
Technical Support Document (TSD)
Preparation of Emissions Inventories for 2016v1 North American Emissions Modeling Platform
                                       
                                September 2020
                                                
                                                
Contacts: 
Alison Eyth, Jeff Vukovich, Caroline Farkas, Madeleine Strum 

U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division 
Emissions Inventory and Analysis Group 
Research Triangle Park, North Carolina 

TABLE OF CONTENTS

List of Tables	iv
List of Figures	vii
List of Appendices	viii
Acronyms	ix
1	Introduction	12
2	Emissions Inventories and Approaches	14
2.1	2016 point sources (ptegu, pt_oilgas, ptnonipm, airports)	18
2.1.1	EGU sector (ptegu)	20
2.1.2	Point source oil and gas sector (pt_oilgas)	21
2.1.3	Non-IPM sector (ptnonipm)	24
2.1.4	Aircraft and ground support equipment (airports)	26
2.2	2016 Nonpoint sources (afdust, ag, np_oilgas, rwc, nonpt)	28
2.2.1	Area fugitive dust sector (afdust)	28
2.2.2	Agriculture Sector (ag)	35
2.2.2.1	Livestock Waste Emissions	36
2.2.2.2	Fertilizer Emissions	37
2.2.3	Nonpoint Oil and Gas Sector (np_oilgas)	40
2.2.4	Residential Wood Combustion (rwc)	42
2.2.5	Nonpoint (nonpt)	43
2.3	2016 Onroad Mobile sources (onroad)	47
2.4	2016 Nonroad Mobile sources (cmv, rail, nonroad)	60
2.4.1	Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)	60
2.4.2	Category 3 Commercial Marine Vessels (cmv_c3)	63
2.4.3	Rail Sources (rail)	67
2.4.4	Nonroad Mobile Equipment Sources (nonroad)	76
2.5	2016 Fires (ptfire, ptagfire)	82
2.5.1	Wild and Prescribed Fires (ptfire)	82
2.5.2	Point source Agriculture Fires (ptagfire)	89
2.6	2016 Biogenic Sources (beis)	92
2.7	Sources Outside of the United States	94
2.7.1	Point Sources in Canada and Mexico (othpt)	94
2.7.2	Fugitive Dust Sources in Canada (othafdust, othptdust)	94
2.7.3	Nonpoint and Nonroad Sources in Canada and Mexico (othar)	95
2.7.4	Onroad Sources in Canada and Mexico (onroad_can, onroad_mex)	95
2.7.5	Fires in Canada and Mexico (ptfire_othna)	95
2.7.6	Ocean Chlorine	95
3	Emissions Modeling	96
3.1	Emissions modeling Overview	96
3.2	Chemical Speciation	99
3.2.1	VOC speciation	102
3.2.1.1	County specific profile combinations	105
3.2.1.2	Additional sector specific considerations for integrating HAP emissions from inventories into speciation	106
3.2.1.3	Oil and gas related speciation profiles	109
3.2.1.4	Mobile source related VOC speciation profiles	110
3.2.2	PM speciation	115
3.2.2.1	Mobile source related PM2.5 speciation profiles	116
3.2.3	NOX speciation	118
3.2.4	Creation of Sulfuric Acid Vapor (SULF)	118
3.3	Temporal Allocation	120
3.3.1	Use of FF10 format for finer than annual emissions	121
3.3.2	Electric Generating Utility temporal allocation (ptegu)	122
3.3.2.1	Base year temporal allocation of EGUs	122
3.3.3	Airport Temporal allocation (airports)	124
3.3.4	Residential Wood Combustion Temporal allocation (rwc)	126
3.3.5	Agricultural Ammonia Temporal Profiles (ag)	130
3.3.6	Oil and gas temporal allocation (np_oilgas)	131
3.3.7	Onroad mobile temporal allocation (onroad)	131
3.3.8	Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire)	135
3.4	Spatial Allocation	137
3.4.1	Spatial Surrogates for U.S. emissions	137
3.4.2	Allocation method for airport-related sources in the U.S.	143
3.4.3	Surrogates for Canada and Mexico emission inventories	144
3.5	Preparation of Emissions for the CAMx model	147
3.5.1	Development of CAMx Emissions for Standard CAMx Runs	147
3.5.2	Development of CAMx Emissions for Source Apportionment CAMx Runs	149
4	Development of 2023 and 2028 Emissions	153
4.1	EGU Point Source Projections (ptegu)	156
4.2	Non-EGU Point and Nonpoint Sector Projections	159
4.2.1	Background on the Control Strategy Tool (CoST)	160
4.2.2	CoST Plant CLOSURE Packet (ptnonipm, pt_oilgas)	164
4.2.3	CoST PROJECTION Packets (afdust, ag, cmv, rail, nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc)	164
4.2.3.1	Fugitive dust growth (afdust)	165
4.2.3.2	Livestock population growth (ag)	165
4.2.3.3	Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)	166
4.2.3.4	Category 3 Commercial Marine Vessels (cmv_c3)	167
4.2.3.5	Oil and Gas Sources (pt_oilgas, np_oilgas)	168
4.2.3.6	Non-EGU point sources (ptnonipm)	171
4.2.3.7	Nonpoint Sources (nonpt)	172
4.2.3.8	Airport sources (airports)	173
4.2.3.9	Residential Wood Combustion (rwc)	173
4.2.4	CoST CONTROL Packets (nonpt, np_oilgas, ptnonipm, pt_oilgas)	174
4.2.4.1	Oil and Gas NSPS (np_oilgas, pt_oilgas)	176
4.2.4.2	RICE NSPS (nonpt, ptnonipm, np_oilgas, pt_oilgas)	178
4.2.4.3	Fuel Sulfur Rules (nonpt, ptnonipm)	181
4.2.4.4	Natural Gas Turbines NOx NSPS (ptnonipm, pt_oilgas)	182
4.2.4.5	Process Heaters NOx NSPS (ptnonipm, pt_oilgas)	184
4.2.4.6	CISWI (ptnonipm)	187
4.2.4.7	Petroleum Refineries NSPS Subpart JA (ptnonipm)	188
4.2.4.8	State-Specific Controls (ptnonipm)	189
4.3	Projections Computed Outside of CoST	190
4.3.1	Nonroad Mobile Equipment Sources (nonroad)	190
4.3.2	Onroad Mobile Sources (onroad)	190
4.3.3	Locomotives (rail)	193
4.3.4	Sources Outside of the United States (onroad_can, onroad_mex, othpt, ptfire_othna, othar, othafdust, othptdust)	194
4.3.4.1	Canadian fugitive dust sources (othafdust, othptdust)	194
4.3.4.2	Point Sources in Canada and Mexico (othpt)	195
4.3.4.3	Nonpoint sources in Canada and Mexico (othar)	196
4.3.4.1	Onroad sources in Canada and Mexico (onroad_can, onroad_mex)	197
5	Emission Summaries	198
6	References	205


List of Tables
Table 2-1.  Platform sectors for the 2016 emissions modeling case	15
Table 2-2. Point source oil and gas sector NAICS Codes	21
Table 2-3. 2014NEIv2-to-2016 projection factors for pt_oilgas sector for 2016v1 inventory	22
Table 2-4. 2016fh pt_oilgas national emissions (excluding offshore) before and after 2014-to-2016 projections (tons/year)	23
Table 2-5. Pennsylvania emissions changes for natural gas transmission sources (tons/year).	23
Table 2-6. SCCs for Census-based growth from 2014 to 2016	24
Table 2-7.  2016v1 platform SCCs for the airports sector	27
Table 2-8. Afdust sector SCCs	28
Table 2-9.  Total impact of fugitive dust adjustments to unadjusted 2016 v1 inventory	32
Table 2-10.  2016v1 platform SCCs for the ag sector	35
Table 2-11.  National back-projection factors for livestock: 2017 to 2016	36
Table 2-12.  Source of input variables for EPIC	39
Table 2-13. 2014NEIv2-to-2016 oil and gas projection factors for CO and OK.	41
Table 2-14. 2016 v1 platform SCCs for RWC sector	42
Table 2-15. Projection factors for RWC by SCC	43
Table 2-16. 2016v1 platform SCCs for Census-based growth	45
Table 2-17. MOVES vehicle (source) types	47
Table 2-18. Submitted data used to prepare onroad activity data	48
Table 2-19. Factors applied to project VMT from 2014 to 2016 to prepare default activity data	49
Table 2-20. Older Vehicle Adjustments Showing the Fraction of IHS Vehicle Populations to Retain for 2016v1 and 2017 NEI	57
Table 2-21. 2016v1 platform SCCs for cmv_c1c2 sector	60
Table 2-22. Vessel groups in the cmv_c1c2 sector	62
Table 2-23. 2016v1 platform SCCs for cmv_c3 sector	64
Table 2-24. 2017 to 2016 projection factors for C3 CMV	67
Table 2-25. 2016v1 SCCs for the Rail Sector	68
Table 2-26. Class I Railroad Reported Locomotive Fuel Use Statistics for 2016	68
Table 2-27. 2016 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal)	70
Table 2-28. Surface Transportation Board R-1 Fuel Use Data  -  2016	71
Table 2-29. 2016 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)[4]	71
Table 2-30. Expenditures and fuel use for commuter rail	74
Table 2-31. Submitted nonroad input tables by agency	80
Table 2-32. Alaska counties/census areas for which nonroad equipment sector-specific emissions are removed in 2016v1	81
Table 2-33. SCCs included in the ptfire sector for the 2016v1 inventory	82
Table 2-34. National fire information databases used in 2016v1 ptfire inventory	83
Table 2-35. List of S/L/T agencies that submitted fire data for 2016v1 with types and formats.	85
Table 2-36. Brief description of fire information submitted for 2016v1 inventory use.	85
Table 2-37. SCCs included in the ptagfire sector for the 2016v1 inventory	89
Table 2-38. Assumed field size of agricultural fires per state(acres)	91
Table 2-39. Hourly Meteorological variables required by BEIS 3.61	93
Table 3-1.  Key emissions modeling steps by sector.	97
Table 3-2.  Descriptions of the platform grids	98
Table 3-3. Emission model species produced for CB6 for CMAQ	100
Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM) for each platform sector	104
Table 3-5. Ethanol percentages by volume by Canadian province	106
Table 3-6.  MOVES integrated species in M-profiles	107
Table 3-7.  Basin/Region-specific profiles for oil and gas	109
Table 3-8.  TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a used for the 2016 Platform	110
Table 3-9.  Select mobile-related VOC profiles 2016	111
Table 3-10.  Onroad M-profiles	112
Table 3-11.  MOVES process IDs	113
Table 3-12.  MOVES Fuel subtype IDs	114
Table 3-13.  MOVES regclass IDs	114
Table 3-14.  SPECIATE4.5 brake and tire profiles compared to those used in the 2011v6.3 Platform	117
Table 3-15.  Nonroad PM2.5 profiles	118
Table 3-16.  NOX speciation profiles	118
Table 3-17.  Sulfate split factor computation	119
Table 3-18.  SO2 speciation profiles	119
Table 3-19.  Temporal settings used for the platform sectors in SMOKE	120
Table 3-20.  U.S. Surrogates available for the 2016v1 modeling platforms	138
Table 3-21.  Off-Network Mobile Source Surrogates	140
Table 3-22.  Spatial Surrogates for Oil and Gas Sources	140
Table 3-23. Selected 2016 CAP emissions by sector for U.S. Surrogates (short tons in 12US1)	141
Table 3-24.  Canadian Spatial Surrogates	144
Table 3-25. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3)	145
Table 3-26. Emission model species mappings for CMAQ and CAMx	148
Table 3-27. State tags for 2023fh1, 2028fh1 USSA modeling	150
Table 4-1.  Overview of projection methods for the 2023 and 2028 regional cases	153
Table 4-2.  EGU sector NOx emissions by State for the 2023 and 2028 regional cases	158
Table 4-3. Subset of CoST Packet Matching Hierarchy	161
Table 4-4. Summary of non-EGU stationary projections subsections	162
Table 4-5. Reductions from all facility/unit/stack-level closures in 2016v1	164
Table 4-6. Increase in total afdust PM2.5 emissions from projections in 2016v1	165
Table 4-7. National projection factors for livestock: 2016 to 2023 and 2028	166
Table 4-8. National projection factors for cmv_c1c2	166
Table 4-9. California projection factors for cmv_c1c2	167
Table 4-10. 2016-to-2023 and 2016-2028 CMV C3 projection factors outside of California	168
Table 4-11. 2016-to-2023 and 2016-2028 CMV C3 projection factors for California	168
Table 4-12.  Year 2014-2017 high-level summary of national oil and gas exploration activity	170
Table 4-13. EIA's 2019 Annual Energy Outlook (AEO) tables used to project industrial sources	171
Table 4-14. Projection factors for RWC	173
Table 4-15. Assumed retirement rates and new source emission factor ratios for NSPS rules	175
Table 4-16. Non-point (np_oilgas) SCCs in 2016v1 modeling platform where Oil and Gas NSPS controls applied	176
Table 4-17. Emissions reductions for np_oilgas sector due to application of Oil and Gas NSPS	177
Table 4-18. Point source SCCs in pt_oilgas sector where Oil and Gas NSPS controls were applied.	177
Table 4-19. VOC reductions (tons/year) for the pt_oilgas sector after application of the Oil and Gas NSPS CONTROL packet for both future years 2023 and 2028.	178
Table 4-20. SCCs and Engine Type in 2016v1 modeling platform where RICE NSPS controls applied for nonpt and ptnonipm sectors.	179
Table 4-21. Non-point Oil and Gas SCCs in 2016v1 modeling platform where RICE NSPS controls applied	179
Table 4-22. Nonpoint Emissions reductions after the application of the RICE NSPS	180
Table 4-23. Ptnonipm Emissions reductions after the application of the RICE NSPS	180
Table 4-24. Oil and Gas Emissions reductions for np_oilgas sector due to application of RICE NSPS	181
Table 4-25. Point source SCCs in pt_oilgas sector where RICE NSPS controls applied.	181
Table 4-26. Emissions reductions (tons/year) in pt_oilgas sector after the application of the RICE NSPS CONTROL packet for future years 2023 and 2028.	181
Table 4-27. Summary of fuel sulfur rule impacts on nonpoint SO2 emissions for 2023 and 2028	182
Table 4-28. Summary of fuel sulfur rule impacts on ptnonipm SO2 emissions for 2023 and 2028	182
Table 4-29. Stationary gas turbines NSPS analysis and resulting emission rates used to compute controls	182
Table 4-30. Ptnonipm SCCs in 2016v1 modeling platform where Natural Gas Turbines NSPS controls applied	184
Table 4-31. Ptnonipm emissions reductions after the application of the Natural Gas Turbines NSPS	184
Table 4-32. Point source SCCs in pt_oilgas sector where Natural Gas Turbines NSPS control applied.	184
Table 4-33. Emissions reductions (tons/year) for pt_oilgas after the application of the Natural Gas Turbines NSPS CONTROL packet for future years 2023 and 2028.	184
Table 4-34. Process Heaters NSPS analysis and 2016v1 new emission rates used to estimate controls	185
Table 4-35. Ptnonipm SCCs in 2016v1 modeling platform where Process Heaters NSPS controls applied.	185
Table 4-36. Ptnonipm emissions reductions after the application of the Process Heaters NSPS	186
Table 4-37. Point source SCCs in pt_oilgas sector where Process Heaters NSPS controls were applied	186
Table 4-38.  NOx emissions reductions (tons/year) in pt_oilgas sector after the application of the Process Heaters NSPS CONTROL packet for futures years 2023 and 2028.	187
Table 4-39. Summary of CISWI rule impacts on ptnonipm emissions for 2023 and 2028	188
Table 4-40. Summary of NSPS Subpart JA rule impacts on ptnonipm emissions for 2023 and 2028	188
Table 4-41. Factors used to Project 2016 VMT to 2023 and 2028	191
Table 4-42. Class I Line-haul Fuel Projections based on 2018 AEO Data	193
Table 4-43. Class I Line-haul Historic and Future Year Projected Emissions	194
Table 4-44. AEO growth rates for rail sub-groups	194
Table 5-1. National by-sector CAP emissions summaries for the 2016fh case, 12US1 grid (tons)	198
Table 5-2. National by-sector CAP emissions summaries for the 2023fh1 case, 12US1 grid (tons)	200
Table 5-3. National by-sector CAP emissions summaries for the 2028fh1 case, 12US1 grid (tons)	201
Table 5-4. National by-sector CAP emissions summaries for the 2016fh case, 36US3 grid (tons)	202
Table 5-5. National by-sector CAP emissions summaries for the 2023fh1 case, 36US3 grid (tons)	203
Table 5-6. National by-sector CAP emissions summaries for the 2028fh1 case, 36US3 grid (tons)	204

