associated with increased
electricity usage by the control devices.

Net Benefits: In the year of full implementation (2013), EPA estimates
the net benefits of the final NESHAP and NSPS are $6.5 billion to $17
billion and $5.8 billion to $16 billion, at 3% and 7% discount rates,
respectively. All estimates are in 2005 dollars for the year 2013.

Organization of this Report

The remainder of this report supports and details the methodology and
the results of the EIA:

Section 2 presents a profile of the affected industry.

Section 3 describes the economic impact analysis and energy impacts.

Section 4 describes the small business impact analysis.

Section 5 presents the air quality modeling of emission reductions.

Section 6 presents the benefits analysis.

Appendix A provides an overview of the economic impact model.

Appendix B discusses the model of the cement plant’s production
decision.

Appendix C presents the social cost methodology.

Table 1-1.	Summary of the Monetized Benefits, Social Costs, and Net
Benefits for the Final Portland Cement NESHAP in 2013 (millions of
2005$)a

Final NESHAP and NSPS 

	3% Discount Rate	7% Discount Rate

Total Monetized Benefitsb	$7,400	to	$18,000	$6,700	to	$16,000

Total Social Costsc	   $926           to                 $950	$926      
    to                 $950

Net Benefits	$6,500	to	$17,000	$5,800	to	$16,000

Nonmonetized Benefitsd	4,400 tons of NOx (includes energy disbenefits)

	5,200 tons of organic HAPs

	5,900 tons of HCl

	16,400 pounds of mercury 

	Health effects from HAPs, NO2, and SO2 exposure

	Ecosystem effects

	Visibility impairment

Final NSPS only

 	3% Discount Rate	7% Discount Rate

Total Monetized Benefitsb	$510	to	$1,300	$460	to	$1,100

Total Social Costsc	$72	$72

Net Benefits	$440	to	$1,200	$390	to	$1,000

Nonmonetized Benefitsd	6,600 tons of NOx

	520 tons of HCl

	Health effects from HAPs, NO2, and SO2 exposure

	Ecosystem effects

	Visibility impairment

Final NESHAP only

	3% Discount Rate	7% Discount Rate

Total Monetized Benefitsb	$7,400	to	$18,000	$6,700	to	$16,000

Total Social Costsc	   $904           to                 $930	   $904   
       to                 $930

Net Benefits	$6,500	to	$17,000	$5,800	to	$16,000

Nonmonetized Benefitsd	5,200 tons of organic HAPs

	5,900 tons of HCl

	16,400 pounds of mercury 

	Health effects from HAPs, SO2 exposure

	Ecosystem effects

	Visibility impairment

Alternative: More Stringent NSPS and Final NESHAP

	3% Discount Rate	7% Discount Rate

Total Monetized Benefitsb	$7,400	to	$18,000	$6,700	to	$16,000

Total Social Costsc	   $955           to                 $979	   $955   
       to                 $979

Net Benefits	$6,500	to	$17,000	$5,700	to	$15,000

Nonmonetized Benefitsd	7,800 tons of NOx (includes energy disbenefits)

	5,200 tons of organic HAPs

	5,900 tons of HCl

	16,400 pounds of mercury 

	Health effects from HAPs, NO2, and SO2 exposure

	Ecosystem effects

	Visibility impairment

a	All estimates are for the implementation year (2013) and are rounded
to two significant figures. 

b	The total monetized benefits reflect the human health benefits
associated with reducing exposure to PM2.5 through reductions of
directly emitted PM2.5 and PM2.5 precursors such as SO2. It is important
to note that the monetized benefits include many but not all health
effects associated with PM2.5 exposure. Benefits are shown as a range
from Pope et al. (2002) to Laden et al. (2006). These models assume that
all fine particles, regardless of their chemical composition, are
equally potent in causing premature mortality because there is no clear
scientific evidence that would support the development of differential
effects estimates by particle type. The total monetized benefits include
the energy disbenefits.

c	The methodology used to estimate social costs for 1 year in the
multimarket model using surplus changes results in the same social costs
for both discount rates.

d	Due to data, methodology, and resource limitations, we were unable to
monetize the benefits associated with these categories of benefits.



Industry Profile

Hydraulic cement (primarily Portland cement) is a key component of an
important construction material: concrete. Concrete is used in a wide
variety of applications (e.g., residential and commercial buildings,
public works projects), and cement demand is influenced by national and
regional trends in these sectors. Recent data for 2007 show that the
U.S. cement industry produced over 90 million metric tons of Portland
cement (Department of Interior [DOI], U.S. Geological Survey [USGS],
2008b  XE "U.S. Geological Survey [USGS], 2008b"  ). The value of total
U.S. sales, including imported cement, was about $11.8 billion, with an
average value of approximately $100 per metric ton. The vast majority of
cement sales went to ready-mixed concrete producers and concrete product
manufacturers (88%). Since 2003, the United States has relied on cement
imports to meet approximately 20% to 23% of its consumption needs.
However, this share dropped to approximately 17% in 2007 as overall
construction demand for cement fell (DOI, USGS, 2008b  XE "DOI, USGS,
2008b"  ).

The remainder of this section provides an introduction to the Portland
cement industry. The purpose is to give the reader a general
understanding of the technical and economic aspects of the industry that
must be addressed in the economic impact analysis. Section 2.1 provides
an overview of the production processes and costs data. Section 2.2
discusses the uses, consumers, and substitutes for cement. Section 2.3
summarizes the organization of the Portland cement industry. The
industry profile concludes with a discussion of historical market data
and the current industry outlook.

The Supply Side

Production Process

As shown in Figure 2-1, the manufacturing process of an integrated
cement plant includes

quarrying and crushing the raw materials,

grinding the carefully proportioned materials to a high degree of
fineness,

firing the raw materials mixture in a rotary kiln to produce clinker,
and

grinding the resulting clinker to a fine powder and mixing with gypsum
to produce cement.

Figure 2-1.	Simplified Flow Sheet of Clinker and Cement Manufacture

There are two processes for manufacturing cement: the wet process and
the dry process. In the wet process, water is added to the raw materials
during the blending process and before feeding the mixture into the
rotary kiln. In contrast, the dry process feeds the blended materials
directly into the rotary kiln in a dry state. Newer dry process plants
also use preheater and precalciner technologies that partially heat and
calcine the blended raw materials before they enter the rotary kiln.
These technologies can increase the overall energy efficiency of the
cement plant and reduce production costs. 

The fuel efficiency differences between the wet and dry processes have
led to a substantial decline in clinker capacity provided by the wet
process over the last 3 decades. Historical data show capacity shares
falling from 52% in 1980 to approximately 22% in 2000 (Van Oss and
Padovani, 2002  XE “Van Oss and Padovani, 2002”  ). Data also show
that the number of wet process plants fell from 32 in 2000 to 23 in 2005
(DOI, USGS, 2007  XE "DOI, USGS, 2007"  ).

Types of Portland Cement

Portland cement manufacturers produce a variety of types of cement in
the United States designed to meet different requirements. The American
Society for Testing Materials (ASTM) specification C-150 provides for
eight types of Portland cement: five standard types (I, II, III, IV, V)
and three additional types that include air-entraining properties (IA,
IIA, IIIA) (PCA, 2008a  XE “PCA, 2008a”  ). We describe these below.

Types I and IA: These types are the usual product used in general
concrete construction, most commonly known as gray cement because of its
color. 

Types II and IIA: These types are intended for use when moderate heat of
hydration is required or for general concrete construction exposed to
moderate sulfate action.

Type III and IIIA: These types are made from raw materials with a
lime-to-silica ratio higher than that of Type I cement and are ground
finer than Type I cements. They contain a higher proportion of
tricalcium silicate than regular Portland cements.

Type IV: This type contains a lower percentage of tricalcium silicate
and tricalcium aluminate than Type I, thus lowering the heat evolution.
Consequently, the percentage of tetracalcium aluminoferrite is
increased. Type IV cements are produced to attain a low heat of
hydration.

Type V: This type resists sulfates better than the other four types.

As shown in Table 2-1, the vast majority of Portland cement shipments in
2005 were Types I and II grey cement. However, Type V
(sulfate-resisting) is a growing market (DOI, USGS, 2007a  XE "DOI,
USGS, 2007a"  ); since 2000, Type V cement has increased its share of
shipments from 4% to 15%. Shipment shares for other types of cement
remained constant during this period.

Production Costs

Portland cement is produced using a combination of variable inputs such
as raw materials, labor, electricity, and fuel. U.S. Census data for the
cement industry (North American Industry Classification System [NAICS]
32731: cement manufacturing) provides an initial overview of aggregated
industry expenditures on these inputs (Department of Commerce [DOC],
Bureau of the Census, 2010  XE “Bureau of the Census, 2008”  ). In
2007, the total value of shipments was $10.6 billion, and the industry
spent approximately $1.7 billion on materials, parts, and packaging, or
16% of the value of shipments. Total compensation for all employees
(includes payroll and fringe benefits) 

Table 2-1.	Portland Cement Shipped from Plants in the United States to
Domestic Customers, by Typea, b

Type	2000	Share	2005	Share

General use and moderate heat (Types I and II) (gray)c	90,644	88%	93,900
77%

High early strength (Type III)	3,815	4%	3,960	3%

Sulfate resisting (Type V)c	4,453	4%	18,100	15%

Whited	894	1%	1,190	1%

Blended	1,296	1%	3,160	3%

Expansive and regulated fast setting	60	0%	6	0%

Othere	1,786	2%	1,997	2%

Totalf	102,947	100%	122,000	100%

a	Includes imported cement. 

b	Data are rounded to no more than three significant digits; may not add
to totals shown.

c	Cements classified as Type II/V hybrids are now commonly reported as
Type V.

d	Mostly Types I and II but may include Types III through V and block
varieties.

e	Includes block, oil well, low heat (Type IV), waterproof, and other
Portland cements.

f	Data are based on an annual survey of plants and importers.

Sources:	U.S. Department of the Interior, U.S. Geological Survey. 2007a 
XE "U.S. Department of the Interior, U.S. Geological Survey. 2007a"  .
2005 Minerals Yearbook, Cement. Washington, DC: U.S. Department of the
Interior. Table 15.

U.S. Department of the Interior, U.S. Geological Survey. 2002  XE
“U.S. Department of the Interior, U.S. Geological Survey. 2002”  .
2001 Minerals Yearbook, Cement. Washington, DC: U.S. Department of the
Interior. Table 15.

amounted to $1.4 billion (13%). Fuels and electricity expenditures were
approximately $1.7 billion (16%).

Raw Material Costs

According to the USGS, approximately 159.7 million tons of raw materials
were required to produce approximately 95.5 million tons of cement in
2005 or 1.67 tons of raw materials per ton of cement. Table 2-2
summarizes the amount of raw material inputs used per ton of cement
produced in the United States between 2000 and 2005. As the data show,
the amount of raw materials required to produce one ton of cement has
remained essentially constant during this 6-year period. 

Table 2-2.	Raw Material Input Ratios for the U.S. Cement Industry: 2000
to 2005

 	2000	2001	2002	2003	2004	2005

Raw material input (103 metric tons) 	144,949	147,300	153,100	150,500
158,200	159,700

Cement production (103 metric tons)	85,178	86,000	86,817	89,592	94,014
95,488

Metric tons of raw material input per ton of cement	1.70	1.71	1.76	1.68
1.68	1.67

Sources:	U.S. Department of the Interior, U.S. Geological Survey.
2002–2007a. 2001–2005 Minerals Yearbook, Cement. Table 6.  XE
“U.S. Department of the Interior, U.S. Geological Survey.
2002–2007a. 2001–2005 Minerals Yearbook, Cement. Table 6.”  
Washington, DC: U.S. Department of the Interior.

U.S. Department of the Interior, U.S. Geological Survey. 2002–2007a.
2001–2005 Minerals Yearbook, Cement. Table 3.  XE “U.S. Department
of the Interior, U.S. Geological Survey. 2002–2007a. 2001–2005
Minerals Yearbook, Cement. Table 3.”   Washington, DC: U.S. Department
of the Interior.

The price of these raw materials varies across regions. Table 2-3 lists
the average price of raw materials per metric ton by state. In 2005, the
prices of raw materials were highest in Hawaii where they sold for an
average of $13.34 per metric ton. The prices of raw materials were
lowest in Michigan, where they sold for an average of $3.89 per metric
ton.

Labor Costs

In 2005, the Portland Cement Association (PCA) reported labor
productivity measures (in terms of metric tons of cement per employee
hour) for 2000 to 2005 in its U.S. and Canadian Labor-Energy Input
Survey. Using these data, we computed a measure of labor hour
requirements to produce cement (see Table 2-4). As these data show, wet
process plants are typically more labor intensive, requiring
approximately 45% more labor hours to produce a metric ton of cement
than dry process plants.

In addition, labor productivity has been improving more quickly in dry
process plants than in those using a wet manufacturing process. Between
2000 and 2005, labor requirements decreased by 15% in dry process
plants, while in wet process plants labor requirements remained
constant. As a result, the wet process labor costs relative to dry
process plants labor costs have risen in recent years (Figure 2-2).

Table 2-3.	Raw Material Costs by Market and State: 2005

State(s)	Price of Raw Materials

($/metric ton)a	State(s)	Price of Raw Materials

($/metric ton)a

AK	6.60	MT	$4.76 

AL	6.57	NC	$8.59 

AR	$6.29 	ND	$4.45 

AZ	$5.75 	NE	$7.10 

CA	$8.37 	NH	$8.02 

CO	$6.85 	NJ	$7.04 

CT	$9.19 	NM	$6.67 

DE	$6.89 	NV	$7.17 

FL	$8.67 	NY	$8.44 

GA	$7.63 	OH	$5.82 

HI	$13.34 	OK	$5.67 

IA	$7.27 	OR	$6.01 

ID	$5.37 	PA	$6.67 

IL	$7.16 	RI	$7.74 

IN	$5.40 	SC	$7.61 

KS	$7.20 	SD	$4.60 

KY	$7.24 	TN	$7.55 

LA	$8.18 	TX	$6.15 

MA	$9.19 	UT	$5.58 

MD	$8.28 	VA	$9.03 

ME	$6.85 	VT	$6.75 

MI	$3.89 	WA	$6.92 

MN	$8.30 	WI	$5.83 

MO	$7.37 	WV	$6.86 

MS	$11.90 	WY	$5.68 

Source:	U.S. Department of the Interior, U.S. Geological Survey. 2007b. 
XE "U.S. Department of the Interior, U.S. Geological Survey. 2006b."  
2005 Minerals Yearbook, Crushed Stone. Table 4. Washington, DC: U.S.
Department of the Interior.

Table 2-4.	Labor Productivity Measures for the U.S. Cement Industry by
Process Type: 2000 to 2005 (employee hours per metric ton)

Year	2000	2001	2002	2003	2004	2005

All plants	0.394	0.388	0.360	0.347	0.338	0.338

Wet process	0.469	0.457	0.450	0.465	0.452	0.463

Dry process	0.376	0.375	0.342	0.328	0.318	0.318

Source:	Portland Cement Association. December 2005  XE “Portland
Cement Association. December 2005”  . U.S. and Canadian Labor-Energy
Input Survey 2005. Skokie, IL: PCA’s Economic Research Department. 

Figure 2-2.	Labor Costs per Metric Ton of Cement ($2005)

Sources:	Portland Cement Association. December 2005  XE “Portland
Cement Association. December 2005”  . U.S. and Canadian Labor-Energy
Input Survey 2005. Skokie, IL: PCA’s Economic Research Department. 

U.S. Department of Labor, Bureau of Labor Statistics (BLS). 2007a  XE
"U.S. Department of Labor, Bureau of Labor Statistics (BLS). 2007a"  .
“Current Employment Statistics (National): Customizable Data Tables”
Available at <http://www.bls.gov/ces/>. As obtained on March 14, 2008. 

U.S. Department of Labor, Bureau of Labor Statistics (BLS). 2008  XE
“Department of Labor, Bureau of Labor Statistics (BLS). 2008”  .
“Consumer Price Index All Items – U.S. City Average Data:
Customizable Data Tables.” Available at <http://www.bls.gov/cpi/>. As
obtained on March 14, 2008.

Energy Costs

Figure 2-3 provides a detailed breakdown of U.S. energy consumption in
2005. As this figure shows, the vast majority of energy in U.S. cement
plants is derived from coal and coke (75%). The remaining 25% of energy
consumption is derived from electricity, waste, natural gas, and
petroleum products. 

PCA also reported energy consumption data by type of U.S. cement plant
(in terms of millions of BTUs per metric ton of cement) (see Table 2-5).
As these data show, wet process plants are typically more energy
intensive, consuming approximately 44% more energy per ton of cement
than dry process plants. In addition, the trends in energy consumption
continue to show that dry plants have become more energy efficient than
wet process plants. Between 2000 and 2005, energy consumption per ton of
cement in dry process plants decreased by 5%; in contrast, wet process
plants’ energy consumption increased slightly during this period. 

Figure 2-3.	Distribution of Energy Consumption

Source:	Portland Cement Association. December 2005.  XE “Portland
Cement Association. December 2005.”   U.S. and Canadian Labor-Energy
Input Survey 2005. Skokie, IL: PCA’s Economic Research Department. 

Table 2-5.	Energy Consumption by Type of U.S. Cement Plant (million BTU
per metric ton)

Year	2000	2001	2002	2003	2004	2005

All plants	4.982	4.93	4.858	4.762	4.755	4.699

Wet process	6.25	6.442	6.676	6.647	6.807	6.387

Dry process	4.673	4.655	4.498	4.433	4.407	4.433

Source:	Portland Cement Association. December 2005  XE “Portland
Cement Association. December 2005”  . U.S. and Canadian Labor-Energy
Input Survey 2005. Skokie, IL: PCA’s Economic Research Department. 

The Demand Side

The demand for Portland cement is considered a “derived” demand
because it depends on the construction demands for its end
product—concrete. A recent study by the U.S. International Trade
Commission suggests that 0.192 metric tons of grey Portland cement were
used per $1,000 of construction in 1998 (USITC, 2006  XE “USITC,
2006”  ). Given cement prices at this time (approximately $75 per
metric ton), Portland cement costs represented only a small share of the
total value of construction expenditures (less than 2%).

Concrete is used in a wide variety of construction applications,
including residential and commercial buildings, and public works
projects such as the national highway system. As shown in Figure 2-4,
ready-mixed concrete producers have historically accounted for over half
of the Portland cement consumption. Although government and
miscellaneous expenditures saw substantial increases in the early 1990s,
their consumption share returned to pre-1990s levels after 1996. The
latest USGS use data show that ready-mixed concrete producers accounted
for 

Figure 2-4.	End Uses of Cement: 1975 to 2003

Source:	Kelly, T. and G. Matos. 2007a.  XE “Kelly, T. and G. Matos.
2007a.”   “Historical Statistics for Mineral and Material
Commodities in the United States: Cement End Use Statistics.” U.S.
Geological Survey Data Series 140, Version 1.2. Available at   HYPERLINK
"http://minerals.usgs.gov/ds/2005/140/" 
http://minerals.usgs.gov/ds/2005/140/ .

74% of cement sales in 2005, followed by concrete product manufacturers
(14%), contractors (6%), and other (6%) (Kelly and Matos, 2007a  XE
"Kelly and Matos, 2007a"  ).

Cement competes with other construction materials such as steel,
asphalt, and lumber. Lumber is the primary substitute in the residential
construction market, while steel is the primary substitute in commercial
applications. Asphalt is a key substitute in transportation projects
such as road and parking lot surfacing. However, concrete has advantages
over these substitutes because it tends to be available locally and has
lower long-term maintenance costs (Van Oss and Padovani, 2002  XE “Van
Oss and Padovani, 2002”  ). 

The PCA regularly reports price trends for these competing building
materials (PCA, 2008b  XE “PCA, 2008b”  ). As shown in Figure 2-5,
steel and asphalt have risen sharply relative to cement since 2003 while
lumber has declined.

 

Figure 2-5.	Producer Price Indices for Competitive Building Materials:
2003 to 2008

Source:	Portland Cement Association. 2008b  XE “Portland Cement
Association. 2008b”  . “Market Research: Producer Price
Indices—Competitive Building Materials.” Available at <  HYPERLINK
"http://www.cement.org/market/"  http://www.cement.org/market/ >.

Industry Organization

Market Structure

A review and description of market characteristics (i.e., degree of
concentration, entry barriers, and product differentiation) can enhance
our understanding of how U.S. cement markets operate. These
characteristics provide indicators of a firm’s ability to influence
market prices by varying the quantity of cement it sells. For example,
in markets with large numbers of sellers and identical products, firms
are unlikely to be able to influence market prices via their production
decisions (i.e., they are “price takers”). However, in markets with
few firms, significant barriers to entry (e.g., licenses, legal
restrictions, or high fixed costs), or products that are similar but can
be differentiated, the firm may have some degree of market power (i.e.,
set or significantly influence market prices).

Cement sales are often concentrated locally among a small number of
firms for two reasons: high transportation costs and production
economies of scale. Transportation costs significantly influence where
cement is ultimately sold; high transportation costs relative to unit
value provide incentives to produce and sell cement locally in regional
markets (USITC, 2006  XE “USITC, 2006”  ). To support this claim,
the empirical literature has typically pointed to Census of
Transportation data showing over 80% of cement shipments were made
within a 200-mile radius (Jans and Rosenbaum, 1997  XE “Jans and
Rosenbaum, 1997”  ) and reported evidence of high transportation costs
per dollar of product value from case studies (Ryan, 2006  XE “Ryan,
2006”  ). The cement industry is also very capital intensive and entry
requires substantial investments. In additional, large plants are
typically more economical because they can produce cement at lower unit
costs; this reduces entry incentives for small-sized cement plants.
Using recent data for planned capacity expansions between 2008 and 2012,
the PCA reports these expansions will cost $5.9 billion and add 25
million metric tons (PCA, 2007  XE “PCA, 2007”  ), or $240 per
metric ton, of new capacity. 

For a given construction application, consumers are likely to view
cement produced by different firms as very good substitutes. American
Society for Testing and Materials (ASTM) specifications tend to ensure
uniform quality, and recent industry reviews (USITC, 2006  XE “USITC,
2006”  ) suggest that there is little or no brand loyalty that allows
firms to differentiate their products.

Manufacturing Plants

During 2005, 107 cement manufacturing plants with 186 cement kilns were
operating in the United States. This section describes the location,
age, production capacity, and employment of these manufacturing
facilities. Section 2.3.2 concludes with a discussion of future trends.
Section 2.3.3 provides a detailed discussion of the characteristics of
the firms owning these facilities. 

Location

Table 2-6 summarizes the geographic location of cement kilns in the
United States and clinker capacity. The top five states in order of
clinker capacity are California, Texas, Pennsylvania, Florida, and
Alabama. Together these states account for 75 (40%) of the kilns in the
United States and 41 million metric tons (44%) of clinker capacity.
Figure 2-6 provides a graphical depiction of the number of kilns
distributed by state. 

Fourteen states (Alaska, Hawaii, Connecticut, Louisiana, New Hampshire,
North Dakota, Wisconsin, Delaware, Massachusetts, New Jersey, Rhode
Island, Minnesota, North Carolina, and Vermont) and the District of
Columbia had no clinker-producing facilities in 2005. 

Table 2-6.	Number of Kilns and Clinker Capacity by State: 2005

 	No. Kilns	Clinker Capacity (103 metric tons per year)

AK	0

	AL	5	5,375

AR	3	831

AZ	8	2,809

CA	20	12,392

CO	2	2,117

CT	0

	DE	0

	FL	7	5,489

GA	2	1,020

HI	0

	IA	4	2,672

ID	2	260

IL	8	2,770

IN	8	3,191

KS	9	2,835

KY	1	1,365

LA	0

	MA	0

	MD	4	2,538

ME	1	392

MI	8	4,243

MN	0

	MO	6	5,169

MS	1	419

MT	2	573

NC	0

	ND



NE	2	845

NH	0

	NJ	0

	NM	2	432

NV	2	452

NY	4	2,886

OH	3	1,115

OK	7	1,869

OR	1	816

(continued)

Table 2-6.	Number of Kilns and Clinker Capacity by State: 2005
(continued)

 	No. Kilns	Clinker Capacity (103 metric tons per year)

PA	21	6,414

RI	0

	SC	6	3,480

SD	3	851

TN	2	1,438

TX	22	11,688

UT	2	1,514

VA	1	1,120

VT	0

	WA	2	1,100

WI	0

	WV	3	708

WY	2	597

Total	186	93,785

Source:	Portland Cement Association (PCA). 2004  XE “Portland Cement
Association (PCA). 2004”  . U.S. and Canadian Portland Cement
Industry: Plant Information Summary. Skokie, IL: PCA’s Economic
Research Department.

Figure 2-6.	Distribution of Cement Kilns in the United States

Source:	Portland Cement Association (PCA). December 2004  XE "Portland
Cement Association (PCA). December 2004"  . U.S. and Canadian Portland
Cement Industry: Plant Information Summary. Skokie, IL: Portland Cement
Association Economic Research Department.

Age

In 2005, 72% (134) of all kilns in the United States used the dry
manufacturing process, and it accounted for 83% (78 million metric tons)
of national clinker capacity. The growing prevalence of the dry process
among cement manufacturers is part of a long-term trend. As the data in
Table 2-7 indicate, no new wet clinker capacity has been added within
the past 30 years. 

Table 2-7.	Number of Kilns and Clinker Capacity by Age and Process Type

 	No. Kilns	Clinker Capacity (103 metric tons per year)	Average Annual
Capacity per Kiln

Total	 	 	 

0–10	26	28,144	1,082.5

11–15	3	2,176	725.3

16–20	5	3,345	669.0

21–25	16	14,982	936.4

26–30	18	11,843	657.9

31–35	16	5,786	361.6

36–40	21	9,285	442.1

41–45	29	8,971	309.3

46–50	32	6,564	205.1

51–55	6	991	165.2

56–60	6	800	133.3

60+	8	898	112.3

Total	186	93,785	504.2

Dry Process	 	 

0–10	26	28,144	1,082.5

11–15	3	2,176	725.3

16–20	5	3,345	669.0

21–25	16	14,982	936.4

26–30	18	11,843	657.9

31–35	10	3,962	396.2

36–40	12	5,498	458.2

41–45	14	3,800	271.4

46–50	16	2,651	165.7

51–55	4	682	170.5

56–60	6	800	133.3

60+	4	328	82.0

Total	134	78,211	583.7

Wet Process	 	 

0–10	0



11–15	0



16–20	0



21–25	0



26–30	0



31–35	6	1,824	304.0

36–40	9	3,787	420.8

41–45	15	5,171	344.7

(continued)

Table 2-7.	Number of Kilns and Clinker Capacity by Age and Process Type
(continued)

 	No. Kilns	Clinker Capacity (103 metric tons per year)	Average Annual
Capacity per Kiln

Wet Process (cont.)	 	 

46–50	16	3,913	244.6

51–55	2	309	154.5

56–60	0



60+	4	570	142.5

Total	52	15,574	299.5

Source:	Portland Cement Association (PCA). 2004  XE “Portland Cement
Association (PCA). 2004”  . U.S. and Canadian Portland Cement
Industry: Plant Information Summary. Skokie, IL: PCA’s Economic
Research Department.

All 68 kilns that have become operational within the past 30 years use
the dry manufacturing process. These new kilns account for 64% (60
million metric tons) of national clinker capacity.

Production Capacity and Utilization

Between 2000 and 2005, apparent annual clinker capacity grew
approximately 17%, while clinker production grew by approximately 14%
(Table 2-8). Because capacity tends to grow more rapidly than
production, total capacity utilization decreased slightly in this period
from 87.5% in 2000 to 85.4% in 2005. 

Table 2-8.	Clinker Capacity, Production, and Capacity Utilization in the
United States: 2000 to 2005

	2000	2001	2002	2003	2004	2005

Apparent annual capacity (103 metric tons)	89,264	100,360	101,000
102,000	105,000	104,000

Production (103 metric tons)	78,138	79,979	82,959	83,315	88,190	88,783

Capacity utilization (%)	87.5%	79.7%	82.1%	81.7%	84.0%	85.4%

Source:	U.S. Department of the Interior, U.S. Geological Survey.
2000–2005. Minerals Yearbook, Cement. Table 5.  XE “U.S. Department
of the Interior, U.S. Geological Survey. 2000–2005. Minerals Yearbook,
Cement. Table 5.”   Washington, DC: U.S. Department of the Interior.
Available at <http://minerals.usgs.gov/minerals/pubs/

commodity/cement/>. As obtained on March 14, 2008.

Much of the vast majority of the growth in clinker capacity came in 2001
when existing Portland cement plants completed major capacity upgrade
projects, resulting in a 12% increase in clinker capacity over the
previous year (USGS, 2002  XE “USGS, 2002”  ). As a result, capacity
utilization fell to 79.7% that year. After 2001, clinker capacity grew
an average of 1% each year, while production grew an average of 2%. As a
result, capacity utilization has risen slowly since 2001. However,
throughout these movements in clinker capacity and production, capacity
utilization tended to remain between 80% and 85%.

Capacity utilization often varies by geographic region as a result of
fluctuations in regional construction activity. For example, 2005 data
show that Idaho, Montana, and Nevada shared a capacity utilization rate
of 95.5%—well above the national average. In contrast, South Carolina
used only 64.5% of its clinker capacity. Table 2-9 provides a complete
listing of capacity utilization rates by state in 2005.

Employment

Each year, the Annual Survey of Manufactures (ASM) collects employment,
payroll, sales, and other data for all manufacturing establishments.
Table 2-10 summarizes the employment data collected by the ASM for the
cement manufacturing industry (NAICS 327310) from 2000 to 2005. As these
data indicate, total employment fell approximately 2% over this 6-year
period, from approximately 17,000 employees in 2000 to 16,900 in 2005. 

Trends

As previously discussed, clinker capacity has been increasing at a
slower pace since 2001. However, according to the PCA, the cement
industry has announced that it will increase clinker capacity by nearly
25 million metric tons between 2007 and 2012. This represents a 27%
increase over U.S. 2006 clinker capacity and amounts to a $5.9 billion
investment (PCA, 2007  XE “PCA, 2007”  ). 

In addition to these expected capacity expansions, likely changes in
U.S. specifications allowing the use of limestone in Portland cement
could also increase production capacity. According to the PCA, domestic
cement supply could increase by as much as 2 million additional tons by
2012. Increases in EPA production variances could also add another 1.1
million metric tons of domestic supply (PCA, 2007  XE “PCA, 2007” 
).

Firm Characteristics

EPA has reviewed industry information and publicly available sales and
employment databases to identify the chain of ownership by accounting
for subsidiaries, divisions, and joint ventures to appropriately group
companies by size. Table 2-11 provides sales and employment data for
27 ultimate parent companies operating Portland cement manufacturing
plants in 2005.

 

Table 2-9.	Capacity Utilization Rates by State: 2005

State	USGS Geographic Area	Utilization Rate (percent)

AL	Alabama	86.7

AR	Arkansas and Oklahoma	90.9

AZ	Arizona and New Mexico	87

CA	California, northern and southern	88.8

CO	Colorado and Wyoming	79.5

FL	Florida	85.9

GA	Georgia, Virginia, West Virginia	78.4

IA	Iowa, Nebraska, South Dakota	85.5

ID	Idaho, Montana, Nevada, Utah	95.5

IL	Illinois	91.4

IN	Indiana	86.8

KS	Kansas	89.1

KY	Kentucky, Mississippi, Tennessee	87.4

MD	Maryland	89.1

ME	Maine and New York	83.6

MI	Michigan	85.5

MO	Missouri	90.3

MS	Kentucky, Mississippi, Tennessee	87.4

MT	Idaho, Montana, Nevada, Utah	95.5

NE	Iowa, Nebraska, South Dakota	85.5

NM	Arizona and New Mexico	87

NV	Idaho, Montana, Nevada, Utah	95.5

NY	Maine and New York	83.6

OH	Ohio	84.7

OK	Arkansas and Oklahoma	90.9

OR	Oregon and Washington	83.3

PA	Pennsylvania, eastern and western	83.7

SC	South Carolina	64.5

Source:	U.S. Department of the Interior, U.S. Geological Survey. 2007b 
XE "U.S. Department of the Interior, U.S. Geological Survey. 2007b"  .
2005 Minerals Yearbook, Cement. Table 5. Washington, DC: U.S.
Department of the Interior.

Table 2-10.	Cement Manufacturing Employment (NAICS 327310): 2000 to 2005

Year	Number of Employees

2000	17,175

2001	17,220

2002	17,660

2003	17,352

2004	16,883

2005	16,877

Sources:	U.S. Department of Commerce, Bureau of the Census. 2006.  XE
“U.S. Department of Commerce, Bureau of the Census. 2006.”   2005
Annual Survey of Manufactures. M05(AS)-1. Washington, DC: Government
Printing Office. Available at
<http://www.census.gov/prod/2003pubs/m01as-1.pdf>. As obtained on March
14, 2008.

	U.S. Department of Commerce, Bureau of the Census. 2003  XE “U.S.
Department of Commerce, Bureau of the Census. 2003”  . 2001 Annual
Survey of Manufactures. M05(AS)-1. Washington, DC: Government Printing
Office. Available at <http://www.census.gov/prod/2003pubs/m01as-1.pdf>.
As obtained on March 14, 2008.

Distribution of Small and Large Companies

Firms are grouped into small and large categories using Small Business
Administration (SBA) general size standard definitions for NAICS codes.
These size standards are presented either by number of employees or by
annual receipt levels, depending on the NAICS code. The manufacture of
Portland cement is covered by NAICS code 327310 for cement
manufacturing. Thus, according to SBA size standards, firms owning
Portland cement manufacturing plants are categorized as small if the
total number of employees at the firm is less than 750; otherwise, the
firm is classified as large. As shown in Table 2-11, potentially
affected firms range in size from 160 to 71,000 employees. A total of 4
firms, or 15%, are categorized as small, while the remaining 23 firms,
or 75%, are large.

Capacity Share

As shown in Table 2-11, the leading companies in terms of capacity at
the end of 2005 were Holcim (U.S.) Inc.; CEMEX, Inc.; Lafarge North
America, Inc.; Buzzi Unicem USA, Inc.; HeidelbergCement AG (owner of
Lehigh Cement Co.); Ash Grove Cement Co.; Texas Industries, Inc.;
Italcementi S.p.A.; Taiheiyo Cement Corporation; Titan Cement; and
VICAT. The top 5 had about 57% of total U.S. clinker capacity, and the
top 10 accounted for 83% of total capacity. Small companies accounted
for less than 5% of clinker capacity.

Table 2-11.	Ultimate Parent Company Summary Data: 2005

Ultimate Parent Name	Annual Sales ($106)	Employ-ment	Type	Small Business
Plants	Kilns	Clinker Capacity (103 metric tons per year)	Capacity Share











Holcim, Inc	$14,034 	59,901	Public	No	14	17	13,089	14.0%

CEMEX, S.A. de C.V.	$18,290 	26,679	Public	No	13	21	12,447	13.3%

Lafarge S.A.	$22,325 	71,000	Public	No	13	23	12,281	13.1%

BUZZI UNICEM SpA	$3,495 	11,815	Private	No	10	19	8,129	8.7%

HeidelbergCement AG	$12,182 	45,958	Public	No	10	13	7,786	8.3%

Ash Grove Cement Company	$1,190 	2,600	Private	No	9	15	6,687	7.1%

Texas Industries, Inc.	$944 	2,680	Public	No	4	15	5,075	5.4%

Italcementi S.p.A.	$5,921 	20,313	Public	No	6	16	4,442	4.7%

Taiheiyo Cement Corporation 	$7,710 	2,061	Private	No	3	7	3,375	3.6%

Titan Cement	$1,589 	1,834	Public	No	2	2	2,612	2.8%

VICAT	$2,137 	6,015	Public	No	2	2	1,933	2.1%

Eagle Materials	$922 	1,600	Public	No	3	5	1,651	1.8%

Mitsubishi Cement Corporation	$1,134 	NA	Joint venture	No	1	1	1,543	1.6%

Rinker Materials	$4,140 	11,193	Private	No	2	2	1,533	1.6%

Hanson America Holdings	$3,000 	14,872	Private	No	1	1	1,497	1.6%

Salt River Materials Group a	$150b 	<750	Tribal Government	Yes	1	4	1,477
1.6%

Grupo Cementos de Chihuahua, S.A. de C.V.	$663 	2,591	Public	No	2	5
1,283	1.4%

Cementos Portland Valderrivas, S.A.	$1,159 	2,674	Public	No	2	6	1,257
1.3%

Zachary Construction	$152 	1,200	Private	No	1	2	868	0.9%

RMC Pacific Materials	$160 	800	Private	No	1	1	812	0.9%

(continued)

Table 2-11.	Ultimate Parent Company Summary Data: 2005 (continued)

Ultimate Parent Name	Annual Sales ($106)	Employ-ment	Type	Small Business
Plants	Kilns	Clinker Capacity (103 metric tons per year)	Capacity Share











Monarch Cement Company	$154 	600	Public	Yes	1	2	787	0.8%

Florida Rock Industries	$1,368 	3,464	Public	No	1	1	726	0.8%

Votorantim Group and Anderson Columbia Company 	$9,518 	30,572	Joint
venture	No	1	1	682	0.7%

Dyckerhoff AG	$1,876 	6,958	Public	No	1	1	586	0.6%

Continental Cement Company, LLC	$50b	<750	Private	Yes	1	1	549	0.6%

Cementos Del Norte	NA	NA	Private	No	1	1	392	0.4%

Snyder Associate Companies	$29 	350	Private	Yes	1	2	286	0.3%

a	Enterprise is owned by Salt River Pima-Maricopa Indian Community.

b	EPA estimate.

Sources:	Dun & Bradstreet, Inc. 2007  XE “Dun & Bradstreet, Inc.
2007”  . D&B million dollar directory. Bethlehem, PA.

LexisNexis. LexisNexis Academic [electronic resource]. Dayton, OH:
LexisNexis  XE “LexisNexis. LexisNexis Academic [electronic resource].
Dayton, OH\: Lexis-Nexis”  .

Company Revenue and Ownership Type

Cement manufacturing is a capital-intensive industry. The vast majority
of stakeholders are large global companies with sales exceeding $1
billion. In 2005, ultimate parent company sales ranged from $30 million
to $22.3 billion (Table 2-11), with average (median) sales of $4,565
($1,589) million. Small companies accounted for 0.3% share by sales.
Ultimate parent companies were either privately or publicly owned or
jointly operated by several companies. A majority of the companies (52%)
were publicly owned. Private companies had a slightly smaller share
(41%), and only two (or 7%) were joint ventures.

Markets

Portland cement is produced and consumed domestically as well as traded
internationally. The United States meets a substantial fraction of its
cement needs through imports; in contrast, it exports only a small
fraction of domestically produced cement to other countries. We provide
value, quantity, and price trends over the past decade for Portland
cement when detailed statistics are available. In the case of
international trade, we can report data only for hydraulic cement, which
includes Portland and masonry cement.

Market Volumes 

Domestic Production 

In 2007, the domestic shipments of Portland cement were 90.6 million
metric tons, reflecting an 8.5% increase from 2000 and, more recently, a
3% decrease from 2006 (see Table 2-12). Year-end stocks remained
relatively level during this period at 7.4 million metric tons. Stocks
fell slightly by 5% since 2006 and equaled 8.9 million tons in 2007. As
Table 2-12 shows, shipments to customers increased steadily since 2000,
reaching 128 million tons in 2006. However, affected by declines in the
housing market, the shipments fell by 9% in 2007. 

Table 2-12.	Historical U.S. Cement Statistics (106 metric tons)

 	2000	2001	2002	2003	2004	2005	2006	2007

Production









Clinker 	78.1	78.5	82.0	81.9	86.7	87.4	88.6	87.2

Portland cement	83.5	84.5	85.3	88.1	92.4	93.9	93.2	90.6

Masonry cement	4.3	4.5	4.4	4.7	5.0	5.4	5.0	4.9

Total cement	87.8	88.9	89.7	92.8	97.4	99.3	98.2	95.5

Shipments to customers	110.0	113.1	110.0	112.9	120.7	127.4	127.9	116.0

Stocks, cement, year end	7.6	6.6	7.6	6.6	6.7	7.4	9.4	8.9

Sources:	U.S. Department of the Interior, U.S. Geological Survey. 2008b 
XE "U.S. Department of the Interior, U.S. Geological Survey. 2008b"  .
Minerals Commodity Summaries, Cement 2008. Washington, DC: U.S.
Department of the Interior. Available at
<http://minerals.usgs.gov/minerals/

pubs/commodity/cement/mcs-2008-cemen.pdf>.

U.S. Department of the Interior, U.S. Geological Survey. 2003  XE
“U.S. Department of the Interior, U.S. Geological Survey. 2003”  .
2002 Minerals Yearbook, Cement. Washington, DC: U.S. Department of the
Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/

commodity/cement/>.

International Trade

Cement imports are a significant share of domestic consumption
(approximately 20%); they also grew by 30% from 2000 to 2006 (see Table
2-13). Major importing countries in 2007 included Canada (18% of total
imports in 2006), China (16%), and Thailand (11%) (DOI, USGS, 2008b  XE
"DOI, USGS, 2008b"  ). In 2007, the falling value of the dollar and
construction activity declines in the housing market tempered the
quantity of import demanded. As a result, the share of U.S. consumption
met by imports fell to its lowest level in 10 years.

Table 2-13.	U.S. Cement Trade Data: 2000 to 2007

 	2000	2001	2002	2003	2004	2005	2006	2007

Exports (106 metric tons)	0.7	0.7	0.9	0.8	0.7	0.8	1.5	1.9

Imports (106 metric tons)	24.6	23.6	22.5	21.0	25.4	30.4	32.1	21.3

Net import share of apparent consumption (%)	20.0	21.0	19.0	20.0	21.0
23.0	23.0	17.0

Sources: U.S. Department of the Interior, U.S. Geological Survey. 2008b 
XE "U.S. Department of the Interior, U.S. Geological Survey. 2008b"  .
Minerals Commodity Summaries, Cement 2008. Washington, DC: U.S.
Department of the Interior. Available at
<http://minerals.usgs.gov/minerals/

pubs/commodity/cement/mcs-2008-cemen.pdf>.

U.S. Department of the Interior, U.S. Geological Survey. 2003  XE
“U.S. Department of the Interior, U.S. Geological Survey. 2003”  .
2002 Minerals Yearbook, Cement. Washington, DC: U.S. Department of the
Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/>.

During the period from 2000 to 2005, U.S. exports remained relatively
constant at about 800,000 tons and typically did not exceed 1% of
production. However, the level of U.S. exports has increased during the
last 2 years. In 2007, U.S. exports totaled 1.9 million metric tons. The
vast majority of U.S. exports of hydraulic cement are supplied to
Canada: U.S. producers shipped a total of 650,000 tons to Canada in
2005, or 85% of total U.S. exports. The remaining fraction of U.S.
exports in 2005 went to the Bahamas, Mexico, and 33 other countries
around the world (DOI, USGS, 2008b  XE "DOI, USGS, 2008b"  ).

Market Prices

Correcting for the effects of inflation, we find that the real price of
cement per metric ton (2005 dollars) has typically ranged between $75
and $95 since 1990 (see Figure 2-7). However, data for the last 2 years
suggest the average price of cement is at its highest level in over 2
decades (approximately $100). Because of transportation constraints,
there are regional differences in the price of cement across states. For
example, remote locations such as Alaska and Hawaii had the highest
deviation from the national average ($48 in 2005) (see Figure 2-8). In
the contiguous states, prices in Arizona, New Mexico, and California
were higher than the national averages, while prices in Texas, Indiana,
and South Carolina were among the lowest. 

Future Projections 

Although estimates of future cement demand are not publicly available,
the Energy Information Administration provides projections for the real
value of shipments for the stone, clay, and glass industry in its AEO
(DOE, 2007  XE “DOE, 2007”  ). The forecasted annual average growth
rate for 2005 to 2030 is approximately 1.7%.

Figure 2-7.	Historical U.S. Cement Price 

Sources:	1990–2003: Kelly, T. and G. Matos. 2007b  XE “Kelly, T. and
G. Matos. 2007b”  . “Historical Statistics for Mineral and Material
Commodities in the United States: Cement Supply and Demand
Statistics.” U.S. Geological Survey Data Series 140, Version 1.2.
Available at <  HYPERLINK "http://minerals.usgs.gov/ds/2005/140/" 
http://minerals.usgs.gov/ds/2005/140/ >. Last modified April 11, 2006.

2004–2007: U.S. Department of the Interior, U.S. Geological Survey.
2008b  XE "U.S. Department of the Interior, U.S. Geological Survey.
2008b"  . Minerals Commodity Summaries, Cement 2008. Washington, DC:
U.S. Department of the Interior. Available at
<http://minerals.usgs.gov/minerals/pubs/commodity/cement/mcs-2008-cemen.
pdf>.

Figure 2-8.	Deviation from National Average Cement Price per Metric Ton
by Region: 2005

Source:	U.S. Department of the Interior, U.S. Geological Survey. 2007a 
XE "U.S. Department of the Interior, U.S. Geological Survey. 2007a"  .
2005 Minerals Yearbook, Cement. Washington, DC: U.S. Department of the
Interior. Table 11. Available at
<http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>.



Economic Impact Analysis

EPA prepares an EIA to provide decision makers with a measure of the
social costs of using resources to comply with a program (EPA, 2000  XE
"EPA, 2000"  ). The social costs can then be compared with estimated
social benefits (as presented in Section 5). As noted in EPA’s (2000)
Guidelines for Preparing Economic Analyses, several tools are available
to estimate social costs and range from simple direct compliance cost
methods to the development of a more complex market analysis that
estimates market changes (e.g., price and consumption) and economic
welfare changes (e.g., changes in consumer and producer surplus). 

The Office of Air Quality Planning and Standards (OAQPS) has adopted the
standard industry-level analysis described in the Office’s resource
manual (EPA, 1999a  XE "EPA, 1999a"  ). This approach is consistent with
previous EPA analyses of the Portland cement industry (EPA, 1998  XE
"EPA, 1998"  ; EPA, 1999b, and 2009a  XE "EPA, 1999b. 2009a"  ) and uses
a single-period static partial-equilibrium model to compare pre-policy
cement market baselines with expected post-policy outcomes in these
markets. The benchmark time horizon for the analysis is the intermediate
run where producers have some constraints on their flexibility to adjust
factors of production. This time horizon allows us to capture important
transitory impacts of the program on existing producers. Key measures in
this analysis include

market-level effects (market prices, changes in domestic production and
consumption, and international trade),

industry-level effects (changes in (i.e. operating profits) and
employment),

facility-level effects (plant utilization changes), and

social costs (changes in producer and consumer surplus).

Absent forecasts and the uncertainties of future economic baselines, the
partial-equilibrium market analysis can only cover a subset of plants
presumed to be operating in conditions similar to 2005. Thus, this
analysis does not reflect changes in the state of the US economy which
may occur by the analysis year of 2013 which could significantly
influence the quantity of cement needed.  As shown in the following
sections, the market analysis covers $378 million of the total $466
million in regulatory program costs, or 81%; simulated post policy
outcomes described throughout Section 3.2 should be interpreted in light
of this modeling choice. EPA analyzed the remaining $88 million in
NESHAP and NSPS regulatory program costs “outside” of the partial
equilibrium market analyses using direct compliance costs methods (see
Section 3.3). EPA provides complete social cost accounting in the
section describing the social cost estimates (Section 3.4) and provides
a discussion of its overall assessment (Section 3.5).

Regulatory Program Costs

EPA is finalizing amendments to the NESHAP from the Portland cement
manufacturing industry and (NSPS for Portland cement plants. The final
amendments to the NESHAP add or revise, as applicable, emission limits
for Hg, THC, and PM from kilns located at a major or an area sources,
and HCl from kilns and located at major sources. EPA is also adopting
separate standards for these pollutants which apply during startup,
shutdown, and operating modes. Finally, EPA is adopting performance
specifications for use of mercury CEMS and updating recordkeeping and
testing requirements. The final amendments to the NSPS add or revise, as
applicable, emission limits for particulate matter (PM), opacity,
nitrogen oxides (NOx), and sulfur dioxide (SO2) for facilities that
commence construction, modification, or reconstruction after June 16,
2008. The final rule also includes additional testing and monitoring
requirements for affected sources. Although EPA’s analysis focuses on
the final NESHAP and NSPS engineering cost estimates, EPA also presents
additional information on different combinations of the regulatory
programs. This information helps stakeholders better understand the size
and scope of the each. These include

final NSPS only,

final NESHAP only, and

alternative: more stringent NSPS and final NESHAP.

For the year 2013, EPA’s engineering cost analysis estimates the total
annualized costs of the final NESHAP and NSPS are $466 million (in 2005
dollars) (see Table 3-1). These costs include a variety of pollution
control expenditures: equipment installation, operating and maintenance,
recordkeeping, and performance-testing activities. Capital costs are
annualized over the expected life of the control equipment which is 20
years for all devices except RTOs which are 15 years.  The majority of
the costs ($455 million, or 98%, are associated with the final NESHAP.
The remaining costs ($11 million) are associated with the final NSPS
limits for SO2 and NOx. Figure 3-1 illustrates the distribution of
annualized compliance costs per metric ton of clinker capacity by
different combinations of the regulatory programs. In Table 3-2, we
report state-level summary statistics for total annualized compliance
costs per metric ton of clinker capacity for the final NESHAP and NSPS
to highlight any regional differences in control costs. 

Table 3-1.	Summary of Direct Total Annualized Compliance Costs (million,
2005$)

	Total Annualized Compliance Costs

Description	Final NSPS Only	More Stringent NSPS Only

Total:	$40a	$56a

	Final NESHAP Only

Partial Equilibrium Analysis (136 Kilns)	$378

NSPS kilns (7 kilns)	$29

Other kilns (13 kilns)	$48

Total:	$455

	Final NESHAP and NSPS	Final NESHAP and More Stringent NSPS

136 Kilns	$378	$378

20 Kilns	$88	$104

Total:	$466	$482

a	The final NSPS only also includes the $29 million in NESHAP costs for
7 kilns. The 7 kilns will also incur an additional $11 in compliance
costs to meet the final NSPS limits for SO2 and NOx. Alternatively, the
7 kilns would also incur an additional $27 in compliance costs to meet
the stringent NSPS limits for SO2 and NOx.

Figure 3-1.	Range of Per-Ton Total Annualized Compliance Costs (2005$)

Table 3-2.	Range of Per-ton Total Annualized Compliance Costs by State
(2005$)

ST	Data

	Average ($/ ton of clinker capacity)	Minimum ($/ ton of clinker
capacity)	Maximum ($/ ton of clinker capacity)

AL	$3	$1	$5

AZ	$3	$1	$6

CA	$4	$3	$5

CO	$2	$1	$3

FL	$3	$1	$5

GA	$1	$1	$1

IA	$6	$4	$8

ID	$10	$9	$10

IL	$6	$1	$8

IN	$9	$5	$14

KS	$6	$6	$6

KY	$4	$4	$4

MD	$6	$3	$9

ME	$1	$1	$1

MI	$5	$4	$6

MO	$5	$4	$5

MT	$2	$2	$2

NE	$6	$5	$6

NM	$2	$2	$2

NV	$2	$2	$2

NY	$3	$1	$4

OH	$5	$5	$5

OK	$8	$4	$13

OR	$4	$4	$4

PA	$5	$2	$7

SC	$4	$4	$4

SD	$2	$1	$2

TN	$3	$1	$5

TX	$5	$1	$8

UT	$5	$1	$9

VA	$4	$4	$4

WA	$1	$1	$2

WV	$7	$6	$8

WY	$7	$5	$8

U.S.	$5	$1	$14

Note:  Includes Final NESHAP only for 136 kilns included in economic
impact model.

Partial-Equilibrium   SEQ CHAPTER \h \r 1 Analysis 

The partial-equilibrium analysis develops a cement market model that
simulates how stakeholders (consumers and firms) might respond to the
additional regulatory program costs. In this section, we provide an
overview of the economic model used during proposal (EPA, 2009  XE "EPA,
2009"  ). Appendix A provides additional details about economic model
updates made since proposal, model equations, and parameters. 

Regional Structure and Baseline Data

Cement sales are often concentrated locally among a small number of
firms for two reasons: high transportation costs and production
economies of scale. Transportation costs significantly influence where
cement is ultimately sold; high transportation costs relative to unit
value provide incentives to produce and sell cement locally in regional
markets (USITC, 2006  XE “USITC, 2006”  ). To support this claim,
the empirical literature has typically pointed to Census of
Transportation data showing over 80% of cement shipments were made
within a 200-mile radius (Jans and Rosenbaum, 1997  XE “Jans and
Rosenbaum, 1997”  ) and reported evidence of high transportation costs
per dollar of product value from case studies (Ryan, 2006  XE “Ryan,
2006”  ). Based on this literature, the Agency assumes that the U.S.
Portland cement industry is divided into a number of independent
regional markets with each having a single market-clearing price.

The freight-on-board (f.o.b.) price of Portland cement for each regional
market is derived as the production weighted average of the state level
f.o.b. prices reported by the USGS for cement (see Table 3-3). The
production of Portland cement within each market is the sum of estimated
individual kiln production levels (EPA, 2009  XE "EPA, 2009"  ) and
include adjustments described in Appendix A (see Table 3-4). We obtained
estimates of Portland cement imports from the USGS and mapped them to
each market based on the port of entry.

Near-Term Cement Plant Production Decisions

A cement company acts in the best interest of its shareholders and
maximizes profits. When deciding whether to make another ton of cement,
the company considers the production effect on profits by comparing the
current market price of cement and the marginal production cost; if
price is above marginal production cost, producing and selling the extra
ton of cement increase profit. The company continues to produce
additional cement until the profit from 

Table 3-3.	Portland Cement Prices by Market ($/metric tons): 2005

Market	Price ($/metric ton)

Atlanta	$81

Baltimore/Philadelphia	$82

Birmingham	$83

Chicago	$67

Cincinnati	$84

Dallas	$75

Denver	$89

Detroit	$93

Florida	$91

Kansas City	$86

Los Angeles	$78

Minneapolis	$92

New York/Boston	$89

Phoenix	$83

Pittsburgh	$88

St. Louis	$84

Salt Lake City	$91

San Antonio	$82

San Francisco	$97

Seattle	$88



producing an extra ton of cement is zero (price equals marginal cost) or
capacity constraints are reached. The decision rule is consistent with
the assumption of pure competition.

Although perfect competition is widely accepted for modeling many
industries regardless of the model time horizon (EPA, 2000  XE "EPA,
2000"  ), the cement industry has two characteristics that influenced
EPA’s modeling choice relating to market structure. First, high
transportation costs and other production economics tend to limit the
number of sellers (particularly over a short time horizon), so each
seller has a substantial regional market share. Timely market entry is
also constrained by the high capital costs that involve purchases and
construction of large rotary kilns that are not readily movable or
transferable to other uses. Second, cement producers offer similar or
identical products. American Society for Testing and Materials (ASTM)
specifications tend to ensure uniform quality, and recent industry
reviews (USITC, 2006  XE "USITC, 2006"  ) suggest that there is little
or no brand loyalty that allows firms to differentiate their products.

Table 3-4.	Portland Cement Markets (106 metric tons): 2005

Market	U.S. Production	Imports	Total

Atlanta	5.8	2.3	8.1

Baltimore/Philadelphia	7.8	0.6	8.5

Birmingham	5.9	2.2	8.1

Chicago	4.7	0.2	4.9

Cincinnati	3.7	0.0	3.7

Dallas	8.1	2.4	10.5

Denver	3.4	0.0	3.4

Detroit	3.8	1.3	5.2

Florida	5.5	5.8	11.4

Kansas City	5.0	0.0	5.0

Los Angeles	10.6	3.8	14.4

Minneapolis	1.7	0.4	2.1

New York/Boston	3.2	2.8	6.0

Phoenix	4.3	0.0	4.3

Pittsburgh	1.5	1.6	3.1

St. Louis	6.0	0.0	6.0

Salt Lake City	2.4	0.1	2.4

San Antonio	5.5	4.6	10.0

San Francisco	3.4	2.8	6.2

Seattle	1.1	2.5	3.6



Given entry barriers, product characteristics, and the need to
understand important near-term/transitory stakeholder outcomes, EPA
continued to use the economic impact model designed for previous
analyses (EPA, 1998, 1999b, 2009  XE "EPA, 1998, 1999b, 2009"  ). The
model considers how regional markets may operate in near-term time
horizons when 1) the number of companies is limited and 2) the companies
sell similar or identical products. Under these circumstances, the
short-run production decision rule that a cement company makes differs
from pure competition. The company continues to consider the production
effect described above; however, the company adds another dimension to
the decision-making process by also considering the market price effect
that is associated with producing an additional ton of cement. Given the
small number of cement producers, adding an extra ton of cement to the
regional market may lower the market cement price and reduce the profits
on all the other cement sold. If the price effect is large enough,
companies may find it more profitable to reduce production below the
levels implied by pure competition. As a result, short-run regional
market prices tend to be higher than marginal production costs (i.e.,
there may be a preexisting market distortion within cement markets prior
to regulation). The size of the existing distortion depends on the
seller’s market share and how responsive cement consumers are to
changes in the cement price. Economic theory suggests the market
distortion will typically be higher the smaller the number of sellers
and when the quantity demanded is less sensitive to price (i.e., the
demand elasticity is inelastic) (see Appendix A).

Economic Impact Model Results

Market-Level Results

Market-level impacts include the regional price and quantity adjustments
for Portland cement, including the changes in imports for the
appropriate regions. As shown in Table 3-5, the average national price
for Portland cement increases by 5%, or $4.50 per metric ton, while
overall U.S. cement consumption falls by approximately 5%. Domestic
production falls by 11%, or 10 million tons per year. Cement imports
increase in response to higher domestic cement prices; imports increase
by 10%, or 3 million metric tons.

Table 3-5.	National-Level Market Impacts: 2005

−6	−4.8%

Domestic production	93	−10	−10.8%

Imports	33	3	10.0%



As shown in Table 3-6, price increases are the highest in regions with
high compliance costs per metric ton. For example, the Cincinnati market
price increase ($10 per metric ton) also includes kilns with higher
average compliance costs and a kiln with the highest per-unit 

Table 3-6.	Regional Compliance Costs and Market Price Changes ($/metric
ton of cement): 2005

	Incremental Compliance Costs

($/metric ton of estimated 

cement production)	Baseline Price	Market Price Change

Market	Mean	Minimum	Maximum

Absolute	Percent

Atlanta	$3.60	$1.10	$5.90	$81.30	$2.80	3.4%

Baltimore/Philadelphia	$6.20	$1.20	$10.00	$81.70	$6.10	7.5%

Birmingham	$3.60	$1.10	$4.80	$82.60	$3.80	4.6%

Chicago	$6.80	$0.90	$10.10	$66.90	$4.80	7.2%

Cincinnati	$8.10	$4.00	$14.10	$84.20	$10.40	12.4%

Dallas	$5.60	$3.50	$8.50	$75.10	$4.90	6.5%

Denver	$3.00	$1.00	$8.10	$88.70	$6.30	7.1%

Detroit	$6.50	$4.00	$10.30	$92.70	$4.20	4.5%

Florida	$3.40	$1.20	$5.50	$90.70	$3.50	3.9%

Kansas City	$8.60	$3.80	$13.80	$86.10	$8.20	9.5%

Los Angeles	$6.00	$3.20	$13.10	$78.20	$4.30	5.5%

Minneapolis	$6.30	$4.50	$8.80	$92.20	$8.50	9.2%

New York/Boston	$2.50	$1.00	$4.50	$89.00	$1.80	2.0%

Phoenix	$1.90	$1.00	$6.00	$83.10	$4.20	5.1%

Pittsburgh	$7.60	$6.90	$8.00	$88.00	$4.60	5.2%

St. Louis	$4.80	$3.80	$5.60	$84.10	$4.50	5.4%

Salt Lake City	$5.90	$1.60	$9.90	$91.40	$10.40	11.4%

San Antonio	$4.00	$0.80	$7.70	$82.30	$3.30	4.0%

San Francisco	$3.10	$1.00	$5.00	$96.90	$3.30	3.4%

Seattle	$1.20	$1.00	$1.40	$88.00	$0.70	0.8%

Grand Total	$5.20	$0.80	$14.10	$83.90	$4.50	5.4%



compliance costs ($14 per metric ton). It is important to note that EPA
uses a time horizon where transportation costs between regions are high
enough that interregional trade is unlikely to occur, at least in the
short run. The regional differences in unit compliance costs and the
significant simulated changes in relative regional prices suggest
domestic cement plants may be more likely to consider short-run
shipments of cement between regional markets. Choices would depend on
the additional benefits of selling cement to these markets and the costs
of transporting the cement outside the regional market. Although EPA has
not quantified this effect, additional flexibility would tend to temper
prices increases in some of these markets.

Imports also tend to limit price increases in certain regions. This
tends to reinforce U.S. production declines because cement plants have
more difficulty passing on compliance costs in the form of higher prices
when compared with similar plants operating in regions without import
competition. Because imports are only modeled for markets with imports
in the baseline without regulation, Table 3-7 separates the results into
markets with and without imports as well as providing the results for
all markets. As shown in Table 3-7, median price increases in regions
with imports are lower than the median price increases in regions
without import competition. In some regions with imports, the reductions
in U.S. production are significant. As shown in Table 3-7, the maximum
simulated U.S. regional production change is 23%. To the extent there
are any unobserved constraints on import supply that are not captured in
the import supply elasticity parameter, price and U.S. production
adjustments for regional markets with imports would tend to become more
similar to regional markets without imports.

Industry-Level Results

As shown in Table 3-8, compliance costs vary by cement plant, and this
variation suggests some plants will be more adversely affected than
others. To assess these differences, EPA collected industry operating
profit data and identified plants with operating profit increases and
losses. Absent plant-specific data, EPA assumed each plant’s baseline
profits were consistent with the median operating profit margin reported
by the PCA (2008c, Table 44 xe "2008c, Table 44" ). In 2005, this value
was $18 per metric ton, or 16%. Using this assumption, total operating
profits for 59 plants (58%) decrease by $387 million with regulation.
These plants tend to have higher per ton compliance costs. The remaining
plants’ compliance burden is offset by higher regional cement prices,
and total plant operating profits increase by $147 million. These 44
plants have lower unit compliance costs compared with their competitors.

Table 3-7.	Summary of Regional Market Impacts 

	Regional Markets

	With Imports	Without Imports	All Markets

Change in Market Price 



	Absolute ($/metric ton)



	Mean	$4.70	$6.40	$4.50

Median	$4.20	$5.40	$4.40

Minimum	$0.70	$4.20	$0.70

Maximum	$10.40	$10.40	$10.40

Percentage of baseline price



	Mean	5.5%	7.5%	5.4%

Median	4.9%	6.2%	5.3%

Minimum	0.8%	5.0%	0.8%

Maximum	11.4%	12.4%	12.4%

Change in Domestic Production



	Absolute (thousand metric tons)



	Mean	−559	−271	−501

Median	−421	−247	−372

Minimum	−74	−189	−74

Maximum	−1,539	−403	−1,539

Percentage of baseline production



	Mean	−11.8%	−6.6%	−10.8%

Median	−11.6%	−5.5%	−10.4%

Minimum	−6.8%	−4.4%	−4.4%

Maximum	−22.8%	−10.9%	−22.8%



Within the group of plants with operating losses, EPA identified 10
domestic plants with negative operating profits and significant
utilization changes that could temporarily idle until market demand
conditions improve (see Table 3-9). The plants have unit compliance
costs close to $8 per ton; they account for approximately 8% of domestic
capacity. These plants are modeled as continuing to operate despite low
capacity utilization and short run negative profits.  The model results
for them are included in the summary results for Tables 3-5, 3-6, 3-7,
and 3-8 but are also reported separately in Table 3-9.

If the plant owners did decide to permanently shut down these plants,
the reduction in domestic supply would lead to additional projected
market price increases. This would lead to an increased production at
other plants, a possible increase in imports (depending if the plant
that chooses to close is in a market where imports are anticipated) and
a decrease in control cost.  This scenario cannot be easily modeled.  In
an effort to bound this effect, the price increase needed to reduce
national consumption by the amount of production that would be lost if
the ten plants dropped from 55.5% capacity utilization to 0.0% capacity
utilization was estimated using the demand elasticity of 0.88.  This
ignores changes in other plants is response to an increased potential
market share and increases in imports. Both of these would tend diminish
the price increase.  The predicted price change was multiplied by the
change in production associated with the ten plants dropping capacity
utilization to zero and multiplied by one half to estimate the change in
surplus associated with the price and quantity change.  This gave a
result of a $10 million increase in social cost. This number was then
reduced by the avoided pollution control cost of $34 million at the ten
plants.  This resulted in a net reduction of $24 million in social cost
due to the adjustment.  Because of the method of estimating this
adjustment it cannot be distributed between producer and consumer
surplus.  An estimate of the social cost is provided with and without
this adjustment.

Table 3-8.	Distribution of Industry 2005

	Changes in Total Operating Profit:

	Plants with Loss	Plants with Gain	All Plants

Number	58	44	102

Cement Capacity (million metric tons)



	Total	55,202	38,145	93,346

Average per plant	952	867	915

Compliance Costs 



	Total (thousand)	$308,740	$68,806	$377,546

Average ($/metric cement)	$5.59	$1.80	$4.04

Capacity Utilization (percent)



	Baseline	100.3%	98.7%	99.6%

With regulation	81.0%	100.3%	88.9%

Change in total operating profits (million)	−$387	$147	−$241



Table 3-9.	Cement Plants with Significant Utilization Changes 2005

	Total

Number	10

Cement Capacity (thousand metric tons)

	Total	7,815

Average per plant	782

Compliance Costs 

	Total (thousand)	$62,222

Average ($/metric ton)	$7.96

Capacity Utilization (%)

	Baseline	99.0%

With regulation	55.5%

Change in Operating Profit (million)	−$116



  SEQ CHAPTER \h \r 1 Job Effects

Precise job effect estimates cannot be estimated with certainty.
Ideally, whenever a regulatory change results in a reallocation of labor
or other factors of production in an economy, a general equilibrium
approach should be applied to estimate the attendant economic impacts.
Unfortunately, time and resource constraints prevented the creation of a
model with the spatial and sectoral resolution necessary to analyze the
final rule. However, Morgenstern et al (2002) provides a theoretical
framework which allows us to approximate some of the relevant general
equilibrium effects by identifying three economic mechanisms by which
pollution abatement activities can indirectly influence:

higher production costs raise market prices, higher prices reduce
consumption, and employment within an industry falls (“demand
effect”);

pollution abatement activities require additional labor services to
produce the same level of output (“cost effect”); and

post-regulation production technologies may be more or less labor
intensive (i.e., more/less labor is required per dollar of output)
(“factor-shift effect”).