List of Figures
Figure 2-1.  Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and cumulative	34
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions	38
Figure 2-3. Representative Counties in 2016v1	59
Figure 2-4. 2017NEI/2016 platform geographical extent (solid) and U.S. ECA (dashed)	61
Figure 2-5. 2016 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT)	69
Figure 2-6. Class I Railroads in the United States[5]	69
Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States	72
Figure 2-8. Class II and III Railroads in the United States[5]	73
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains	75
Figure 2-10. Processing flow for fire emission estimates in the 2016v1 inventory	87
Figure 2-11. Default fire type assignment by state and month in cases where a satellite detect is only source of fire information.	88
Figure 2-12.  Blue Sky Modeling Framework	88
Figure 2-13. Normbeis3 data flows	93
Figure 2-14. Tmpbeis3 data flow diagram.	94
Figure 3-1. Air quality modeling domains	99
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation	104
Figure 3-3.  Profiles composited for the new PM gas combustion related sources	115
Figure 3-4.  Comparison of PM profiles used for Natural gas combustion related sources	116
Figure 3-5.  Eliminating unmeasured spikes in CEMS data	122
Figure 3-6.  Seasonal diurnal profiles for EGU emissions in a Virginia Region	123
Figure 3-7.  Diurnal Profile for all Airport SCCs	124
Figure 3-8.  Weekly profile for all Airport SCCs	125
Figure 3-9.  Monthly Profile for all Airport SCCs	125
Figure 3-10.  Alaska Seaplane Profile	126
Figure 3-11.  Example of RWC temporal allocation in 2007 using a 50 versus 60 ˚F threshold	127
Figure 3-12.  RWC diurnal temporal profile	128
Figure 3-13.  Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr)	129
Figure 3-14.  Day-of-week temporal profiles for OHH and Recreational RWC	129
Figure 3-15.  Annual-to-month temporal profiles for OHH and recreational RWC	130
Figure 3-16.  Example of animal NH3 emissions temporal allocation approach, summed to daily emissions	131
Figure 3-17.  Example of temporal variability of NOX emissions	132
Figure 3-18.  Sample onroad diurnal profiles for Fulton County, GA	133
Figure 3-19.  Counties for which MOVES Speeds and Temporal Profiles could be Populated	134
Figure 3-20.  Example of Temporal Profiles for Combination Trucks	135
Figure 3-21.  Agricultural burning diurnal temporal profile	136
Figure 3-22.  Prescribed and Wildfire diurnal temporal profiles	137
Figure 4-1.  EIA Oil and Gas Supply Regions as of AEO2019	169

List of Appendices
 Appendix A: CB6 Assignment for New Species
 Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE4.5 that were used in the 2014 v7.2 Platform
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
Acronyms
AADT
Annual average daily traffic
AE6
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0 
AEO
Annual Energy Outlook
AERMOD
American Meteorological Society/Environmental Protection Agency Regulatory Model
AIS
Automated Identification System
APU
Auxiliary power unit
BEIS
Biogenic Emissions Inventory System
BELD
Biogenic Emissions Land use Database
BenMAP
Benefits Mapping and Analysis Program
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAMD
EPA's Clean Air Markets Division
CAMX
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CB05
Carbon Bond 2005 chemical mechanism
CB6
Version 6 of the Carbon Bond mechanism
CBM
Coal-bed methane
CDB
County database (input to MOVES model)
CEMS
Continuous Emissions Monitoring System
CISWI
Commercial and Industrial Solid Waste Incinerators
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
CNG
Compressed natural gas
CO
Carbon monoxide
CONUS
Continental United States
CoST
Control Strategy Tool
CRC
Coordinating Research Council
CSAPR
Cross-State Air Pollution Rule
E0, E10, E85
0%, 10% and 85% Ethanol blend gasoline, respectively
ECA
Emissions Control Area
ECCC
Environment and Climate Change Canada
EF
Emission Factor
EGU
Electric Generating Units
EIA
EIS
Energy Information Administration
Emissions Inventory System
EPA
Environmental Protection Agency
EMFAC
EMission FACtor (California's onroad mobile model)
EPIC
Environmental Policy Integrated Climate modeling system
FAA
Federal Aviation Administration
FCCS
Fuel Characteristic Classification System
FEST-C
Fertilizer Emission Scenario Tool for CMAQ
FF10
Flat File 2010
FINN
Fire Inventory from the National Center for Atmospheric Research
FIPS
Federal Information Processing Standards
FHWA
Federal Highway Administration
HAP
Hazardous Air Pollutant
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
I/M
Inspection and Maintenance
IMO
International Marine Organization
IPM
Integrated Planning Model
LADCO
Lake Michigan Air Directors Consortium
LDV
Light-Duty Vehicle
LPG
Liquified Petroleum Gas
MACT
Maximum Achievable Control Technology
MARAMA
Mid-Atlantic Regional Air Management Association
MATS
Mercury and Air Toxics Standards
MCIP
Meteorology-Chemistry Interface Processor
MMS
Minerals Management Service (now known as the Bureau of Energy Management, Regulation and Enforcement (BOEMRE)
MOVES
Motor Vehicle Emissions Simulator
MSA
Metropolitan Statistical Area
MTBE
Methyl tert-butyl ether
MWC
Municipal waste combustor
MY
Model year
NAAQS
National Ambient Air Quality Standards
NAICS
North American Industry Classification System
NBAFM
Naphthalene, Benzene, Acetaldehyde, Formaldehyde and Methanol
NCAR
National Center for Atmospheric Research
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NESCAUM
Northeast States for Coordinated Air Use Management
NH3
Ammonia
NLCD
National Land Cover Database
NOAA
National Oceanic and Atmospheric Administration
NONROAD
OTAQ's model for estimation of nonroad mobile emissions
NOX
Nitrogen oxides
NSPS
New Source Performance Standards
OHH
Outdoor Hydronic Heater
OTAQ
EPA's Office of Transportation and Air Quality
ORIS
Office of Regulatory Information System
ORD
EPA's Office of Research and Development
OSAT
Ozone Source Apportionment Technology
PFC
Portable Fuel Container
PM2.5
Particulate matter less than or equal to 2.5 microns
PM10
Particulate matter less than or equal to 10 microns
ppm
arts per million
ppmv
Parts per million by volume
PSAT
Particulate Matter Source Apportionment Technology
RACT
Reasonably Available Control Technology
RBT
Refinery to Bulk Terminal
RIA
Regulatory Impact Analysis
RICE
Reciprocating Internal Combustion Engine
RWC
Residential Wood Combustion
RPD
Rate-per-vehicle (emission mode used in SMOKE-MOVES)
RPH
Rate-per-hour (emission mode used in SMOKE-MOVES)
RPP
Rate-per-profile (emission mode used in SMOKE-MOVES)
RPV
Rate-per-vehicle (emission mode used in SMOKE-MOVES)
RVP
Reid Vapor Pressure
SCC
Source Classification Code
SMARTFIRE2
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation version 2
SMOKE
Sparse Matrix Operator Kernel Emissions
SO2
Sulfur dioxide
SOA
Secondary Organic Aerosol
SIP
State Implementation Plan
SPDPRO
S/L/T
Hourly Speed Profiles for weekday versus weekend
state, local, and tribal
TAF
Terminal Area Forecast 
TCEQ
Texas Commission on Environmental Quality
TOG
Total Organic Gas
TSD
Technical support document
USDA
VIIRS
United States Department of Agriculture
Visible Infrared Imaging Radiometer Suite
VOC
Volatile organic compounds
VMT
Vehicle miles traveled
VPOP
Vehicle Population
WRAP
Western Regional Air Partnership
WRF
Weather Research and Forecasting Model
2014NEIv2
2014 National Emissions Inventory (NEI), version 2



Introduction
The U.S. Environmental Protection Agency (EPA), working in conjunction with the National Emissions Inventory Collaborative, developed an air quality modeling platform for criteria air pollutants to represent the years of 2016, 2023 and 2028.  The starting point for the 2016 inventory was the 2014 National Emissions Inventory (NEI), version 2 (2014NEIv2), although many inventory sectors were updated to represent the year 2016 through the incorporation of 2016-specific state and local data along with nationally-applied adjustment methods.  The year 2023 and year 2028 inventories were developed starting with the 2016 inventory using sector-specific methods as described below.  The inventories support several applications, including modeling in support of the Revised Cross State Air Pollution Rule (CSAPR) Update for the 2008 Ozone National Ambient Air Quality Standards (NAAQS). 

The air quality modeling platform consists of all the emissions inventories and ancillary data files used for emissions modeling, as well as the meteorological, initial condition, and boundary condition files needed to run the air quality model.  This document focuses on the emissions modeling data and techniques including the emission inventories, the ancillary data files, and the approaches used to transform inventories for use in air quality modeling.  

The National Emissions Inventory Collaborative is a partnership between state emissions inventory staff, multi-jurisdictional organizations (MJOs), federal land managers (FLMs), EPA, and others to develop a North American air pollution emissions modeling platform with a base year of 2016 for use in air quality planning. The Collaborative planned for three versions of the 2016 platform: alpha, beta, and Version 1.0. This numbering format for this platform is different from previous EPA platforms which had the first number based on the version of the NEI, and the second number as a platform iteration for that NEI year (e.g., 7.3 where 7 represents 2014 NEI-based platforms, and 3 means the third iteration of the platform).  For the emissions modeling documented in this technical support document (TSD), the emissions values for most sectors are the same as those in the Inventory Collaborative 2016v1 Emissions Modeling Platform, available from http://views.cira.colostate.edu/wiki/wiki/10202.  In the file packages for this platform, the platform may sometimes be known as the 2016v7.3 platform. The specification sheets posted on the 2016v1 platform release page on the Wiki provide many details regarding the inventories and emissions modeling techniques in addition to those addressed in this TSD.  

Some updates were made to the 2016v1 platform after the fall 2019 release that were included in the Revised CSAPR Update modeling, including some minor revisions to commercial marine vessel (CMV) emissions, and electric generating unit (EGU) emissions developed in January 2020.  Updates to 2016v1 to correct airport emissions and 2016 EGU processing made in June and July of 2020 were not included in the CSAPR Update modeling because the modeling was already complete by that time. The updated data and a description of them are available on the EPA FTP site ftp://newftp.epa.gov/air/emismod/2016/v1/postv1_updates/. 

This 2016 emissions modeling platform includes all criteria air pollutants (CAPs) and precursors, and a group of hazardous air pollutants (HAPs).  The group of HAPs are those explicitly used by the chemical mechanism in the Community Multiscale Air Quality (CMAQ) model (Appel et al., 2018) for ozone/particulate matter (PM): chlorine (Cl), hydrogen chloride (HCl), benzene, acetaldehyde, formaldehyde, methanol, naphthalene.  The modeling domain includes the lower 48 states and parts of Canada and Mexico.  The modeling cases for this platform were developed for the Comprehensive Air Quality Model with Extensions (CAMx).  However, the emissions modeling process first prepares outputs in the format used by CMAQ, after which those emissions data are converted to the formats needed by CAMx.

The 2016 platform used in this study consists of a 2016 base case, a 2023 case, and a 2028 case with the abbreviations 2016fh_16j, 2023fh1_16j, and 2028fh1_16j, respectively. Additional cases that included source apportionment by state and in some cases inventory sector were also developed. This platform accounts for atmospheric chemistry and transport within a state-of-the-art photochemical grid model. In the case abbreviation 2016fh_16j, 2016 is the year represented by the emissions; the "f" represents the base year emissions modeling platform iteration, which here shows that it is 2014 NEI-based (whereas for 2011 NEI-based platforms, this letter was "e"); and the "h" stands for the eighth configuration of emissions modeled for a 2014-NEI based modeling platform.  The cases named 2023fh1_16j and 2028fh1_16j are the same as the original 2023 and 2028 future year cases, except that they include EGU emissions that were developed in January 2020 and slightly updated commercial marine vessel emissions.

The 2016v1 emissions modeling platform includes point sources, nonpoint sources, commercial marine vessels (CMV), onroad and nonroad mobile sources, and fires for the U.S., Canada, and Mexico.  Some platform categories use more disaggregated data than are made available in the NEI. For example, in the platform, onroad mobile source emissions are represented as hourly emissions by vehicle type, fuel type process and road type while the NEI emissions are aggregated to vehicle type/fuel type totals and annual temporal resolution.  Temporal, spatial and other changes in emissions between the NEI and the emissions input into the platform are described primarily in the platform specification sheets, although a full NEI was not developed for the year 2016 because only point sources above a certain potential to emit must be submitted for years between the full triennial NEI years (e.g., 2014, 2017, 2020). Emissions from Canada and Mexico are used for the modeling platform but are not part of the NEI.  

The primary emissions modeling tool used to create the air quality model-ready emissions was the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (http://www.smoke-model.org/), version 4.7 (SMOKE 4.7) with some updates.  Emissions files were created for a 36-km national grid and for a 12-km national grid, both of which include the contiguous states and parts of Canada and Mexico as shown in Figure 3-1.  

The gridded meteorological model used to provide input data for the emissions modeling was developed using the Weather Research and Forecasting Model (WRF, https://ral.ucar.edu/solutions/products/weather-research-and-forecasting-model-wrf ) version 3.8, Advanced Research WRF core (Skamarock, et al., 2008).  The WRF Model is a mesoscale numerical weather prediction system developed for both operational forecasting and atmospheric research applications.  The WRF was run for 2016 over a domain covering the continental U.S. at a 12km resolution with 35 vertical layers.  The run for this platform included high resolution sea surface temperature data from the Group for High Resolution Sea Surface Temperature (GHRSST) (see https://www.ghrsst.org/) and is given the EPA meteorological case label "16j."  The full case name includes this abbreviation following the emissions portion of the case name to fully specify the name of the case as "2016fh_16j."