Several empirical studies, including Morgenstern et al. (2002 xe
"Morgenstern et al. (2002" ), suggest the net employment decline is zero
or economically small (e.g., Cole and Elliot, 2007 xe "Cole and Elliot,
2007" ; Berman and Bui, 2001 xe "Berman and Bui, 2001" ). However,
others show the question has not been resolved in the literature
(Henderson, 1996 xe "Henderson, 1996" ; Greenstone, 2002 xe "Greenstone,
2002" ). Morgenstern et al. use a 6-year panel (U.S. Census data for
plant-level prices, inputs [(including labor], outputs, and
environmental expenditures) to econometrically estimate the production
technologies and industry-level demand elasticities. Their
identification strategy leverages repeat plant-level observations over
time and uses plant-level and year fixed effects (e.g., dummy variables
for plant and years). After estimating their model, Morgenstern show and
compute the change in employment associated with an additional $1
million ($1987) in environmental spending. Their estimates cover four
manufacturing industries (pulp and paper, plastics, petroleum, and
steel) and Morgenstern et al. present results separately for the cost,
factor shift, and demand effects, as well as the net effect. They also
estimate and report an industry-wide average parameter that combines the
four industry-wide estimates and weight them by each industry’s share
of environmental expenditures.

Historically, EPA has most often estimated employment changes associated
with plant closures due to environmental regulation or changes in output
for the regulated industry (EPA, 1999a; EPA, 2000). This partial
equilibrium approach focuses only on the “demand” portion of the
projected change in employment and neglects other employment changes. 
EPA provides this estimate because it employs the most detailed modeling
for the industry being regulated even if it does not capture all types
of employment impacts.   In addition to the employment effects
identified by Morgenstern et al., we also expect that the substitutes
for cement (e.g., asphalt) would expand production as consumers shift
away from cement to other products.  This would also lead to increased
employment in those industries. Focusing only on the “demand
effect”, it can be seen that the estimate from the historical approach
is within the range presented by the Morgenstern “demand effect”
portion. This strengthens our comfort in the reasonableness of both
estimates.  In April of this year, EPA started including an estimate
based on the Morgenstern approach because it is thought to be a broader
measure of the employment impacts of this type of environmental
regulation.  Thus, this analysis goes beyond what EPA has typically done
because the parameters estimated in the Morgenstern paper were used to
estimate all three effects (“demand,” “cost,” and “factor
shift”). This transfer of results from the Morgenstern study is
uncertain but avoids ignoring the “cost effect” and the
“factor-shift effect.”

−10%.   By comparison, using the Morgenstern approach, we estimate
that the net employment effects could range between 600 job losses to
1,300 job gains. 

EPA has solely used this historical estimate in the past as a measure of
the projected employment change associated with a regulation.  However
there are a number of serious shortcomings with this approach.  First,
and foremost, the historical approach only looks at the employment
effects on the regulated industry from reduced output.  Second, to
arrive at that estimate, EPA needed to string together a number of
strong assumptions.  The employment impacts are independent of the
performance of the overall economy.  This rule takes effect in three
years.  If the economy is strong, the demand for cement strong, it is
unlikely that any contraction in the industry will take place, even with
the regulation.  Second, we assume that all plants have the same limited
ability to pass on the higher costs.  In reality, plants should be
modeled as oligopolists for each of their regional markets.  Finally,
EPA assumed that employment is directly proportional to output.  This is
unlikely, and biases the results towards higher employment losses.  The
Morgenstern methodology is a more complete consideration of probable
impacts of a regulation on the economy.

Table 3-10.	Job Losses/Gains Associated with the Final Rule

Method	1,000 Jobs

Partial equilibrium model 	−1.5

(demand effect only)	 

Literature-based estimate (net effect [A + B + C below])	0.3

(−0.6 to +1.3)

A. Literature-based estimate: Demand effect	−0.8

(−1.7 to +0.1)

B. Literature-based estimate: Cost effect	0.5

(+0.2 to +0.9)

C. Literature-based estimate: Factor shift effect	0.6

(+0 to +1.2)



ble from other studies. To do this, we multiplied the point estimate for
the total demand effect (−3.56 jobs per million [$1987] of
environmental compliance expenditure) by the total environmental
compliance expenditures used in the partial equilibrium model. For
example, the jobs effect estimate for is estimated to be 807 jobs
(−3.56 × $378 million × 0.6). Demand effect results are provided in
Table 3-10.  It is not appropriate to substitute the data from that
approach in to the Morgenstern due to the incompatibilities of the
underlying data.  Since the result from the historical approach is
within the confidence bounds for the Morgenstern results for the
“demand effect”, we are comfortable that the more general
Morgenstern result is a good representation of the change in employment.

We also present the results of using the Morgenstern paper to estimate
employment “cost” and “factor-shift” effects. Although using the
Morgenstern parameters to estimate these “cost” and
“factor-shift” employment changes is uncertain, it is helpful to
compare the potential job gains from these effects to the job losses
associated with the “demand” effect. Table 3-10 shows that using the
“cost” and “factor shift” employment effects may offset
employment loss estimates using either “demand” effect employment
losses. The 95% confidence intervals are shown for all of the estimates
based on the Morgenstern parameters. As shown, at the 95% confidence
level, we cannot be certain if net employment changes are positive or
negative.

Although the Morgenstern paper provides additional information about the
potential job effects of environmental protection programs, there are
several qualifications EPA considered as part of the analysis. First,
EPA has used the weighted average parameter estimates for a narrow set
of manufacturing industries (pulp and paper, plastics, petroleum, and
steel). Absent other data and estimates, this approach seems reasonable
and the estimates come from a respected peer-reviewed source. However,
EPA acknowledges the final rule covers an industry not considered in the
original empirical study. By transferring the estimates to the cement
sector, we make the assumption that estimates are similar in size. In
addition, EPA assumes also that Morgenstern et al.’s estimates derived
from the 1979–1991 are still applicable for policy taking place in
2013, almost 20 years later. Second, the economic impact model only
considers near-term employment effects in the cement industry where
production technologies are fixed. As a result, the economic impact
model places more emphasis on the short-term “demand effect,”
whereas the Morgenstern paper emphasizes other important long-term
responses. For example, positive job gains associated with “factor
shift effects” are more plausible when production choices become more
flexible over time and industries can substitute labor for other
production inputs. Third, the Morgenstern paper estimates rely on sector
demand elasticities that are different (typically bigger) from the
demand elasticity parameter used in the cement model. As a result, the
demand effects are not directly comparable with the demand effects
estimated by the cement model. Fourth, Morgenstern identifies the
industry average as economically and statistically insignificant effect
(i.e., the point estimates are small, measured imprecisely, and not
distinguishable from zero). EPA acknowledges this fact and has reported
the 95% confidence intervals in Table 3-10. Fifth, Morgenstern’s
methodology assumes large plants bear most of the regulatory costs. By
transferring the estimates, EPA assumes a similar distribution of
regulatory costs by plant size and that the regulatory burden does not
disproportionately fall on smaller plants.

Other Economic Analyses: Direct Compliance Cost Methods   SEQ CHAPTER \h
\r 1 

In addition to the market-level partial equilibrium analysis, EPA
developed a separate economic analysis for the remaining 20 kilns that
EPA anticipates will be affected by the final rule. These costs ($88
million, or 19%) were not included in the economic impact model analysis
because of uncertainties and difficulties with developing an appropriate
set of baseline cement market conditions for future years. 

The total annualized costs for two white cement kilns are $2 million, or
approximately $9 per metric ton of cement production. Using reported
2005 data from the USGS on the average mill net value of white cement
($176 per metric ton), this cost represents 5% of the product value. 

EPA also conducted sales tests for 18 other kilns that were not included
in the partial equilibrium analysis. The total annualized NESHAP cost
for these 18 kilns is approximately $75 million. The median cost per ton
is approximately $3.80 and ranges from $1.90 to $4.40 per ton of cement
production. In addition, 7 of these 18 kilns would face an additional
control cost above the NESHAP (approximately $1 dollar per metric ton)
to meet the NSPS limits for SO2 and NOx.

The USGS reports that the real price of cement per metric ton (2005
dollars) has typically ranged between $75 and $100 since 1990. A sales
test using these price data shows cost-to-sales ratios (CSRs) could
range between 2% and 6% 

Sales Test Ratio = Control Costs ($/ton)/F.O.B Cement Prices ($/ton).

From 2000 to 2006, the PCA reports that the average operating profit
rates for the industry ranged from 17 to 21% (PCA, 2008c  XE "PCA,
2008c"  ). If these profit data are representative of operating profit
rates for new kilns, kilns could potentially significantly reduce their
operating profit rates. As a result, companies may have the incentive to
look for less expensive alternatives to meet the emission standards. If
these alternatives are limited or not cost effective, the final rule may
lead companies to consider delaying rates of construction of new kilns
until market conditions change (e.g., increases in demand that lead to
rising cement prices) to cover additional control costs. 

  SEQ CHAPTER \h \r 1 Social Cost Estimates

For the kilns modeled in our partial equilibrium model, the market
adjustments in price and quantity were used to estimate the changes in
aggregate economic welfare using applied welfare economics principles
(see Appendix C).  Higher cement prices and reduced consumption lead to
consumer welfare losses ($540 million). Domestic producers (in
aggregate) experience a net loss of $239 million. As noted in the
previous section, individual domestic producers may gain or lose
depending on the change in compliance costs versus the change in the
regional market prices. The total domestic surplus loss (consumer and
producers) totals $792 million. 

  For the kilns not modeled in our partial equilibrium model, the $88
million in engineering costs were multiplied by 1.8 to approximate the
likely additional social cost associated with oligopoly market response.
 Thus the social cost estimate for the 20 kilns not in the partial
equilibrium model is $158 million.  Because of the approximation used,
we cannot estimate how the $158 million is distributed between consumers
and producers.

Table 3-11.	Distribution of Social Costs ($106): 2005

	Social Cost Estimates

	Description	Final NESHAP and NSPS	Final NESHAP and More Stringent NSPS
EIA Social Cost Method

Change in consumer surplus	$551	$551	Partial-equilibrium model

(baseline year 2005)

Change in domestic producer surplus	$241	$241	Partial-equilibrium model

(baseline year 2005)





	20 Kilns

	$158	$187	Direct Compliance Method

(scaled by 1.8 for oligopoly)



Change in domestic surplus	$950	$979	Combined methods

Adjustment if  ten low utilization facilities idle or close 	-$24	-$24







Total:	$926-$950	$955-$979	Change with and without adjustment

	Final NSPS Only	More Stringent NSPS Only

	Total:	$72	$101	Direct compliance cost method (scaled by 1.8 for
oligopoly)

	Final NESHAP Only

	Change in consumer surplus	$551	Partial-equilibrium model

(baseline year 2005)

Change in domestic producer surplus	$241	Partial-equilibrium model

(baseline year 2005)





NSPS kilns (7 kilns)	$52	Direct compliance cost method

(scaled by 1.8 for oligopoly)

Other kilns (13 kilns)	$86	Direct compliance cost method

(scaled by 1.8 for oligopoly)

Change in domestic surplus	$930	Combined methods

Adjustment if  ten low utilization facilities idle or close	$-24

	Total:	$904-$930	Change with and without adjustment



The estimated social cost of the final rule is $880 million. This
estimate includes the results for existing kilns included in the
partial-equilibrium analysis ($792 million),the final NESHAP direct
compliance costs 20 kilns not included in the economic impact model,
($77 million), and the additional NSPS direct compliance cost for 7
kilns coming on line in the future ($11 million). The social estimates
are significantly higher than the engineering analysis estimate of
annualized costs totaling $466 million. This is a direct consequence of
EPA’s assumptions about existing market structure discussed
extensively in previous cement industry rulemakings and in Section 2 and
Appendix B of this RIA. Under baseline conditions without regulation,
the existing domestic cement plants are assumed to choose a production
level that is less than the level produced under perfect competition. As
a result, a preexisting market distortion exists in the cement markets
covered by the final rule (i.e., the observed baseline market price is
higher than the [unobserved] market price that a model of perfect
competition would predict). The imposition of additional regulatory
costs tends to widen the gap between price and marginal cost in these
markets and contributes to additional social costs. The above social
costs for 2013 include annualized capital costs over the expected
lifetime of the equipment and an opportunity cost of capital (7%)
discount rate. To facilitate comparisons of benefits and costs when
estimates vary of time across multiple years, EPA typically estimates a
“consumption equivalent” present value measure of costs. This could
be computed using a consumption rate of interest of 3% and 7%. However,
this calculation was not necessary since the cost and benefit analyses
only produce estimates for a single year (OAQPS, 1999a  XE "OAQPS,
1999a"  ).

Energy Impacts

Executive Order 13211 (66 FR 28355, May 22, 2001) provides that agencies
will prepare and submit to the Administrator of the Office of
Information and Regulatory Affairs, OMB, a Statement of Energy Effects
for certain actions identified as “significant energy actions.”
Section 4(b) of Executive Order 13211 defines “significant energy
actions” as any action by an agency (normally published in the Federal
Register) that promulgates or is expected to lead to the promulgation of
a rule or regulation, including notices of inquiry, advance notices of
final rulemaking, and notices of final rulemaking: (1) (i) that is a
significant regulatory action under Executive Order 12866 or any
successor order, and (ii) is likely to have a significant adverse
effect on the supply, distribution, or use of energy; or (2) that is
designated by the Administrator of the Office of Information and
Regulatory Affairs as a significant energy action.

This rule is not a significant energy action as designated by the
Administrator of the Office of Information and Regulatory Affairs
because it is not likely to have a significant adverse impact on the
supply, distribution, or use of energy. EPA has prepared an analysis of
energy impacts that explains this conclusion below.

To enhance understanding regarding the regulation’s influence on
energy consumption, EPA examined publicly available data describing the
cement sector’s energy consumption. The AEO 2010 (DOE, 2010  XE "DOE,
2010"  ) provides energy consumption data. As shown in Table 3-12, this
industry accounts for approximately 0.4% of the U.S. total energy
consumption. As a result, any 

Table 3-12.	U.S. Cement Sector Energy Consumption (Trillion BTUs)a:
2013

 	Quantity	Share of Total Energy Use

Residual fuel oil	0.9	0.00%

Distillate fuel oil	10.8	0.00%

Petroleum coke	47.3	0.10%

Other petroleumb	30.2	0.00%

Petroleum subtotal	89.2	0.10%

Natural gas	19.8	0.00%

Steam coal	206.6	0.20%

Metallurgical coal	6.8	0.00%

Coal subtotal	213.4	0.20%

Purchased electricity	38.9	0.00%

Total 	399.44	0.40%

Delivered Energy Use	72,407	72.20%

Total Energy Use	100,592	100.00%

a Fuel consumption includes consumption for combined heat and power.

b Includes petroleum coke, lubricants, and miscellaneous petroleum
products.

Source:	U.S. Department of Energy, Energy Information Administration.
2010. Supplemental Tables to the Annual Energy Outlook 2010.  XE "U.S.
Department of Energy, Energy Information Administration. 2010.
Supplemental Tables to the Annual Energy Outlook 2010."   Table 10 and
Table 39. Available at
<http://www.eia.doe.gov/oiaf/aeo/supplement/supref.html>.

energy consumption changes attributable to the regulatory program should
not significantly influence the supply, distribution, or use of energy.
EPA has also estimated the amount of additional electricity consumption
associated with add-on controls. The analysis shows the additional
national electrical demand to be 780 million kWh per year and the
natural gas use to be 1.2 million MMBTU per year for existing kilns. For
new kilns, assuming that of the 16 new kilns to start up by 2013, all 16
will add alkaline scrubbers and ACI systems, the electrical demand is
estimated to be 199 million kWh per year. This is less than 0.1% of AEO
2010 forecasts of total electricity and natural gas use.

Assessment

Although the economic analyses presented in this section cannot provide
precise estimates of the final NESHAP’s and NSPS’s economic impacts,
the evidence presented in this section suggests that the economic
impacts may be significant across several dimensions (price,
consumption, production, and international trade). There are several
broad issues we emphasize as stakeholders review the analysis. First,
OAQPS’s partial equilibrium analysis of NESHAPs has traditionally been
designed to assess small (marginal) changes in industry conditions. The
overall engineering cost analysis estimates are significant relative to
the size of the U.S. cement market; EPA acknowledges that use of demand
and import supply elasticities can be tenuous in these cases because the
exact functional relationships (demand and supply) are less certain when
simulated outcomes move further away from the observed pre-policy
equilibrium. Second, the partial equilibrium assumes that transportation
costs between regions are high enough that interregional trade is
unlikely to occur, at least in the short run. Allowing interregional
trade would expand the cement market definitions and increase the number
of producers in each market. As discussed above, as the number of
producers in a market increases, the production decision becomes more
consistent with decisions made in pure competition; the additional
trading opportunities may tend to moderate the relative price changes
simulated within the model. Third, as discussed earlier in this section,
the choice of market structure increases the agency’s social cost
estimate; it is almost 2 times higher than a model that assumes perfect
competition. Therefore, the analysis may overstate the social costs of
the rule. EPA continues to believe the market structure is reasonable
and provides an upper-bound social cost estimate for the following
reasons: (1) high transportation costs and other production economics
tend to limit the number of sellers (particularly over a short time
horizon), so each seller has a substantial regional market share; (2)
timely market entry is also constrained by the high capital costs that
involve purchases and construction of large rotary kilns that are not
readily movable or transferable to other uses; (3) cement producers
offer very similar or identical products; and (4) the Office of
Management and Budget (OMB) explicitly mentions the need to consider
market power–related welfare costs in evaluating regulations under
Executive Order 12866. 



Small Business Impact Analysis

The Regulatory Flexibility Act (RFA) generally requires an agency to
prepare a regulatory flexibility analysis of any rule subject to notice
and comment rulemaking requirements under the Administrative Procedure
Act or any other statute unless the agency certifies that the rule will
not have a significant economic impact on a substantial number of small
entities (SISNOSE). The first step in this assessment was to determine
whether the rule will have SISNOSE. To make this determination, EPA used
a screening and market analysis to indicate whether EPA can certify the
rule as not having a SISNOSE. The elements of this analysis included

identifying affected small entities,

selecting and describing the measures and economic impact thresholds
used in the analysis, and

completing the assessment and determining the SISNOSE certification
category.

Identify Affected Small Entities

For the purposes of assessing the impacts of the final rule on small
entities, small entity is defined as (1) a small business as defined by
the Small Business Administration’s regulations at 13 CFR 121.201;
according to these size standards, ultimate parent companies owning
Portland cement manufacturing plants are categorized as small if the
total number of employees at the firm is fewer than 750 (see Table 4-1
for list); (2) a small governmental jurisdiction that is a government of
a city, county, town, school district, or special district with a
population of less than 50,000; and (3) a small organization that is any
not-for-profit enterprise that is independently owned and operated and
is not dominant in its field. As reported in Section 2, EPA has
identified four small entities (see Table 4-1). One of the four entities
is owned by a small Tribal government (Salt River Pima-Maricopa Indian
Community). The remaining three entities are small businesses.

Sales and Revenue Test Screening Analysis

In the next step of the analysis, EPA assessed how the regulatory
program may influence the profitability of ultimate parent companies by
comparing pollution control costs to total sales (i.e., a “sales”
test). To do this, we divided an ultimate parent company’s total
annualized compliance costs by its reported revenue:

Table 4-1.	Small Entity Analysis

Owner 	Entity Type	Annual Sales ($106)	Employees	Plants	Kilns	Clinker
Capacity (103 metric tons per year)	Cost-to-Sales Ratio

Salt River Materials Groupa	Tribal government	$184b	NA	1	1	1,477	0.7%

Monarch Cement Company	Business	$154 	600	1	2	787	3.0%

Continental Cement Company, LLC	Business	$93c	<750	1	1	1,164 	0.0%

Snyder Associate Companies	Business	$29 	350	1	2	286	2.0%

a	Enterprise is owned by Salt River Pima-Maricopa Indian Community.

b	EPA estimate. Estimate uses revenue data for four of the six
enterprises owned by Salt River Pima-Maricopa Indian Community.

c	EPA estimate. Estimate uses cement production levels and average
market prices.

 	(4.1)

where 

CSR	=	cost-to-sales ratio,

TACC	=	total annualized compliance costs,

i	=	index of the number of affected plants owned by company j,

n	=	number of affected plants, and

TRj	=	total sales from all operations of ultimate parent company j or
annual government revenue.

The results of the screening analysis, presented in Table 4-1, show that
no small businesses have a CSR greater than 3%. Two small business have
an estimated CSR between 1 and 3%. 

Additional Market Analysis

In additional to the screening analysis, EPA also examined small entity
effects after accounting for market adjustments. Under this assumption,
the entities recover some of the regulatory program costs as the market
price adjusts in response to higher cement production costs. Even after
accounting for these adjustments, small entity operating profits fall by
less than 1 million. 

Assessment

After considering the economic impact of this final rule on small
entities, EPA has determined it will not have a significant economic
impact on the four small entities. No small companies have cost-to-sales
ratios greater than 3% and  only 4 of the over 40 cement companies are
small entities.



Air Quality Modeling of Emission Reductions

Synopsis

This section describes the air quality modeling performed by EPA in
support of the Portland cement NESHAP and NSPS. A national scale air
quality modeling analysis was performed to estimate the impact of the
sector emissions changes on future years: annual and 24-hour PM2.5
concentrations, total Hg deposition, as well as visibility impairment.
Air quality benefits are estimated with the Comprehensive Air Quality
Model with Extensions (CAMx) model. CAMx simulates the numerous physical
and chemical processes involved in the formation, transport, and
destruction of ozone, PM, and air toxics. In addition to the CAMx model,
the modeling platform includes the emissions, meteorology, and initial
and boundary condition data which are inputs to this model.

Emissions and air quality modeling decisions are made early in the
analytical process. For this reason, it is important to note that the
inventories used in the air quality modeling and the benefits modeling
are slightly different than the final adjusted cement kiln sector
inventories presented in the RIA. However, the air quality inventories
and the final rule inventories are generally consistent, so the air
quality modeling adequately reflects the effects of the rule.

The 2005-based CAMx modeling platform was used as the basis for the air
quality modeling for this final rule. This platform represents a
structured system of connected modeling-related tools and data that
provide a consistent and transparent basis for assessing the air quality
response to projected changes in emissions. The base year of data used
to construct this platform includes emissions and meteorology for 2005.
The platform is intended to support a variety of regulatory and research
model applications and analyses. This modeling platform and analysis is
described fully below. Additional details about the modeling system are
available in a separate technical support document: Air Quality Modeling
Technical Support Document: National Emission Standards for Hazardous
Air Pollutants from the Portland Cement Manufacturing Industry (U.S.
EPA, 2010c).

Photochemical Model Background

CAMx version 5.10 is a freely available computer model that simulates
the formation and fate of photochemical oxidants, primary and secondary
PM concentrations, and air toxics, over regional and urban spatial
scales for given input sets of meteorological conditions and emissions.
CAMx includes numerous science modules that simulate the emission,
production, decay, deposition and transport of organic and inorganic
gas-phase and particle-phase pollutants in the atmosphere (Nobel,
McDonald-Buller et al., 2001  XE "Nobel, McDonald-Buller et al., 2001" 
; Baker and Scheff, 2007  XE "Baker and Scheff, 2007"  ; Russell, 2008 
XE "Russell, 2008"  ). 

CAMx is applied with ISORROPIA inorganic chemistry (Nenes et al., 1999 
XE "Nenes et al., 1999"  ), a semivolatile equilibrium scheme to
partition condensable organic gases between gas and particle phase
(Strader et al., 1999  XE "Strader et al., 1999"  ), Regional Acid
Deposition Model (RADM) aqueous phase chemistry (Chang et al., 1987  XE
"Chang et al., 1987"  ), and Carbon Bond 05 (CB05) gas-phase chemistry
module (Gery et al., 1989  XE "Gery et al., 1989"  ; ENVIRON, 2008  XE
"ENVIRON, 2008"  ). All modeling domains were modeled for the entire
year of 2005. Data from the entire year were used when looking at the
estimation of PM2.5, total Hg deposition, and visibility impacts from
the regulation.

Model Domain and Grid Resolution

The modeling analyses were performed for a domain covering the
continental United States, as shown in Figure 5-1. This domain has a
parent horizontal grid of 36 km with two finer-scale 12 km grids over
portions of the eastern and western United States. The model extends
vertically from the surface to 100 millibars (approximately 15 km) using
a sigma-pressure coordinate system. Air quality conditions at the outer
boundary of the 36 km domain were taken from a global model and did not
change over the simulations. In turn, the 36 km grid was only used to
establish the incoming air quality concentrations along the boundaries
of the 12 km grids. Only the finer grid data were used in determining
the impacts of the emission standard program changes. Table 5-1 provides
some basic geographic information regarding the photochemical model
domains.

Emissions Input Data

The emissions data used in the base year and future reference and future
emissions adjustment case are based on the 2005 v4 platform. The
emissions cases use some different emissions data than the official v4
platform to use data intended only for the rule development and not for
general use. Unlike the 2005 v4 platform, the configuration for this
modeling application included some additional HAPs and a cement kiln
sector emissions inventory more consistent with the engineering analysis
of potential control options. 

The 2013 reference case is intended to represent the emissions
associated with growth and controls in that year. The U.S. EGU point
source emissions estimates for the future year reference and control
case are based on an Integrated Planning Model (IPM) run for criteria
pollutants, HCl, and Hg in 2013 (although HCl was not modeled). Both
control and growth factors were applied to a subset of the 2005 non-EGU
point and nonpoint to create the 2013 reference case. The 2002 v3.1
platform 2020 projection factors were the starting point for most of the
2013 SMOKE-based projections. 

Figure 5-1.	Map of the Photochemical Modeling Domaina

a	The black outer box denotes the 36 km national modeling domain; the
red inner box is the 12 km western U.S. grid; and the blue inner box is
the 12 km eastern U.S. grid. 

Table 5-1.	Geographic Elements of Domains Used in Photochemical Modeling

	Photochemical Modeling Configuration

	National Grid	Western U.S. Fine Grid	Eastern U.S. Fine Grid

Map Projection	Lambert Conformal Projection

Grid Resolution	36 km	12 km	12 km

Coordinate Center	97 deg W, 40 deg N

True Latitudes	33 deg N and 45 deg N

Dimensions	148 x 112 x 14	213 x 192 x 14	279 x 240 x 14

Vertical extent	14 Layers: Surface to 100 millibar level



The 2013 reference scenario for the cement kiln sector assumed no growth
or control for the industry from the 2005 sector emissions estimates
with the exception that facilities that closed between 2005 and 2010
were removed from the 2013 inventory. The length of time required to
conduct emissions and photochemical modeling precludes using the final
facility-specific emissions estimates based on controls implemented for
this rule. A 2013 “control” or emissions adjustment case was
developed by removing all Portland cement sector emissions from the 2013
baseline inventory. This “zero-out” of the sector creates a policy
space where potential controls would be maximized at all locations.
Since this is unrealistic, the air quality estimates from the 2013
“zero-out” or “control” case are adjusted to reflect nationwide
estimates of control percentages by pollutant. It is important to note
that the scenario without cement kilns includes the zeroing-out of
emissions from hazardous waste kilns.  Out of 181 kilns nationwide,
there are 14 hazardous waste kilns, which represent 10 to 20% of total
kiln emissions.  This leads to a slight overestimate of the reduction in
PM2.5 levels and mercury deposition.  

Table 5-2.	Cement Kiln Emissions in 2005 Base and Estimated Future Year
(2013) in tons per year

Specie	2005	2013

Nitrogen Oxides	216,525	199,391

Volatile Organic Compounds	8,817	8,419

Sulfur Dioxide	158,560	149,013

PrimaryPM2.5	16,758	15,403

PM2.5 Mercury	0.8	0.7

Reactive Gas Phase Mercury	6.2	6.0

Elemental Mercury	3.8	3.6



	The air quality estimates associated with 2013 zero-out of the cement
kiln sector are adjusted nationally to reflect various options.

A 90% reduction in mercury emissions for the NSPS and NESHAP , more
stringent NSPS and NESHAP, and NESHAP only

82% reductions in SOX and 86% reductions in primarily emitted PM2.5 for
the NSPS and NESHAP, more stringent NSPS and NESHAP,  and NESHAP only

6% reductions in SOX and 5% reductions in primarily emitted PM2.5 for
NSPS only

As part of the analysis for this rulemaking, the modeling system was
used to calculate daily and annual PM2.5 concentrations, annual total Hg
deposition levels, and visibility impairment. Model predictions are used
in a relative sense to estimate scenario-specific future-year design
values of PM2.5 and ozone. Specifically, we compare a 2013 reference
scenario, a scenario without the cement kiln controls, to a 2013 control
scenario that includes the adjustments to the cement kiln sector. This
is done by calculating the simulated air quality ratios between any
particular future year simulation and the 2005 base. These predicted
ratios are then applied to ambient base year design values. The design
value projection methodology used here followed EPA guidance for such
analyses (U.S. EPA, 2007). Additionally, the raw model outputs are also
used in a relative sense as inputs to the health and welfare impact
functions of the benefits analysis. Only model predictions for Hg
deposition were analyzed using absolute model changes, although these
parameters also considered percentage changes between the control case
and two future baselines.

Model Results: Air Quality Impacts

As described above, we performed a series of air quality modeling
simulations for the continental United States to assess the impacts of
emissions adjustments to the Portland cement kiln sector. We looked at
impacts on future ambient PM2.5, total Hg deposition levels, and
visibility impairment. In this section, we present information on
current and projected levels of pollution for 2013.

This section summarizes the results of our modeling of differences in
total Hg deposition impacts in the future based on changes to the cement
kiln emissions. Specifically, we compare a 2013 reference scenario to a
2013 emissions change scenario (approximating a nationwide 90% reduction
to mercury emissions). Model results for the eastern and central United
States indicate that total Hg deposition (wet and dry forms) would be
reduced by a total of 63,518 µg/m2. A reduction of 26,047 µg/m2 is
estimated for the western United States. The reductions to total annual
Hg deposition estimated by the photochemical model show that the
reductions tend to be greatest nearest the sources. 

This section summarizes the results of our modeling of annual average
PM2.5 air quality impacts in the future due to reductions in emissions
from this sector. Specifically, we compare a 2013 reference scenario to
a 2013 control scenario. The modeling assessment indicates that a
decrease up to 0.3 µg/m3 in annual PM2.5 design values is possible
given an area’s proximity to controlled sources and the amount of
reduced sulfur dioxide emissions. The median reduction over all monitor
locations is 0.09 µg/m3. An annual PM2.5 design value is the
concentration that determines whether a monitoring site meets the annual
NAAQS for PM2.5. The full details involved in calculating an annual
PM2.5 design value are given in Appendix N of 40 CFR part 50. Projected
air quality benefits are estimated using procedures outlined by EPA
modeling guidance (U.S. EPA, 2007  XE "U.S. EPA, 2007"  ).

This section summarizes the results of our modeling of 24-hour average
PM2.5 air quality impacts in the future due to reductions in emissions
from this sector. Specifically, we compare a 2013 reference scenario to
a 2013 control scenario. The modeling assessment indicates that a
decrease up to 0.5 µg/m3 in 24-hour average PM2.5 design values at most
monitor locations in the United States is possible given an area’s
proximity to controlled sources and the amount of reduced sulfur dioxide
emissions. The median reduction over all monitor locations is 0.1
µg/m3. A 24-hour PM2.5 design value is the concentration that
determines whether a monitoring site meets the 24-hour NAAQS for PM2.5.
The full details involved in calculating a 24-hour PM2.5 design value
are given in Appendix N of 40 CFR part 50. Projected air quality
benefits are estimated using procedures outlined by EPA modeling
guidance (U.S. EPA, 2007  XE "U.S. EPA, 2007"  ). 

Air quality modeling conducted for this final rule was used to project
visibility conditions in 138 mandatory Class I federal areas across the
United States in 2013 (U.S. EPA, 2007  XE "U.S. EPA, 2007"  ). The level
of visibility impairment in an area is based on the light-extinction
coefficient and a unitless visibility index, called a “deciview,”
that is used in the valuation of visibility. The deciview metric
provides a scale for perceived visual changes over the entire range of
conditions, from clear to hazy. Under many scenic conditions, the
average person can generally perceive a change of one deciview. Higher
deciview values are indicative of worse visibility. Thus, an improvement
in visibility is a decrease in deciview value. The modeling assessment
indicates that a decrease up to 0.31 deciviews in annual 20% worst
visibility days is possible given an area’s proximity to controlled
sources and the amount of reduced sulfur dioxide emissions. Median
reductions are 0.01 deciviews to the 20% worst days and 20% best days
over all monitor locations. 

Limitations (Uncertainties) Associated with the Air Quality Modeling

Any deficiencies with the emissions or meteorological inputs may lead to
control scenario estimates that may not fully characterize the source
contribution mix at a receptor location. This application used a
complete year of meteorology to capture the variety of meteorological
formation regimes conducive to eleveted pollution. However, it is
possible that the meteorology used for these model applications may not
represent all elevated pollution formation regimes at every individual
receptor location in the continental United States.



Benefits of Emissions Reductions

Synopsis

In this section, we provide an estimate of the monetized benefits
associated with reducing exposure to particulate matter (PM) for the
final Portland Cement NESHAP and NSPS.  The PM reductions are the result
of emission limits on PM as well as emission limits on other pollutants,
including hazardous air pollutants (HAPs) for the NESHAP and criteria
pollutants for the NSPS.  The total PM2.5 reductions are the consequence
of the technologies installed to meet these multiple limits.  These
estimates include the number of cases of avoided morbidity and premature
mortality among populations exposed to PM2.5, as well as the monetized
value of those avoided cases.  Using a 3% discount rate, we estimate the
total monetized benefits of the final Cement NESHAP and NSPS to be $7.4
billion to $18 billion in the implementation year (2013).  Using a 7%
discount rate, we estimate the total monetized benefits of the final
Cement NESHAP and NSPS to be $6.7 billion to $17 billion in the
implementation year. All estimates are in 2005$. These estimates include
the energy disbenefits associated with increased electricity usage by
the control devices.

These monetized estimates reflect EPA’s most current interpretation of
the scientific literature and several methodology updates introduced in
the proposal analysis.  In addition, these estimates incorporate an
array of improvements since the proposal, including cement
sector-specific air quality modeling data, revised
value-of-a-statistical-life (VSL), lowest measure level (LML)
assessment, qualitative benefits for ecosystems and HAPs, and mercury
deposition maps.  Higher or lower estimates of benefits are possible
using other assumptions; examples of this are provided in Figure 6-1. 
Data, resource, and methodological limitations prevented EPA from
monetizing the benefits from several important benefit categories,
including benefits from reducing hazardous air pollutants, ecosystem
effects, and visibility impairment.  The benefits from reducing other
air pollutants have not been monetized in this analysis, including
reducing 4,400 tons of NOx, 5,800 tons of HCl, 5,200 tons of organic
HAPs, and 16,400 pounds of mercury each year.

Figure 6-1.	Total Monetized PM2.5 Benefits for the Final Cement NESHAP
and NSPS in 2013 a

a	This graph shows the estimated benefits at discount rates of 3% and 7%
using effect coefficients derived from the Pope et al. study and the
Laden et al study, as well as 12 effect coefficients derived from
EPA’s expert elicitation on PM mortality. The results shown are not
the direct results from the studies or expert elicitation; rather, the
estimates are based in part on the concentration-response function
provided in those studies. These estimates do not include benefits from
reducing HAP emissions, but they do include the energy disbenefits. Due
to data, methodology, and resource limitations, we were unable to
monetize the benefits associated with several categories of benefits,
including exposure to HAPs, NO2, and SO2, ecosystem effects, and
visibility effects.

Calculation of PM2.5 Human Health Benefits

In addition to pollutants we cannot monetize, this rulemaking would
reduce emissions of PM2.5 and SO2.  Because SO2 is also a precursor to
PM2.5, reducing and SO2 emissions would also reduce PM2.5 formation,
human exposure, and the incidence of PM2.5-related health effects.  The
PM reductions are the result of emission limits on PM as well as
emission limits on other pollutants, including hazardous air pollutants
for the NESHAP and criteria pollutants for the NSPS.  The total PM2.5
reductions are the consequence of the technologies installed to meet
these multiple limits. 

Methodology Improvements since Proposal

This benefits analysis incorporates an array of policy and technical
improvements since the proposal RIA in 2009 (U.S. EPA, 2009a),
including:

Cement sector-specific air quality modeling data. The benefits estimates
for this final analysis are based on air quality data modeled by CAMx
that reflect the emissions from the cement sector and the reductions
anticipated as a result of this rule. This data provides a superior
representation of the geographic distribution of the emission reductions
and resulting ambient concentrations than the national average
benefit-per-ton estimates used in the proposal. For more information
regarding the modeling inputs and assumptions, please see Section 5 of
this RIA.  

Use of a revised Value of Statistical Life (VSL). The Agency continues
to update its guidance on valuing mortality risk reductions and until a
final report is available, EPA now uses a single, peer-reviewed mean VSL
estimate of $6.3 million (2000$). We discuss this issue in more detail
in Section 6.2.5. 

Lowest Measured Level (LML) assessment. Consistent with the rationale
outlined in the proposal RIA, EPA now estimates PM-related mortality
without assuming an arbitrary threshold in the concentration-response
function.  Consistent with recent scientific advice, we are replacing
the previous threshold sensitivity analysis with a new LML assessment to
highlight the uncertainty associated with benefits estimated at low air
quality levels.  We discuss this issue in more detail in Section 6.2.4
and provide the results of this LML assessment in Section 6.3.  

Qualitative benefits for ecosystems and HAPs.  Data, resource, and
methodological limitations prevented EPA from quantifying or monetizing
the benefits from several important benefit categories, including
benefits from reducing toxic air pollutant emissions, ecosystem effects,
and visibility impairment.  Instead, we provide a qualitative
description of the benefits anticipated as a result of the emission
reductions from this rule.  These unquantified benefits are described in
Section 6.5.

Mercury deposition. The air quality modeling data provide an estimate of
the reduction in mercury deposition associated with the mercury emission
reductions anticipated as a result of this rule.  We provide maps of the
reduced mercury deposition in Section 6.3.2.1.  Due to time and resource
limitations, we were unable to model mercury methylation,
bioaccumulation in fish tissue, and human consumption of
mercury-contaminated fish that would be needed in order to estimate the
human health benefits from reducing mercury emissions.

Benefits Analysis Approach

We follow a “damage-function” approach in calculating total benefits
of the modeled changes in environmental quality.  This approach
estimates changes in individual health and welfare endpoints and assigns
values to those changes assuming independence of the individual values. 
Total benefits are calculated simply as the sum of the values for all
non-overlapping health and welfare endpoints. The “damage-function”
approach is the standard method for assessing costs and benefits of
environmental quality programs and has been used in several recent
published analyses (Levy et al., 2009; Hubbell et al., 2009; Tagaris et
al., 2009).

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.  For changes in PM, a health impact
analysis (HIA) must first be conducted to convert air quality changes
into effects that can be assigned dollar values.  For this RIA, the
health impacts analysis is limited to those health effects that are
directly linked to ambient levels of air pollution and specifically to
those linked to PM.  We also provide qualitative discussions of the
impact of changes in other environmental and ecological effects,
including the benefits associated with decreasing deposition of sulfur
to terrestrial and aquatic ecosystems, but we are unable to place an
economic value on these changes due to time and resource limitations.

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
Kunzli et al. (2001) and other recent health impact analyses, our
estimates are based on the best available methods of benefits transfer. 
Benefits transfer is the science and art of adapting primary research
from similar contexts to obtain the most accurate measure of benefits
for the environmental quality change under analysis.  Adjustments are
made for the level of environmental quality change, the
socio-demographic and economic characteristics of the affected
population, and other factors to improve the accuracy and robustness of
benefits estimates.

Health Impact Analysis (HIA)

The HIA quantifies the changes in the incidence of adverse health
impacts resulting from changes in human exposure to PM2.5 air quality.
HIAs are a well-established approach for estimating the retrospective or
prospective change in adverse health impacts resulting from
population-level changes in exposure to pollutants (Levy et al. 2009).
Analysts have applied the HIA approach to estimate human health impacts
resulting from hypothetical changes in pollutant levels (Hubbell et al.
2005; Davidson et al. 2007, Tagaris et al. 2009).  For this analysis, we
used the environmental Benefits Mapping and Analysis Program (BenMAP),
which is a PC-based tool that can systematize health impact analyses by
applying a database of key input parameters, including health impact
functions and population projections.

The HIA approach used in this analysis involves three basic steps: (1)
utilizing CAMx-generated projections of PM2.5 air quality and estimating
the change in the spatial distribution of the ambient air quality; (2)
determining the subsequent change in population-level exposure; (3)
calculating health impacts by applying concentration-response
relationships drawn from the epidemiological literature (Hubbell et al.
2009) to this change in population exposure. 

A typical health impact function might look as follows:

where y0 is the baseline incidence rate for the health endpoint being
quantified (for example, a health impact function quantifying changes in
mortality would use the baseline, or background, mortality rate for the
given population of interest); Pop is the population affected by the
change in air quality; (x is the change in air quality; and β is the
effect coefficient drawn from the epidemiological study. For this
analysis, we systematize the HIA calculation process using BenMAP’s
library of existing air quality monitoring data, population data and
health impact functions.  Figure 6-2 provides a simplified overview of
this approach, and Figure 6-3 identifies the data inputs and outputs for
the BenMAP model.  

Figure 6-2. Illustration of BenMAP Approach

 

Figure 6-3. Data inputs and outputs for the BenMAP model

The benefits estimates in this analysis were derived using modified
versions of the health impact functions used in the PM NAAQS Regulatory
Impact Analysis (RIA) (U.S. EPA, 2006).  While many of the functions are
identical to those used in the PM NAAQS RIA, we have updated a few of
the underlying assumptions over the last few years.  For a detailed
description of the underlying functions, studies, baseline incidence
rates, and population data used in this analysis, please refer to
Chapter 5 of the recently proposed Transport Rule (U.S. EPA, 2010a). 
Table 6-1 identifies which human health and welfare endpoints are
included in the monetized benefits and which endpoints are unquantified.
 In summary, the monetized PM benefits include premature mortality and
11 morbidity endpoints.  

Table 6-1.	Human Health and Welfare Effects of Pollutants Affected 

Pollutant/ Effect	Quantified and monetized in primary estimate
Unquantified

PM: healtha	Premature mortality based on cohort study estimatesb	Low
birth weight

	Premature mortality based on expert elicitation estimates	Pulmonary
function

	Hospital admissions: respiratory and cardiovascular	Chronic respiratory
diseases other than chronic bronchitis

	Emergency room visits for asthma	Non-asthma respiratory emergency room
visits

	Nonfatal heart attacks (myocardial infarctions)	UVb exposure (+/-)c

	Lower and upper respiratory illness



Minor restricted activity days



Work loss days



Asthma exacerbations (among asthmatic populations



Respiratory symptoms (among asthmatic populations)



Infant mortality

	PM: welfare

Visibility in Class I areas in SE, SW, and CA regions



Visibility in residential areas

Visibility in non-class I areas and class 1 areas in NW, NE, and Central
regions



UVb exposure (+/-)c

Global climate impactsc

SO2: health

Respiratory hospital admissions



Asthma emergency room visits



Asthma exacerbation



Acute respiratory symptoms



Premature mortality



Pulmonary function

SOX: welfare

Commercial fishing and forestry from acidic deposition effects



Recreation in terrestrial and aquatic ecosystems from acid deposition
effects



Increased mercury methylation

Mercury: health

Incidence of neurological disorders



Incidence of learning disabilities



Incidences in developmental delays



Potential cardiovascular effects including:

--Altered blood pressure regulation

--Increased heart rate variability

--Incidences of Myocardial infarction

Potential reproductive effects

Mercury: welfare

Impact on birds and mammals (e.g. reproductive effects)



Impacts to commercial, subsistence and recreational fishing

a	In addition to primary economic endpoints, there are a number of
biological responses that have been associated with PM health effects
including morphological changes and altered host defense mechanisms. 
The public health impact of these biological responses may be partly
represented by our quantified endpoints.

b	Cohort estimates are designed to examine the effects of long term
exposures to ambient pollution, but relative risk estimates may also
incorporate some effects due to shorter term exposures (see Kunzli et
al., 2001 xe "Kunzli, 2001"  for a discussion of this issue). While some
of the effects of short term exposure are likely to be captured by the
cohort estimates, there may be additional premature mortality from short
term PM exposure not captured in the cohort estimates included in the
primary analysis.

c	May result in benefits or disbenefits.

Estimating PM2.5-related premature mortality

Consistent with the proposal RIA for this rule (U.S. EPA, 2009a  XE
"U.S. EPA, 2009a"  ), the PM2.5 benefits estimates utilize the
concentration-response functions as reported in the epidemiology
literature, as well as the 12 functions obtained in EPA’s expert
elicitation study as a characterization of uncertainty. 

One estimate is based on the concentration-response (C-R) function
developed from the extended analysis of American Cancer Society (ACS)
cohort, as reported in Pope et al. (2002) xe "Pope et al. (2002)" , a
study that EPA has previously used to generate its primary benefits
estimate.  When calculating the estimate, EPA applied the effect
coefficient as reported in the study without an adjustment for assumed
concentration threshold of 10 µg/m3 as was done in recent (2006-2009)
Office of Air and Radiation RIAs.

One estimate is based on the C-R function developed from the extended
analysis of the Harvard Six Cities cohort, as reported by Laden et al.
(2006).  This study, published after the completion of the Staff Paper
for the 2006 PM2.5 NAAQS, has been used as an alternative estimate in
the PM2.5 NAAQS RIA and PM2.5 benefits estimates in RIAs completed since
the PM2.5 NAAQS.  When calculating the estimate, EPA applied the effect
coefficient as reported in the study without an adjustment for assumed
concentration threshold of 10 µg/m3 as was done in recent (2006-2009)
RIAs. 

Twelve estimates are based on the C-R functions from EPA’s expert
elicitation study (IEc, 2006; Roman et al., 2008) on the PM2.5
-mortality relationship and interpreted for benefits analysis in EPA’s
final RIA for the PM2.5 NAAQS.  For that study, twelve experts (labeled
A through L) provided independent estimates of the PM2.5 -mortality
concentration-response function.  EPA practice has been to develop
independent estimates of PM2.5 -mortality estimates corresponding to the
concentration-response function provided by each of the twelve experts,
to better characterize the degree of variability in the expert
responses.

The effect coefficients are drawn from epidemiology studies examining
two large population cohorts: the American Cancer Society cohort (Pope
et al., 2002  XE "Pope et al., 2002"  ) and the Harvard Six Cities
cohort (Laden et al., 2006  XE "Laden et al., 2006"  ). These are
logical choices for anchor points in our presentation because, while
both studies are well designed and peer reviewed, there are strengths
and weaknesses inherent in each, which we believe argues for using both
studies to generate benefits estimates. Previously, EPA had calculated
benefits based on these two empirical studies, but derived the range of
benefits, including the minimum and maximum results, from an expert
elicitation of the relationship between exposure to PM2.5 and premature
mortality (Roman et al., 2006  XE "Roman et al., 2008"  ).  Within this
assessment, we include the benefits estimates derived from the
concentration-response function provided by each of the twelve experts
to better characterize the uncertainty in the concentration-response
function for mortality and the degree of variability in the expert
responses. Because the experts used these cohort studies to inform their
concentration-response functions, benefits estimates using these
functions generally fall between results using these epidemiology
studies (see Figure 6-1). In general, the expert elicitation results
support the conclusion that the benefits of PM2.5 control are very
likely to be substantial.

EPA strives to use the best available science to support our benefits
analyses, and we recognize that interpretation of the science regarding
air pollution and health is dynamic and evolving. This analysis
continues to use the updated assumptions first applied in the proposal
RIA for this rule (U.S. EPA, 2009a  XE "U.S. EPA, 2009a"  ), including
the updated population dataset in BenMAP 3.0 and the functions directly
from the epidemiology studies without an adjustment for an assumed
threshold.  Removing the threshold assumption is a key difference
between the method used in this analysis of PM benefits and the methods
used in RIAs prior to the proposal RIA for this rule, and we now
calculate incremental benefits down to the lowest modeled PM2.5 air
quality levels.    Prior to the proposal RIA for this rule, EPA
presented the results using an assumed threshold at 10 µg/m3 in the
PM-mortality health impact function as the primary PM-related benefits
results.  Using a threshold of 10 µg/m3 was an arbitrary choice, and we
could have assumed thresholds at other points in the lower end of the
observed range the analysis. Since the proposal RIA for this rule, EPA
included a sensitivity analysis with an assumed threshold at 10 µg/m3
to illustrate that the fraction of benefits that occur at lower air
pollution concentration levels are inherently more uncertain.  

In the proposal RIA for this rule, EPA solicited comment on the use of
the no-threshold model for benefits analysis within the preamble. Based
on our review of the public comments as well as the current body of
scientific literature, EPA now estimates PM-related mortality without
applying an assumed concentration threshold.  EPA’s Integrated Science
Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently
reviewed by EPA’s Clean Air Scientific Advisory Committee (U.S.
EPA-SAB, 2009a; U.S. EPA-SAB, 2009b), concluded that the scientific
literature consistently finds that a no-threshold log-linear model most
adequately portrays the PM-mortality concentration-response relationship
while recognizing potential uncertainty about the exact shape of the
concentration-response function. Since then, the Health Effects
Subcommittee (U.S. EPA-SAB, 2010) of EPA’s Council concluded, “The
HES fully supports EPA’s decision to use a no-threshold model to
estimate mortality reductions. This decision is supported by the data,
which are quite consistent in showing effects down to the lowest
measured levels. Analyses of cohorts using data from more recent years,
during which time PM concentrations have fallen, continue to report
strong associations with mortality. Therefore, there is no evidence to
support a truncation of the CRF.”  In conjunction with the underlying
scientific literature, this document provided a basis for reconsidering
the application of thresholds in PM2.5 concentration-response functions
used in EPA’s RIAs.  For a summary of these scientific review
statements and the panel members please consult the Technical Support
Document (TSD) entitled Summary of Expert Opinions on the Existence of a
Threshold in the Concentration-Response Function for PM-related
Mortality (U.S. EPA, 2010b), which is provided in Appendix D of this
RIA.

Consistent with recent scientific advice, we are replacing the previous
threshold sensitivity analysis with a new “Lowest Measured Level”
(LML) assessment.  This approach summarizes the distribution of avoided
PM mortality impacts according to the baseline PM2.5 levels experienced
by the population receiving the PM2.5 mortality benefit. In the results
section, we identify on the figures the lowest air quality levels
measured in each of the primary cohort studies that estimate PM-related
mortality. This information allows readers to determine the portion of
PM-related mortality benefits occurring above or below the LML of each
study; in general, our confidence in the estimated PM mortality
decreases as we consider air quality levels further below the LML in the
two epidemiological studies. 

While an LML assessment provides some insight into the level of
uncertainty in the estimated PM mortality benefits, EPA does not view
the LML as a threshold and continues to quantify PM-related mortality
impacts using a full range of modeled air quality concentrations. 
Unlike an assumed threshold, which is a modeling assumption that reduces
the magnitude of the estimated health impacts, the LML is a
characterization of the fraction of benefits that are more uncertain. 
It is important to emphasize that just because we have greater
confidence in the benefits above the LML, this does not mean that we
have no confidence that benefits occur below the LML.    

Analyses of these cohorts using data from more recent years, during
which time PM concentrations have fallen, continue to report strong
associations with mortality.  As we model mortality impacts among
populations exposed to levels of PM2.5 that are successively lower than
the LML of each study, our confidence in the results diminishes. As air
pollution emissions continue to decrease over time, there will be more
people in areas where we do not have published epidemiology studies. 
However, each successive cohort study has shown evidence of effects at
successively lower levels of PM2.5.  As more large cohort studies follow
populations over time, we will likely have more studies with lower LML
as air quality levels continue to improve.  Even in the absence of a
definable threshold, we have more confidence in the benefits estimates
above the LML of the large cohort studies.  To account for the
uncertainty in each of the studies that we base our mortality estimates
on, we provide the LML for each of the cohort studies.  However, the
finding of effects at the lowest LML from recent studies indicate that
confidence in PM2.5-related mortality effects down to at least 7.5
µg/m3 is high.  

For these rules the SO2 reductions represent a large fraction of the
total benefits from reducing PM2.5 , but it is not possible to isolate
the portion if the total benefits attributable to the emission
reductions of SO2 resulting from the application of HCl controls.  The
benefits models assume that all fine particles, regardless of their
chemical composition, are equally potent in causing premature mortality
because there is no clear scientific evidence that would support the
development of differential effects estimates by particle type.

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. Please refer
to Table 5-11 in the recently proposed Transport Rule (U.S. EPA, 2010a)
for a detailed description of the underlying valuation functions and the
monetized unit values for each endpoint incorporated into this analysis.
 The monetized mortality benefits dominate the total benefits estimates.
 

As is the nature of RIAs, the assumptions and methods used to estimate
air quality benefits evolve over time to reflect the Agency’s most
current interpretation of the scientific and economic literature. For a
period of time (2004–2006), the Office of Air and Radiation (OAR)
valued mortality risk reductions using a value-of-a-statistical-life
(VSL) estimate derived from a limited analysis of some of the available
studies. OAR arrived at a VSL using a range of $1 million to $10 million
(2000$) consistent with two meta-analyses of the wage-risk literature.
The $1 million value represented the lower end of the interquartile
range from the Mrozek and Taylor (2002)  XE "Mrozek and Taylor (2002)"  
meta-analysis of 33 studies. The $10 million value represented the upper
end of the interquartile range from the Viscusi and Aldy (2003)  XE
"Viscusi and Aldy (2003)"   meta-analysis of 43 studies. The mean
estimate of $5.5 million (2000$) was also consistent with the mean VSL
of $5.4 million estimated in the Kochi et al. (2006)  XE "Kochi et al.
(2006)"   meta-analysis. However, the Agency neither changed its
official guidance on the use of VSL in rule-makings nor subjected the
interim estimate to a scientific peer-review process through the Science
Advisory Board (SAB) or other peer-review group. 

During this time, the Agency continued work to update its guidance on
valuing mortality risk reductions, including commissioning a report from
meta-analytic experts to evaluate methodological questions raised by EPA
and the SAB on combining estimates from the various data sources. In
addition, the Agency consulted several times with the Science Advisory
Board Environmental Economics Advisory Committee (SAB-EEAC) on the
issue. With input from the meta-analytic experts, the SAB-EEAC advised
the Agency to update its guidance using specific, appropriate
meta-analytic techniques to combine estimates from unique data sources
and different studies, including those using different methodologies
(i.e., wage-risk and stated preference) (U.S. EPA-SAB, 2007  XE "U.S.
EPA-SAB, 2007"  ). 

Until updated guidance is available, the Agency determined that a
single, peer-reviewed estimate applied consistently best reflects the
SAB-EEAC advice it has received. Therefore, the Agency has decided to
apply the VSL that was vetted and endorsed by the SAB in the Guidelines
for Preparing Economic Analyses (U.S. EPA, 2000  XE "U.S. EPA, 2000"  )
while the Agency continues its efforts to update its guidance on this
issue. This approach calculates a mean value across VSL estimates
derived from 26 labor market and contingent valuation studies published
between 1974 and 1991. The mean VSL across these studies is $6.3 million
(2000$). The Agency is committed to using scientifically sound,
appropriately reviewed evidence in valuing mortality risk reductions and
has made significant progress in responding to the SAB-EEAC’s specific
recommendations. 

Health Benefits Results

Table 6-2 provides a summary of the monetized PM2.5 benefits for the
final Portland Cement NESHAP and NSPS using the anchor points of Pope et
al. and Laden et al. as well as the results from the expert elicitation
on PM mortality at discount rates of 3% and 7%. Table 6-3 provides a
summary of the reductions in health incidences as a result of the
pollution reductions for the final Portland Cement NESHAP and NSPS.
Table 6-4 compares the monetized PM2.5 benefits attributable to the
final NSPS only, the final NESHAP only, and the more stringent NSPS and
final NESHAP.  Figure 6-4 illustrates the relative breakdown of the
monetized PM2.5 health benefits. Figure 6-5 provides a graphical
representation of all 14 of the PM2.5 benefits, at both a 3 percent and
7% discount rate.

The very large proportion of the avoided PM-related impacts we estimate
in this analysis occur among populations exposed at or above the lowest
LML of the cohort studies (Figures 6-6 and 6-7), increasing our
confidence in the PM mortality analysis. Figure 6-6 shows a bar chart of
the percentage of the estimated mortalities at each PM2.5 level.  Figure
6-7 shows a cumulative distribution function of the same data.  Both
figures identify the LML for each of the major cohort studies.  

Using the Pope et al. (2002) study, approximately 94% of the mortality
impacts occur among populations with baseline exposure to annual mean
PM2.5 levels at or above the LML of 7.5 µg/m3.  Using the Laden et al.
(2006) study, 40% of the mortality impacts occur at or above the LML of
10 µg/m3.  As we model mortality impacts among populations exposed to
levels of PM2.5 that are successively lower than the LML of the lowest
cohort study, our confidence in the results diminishes. However, the
analysis above confirms that the great majority of the impacts occur at
or above the lowest cohort study’s LML. It is important to emphasize
that we have high confidence in PM2.5-related effects down to the lowest
LML of the major cohort studies.  

Table 6-2.	Summary of Monetized Benefits Estimates for Final Cement
NESHAP and NSPS in 2013 (millions of 2005$)a

 	3%	7%

Based on Epidemiology Literature

Pope et al.	$7,600	$6,900

	($620--$23,000)	($560--$21,000)

Laden et al.	$19,000	$17,000

	($1,600--$55,000)	($1,500--$49,000)

Based on Expert Elicitation

Expert A	$20,000	$18,000

	($1,100--$65,000)	($1,000--$59,000)

Expert B	$15,000	$13,000

	($550--$61,000)	($500--$55,000)

Expert C	$15,000	$14,000

	($870--$57,000)	($790--$52,000)

Expert D	$11,000	$9,700

	($690--$34,000)	($620--$31,000)

Expert E	$24,000	$22,000

	($2,100--$73,000)	($1,900--$66,000)

Expert F	$14,000	$12,000

	($1,300--$41,000)	($1,200--$37,000)

Expert G	$9,000	$8,200

	($56--$33,000)	($53--$30,000)

Expert H	$11,000	$10,000

	($75--$44,000)	($71--$40,000)

Expert I	$15,000	$13,000

	($820--$50,000)	($740--$45,000)

Expert J	$12,000	$11,000

	($900--$47,000)	($810--$42,000)

Expert K	$2,900	$2,700

	($56--$19,000)	($53--$17,000)

Expert L	$10,000	$9,100

	($370--$39,000)	($330--$35,000)

a	All estimates are for the implementation year (2013), and are rounded
to two significant figures so numbers may not sum across columns. All
fine particles are assumed to have equivalent health effects. These
estimates do not include benefits from reducing HAP emissions, and they
do not include the energy disbenefits described in the next section.

Table 6-3.	Summary of Reductions in Health Incidences and Monetized
Benefits from PM2.5 Benefits for the Final Cement NESHAP and NSPS in
2013 (95th percentile confidence interval)a

Health Endpoint	Incidence	3% Discount 

(millions of 2005$)	7% Discount (millions of 2005$)

Avoided Premature Mortality



	Pope et al. (ACS cohort)	960	$7,000	$6,300

	(320--1,600)	($0,560--$21,000)	($0,500--$19,000)

Laden et al. (H6C cohort)	2,500	$18,000	$16,000

	(1,200--3,700)	($1,600--$53,000)	($1,400--$47,000)

Woodruff et al. (Infant Mortality)	4	$35	$35

	(-4--13)	(-$38--$160)	(-$38--$160)

Avoided Morbidity



	Chronic Bronchitis	650	$19	$19

	(70--1,200)	($1.1--$90)	($1.10--$90.00)

Acute Myocardial Infarction	1,500	$11	$11

	(470--2,600)	($2.0--$27)	($1.8--$26)

Hospital Admissions, Respiratory	240	$0.21	$0.21

	(100--360)	($0.10--$0.31)	($0.10--$0.31)

Hospital Admissions, Cardiovascular	500	$0.90	$0.90

	(360--590)	($0.47--$1.20)	($0.47--$1.2)

Emergency Room Visits, Respiratory	1,000	$0.03	$0.03

	(550--1,500)	($0.01--$0.04)	($0.01--$0.04)

Acute Bronchitis	1,500	$0.01	$0.01

	(-200--3,200)	($0.00--$0.02)	($0.00--$0.02)

Work Loss Days	130,000	$1.2	$1.2

	(110,000--140,000)	($1.1--$1.4)	($1.1--$1.4)

Asthma Exacerbation	17,000	$0.06	$0.06

	(1,200--52,000)	($0.00--$0.21)	($0.00--$0.21)

Minor Restricted Activity Days	750,000	$3.0	$3.0

	(620,000--880,000)	($1.6--$4.6)	($1.6--$4.6)

Lower Respiratory Symptoms	18,000	$0.02	$0.02

	(7,800--28,000)	($0.01--$0.05)	($0.01--$0.05)

Upper Respiratory Symptoms	14,000	$0.03	$0.03

	(3,400--24,000)	($0.01--$0.07)	($0.01--$0.07)

a	All estimates are for the analysis year (2013) and are rounded to
whole numbers with two significant figures. All fine particles are
assumed to have equivalent health effects. These estimates do not
include benefits from reducing HAP emissions, and they do not include
the energy disbenefits described in the next section.



Figure 6-4.	Breakdown of Monetized PM2.5 Health Benefits using Mortality
Function from Pope et al. (2002)a

a	This pie chart breakdown is illustrative, using the results based on
Pope et al. (2002)  XE "Pope et al. (2002)"   as an example. Using the
Laden et al. (2006)  XE "Laden et al. (2006)"   function for premature
mortality, the percentage of total monetized benefits due to adult
mortality would be 97%. This chart shows the breakdown using a 3%
discount rate, and the results would be similar if a 7% discount rate
was used. The monetized estimates do not include benefits from reducing
HAP emissions or NOx, and they do not include the energy disbenefits
described in the next section.

Table 6-4.	Comparison of Monetized Benefits and Emission Reductions for
Final Cement NESHAP and NSPS in 2013 (2005$)a

 	 	Final NESHAP and NSPS	Final NSPS only	Final NESHAP only	Final
NESHAP and Stringent NSPS

3%	Pope	$7,600	$510	$7,600	$7,600

	Laden	$19,000	$1,300	$19,000	$19,000

7%	Pope 	$6,900	$460	$6,900	$6,900

	Laden	$17,000	$1,100	$17,000	$17,000

Emission 

Reductions 	PM (tpy)	11,000	590	11,000	11,000

	SO2 (tpy)	124,000	9,000	124,000	124,000

	NOx (tpy)	6,600	6,600	0	11,000

	HCl (tpy)	5,900	520	5,900	5,900

	Organic HAPs (tpy)	5,200	0	5,200	5,200

	Hg (pounds)	16,400	0	16,400	16,400

a	All estimates are for the analysis year (2013) and are rounded to
whole numbers with two significant figures. All fine particles are
assumed to have equivalent health effects. The monetized estimates do
not include benefits from reducing HAP emissions or NOx, and they do not
include the energy disbenefits described in the next section.

Figure 6-5.	Total Monetized PM2.5 Benefits for the Final Cement NESHAP
and NSPS in 2013a

a	This graph shows the estimated benefits at discount rates of 3% and 7%
using effect coefficients derived from the Pope et al. study and the
Laden et al study, as well as 12 effect coefficients derived from
EPA’s expert elicitation on PM mortality. The results shown are not
the direct results from the studies or expert elicitation; rather, the
estimates are based in part on the concentration-response function
provided in those studies. These estimates do not include benefits from
reducing HAP emissions, and they do not include the energy disbenefits
described in the next section.

Figure 6-6.	Percentage of Total PM-Related Mortalities Avoided by
Baseline Air Quality Level for Final Portland Cement NESHAP and NSPS a

a	Approximately 94% of the mortality impacts occur among populations
with baseline exposure to annual mean PM2.5 levels at or above 7.5
µg/m3, which is the lowest air quality level considered in the ACS
cohort study by Pope et al. (2002).

Figure 6-7.	Cumulative Percentage of Total PM-related Mortalities
Avoided by Baseline Air Quality Level for Final Portland Cement NESHAP
and NSPS a

a	Approximately 94% of the mortality impacts occur among populations
with baseline exposure to annual mean PM2.5 levels at or above 7.5
µg/m3, which is the lowest air quality level considered in the ACS
cohort study by Pope et al. (2002).

Energy Disbenefits

Electricity usage associated with the operation of control devices is
anticipated to increase emissions of criteria pollutants from utility
boilers that supply electricity to the Portland cement facilities. We
estimate increased energy demand associated with the installation of
scrubbers, ACI systems, and RTO. The increases for kilns subject to
existing source standards are estimated to be 2,000 tpy of NOx, 1,000
tpy of CO, 3,500 tpy of SO2 and about 100 tpy of PM. For kilns subject
to new source standards increases in secondary air pollutants are
estimated to be 200 tpy of NOX, 100 tpy of CO, 400 tpy of SO2 and 10 tpy
of PM.  We also estimated increases of CO2 to be 1.1 million tpy for
kilns subject to existing source standards and 4,000 tpy for kilns
subject to new source standards.  The increase in electricity usage for
the pumps used in the SNCR system to deliver reagent to the kiln is
negligible.