This document contains five sections and several appendices.  Section 2 describes the 2016 and 2028 inventories input to SMOKE.  Section 3 describes the emissions modeling and the ancillary files used with the emission inventories.  Methods to develop future year emissions are described in Section 4. Data summaries are provided in Section 5.  Section 6 provides references.  The Appendices provide additional details about specific technical methods or data. 
Emissions Inventories and Approaches
This section summarizes the emissions data that make up the 2016v1 platform.  This section provides details about the data contained in each of the platform sectors for the base year and the future year.    
The original starting point for the emission inventories was the 2014NEIv2 although emissions for most sectors have been updated to better represent the year 2016. Documentation for the 2014NEIv2, including a TSD, is available at https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-nei-technical-support-document-tsd Documentation for each 2016v1 emissions sector in the form of specification sheets is available on the 2016v1 page of Inventory Collaborative Wiki (http://views.cira.colostate.edu/wiki/wiki/10202). In addition to the NEI-based data for the broad categories of point, nonpoint, onroad, nonroad, and events (i.e., fires), emissions from the Canadian and Mexican inventories and several other non-NEI data sources are included in the 2016 platform.  

The triennial NEI data for CAPs are largely compiled from data submitted by state, local and tribal (S/L/T) air agencies.  HAP emissions data are also from the S/L/T agencies, but, are often augmented by the EPA because they are voluntarily submitted.  The EPA uses the Emissions Inventory System (EIS) to compile the NEI.  The EIS includes hundreds of automated quality assurance checks to help improve data quality, and also supports tracking release point (e.g., stack) coordinates separately from facility coordinates.  The EPA collaborates extensively with S/L/T agencies to ensure a high quality of data in the NEI.  Using the 2014NEIv2 as a starting point, the National Inventory Collaborative worked to develop a modeling platform that more closely represents the year 2016. All emissions modeling sectors were modified in some way to better represent the year 2016 for the 2016v1 platform. 

The point source emission inventories for the platform include partially updated emissions to represent 2016 based on state-submitted data and adjustments to much of the remaining 2014 data to better represent 2016. Agricultural and wildland fire emissions represent the year 2016. Most nonpoint source sectors started with 2014NEIv2 emissions and were adjusted to better represent the year 2016. Fertilizer emissions, nonpoint oil and gas emissions, and onroad and nonroad mobile source emissions represent the year 2016. For CMV emissions, emissions were developed based on 2017 NEI CMV emissions and the sulfur dioxide (SO2) emissions reflect rules that reduced sulfur emissions for CMV that took effect in the year 2015. For fertilizer ammonia emissions, a 2016-specific emissions inventory is used in this platform. Nonpoint oil and gas emissions were developed using 2016-specific data for oil and gas wells and their 2016 production levels. 

Onroad and nonroad mobile source emissions were developed using the Motor Vehicle Emission Simulator (MOVES).  Onroad emissions for the platform were developed based on emissions factors output from MOVES2014b for the year 2016, run with inputs derived from the 2014NEIv2 including activity data (e.g., vehicle miles traveled and vehicle populations) provided by state and local agencies or otherwise projected to the year 2016.  MOVES2014b was also used to generate nonroad emissions because it included important updates related to nonroad engine population growth rates and spatial allocation factors.  

For the purposes of preparing the air quality model-ready emissions, emissions from the five NEI data categories are split into finer-grained sectors used for emissions modeling.  The significance of an emissions modeling or "platform sector" is that the data are run through the SMOKE programs independently from the other sectors except for the final merge (Mrggrid).  The final merge program combines the sector-specific gridded, speciated, hourly emissions together to create CMAQ-ready emission inputs. For studies that use CAMx, these CMAQ-ready emissions inputs are converted into the file formats needed by CAMx.

Table 2-1 presents an overview the sectors in the 2016 platform and how they generally relate to the 2014NEIv2 as their starting point.  The platform sector abbreviations are provided in italics.  These abbreviations are used in the SMOKE modeling scripts, inventory file names, and throughout the remainder of this document. Through the Collaborative workgroups, state and local agencies provided data used in the development of most sectors.
Table 2-1.  Platform sectors for the 2016 emissions modeling case
                         Platform Sector: abbreviation
                               NEI Data Category
             Description and resolution of the data input to SMOKE
EGU units:
Ptegu
                                     Point
Point source electric generating units (EGUs) for 2016 from the Emissions Inventory System (EIS), based on 2014NEIv2 with most sources updated to 2016. Includes some specific S/L/T updates. The inventory emissions are replaced with hourly 2016 Continuous Emissions Monitoring System (CEMS) values for nitrogen oxides (NOX) and SO2 for any units that are matched to the NEI, and other pollutants for matched units are scaled from the 2016 point inventory using CEMS heat input.  Emissions for all sources not matched to CEMS data come from the raw inventory. Annual resolution for sources not matched to CEMS data, hourly for CEMS sources.
Point source oil and gas: 
pt_oilgas
                                     Point
Point sources for 2016 including S/L/T updates for oil and gas production and related processes based on facilities with the following NAICS: 2111, 21111, 211111, 211112 (Oil and Gas Extraction); 213111 (Drilling Oil and Gas Wells); 213112 (Support Activities for Oil and Gas Operations); 2212, 22121, 221210 (Natural Gas Distribution); 48611, 486110 (Pipeline Transportation of Crude Oil); 4862, 48621, 486210 (Pipeline Transportation of Natural Gas).  Includes offshore oil and gas platforms in the Gulf of Mexico (FIPS=85). Oil and gas point sources that were not already updated to year 2016 in the baseline inventory were projected from 2014 to 2016. Annual resolution.
Aircraft and ground support equipment: airports
                                     Point
Emissions from aircraft up to 3,000 ft elevation and emissions from ground support equipment based on 2017 NEI data. Note that these emissions were found to be overestimated in June 2020.
Remaining non-EGU point:
ptnonipm
                                     Point
All 2016 point source inventory records not matched to the ptegu, airports, or pt_oilgas sectors, including updates submitted by state and local agencies. Year 2016 rail yard emissions were developed by the rail workgroup.  Annual resolution.
Agricultural:
ag
                                   Nonpoint
Nonpoint livestock and fertilizer application emissions.  Livestock includes ammonia and other pollutants (except PM2.5) and was backcasted from a draft version of 2017NEI based on animal population data from the U.S. Department of Agriculture (USDA) National Agriculture Statistics Service Quick Stats, where available.  Fertilizer includes only ammonia and is estimated for 2016 using the FEST-C model. County and monthly resolution.
Agricultural fires with point resolution: ptagfire
                                   Nonpoint
2016 agricultural fire sources based on EPA-developed data with state updates, represented as point source day-specific emissions. They are in the nonpoint NEI data category, but in the platform, they are treated as point sources.  Mostly at daily resolution with some state-submitted data at monthly resolution.
Area fugitive dust:
afdust
                                   Nonpoint
PM10 and PM2.5 fugitive dust sources from the 2014NEIv2 nonpoint inventory with paved road dust grown to 2016 levels; including building construction, road construction, agricultural dust, and road dust.  The NEI emissions are reduced during modeling according to a transport fraction (newly computed for the 2016 beta platform) and a meteorology-based (precipitation and snow/ice cover) zero-out.  Afdust emissions from the portion of Southeast Alaska inside the 36US3 domain are processed in a separate sector called `afdust_ak'. County and annual resolution.  
Biogenic:
beis
                                   Nonpoint
Year 2016, hour-specific, grid cell-specific emissions generated from the BEIS3.61 model within SMOKE, including emissions in Canada and Mexico using BELD v4.1 "water fix" land use data (including improved treatment of water grid cells). 
Category 1, 2 CMV:
cmv_c1c2
                                   Nonpoint
Category 1 and category 2 (C1C2) commercial marine vessel (CMV) emissions sources backcast to 2016 from the 2017NEI using a multiplier of 0.98.emissions. Includes C1C2 emissions in U.S. state and Federal waters, and also all non-U.S. C1C2 emissions including those in Canadian waters. Gridded and hourly resolution. 
Category 3 CMV:
cmv_c3
                                   Nonpoint
Category 3 (C3) CMV emissions converted to point sources based on the center of the grid cells. Includes C3 emissions in U.S. state and Federal waters, and also all non-U.S. C3 emissions including those in Canadian waters. Emissions are backcast to 2016 from 2017NEI emissions based on factors derived from U.S. Army Corps of Engineers Entrance and Clearance data and information about the ships entering the ports. Gridded and hourly resolution.
Locomotives : 
rail
                                   Nonpoint
Line haul rail locomotives emissions developed by the rail workgroup based on 2016 activity and emission factors.  Includes freight and commuter rail emissions and incorporates state and local feedback.  County and annual resolution.
Remaining nonpoint:
nonpt
                                   Nonpoint
2014NEIv2 nonpoint sources not included in other platform sectors with sources proportional to human population activity data grown to year 2016; incorporates state and local feedback. County and annual resolution. 
Nonpoint source oil and gas: 
np_oilgas
                                   Nonpoint
2016 nonpoint oil and gas emissions output from the NEI oil and gas tool along with state and local feedback. County and annual resolution.
Residential Wood Combustion:
rwc
                                   Nonpoint
2014NEIv2 nonpoint sources from residential wood combustion (RWC) processes projected to the year 2016.  County and annual resolution.
Nonroad:
nonroad
                                    Nonroad
2016 nonroad equipment emissions developed with the MOVES2014b model which incorporates updated equipment growth rates.  MOVES was used for all states except California and Texas, which submitted emissions.  County and monthly resolution.
Onroad:
onroad
                                    Onroad
2016 onroad mobile source gasoline and diesel vehicles from moving and non-moving vehicles that drive on roads, along with vehicle refueling.  Includes the following modes: exhaust, extended idle, auxiliary power units, evaporative, permeation, refueling, and brake and tire wear.  For all states except California, developed using winter and summer MOVES emissions tables produced by MOVES2014b coupled with activity data projected to year 2016 or provided by S/L/T agencies.  SMOKE-MOVES was used to compute emissions from the emission factors and activity data.  Onroad emissions for Alaska, Hawaii, Puerto Rico and the Virgin Islands were computed using the same method as the continental U.S.,but are part of the onroad_nonconus sector.
Onroad California:
onroad_ca_adj 
                                    Onroad
2016 California-provided CAP onroad mobile source gasoline and diesel vehicles based on the EMFAC model, which ere gridded and temporalized using MOVES2014b results.  Volatile organic compound (VOC) HAP emissions derived from California-provided VOC emissions and MOVES-based speciation.
Point source fires- ptfire
                                    Events
Point source day-specific wildfires and prescribed fires for 2016 computed using Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Framework (Sullivan, 2008 and Raffuse, 2007) for both flaming and smoldering processes (i.e., SCCs 281XXXX002). Smoldering is forced into layer 1 (by adjusting heat flux). Incorporates state inputs.  Daily resolution.
Non-US. Fires:
ptfire_othna
                                      N/A
Point source day-specific wildfires and prescribed fires for 2016 provided by Environment Canada with data for missing months, and for Mexico and Central America, filled in using fires from the Fire Inventory (FINN) from National Center for Atmospheric Research (NCAR) fires (NCAR, 2016 and Wiedinmyer, C., 2011).   Daily resolution.
Other Area Fugitive dust sources not from the NEI:
othafdust
                                      N/A
Fugitive dust sources of particulate matter emissions excluding land tilling from agricultural activities, from Environment and Climate Change Canada (ECCC) 2015 emission inventory, except that construction dust emissions were reduced to levels compatible with their 2010 inventory.  A transport fraction adjustment is applied along with a meteorology-based (precipitation and snow/ice cover) zero-out. County and annual resolution.  
Other Point Fugitive dust sources not from the NEI:
othptdust
                                      N/A
Fugitive dust sources of particulate matter emissions from land tilling from agricultural activities, ECCC 2015 emission inventory, but wind erosion emissions were removed.  A transport fraction adjustment is applied along with a meteorology-based (precipitation and snow/ice cover) zero-out. Data were originally provided on a rotated 10-km grid for beta, but were smoothed so as to avoid the artifact of grid lines in the processed emissions.  Monthly resolution.
Other point sources not from the NEI:
othpt
                                      N/A
Point sources from the ECCC 2015 emission inventory, including agricultural ammonia, along with emissions from Mexico's 2008 inventory projected to 2014 and 2018 and then interpolated to 2016. Agricultural data were originally provided on a rotated 10-km grid for beta, but were smoothed so as to avoid the artifact of grid lines in the processed emissions.  Monthly resolution for Canada agricultural and airport emissions, annual resolution for the remainder of Canada and all of Mexico.  
Other non-NEI nonpoint and nonroad:
othar
                                      N/A
Year 2015 Canada (province or sub-province resolution) emissions from the ECCC inventory: monthly for nonroad sources; annual for rail and other nonpoint Canada sectors.  Year 2016 Mexico (municipio resolution) emissions, interpolated from 2014 and 2018 inventories that were projected from their 2008 inventory: annual nonpoint and nonroad mobile inventories.  
Other non-NEI onroad sources:
onroad_can
                                      N/A
Monthly year 2015 Canada (province resolution or sub-province resolution, depending on the province) from the ECCC onroad mobile inventory. 
Other non-NEI onroad sources:
onroad_mex
                                      N/A
Monthly year 2016 Mexico (municipio resolution) onroad mobile inventory based on MOVES-Mexico runs for 2014 and 2018 then interpolated to 2016.

Other natural emissions are also merged in with the above sectors: ocean chlorine and sea salt. The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cl2) concentrations in oceanic air masses (Bullock and Brehme, 2002).  In CMAQ, the species name is "CL2".  The sea salt emissions were developed with version 4.1 of the OCEANIC pre-processor that comes with the CAMx model. The preprocessor estimates time/space-varying emissions of aerosol sodium, chloride and sulfate; gas-phase chlorine and bromine associated with sea salt; gaseous halo-methanes; and dimethyl sulfide (DMS). These additional oceanic emissions are incorporated into the final model-ready emissions files for CAMx. 

The emission inventories in SMOKE input formats for the platform are available from EPA's Air Emissions Modeling website: https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms, under the section entitled "2016v1 Platform".  The platform "README" file indicates the particular zipped files associated with each platform sector.  A number of reports (i.e., summaries) are available with the data files for the 2016 platform.  The types of reports include state summaries of inventory pollutants and model species by modeling platform sector and county annual totals by modeling platform sector. Additional types of data including outputs from SMOKE and inputs to CAMx are available from the Intermountain West Data Warehouse.
2016 point sources (ptegu, pt_oilgas, ptnonipm, airports)
Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude/longitude) are specified, as in the case of an individual facility.  A facility may have multiple emission release points that may be characterized as units such as boilers, reactors, spray booths, kilns, etc.  A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas).  This section describes NEI point sources within the contiguous U.S. and the offshore oil platforms which are processed by SMOKE as point source inventories.  A full NEI is compiled every three years including 2011, 2014 and 2017. In the intervening years, emissions information about point sources that exceed certain potential to emit threshold are required to be submitted to the EIS that is used to compile the NEI.  A comprehensive description of how EGU emissions were characterized and estimated in the 2014 NEI is located in Section 3.4 in the 2014NEIv2 TSD. The methods for emissions estimation are similar for the interim year of 2016, but there is no TSD available specific to the 2016 point source submissions to EIS.  Additional information on state submissions through the collaborative process are available in the collaborative specification sheets.

The point source file used for the modeling platform is exported from EIS into the Flat File 2010 (FF10) format that is compatible with SMOKE (see https://www.cmascenter.org/smoke/documentation/4.7/html/ch08s02s08.html). 

For the 2016v1 platform, the export of point source emissions, including stack parameters and locations from EIS, was done on June 12, 2018.  The flat file was modified to remove sources without specific locations (i.e., their FIPS code ends in 777).  Then the point source FF10 was divided into four NEI-based platform point source sectors: the EGU sector (ptegu), point source oil and gas extraction-related emissions (pt_oilgas), airport emissions were put into the airports sector, and the remaining non-EGU sector also called the non-IPM (ptnonipm) sector. The split was done at the unit level for ptegu and facility level for pt_oilgas such that a facility may have units and processes in both ptnonipm and ptegu, but cannot be in both pt_oilgas and any other point sector. Additional information on updates made through the collaborative process is available in the collaborative specification sheets.
 