6.4.1	PM2.5 Disbenefits

The additional energy usage required for the emission control devices
would increase emissions of PM, NOx, SO2.  Because NOx and SO2 are also
precursors to PM2.5, increasing these emissions would also increase
PM2.5 formation, human exposure, and the incidence of PM2.5-related
health effects.  Due to time and resource limitations, it was not
possible to provide a comprehensive estimate of the PM2.5-related
disbenefits using air quality modeling.  Instead, we used the
“benefit-per-ton” approach to estimate these disbenefits based on
the methodology described in Fann, Fulcher, and Hubbell (2009). These
PM2.5 benefit-per-ton estimates provide the total monetized human health
benefits (the sum of premature mortality and premature morbidity) of
reducing one ton of PM2.5 from a specified source. EPA has used the
benefit per-ton technique in several previous RIAs, including the
proposal for this rule (U.S. EPA, 2009a).  For this analysis, we use the
benefit-per-ton estimates associated with the EGU sector.  It is
important to note that the disbenefits associated with directly emitted
PM are overestimated in this analysis because we assume that all of the
increased PM tons are in the PM2.5 fraction.  Table 6-5 summarizes the
benefit-per-ton estimates and the monetized PM2.5 disbenefits at
discount rates of 3% and 7%.  

Table 6-5.	Summary of Monetized PM2.5 Energy Disbenefits for the Final
Portland Cement NSPS and NESHAP in 2013 (2005$)

Pollutant	Emissions Reductions (tons)	Benefit per ton (Pope, 3%)	Benefit


per ton (Laden, 3%)	Benefit per ton (Pope, 7%)	Benefit

 per ton (Laden, 7%)	Monetized PM2.5 Disbenefits (millions, 3%)
Monetized PM2.5 Disbenefits (millions, 7%)

Direct PM2.5 	110	$210,000	$510,000	$190,000	$460,000	$23	to	$56	$21	to
$50

PM2.5 Precursors











	SO2	3,900	$37,000	$91,000	$34,000	$82,000	$150	to	$360	$130	to	$320

NOX	2,200	$6,800	$17,000	$6,100	$15,000	$15	to	$36	$13	to	$33

 	 	 	 	 	Total	$180	to	$450	$170	to	$400

a	All estimates are for the implementation year (2013), and are rounded
to two significant figures so numbers may not sum across columns. All
fine particles are assumed to have equivalent health effects, but the
benefit per ton estimates vary because each ton of precursor reduced has
a different propensity to become PM2.5. The monetized disbenefits
incorporate the conversion from precursor emissions to ambient fine
particles. Confidence intervals are unavailable for this analysis
because of the benefit-per-ton methodology. The disbenefits associated
with directly emitted PM are overestimated in this analysis because we
assume that all of the increased PM tons are in the PM2.5 fraction.

6.4.2	Social Cost of Carbon and Greenhouse Gas Disbenefits

EPA has assigned a dollar value to reductions in carbon dioxide (CO2)
emissions using recent estimates of the “social cost of carbon”
(SCC).  The SCC is an estimate of the monetized damages associated with
an incremental increase in carbon emissions in a given year.  It is
intended to include (but is not limited to) changes in net agricultural
productivity, human health, property damages from increased flood risk,
and the value of ecosystem services due to climate change.  The SCC
estimates used in this analysis were developed through an interagency
process that included EPA and other executive branch entities, and
concluded in February 2010.  EPA first used these SCC estimates in the
benefits analysis for the final joint EPA/DOT Rulemaking to establish
Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate
Average Fuel Economy Standards; see the rule’s preamble for discussion
about application of SCC (75 FR 25324; 5/7/10).  The SCC Technical
Support Document (SCC TSD) provides a complete discussion of the methods
used to develop these SCC estimates.  

The interagency group selected four SCC values for use in regulatory
analyses, which we have applied in this analysis: $5, $21, $35, and $65
per metric ton of CO2 emissions in 2010, in 2007 dollars.  The first
three values are based on the average SCC from three integrated
assessment models, at discount rates of 2.5, 3, and 5 percent,
respectively.  SCCs at several discount rates are included because the
literature shows that the SCC is quite sensitive to assumptions about
the discount rate, and because no consensus exists on the appropriate
rate to use in an intergenerational context. The fourth value is the
95th percentile of the SCC from all three models at a 3 percent discount
rate.  It is included to represent higher-than-expected impacts from
temperature change further out in the tails of the SCC distribution. Low
probability, high impact events are incorporated into all of the SCC
values through explicit consideration of their effects in two of the
three models as well as the use of a probability density function for
equilibrium climate sensitivity.  Treating climate sensitivity
probabilistically results in more high temperature outcomes, which in
turn lead to higher projections of damages.

The SCC increases over time because future emissions are expected to
produce larger incremental damages as physical and economic systems
become more stressed in response to greater climatic change.  Note that
the interagency group estimated the growth rate of the SCC directly
using the three integrated assessment models rather than assuming a
constant annual growth rate. This helps to ensure that the estimates are
internally consistent with other modeling assumptions.   The SCC
estimates for the analysis years of 2013, in 2005 dollars are provided
in Table 6-6.

When attempting to assess the incremental economic impacts of carbon
dioxide emissions, the analyst faces a number of serious challenges.  A
recent report from the National Academies of Science (NRC, 2008) points
out that any assessment will suffer from uncertainty, speculation, and
lack of information about (1) future emissions of greenhouse gases, (2)
the effects of past and future emissions on the climate system, (3) the
impact of changes in climate on the physical and biological environment,
and (4) the translation of these environmental impacts into economic
damages.  As a result, any effort to quantify and monetize the harms
associated with climate change will raise serious questions of science,
economics, and ethics and should be viewed as provisional.  

The interagency group noted a number of limitations to the SCC analysis,
including the incomplete way in which the integrated assessment models
capture catastrophic and non-catastrophic impacts, their incomplete
treatment of adaptation and technological change, uncertainty in the
extrapolation of damages to high temperatures, and assumptions regarding
risk aversion.  The limited amount of research linking climate impacts
to economic damages makes the interagency modeling exercise even more
difficult.  The interagency group hopes that over time researchers and
modelers will work to fill these gaps and that the SCC estimates used
for regulatory analysis by the Federal government will continue to
evolve with improvements in modeling. Additional details on these
limitations are discussed in the SCC TSD.

In light of these limitations, the interagency group has committed to
updating the current estimates as the science and economic understanding
of climate change and its impacts on society improves over time. 
Specifically, the interagency group has set a preliminary goal of
revisiting the SCC values within two years or at such time as
substantially updated models become available, and to continue to
support research in this area. 

Applying the global SCC estimates to the estimated increases in CO2
emissions for the range of policy scenarios, we estimate the dollar
value of the climate-related disbenefits captured by the models for each
analysis year.  For internal consistency, the annual disbenefits are
discounted back to NPV terms using the same discount rate as each SCC
estimate (i.e. 5%, 3%, and 2.5%) rather than 3% and 7%. These estimates
are provided in Table 6-7.

Table 6-6.  Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for
2013 a

Discount Rate and Statistic	SCC estimate (2005$)

5%     Average	$5.0 

3%     Average	$21.5 

2.5%  Average 	$34.9 

3%     95%ile	$65.6 

a The SCC values are dollar-year and emissions-year specific. SCC values
represent only a partial accounting of climate impacts.

Table 6-7.  Monetized Disbenefits of CO2 Emission Increases in 2013 a

Discount Rate and Statistic	SCC-derived disbenefits

(millions of 2005$)

5%     Average	$5.1 

3%     Average	$22 

2.5%  Average 	$36 

3%     95%ile	$67 

a The SCC values are dollar-year and emissions-year specific. SCC values
represent only a partial accounting of climate impacts.

6.4.3	Total Monetized Disbenefits

The additional energy usage required for the emission control devices
would increase emissions of several pollutants.  In this analysis, we
were able to monetize the disbenefits associated with the increased
emissions of PM, NOX, SO2, and CO2, but we were unable to monetize the
disbenefits associated with the increased emissions of CO.  We estimate
that the total monetized disbenefits at a 3% discount rate are $210 to
$470 million.  Therefore, these disbenefits reduce the total monetized
benefits to $7.4 billion to $18 billion and $6.7 billion to $17 billion,
at discount rates of 3% and 7% respectively.

In addition, we were unable to quantify the emission increases or
monetize the disbenefits associated with “leakage” of emissions to
other counties.  This benefits analysis only incorporates the domestic
emission changes, but this regulation could lead to increased imports
and production in other countries.  For this analysis, because we do not
have sufficient information on origin of these imports, the specific
location of the additional emissions, or the level of control on those
facilities, we are unable to estimate the potential disbenefits
associated with increased emissions in other countries that might occur
as a result of this regulation.  However, the monetized benefits
estimates do not account for the decrease in domestic emissions
associated with the decrease in domestic production and transportation. 
The economic analysis estimates that domestic production would decrease
by 10 million tons, but imports would increase by only 3 million tons. 
The net effect on global pollutants like CO2 and mercury is difficult to
determine because it depends on many factors, and quantifying the
benefits associated with either omission is beyond the scope of this
analysis. 

Unquantified or Nonmonetized Benefits

The monetized benefits estimated in this RIA only reflect the portion of
benefits attributable to the health impacts associated with exposure to
ambient fine particles. Data, resource, and methodological limitations
prevented EPA from quantifying or monetizing the benefits from several
important benefit categories, including benefits from reducing toxic
emissions, ecosystem effects, and visibility impairment. The health
benefits from reducing hazardous air pollutants (HAPs) have not been
monetized in this analysis. In addition to being a PM2.5 precursor, SO2
emissions also contribute to adverse effects from acidic deposition in
aquatic and terrestrial ecosystems, increase mercury methylation, as
well as visibility impairment.

Other SO2 and PM Benefits

In addition to being a precursor to PM2.5, SO2 emissions are also
associated with a variety of respiratory health effects. Unfortunately,
we were unable to estimate the health benefits associated with reduced
SO2 exposure in this analysis because we do not have air quality
modeling data available. Without knowing the location of the emission
reductions and the resulting ambient concentrations, we were unable to
estimate the exposure to SO2 for nearby populations. Therefore, this
analysis only quantifies and monetizes the PM2.5 benefits associated
with the reductions in SO2 emissions.

Following an extensive evaluation of health evidence from epidemiologic
and laboratory studies, the Integrated Science Assessment (ISA) for
Sulfur Dioxide concluded that there is a causal relationship between
respiratory health effects and short-term exposure to SO2 (U.S. EPA,
2008b). According to summary of the ISA in EPA’s risk and exposure
assessment (REA) for the SO2 NAAQS“the immediate effect of SO2 on the
respiratory system in humans is bronchoconstriction” (U.S. EPA,
2009c). In addition, the REA summarized from the ISA that “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 at concentrations between 20 and 100
ppb, both in terms of increasing severity of effect and percentage of
asthmatics adversely affected (U.S. EPA, 2009c).  Based on our review of
this information, we identified four 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. 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, there was a lack of robustness of
the observed associations to adjustment for pollutants.

SO2 emissions also contribute to adverse welfare effects from acidic
deposition, visibility impairment, and enhanced mercury methylation.
Deposition of sulfur causes acidification, which can cause a loss of
biodiversity of fishes, zooplankton, and macro invertebrates in aquatic
ecosystems, as well as a decline in sensitive tree species, such as red
spruce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial
ecosystems. In the northeastern United States, the surface waters
affected by acidification are a source of food for some recreational and
subsistence fishermen and for other consumers and support several
cultural services, including aesthetic and educational services and
recreational fishing. Biological effects of acidification in terrestrial
ecosystems are generally linked to aluminum toxicity, which can cause
reduced root growth, which restricts the ability of the plant to take up
water and nutrients. These direct effects can, in turn, increase the
sensitivity of these plants to stresses, such as droughts, cold
temperatures, insect pests, and disease leading to increased mortality
of canopy trees. Terrestrial acidification affects several important
ecological services, including declines in habitat for threatened and
endangered species (cultural), declines in forest aesthetics (cultural),
declines in forest productivity (provisioning), and increases in forest
soil erosion and reductions in water retention (cultural and
regulating). (U.S. EPA, 2008c)

Reducing SO2 and PM emissions would improve the level of visibility
throughout the United States. Fine particles with significant
light-extinction efficiencies include sulfates, nitrates, organic
carbon, elemental carbon, and soil (Sisler, 1996). These suspended
particles and gases degrade visibility by scattering and absorbing
light. Higher visibility impairment levels in the East are due to
generally higher concentrations of fine particles, particularly
sulfates, and higher average relative humidity levels. In fact,
particulate sulfate is the largest contributor to regional haze in the
eastern U.S. (i.e., 40% or more annually and 75% during summer). In the
western U.S., particulate sulfate contributes to 20-50% of regional haze
(U.S. EPA, 2009b). 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.  Due to time and
resource limitations, we were unable to estimate the monetized benefits
associated with visibility improvements.  Previous analyses (U.S. EPA,
2006; U.S. EPA, 2010c) show that visibility benefits are a significant
welfare benefit category.

HAP Benefits

Americans are exposed to ambient concentrations of air toxics at levels
which have the potential to cause adverse health effects. The levels of
air toxics to which people are exposed vary depending on where people
live and work and the kinds of activities in which they engage. In order
to identify and prioritize air toxics, emission source types and
locations which are of greatest potential concern, U.S. EPA conducts the
National-Scale Air Toxics Assessment (NATA). The most recent NATA was
conducted for calendar year 2002, and was released in June 2009. NATA
for 2002 includes four steps:

1) Compiling a national emissions inventory of air toxics emissions from
outdoor sources

2) Estimating ambient concentrations of air toxics across the United
States

3) Estimating population exposures across the United States

4) Characterizing potential public health risk due to inhalation of air
toxics including both cancer and noncancer effects

Noncancer health effects can result from chronic, subchronic, or acute
inhalation exposures to air toxics, and include neurological,
cardiovascular, liver, kidney, and respiratory effects as well as
effects on the immune and reproductive systems. According to the 2002
NATA, nearly the entire U.S. population was exposed to an average
concentration of air toxics that has the potential for adverse noncancer
respiratory health effects.  Figures 6-8 and 6-9 depict estimated
county-level carcinogenic risk and noncancer respiratory hazard from the
assessment. The respiratory hazard is dominated by a single pollutant,
acrolein.

This rule is anticipated to reduce 16,400 pounds of mercury, 5,800 tons
of HCl, and 5,200 tons of organic HAPs each year.  Due to data,
resource, and methodology limitations, we were unable to estimate the
benefits associated with the thousands tons of hazardous air pollutants
that would be reduced as a result of this rule. Available emissions data
show that several different HAPs are emitted from Portland cement
manufacturing plants, either released from kilns systems, raw material
dryers, clinker coolers, raw mills, finish mills, storage bins,
conveying system transfer points, bagging systems, or bulk loading and
unloading systems.

Figure 6-8.	Estimated County Level Carcinogenic Risk from HAP exposure
from outdoor sources (NATA, 2002)

Figure 6-9.	Estimated County Level Noncancer (Respiratory) Risk from HAP
exposure from outdoor sources (NATA, 2002)

Mercury

Mercury is a highly neurotoxic contaminant that enters the food web as a
methylated compound, methylmercury (U.S. EPA, 2008c). The contaminant is
concentrated in higher trophic levels, including fish eaten by humans.
Experimental evidence has established that only inconsequential amounts
of methylmercury can be produced in the absence of sulfate (U.S. EPA,
2008c). Current evidence indicates that in watersheds where mercury is
present, increased sulfate deposition very likely results in
methylmercury accumulation in fish (Drevnick et al., 2007; Munthe et al,
2007). The SO2 ISA concluded that evidence is sufficient to infer a
casual relationship between sulfur deposition and increased mercury
methylation in wetlands and aquatic environments (U.S. EPA, 2008c).

In addition to the role of sulfate deposition on methylation, these
rules would also reduce mercury emissions. Mercury is emitted to the air
from various man-made and natural sources. These emissions transport
through the atmosphere and eventually deposit to land or water bodies.
This deposition can occur locally, regionally, or globally, depending on
the form of mercury emitted and other factors such as the weather. The
form of mercury emitted varies depending on the source type and other
factors. Available data indicate that the mercury emissions from these
sources are a mixture of gaseous elemental mercury (35%), inorganic
divalent mercury (reactive gas phase mercury) (58%), and particulate
bound mercury (7%) (U.S. EPA, 2010c). Gaseous elemental mercury can be
transported very long distances, even globally, to regions far from the
emissions source (becoming part of the global “pool”) before
deposition occurs. Inorganic divalent and particulate bound mercury have
a shorter atmospheric lifetime and can deposit to land or water bodies
closer to the emissions source. Furthermore, elemental mercury in the
atmosphere can undergo transformation into divalent mercury, providing a
significant pathway for deposition of emitted elemental mercury.

Potential exposure routes to mercury emissions include both direct
inhalation and consumption of fish containing methylmercury. The primary
route of human exposure to mercury emissions from industrial sources is
generally indirectly through the consumption of fish containing
methylmercury. As described above, mercury that has been emitted to the
air eventually settles into water bodies or onto land where it can
either move directly or be leached into waterbodies. Once deposited,
certain microorganisms can change it into methylmercury, a highly toxic
form that builds up in fish, shellfish and animals that eat fish.
Consumption of fish and shellfish are the main sources of methylmercury
exposure to humans. Methylmercury builds up more in some types of fish
and shellfish than in others. The levels of methylmercury in fish and
shellfish vary widely depending on what they eat, how long they live,
and how high they are in the food chain. Most fish, including ocean
species and local freshwater fish, contain some methylmercury. For
example, in recent studies by EPA and the U.S. Geological Survey (USGS)
of fish tissues, every fish sampled contained some methylmercury
(Scudder, 2009).

The majority of fish consumed in the U.S. are ocean species. The
methylmercury concentrations in ocean fish species are primarily
influenced by the global mercury pool. However, the methylmercury found
in local fish can be due, at least partly, to mercury emissions from
local sources. Research shows that most people’s fish consumption does
not cause a mercury-related health concern. However, certain people may
be at higher risk because of their routinely high consumption of fish
(e.g., tribal and other subsistence fishers and their families who rely
heavily on fish for a substantial part of their diet). It has been
demonstrated that high levels of methylmercury in the bloodstream of
unborn babies and young children may harm the developing nervous system,
making the child less able to think and learn. Moreover, mercury
exposure at high levels can harm the brain, heart, kidneys, lungs, and
immune system of people of all ages.

Several studies suggest that the methylmercury content of fish may
reduce these cardio-protective effects of fish consumption.  Some of
these studies also suggest that methylmercury may cause adverse effects
to the cardiovascular system.  For example, the NRC (2000) review of the
literature concerning methylmercury health effects took note of two
epidemiological studies that found an association between dietary
exposure to methylmercury and adverse cardiovascular effects.  Moreover,
in a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al.
(1995) observed a relationship between methylmercury exposure via fish
consumption and acute myocardial infarction (AMI or heart attacks),
coronary heart disease, cardiovascular disease, and all-cause mortality.
 The NRC also noted a study of 917 seven year old children in the Faroe
Islands, whose initial exposure to methylmercury was in utero although
post natal exposures may have occurred as well.  At seven years of age,
these children exhibited an increase in blood pressure and a decrease in
heart rate variability. Based on these and other studies, NRC concluded
in 2000 that, while “the data base is not as extensive for
cardiovascular effects as it is for other end points (i.e. neurologic
effects) the cardiovascular system appears to be a target for
methylmercury toxicity.”  

Since publication of the NRC report there have been some 30 published
papers presenting the findings of studies that have examined the
possible cardiovascular effects of methylmercury exposure.  These
studies include epidemiological, toxicological, and toxicokinetic
investigations.  Over a dozen review papers have also been published.  
If there is a causal relationship between methylmercury exposure and
adverse cardiovascular effects, then reducing exposure to methylmercury
would result in public health benefits from reduced cardiovascular
effects.

In early 2010, EPA sponsored a workshop in which a group of experts were
asked to assess the plausibility of a causal relationship between
methylmercury exposure and cardiovascular health effects and to advise
EPA on methodologies for estimating population level cardiovascular
health impacts of reduced methylmercury exposure. The report from that
workshop is in preparation.

Portland cement manufacturing plants emitted about 16 tons of mercury in
the air in 2006 in the U.S. Based on the EPA’s National Emission
Inventory, and about 103 tons of mercury were emitted from all
anthropogenic sources in the U.S. in 2005. Moreover, the United Nations
has estimated that about 2,100 tons of mercury were emitted worldwide by
anthropogenic sources in 2005. We believe that total mercury emissions
in the U.S. and globally in 2006 were about the same magnitude in 2005.
Therefore, we estimate that in 2006, these sources emitted about 16% of
the total anthropogenic mercury emissions in the U.S. and about 0.8% of
the global emissions in 2005. 

Using 2008 inventory estimates, the mercury emissions from Portland
cement kilns only were approximately 9.1 tons.  Overall, the NESHAP and
NSPS would reduce mercury emissions by about 8.2 tons (90%) per year
from current levels, and therefore, contribute to reductions in mercury
exposures and health effects.  Due to time and resource limitations, we
were unable to model mercury methylation, bioaccumulation in fish
tissue, and human consumption of mercury-contaminated fish that would be
needed in order to estimate the human health benefits from reducing
mercury emissions.  However, we were able to model the change in mercury
deposition using CAMx for the final Portland Cement NESHAP and NSPS. 
These modeling results indicate significantly reduced total mercury
deposition (wet and dry forms), including reducing deposition by up to
30% in the West and up to 17% in the East in 2013.  This modeling
indicates that mercury deposition reductions tend to be greatest nearest
the sources. Figure 6-10 shows the change in mercury deposition as a
result of the final Portland Cement NESHAP and NSPS in the Eastern U.S.,
and Figure 6.11 shows the change in mercury deposition in the Western
U.S.

Figure 6-10.	Reductions in Total Mercury Deposition (µg/m2) in the
Eastern U.S.

Figure 6-11.	Reductions in Total Mercury Deposition (µg/m2) in the
Western U.S.

Hydrogen Chloride (HCl) 

Hydrogen chloride gas is intensely irritating to the mucous membranes of
the nose, throat, and respiratory tract. Brief exposure to 35 ppm causes
throat irritation, and levels of 50 to 100 ppm are barely tolerable for
1 hour. The greatest impact is on the upper respiratory tract; exposure
to high concentrations can rapidly lead to swelling and spasm of the
throat and suffocation. Most seriously exposed persons have immediate
onset of rapid breathing, blue coloring of the skin, and narrowing of
the bronchioles. Patients who have massive exposures may develop an
accumulation of fluid in the lungs. Exposure to hydrogen chloride can
lead to Reactive Airway Dysfunction Syndrome (RADS), a chemically- or
irritant-induced type of asthma. Children may be more vulnerable to
corrosive agents than adults because of the relatively smaller diameter
of their airways. Children may also be more vulnerable to gas exposure
because of increased minute ventilation per kg and failure to evacuate
an area promptly when exposed. Hydrogen chloride has not been classified
for carcinogenic effects. 

Toluene

Toluene is found in evaporative as well as exhaust emissions from motor
vehicles. Under the 2005 Guidelines for Carcinogen Risk Assessment,
there is inadequate information to assess the carcinogenic potential of
toluene because studies of humans chronically exposed to toluene are
inconclusive, toluene was not carcinogenic in adequate inhalation cancer
bioassays of rats and mice exposed for life, and increased incidences of
mammary cancer and leukemia were reported in a lifetime rat oral
bioassay.

The central nervous system (CNS) is the primary target for toluene
toxicity in both humans and animals for acute and chronic exposures. CNS
dysfunction (which is often reversible) and narcosis have been
frequently observed in humans acutely exposed to low or moderate levels
of toluene by inhalation; symptoms include fatigue, sleepiness,
headaches, and nausea. Central nervous system depression has been
reported to occur in chronic abusers exposed to high levels of toluene.
Symptoms include ataxia, tremors, cerebral atrophy, nystagmus
(involuntary eye movements), and impaired speech, hearing, and vision.
Chronic inhalation exposure of humans to toluene also causes irritation
of the upper respiratory tract, eye irritation, dizziness, headaches,
and difficulty with sleep.

Human studies have also reported developmental effects, such as CNS
dysfunction, attention deficits, and minor craniofacial and limb
anomalies, in the children of women who abused toluene during pregnancy.
A substantial database examining the effects of toluene in subchronic
and chronic occupationally exposed humans exists. The weight of evidence
from these studies indicates neurological effects (i.e., impaired color
vision, impaired hearing, decreased performance in neurobehavioral
analysis, changes in motor and sensory nerve conduction velocity,
headache, dizziness) as the most sensitive endpoint.

Formaldehyde

Since 1987, EPA has classified formaldehyde as a probable human
carcinogen based on evidence in humans and in rats, mice, hamsters, and
monkeys. EPA is currently reviewing recently published epidemiological
data. For instance, research conducted by the National Cancer Institute
(NCI) found an increased risk of nasopharyngeal cancer and
lymphohematopoietic malignancies such as leukemia among workers exposed
to formaldehyde., In an analysis of the lymphohematopoietic cancer
mortality from an extended follow-up of these workers, NCI confirmed an
association between lymphohematopoietic cancer risk and peak exposures.
A recent National Institute of Occupational Safety and Health (NIOSH)
study of garment workers also found increased risk of death due to
leukemia among workers exposed to formaldehyde. Extended follow-up of a
cohort of British chemical workers did not find evidence of an increase
in nasopharyngeal or lymphohematopoietic cancers, but a continuing
statistically significant excess in lung cancers was reported.

In the past 15 years there has been substantial research on the
inhalation dosimetry for formaldehyde in rodents and primates by the
CIIT Centers for Health Research (formerly the Chemical Industry
Institute of Toxicology), with a focus on use of rodent data for
refinement of the quantitative cancer dose-response assessment.,,
CIIT’s risk assessment of formaldehyde incorporated mechanistic and
dosimetric information on formaldehyde. However, it should be noted that
recent research published by EPA indicates that when two-stage modeling
assumptions are varied, resulting dose-response estimates can vary by
several orders of magnitude.,,, These findings are not supportive of
interpreting the CIIT model results as providing a conservative (health
protective) estimate of human risk. EPA research also examined the
contribution of the two-stage modeling for formaldehyde towards
characterizing the relative weights of key events in the mode-of-action
of a carcinogen. For example, the model-based inference in the published
CIIT study that formaldehyde’s direct mutagenic action is not relevant
to the compound’s tumorigenicity was found not to hold under
variations of modeling assumptions.

Based on the developments of the last decade, in 2004, the working group
of the IARC concluded that formaldehyde is carcinogenic to humans (Group
1), on the basis of sufficient evidence in humans and sufficient
evidence in experimental animals - a higher classification than previous
IARC evaluations. After reviewing the currently available
epidemiological evidence, the IARC (2006) characterized the human
evidence for formaldehyde carcinogenicity as “sufficient,” based
upon the data on nasopharyngeal cancers; the epidemiologic evidence on
leukemia was characterized as “strong.” EPA is reviewing the recent
work cited above from the NCI and NIOSH, as well as the analysis by the
CIIT Centers for Health Research and other studies, as part of a
reassessment of the human hazard and dose-response associated with
formaldehyde.

Formaldehyde exposure also causes a range of noncancer health effects,
including irritation of the eyes (burning and watering of the eyes),
nose and throat. Effects from repeated exposure in humans include
respiratory tract irritation, chronic bronchitis and nasal epithelial
lesions such as metaplasia and loss of cilia. Animal studies suggest
that formaldehyde may also cause airway inflammation – including
eosinophil infiltration into the airways. There are several studies that
suggest that formaldehyde may increase the risk of asthma –
particularly in the young.,

Dioxins (Chlorinated dibenzodioxins (CDDs) 

A number of effects have been observed in people exposed to 2,3,7,8-TCDD
levels that are at least 10 times higher than background levels. The
most obvious health effect in people exposure to relatively large
amounts of 2,3,7,8-TCDD is chloracne. Chloracne is a severe skin disease
with acne-like lesions that occur mainly on the face and upper body.
Other skin effects noted in people exposed to high doses of 2,3,7,8-TCDD
include skin rashes, discoloration, and excessive body hair. Changes in
blood and urine that may indicate liver damage also are seen in people.
Alterations in the ability of the liver to metabolize (or breakdown)
hemoglobin, lipids, sugar, and protein have been reported in people
exposed to relatively high concentrations of 2,3,7,8-TCDD. Most of the
effects are considered mild and were reversible. However, in some people
these effects may last for many years. Slight increases in the risk of
diabetes and abnormal glucose tolerance have been observed in some
studies of people exposed to 2,3,7,8-TCDD. We do not have enough
information to know if exposure to 2,3,7,8-TCDD would result in
reproductive or developmental effects in people, but animal studies
suggest that this is a potential health concern. 

In certain animal species, 2,3,7,8-TCDD is especially harmful and can
cause death after a single exposure. Exposure to lower levels can cause
a variety of effects in animals, such as weight loss, liver damage, and
disruption of the endocrine system. In many species of animals,
2,3,7,8-TCDD weakens the immune system and causes a decrease in the
system's ability to fight bacteria and viruses at relatively low levels
(approximately 10 times higher than human background body burdens). In
other animal studies, exposure to 2,3,7,8-TCDD has caused reproductive
damage and birth defects. Some animal species exposed to CDDs during
pregnancy had miscarriages and the offspring of animals exposed to
2,3,7,8-TCDD during pregnancy often had severe birth defects including
skeletal deformities, kidney defects, and weakened immune responses. In
some studies, effects were observed at body burdens 10 times higher than
human background levels. 

Furans (Chlorinated dibenzofurans (CDFs)) 

Most of the information on the adverse health effects comes from studies
in people who were accidentally exposed to food contaminated with CDFs.
The amounts that these people were exposed to were much higher than are
likely from environmental exposures or from a normal diet. Skin and eye
irritations, especially severe acne, darkened skin color, and swollen
eyelids with discharge, were the most obvious health effects of the CDF
poisoning. CDF poisoning also caused vomiting and diarrhea, anemia, more
frequent lung infections, numbness, effects on the nervous system, and
mild changes in the liver. Children born to exposed mothers had skin
irritation and more difficulty learning, but it is unknown if this
effect was permanent or caused by CDFs alone or CDFs and polychlorinated
biphenyls in combination.

Many of the same effects that occurred in people accidentally exposed
also occurred in laboratory animals that ate CDFs. Animals also had
severe weight loss, and their stomachs, livers, kidneys, and immune
systems were seriously injured. Some animals had birth defects and
testicular damage, and in severe cases, some animals died. These effects
in animals were seen when they were fed large amounts of CDFs over a
short time, or small amounts over several weeks or months. Nothing is
known about the possible health effects in animals from eating CDFs over
a lifetime.

Benzene

According to NATA for 2002, benzene is the largest contributor to cancer
risk of all 124 pollutants quantitatively assessed in the 2002 NATA. 
The EPA’s IRIS database lists benzene as a known human carcinogen
(causing leukemia) by all routes of exposure, and concludes that
exposure is associated with additional health effects, including genetic
changes in both humans and animals and increased proliferation of bone
marrow cells in mice. EPA states in its IRIS database that data indicate
a causal relationship between benzene exposure and acute lymphocytic
leukemia and suggest a relationship between benzene exposure and chronic
non-lymphocytic leukemia and chronic lymphocytic leukemia. The
International Agency for Research on Carcinogens (IARC) has determined
that benzene is a human carcinogen and the U.S. Department of Health and
Human Services (DHHS) has characterized benzene as a known human
carcinogen.   A number of adverse noncancer health effects including
blood disorders, such as preleukemia and aplastic anemia, have also been
associated with long-term exposure to benzene. The most sensitive
noncancer effect observed in humans, based on current data, is the
depression of the absolute lymphocyte count in blood. In addition,
recent work, including studies sponsored by the Health Effects Institute
(HEI), provides evidence that biochemical responses are occurring at
lower levels of benzene exposure than previously known. EPA’s IRIS
program has not yet evaluated these new data.

Other Air Toxics

In addition to the compounds described above, other compounds in gaseous
hydrocarbon and PM emissions would be affected by this rule, including
metal and organic HAPs. Information regarding the health effects of
these compounds can be found in EPA’s IRIS database.

Limitations and Uncertainties

The National Research Council (NRC) (2002) concluded that EPA’s
general methodology for calculating the benefits of reducing air
pollution is reasonable and informative in spite of inherent
uncertainties.  To address these inherent uncertainties, NRC highlighted
the need to conduct rigorous quantitative analysis of uncertainty and to
present benefits estimates to decisionmakers in ways that foster an
appropriate appreciation of their inherent uncertainty.  In response to
these comments, EPA’s Office of Air and Radiation (OAR) is developing
a comprehensive strategy for characterizing the aggregate impact of
uncertainty in key modeling elements on both health incidence and
benefits estimates.  Components of that strategy include emissions
modeling, air quality modeling, health effects incidence estimation, and
valuation. 