The EGU emissions are split out from the other sources to facilitate the use of distinct SMOKE temporal processing and future-year projection techniques.  The oil and gas sector emissions (pt_oilgas) were processed separately for summary tracking purposes and distinct future-year projection techniques from the remaining non-EGU emissions (ptnonipm). 

The inventory pollutants processed through SMOKE for all point source sectors were:  carbon monoxide (CO), NOX, VOC, SO2, ammonia (NH3), particles less than 10 microns in diameter (PM10), and particles less than 2.5 microns in diameter (PM2.5), and all of the air toxics listed in Table 3-3.  The Naphthalene, Benzene, Acetaldehyde, Formaldehyde, and Methanol (NBAFM) species are explicit in the CB6-CMAQ chemical mechanism and are taken from the HAP emissions in the flat file (if present for a source) as opposed to using emissions generated through VOC speciation, as is normally done for non-toxics modeling applications.  To prevent double counting of mass, NBAFM species are removed from VOC speciation profiles, thus resulting in speciation profiles that may sum to less than 1.  This is called the "no-integrate" VOC speciation case and is discussed in detail in Section 3.2.1.1.  The resulting VOC in the modeling system may be higher or lower than the VOC emissions in the NEI; they would only be the same if the HAP inventory and speciation profiles were exactly consistent.  For HAPs other than those in NBAFM, there is no concern for double-counting since CMAQ handles these outside the CB6 mechanism.

The ptnonipm and pt_oilgas sector emissions were provided to SMOKE as annual emissions.  For those ptegu sources with CEMS data that could be matched to the point inventory from EIS, hourly CEMS NOX and SO2 emissions were used rather than the annual total NEI emissions. For all other pollutants at matched units, the annual emissions were used as-is from the NEI, but were allocated to hourly values using heat input from the CEMS data.  For the sources in the ptegu sector not matched to CEMS data, daily emissions were created using an approach described in Section 2.1.1. For non-CEMS units other than municipal waste combustors and cogeneration units, IPM region- and pollutant-specific diurnal profiles were applied to create hourly emissions.  
EGU sector (ptegu)
	The ptegu sector contains emissions from EGUs in the 2016 NEI point inventory that could be matched to units found in the National Electric Energy Data System (NEEDS) v6 database.  The matching was prioritized according to the amount of the emissions produced by the source.  In the SMOKE point flat file, emission records for sources that have been matched to the NEEDS database have a value filled into the IPM_YN column based on the matches stored within EIS. The 2016 NEI point inventory consists of data submitted by S/L/T agencies and EPA to the EIS for Type A (i.e., large) point sources. Those EGU sources in the 2014 NEIv2 inventory that were not submitted or updated for 2016 and not identified as retired were retained. The retained 2014 NEIv2 EGUs in CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV were projected from 2014 to 2016 values using factors provided by the Mid-Atlantic Regional Air Management Association  (MARAMA).

Higher generation capacity units in the ptegu sector are matched to 2016 CEMS data from EPA's Clean Air Markets Division (CAMD) via ORIS facility codes and boiler ID.  For the matched units, SMOKE replaces the 2016 emissions of NOX and SO2 with the CEMS emissions, thereby ignoring the annual values specified in the NEI.  For other pollutants at matched units, the hourly CEMS heat input data are used to allocate the NEI annual emissions to hourly values.  All stack parameters, stack locations, and Source Classification Codes (SCC) for these sources come from the NEI or updates provided by data submitters outside of EIS.  Because these attributes are obtained from the NEI, the chemical speciation of VOC and PM2.5 for the sources is selected based on the SCC or in some cases, based on unit-specific data.  If CEMS data exists for a unit, but the unit is not matched to the NEI, the CEMS data for that unit is not used in the modeling platform.  However, if the source exists in the NEI and is not matched to a CEMS unit, the emissions from that source are still modeled using the annual emission value in the NEI temporally allocated to hourly values.  The EGU flat file inventory is split into a flat file with CEMS matches and a flat file without CEMS matches to support analysis and temporalization.

In the SMOKE point flat file, emission records for point sources matched to CEMS data have values filled into the ORIS_FACILITY_CODE and ORIS_BOILER_ID columns.  The CEMS data in SMOKE-ready format is available at http://ampd.epa.gov/ampd/ near the bottom of the "Prepackaged Data" tab.  Many smaller emitters in the CEMS program are not identified with ORIS facility or boiler IDs that can be matched to the NEI due to inconsistencies in the way a unit is defined between the NEI and CEMS datasets, or due to uncertainties in source identification such as inconsistent plant names in the two data systems.  Also, the NEEDS database of units modeled by IPM includes many smaller emitting EGUs that do not have CEMS.  Therefore, there will be more units in the NEEDS database than have CEMS data.  The temporal allocation of EGU units matched to CEMS is based on the CEMS data, whereas regional profiles are used for most of the remaining units.  More detail can be found in Section 3.3.2.

Some EIS units match to multiple CAMD units based on cross-reference information in the EIS alternate identifier table. The multiple matches are used to take advantage of hourly CEMS data when a CAMD unit specific entry is not available in the inventory. Where a multiple match is made the EIS unit is split and the ORIS facility and boiler IDs are replaced with the individual CAMD unit IDs. The split EIS unit NOX and SO2 emissions annual emissions are replaced with the sum of CEMS values for that respective unit. All other pollutants are scaled from the EIS unit into the split CAMD unit using the fraction of annual heat input from the CAMD unit as part of the entire EIS unit. The NEEDS ID in the "ipm_yn" column of the flat file is updated with a "_M_" between the facility and boiler identifiers to signify that the EIS unit had multiple CEMS matches. The inventory records with multiple matches had the EIS unit identifiers appended with the ORIS boiler identifier to distinguish each CEMS record in SMOKE.

For sources not matched to CEMS data, except for municipal waste combustors (MWCs) waste-to-energy and cogeneration units, daily emissions were computed from the NEI annual emissions using average CEMS data profiles specific to fuel type, pollutant, and IPM region.  To allocate emissions to each hour of the day, diurnal profiles were created using average CEMS data for heat input specific to fuel type and IPM region.  See Section 3.3.2 for more details on the temporal allocation approach for ptegu sources.  MWC and cogeneration units were specified to use uniform temporal allocation such that the emissions are allocated to constant levels for every hour of the year. These sources do not use hourly CEMs, and instead use a PTDAY file with the same emissions for each day, combined with a uniform hourly temporal profile applied by SMOKE.
Point source oil and gas sector (pt_oilgas)
The pt_oilgas sector consists of point source oil and gas emissions in United States, primarily pipeline-transportation and some upstream exploration and production. Sources in the pt_oilgas sector consist of sources which are not electricity generating units (EGUs) and which have a North American Industry Classification System (NAICS) code corresponding to oil and gas exploration, production, pipeline-transportation or distribution. The pt_oilgas sector was separated from the ptnonipm sector by selecting sources with specific NAICS codes shown in Table 2-2.  The use of NAICS to separate out the point oil and gas emissions forces all sources within a facility to be in this sector, as opposed to ptegu where sources within a facility can be split between ptnonipm and ptegu sectors.

Table 2-2. Point source oil and gas sector NAICS Codes
NAICS
Type of point source
NAICS description
2111, 21111
Production
Oil and Gas Extraction 
211111 
Production
Crude Petroleum and Natural Gas Extraction 
211112
Production
Natural Gas Liquid Extraction
213111
Production
Drilling Oil and Gas Wells 
213112
Support
Support Activities for Oil and Gas Operations
2212, 22121, 221210
Distribution
Natural Gas Distribution
4862, 48621, 486210
Transmission
Pipeline Transportation of Natural Gas 
48611, 486110
Transmission
Pipeline Transportation of Crude Oil 

The starting point for the 2016v1 emissions platform pt_oilgas inventory was the 2016 point source NEI. The 2016 NEI includes data submitted by S/L/T  agencies and EPA to the EIS for Type A (i.e., large) point sources. Point sources in the 2014 NEIv2 not submitted for 2016 were pulled forward from the 2014 NEIv2 unless they had been marked as shut down.  For the federally-owned offshore point inventory of oil and gas platforms, a 2014 inventory was developed by the U.S. Department of the Interior, Bureau of Ocean and Energy Management, Regulation, and Enforcement (BOEM).

The 2016 pt_oilgas inventory includes sources with updated data for 2016 and sources carried forward from the 2014NEIv2 point inventory. Each type of source can be identified based on the calc_year field in the flat file 2010 (FF10) formatted inventory files, which is set to either 2016 or 2014. The pt_oilgas inventory was split into two components: one for 2016 sources, and one for 2014 sources. The 2016 sources were used in 2016v1 platform without further modification.  Updates were made to selected West Virginia Type B facilities based on comments from the state.

For pt_oilgas emissions that were carried forward from the 2014NEIv2, the emissions were projected to represent the year 2016. Each state/ SCC/NAICS combination in the inventory was classified as either an oil source, a natural gas source, a combination of oil and gas, or designated as a "no growth" source. Growth factors were based on historical state production data from the Energy Information Administration (EIA) and are listed in Table 2. National 2016 pt_oilgas emissions before and after application of 2014-to-2016 projections are shown in Table 3. The historical production data for years 2014 and 2016 for oil and natural gas were taken from the following websites:

:: https://www.eia.gov/dnav/pet/pet_crd_crpdn_adc_mbbl_a.htm (Crude production)
:: http://www.eia.gov/dnav/ng/ng_sum_lsum_a_epg0_fgw_mmcf_a.htm (Natural gas production)

The "no growth" sources include all offshore and tribal land emissions, and all emissions with a NAICS code associated with distribution, transportation, or support activities. As there were no 2015 production data in the EIA for Idaho, no growth was assumed for this state; the only pt_oilgas sources in Idaho were pipeline transportation related. Maryland and Oregon had no oil production data on the EIA website. The factors provided in Table 2-8 were applied to sources with NAICS = 2111, 21111, 211111, 211112, and 213111 and with production-related SCC processes.  Table 2-3 provides a national summary of emissions before and after this 2 year projection for these sources in the pt_oilgas sector.  Table 2-4 shows the national emissions for pt_oilgas following the projection to 2016. 
Table 2-3. 2014NEIv2-to-2016 projection factors for pt_oilgas sector for 2016v1 inventory
State
                              Natural Gas growth
                                  Oil growth
                          Combination gas/oil growth
Alabama
                                     -9.0%
                                    -17.5%
                                    -13.2%
Alaska
                                     1.9%
                                     -1.1%
                                     0.4%
Arizona
                                    -55.7%
                                    -85.7%
                                    -70.7%
Arkansas
                                    -26.7%
                                     13.6%
                                     -6.6%
California
                                    -14.2%
                                     -9.1%
                                    -11.7%
Colorado
                                     3.5%
                                     22.0%
                                     12.8%
Florida
                                     8.0%
                                    -13.2%
                                     -2.6%
Idaho
                                     0.0%
                                     0.0%
                                     0.0%
Illinois
                                     13.2%
                                     -9.5%
                                     1.8%
Indiana
                                     -6.2%
                                    -27.5%
                                    -16.9%
Kansas
                                    -15.0%
                                    -23.4%
                                    -19.2%
Kentucky
                                     -1.6%
                                    -23.1%
                                    -12.4%
Louisiana
                                    -11.0%
                                    -17.4%
                                    -14.2%
Maryland
                                     70.0%
                                      N/A
                                      N/A
Michigan
                                    -12.6%
                                    -23.4%
                                    -18.0%
Mississippi
                                    -10.9%
                                    -16.3%
                                    -13.6%
Missouri
                                    -66.7%
                                    -37.2%
                                    -52.0%
Montana
                                    -11.9%
                                    -22.5%
                                    -17.2%
Nebraska
                                     27.3%
                                    -25.0%
                                     1.2%
Nevada
                                     0.0%
                                    -12.3%
                                     -6.2%
New Mexico
                                     1.4%
                                     17.4%
                                     9.4%
New York
                                    -33.4%
                                    -36.8%
                                    -35.1%
North Dakota
                                     31.4%
                                     -4.3%
                                     13.6%
Ohio
                                    181.0%
                                     44.4%
                                    112.7%
Oklahoma
                                     5.9%
                                     6.9%
                                     6.4%
Oregon
                                    -18.0%
                                      N/A
                                      N/A
Pennsylvania
                                     24.8%
                                     -7.9%
                                     8.5%
South Dakota
                                    -33.9%
                                    -21.7%
                                    -27.8%
Tennessee
                                    -31.9%
                                    -22.1%
                                    -27.0%
Texas
                                     -6.1%
                                     1.0%
                                     -2.6%
Utah
                                    -19.8%
                                    -25.4%
                                    -22.6%
Virginia
                                    -10.0%
                                    -50.0%
                                    -30.0%
West Virginia
                                     28.9%
                                     0.7%
                                     14.8%
Wyoming
                                     -7.5%
                                     -4.7%
                                     -6.1%

Table 2-4. 2016fh pt_oilgas national emissions (excluding offshore) before and after 2014-to-2016 projections (tons/year)
Pollutant
                              Before projections
                               After projections
                             % change 2014 to 2016
CO
                                    175,929
                                    177,690
                                     1.0%
NH3
                                     4,347
                                     4,338
                                     -0.2%
NOX
                                    377,517
                                    379,866
                                     0.6%
PM10-PRI
                                    12,630
                                    12,397
                                     -1.8%
PM25-PRI
                                    11,545
                                    11,286
                                     -2.2%
SO2
                                    35,236
                                    34,881
                                     -1.0%
VOC
                                    127,242
                                    129,253
                                     1.6%

The state of Pennsylvania provided new emissions data for natural gas transmission sources for year 2016. The PA point source data replaced the emissions used in 2016beta. Table 2-5 illustrates the change in emissions with this update.  
Table 2-5. Pennsylvania emissions changes for natural gas transmission sources (tons/year).
                                     State
                                  State FIPS
                                     NAICS
                                   Pollutant
                                   2016 beta
                                    2016 v1
                                2016 v1 - beta
                                 Pennsylvania
                                      42
                                    486210
                                      CO
                                     2,787
                                     2,385
                                      403
                                 Pennsylvania
                                      42
                                    486210
                                      NOX
                                     5,737
                                     5,577
                                      160
                                 Pennsylvania
                                      42
                                    486210
                                   PM10-PRI
                                      400
                                      227
                                      173
                                 Pennsylvania
                                      42
                                    486210
                                   PM25-PRI
                                      399
                                      209
                                      191
                                 Pennsylvania
                                      42
                                    486210
                                      SO2
                                      30
                                      33
                                      -3
                                 Pennsylvania
                                      42
                                    486210
                                      VOC
                                     1,221
                                     1,149
                                      71

Non-IPM sector (ptnonipm)
With minor exceptions, the ptnonipm sector contains point sources that are not in the airport, ptegu or pt_oilgas sectors.  For the most part, the ptnonipm sector reflects the non-EGU sources of the NEI point inventory; however, it is likely that some small low-emitting EGUs not matched to the NEEDS database or to CEMS data are present in the ptnonipm sector. The ptnonipm emissions in the 2016v1 platform have been updated from the 2016 NEI point inventory with the following changes.

Non-IPM Projection from 2014 to 2016 inside MARAMA region

2014-to-2016 projection packets for all nonpoint sources were provided by MARAMA for the following states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV. 

New Jersey provided their own projection factors for projection from 2014 to 2016 which were mostly the same as those provided by MARAMA, except for three SCCs with differences (SCCs: 2302070005, 2401030000, 2401070000). For those three SCCs, the projection factors provided by New Jersey were used instead of the MARAMA factors.

Non-IPM Projection from 2014 to 2016 outside MARAMA region

In areas outside of the MARAMA states, historical census population, sometimes by county and sometimes by state, was used to project select nonpt sources from the 2014NEIv2 to 2016v1 platform. The population data was downloaded from the US Census Bureau. Specifically, the "Population, Population Change, and Estimated Components of Population Change: April 1, 2010 to July 1, 2017" file (https://www2.census.gov/programs-surveys/popest/datasets/2010-2017/counties/totals/co-est2017-alldata.csv). A ratio of 2016 population to 2014 population was used to create a growth factor that was applied to the 2014NEIv2 emissions with SCCs matching the population-based SCCs listed in Table 2-6 Positive growth factors (from increasing population) were not capped, but negative growth factors (from decreasing population) were flatlined for no growth.   
Table 2-6. SCCs for Census-based growth from 2014 to 2016
SCC
Tier 1 Description
Tier 2 Description
Tier 3 
Description
Tier 4 
Description
2302002100
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Charbroiling
Conveyorized Charbroiling
2302002200
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Charbroiling
Under-fired Charbroiling
2302003000
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Total
2302003100
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Flat Griddle Frying
2302003200
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Clamshell Griddle Frying
2401001000
Solvent Utilization
Surface Coating
Architectural Coatings
Total: All Solvent Types
2401002000
Solvent Utilization
Surface Coating
Architectural Coatings - Solvent-based
Total: All Solvent Types
2401003000
Solvent Utilization
Surface Coating
Architectural Coatings - Water-based
Total: All Solvent Types
2401100000
Solvent Utilization
Surface Coating
Industrial Maintenance Coatings
Total: All Solvent Types
2401200000
Solvent Utilization
Surface Coating
Other Special Purpose Coatings
Total: All Solvent Types
2425000000
Solvent Utilization
Graphic Arts
All Processes
Total: All Solvent Types
2425010000
Solvent Utilization
Graphic Arts
Lithography
Total: All Solvent Types
2425020000
Solvent Utilization
Graphic Arts
Letterpress
Total: All Solvent Types
2425030000
Solvent Utilization
Graphic Arts
Rotogravure
Total: All Solvent Types
2425040000
Solvent Utilization
Graphic Arts
Flexography
Total: All Solvent Types
2440020000
Solvent Utilization
Miscellaneous Industrial
Adhesive (Industrial) Application
Total: All Solvent Types
2460000000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Processes
Total: All Solvent Types
2460100000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Personal Care Products
Total: All Solvent Types
2460200000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Household Products
Total: All Solvent Types
2460400000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Automotive Aftermarket Products
Total: All Solvent Types
2460500000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Coatings and Related Products
Total: All Solvent Types
2460600000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Adhesives and Sealants
Total: All Solvent Types
2460800000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All FIFRA Related Products
Total: All Solvent Types
2460900000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
Miscellaneous Products (Not Otherwise Covered)
Total: All Solvent Types
2461800000
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Total: All Solvent Types
2461800001
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Surface Application
2461800002
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Soil Incorporation
2461870999
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: Non-Agricultural
Not Elsewhere Classified
2465800000
Solvent Utilization
Miscellaneous Non-industrial: Consumer
Pesticide Application
Total: All Solvent Types
                                                                     2501011011
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Permeation
                                                                     2501011012
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Evaporation (includes Diurnal losses)
                                                                     2501011013
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Spillage During Transport
                                                                     2501011014
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Refilling at the Pump - Vapor Displacement
                                                                     2501011015
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Refilling at the Pump - Spillage
                                                                     2501012011
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Permeation
                                                                     2501012012
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Evaporation (includes Diurnal losses)
                                                                     2501012013
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Spillage During Transport
                                                                     2501012014
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Refilling at the Pump - Vapor Displacement
                                                                     2501012015
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Refilling at the Pump - Spillage
                                                                     2630020000
Waste Disposal
Treatment and Recovery
Wastewater Treatment, Public Owned
Total Processed
                                                                     2640000000
Waste Disposal
Treatment and Recovery
TSDFs, All TSDF Types
Total: All Processes
                                                                     2810025000
Miscellane-ous Area Sources
Other Combustion
Residential Grilling
Total
                                                                     2810060100
Miscellane-ous Area Sources
Other Combustion
Cremation
Humans

Other non-IPM updates in 2016v1

In New Jersey, emissions for SCCs for Industrial (2102004000) and Commercial/Institutional (2103004000) Distillate Oil, Total: Boilers and Internal Combustion (IC) Engines were removed at that state's request. These emissions were derived from EPA estimates, and double counted emissions that were provided by New Jersey and assigned to other SCCs.

The state of New Jersey also requested that animal waste NH3 emissions from the following SCCs be removed: 2806010000  -  Cats, 2806015000  -  Dogs, 2807020001  -  Black Bears, 2807020002  -  Grizzly Bears, 2807025000  -  Elk, 2807030000  -  Deer, and 2810010000  -  Human Perspiration and Respiration. These emissions existed in CA, DE, ME, NJ, and UT, and were removed from all states.

The state of Alaska reported several nonpoint sources that were missing in 2014NEIv2. Some of the sources reported by Alaska were identified in our EGU inventory and removed from the new nonpoint inventory. The rest of the stationary sources were converted to an FF10-formatted nonpoint inventory and included in 2016v1 platform in the nonpt sector.

The state of Alabama requested that their Industrial, Commercial, Institutional (ICI) Wood emissions (2102008000), which totaled more than 32,000 tons/year of PM2.5 emissions in the beta version of this emissions modeling platform and were significantly higher than other states' ICI Wood emissions, be removed from 2016v1 platform.

The state of New York provided a new set of non-residential wood combustion emissions for inclusion in 2016v1 platform. These new combustion emissions replace the emissions derived from the MARAMA projection.
Aircraft and ground support equipment (airports)
The airport sector contains emissions of all pollutants from aircraft, categorized by their itinerant class (i.e., commercial, air taxi, military, or general), as well as emissions from ground support equipment. The starting point for the 2016 version 1 (v1) platform airport inventory is the airport emissions from the 2017 National Emissions Inventory (NEI). The SCCs included in the airport sector are shown in Table 2-7.
Table 2-7.  2016v1 platform SCCs for the airports sector
SCC
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2265008005
Mobile Sources
Off-highway Vehicle Gasoline, 4-stroke
Airport Ground Support Equipment
Airport Ground Support Equipment
2267008005
Mobile Sources
LPG
Airport Ground Support Equipment
Airport Ground Support Equipment
2268008005
Mobile Sources
compressed natural gas (CNG)
Airport Ground Support Equipment
Airport Ground Support Equipment
2270008005
Mobile Sources
Off-highway Vehicle Diesel
Airport Ground Support Equipment
Airport Ground Support Equipment
2275001000
Mobile Sources
Aircraft
Military Aircraft
Total
2275020000
Mobile Sources
Aircraft
Commercial Aircraft
Total: All Types
2275050011
Mobile Sources
Aircraft
General Aviation
Piston
2275050012
Mobile Sources
Aircraft
General Aviation
Turbine
2275060011
Mobile Sources
Aircraft
Air Taxi
Piston
2275060012
Mobile Sources
Aircraft
Air Taxi
Turbine
2275070000
Mobile Sources
Aircraft
Aircraft Auxiliary Power Units
Total
40600307
Chemical Evaporation
Transportation and Marketing of Petroleum Products
Gasoline Retail Operations  -  Stage I
Underground Tank Breathing and Emptying
20200102
Internal Combustion Engines
Industrial
Distillate Oil (Diesel)
Reciprocating

The 2016v1 airport emissions inventory was created from the 2017NEI airport emissions that were estimated using the Federal Aviation Administration's (FAA's) Aviation Environmental Design Tool (AEDT). Additional information about the 2017NEI airport inventory and the AEDT can be found in the 2017 National Emissions Inventory Technical Support Document (https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-technical-support-document-tsd). The 2017NEI emissions were adjusted from 2017 to represent year 2016 emissions using FAA data. Adjustment factors were created using airport-specific numbers, where available, or the state default by itinerant class (commercial, air taxi, and general) where there were not airport-specific values in the FAA data. Emissions growth for facilities is capped at 500% and the state default growth is capped at 200%. Military state default values were kept flat to reflect uncertainly in the data regarding these sources.

2016 Nonpoint sources (afdust, ag, np_oilgas, rwc, nonpt)
This section describes the stationary nonpoint sources in the NEI nonpoint data category.  Locomotives, C1 and C2 CMV, and C3 CMV are included in the NEI nonpoint data category, but are mobile sources that are described in Section 2.4. 

The nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE due to the configuration of the spatial surrogates.  Part of the reason for this is to prevent possible double-counting with county-level emissions and also because spatial surrogates for tribal data are not currently available.  These omissions are not expected to have an impact on the results of the air quality modeling at the 12-km resolution used for this platform.

The following subsections describe how the sources in the NEI nonpoint inventory were separated into modeling platform sectors, along with any data that were updated replaced with non-NEI data. 
Area fugitive dust sector (afdust)
The area-source fugitive dust (afdust) sector contains PM10 and PM2.5 emission estimates for nonpoint SCCs identified by EPA as dust sources.  Categories included in the afdust sector are paved roads, unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production, and mining and quarrying.  It does not include fugitive dust from grain elevators, coal handling at coal mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as point sources so they are properly located. Table 2-8 is a listing of the Source Classification Codes (SCCs) in the afdust sector.
Table 2-8. Afdust sector SCCs 
SCC
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
                                                                     2275085000
Mobile Sources
Aircraft
Unpaved Airstrips
Total
                                                                     2294000000
Mobile Sources
Paved Roads
All Paved Roads
Total: Fugitives
                                                                     2294000002
Mobile Sources
Paved Roads
All Paved Roads
Total: Sanding/Salting - Fugitives
                                                                     2296000000
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
                                                                     2311000000
Industrial Processes
Construction: SIC 15 - 17
All Processes
Total
                                                                     2311010000
Industrial Processes
Construction: SIC 15 - 17
Residential
Total
                                                                     2311010070
Industrial Processes
Construction: SIC 15 - 17
Residential
Vehicle Traffic
                                                                     2311020000
Industrial Processes
Construction: SIC 15 - 17
Industrial/Commercial/ Institutional
Total
                                                                     2311030000
Industrial Processes
Construction: SIC 15 - 17
Road Construction
Total
                                                                     2325000000
Industrial Processes
Mining and Quarrying: SIC 14
All Processes
Total
                                                                     2325060000
Industrial Processes
Mining and Quarrying: SIC 10
Lead Ore Mining and Milling
Total
                                                                     2801000000
Miscellaneous Area Sources
Ag. Production - Crops
Agriculture  -  Crops
Total
                                                                     2801000003
Miscellaneous Area Sources
Ag. Production - Crops
Agriculture  -  Crops
Tilling
                                                                     2801000005
Miscellaneous Area Sources
Ag. Production - Crops
Agriculture  -  Crops
Harvesting
                                                                     2801000007
Miscellaneous Area Sources
Ag. Production - Crops
Agriculture  -  Crops
Loading
                                                                     2801000008
Miscellaneous Area Sources
Ag. Production - Crops
Agriculture - Crops
Transport
                                                                     2805001000
Miscellaneous Area Sources
Ag. Production - Livestock
Beef cattle - finishing operations on feedlots (drylots)
Dust Kicked-up by Hooves (use 28-05-020, -001, -002, or -003 for Waste
                                                                     2805001100
Miscellaneous Area Sources
Ag. Production - Livestock
Beef cattle - finishing operations on feedlots (drylots)
Confinement
                                                                     2805001200
Miscellaneous Area Sources
Agriculture Production  -  Livestock
Beef cattle - finishing operations on feedlots (drylots)
Manure handling and storage
                                                                     2805001300
Miscellaneous Area Sources
Agriculture Production  -  Livestock
Beef cattle - finishing operations on feedlots (drylots)
Land application of manure
                                                                     2805002000
Miscellaneous Area Sources
Ag. Production - Livestock
Beef cattle production composite
Not Elsewhere Classified
                                                                     2805003100
Miscellaneous Area Sources
Ag. Production - Livestock
Beef cattle - finishing operations on pasture/range
Confinement
                                                                     2805007100
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - layers with dry manure management systems
Confinement
                                                                     2805007300
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - layers with dry manure management systems
Land application of manure
                                                                     2805008100
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - layers with wet manure management systems
Confinement
                                                                     2805008200
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - layers with wet manure management systems
Manure handling and storage
                                                                     2805008300
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - layers with wet manure management systems
Land application of manure
                                                                     2805009100
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - broilers
Confinement
                                                                     2805009200
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - broilers
Manure handling and storage
                                                                     2805009300
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - broilers
Land application of manure
                                                                     2805010100
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - turkeys
Confinement
                                                                     2805010200
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - turkeys
Manure handling and storage
                                                                     2805010300
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - turkeys
Land application of manure
                                                                     2805018000
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle composite
Not Elsewhere Classified
                                                                     2805019100
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - flush dairy
Confinement
                                                                     2805019200
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - flush dairy
Manure handling and storage
                                                                     2805019300
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - flush dairy
Land application of manure
                                                                     2805020002
Miscellaneous Area Sources
Ag. Production - Livestock
Cattle and Calves Waste Emissions
Beef Cows
                                                                     2805021100
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - scrape dairy
Confinement
                                                                     2805021200
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - scrape dairy
Manure handling and storage
                                                                     2805021300
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - scrape dairy
Land application of manure
                                                                     2805022100
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - deep pit dairy
Confinement
                                                                     2805022200
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - deep pit dairy
Manure handling and storage
                                                                     2805022300
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - deep pit dairy
Land application of manure
                                                                     2805023100
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - drylot/pasture dairy
Confinement
                                                                     2805023200
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - drylot/pasture dairy
Manure handling and storage
                                                                     2805023300
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - drylot/pasture dairy
Land application of manure
                                                                     2805025000
Miscellaneous Area Sources
Ag. Production - Livestock
Swine production composite
Not Elsewhere Classified (see also 28-05-039, -047, -053)
                                                                     2805030000
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry Waste Emissions
Not Elsewhere Classified (see also 28-05-007, -008, -009)
                                                                     2805030007
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry Waste Emissions
Ducks
                                                                     2805030008
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry Waste Emissions
Geese
                                                                     2805035000
Miscellaneous Area Sources
Ag. Production - Livestock
Horses and Ponies Waste Emissions
Not Elsewhere Classified
                                                                     2805039100
Miscellaneous Area Sources
Ag. Production - Livestock
Swine production - operations with lagoons (unspecified animal age)
Confinement
                                                                     2805039200
Miscellaneous Area Sources
Ag. Production - Livestock
Swine production - operations with lagoons (unspecified animal age)
Manure handling and storage
                                                                     2805039300
Miscellaneous Area Sources
Ag. Production - Livestock
Swine production - operations with lagoons (unspecified animal age)
Land application of manure
                                                                     2805040000
Miscellaneous Area Sources
Ag. Production - Livestock
Sheep and Lambs Waste Emissions
Total
                                                                     2805045000
Miscellaneous Area Sources
Ag. Production  -  Livestock
Goats Waste Emissions
Not Elsewhere Classified
                                                                     2805047100
Miscellaneous Area Sources
Ag. Production  -  Livestock
Swine production - deep-pit house operations (unspecified animal age)
Confinement
                                                                     2805047300
Miscellaneous Area Sources
Ag. Production  -  Livestock
Swine production - deep-pit house operations (unspecified animal age)
Land application of manure
                                                                     2805053100
Miscellaneous Area Sources
Ag. Production  -  Livestock
Swine production - outdoor operations (unspecified animal age)
Confinement

The starting point for the afdust emissions is the 2014 National Emissions Inventory version 2.  The methodologies to estimate emissions for each SCC in the preceding table are described in the 2014 NEI version 2 Technical Support Document.   The 2014 emissions were adjusted to better represent 2016 as described below.
MARAMA States area fugitive dust emissions
The MARAMA states include Connecticut, Delaware, the District of Columbia (DC), Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Vermont, Virginia, and West Virginia. MARAMA submitted county-specific projection factors for their states to project afdust emissions from the 2014NEI2 to 2016 for paved roads (SCC 2294000000), residential construction dust (SCC 2311010000), industrial/commercial/institutional construction dust (SCC 2311020000), road construction dust (SCC 2311030000), dust from mining and quarrying (SCC 2325000000), agricultural crop tilling dust (SCC 2801000003), and agricultural dust kick-up from beef cattle hooves (SCC 2805001000). Other afdust emissions, including unpaved road dust emissions, were held constant at 2014NEIv2 values.