In this analysis, we use three methods to assess uncertainty
quantitatively: Monte Carlo analysis, alternate concentration-response
functions for PM mortality, and LML assessment.  We also provide a
qualitative assessment for those aspects that we are unable to address
quantitatively in this analysis.  Each of these analyses is described in
detail in the following sections.  

This analysis includes many data sources as inputs, including emission
inventories, air quality data from models (with their associated
parameters and inputs), population data, health effect estimates from
epidemiology studies, and economic data for monetizing benefits.  Each
of these inputs may be uncertain and would affect the benefits estimate.
 When the uncertainties from each stage of the analysis are compounded,
small uncertainties can have large effects on the total quantified
benefits.  In this analysis, we are unable to quantify the cumulative
effect of all of these uncertainties, but we provide the following
analyses to characterize many of the largest sources of uncertainty.  

Monte Carlo analysis

Similar to other recent RIAs, we used Monte Carlo methods for
characterizing random sampling error associated with the concentration
response functions and economic valuation functions.  Monte Carlo
simulation uses random sampling from distributions of parameters to
characterize the effects of uncertainty on output variables, such as
incidence of morbidity.  Specifically, we used Monte Carlo methods to
generate confidence intervals around the estimated health impact and
dollar benefits.  The reported standard errors in the epidemiological
studies determined the distributions for individual effect estimates, as
shown in Tables 6-2 and 6-3.  

Alternate concentration-response functions for PM mortality

PM2.5 mortality benefits are the largest benefit category that we
monetized in this analysis.  To better understand the
concentration-response relationship between PM2.5 exposure and premature
mortality, EPA conducted an expert elicitation in 2006 (Roman et al.,
2008 xe "Roman et al., 2008" ; IEc, 2006 xe "IEc, 2006" ).  In general,
the results of the expert elicitation support the conclusion that the
benefits of PM2.5 control are very likely to be substantial.  In
previous RIAs, EPA presented benefits estimates using concentration
response functions derived from the PM2.5 Expert Elicitation as a range
from the lowest expert value (Expert K) to the highest expert value
(Expert E).  However, this approach did not indicate the agency’s
judgment on what the best estimate of PM benefits may be, and EPA’s
Science Advisory Board described this presentation as misleading. 
Therefore, we began to present the cohort-based studies (Pope et al,
2002; and Laden et al., 2006) as our core estimates in the proposal RIA
for this rule (U.S. EPA, 2009a).  Using alternate relationships between
PM2.5 and premature mortality supplied by experts, higher and lower
benefits estimates are plausible, but most of the expert-based estimates
fall between the two epidemiology-based estimates (Roman et al., 2008 xe
"Roman et al., 2008" ).  

In this analysis, we present the results derived from the expert
elicitation as indicative of the uncertainty associated with a major
component of the health impact functions, and we provide the independent
estimates derived from each of the twelve experts to better characterize
the degree of variability in the expert responses.  In this section, we
provide the results using the concentration-response functions derived
from the expert elicitation in both tabular (Tables 6-2 and 6-3) and
graphical form (Figure 6-5).  Please note that these results are not the
direct results from the studies or expert elicitation; rather, the
estimates are based in part on the concentration-response function
provided in those studies.  

LML assessment

PM2.5 mortality benefits are the largest benefit category that we
monetized in this analysis.  To better characterize the uncertainty
associated with mortality impacts that are estimated to occur in areas
with low baseline levels of PM2.5, we included the LML assessment.  We
have more confidence in the mortality impacts among populations exposed
to levels of PM2.5 above the lowest LML of the large cohort studies, and
our confidence in the results diminish as we model that are lower than
the LML. While an LML assessment provides some insight into the level of
uncertainty in the estimated PM mortality benefits, EPA does not view
the LML as a threshold and continues to quantify PM-related mortality
impacts using a full range of modeled air quality concentrations.  It is
important to emphasize that just because we have greater confidence in
the benefits above the LML, this does not mean that we have no
confidence that benefits occur below the LML.  In section 6.3, we
provide the results of the LML assessment in Figures 6-6 and 6-7.

Qualitative assessment of uncertainty and other analysis limitations

Although we strive to incorporate as many quantitative assessments of
uncertainty, there are several aspects for which we are only able to
address qualitatively.  These aspects are important factors to consider
when evaluating the relative benefits of the attainment strategies for
each of the alternative standards: 

Above we present the estimates of the total monetized benefits, based on
our interpretation of the best available scientific literature and
methods and supported by the SAB-HES and the NAS (NRC, 2002  XE "NRC,
2002"  ). The benefits estimates are subject to a number of assumptions
and uncertainties. For example, the key assumptions underlying the
estimates for premature mortality, which typically account for at least
90% of the total monetized benefits, 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 produced via transported
precursors emitted from EGUs may differ significantly from direct PM2.5
released from diesel engines and other industrial sources, but no clear
scientific grounds exist for supporting differential effects estimates
by particle type. 

We assume that the health impact function for fine particles is 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 fine particle standard and those that do not meet
the standard down to the lowest modeled concentrations. 

To characterize the uncertainty in the relationship between PM2.5 and
premature mortality (which typically accounts for 85% to 95% of total
monetized benefits), we include a set of twelve estimates based on
results of the expert elicitation study in addition to our core
estimates. Even these multiple characterizations omit the uncertainty in
air quality estimates, 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 PM2.5 estimates.
This information should be interpreted within the context of the larger
uncertainty surrounding the entire analysis. For more information on the
uncertainties associated with PM2.5 benefits, please consult the PM2.5
NAAQS RIA (Table 5.5).

In addition, there is some uncertainty associated with the specificity
of the air quality inputs to benefits model for this particular
regulatory scenario.  By assuming that each kiln proportionately reduces
their emissions by the same percentage as the national percentage
reduction, we may be slightly under or overestimating the air quality
impacts at specific locations and the associated monetized benefits.  By
including the hazardous waste kilns in the emissions inventory, we may
be slightly overestimating the air quality impacts and monetized
benefits.  By omitting the decrease in domestic cement production and
transportation, we are underestimating the air quality impacts and
monetized benefits.  By omitting the increase in cement imports, we may
be overestimating the monetized benefits by not accounting for
additional global pollutants.  By using national average benefit-per-ton
estimates to calculate the energy disbenefits, we may be under or
overestimating these monetized disbenefits.  Despite our inability to
fully characterize and quantify these relatively small effects, we
believe that, on net, the air quality impacts and associated monetized
benefits are representative of the magnitude of benefits anticipated
from this regulation.  

As previously described, we strive to monetize as many of the benefits
anticipated from this rule as possible, but the monetized benefits
estimated in this RIA inevitably only reflect the portion of benefits. 
Specifically, only the benefits attributable to the health impacts
associated with exposure to ambient fine particles have been monetized
in this analysis. Data, resource, and methodological limitations
prevented EPA from quantifying or monetizing the benefits from several
important benefit categories, including benefits from reducing toxic
emissions, ecosystem effects, and visibility impairment. Data
limitations include limited monitoring for HAPs, incomplete emissions
inventories for HAPs, and limited photochemical air quality modeling for
non-mercury HAPs.  Resource limitations include limited staff and
extramural funding in conjunction with a heavy regulatory workload. 
Methodological limitations include an absence of concentration-response
functions for many HAP health effects, with issues such as exposure
misclassification, small number of cases, confounding, and extrapolation
of toxicological effects down to ambient levels (IEc, 2008).  Despite
our inability to monetize all of the benefit categories, the monetized
benefits still exceed the costs by a substantial margin.  

This RIA does not include the type of detailed uncertainty assessment
found in the PM NAAQS RIA.  However, the results of the Monte Carlo
analyses of the health and welfare benefits presented in Chapter 5 of
the PM RIA can provide some evidence of the uncertainty surrounding the
benefits results presented in this analysis.

Comparison of Benefits and Costs

Using a 3% discount rate, we estimate the total monetized benefits of
the final Portland Cement NESHAP and NSPS to be $7.4 billion to $18
billion in the implementation year (2013).  Using a 7% discount rate, we
estimate the total monetized benefits of the final Portland Cement
NESHAP and NSPS to be $6.7 billion to $16 billion.  These estimates
include the energy disbenefits associated with increased electricity
usage by the control devices.  The annualized social costs of the final
NESHAP and NSPS are $880 million.  Thus, net benefits are $6.5 billion
to $17 billion at a 3% discount rate for the benefits and $5.8 billion
to $16 billion at a 7% discount rate.  In addition, the benefits from
reducing 16,400 pounds of mercury, 4,400 tons of NOx, 5,800 tons of HCl,
and 5,200 tons of organic HAPs each year have not been included in these
estimates. All estimates are in 2005$.  

Table 6-5 shows a summary of the monetized benefits, social costs, and
net benefits for the final Portland Cement NESHAP and NSPS, the final
NSPS only, the final NESHAP only, and the more stringent NSPS and final
NESHAP.  Figures 6-12 and 6-13 show the full range of net benefits
estimates (i.e., annual benefits minus annualized costs) utilizing the
14 different PM2.5 mortality functions at discount rates of 3% and 7%. 
Data, resource, and methodological limitations prevented EPA from
monetizing the benefits from several important benefit categories,
including benefits from reducing hazardous air pollutants, ecosystem
effects, and visibility impairment.  EPA believes that the benefits are
likely to exceed the costs under this rulemaking even when taking into
account uncertainties in the cost and benefit estimates.

Table 6-8.	Summary of the Monetized Benefits, Social Costs, and Net
Benefits for the final Portland Cement NESHAP in 2013 (millions of
2005$)1

Final NESHAP and NSPS 

	3% Discount Rate	7% Discount Rate

Total Monetized Benefits2	$7,400	to	$18,000	$6,700	to	$16,000

Total Social Costs3	   $926           to                 $950 	   $926  
        to                 $950

Net Benefits	$6,500	to	$17,000	$5,800	to	$16,000

Non-monetized Benefitsd	4,400 tons of NOx (includes energy disbenefits)

	5,200 tons of organic HAPs

	5,900 tons of HCl

	16,400 pounds of mercury 

	Health effects from HAPs, NO2, and SO2 exposure

	Ecosystem effects

	Visibility impairment

Final NSPS only

 	3% Discount Rate	7% Discount Rate

Total Monetized Benefits2	$510	to	$1,300	$460	to	$1,100

Total Social Costs3	$72	$40

Net Benefits	$470	to	$1,300	$420	to	$1,100

Non-monetized Benefitsd	6,600 tons of NOx

	520 tons of HCl

	Health effects from HAPs, NO2, and SO2 exposure

	Ecosystem effects

	Visibility impairment

Final NESHAP only

	3% Discount Rate	7% Discount Rate

Total Monetized Benefits2	$7,400	to	$18,000	$6,700	to	$16,000

Total Social Costs3	   $904           to                 $930	   $904   
       to                 $930

Net Benefits	$6,500	to	$17,000	$5,800	to	$16,000

Non-monetized Benefitsd	5,200 tons of organic HAPs

	5,900 tons of HCl

	16,400 pounds of mercury 

	Health effects from HAPs, SO2 exposure

	Ecosystem effects

	Visibility impairment

Alternative: More Stringent NSPS and Final NESHAP

	3% Discount Rate	7% Discount Rate

Total Monetized Benefits2	$7,400	to	$18,000	$6,700	to	$16,000

Total Social Costs3	   $955           to                 $979	   $955   
       to                 $979

Net Benefits	$6,500	to	$17,000	$5,700	to	$15,000

Non-monetized Benefits4	7,800 tons of NOx (includes energy disbenefits)

	5,200 tons of organic HAPs

	5,900 tons of HCl

	16,400 pounds of mercury 

	Health effects from HAPs, NO2, and SO2 exposure

	Ecosystem effects

	Visibility impairment

1	All estimates are for the implementation year (2013), and are rounded
to two significant figures.  

2	The total monetized benefits reflect the human health benefits
associated with reducing exposure to PM2.5 through reductions of
directly emitted PM2.5 and PM2.5 precursors such as SO2. It is important
to note that the monetized benefits include many but not all health
effects associated with PM2.5 exposure. Benefits are shown as a range
from Pope et al. (2002) to Laden et al. (2006). These models assume that
all fine particles, regardless of their chemical composition, are
equally potent in causing premature mortality because there is no clear
scientific evidence that would support the development of differential
effects estimates by particle type. The total monetized benefits include
the energy disbenefits. 

3	The methodology used to estimate social costs for one year in the
multimarket model using surplus changes results in the same social costs
for both discount rates. 

4	Due to data, methodology, and resource limitations, we were unable to
monetize the benefits associated with these categories of benefits.

Figure 6-12.	Net Benefits for the Final Portland Cement NESHAP and NSPS
at 3% Discount Rate a

a	Net Benefits are quantified in terms of PM2.5 benefits for
implementation year (2013). This graph shows 14 benefits estimates
combined with the cost estimate. All combinations are treated as
independent and equally probable. All fine particles are assumed to have
equivalent health effects, but the benefit per ton estimates vary
because each ton of precursor reduced has a different propensity to
become PM2.5. The monetized benefits incorporate the conversion from
precursor emissions to ambient fine particles. These net benefits
include the energy disbenefits. Due to data, methodology, and resource
limitations, we were unable to monetize the benefits associated with
several categories of benefits, including exposure to HAPs, NO2, and
SO2, ecosystem effects, and visibility effects.

Figure 6-13.	Net Benefits for the Final Portland Cement NESHAP and NSPS
at 7% Discount Rate a

a	Net Benefits are quantified in terms of PM2.5 benefits for
implementation year (2013). This graph shows 14 benefits estimates
combined with the cost estimate. All combinations are treated as
independent and equally probable. All fine particles are assumed to have
equivalent health effects, but the benefit per ton estimates vary
because each ton of precursor reduced has a different propensity to
become PM2.5. The monetized benefits incorporate the conversion from
precursor emissions to ambient fine particles. These net benefits
include the energy disbenefits. Due to data, methodology, and resource
limitations, we were unable to monetize the benefits associated with
several categories of benefits, including exposure to HAPs, NO2, and
SO2, ecosystem effects, and visibility effects.



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Exposure Assessment to Support the Review of the SO2 Primary National
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on the Internet at
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<http://www.epa.gov/ttn/ecas/ria.html>. 

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Appendix A

Short-Run Regional Portland Cement Economic Model

The Office of Air Quality Planning and Standards (OAQPS) has adopted
the standard-industry level analysis described in the Office’s
resource manual (EPA, 1999a  XE "EPA, 1999a"  ). This approach is
consistent with previous EPA analyses of the Portland cement industry
(EPA, 1998; EPA, 1999b  XE "EPA, 1998"    XE "EPA, 1999b"  ) and uses a
single-period static partial-equilibrium model to compare prepolicy
cement market baselines with expected postpolicy outcomes in these
markets. The benchmark time horizon for the analysis is the intermediate
run where producers have some constraints on their flexibility to adjust
factors of production. This time horizon allows us to capture important
transitory impacts of the program on existing producers. Key measures in
this analysis include

market-level effects (market prices, changes in domestic production and
consumption, and international trade),

industry-level effects (changes in operating profits and employment),

facility-level effects (plant utilization changes), and

social costs (changes in producer and consumer surplus).

In this appendix, we provide additional details about economic model
updates, model equations and parameters.

A.1	Economic Impact Model Updates Since Proposal

The need for a complete set of statistics makes the use of a 2005
baseline the best choice for a typical year. At the time of proposal
model development, it was the latest year for which the PCA had
published their plant information summary and complete statistics for
updating variable cost functions were available. Details of model
development are provided in EPA (2009  XE "EPA (2009"  ), Appendix A.
Since proposal, EPA identified several plants where operations had
changed (see Table A-1). As a result, EPA modified the baseline U.S.
production quantities to approximate these changes and maintain
consistency with 2005 market conditions (Table A-2). 

EPA also recognizes that the demand for cement is a derived demand
because it depends on demand for sectors such as housing and
construction. As a result, business cycles also significantly influence
the cement industry (see Table A-3). If 2013 is more or less favorable
for the cement industry than 2005, then impacts would be expected to
change accordingly.

Table A-1.	Economic Model Population Updates: 2005

Market	Approximate Clinker Capacity Removed

(thousand metric tons)	Description	Approximate Clinker Capacity Added

(thousand metric tons)	Net Change in Market Plant Population 

Atlanta	300	Closure 	0	−1

Baltimore/Philadelphia	500	Closure 	400	−1

Chicago	600	Replacement 	1,100	0

Dallas	800	Replacement	800	0

Detroit	900	Closure	0	-1

Kansas City	300	Closure 	0	−1

Los Angeles	1,100	Replacement 	2,200	0

Phoenix	600	Replacement 	700	+1

San Antonio	300	 Closure	0	0

St. Louis	500	Replacement	1,200	0



Market	U.S. Production

Proposal	U.S. Production Revised	Difference

Atlanta	6.1	5.8	−0.3

Baltimore/Philadelphia	8.0	7.8	−0.2

Birmingham	5.9	5.9	0.0

Chicago	4.3	4.7	0.4

Cincinnati	3.7	3.7	0.0

Dallas	8.2	8.1	−0.1

Denver	3.4	3.4	0.0

Detroit	4.8	3.8	−1.0

Florida	5.6	5.5	−0.1

Kansas City	5.3	5.0	−0.3

Los Angeles	9.6	10.6	1.0

Minneapolis	1.7	1.7	0.0

New York/Boston	3.2	3.2	0.0

Phoenix	4.1	4.3	0.2

Pittsburgh	1.5	1.5	0.0

St. Louis	5.4	6.0	0.6

Salt Lake City	2.4	2.4	0.0

San Antonio	5.7	5.5	−0.2

San Francisco	3.4	3.4	0.0

Seattle	1.1	1.1	0.0

Total, Grey	93.6	93.6	0.0

Source: EPA calculations.

Table A-3.	Recent Market Trends

Economic Variable	2005	2006	2007	2008	2009a

Clinker production (million metric tons)	87	89	86	78	58

Price, average mill value ($/metric ton)	$91	$102	$104	$103	$100

Employment (thousand)	16	16	16	15	14

Share of consumption provided by imports (percent)	25	27	19	11	8

aestimated.

Source: USGS, 2010  XE "USGS, 2010"  . Mineral Commodity Survey 2010.
http://minerals.usgs.gov/minerals/pubs/commodity/cement/mcs-2010-cemen.p
df

A.2	Partial Equilibrium Model

The partial equilibrium analysis performed for this rule uses the cement
market model developed during proposal (U.S. EPA, 2009  XE "U.S. EPA,
2009"  ). The model simulates how stakeholders (consumers and firms) may
respond to the additional regulatory program costs. In the near term,
the regional cement markets are assumed to have few sellers that offer
similar/identical products. As a result, EPA used an oligopoly market
structure. As described in Section 3, this market structure assumption
suggests that the observed baseline market price will be higher than
marginal production costs (i.e., there may be a preexisting market
distortion prior to regulation). To provide some intuition about factors
that influence the size of the existing distortion, we express a
seller’s “best” supply decision as a function of the market price,
the seller’s market share, the market demand elasticity, and the
seller’s marginal costs (see Varian [1992], pp. 289–290  XE "Varian
[1992], pp. 289–290"  ):

Price × (1 + Market Sharei/Demand Elasticity) = Marginal Costi.

This equation shows the relationship between the oligopoly model and
perfect competition. The market distortion will typically be higher when
market sharei is high (there are few sellers) and in markets where the
quantity demanded is less sensitive to price (i.e., the demand
elasticity is inelastic).

A.2.1	 Model Equations

To estimate the economic impacts of the regulation, EPA used four linear
equations to calculate the following unknown variables:

change in domestic plant production (dqi), 

change in imports (dqimports), 

change in cement market quantity (dQ), and

change in cement price (dP). 

Equation 1: Domestic Supply. For each plant, we describe its response to
the regulatory program as follows. The total compliance cost per ton
(ci) is applied to each kiln, and the difference in the highest cost
kiln with-regulation and the highest cost kiln in the baseline
approximates the plant’s change in the marginal cost of production
(dMCi). In with-regulation equilibrium, the change in marginal revenue
(dMRi) must equal the change in the marginal cost (dMCi) for each plant.

dmarginal Revenuei = dmarginal Costi

or

 

Equation 2: Supply of Imports. If applicable to the market, an equation
describing the supply of cement from other countries was included: 

dimports = import supply elasticity × (dprice/baseline price) ×
baseline imports.

For import supply, EPA used the latest empirical work on how other
countries who export (i.e., supply imports) to the United States respond
to price changes. Broda et al. (2008  XE "Broda et al. (2008"  ) report
that the export supply elasticity for commodities imported by the United
States was approximately two. This implies that a 1% increase in prices
results in a 2% increase in the volume of exports for a typical good.

Equation 3: Market Supply. Market supply of Portland cement equals the
change in domestic production and imports:

dmarket Q = dtotal domestic production + dimports.

This condition ensures that the market quantity is consistent with the
individual supply decisions of domestic plants and imports in the new
with-regulation equilibrium for each regional market.

Equation 4: Market Demand. The demand for Portland cement is derived
from the demand for concrete products, which, in turn, is derived
largely from the demand for construction. Based on a linear demand
equation, the market demand condition for Portland cement must hold
based on the projected change in market price, that is,

dMarketQ = demand elasticity (dprice/baseline price) × baseline
consumption.

estimate of −0.88 (EPA, 1998  XE "EPA 1998"  ). This value suggests
that a 1% increase in the cement price would lead to a 0.88% reduction
in cement consumption. 

Appendix B

The Cement Plant’s Production Decision:

A Mathematical Representation 

This appendix provides additional detail about the cement ’s
production decision used in the economic model (see Equation 1 in
Section 3 of the RIA). Table B-1 identifies and describes the key
variables of the cement plant’s profit function.

Table B-1.	Variable Descriptions

 	Market output

qi	Domestic plant i’s output

FCi	Plant fixed costs

VCi	Plant variable costs



Step 1: First, we assume the plant’s goal is to maximize profits:

  .

Step 2: The first-order conditions for a profit maximum are:

  .

Step 3: Apply two key assumptions in the Cournot price model:

Plant’s (i) recognizes its own production decisions influence the
market price: 

 

Plant (i) output decisions do not affect those of any other plant (j)
(e.g., there is no strategic action among cement plants):

 

Step 4: Next, multiply second term by

 

  .

Step 5: Rearranging terms:

  .

Step 6: Use and apply the following definitions:

 

  .

We derive the following expression:

  .

Step 7: The total differential of this equation is determined and gives
us the optimal decision rule for the plant:

  .

Appendix C

Social Cost Methodology

The Office of Air Quality Planning and Standards (OAQPS) has adopted
the standard industry-level analysis described in the Office’s
resource manual (EPA, 1999a  XE “U.S. EPA, 1999a”  ). This approach
is consistent with previous EPA analyses of the Portland cement industry
(EPA, 1998  XE “U.S. EPA, 1998”  ; EPA, 1999b  XE “U.S. EPA,
1999b”  ) and uses a single-period static partial-equilibrium model to
compare prepolicy cement market baselines with expected postpolicy
outcomes in these markets. The benchmark time horizon for the analysis
is the intermediate run where producers have some constraints on their
flexibility to adjust factors of production. This time horizon allows us
to capture important transitory impacts of the program on existing
producers. The model provides an estimate of the social costs (changes
in producer and consumer surplus) associated with controls applying to
existing kilns (see Section 4). Since the social cost methodology is
identical to the approach used in previous cement analysis (EPA, 1998,
Appendix C), we have included elements of the previous report’s
Appendix C in this RIA.

Figure C-1 illustrates the conceptual framework for evaluating the
social cost and distributive impacts under the imperfectly competitive
structure of U.S. cement markets. The baseline equilibrium is given by
the price, P0, and the quantity, Q0. Without the regulation, the total
benefits of consuming cement are given by the area under the demand
curve up to the market output, Q0. This equals the area filled by the
letters ABCDEFGHIJ. The total variable cost to society of producing Q0
equals the area under the MC function, given by the area IJ. Thus, the
total surplus value to society from the production and consumption of
output level Q0 equals the total benefits minus the total costs, or the
area filled by the letters ABCDEFGH. 

This total surplus value to society can be further divided into producer
surplus and consumer surplus. Producer surplus accrues to the suppliers
of cement and reflects the value they receive in the market for
producing Q0 units of cement less their costs of production, i.e., their
profits. As shown in Figure C-1, producer surplus is given by the area
DEFGH, which is the difference between cement revenues (i.e., area
DEFGHIJ) and production costs (area IJ). Consumer surplus accrues to the
consumers of cement and reflects the value they place on consumption
(total benefits of consumption) less what they must pay on the market,
i.e., P0. Consumer surplus is thereby given by the area ABC.

Figure C-1.	Social Cost of Regulation Under Imperfect Competition

The final rule will increase the marginal cost of producing cement and
thereby shift this curve upward by the amount of the incremental
compliance costs. As shown in Figure C-1, this results in a new market
equilibrium that occurs at a higher market price for cement, P1, and a
lower level of output, Q1. In this scenario, the total benefits of
consumption are equal to area ABDFI and the total variable costs of
production are equal to area FI. This yields a with-regulation social
surplus equal to area ABD with area BD representing the new producer
surplus and area A being the new consumer surplus. The social cost of
the regulation equals the total change in social surplus caused by the
regulation. Therefore, the social cost of the regulation is represented
by the area FGHEC in Figure C-1.

The distributive effects are estimated by separating the social cost
into producer surplus and consumer surplus losses. First, the change in
producer surplus is given by

	ΔPS = B – F – (G+H+E)	(C.1)

Producers gain B from the increase in price (a transfer from consumers
to producers), but lose F from the increase in production costs due to
the incremental compliance costs. Furthermore, the reduction of cement
production leads to foregone baseline profits of G+H+E.

The change in consumer surplus is given by

	ΔCS = – (B + C)	(C.2)

This change results from the reduction in consumer surplus from the
baseline value of ABC to the with-regulation value of A. In this case,
consumers lose area B as a transfer to producers through the increase in
the price they pay for the with-regulation level of cement consumption,
while the reduction in cement consumption due to regulation leads to
foregone baseline value of consumption equal to area C.

The social cost or total change in social surplus can then be derived
simply by adding the changes in producer and consumer surplus, i.e., 

al Cost = ΔPS + ΔCS = – (F + G + H + E + C)	(C.3)

This estimate can be compared to the engineering estimate of incremental
compliance cost to demonstrate the difference between these two
estimates of social cost. The incremental compliance cost estimate is
given by the area FGH, which is simply the constant cost per unit times
the baseline output level of cement. The social cost estimate from
Equation (C.3) above, however, exceeds the engineering estimate by the
area EC. In other words, the incremental compliance cost estimate
understates the social costs of the regulation. The reason for this
follows directly from the imperfectly competitive structure of the
markets for cement. A comparison with the outcome under perfect
competition will assist in illustrating this point.

Suppose that the MR curve in Figure C-1 was the demand function for a
competitive market, rather than the marginal revenue function for an
imperfectly competitive producer. Similarly, let the MC function be the
aggregate supply function for all producers in the market. The market
equilibrium is still determined at the intersection of MC and MR, but
given the revised interpretation of MR as the competitive demand
function, the baseline (competitive) market price, P0C, is now equal to
MC and Q0 is now interpreted as the competitive level of cement demand.
In this case, all social surplus goes to the consumer. This is because
producers receive a price that just covers their costs of production. 

In the with-regulation perfectly competitive equilibrium, the market
price would rise by the per unit control cost amount to P1c. The social
cost of the regulation is given entirely by the loss in consumer surplus
as given by area FG. As shown in Figure C-1, this estimate of social
cost is less than the incremental compliance cost estimate (i.e., area
FGH) so that the engineering estimate overstates the social cost of the
regulation under perfect competition. The overstatement results from the
fact that the incremental compliance costs are estimated based on the
baseline market level of cement output. With regulation, output is
projected to decline to Q1, so that the actual incremental compliance
costs incurred by the industry are given by area F. Area G represents
the foregone value of cement consumption to consumers, also referred to
as the deadweight loss (analogous to area C under the imperfect
competition scenario). 

−[(F+G+H+E+C) – (F+G)] = −(H + E + C)	(C.4)

The difference between these two measures results from the fact that the
price paid by consumers (i.e., marginal value to society for cement)
exceeds the cost of producing cement (i.e., the marginal cost to society
of producing cement). As shown in Figure C-1, this difference in social
cost is equal to the area between the demand curve (D) and the marginal
revenue curve (MR) that exist under imperfectly competitive market
structure. This area does not exist under perfect competition because
the MR curve is interpreted as the demand curve so that the price paid
by consumers equals the marginal cost of producing cement. The
pre-existing social inefficiency of imperfect competition is exacerbated
as the regulation moves society further away from the socially optimal
level of cement production, which results in social costs greater than
the incremental compliance cost imposed on the cement industry.

Appendix D

Summary of Expert Opinions on the Existence of a Threshold in the
Concentration-Response Function for PM2.5-related Mortality



Summary of Expert Opinions on the Existence of a Threshold in the
Concentration-Response Function for PM2.5-related Mortality

Technical Support Document (TSD)

July 2010

Compiled by:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impact Division

Air Benefit-Cost Group

Research Triangle Park, North Carolina

Contents:

A. HES comments on 812 Analysis (2010)

B. American Heart Association Scientific Statement (2010)

C. Integrated Science Assessment for Particulate Matter (2009)

D. CASAC comments on PM ISA and REA (2009)

E. Krewski et al. (2009)

F. Schwartz et al. (2008)

G. Expert Elicitation on PM Mortality (2006, 2008)

H. CASAC comments on PM Staff Paper (2005)

I. HES comments on 812 Analysis (2004)

J. NRC (2002)



A. HES Comments on 812 Analysis (2010)

U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB). 2010. Review of EPA’s DRAFT Health Benefits of the Second
Section 812 Prospective Study of the Clean Air Act. EPA-COUNCIL-10-001.
June. Available on the Internet at
<http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB28525774500
738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>.

Pg 2: “The HES generally agrees with other decisions made by the EPA
project team with respect to PM, in particular, the PM mortality effect
threshold model, the cessation lag model, the inclusion of infant
mortality estimation, and differential toxicity of PM.”

Pg 2: “Further, the HES fully supports EPA’s use of a no-threshold
model to estimate the mortality reductions associated with reduced PM
exposure.”

Pg 6: “The HES also supports the Agency’s choice of a no-threshold
model for PM-related effects.”

Pg 13: “The HES fully supports EPA’s decision to use a no-threshold
model to estimate mortality reductions. This decision is supported by
the data, which are quite consistent in showing effects down to the
lowest measured levels. Analyses of cohorts using data from more recent
years, during which time PM concentrations have fallen, continue to
report strong associations with mortality. Therefore, there is no
evidence to support a truncation of the CRF.”

HES Panel Members

Dr. John Bailar, Chair of the Health Effects Subcommittee, Scholar in
Residence, The National Academies, Washington, DC

Dr. Michelle Bell, Associate Professor, School of Forestry and
Environmental Studies, Yale 

University, New Haven, CT

Dr. James K. Hammitt, Professor, Department of Health Policy and
Management, Harvard School of Public Health, Boston, MA

Dr. Jonathan Levy, Associate Professor, Department of Environmental
Health, Harvard School of Public Health, Boston, MA

Dr. C. Arden Pope, III Professor, Department of Economics, Brigham Young
University, Provo, UT

Mr. John Fintan Hurley, Research Director, Institute of Occupational
Medicine (IOM), Edinburgh, United Kingdom, UK

Dr. Patrick Kinney, Professor, Department of Environmental Health
Sciences, Mailman School of Public Health, Columbia University, New
York, NY

Dr. Michael T. Kleinman, Professor, Department of Medicine, Division of
Occupational and Environmental Medicine, University of California,
Irvine, Irvine, CA

Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of
Environmental Health Hazard Assessment, California Environmental
Protection Agency, Oakland, CA

Dr. Rebecca Parkin, Professor and Associate Dean, Environmental and
Occupational Health, School of Public Health and Health Services, The
George Washington University Medical Center, Washington, DC

B. Scientific Statement from American Heart Association (2010)

Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux
AV, Holguin F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D,
Smith SC Jr, Whitsel L, Kaufman JD; on behalf of the American Heart
Association Council on Epidemiology and Prevention, Council on the
Kidney in Cardiovascular Disease, and Council on Nutrition, Physical
Activity and Metabolism. (2010). “Particulate matter air pollution and
cardiovascular disease: an update to the scientific statement from the
American Heart Association.” Circulation. 121: 2331-2378.

Pg 2338: “Finally, there appeared to be no lower-limit threshold below
which PM10 was not associated with excess mortality across all
regions.”

Pg 2350: “There also appears to be a monotonic (eg, linear or
log-linear) concentration-response relationship between PM2.5 and
mortality risk observed in cohort studies that extends below present-day
regulations of 15 µg/m3 for mean annual levels, without a discernable
“safe” threshold.” (cites Pope 2004, Krewski 2009, and Schwartz
2008)

Pg 2364: “The PM2.5 concentration– cardiovascular risk relationships
for both short- and long-term exposures appear to be monotonic,
extending below 15 µg/m3 (the 2006 annual NAAQS level) without a
discernable “safe” threshold.”