Non-MARAMA States area fugitive dust emissions
For paved roads (SCC 2294000000) in non-MARAMA states, the 2014NEIv2 paved road emissions in afdust were projected to year 2016 based on differences in county total vehicle miles traveled (VMT) between 2014 and 2016:
2016 afdust paved roads = 2014 afdust paved roads * (2016 county total VMT) / (2014 county total VMT)
The development of the 2016 VMT is described in the onroad documentation. All emissions other than those for paved roads are held constant in the 2016v1 inventory, including unpaved roads for these states.
Area Fugitive Dust Transport Fraction
The afdust sector is separated from other nonpoint sectors to allow for the application of a "transport fraction," and meteorological/precipitation reductions.  These adjustments are applied using a script that applies land use-based gridded transport fractions based on landscape roughness, followed by another script that zeroes out emissions for days on which at least 0.01 inches of precipitation occurs or there is snow cover on the ground.  The land use data used to reduce the NEI emissions determines the amount of emissions that are subject to transport.  This methodology is discussed in Pouliot, et al., 2010, and in "Fugitive Dust Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012).  Both the transport fraction and meteorological adjustments are based on the gridded resolution of the platform (i.e., 12km grid cells); therefore, different emissions will result if the process were applied to different grid resolutions.  A limitation of the transport fraction approach is the lack of monthly variability that would be expected with seasonal changes in vegetative cover.  While wind speed and direction are not accounted for in the emissions processing, the hourly variability due to soil moisture, snow cover and precipitation is accounted for in the subsequent meteorological adjustment.

For the data compiled into the 2014NEIv2, meteorological adjustments are applied to paved and unpaved road SCCs but not transport adjustments.  For the 2014NEIv1, the meteorological adjustments were inadvertently not applied. This created a large difference between the 2014NEIv1 and 2014NEIv2 dust emissions which did not impact the modeling platform because the modeling platform applies meteorological adjustments and transport adjustments based on unadjusted NEI values (for both v1 and v2).   Thus, for the 2014NEIv2, the meteorological adjustments that were applied (to paved and unpaved road SCCs) had to be backed out so that the entire sector could be processed consistently in SMOKE and the same grid-specific transport fractions and meteorological adjustments could be applied sector-wide.  Because it was determined that some counties in 2014NEIv2 did not have the adjustment applied, their emissions were used as-is. Thus, the FF10 that is run through SMOKE consists of 100% unadjusted emissions, and after SMOKE all afdust sources have both transport and meteorological adjustments applied.  The total impacts of the transport fraction and meteorological adjustments for 2016v1 are shown in Table 2-9. Note that while totals from AK, HI, PR, and VI are included at the bottom of the table, they are from non-continental U.S. (non-CONUS) modeling domains.

Table 2-9.  Total impact of fugitive dust adjustments to unadjusted 2016 v1 inventory 
State
                                Unadjusted PM10
                               Unadjusted PM2.5
                                Change in PM10
                                Change in PM2.5
                                PM10 Reduction
                                PM2.5 Reduction
Alabama
                                                                        535,218
                                                                         63,682
                                                                       -372,853
                                                                        -44,336
                                      70%
                                      70%
Arizona
                                                                        264,628
                                                                         32,808
                                                                        -96,814
                                                                        -11,809
                                      37%
                                      36%
Arkansas
                                                                        321,488
                                                                         49,397
                                                                       -211,050
                                                                        -31,802
                                      66%
                                      64%
California
                                                                        314,917
                                                                         41,395
                                                                       -134,347
                                                                        -17,059
                                      43%
                                      41%
Colorado
                                                                        242,327
                                                                         36,848
                                                                       -121,263
                                                                        -17,718
                                      50%
                                      48%
Connecticut
                                                                         23,740
                                                                          3,385
                                                                        -17,548
                                                                         -2,510
                                      74%
                                      74%
Delaware
                                                                         14,566
                                                                          2,502
                                                                         -8,843
                                                                         -1,533
                                      61%
                                      61%
District of Columbia
                                                                          2,619
                                                                            378
                                                                         -1,627
                                                                           -236
                                      62%
                                      62%
Florida
                                                                        721,379
                                                                         82,397
                                                                       -412,621
                                                                        -46,899
                                      57%
                                      57%
Georgia
                                                                        557,354
                                                                         66,609
                                                                       -389,482
                                                                        -46,272
                                      70%
                                      69%
Idaho
                                                                        454,301
                                                                         55,978
                                                                       -241,373
                                                                        -28,363
                                      53%
                                      51%
Illinois
                                                                        997,748
                                                                        143,992
                                                                       -619,594
                                                                        -88,735
                                      62%
                                      62%
Indiana
                                                                        718,027
                                                                         84,663
                                                                       -498,442
                                                                        -58,430
                                      69%
                                      69%
Iowa
                                                                        387,029
                                                                         60,253
                                                                       -222,941
                                                                        -34,557
                                      58%
                                      57%
Kansas
                                                                        613,183
                                                                         99,486
                                                                       -277,007
                                                                        -44,234
                                      45%
                                      44%
Kentucky
                                                                        312,872
                                                                         42,952
                                                                       -233,163
                                                                        -31,762
                                      75%
                                      74%
Louisiana
                                                                        266,812
                                                                         35,788
                                                                       -172,875
                                                                        -22,923
                                      65%
                                      64%
Maine
                                                                         38,345
                                                                          5,963
                                                                        -31,893
                                                                         -4,978
                                      83%
                                      83%
Maryland
                                                                        105,892
                                                                         16,672
                                                                        -68,246
                                                                        -10,824
                                      64%
                                      65%
Massachusetts
                                                                        148,284
                                                                         18,297
                                                                       -112,998
                                                                        -13,852
                                      76%
                                      76%
Michigan
                                                                        390,994
                                                                         48,838
                                                                       -286,999
                                                                        -35,560
                                      73%
                                      73%
Minnesota
                                                                        405,052
                                                                         61,723
                                                                       -250,646
                                                                        -37,609
                                      62%
                                      61%
Mississippi
                                                                        434,575
                                                                         53,546
                                                                       -299,888
                                                                        -36,494
                                      69%
                                      68%
Missouri
                                                                      1,604,501
                                                                        185,103
                                                                     -1,084,830
                                                                       -124,078
                                      68%
                                      67%
Montana
                                                                        432,844
                                                                         62,062
                                                                       -236,341
                                                                        -32,695
                                      55%
                                      53%
Nebraska
                                                                        349,373
                                                                         55,303
                                                                       -165,083
                                                                        -25,739
                                      47%
                                      47%
Nevada
                                                                        161,820
                                                                         23,360
                                                                        -54,899
                                                                         -7,953
                                      34%
                                      34%
New Hampshire
                                                                         22,330
                                                                          4,607
                                                                        -18,436
                                                                         -3,803
                                      83%
                                      83%
New Jersey
                                                                         40,336
                                                                          9,118
                                                                        -26,776
                                                                         -6,035
                                      66%
                                      66%
New Mexico
                                                                        490,617
                                                                         54,236
                                                                       -200,695
                                                                        -22,038
                                      41%
                                      41%
New York
                                                                        264,041
                                                                         44,137
                                                                       -196,162
                                                                        -32,785
                                      74%
                                      74%
North Carolina
                                                                        206,465
                                                                         30,017
                                                                       -141,501
                                                                        -20,610
                                      69%
                                      69%
North Dakota
                                                                        473,241
                                                                         82,478
                                                                       -249,646
                                                                        -43,138
                                      53%
                                      52%
Ohio
                                                                        931,847
                                                                        116,560
                                                                       -638,127
                                                                        -79,098
                                      68%
                                      68%
Oklahoma
                                                                        450,904
                                                                         67,915
                                                                       -232,046
                                                                        -33,983
                                      51%
                                      50%
Oregon
                                                                        659,099
                                                                         73,832
                                                                       -456,949
                                                                        -49,830
                                      69%
                                      67%
Pennsylvania
                                                                        242,608
                                                                         37,707
                                                                       -179,647
                                                                        -27,959
                                      74%
                                      74%
Rhode Island
                                                                          4,935
                                                                            785
                                                                         -3,503
                                                                           -556
                                      71%
                                      71%
South Carolina
                                                                        164,477
                                                                         22,016
                                                                       -110,278
                                                                        -14,795
                                      67%
                                      67%
South Dakota
                                                                        339,195
                                                                         63,248
                                                                       -169,300
                                                                        -31,302
                                      50%
                                      49%
Tennessee
                                                                        295,092
                                                                         43,414
                                                                       -204,746
                                                                        -29,995
                                      69%
                                      69%
Texas
                                                                      1,264,131
                                                                        180,314
                                                                       -636,591
                                                                        -87,931
                                      50%
                                      49%
Utah
                                                                        209,800
                                                                         26,453
                                                                       -111,587
                                                                        -13,771
                                      53%
                                      52%
Vermont
                                                                         22,437
                                                                          3,275
                                                                        -18,644
                                                                         -2,699
                                      83%
                                      82%
Virginia
                                                                        286,237
                                                                         37,007
                                                                       -211,882
                                                                        -27,348
                                      74%
                                      74%
Washington
                                                                        242,907
                                                                         41,851
                                                                       -135,713
                                                                        -23,281
                                      56%
                                      56%
West Virginia
                                                                        123,003
                                                                         15,127
                                                                       -105,093
                                                                        -12,911
                                      85%
                                      85%
Wisconsin
                                                                        690,830
                                                                         89,899
                                                                       -486,508
                                                                        -62,683
                                      70%
                                      70%
Wyoming
                                                                        240,156
                                                                         29,140
                                                                       -123,388
                                                                        -14,561
                                      51%
                                      50%
Domain Total (12km CONUS)
                                                                     18,484,575
                                                                      2,506,516
                                                                    -11,280,883
                                                                     -1,500,070
                                      61%
                                      60%
Alaska
                                                                        112,025
                                                                         11,562
                                                                       -101,822
                                                                        -10,508
                                      91%
                                      91%
Hawaii
                                                                        109,120
                                                                         11,438
                                                                        -73,612
                                                                         -7,673
                                      67%
                                      67%
Puerto Rico
                                                                          5,889
                                                                          1,313
                                                                         -4,355
                                                                           -984
                                      74%
                                      75%
Virgin Islands
                                                                          3,493
                                                                            467
                                                                         -1,477
                                                                           -195
                                      42%
                                      42%

Figure 2-1 illustrates the impact of each step of the adjustment.  The reductions due to the transport fraction adjustments alone are shown at the top of the figure.  The reductions due to the precipitation adjustments alone are shown in the middle of the figure.  The cumulative emission reductions after both transport fraction and meteorological adjustments are shown at the bottom of the figure.  The top plot shows how the transport fraction has a larger reduction effect in the east, where forested areas are more effective at reducing PM transport than in many western areas.  The middle plot shows how the meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust emissions.

Figure 2-1.  Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and cumulative
                                       
                                       

Agriculture Sector (ag)
The ag sector includes NH3 emissions from fertilizer and emissions of all pollutants other than PM2.5 from livestock in the nonpoint (county-level) data category of the 2017NEI. PM2.5 from livestock are in the Area Fugitive Dust (afdust) sector. Combustion emissions from agricultural equipment, such as tractors, are in the Nonroad sector.  The sector now includes VOC and HAP VOC in addition to NH3. The 2016 version 1 (v1) platform uses a 2016-specific fertilizer inventory from the USDA's Environmental Policy Integrated Climate (EPIC) model combined with a 2016 USDA-based county-level back-projection of 2017NEI livestock emissions. The SCCs included in the ag sector are shown in Table 2-10.
Table 2-10.  2016v1 platform SCCs for the ag sector
SCC
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
                                                                     2801700099
Miscellaneous Area Sources
Ag.  Production - Crops
Fertilizer Application
Miscellaneous Fertilizers
                                                                     2805002000
Miscellaneous Area Sources
Ag.  Production - Livestock
Beef cattle production composite
Not Elsewhere Classified
                                                                     2805007100
Miscellaneous Area Sources
Ag.  Production - Livestock
Poultry production - layers with dry manure management systems
Confinement
                                                                     2805009100
Miscellaneous Area Sources
Ag.  Production - Livestock
Poultry production - broilers
Confinement
                                                                     2805010100
Miscellaneous Area Sources
Ag.  Production - Livestock
Poultry production - turkeys
Confinement
                                                                     2805018000
Miscellaneous Area Sources
Ag.  Production - Livestock
Dairy cattle composite
Not Elsewhere Classified
                                                                     2805025000
Miscellaneous Area Sources
Ag.  Production - Livestock
Swine production composite
Not Elsewhere Classified (see also 28-05-039, -047, -053)
                                                                     2805035000
Miscellaneous Area Sources
Ag.  Production - Livestock
Horses and Ponies Waste Emissions
Not Elsewhere Classified
                                                                     2805040000
Miscellaneous Area Sources
Ag.  Production - Livestock
Sheep and Lambs Waste Emissions
Total
                                                                     2805045000
Miscellaneous Area Sources
Ag.  Production - Livestock
Goats Waste Emissions
Not Elsewhere Classified

 Livestock Waste Emissions
The 2016v1 platform livestock emissions consist of a back-projection of 2017NEI livestock emissions to the year 2016 and include NH3 and VOC. The livestock waste emissions from 2017NEI contain emissions for beef cattle, dairy cattle, goats, horses, poultry, sheep, and swine. The data come from both state-submitted emissions and EPA-calculated emission estimates. Further information about the 2017NEI emissions can be found in the 2017 National Emissions Inventory Technical Support Document (https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-technical-support-document-tsd). Back-projection factors for 2016 emission estimates are based on animal population data from the USDA National Agriculture Statistics Service Quick Stats (https://www.nass.usda.gov/Quick_Stats/). These estimates are developed by data collected from annual agriculture surveys and the Census of Agriculture that is completed every five years. These data include estimates for beef, layers, broilers, turkeys, dairy, swine, and sheep. Each SCC in the 2017NEI livestock inventory, except for 2805035000 (horses and ponies) and 2805045000 (goats), was mapped to one of these USDA categories. Then, back-projection factors were calculated based on USDA animal populations for 2016 and 2017.  Emissions for animal categories for which population data were not available (e.g. horses, goats) were held constant in the projection.

Back-projection factors were calculated at the county level, but only where county-level data was available for a specific animal category. County-level factors were limited to a range of 0.8 to 1.2. Data were not available for every animal category in every county. State-wide back-projection factors based on state total animal populations were calculated and applied to counties where county-specific data was not available for a given animal category. However, data were often not available for every animal category in every state. For categories other than beef and dairy, data are not available for most states. In cases of missing state-level data, a national back-projection factor was applied. Back-projection factors were not pollutant-specific and were applied to all pollutants. The national back-projection factors, which were only used when county or state data were not available, are shown in Table 2-11. The national factors were created using a ratio between animal inventory counts for 2017 and 2016 from the USDA National livestock inventory projections published in February 2018 (https://www.ers.usda.gov/webdocs/outlooks/87459/oce-2018-1.pdf?v=7587.1).
Table 2-11.  National back-projection factors for livestock: 2017 to 2016
beef
-1.8%
swine
-3.6%
broilers
-2.0%
turkeys
-0.3%
layers
-2.3%
dairy
-0.4%
sheep
+0.4%

 Fertilizer Emissions
Fertilizer emissions for 2016 are based on the Fertilizer Emission Scenario Tool for CMAQ (FEST-C) model (https://www.cmascenter.org/fest-c/). The bidirectional version of CMAQ (v5.3) and the Fertilizer Emissions Scenario Tool for CMAQ FEST-C (v1.3) were used to estimate ammonia (NH3) emissions from agricultural soils. The approach to estimate year-specific fertilizer emissions consists of these steps: 
 Run FEST-C to produce nitrate (NO3), Ammonium (NH4+, including Urea), and organic (manure) nitrogen (N) fertilizer usage estimates
 Use USDA Economic Research Services crop specific fertilizer use data and state submitted data to adjust the FEST-C fertilizer totals to match the USDA and State submitted. 
 Run the CMAQ model with bidirectional ("bidi") NH3 exchange to generate gaseous ammonia NH3 emission estimates. 
 Calculate county-level emission factors as the ratio of bidirectional CMAQ NH3 fertilizer emissions to FEST-C total N fertilizer application.
 Assign the NH3 emissions to one SCC: "...Miscellaneous Fertilizers" (2801700099).

FEST-C is the software program that processes land use and agricultural activity data to develop inputs for the CMAQ model when run with bidirectional exchange. FEST-C reads land use data from the Biogenic Emissions Landuse Dataset (BELD), meteorological variables from the Weather Research and Forecasting model, and nitrogen deposition data from a previous or historical average CMAQ simulation. FEST-C, then uses the EPIC modeling system (https://epicapex.tamu.edu/epic/) to simulate the agricultural practices and soil biogeochemistry and provides information regarding fertilizer timing, composition, application method and amount.

An iterative calculation was applied to estimate fertilizer emissions for the 2016 platform.   We first estimate fertilizer application by crop type using FEST-C modeled data. After receipt and addressing of comments to the extent possible, we then adjusted the fertilizer application estimates using state submitted data, (currently only Iowa), and USDA Economic Research Service state and crop specific survey data. The USDA and state submitted annual fertilizer data was used to estimate the ratio of UDSA/state fertilizer use to FEST-C annual total fertilizer estimates for each state and crop with USDA or state data. This ratio is then applied to the FEST-C fertilizer application rates for each state and crop with data.  A maximum annual fertilization rate was estimated from the FEST-C simulation and annual adjusted totals were limited to this rate to prevent unrealistically higher fertilization rates. Then we ran the CMAQ v5.3 model with the Surface Tiled Aerosol and Gaseous Exchange (STAGE) deposition option with bidirectional exchange to estimate fertilizer and biogenic NH3 emissions.  We use this approach for three reasons: (1) FEST-C estimates fertilizer applications based on crop nutrient needs which is typically lower than real world fertilization rates; (2) FEST-C fertilizer timing and application methods are assumed to be correct; and (3) We desired a method to incorporate state submitted and USDA reported data into the final fertilization emission estimates. 

Example Calculation:
Adjustment of FEST-C fertilizer rates using state or USDA data:
Fertajusted,crop=maxFertsubmitted,crop1ncropFertFEST-C,cropFertFEST-C,crop, Fertmax,crop
Where Fertadjusted,crop is the FEST-C 12km grid cell adjusted fertilization rate, FertSubmitted,crop is the USDA or State submitted state mean annual application data for the specified crop, in kg ha[-1], FERTFEST-C,crop is the initial FEST-C 12km grid cell fertilization rate for the state being considered, ncrop is the number of grid cells with fertilization use for the specified crop in the state, and Fertmax,crop is the maximum fertilization rate estimated from EPIC for the crop.
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions
                                       

Fertilizer Activity Data

The following activity parameters were input into the EPIC model:

 Grid cell meteorological variables from WRF (see Table 3)
 Initial soil profiles/soil selection
 Presence of 21 major crops: irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn, silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans, spring wheat, winter wheat, canola, and other crops (e.g.. lettuce, tomatoes, etc.) 
 Fertilizer sales to establish the type/composition of nutrients applied
 Management scenarios for the 10 USDA production regions. These include irrigation, tile drainage, intervals between forage harvest, fertilizer application method (injected versus surface applied), and equipment commonly used in these production regions.

The WRF meteorological model was used to provide grid cell meteorological parameters for year 2016 using a national 12-km rectangular grid covering the continental U.S. The meteorological parameters in Table 2-12 were used as EPIC model inputs.
Table 2-12.  Source of input variables for EPIC
EPIC input variable 
Variable Source
Daily Total Radiation (MJ m[2] )
WRF
Daily Maximum 2-m Temperature (C)
WRF
Daily minimum 2-m temperature (C)
WRF
Daily Total Precipitation (mm)
WRF
Daily Average Relative Humidity (unitless)
WRF
Daily Average 10-m Wind Speed (m s[-1] )
WRF
Daily Total Wet Deposition Oxidized N (g/ha)
CMAQ
Daily Total Wet Deposition Reduced N (g/ha)
CMAQ
Daily Total Dry Deposition Oxidized N (g/ha)
CMAQ
Daily Total Dry Deposition Reduced N (g/ha)
CMAQ
Daily Total Wet Deposition Organic N (g/ha)
CMAQ

Initial soil nutrient and pH conditions in EPIC were based on the 1992 USDA Soil Conservation Service (CSC) Soils-5 survey. The EPIC model then was run for 25 years using current fertilization and agricultural cropping techniques to estimate soil nutrient content and pH for the 2016 EPIC/WRF/CMAQ simulation. 

The presence of crops in each model grid cell was determined through the use of USDA Census of Agriculture data (2012) and USGS National Land Cover data (2011). These two data sources were used to compute the fraction of agricultural land in a model grid cell and the mix of crops grown on that land.

Fertilizer sales data and the 6-month period in which they were sold were extracted from the 2014 Association of American Plant Food Control Officials (AAPFCO, http://www.aapfco.org/publications.html). AAPFCO data were used to identify the composition (e.g., urea, nitrate, organic) of the fertilizer used, and the amount applied is estimated using the modeled crop demand. These data were useful in making a reasonable assignment of what kind of fertilizer is being applied to which crops.

Management activity data refers to data used to estimate representative crop management schemes. The USDA Agricultural Resource Management Survey (ARMS, https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Ag_Resource_Management/) was used to provide management activity data. These data cover 10 USDA production regions and provide management schemes for irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn, silage corn, cottonoats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans, spring wheat, winter wheat, canola, and other crops (e.g. lettuce, tomatoes, etc.).

Fertilizer Emission Factors

The emission factors were derived from the 2016 CMAQ FEST-C outputs adjusted using USDA Economic Research Service (ERS) state and crop specific reported annual fertilizer rates. Total fertilizer emission factors for each month and county were computed by taking the ratio of total fertilizer NH3 emissions (short tons) to total nitrogen fertilizer application (short tons).
12 km by 12 km gridded NH3 emissions were mapped to a county shape file polygon.  The cell was assigned to a county if the grid centroid fell within the county boundary.
Nonpoint Oil and Gas Sector (np_oilgas)
While the major emissions sources associated with oil and gas collection, processing, and distribution have traditionally been included in the National Emissions Inventory (NEI) as point sources (e.g., gas processing plants, pipeline compressor stations, and refineries), the activities occurring "upstream" of these types of facilities have not been as well characterized in the NEI.  Here, upstream activities refer to emission units and processes associated with the exploration and drilling of oil and gas wells, and the equipment used at the wellsite to then extract the product from the well and deliver it to a central collection point or processing facility. The types of unit processes found at upstream sites include separators, dehydrators, storage tanks, and compressor engines.

The nonpoint oil and gas (np_oilgas) sector, which consists of oil and gas exploration and production sources, both onshore and offshore (state-owned only). In the 2016v1 platform, these emissions are mostly based on the EPA Oil and Gas Tool run with data specific to the year 2016, with some states submitting their own inventory data. Because of the growing importance of these emissions, special consideration is given to the speciation, spatial allocation, and monthly temporalization of nonpoint oil and gas emissions, instead of relying on older, more generalized profiles.

EPA Oil and Gas Tool

EPA developed the 2016 Nonpoint Oil and Gas Emission Estimation Tool (the "Tool") to estimate the non-point oil and gas inventory for the 2016v1 platform.  The Tool was previously used to estimate emissions for the 2014 NEI. Year 2016 oil and gas activity data were supplied to EPA by some state air agencies, and where state data were not supplied to EPA, EPA populated the 2016v1 inventory with the best available data. The Tool is an Access database that utilizes county-level activity data (e.g. oil production and well counts), operational characteristics (types and sizes of equipment), and emission factors to estimate emissions.    The Tool creates a CSV-formatted emissions dataset covering all national nonpoint oil and gas emissions. This dataset is then converted to FF10 format for use in SMOKE modeling.  A separate report named "2016 Nonpoint Oil and Gas Emission Estimation Tool V1_0 December_2018.docx" was generated that provides technical details of how the tool was applied for the 2016v1 platform. 
 
In the 2016beta platform, it was found that the number of active wells in the state of Illinois was too high (~48,000 total wells).   After various discussions and other communications with the Illinois Environmental Protection Agency (IEPA), a more accurate number of active of wells (~20,000 total wells) was obtained and the new data were used in a rerun of the Oil and Gas Tool to produce new emissions for the state of Illinois. These new emissions estimates for Illinois are in the 2016v1 modeling platform. The reduction in total number of active wells resulted in NOX and VOC emissions being reduced by about 14,000 tons and 48,000 tons, respectively, in 2016v1 when compared to 2016beta emissions.

Nonpoint Oil and Gas Alternative Datasets

Some states provided, or recommended use of, a separate emissions inventory for use in 2016v1 platform instead of emissions derived from the EPA Oil and Gas Tool. For example, the California Air Resources Board (CARB) developed their own np_oilgas emissions inventory for 2016 for California that were used for the 2016v1 platform. 

In Pennsylvania for the 2016v1 modeling platform, the emissions associated with unconventional wells for year 2016 were supplied by the Pennsylvania Department of Environmental Protection (PA DEP). The Oil and Gas Tool was used to produce the conventional well emissions for 2016. Together these unconventional and conventional well emissions represent the total non-point oil and gas emissions for Pennsylvania. The resulting NOX emissions for Pennsylvania were increased by about 16,000 tons in 2016v1 when compared to the 2016beta emissions.   The VOC emissions were reduced by about 56,000 tons in 2016v1 due to these emissions changes in Pennsylvania.

Colorado Department of Public Health and Environment (CDPHE) requested that the 2014NEIv2 be projected to 2016 instead of using data from the EPA Oil and Gas Tool. For Colorado projections were applied to CO, NOX, PM, and SO2, but not VOC.  VOC emissions for year 2016 were assumed to equal year 2014 levels for Colorado.  Projection factors for Colorado are listed in Table 2-13 and are based on historical production trends.

Oklahoma Department of Environmental Quality requested that np_oilgas emissions from 2014NEIv2 be projected to 2016 for all source except lateral compressors. Projection factors for Oklahoma np_oilgas production, based on historical production data, are listed in Table 2-13. For lateral compressor emissions in Oklahoma, the EPA Oil and Gas Tool inventory for 2016 was used, except with a 72% cut applied to all emissions. Exploration np_oilgas emissions in Oklahoma are based on the EPA Oil and Gas Tool inventory for 2016, without modification.
Table 2-13. 2014NEIv2-to-2016 oil and gas projection factors for CO and OK.
State/region
Emissions type
                                    Factor
Pollutant(s)
Colorado
Oil
                                    +22.0%
CO, NOX, SO2
Colorado
Natural Gas
                                     +3.5%
CO, NOX, PM, SO2
Colorado
Combination Oil + NG
                                    +12.8%
CO, NOX, PM, SO2
Oklahoma
Oil Production
                                     +6.9%
All
Oklahoma
Natural Gas Production
                                     +5.9%
All
Oklahoma
Combination Oil + NG Production
                                     +6.4%
All
Oklahoma
Coal Bed Methane Production
                                    -30.0%
All

Residential Wood Combustion (rwc)
The RWC sector includes residential wood burning devices such as fireplaces, fireplaces with inserts, free standing woodstoves, pellet stoves, outdoor hydronic heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepits and chimneys.  Free standing woodstoves and inserts are further differentiated into three categories: 1) conventional (not EPA certified); 2) EPA certified, catalytic; and 3) EPA certified, noncatalytic. Generally, the conventional units were constructed prior to 1988.  Units constructed after 1988 had to meet EPA emission standards and they are either catalytic or non-catalytic.  The source classification codes (SCCs) in the RWC sector are listed in Table 2-14.
Table 2-14. 2016 v1 platform SCCs for RWC sector
SCC
Tier 1 Description
Tier 2 Description
Tier 3 Description
Tier 4 Description
                                                                     2104008100
Stationary Source Fuel Combustion
Residential
Wood
Fireplace: general
                                                                     2104008210
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts; non-EPA certified
                                                                     2104008220
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts; EPA certified; non-catalytic
                                                                     2104008230
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts; EPA certified; catalytic
                                                                     2104008310
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: freestanding, non-EPA certified
                                                                     2104008320
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: freestanding, EPA certified, non-catalytic
                                                                     2104008330
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: freestanding, EPA certified, catalytic
                                                                     2104008400
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: pellet-fired, general (freestanding or FP insert)
                                                                     2104008510
Stationary Source Fuel Combustion
Residential
Wood
Furnace: Indoor, cordwood-fired, non-EPA certified
                                                                     2104008610
Stationary Source Fuel Combustion
Residential
Wood
Hydronic heater: outdoor
                                                                     2104008700
Stationary Source Fuel Combustion
Residential
Wood
Outdoor wood burning device, NEC (fire-pits, chimeas, etc)
                                                                     2104009000
Stationary Source Fuel Combustion
Residential
Firelog
Total: All Combustor Types

For all states other than California, Washington, and Oregon RWC emissions from the NEI2014v2 were projected to 2016 using projection factors derived by MARAMA based on implementing the projection methodology from EPA's 2011 platform into a spreadsheet tool. Projection factors are by SCC and SCC-pollutant; SCC-only factors (i.e., factors that do not specify a pollutant) are applied to all pollutants without an SCC-pollutant factor. Table 2-15 lists the SCC-based projection factors applied to RWC sources.
Table 2-15. Projection factors for RWC by SCC
SCC