Pg 2365: “This updated review by the AHA writing group corroborates
and strengthens the conclusions of the initial scientific statement. In
this context, we agree with the concept and continue to support measures
based on scientific evidence, such as the US EPA NAAQS, that seek to
control PM levels to protect the public health. Because the evidence
reviewed supports that there is no safe threshold, it appears that
public health benefits would accrue from lowering PM2.5 concentrations
even below present-day annual (15 µg/m3) and 24-hour (35 µg/m3) NAAQS,
if feasible, to optimally protect the most susceptible populations.”

Pg 2366: “Although numerous insights have greatly enhanced our
understanding of the PM-cardiovascular relationship since the first AHA
statement was published, the following list represents broad strategic
avenues for future investigation: ... Determine whether any “safe”
PM threshold concentration exists that eliminates both acute and chronic
cardiovascular effects in healthy and susceptible individuals and at a
population level.”

Scientific Statement Authors

Dr. Robert D. Brook, MD

Dr. Sanjay Rajagopalan, MD

Dr. C. Arden Pope, PhD

Dr. Jeffrey R. Brook, PhD

Dr. Aruni Bhatnagar, PhD, FAHA

Dr. Ana V. Diez-Roux, MD, PhD, MPH

Dr. Fernando Holguin, MD

Dr. Yuling Hong, MD, PhD, FAHA

Dr. Russell V. Luepker, MD, MS, FAHA

Dr. Murray A. Mittleman, MD, DrPH, FAHA

Dr. Annette Peters, PhD 

Dr. David Siscovick, MD, MPH, FAHA

Dr. Sidney C. Smith, Jr, MD, FAHA

Dr. Laurie Whitsel, PhD

Dr. Joel D. Kaufman, MD, MPH



C. Integrated Science Assessment for Particulate Matter (2009)

U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated
Science Assessment for Particulate Matter (Final Report).
EPA-600-R-08-139F. National Center for Environmental Assessment – RTP
Division. December. Available on the Internet at
<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

Pg 1-22: “An important consideration in characterizing the public
health impacts associated with exposure to a pollutant is whether the
concentration-response relationship is linear across the full
concentration range encountered, or if nonlinear relationships exist
along any part of this range. Of particular interest is the shape of the
concentration-response curve at and below the level of the current
standards. The shape of the concentration-response curve varies,
depending on the type of health outcome, underlying biological
mechanisms and dose. At the human population level, however, various
sources of variability and uncertainty tend to smooth and
“linearize” the concentration-response function (such as the low
data density in the lower concentration range, possible influence of
measurement error, and individual differences in susceptibility to air
pollution health effects). In addition, many chemicals and agents may
act by perturbing naturally occurring background processes that lead to
disease, which also linearizes population concentration-response
relationships (Clewell and Crump, 2005, 156359; Crump et al., 1976,
003192; Hoel, 1980, 156555). These attributes of population
dose-response may explain why the available human data at ambient
concentrations for some environmental pollutants (e.g., PM, O3, lead
[Pb], ETS, radiation) do not exhibit evident thresholds for health
effects, even though likely mechanisms include nonlinear processes for
some key events. These attributes of human population dose-response
relationships have been extensively discussed in the broader
epidemiologic literature (Rothman and Greenland, 1998, 086599).”

Pg 2-16: “In addition, cardiovascular hospital admission and mortality
studies that examined the PM10 concentration-response relationship found
evidence of a log-linear no-threshold relationship between PM exposure
and cardiovascular-related morbidity (Section 6.2) and mortality
(Section 6.5).”

Pg 2-25: “2.4.3. PM Concentration-Response Relationship

An important consideration in characterizing the PM-morbidity and
mortality association is whether the concentration-response relationship
is linear across the full concentration range that is encountered or if
there are concentration ranges where there are departures from linearity
(i.e., nonlinearity). In this ISA studies have been identified that
attempt to characterize the shape of the concentration-response curve
along with possible PM “thresholds” (i.e., levels which PM
concentrations must exceed in order to elicit a health response). The
epidemiologic studies evaluated that examined the shape of the
concentration-response curve and the potential presence of a threshold
have focused on cardiovascular hospital admissions and ED visits and
mortality associated with short-term exposure to PM10 and mortality
associated with long-term exposure to PM2.5. 

“A limited number of studies have been identified that examined the
shape of the PM cardiovascular hospital admission and ED visit
concentration-response relationship. Of these studies, some conducted an
exploratory analysis during model selection to determine if a linear
curve most adequately represented the concentration-response
relationship; whereas, only one study conducted an extensive analysis to
examine the shape of the concentration-response curve at different
concentrations (Section 6.2.10.10). Overall, the limited evidence from
the studies evaluated supports the use of a no-threshold, log-linear
model, which is consistent with the observations made in studies that
examined the PM-mortality relationship.

“Although multiple studies have previously examined the PM-mortality
concentration-response relationship and whether a threshold exists, more
complex statistical analyses continue to be developed to analyze this
association. Using a variety of methods and models, most of the studies
evaluated support the use of a no-threshold, log-linear model; however,
one study did observe heterogeneity in the shape of the
concentration-response curve across cities (Section 6.5). Overall, the
studies evaluated further support the use of a no-threshold log-linear
model, but additional issues such as the influence of heterogeneity in
estimates between cities, and the effect of seasonal and regional
differences in PM on the concentration-response relationship still
require further investigation.

“In addition to examining the concentration-response relationship
between short-term exposure to PM and mortality, Schwartz et al. (2008,
156963) conducted an analysis of the shape of the concentration-response
relationship associated with long-term exposure to PM. Using a variety
of statistical methods, the concentration-response curve was found to be
indistinguishable from linear, and, therefore, little evidence was
observed to suggest that a threshold exists in the association between
long-term exposure to PM2.5 and the risk of death (Section 7.6).”

Pg 6-75: “6.2.10.10. Concentration Response

The concentration-response relationship has been extensively analyzed
primarily through studies that examined the relationship between PM and
mortality. These studies, which have focused on short- and long-term
exposures to PM have consistently found no evidence for deviations from
linearity or a safe threshold (Daniels et al., 2004, 087343; Samoli et
al., 2005, 087436; Schwartz, 2004, 078998; Schwartz et al., 2008,
156963) (Sections 6.5.2.7 and 7.1.4). Although on a more limited basis,
studies that have examined PM effects on cardiovascular hospital
admissions and ED visits have also analyzed the PM
concentration-response relationship, and contributed to the overall body
of evidence which suggests a log-linear, no-threshold PM
concentration-response relationship.

“The results from the three multicity studies discussed above support
no-threshold log-linear models, but issues such as the possible
influence of exposure error and heterogeneity of shapes across cities
remain to be resolved. Also, given the pattern of seasonal and regional
differences in PM risk estimates depicted in recent multicity study
results (e.g., Peng et al., 2005, 087463), the very concept of a
concentration-response relationship estimated across cities and for
all-year data may not be very informative.”

Pg 6-197: “6.5.2.7. Investigation of Concentration-Response
Relationship

The results from large multicity studies reviewed in the 2004 PM AQCD
(U.S. EPA, 2004, 056905) suggested that strong evidence did not exist
for a clear threshold for PM mortality effects. However, as discussed in
the 2004 PM AQCD (U.S. EPA, 2004, 056905), there are several challenges
in determining and interpreting the shape of PM-mortality
concentration-response functions and the presence of a threshold,
including: (1) limited range of available concentration levels (i.e.,
sparse data at the low and high end); (2) heterogeneity of susceptible
populations; and (3) investigate the PM-mortality concentration-response
relationship.

threshold at around 50 μg/m3 PM10, as shown in Figure 6-35. For
all-cause and cardio-respiratory deaths, based on the Akaike’s
Information Criterion (AIC), a log-linear model without threshold was
preferred to the threshold model and to the spline model.

“The HEI review committee commented that interpretation of these
results required caution, because (1) the measurement error could
obscure any threshold; (2) the city-specific concentration-response
curves exhibited a variety of shapes; and (3) the use of AIC to choose
among the models might not be appropriate due to the fact it was not
designed to assess scientific theories of etiology. Note, however, that
there has been no etiologically credible reason suggested thus far to
choose one model over others for aggregate outcomes. Thus, at least
statistically, the result of Daniels et al. (2004, 087343) suggests that
the log-linear model is appropriate in describing the relationship
between PM10 and mortality.

between 15 and 25 μg/m3, between 25 and 34 μg/m3, between 35 and 44
μg/m3, and 45 μg/m3 and above. In the model, days with concentrations
below 15 μg/m3 served as the reference level. This model was fit using
the single stage method, combining strata across all cities in the
case-crossover design. Figure 6-36 shows the resulting relationship,
which does not provide sufficient evidence to suggest that a threshold
exists. The authors did not examine city-to-city variation in the
concentration-response relationship in this study.

“PM10 and mortality in 22 European cities (and BS in 15 of the cities)
participating in the APHEA project. In nine of the 22 cities, PM10
levels were estimated using a regression model relating co-located PM10
to BS or TSP. They used regression spline models with two knots (30 and
50 μg/m3) and then combined the individual city estimates of the
splines across cities. The investigators concluded that the association
between PM and mortality in these cities could be adequately estimated
using the log-linear model. However, in an ancillary analysis of the
concentration-response curves for the largest cities in each of the
three distinct geographic areas (western, southern, and eastern European
cities): London, England; Athens, Greece; and Cracow, Poland, Samoli et
al. (2005, 087436) observed a difference in the shape of the
concentration-response curve across cities. Thus, while the combined
curves (Figure 6-37) appear to support no-threshold relationships
between PM10 and mortality, the heterogeneity of the shapes across
cities makes it difficult to interpret the biological relevance of the
shape of the combined curves.

“The results from the three multicity studies discussed above support
no-threshold log-linear models, but issues such as the possible
influence of exposure error and heterogeneity of shapes across cities
remain to be resolved. Also, given the pattern of seasonal and regional
differences in PM risk estimates depicted in recent multicity study
results (e.g., Peng et al., 2005, 087463), the very concept of a
concentration-response relationship estimated across cities and for
all-year data may not be very informative.”

Authors of ISA

Dr. Lindsay Wichers Stanek (PM Team Leader)—National Center for
Environmental Assessment (NCEA), U.S. Environmental Protection Agency
(U.S. EPA), Research Triangle Park, NC

Dr. Jeffrey Arnold—NCEA, U.S. EPA, Research Triangle Park, NC (now at
Institute for Water Resources, U.S. Army Corps of Engineers, Washington,
D.C)

Dr. Christal Bowman—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. James S. Brown—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Barbara Buckley—NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Allen Davis—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Jean-Jacques Dubois—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Steven J. Dutton—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Tara Greaver—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Erin Hines—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Douglas Johns—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Ellen Kirrane—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Dennis Kotchmar—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Thomas Long—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Thomas Luben—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Qingyu Meng—Oak Ridge Institute for Science and Education,
Postdoctoral Research Fellow to NCEA, U.S. EPA, Research Triangle Park,
NC

Dr. Kristopher Novak—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Joseph Pinto—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Jennifer Richmond-Bryant—NCEA, U.S. EPA, Research Triangle Park,
NC

Dr. Mary Ross—NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Jason Sacks—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Timothy J. Sullivan—E&S Environmental Chemistry, Inc., Corvallis,
OR

Dr. David Svendsgaard—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Lisa Vinikoor—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. William Wilson—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Lori White— NCEA, U.S. EPA, Research Triangle Park, NC (now at
National Institute for Environmental Health Sciences, Research Triangle
Park, NC)

Dr. Christy Avery—University of North Carolina, Chapel Hill, NC

Dr. Kathleen Belanger —Center for Perinatal, Pediatric and
Environmental Epidemiology, Yale University, New Haven, CT

Dr. Michelle Bell—School of Forestry & Environmental Studies, Yale
University, New Haven, CT

Dr. William D. Bennett—Center for Environmental Medicine, Asthma and
Lung Biology, University of North Carolina, Chapel Hill, NC

Dr. Matthew J. Campen—Lovelace Respiratory Research Institute,
Albuquerque, NM

Dr. Leland B. Deck— Stratus Consulting, Inc., Washington, DC

Dr. Janneane F. Gent—Center for Perinatal, Pediatric and Environmental
Epidemiology, Yale University, New Haven, CT

Dr. Yuh-Chin Tony Huang—Department of Medicine, Division of Pulmonary
Medicine, Duke University Medical Center, Durham, NC

Dr. Kazuhiko Ito—Nelson Institute of Environmental Medicine, NYU
School of Medicine, Tuxedo, NY

Mr. Marc Jackson—Integrated Laboratory Systems, Inc., Research
Triangle Park, NC

Dr. Michael Kleinman—Department of Community and Environmental
Medicine, University of California, Irvine

Dr. Sergey Napelenok—National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Dr. Marc Pitchford—National Oceanic and Atmospheric Administration,
Las Vegas, NV

Dr. Les Recio—Genetic Toxicology Division, Integrated Laboratory
Systems, Inc., Research Triangle Park, NC

Dr. David Quincy Rich—Department of Epidemiology, University of
Medicine and Dentistry of New Jersey, Piscataway, NJ

Dr. Timothy Sullivan— E&S Environmental Chemistry, Inc., Corvallis, OR

Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo,
NY

Dr. Gregory Wellenius—Cardiovascular Epidemiology Research Unit, Beth
Israel Deaconess Medical Center, Boston, MA

Dr. Eric Whitsel—Departments of Epidemiology and Medicine, University
of North Carolina, Chapel Hill, NC

Peer Reviewers

Dr. Sara Dubowsky Adar, Department of Epidemiology, University of
Washington, Seattle, WA

Mr. Chad Bailey, Office of Transportation and Air Quality, Ann Arbor, MI

Mr. Richard Baldauf, Office of Transportation and Air Quality, Ann
Arbor, MI

Dr. Prakash Bhave, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Mr. George Bowker, Office of Atmospheric Programs, U.S. EPA, Washington,
D.C.

Dr. Judith Chow, Division of Atmospheric Sciences, Desert Research
Institute, Reno, NV 

Dr. Dan Costa, U.S. EPA, Research Triangle Park, NC

Dr. Ila Cote, NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Robert Devlin, National Health and Environmental Effects Research
Laboratory, U.S. EPA, Research Triangle Park, NC

Dr. David DeMarini, National Health and Environmental Effects Research
Laboratory, U.S. EPA, Research Triangle Park, NC

Dr. Neil Donahue, Department of Chemical Engineering, Carnegie Mellon
University, Pittsburgh, PA

Dr. Aimen Farraj, National Health and Environmental Effects Research
Laboratory, U.S. EPA, Research Triangle Park, NC

Dr. Mark Frampton, Department of Environmental Medicine, University of
Rochester Medical Center, Rochester, NY

Mr. Neil Frank, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Mr. Tyler Fox, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Dr. Jim Gauderman, Department of Environmental Medicine, Department of
Preventive Medicine, University of Southern California, Los Angeles, CA

Dr. Barbara Glenn, National Center for Environmental Research, U.S. EPA,
Washington, D.C.

Dr. Terry Gordon, School of Medicine, New York University, Tuxedo, NY

Mr. Tim Hanley, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Dr. Jack Harkema, Department of Pathobiology and Diagnostic
Investigation, Michigan State University, East Lansing, MI

Ms. Beth Hassett-Sipple, Office of Air Quality Planning and Standards,
U.S. EPA, Research Triangle Park, NC

Dr. Amy Herring, Department of Biostatistics, University of North
Carolina, Chapel Hill, NC

Dr. Israel Jirak, Department of Meteorology, Embry-Riddle Aeronautical
University, Prescott, AZ

Dr. Mike Kleeman, Department of Civil and Environmental Engineering,
University of California, Davis, CA

Dr. Petros Koutrakis, Exposure, Epidemiology and Risk Program, Harvard
School of Public Health, Boston, MA

Dr. Sagar Krupa, Department of Plant Pathology, University of Minnesota,
St. Paul, MN

Mr. John Langstaff, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Dr. Meredith Lassiter, Office of Air Quality Planning and Standards,
U.S. EPA, Research Triangle Park, NC

Mr. Phil Lorang, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Dr. Karen Martin, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Ms. Connie Meacham, NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Tom Pace, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Dr. Jennifer Peel, Department of Environmental and Radiological Health
Sciences, College of Veterinary Medicine and Biomedical Sciences,
Colorado State University, Fort Collins, CO

Dr. Zackary Pekar, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Mr. Rob Pinder, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Mr. Norm Possiel, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Dr. Sanjay Rajagopalan, Division of Cardiovascular Medicine, Ohio State
University, Columbus, OH

Dr. Pradeep Rajan, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Mr. Venkatesh Rao, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Ms. Joann Rice, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Mr. Harvey Richmond, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Ms. Victoria Sandiford, Office of Air Quality Planning and Standards,
U.S. EPA, Research Triangle Park, NC

Dr. Stefanie Sarnat, Department of Environmental and Occupational
Health, Emory University, Atlanta, GA

Dr. Frances Silverman, Gage Occupational and Environmental Health,
University of Toronto, Toronto, ON

Mr. Steven Silverman, Office of General Council, U.S. EPA, Washington,
D.C.

Dr. Barbara Turpin, Department of Environmental Sciences, Rutgers
University, New Brunswick, NJ

Dr. Robert Vanderpool, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Dr. John Vandenberg (Director)—NCEA-RTP Division, U.S. EPA, Research
Triangle Park, NC

Dr. Alan Vette, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Ms. Debra Walsh (Deputy Director)—NCEA-RTP Division, U.S. EPA,
Research Triangle Park, NC

Mr. Tim Watkins, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Dr. Christopher Weaver, NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Lewis Weinstock, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Ms. Karen Wesson, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Dr. Jason West, Department of Environmental Sciences and Engineering,
University of North Carolina, Chapel Hill, NC

Mr. Ronald Williams, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Dr. George Woodall, NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Antonella Zanobetti, Department of Environmental Health, Harvard
School of Public Health, Boston, MA

D. CASAC comments on PM ISA and REA (2009)

U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB). 2009. Review of EPA’s Integrated Science Assessment for
Particulate Matter (First External Review Draft, December 2008).
EPA-COUNCIL-09-008. May. Available on the Internet at
<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006b
e86e/73ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pd
f>.

Pg 9: “There is an appropriate discussion of the time-series studies,
but this section needs to have an explicit finding that the evidence
supports a relationship between PM and mortality that is seen in these
studies. This conclusion should be followed by the discussion of
statistical methodology and the identification of any threshold that may
exist.”

U.S. Environmental Protection Agency Science Advisory Board (U.S.
EPA-SAB). 2009. Consultation on EPA’s Particulate Matter National
Ambient Air Quality Standards: Scope and Methods Plan for Health Risk
and Exposure Assessment. EPA-COUNCIL-09-009. May. Available on the
Internet at
<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006b
e86e/723FE644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pd
f>.

Pg 6: “On the issue of cut-points raised on 3-18, the authors should
be prepared to offer a scientifically cogent reason for selection of a
specific cut-point, and not simply try different cut-points to see what
effect this has on the analysis. The draft ISA was clear that there is
little evidence for a population threshold in the C-R function.”

U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB). 2009. Review of Integrated Science Assessment for Particulate
Matter (Second External Review Draft, July 2009). EPA-CASAC-10-001.
November. Available on the Internet at
<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006b
e86e/151B1F83B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pd
f>.

Pg 2: “The paragraph on lines 22-30 of page 2-37 is not clearly
written. Twice in succession it states that the use of a no-threshold
log-linear model is supported, but then cites other studies that suggest
otherwise. It would be good to revise this paragraph to more clearly
state – well, I’m not sure what. Probably that more research is
needed.”

CASAC Panel Members

Dr. Jonathan M. Samet, Professor and Chair, Department of Preventive
Medicine, University of Southern California, Los Angeles, CA 

Dr. Joseph Brain, Philip Drinker Professor of Environmental Physiology,
Department of Environmental Health, Harvard School of Public Health,
Harvard University, Boston, MA 

Dr. Ellis B. Cowling, University Distinguished Professor At-Large
Emeritus, Colleges of Natural Resources and Agriculture and Life
Sciences, North Carolina State University, Raleigh, NC 

Dr. James Crapo, Professor of Medicine, Department of Medicine, National
Jewish Medical and Research Center, Denver, CO 

Dr. H. Christopher Frey, Professor, Department of Civil, Construction
and Environmental Engineering, College of Engineering, North Carolina
State University, Raleigh, NC 

Dr. Armistead (Ted) Russell, Professor, Department of Civil and
Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 

Dr. Lowell Ashbaugh, Associate Research Ecologist, Crocker Nuclear Lab,
University of California, Davis, Davis, CA 

Prof. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine,
University of Southern California, Los Angeles, CA 

Dr. Wayne Cascio, Professor, Medicine, Cardiology, Brody School of
Medicine at East Carolina University, Greenville, NC 

Dr. David Grantz, Director, Botany and Plant Sciences and Air Pollution
Research Center, Riverside Campus and Kearney Agricultural Center,
University of California, Parlier, CA 

Dr. Joseph Helble, Dean and Professor, Thayer School of Engineering,
Dartmouth College, Hanover, NH 

Dr. Rogene Henderson, Senior Scientist Emeritus, Lovelace Respiratory
Research Institute, Albuquerque, NM 

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department
of Chemical Engineering, Clarkson University, Potsdam, NY 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental
Medicine, New York University School of Medicine, Tuxedo, NY 

Dr. Helen Suh MacIntosh, Associate Professor, Environmental Health,
School of Public Health, Harvard University, Boston, MA 

Dr. William Malm, Research Physicist, National Park Service Air
Resources Division, Cooperative Institute for Research in the
Atmosphere, Colorado State University, Fort Collins, CO 

Mr. Charles Thomas (Tom) Moore, Jr., Air Quality Program Manager,
Western Governors' Association, Cooperative Institute for Research in
the Atmosphere, Colorado State University, Fort Collins, CO 

Dr. Robert F. Phalen, Professor, Department of Community & Environmental
Medicine; Director, Air Pollution Health Effects Laboratory; Professor
of Occupational & Environmental Health, Center for Occupation &
Environment Health, College of Medicine, University of California
Irvine, Irvine, CA 

Dr. Kent Pinkerton, Professor, Regents of the University of California,
Center for Health and the Environment, University of California, Davis,
CA 

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control
Division, Department of Environmental Conservation, Vermont Agency of
Natural Resources, Waterbury, VT 

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing
Laboratory, Harvard Medical School, Boston, MA 

Dr. Sverre Vedal, Professor, Department of Environmental and
Occupational Health Sciences, School of Public Health and Community
Medicine, University of Washington, Seattle, WA 

Dr. Donna Kenski, Data Analysis Director, Lake Michigan Air Directors
Consortium, Rosemont, IL

Dr. Kathy Weathers, Senior Scientist, Cary Institute of Ecosystem
Studies, Millbrook, NY

E. Krewski et al. (2009)

Krewski, Daniel, Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward
Hughes, Yuanli Shi, Michelle C. Turner, C. Arden Pope III, George
Thurston, Eugenia E. Calle, and Michael J. Thun with Bernie Beckerman,
Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K. Moore, K. Bruce Newbold, Tim
Ramsay, Zev Ross, Hwashin Shin, and Barbara Tempalski. (2009). Extended
follow-up and spatial analysis of the American Cancer Society study
linking particulate air pollution and mortality. HEI Research Report,
140, Health Effects Institute, Boston, MA.

Pg 119: [About Pope et al. (2002)] “Each 10-μg/m3 increase in
long-term average ambient PM2.5 concentrations was associated with
approximately a 4%, 6%, or 8% increase in risk of death from all causes,
cardiopulmonary disease, and lung cancer, respectively. There was no
evidence of a threshold exposure level within the range of observed
PM2.5 concentrations. “

Krewski (2009). Letter from Dr. Daniel Krewski to HEI’s Dr. Kate Adams
(dated July 7, 2009) regarding “EPA queries regarding HEI Report
140”. Dr. Adams then forwarded the letter on July 10, 2009 to EPA’s
Beth Hassett-Sipple. (letter placed in docket #EPA-HQ-OAR-2007-0492).

Pg 4: “6. The Health Review Committee commented that the Updated
Analysis completed by

Pope et al. 2002 reported “no evidence of a threshold exposure level
within the range of observed PM2.5 concentrations” (p. 119). In the
Extended Follow-Up study, did the analyses provide continued support for
a no-threshold response or was there evidence of a threshold?

“Response: As noted above, the HEI Health Review Committee commented
on the lack of evidence for a threshold exposure level in Pope et al.
(2002) with follow-up through the year 1998. The present report, which
included follow-up through the year 2000, also does not appear to
demonstrate the existence of a threshold in the exposure-response
function within the range of observed PM2.5 concentrations.”

HEI Health Review Committee Members

Dr. Homer A. Boushey, MD, Chair, Professor of Medicine, Department of
Medicine, University of California–San Francisco 

Dr. Ben Armstrong, Reader, in Epidemiological Statistics, Department of
Public Health and Policy, London School of Hygiene and Tropical
Medicine, United Kingdom 

Dr. Michael Brauer, ScD, Professor, School of Environmental Health,
University of British Columbia, Canada 

Dr. Bert Brunekreef, PhD, Professor of Environmental Epidemiology,
Institute of Risk Assessment Sciences, University of Utrecht, The
Netherlands 

Dr. Mark W. Frampton, MD, Professor of Medicine & Environmental
Medicine, University of Rochester Medical Center, Rochester, NY 

Dr. Stephanie London, MD, PhD, Senior Investigator, Epidemiology Branch,
National Institute of Environmental Health Sciences 

Dr. William N. Rom, MD, MPH, Sol and Judith Bergstein Professor of
Medicine and Environmental Medicine and Director of Pulmonary and
Critical Care Medicine, New York University Medical Center 

Dr. Armistead Russell, Georgia Power Distinguished Professor of
Environmental Engineering, School of Civil and Environmental
Engineering, Georgia Institute of Technology 

Dr. Lianne Sheppard, PhD, Professor, Department of Biostatistics,
University of Washington

F. Schwartz et al. (2008)

Schwartz J, Coull B, Laden F. (2008). The Effect of Dose and Timing of
Dose on the Association between Airborne Particles and Survival.
Environmental Health Perspectives. 116: 64-69.

15 μg/m3.” 

Pg 68: “In conclusion, penalized spline smoothing and model averaging
represent reasonable, feasible approaches to addressing questions of the
shape of the exposure–response curve, and can provide valuable
information to decisionmakers. In this example, both approaches are
consistent, and suggest that the association of particles with mortality
has no threshold down to close to background levels.”



G. Expert Elicitation on PM-Mortality (2006, 2008)

Industrial Economics, Inc., 2006. Expanded Expert Judgment Assessment of
the Concentration-Response Relationship Between PM2.5 Exposure and
Mortality. Prepared for the U.S.EPA, Office of Air Quality Planning and
Standards, September. Available on the Internet at
<http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf>.

Pg v: “Each expert was given the option to integrate their judgments
about the likelihood of a causal relationship and/or threshold in the
C-R function into his distribution or to provide a distribution
"conditional on" one or both of these factors.”

Pg vii: “Only one of 12 experts explicitly incorporated a threshold
into his C-R function.3 The rest believed there was a lack of empirical
and/or theoretical support for a population threshold. However, three
other experts gave differing effect estimate distributions above and
below some cut-off concentration. The adjustments these experts made to
median estimates and/or uncertainty at lower PM2.5 concentrations were
modest.”

μg/m3, and a 20% chance that it would fall between 5 and 10 μg/m3.”

Pg ix: “Compared to the pilot study, experts in this study were in
general more confident in a causal relationship, less likely to
incorporate thresholds, and reported higher mortality effect estimates.
The differences in results compared with the pilot appear to reflect the
influence of new research on the interpretation of the key
epidemiological studies that were the focus of both elicitation studies,
more than the influence of changes to the structure of the protocol.”

Pg 3-25: “3.1.8 THRESHOLDS 

The protocol asked experts for their judgments regarding whether a
threshold exists in the PM2.5 mortality C-R function. The protocol
focused on assessing expert judgments regarding theory and evidential
support for a population threshold (i.e., the concentration below which
no member of the study population would experience an increased risk of
death).32 If an expert wished to incorporate a threshold in his
characterization of the concentration-response relationship, the team
then asked the expert to specify the threshold PM2.5 concentration
probabilistically, incorporating his uncertainty about the true
threshold level.

“From a theoretical and conceptual standpoint, all experts generally
believed that individuals exhibit thresholds for PM-related mortality.
However, 11 of them discounted the idea of a population threshold in the
C-R function on a theoretical and/or empirical basis. Seven of these
experts noted that theoretically one would be unlikely to observe a
population threshold due to the variation in susceptibility at any given
time in the study population resulting from combinations of genetic,
environmental, and socioeconomic factors.33 All 11 thought that there
was insufficient empirical support for a population threshold in the C-R
function. In addition, two experts (E and L) cited analyses of the ACS
cohort data in Pope et al. (2002) and another (J) cited Krewski et al.
(2000a & b) as supportive of a linear relationship in the study range. 

“Seven of the experts favored epidemiological studies as ideally the
best means of addressing the population threshold issue, because they
are best able to evaluate the full range of susceptible individuals at
environmentally relevant exposure levels. However, those who favored
epidemiologic studies generally acknowledged that definitive studies
addressing thresholds would be difficult or impossible to conduct,
because they would need to include a very large and diverse population
with wide variation in exposure and a long follow-up period.
Furthermore, two experts (B and I) cited studies documenting
difficulties in detecting a threshold using epidemiological studies
(Cakmak et al. 1999, and Brauer et al., 2002, respectively). The experts
generally thought that clinical and toxicological studies are best
suited for researching mechanisms and for addressing thresholds in very
narrowly defined groups. One expert, B, thought that a better
understanding of the detailed biological mechanism is critical to
addressing the question of a threshold.

“One expert, K, believed it was possible to make a conceptual argument
for a population threshold. He drew an analogy with smoking, indicating
that among heavy smokers, only a proportion of them gets lung cancer or
demonstrates an accelerated decline in lung function. He thought that
the idea that there is no level that is biologically safe is
fundamentally at odds with toxicological theory. He did not think that a
population threshold was detectable in the currently available
epidemiologic studies. He indicated that some of the cohort studies
showed greater uncertainty in the shape of the C-R function at lower
levels, which could be indicative of a threshold. 

“Expert K chose to incorporate a threshold into his C-R function. He
indicated that he was 50% sure that a threshold existed. If there were a
threshold, he thought that there was an 80% chance that it would be less
than or equal to 5 μg/m3, and a 20% chance that it would fall between 5
and 10 μg/m3.”

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey
M. Richmond, Bryan J. Hubbell, and Patrick L. Kinney. (2008). “Expert
Judgment Assessment of the Mortality Impact of Changes in Ambient Fine
Particulate Matter in the U.S.” Environ. Sci. Technol.,
42(7):2268-2274.

 was log-linear across the entire study range (ln(mortality) ) β ×
PM). Four experts (B, F, K, and L) specified a “piecewise”
log-linear function, with different β coefficients for PM
concentrations above and below an expert-specified break point. This
approach allowed them to express increased uncertainty in mortality
effects seen at lower concentrations in major epidemiological studies.
Expert K thought the relationship would be log-linear above a
threshold.”

T, to his function, which he described probabilistically. He specified
P(T > 0) = 0.5. Given T > 0, he indicated P(T ≤ 5 μg/m3) = 0.8 and
P(5 μg/m3 < T ≤ 10 μg/m3) = 0.2. Figure 3 does not include the
impact of applying expert K’s threshold, as the size of the reduction
in benefits will depend on the distribution of baseline PM levels in a
benefits analysis.”

Experts:

Dr. Doug W. Dockery, Harvard School of Public Health

Dr. Kazuhiko Ito, Nelson Institute of Environmental Medicine, NYU School
of Medicine, Tuxedo, NY

Dr. Dan Krewski, University of Ottawa

Dr. Nino Künzli, University of Southern California Keck School of
Medicine 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental
Medicine, New York University School of Medicine, Tuxedo, NY 

Dr. Joe Mauderly, Lovelace Respiratory Research Institute

Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of
Environmental Health Hazard Assessment, California Environmental
Protection Agency, Oakland, CA

Dr. Arden Pope, Professor, Department of Economics, Brigham Young
University, Provo, UT

Dr. Richard Schlesinger, Pace University

Dr. Joel Schwartz, Harvard School of Public Health

Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo,
NY

Dr. Mark Utell, University of Rochester School of Medicine and
Dentistry

H. CASAC comments on PM Staff Paper (2005)

U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB). 2005. EPA’s Review of the National Ambient Air Quality
Standards for Particulate Matter (Second Draft PM Staff Paper, January
2005). EPA-SAB-CASAC-05-007. June. Available on the Internet at
<http://yosemite.epa.gov/sab/sabproduct.nsf/E523DD36175EB5AD8525701B0073
32AE/$File/SAB-CASAC-05-007_unsigned.pdf>.

h impacts, the Panel favored the primary use of an assumed threshold of
10 μg/m3. The original approach of using background or LML, as well as
the other postulated thresholds, could still be used in a sensitivity
analysis of threshold assumptions.

“The analyses in this chapter highlight the impact of assumptions
regarding thresholds, or lack of threshold, on the estimates of risk.
The uncertainty associated with threshold or nonlinear models needs more
thorough discussion. A major research need is for more work to determine
the existence and level of any thresholds that may exist or the shape of
nonlinear concentration-response curves at low levels of exposure that
may exist, and to reduce uncertainty in estimated risks at the lowest PM
concentrations.”

CASAC Panel Members

Dr. Rogene Henderson, Scientist Emeritus, Lovelace Respiratory Research
Institute, Albuquerque, NM 

Dr. Ellis Cowling, University Distinguished Professor-at-Large, North
Carolina State University, Colleges of Natural Resources and Agriculture
and Life Sciences, North Carolina State University, Raleigh, NC 

Dr. James D. Crapo, Professor, Department of Medicine, Biomedical
Research and PatientCare, National Jewish Medical and Research Center,
Denver, CO 

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department
of Chemical Engineering, Clarkson University, Potsdam, NY 

Dr. Jane Q. Koenig, Professor, Department of Environmental Health,
School of Public Health and Community Medicine, University of
Washington, Seattle, WA

Dr. Petros Koutrakis, Professor of Environmental Science, Environmental
Health , School of Public Health, Harvard University (HSPH), Boston, MA 

Dr. Allan Legge, President, Biosphere Solutions, Calgary, Alberta 

Dr. Paul J. Lioy, Associate Director and Professor, Environmental and
Occupational Health Sciences Institute, UMDNJ - Robert Wood Johnson
Medical School, NJ 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental
Medicine, New York University School of Medicine, Tuxedo, NY 

Dr. Joe Mauderly, Vice President, Senior Scientist, and Director,
National Environmental Respiratory Center, Lovelace Respiratory Research
Institute, Albuquerque, NM 

Dr. Roger O. McClellan, Consultant, Albuquerque, NM 

Dr. Frederick J. Miller, Consultant, Cary, NC

Dr. Gunter Oberdorster, Professor of Toxicology, Department of
Environmental Medicine, School of Medicine and Dentistry, University of
Rochester, Rochester, NY 

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control
Division, Department of Environmental Conservation, Vermont Agency of
Natural Resources, Waterbury, VT 

Dr. Robert D. Rowe, President, Stratus Consulting, Inc., Boulder, CO 

Dr. Jonathan M. Samet, Professor and Chair, Department of Epidemiology,
Bloomberg School of Public Health, Johns Hopkins University, Baltimore,
MD 

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing
Laboratory, Harvard Medical School, Boston, MA 

Dr. Sverre Vedal, Professor of Medicine, School of Public Health and
Community Medicine University of Washington, Seattle, WA 

Mr. Ronald White, Research Scientist, Epidemiology, Bloomberg School of
Public Health, Johns Hopkins University, Baltimore, MD 

Dr. Warren H. White, Visiting Professor, Crocker Nuclear Laboratory,
University of California -Davis, Davis, CA 

Dr. George T. Wolff, Principal Scientist, General Motors Corporation,
Detroit, MI 

Dr. Barbara Zielinska, Research Professor, Division of Atmospheric
Science, Desert Research Institute, Reno, NV



I. HES Comments on 812 Analysis (2004)

U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB). 2004. Advisory on Plans for Health Effects Analysis in the
Analytical Plan for EPA’s Second Prospective Analysis – Benefits and
Costs of the Clean Air Act, 1990-2020. Advisory by the Health Effects
Subcommittee of the Advisory Council on Clean Air Compliance Analysis.
EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at
<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400
433D5D/$File/council_adv_04002.pdf>.

Pg 20: “The Subcommittee agrees that the whole range of uncertainties,
such as the questions of causality, shape of C-R functions and
thresholds, relative toxicity, years of life lost, cessation lag
structure, cause of death, biologic pathways, or susceptibilities may be
viewed differently for acute effects versus long-term effects. 

“For the studies of long-term exposure, the HES notes that Krewski et
al. (2000) have conducted the most careful work on this issue. They
report that the associations between PM2.5 and both all-cause and
cardiopulmonary mortality were near linear within the relevant ranges,
with no apparent threshold. Graphical analyses of these studies (Dockery
et al., 1993, Figure 3 and Krewski et al., 2000, page 162) also suggest
a continuum of effects down to lower levels. Therefore, it is reasonable
for EPA to assume a no threshold model down to, at least, the low end of
the concentrations reported in the studies.”

HES Panel Members

Dr. Bart Ostro, California Office of Environmental Health Hazard
Assessment (OEHHA), Oakland, CA 

Mr. John Fintan Hurley, Institute of Occupational Medicine (IOM),
Edinburgh, Scotland 

Dr. Patrick Kinney, Columbia University, New York, NY 

Dr. Michael Kleinman, University of California, Irvine, CA 

Dr. Nino Künzli, University of Southern California, Los Angeles, CA 

Dr. Morton Lippmann, New York University School of Medicine, Tuxedo, NY
Dr. Rebecca Parkin, The George Washington University, Washington, DC

Dr. Trudy Cameron, University of Oregon, Eugene, OR 

Dr. David T. Allen, University of Texas, Austin, TX 

Ms. Lauraine Chestnut, Stratus Consulting Inc., Boulder, CO 

Dr. Lawrence Goulder, Stanford University, Stanford, CA 

Dr. James Hammitt, Harvard University, Boston, MA 

Dr. F. Reed Johnson, Research Triangle Institute, Research Triangle
Park, NC 

Dr. Charles Kolstad, University of California, Santa Barbara, CA 

Dr. Lester B. Lave, Carnegie Mellon University, Pittsburgh, PA 

Dr. Virginia McConnell, Resources for the Future, Washington, DC 

Dr. V. Kerry Smith, North Carolina State University, Raleigh, NC 

Other Panel Members

Dr. John Evans, Harvard University, Portsmouth, NH Dr. Dale Hattis,
Clark University, Worcester, MA Dr. D. Warner North, NorthWorks Inc.,
Belmont, CA Dr. Thomas S. Wallsten, University of Maryland, College
Park, MD



J. NRC – Committee on Estimating the Health Risk Reduction Benefits of
Proposed Air Pollution Regulations (2002)

National Research Council (NRC). 2002. Estimating the Public Health
Benefits of Proposed Air Pollution Regulations. Washington, DC: The
National Academies Press.

Pg 109: “Linearity and Thresholds

“The shape of the concentration-response functions may influence the
overall estimate of benefits. The shape is particularly important for
lower ambient air pollution concentrations to which a large portion of
the population is exposed. For this reason, the impact of the existence
of a threshold may be considerable.

“In epidemiological studies, air pollution concentrations are usually
measured and modeled as continuous variables. Thus, it may be feasible
to test linearity and the existence of thresholds, depending on the
study design. In time-series studies with the large number of repeated
measurements, linearity and thresholds have been formally addressed with
reasonable statistical power. For pollutants such as PM10 and PM2.5,
there is no evidence for any departure of linearity in the observed
range of exposure, nor any indication of a threshold. For example,
examination of the mortality effects of short-term exposure to PM10 in
88 cities indicates that the concentration-response functions are not
due to the high concentrations and that the slopes of these functions do
not appear to increase at higher concentrations (Samet et al. 2000).
Many other mortality studies have examined the shape of the
concentration-response function and indicated that a linear
(nonthreshold) model fit the data well (Pope 2000). Furthermore, studies
conducted in cities with very low ambient pollution concentrations have
similar effects per unit change in concentration as those studies
conducted in cities with higher concentrations. Again, this finding
suggests a fairly linear concentration-response function over the
observed range of exposures.

“Regarding the studies of long-term exposure, Krewski et al. (2000)
found that the assumption of a linear concentration-response function
for mortality outcomes was not unreasonable. However, the statistical
power to assess the shape of these functions is weakest at the upper and
lower end of the observed exposure ranges. Most of the studies examining
the effects of long-term exposure on morbidity compare subjects living
in a small number of communities (Dockery et al. 1996;
Ackermmann-Liebrich 1997; Braun-Fahrländer et al. 1997). Because the
number of long-term effects studies are few and the number of
communities studied is relatively small (8 to 24), the ability to test
formally the absence or existence of a no-effect threshold is not
feasible. However, even if thresholds exist, they may not be at the same
concentration for all health outcomes.

“A review of the time-series and cohort studies may lead to the
conclusion that although a threshold is not apparent at commonly
observed concentrations, one may exist at lower levels. An important
point to acknowledge regarding thresholds is that for health benefits
analysis a key threshold is the population threshold (the lowest of the
individual thresholds). However, the population threshold would be very
difficult to observe empirically through epidemiology, because
epidemiology integrates information from very large groups of people
(thousands). Air pollution regulations affect even larger groups of
people (millions). It is reasonable to assume that among such large
groups susceptibility to air pollution health effects varies
considerably across individuals and depends on a large set of underlying
factors, including genetic makeup, age, exposure measurement error,
preexisting disease, and simultaneous exposures from smoking and
occupational hazards. This variation in individual susceptibilities and
the resulting distribution of individual thresholds underlies the
concentration-response function observed in epidemiology. Thus, until
biologically based models of the distribution of individual thresholds
are developed, it may be productive to assume that the population
concentration-response function is continuous and to focus on finding
evidence of changes in its slope as one approaches lower concentrations.

EPA’s Use of Thresholds

“In EPA’s benefits analyses, threshold issues were discussed and
interpreted. For the PM and ozone National Ambient Air Quality Standards
(NAAQS), EPA investigated the effects of a potential threshold or
reference value below which health consequences were assumed to be zero
(EPA 1997). Specifically, the high-end benefits estimate assumed a
12-microgram per cubic meter (µg/m3) mean threshold for mortality
associated with long-term exposure to PM2.5. The low-end benefits
estimate assumed a 15-µg/m3 threshold for all PM-related health
effects. The studies, however, included concentrations as low as 7.5
µg/m3. For the Tier 2 rule and the HD engine and diesel-fuel rule, no
threshold was assumed (EPA 1999, 2000). EPA in these analyses
acknowledged that there was no evidence for a threshold for PM.

“Several points should be noted regarding the threshold assumptions.
If a threshold is assumed where one was not apparent in the original
study, then the data should be refit and a new curve generated with the
assumption of a zero slope over a segment of the concentration-response
function that was originally found to be positively sloped. The
assumption of a zero slope over a portion of the curve will force the
slope in the remaining segment of the positively sloped
concentration-response function to be greater than was indicated in the
original study. A new concentration-response function was not generated
for EPA’s benefits analysis for the PM and ozone NAAQS for which
threshold assumptions were made. The generation of the steeper slope in
the remaining portion of the concentration-response function may fully
offset the effect of assuming a threshold. These aspects of assuming a
threshold in a benefits analysis where one was not indicated in the
original study should be conveyed to the reader. The committee notes
that the treatment of thresholds should be evaluated in a consistent and
transparent framework by using different explicit assumptions in the
formal uncertainty analyses (see  HYPERLINK
"http://www.nap.edu/openbook.php?record_id=10511&page=126" \l
"p20005c9f9970126001" Chapter 5 ).”

Pg 117: “Although the assumption of no thresholds in the most recent
EPA benefits analyses was appropriate, EPA should evaluate threshold
assumptions in a consistent and transparent framework using several
alternative assumptions in the formal uncertainty analysis.”

Pg 136: “Two additional illustrative examples are thresholds for
adverse effects and lag structures. HYPERLINK
"http://www.nap.edu/openbook.php?record_id=10511&page=136" \l
"p20005c9f8960136001" 2  EPA considers implausible any threshold for
mortality in the particulate matter (PM) exposure ranges under
consideration (EPA 1999a, p. 3-8). Although the agency conducts
sensitivity analyses incorporating thresholds, it provides no judgment
as to their relative plausibility. In a probabilistic uncertainty
analysis, EPA could assign appropriate weights to various threshold
models. For PM-related mortality in the Tier 2 analysis, the committee
expects that this approach would have resulted in only a slight widening
of the probability distribution for avoided mortality and a slight
reduction in the mean of that distribution, thus reflecting EPA’s
views about the implausibility of thresholds. The committee finds that
such formal incorporation of EPA’s expert judgments about the
plausibility of thresholds into its primary analysis would have been an
improvement.

“Uncertainty about thresholds is a special aspect of uncertainty about
the shape of concentration-response functions. Typically, EPA and
authors of epidemiological studies assume that these functions are
linear on some scale. Often, the scale is a logarithmic transformation
of the risk or rate of the health outcome, but when a rate or risk is
low, a linear function on the logarithmic scale is approximately linear
on the scale of the rate or risk itself. Increasingly, epidemiological
investigators are employing analytic methods that permit the estimation
of nonlinear shapes for concentration-response functions (Greenland et
al. 1999). As a consequence, EPA will need to be prepared to incorporate
nonlinear concentration-response functions from epidemiological studies
into the agency’s health benefits analyses. Any source of error or
bias that can distort an epidemiological association can also distort
the shape of an estimated concentration -response function, as can
variation in individual susceptibility (Hattis and Burmaster 1994;
Hattis et al. 2001).”

Pg 137: “In principle, many components of the health benefits model
need realistic probabilistic models (see Table 5-1 for a listing of such
components), in addition to concentration-response thresholds and time
lags between exposure and response. For example, additional features of
the concentration-response function—such as projection of the results
from the study population to the target populations (which may have
etiologically relevant characteristics outside the range seen in the
study population) and the projection of baseline frequencies of
morbidity and mortality into the future—must be characterized
probabilistically. Other uncertainties that might affect the probability
distributions are the estimations of population exposure (or even
concentration) from emissions, estimates of emissions themselves, and
the relative toxicity of various classes of particles. Similarly, many
aspects of the analysis of the impact of regulation on ambient
concentrations and on population exposure involve considerable
uncertainty and, therefore, may be beneficially modeled in this way.
Depending on the analytic approach used, joint probability distributions
will have to be specified to incorporate correlations between model
components that are structurally dependent upon each other, or the
analysis will have to be conducted in a sequential fashion that follows
the model for the data-generating process.

“EPA should explore alternative options for incorporating expert
judgment into its probabilistic uncertainty analyses. The agency
possesses considerable internal expertise, which should be employed as
fully as possible. Outside experts should also be consulted as needed,
individually or in panels. In all cases, when expert judgment is used in
the construction of a model component, the experts should be identified
and the rationales and empirical bases for their judgments should be
made available.”

NRC members

Dr. JOHN C. BAILAR, III (Chair), (emeritus) University of Chicago,
Chicago, Illinois

Dr. HUGH ROSS ANDERSON, University of London, London, England

Dr. MAUREEN L. CROPPER, University of Maryland, College Park

Dr. JOHN S. EVANS, Harvard University, Boston, Massachusetts

Dr. DALE B. HATTIS, Clark University, Worcester, Massachusetts

Dr. ROGENE F. HENDERSON, Lovelace Respiratory Research Institute,
Albuquerque, New Mexico

Dr. PATRICK L. KINNEY, Columbia University, New York, New York

Dr. NINO KÜNZLI, University of Basel, Basel, Switzerland; as of
September 2002, University of Southern California, Los Angeles

Dr. BART D. OSTRO, California Environmental Protection Agency, Oakland

Dr. CHARLES POOLE, University of North Carolina, Chapel Hill

Dr. KIRK R. SMITH, University of California, Berkeley

Dr. PETER A. VALBERG, Gradient Corporation, Cambridge, Massachusetts

Dr. SCOTT L. ZEGER, Johns Hopkins University, Baltimore, Maryland

 USGS notes these shipment data include cement imports (primarily Types
I, II, and V).

 Wages paid to production workers were $0.8 billion (8% of the value of
shipments) at an average hourly rate of $27.

 Throughout this report, we use PCA’s method to calculate labor and
energy efficiency. This measure is a weighted sum of clinker and
finished cement production. Weights for labor are 85% clinker and 15%
finished cement production. Weights for energy are 92% clinker and 8%
finished cement production (PCA, 2005  XE "PCA, 2005"  ).

 The labor costs reported in Figure 2-3 were calculated by first
multiplying the number of employee hours per metric ton of cement
reported in Table 2-4 by the average hourly earnings of production
workers for each year (BLS, 2007a and 2007b  XE "BLS, 2007"  ). Next,
these cost estimates were adjusted for inflation and expressed in 2005
dollars by using the consumer price index (CPI) (BLS, 2008  XE "DOC,
BLS, 2008"  ). 

 The 2002 Economic Census reports that the national Herfindahl-Hirschman
Index (HHI) for cement (North American Industry Classification System
[NAICS] 32731) is 568. However, this measure is likely not
representative of actual concentration that exists in regional markets.

 A recent USITC study of California cement markets found more than 75%
of gray Portland cement shipments in the state were shipped to customers
within 200 miles of the cement producer (USITC, 2006  XE "USITC, 2006" 
).

 In cases where no employment data were available, we used information
from previous EPA analyses to determine firm size.

 The 2002 Economic Census reports that the national Herfindahl-Hirschman
Index (HHI) for cement—North American Industry Classification System
(NAICS) 32731—is 568. However, this measure is likely not
representative of actual concentration that exists in regional markets.

 A recent USITC study of California cement markets found more than 75%
of gray Portland cement shipments in the state were shipped to customers
within 200 miles of the cement producer (USITC, 2006  XE "USITC, 2006" 
).

 In addition, large plants are typically more economical because they
can produce cement at lower unit costs; this reduces entry incentives
for smaller capacity cement plants.

 This economic model is formally known as a multi-firm Cournot oligopoly
model.

 This ultimately influences the partial-equilibrium model’s estimates
of the social cost of the regulatory program since bigger existing
market distortions tend to widen the gap between price and marginal cost
in these markets and lead to higher deadweight loss estimates than under
the case of perfectly competitive markets. The Office of Management and
Budget (OMB) explicitly mentions the need to consider market
power–related welfare costs in evaluating regulations under Executive
Order 12866 (EPA, 1999a  XE "EPA, 1999a"  ).

 The per-unit compliance costs were calculated by dividing the total
annualized cost per kiln by the kiln’s estimated cement production
within the economic impact model.  

 To place this reduction in context, it is a similar to the decline
experienced during the latest economic downturn; approximately 2,000
jobs (see Appendix A, Table A-3). 

 Since Morgenstern’s analysis reports environmental expenditures in
1987 dollars, we make an inflation adjustment to the engineering cost
analysis using the consumer price index (195.3/113.6) = 0.6) 

 In addition, large plants are typically more economical because they
can produce cement at lower unit costs; this reduces entry incentives
for smaller capacity cement plants.

 For this analysis, we used BenMAP version 3.0 (Abt Associates, 2008).
This model is available for free download on the Internet at
<http://www.epa.gov/air/benmap>.

 These two studies specify multi-pollutant models that control for SO2,
among other pollutants.

 Please see the Section 5.2 of the proposal RIA for this rule for more
information regarding the change in the presentation of benefits
estimates.

The benefits methodology has also been updated since the proposal RIA to
incorporate a revised VSL, as discussed in the next section.

It is important to note that uncertainty regarding the shape of the
concentration-response function is conceptually distinct from an assumed
threshold.  An assumed threshold (below which there are no health
effects) is a discontinuity, which is a specific example of
non-linearity.

 The comment period for the proposed rule closed on September 4, 2009
(Docket ID No. EPA–HQ–OAR–2002–0051 available at  HYPERLINK
"http://www.regulations.gov" http://www.regulations.gov ).  All public
comments received as well as the responses to those comments are
available in this docket.  

 To comply with Circular A-4, EPA provides monetized benefits using
discount rates of 3% and 7% (OMB, 2003  XE "OMB, 2003"  ). These
benefits are estimated for a specific analysis year (i.e., 2013), and
most of the PM benefits occur within that year with two exceptions:
acute myocardial infarctions (AMIs) and premature mortality. For AMIs,
we assume 5 years of follow-up medical costs and lost wages. For
premature mortality, we assume that there is a “cessation” lag
between PM exposures and the total realization of changes in health
effects. Although the structure of the lag is uncertain, EPA follows the
advice of the SAB-HES to assume a segmented lag structure characterized
by 30% of mortality reductions in the first year, 50% over years 2 to 5,
and 20% over the years 6 to 20 after the reduction in PM2.5 (U.S.
EPA-SAB, 2004  XE "U.S. EPA-SAB, 2004"  ). Changes in the lag
assumptions do not change the total number of estimated deaths but
rather the timing of those deaths. Therefore, discounting only affects
the AMI costs after the analysis year and the valuation of premature
mortalities that occur after the analysis year. As such, the monetized
benefits using a 7% discount rate are only approximately 10% less than
the monetized benefits using a 3% discount rate. 

After adjusting the VSL to account for a different currency year (2005$)
and to account for income growth to 2015, the $5.5 million VSL is $7.2
million.

In the (draft) update of the Economic Guidelines (U.S. EPA, 2006  XE
"U.S. EPA, 2008"  ), EPA retained the VSL endorsed by the SAB with the
understanding that further updates to the mortality risk valuation
guidance would be forthcoming in the near future. Therefore, this report
does not represent final agency policy.

In this analysis, we adjust the VSL to account for a different currency
year (2005$) and to account for income growth to 2015.  After applying
these adjustments to the $6.3 million value, the VSL is $8.3 million.

 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document:
Social Cost of Carbon for Regulatory Impact Analysis Under Executive
Order 12866, Interagency Working Group on Social Cost of Carbon, with
participation by Council of Economic Advisers, Council on Environmental
Quality, Department of Agriculture, Department of Commerce, Department
of Energy, Department of Transportation, Environmental Protection
Agency, National Economic Council, Office of Energy and Climate Change,
Office of Management and Budget, Office of Science and Technology
Policy, and Department of Treasury (February 2010).  Also available at  
HYPERLINK "http://www.epa.gov/otaq/climate/regulations.htm" 
http://www.epa.gov/otaq/climate/regulations.htm 

 The interagency group decided that these estimates apply only to CO2
emissions.  Given that warming profiles and impacts other than
temperature change (e.g. ocean acidification) vary across GHGs, the
group concluded “transforming gases into CO2-equivalents using GWP,
and then multiplying the carbon-equivalents by the SCC, would not result
in accurate estimates of the social costs of non-CO2 gases” (SCC TSD,
pg 13).  

 It is possible that other benefits or costs of proposed regulations
unrelated to CO2 emissions will be discounted at rates that differ from
those used to develop the SCC estimates.

 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/

 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/

 Chronic exposure is defined in the glossary of the Integrated Risk
Information (IRIS) database (http://www.epa.gov/iris) as repeated
exposure by the oral, dermal, or inhalation route for more than
approximately 10% of the life span in humans (more than approximately 90
days to 2 years in typically used laboratory animal species).

 Defined in the IRIS database as exposure to a substance spanning
approximately 10% of the lifetime of an organism.

 Defined in the IRIS database as exposure by the oral, dermal, or
inhalation route for 24 hours or less.

 The NATA modeling framework has a number of limitations which prevent
its use as the sole basis for setting regulatory standards. These
limitations and uncertainties are discussed on the 2002 NATA website.
Even so, this modeling framework is very useful in identifying air toxic
pollutants and sources of greatest concern, setting regulatory
priorities, and informing the decision making process.  U.S. EPA. (2009)
2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/

 National Research Council (NRC). 2000. Toxicological Effects of
Methylmercury. Committee on the Toxicological Effects of Methylmercury,
Board on Environmental Studies and Toxicology. National Academies Press.
Washington, DC. pp.168-173.

Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury
from fish lipid peroxidation, and the risk of myocardial infarction and
coronary, cardiovascular and any death in Eastern Finnish men.”
Circulation, 91 (3):645-655.

Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weihe, and  Grandjean,
P., 1999. “Prenatal Methylmercury Exposure As A Cardiovascular Risk
Factor At Seven Years of Age”, Epidemiology, pp370-375.

National Research Council (NRC). 2000. Toxicological Effects of
Methylmercury. Committee on the Toxicological Effects of Methylmercury,
Board on Environmental Studies and Toxicology. National Academies Press.
Washington, DC.  p. 229.

 See Section 5 of this RIA for more information on the air quality
modeling.  

 

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Toxic Substances and Disease Registry (ATSDR). Medical Management
Guidelines for Hydrogen Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA:
U.S. Department of Health and Human Services, Public Health Service.
Available on the Internet at < HYPERLINK
"http://www.atsdr.cdc.gov/Mhmi/mmg173.html"
http://www.atsdr.cdc.gov/Mhmi/mmg173.html >.

 All health effects language for this section came from: U.S. EPA. 2005.
“Full IRIS Summary for Toluene (CASRN 108-88-3)” Environmental
Protection Agency, Integrated Risk Information System (IRIS), Office of
Health and Environmental Assessment, Environmental Criteria and
Assessment Office, Cincinnati, OH. Available on the Internet at <
HYPERLINK "http://www.epa.gov/iris/subst/0118.htm"
http://www.epa.gov/iris/subst/0118.htm >.

 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and
Certain Home Residents from Exposure to Formaldehyde, Office of
Pesticides and Toxic Substances, April 1987.

 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A.
2003. Mortality from lymphohematopoetic malignancies among workers in
formaldehyde industries. Journal of the National Cancer Institute 95:
1615-1623.

 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A.
2004. Mortality from solid cancers among workers in formaldehyde
industries. American Journal of Epidemiology 159: 1117-1130.

 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes,
R. B.; Hoover, R. N.; Hauptmann, M. 2009. Mortality from
lymphohematopoietic malignancies among workers in formaldehyde
industries: The National Cancer Institute cohort. J. National Cancer
Inst. 101: 751-761.

 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers
exposed to formaldehyde: an update. Occup. Environ. Med. 61: 193-200.

 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Extended follow-up of a
cohort of British chemical workers exposed to formaldehyde. J National
Cancer Inst. 95:1608-1615.

 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston,
and FJ Miller. 2003. Biologically motivated computational modeling of
formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432-447.

 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston,
and FJ Miller. 2004. Human respiratory tract cancer risks of inhaled
formaldehyde: Dose-response predictions derived from
biologically-motivated computational modeling of a combined rodent and
human dataset. Tox Sci 82: 279-296.

 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde:
Hazard characterization and dose-response assessment for carcinogenicity
by the route of inhalation. CIIT, September 28, 1999. Research Triangle
Park, NC.

 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal
Growth Models for Formaldehyde with Relevance to Other
Biologically-Based Dose Response (BBDR) Models. U.S. Environmental
Protection Agency, Washington, D.C., EPA/600/R-08/103, 2006

 Subramaniam, R; Chen, C; Crump, K; .et .al. (2006) Uncertainties in
biologically-based modeling of formaldehyde-induced cancer risk:
identification of key issues. Risk Anal 28(4):907-923.

 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in
the CIIT 2-stage model for formaldehyde-induced nasal cancer in the F344
rat: a limited sensitivity analysis-I. Risk Anal 27:1237

 Crump, K; Chen, C; Fox, J; .et .al. (2006) Sensitivity analysis of
biologically motivated model for formaldehyde-induced respiratory cancer
in humans. Ann Occup Hyg 52:481-495.

 Crump, K; Chen, C; Fox, J; .et .al. (2006) Sensitivity analysis of
biologically motivated model for formaldehyde-induced respiratory cancer
in humans. Ann Occup Hyg 52:481-495.

 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in
the CIIT 2-stage model for formaldehyde-induced nasal cancer in the F344
rat: a limited sensitivity analysis-I. Risk Anal 27:1237

 International Agency for Research on Cancer (2006) Formaldehyde,
2-Butoxyethanol and 1-tert-Butoxypropan-2-ol. Monographs Volume 88.
World Health Organization, Lyon, France.

 Agency for Toxic Substances and Disease Registry (ATSDR). 1999.
Toxicological profile for Formaldehyde. Atlanta, GA: U.S. Department of
Health and Human Services, Public Health Service.
http://www.atsdr.cdc.gov/toxprofiles/tp111.html

 WHO (2002) Concise International Chemical Assessment Document 40:
Formaldehyde. Published under the joint sponsorship of the United
Nations Environment Programme, the International Labour Organization,
and the World Health Organization, and produced within the framework of
the Inter-Organization Programme for the Sound Management of Chemicals.
Geneva.

 All health effects language for this section came from: Agency for
Toxic Substances and Disease Registry (ATSDR). 1999. ToxFAQs for
Chlorinated Dibenzo-p-dioxins (CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6).
Atlanta, GA: U.S. Department of Health and Human Services, Public Health
Service. Available on the Internet at  HYPERLINK
"http://www.atsdr.cdc.gov/tfacts104.html"
http://www.atsdr.cdc.gov/tfacts104.html .

 All health effects language for this section came from: Agency for
Toxic Substances and Disease Registry (ATSDR). 1995. ToxFAQs™ for
Chlorodibenzofurans (CDFs). Atlanta, GA: U.S. Department of Health and
Human Services, Public Health Service. Available on the Internet at
<http://www.atsdr.cdc.gov/tfacts32.html>.

 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/

 U.S. EPA. 2000. Integrated Risk Information System File for Benzene.
This material is available electronically at:
http://www.epa.gov/iris/subst/0276.htm.

International Agency for Research on Cancer, IARC monographs on the
evaluation of carcinogenic risk of chemicals to humans, Volume 29, Some
industrial chemicals and dyestuffs, International Agency for Research on
Cancer, World Health Organization, Lyon, France, p. 345-389, 1982.

Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henry, V.A. (1992)
Synergistic action of the benzene metabolite hydroquinone on
myelopoietic stimulating activity of granulocyte/macrophage
colony-stimulating factor in vitro, Proc. Natl. Acad. Sci. 89:3691-3695.

 International Agency for Research on Cancer (IARC). 1987. Monographs on
the evaluation of carcinogenic risk of chemicals to humans, Volume 29,
Supplement 7, Some industrial chemicals and dyestuffs, World Health
Organization, Lyon, France.

U.S. Department of Health and Human Services National Toxicology Program
11th Report on Carcinogens available at:
http://ntp.niehs.nih.gov/go/16183.

 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene.
Environ. Health Perspect. 82: 193-197.

Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State
of the Art Reviews. 3: 541-554.

 Rothman, N., G.L. Li, M. Dosemeci, W.E. Bechtold, G.E. Marti, Y.Z.
Wang, M. Linet, L.Q. Xi, W. Lu, M.T. Smith, N. Titenko-Holland, L.P.
Zhang, W. Blot, S.N. Yin, and R.B. Hayes (1996) Hematotoxicity among
Chinese workers heavily exposed to benzene. Am. J. Ind. Med. 29:
236-246.

U.S. EPA 2002 Toxicological Review of Benzene (Noncancer Effects).
Environmental Protection Agency, Integrated Risk Information System
(IRIS), Research and Development, National Center for Environmental
Assessment, Washington DC. This material is available electronically at
http://www.epa.gov/iris/subst/0276.htm.

 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.; Cohen, B.; Melikian,
A.; Eastmond, D.; Rappaport, S.; Li, H.; Rupa, D.; Suramaya, R.;
Songnian, W.; Huifant, Y.; Meng, M.; Winnik, M.; Kwok, E.; Li, Y.; Mu,
R.; Xu, B.; Zhang, X.; Li, K. (2003). HEI Report 115, Validation &
Evaluation of Biomarkers in Workers Exposed to Benzene in China.

Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohen, et al. (2002).
Hematological changes among Chinese workers with a broad range of
benzene exposures. Am. J. Industr. Med. 42: 275- 285.

Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et al. (2004).
Hematotoxically in Workers Exposed to Low Levels of Benzene. Science
306: 1774-1776. Turtletaub, K.W. and Mani, C. (2003). Benzene metabolism
in rodents at doses relevant to human exposure from Urban Air. Research
Reports Health Effect Inst. Report No.113.

 U.S. EPA Integrated Risk Information System (IRIS) database is
available at: www.epa.gov/iris

 For more information on the annualized costs, please refer to Section 4
of this RIA.

 There are different commonly used models of oligopoly in the economics
literature. They differ with respect to the assumption about how a
company believes competing companies will react to its own production
decision. EPA selected the Cournot model where the company assumes
competing companies’ output is fixed in its own production decision.

 To highlight and make transparent the assumptions regarding seller
behavior, this equation is formally derived in Appendix B.

  PAGE  iii 

  PAGE  1-1 

  PAGE  2-1 

  PAGE   \* MERGEFORMAT  iii 

  PAGE   \* MERGEFORMAT  7-1 

A-  PAGE  6 

B-  PAGE  2 

C-  PAGE   \* MERGEFORMAT  4 

  PAGE   \* MERGEFORMAT  5-1 

D-  PAGE   \* MERGEFORMAT  30 

Added number to table without track changes.

Blue identifies a user-selected input within the BenMAP model

Green identifies a data input generated outside of the BenMAP model

Monetized PM2.5-related Benefits

Background Incidence and Prevalence Rates

Woods & Poole Population Projections

PM2.5 Related Health Impacts

PM2.5 Incremental Air Quality Change

Population Projections

Economic Valuation Functions

PM2.5 Health Functions

Baseline and Post-Control PM2.5 Concentrations

Census Population Data