SCC description
Pollutant

                                 2014-to-2016
2104008100
Fireplace: general

                                     2.00%
2104008210
Woodstove: fireplace inserts; non-EPA certified

                                    -3.40%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic
PM10-PRI
                                     2.29%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic
PM25-PRI
                                     2.29%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic

                                     5.25%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic
PM10-PRI
                                     2.44%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic
PM25-PRI
                                     2.44%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic

                                     5.25%
2104008310
Woodstove: freestanding, non-EPA certified
CO
                                    -2.35%
2104008310
Woodstove: freestanding, non-EPA certified
PM10-PRI
                                    -2.17%
2104008310
Woodstove: freestanding, non-EPA certified
PM25-PRI
                                    -2.17%
2104008310
Woodstove: freestanding, non-EPA certified
VOC
                                    -2.06%
2104008310
Woodstove: freestanding, non-EPA certified

                                    -2.35%
2104008320
Woodstove: freestanding, EPA certified, non-catalytic
PM10-PRI
                                     2.29%
2104008320
Woodstove: freestanding, EPA certified, non-catalytic
PM25-PRI
                                     2.29%
2104008320
Woodstove: freestanding, EPA certified, non-catalytic

                                     5.25%
2104008330
Woodstove: freestanding, EPA certified, catalytic
PM10-PRI
                                     2.47%
2104008330
Woodstove: freestanding, EPA certified, catalytic
PM25-PRI
                                     2.47%
2104008330
Woodstove: freestanding, EPA certified, catalytic

                                     5.25%
2104008400
Woodstove: pellet-fired, general (freestanding or FP insert)
PM10-PRI
                                    14.40%
2104008400
Woodstove: pellet-fired, general (freestanding or FP insert)
PM25-PRI
                                    14.40%
2104008400
Woodstove: pellet-fired, general (freestanding or FP insert)

                                    14.38%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
CO
                                    -9.70%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM10-PRI
                                    -6.15%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM25-PRI
                                    -6.15%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
VOC
                                    -9.74%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified

                                    -9.70%
2104008610
Hydronic heater: outdoor
PM10-PRI
                                     2.99%
2104008610
Hydronic heater: outdoor
PM25-PRI
                                     2.99%
2104008610
Hydronic heater: outdoor

                                     2.00%
2104008700
Outdoor wood burning device, NEC (fire-pits, chimineas, etc)

                                     2.00%
2104009000
Fire log total

                                     2.00%

For California, Oregon, and Washington, the RWC emissions were held constant at NEI2014v2 levels for 2016. This approach is consistent with the RWC projections used in the EPA's 2011 emissions modeling platform.

After the 2014NEIv2 was published, it was determined that the 2014NEIv2 RWC inventory was missing woodstove emissions for certain pollutants in Idaho. The missing emissions for woodstove SCCs 2104008210, 2104008230, 2104008310, 2104008330 were added to the inventory prior to projecting it to 2016 for the v1 platform.
Nonpoint (nonpt)
The starting point for the 2016v1 platform nonpt inventory is the 2014NEIv2, including all nonpoint sources that are not included in the afdust, ag, cmv_c1c2, cmv_c3, np_oilgas, rail, or rwc sectors. The types of sources in the nonpt sector include, but are not limited to:
 stationary source fuel combustion, including industrial, commercial, and residential and orchard heaters; 
 commercial sources such as commercial cooking; 
 industrial processes such as chemical manufacturing, metal production, mineral processes, petroleum refining, wood products, fabricated metals, and refrigeration; 
 solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances, and motor vehicles; 
 solvent utilization for degreasing of furniture, metals, auto repair, electronics, and manufacturing;
 solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care products, household products, adhesives and sealants; 
 solvent utilization for asphalt application and roofing, and pesticide application; 
 storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline service stations, aviation, and marine vessels; 
 storage and transport of chemicals;
 waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;
 cellulosic biorefining;
 miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair shops.
The nonpoint emissions in 2016v1 platform are equivalent to those in the 2014NEIv2 except for the following changes:

Nonpoint projection to 2016 inside MARAMA region

2014-to-2016 projection packets for all nonpoint sources were provided by MARAMA for the following states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV. 

New Jersey provided their own projection factors for projection from 2014 to 2016 which were mostly the same as those provided by MARAMA, except for three SCCs with differences (SCCs: 2302070005, 2401030000, 2401070000). For those three SCCs, the projection factors provided by New Jersey were used instead of the MARAMA factors.

Nonpoint projection to 2016 outside MARAMA region

In areas outside of the MARAMA states, historical census population, sometimes by county and sometimes by state, was used to project select nonpt sources from the 2014NEIv2 to 2016v1 platform. The population data was downloaded from the US Census Bureau. Specifically, the "Population, Population Change, and Estimated Components of Population Change: April 1, 2010 to July 1, 2017" file (https://www2.census.gov/programs-surveys/popest/datasets/2010-2017/counties/totals/co-est2017-alldata.csv). A ratio of 2016 population to 2014 population was used to create a growth factor that was applied to the 2014NEIv2 emissions with SCCs matching the population-based SCCs listed in Table 2-16. Positive growth factors (from increasing population) were not capped, but negative growth factors (from decreasing population) were flatlined for no growth.
Table 2-16. 2016v1 platform SCCs for Census-based growth
SCC
Tier 1 Description
Tier 2 Description
Tier 3 
Description
Tier 4 
Description
2302002100
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Charbroiling
Conveyorized Charbroiling
2302002200
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Charbroiling
Under-fired Charbroiling
2302003000
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Total
2302003100
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Flat Griddle Frying
2302003200
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Clamshell Griddle Frying
2401001000
Solvent Utilization
Surface Coating
Architectural Coatings
Total: All Solvent Types
2401002000
Solvent Utilization
Surface Coating
Architectural Coatings - Solvent-based
Total: All Solvent Types
2401003000
Solvent Utilization
Surface Coating
Architectural Coatings - Water-based
Total: All Solvent Types
2401100000
Solvent Utilization
Surface Coating
Industrial Maintenance Coatings
Total: All Solvent Types
2401200000
Solvent Utilization
Surface Coating
Other Special Purpose Coatings
Total: All Solvent Types
2425000000
Solvent Utilization
Graphic Arts
All Processes
Total: All Solvent Types
2425010000
Solvent Utilization
Graphic Arts
Lithography
Total: All Solvent Types
2425020000
Solvent Utilization
Graphic Arts
Letterpress
Total: All Solvent Types
2425030000
Solvent Utilization
Graphic Arts
Rotogravure
Total: All Solvent Types
2425040000
Solvent Utilization
Graphic Arts
Flexography
Total: All Solvent Types
2440020000
Solvent Utilization
Miscellaneous Industrial
Adhesive (Industrial) Application
Total: All Solvent Types
2460000000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Processes
Total: All Solvent Types
2460100000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Personal Care Products
Total: All Solvent Types
2460200000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Household Products
Total: All Solvent Types
2460400000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Automotive Aftermarket Products
Total: All Solvent Types
2460500000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Coatings and Related Products
Total: All Solvent Types
2460600000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Adhesives and Sealants
Total: All Solvent Types
2460800000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All FIFRA Related Products
Total: All Solvent Types
2460900000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
Miscellaneous Products (Not Otherwise Covered)
Total: All Solvent Types
2461800000
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Total: All Solvent Types
2461800001
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Surface Application
2461800002
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Soil Incorporation
2461870999
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: Non-Agricultural
Not Elsewhere Classified
2465800000
Solvent Utilization
Miscellaneous Non-industrial: Consumer
Pesticide Application
Total: All Solvent Types
                                                                     2501011011
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Permeation
                                                                     2501011012
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Evaporation (includes Diurnal losses)
                                                                     2501011013
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Spillage During Transport
                                                                     2501011014
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Refilling at the Pump - Vapor Displacement
                                                                     2501011015
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Refilling at the Pump - Spillage
                                                                     2501012011
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Permeation
                                                                     2501012012
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Evaporation (includes Diurnal losses)
                                                                     2501012013
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Spillage During Transport
                                                                     2501012014
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Refilling at the Pump - Vapor Displacement
                                                                     2501012015
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Refilling at the Pump - Spillage
                                                                     2630020000
Waste Disposal
Treatment and Recovery
Wastewater Treatment, Public Owned
Total Processed
                                                                     2640000000
Waste Disposal
Treatment and Recovery
TSDFs, All TSDF Types
Total: All Processes
                                                                     2810025000
Miscellaneous Area Sources
Other Combustion
Residential Grilling
Total
                                                                     2810060100
Miscellaneous Area Sources
Other Combustion
Cremation
Humans
2016 Onroad Mobile sources (onroad)
Onroad mobile source include emissions from motorized vehicles operating on public roadways.  These include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks, heavy-duty trucks, and buses.  The sources are further divided by the fuel they use, including diesel, gasoline, E-85, and compressed natural gas (CNG) vehicles.  The sector characterizes emissions from parked vehicle processes (e.g., starts, hot soak, and extended idle) as well as from on-network processes (i.e., from vehicles as they move along the roads).  Except for California, all onroad emissions are generated using the SMOKE-MOVES emissions modeling framework that leverages MOVES-generated emission factors, county and SCC-specific activity data, and hourly meteorological data.  The onroad source classification codes (SCCs) in the modeling platform are more finely resolved than those in the National Emissions Inventory (NEI). The NEI SCCs distinguish vehicles and fuels.  The SCCs used in the model platform also distinguish between emissions processes (i.e., off-network, on-network, and extended idle), and road types.

Onroad emissions were computed with SMOKE-MOVES by multiplying specific types of vehicle activity data by the appropriate emission factors. This section includes discussions of the activity data and the emission factor development. The vehicles (aka source types) for which MOVES computes emissions are shown in Table 2-17. SMOKE-MOVES was run for specific modeling grids.  Emissions for the contiguous U.S. states and Washington, D.C., were computed for a grid covering those areas. Emissions for Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands were computed by running SMOKE-MOVES for distinct grids covering each of those regions and are included in the onroad_nonconus sector. In some summary reports these non-CONUS emissions are aggregated with emissions from the onroad sector. 
Table 2-17. MOVES vehicle (source) types
                              MOVES vehicle type
                                  Description
                               HPMS vehicle type
                                      11
Motorcycle
                                      10
                                      21
Passenger Car
                                      25
                                      31
Passenger Truck
                                      25
                                      32
Light Commercial Truck
                                      25
                                      41
Intercity Bus
                                      40
                                      42
Transit Bus
                                      40
                                      43
School Bus
                                      40
                                      51
Refuse Truck
                                      50
                                      52
Single Unit Short-haul Truck
                                      50
                                      53
Single Unit Long-haul Truck
                                      50
                                      54
Motor Home
                                      50
                                      61
Combination Short-haul Truck
                                      60
                                      62
Combination Long-haul Truck
                                      60

Onroad Activity Data Development

SMOKE-MOVES uses vehicle miles traveled (VMT), vehicle population (VPOP), and hours of hoteling, to calculate emissions. These datasets are collectively known as "activity data". For each of these activity datasets, first a national dataset was developed; this national dataset is called the "EPA default" dataset. Second, data submitted by state agencies were incorporated where available, in place of the EPA default data. EPA default activity was used for California, but the emissions were scaled to California-supplied values during the emissions processing.  The agencies for which submitted VMT and VPOP data were used for 2016 platforms are shown in Table 2-18 along with the timing of the submission: 2014v1 or 2016 beta or 2016 v1.  Data submitted for the 2014 NEI were adjusted before they were used for 2016 platforms.
Table 2-18. Submitted data used to prepare onroad activity data
Agency
                                   2016 VMT
                                   2016 VPOP
Alaska
                                 yes (2014v1)
                                 yes (2014v1)
Arizona - Maricopa
                                 yes (2014v1)
                                 yes (2014v1)
Arizona - Pima
                                   yes (v1)
                                   yes (v1)
Colorado
                                  yes (beta)
                                   yes (v1)
Connecticut
                                  yes (beta)
                                 yes (2014v1)
Delaware
                                 yes (2014v1)
                                 yes (2014v1)
District of Columbia
                                 yes (2014v1)
                                 yes (2014v1)
Georgia
                                  yes (beta)
                                  yes (beta)
Idaho
                                 yes (2014v1)
                                 yes (2014v1)
Illinois - Chicago area
                                   yes (v1)
                                   yes (v1)
Illinois - rest of state
                                  yes (beta)
                                 yes (2014v1)
Indiana - Louisville area
                                   yes (v1)
                                       
Kentucky - Jefferson
                                   yes (v1)
                                 yes (2014v1)
Kentucky - Louisville exurbs
                                   yes (v1)
                                       
Maine
                                 yes (2014v2)
                                 yes (2014v2)
Maryland
                                  yes (beta)
                                  yes (beta)
Massachusetts
                                   yes (v1)
                                   yes (v1)
Michigan - Detroit area
                                  yes (beta)
                                 yes (2014v1)
Michigan - rest of state
                                  yes (beta)
                                 yes (2014v1)
Minnesota
                                  yes (beta)
                                 yes (2014v1)
Missouri
                                 yes (2014v1)
                                 yes (2014v1)
Nevada - Clark
                                  yes (beta)
                                  yes (beta)
Nevada - Washoe
                                 yes (2014v1)
                                 yes (2014v1)
New Hampshire
                                  yes (beta)
                                  yes (beta)
New Jersey
                                  yes (beta)
                                   yes (v1)
New Mexico - Bernalillo
                                 yes (2014v1)
                                 yes (2014v1)
New York
                                 yes (2014v1)
                                 yes (2014v1)
North Carolina
                                  yes (beta)
                                  yes (beta)
Ohio
                                 yes (2014v1)
                                 yes (2014v1)
Oregon
                                 yes (2014v1)
                                 yes (2014v1)
Pennsylvania
                                  yes (beta)
                                  yes (beta)
Rhode Island
                                 yes (2014v1)
                                 yes (2014v1)
South Carolina
                                  yes (beta)
                                  yes (beta)
Tennessee - Davidson
                                 yes (2014v1)
                                 yes (2014v1)
Tennessee - Knox
                                 yes (2014v1)
                                 yes (2014v1)
Tennessee - rest of state
                                 yes (2014v2)
                                 yes (2014v2)
Texas
                                 yes (2014v1)
                                 yes (2014v1)
Vermont
                                 yes (2014v2)
                                 yes (2014v2)
Virginia
                                  yes (beta)
                                 yes (2014v2)
Washington
                                 yes (2014v2)
                                 yes (2014v2)
West Virginia
                                  yes (beta)
                                  yes (beta)
Wisconsin
                                  yes (beta)
                                  yes (beta)


Vehicle Miles Traveled (VMT)

EPA calculated default 2016 state VMT by projecting the 2014NEIv2 platform VMT to 2016. The 2014NEIv2 Technical Support Document has details on the development of those VMT (https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-nei-technical-support-document-tsd). The data projected to 2016 were used for states that did not submit 2016 VMT data. Projection factors to grow state VMT from 2014 to 2016 were based on state-level VMT data from the Federal Highway Administration (FHWA) VM-2 reports (https://www.fhwa.dot.gov/policyinformation/statistics/2014/vm2.cfm and https://www.fhwa.dot.gov/policyinformation/statistics/2016/vm2.cfm). For most states, separate factors were calculated for urban VMT and rural VMT. Some states have a very different distribution of urban activity versus rural activity between 2014NEIv2 and the FHWA data, due to inconsistencies in the definition of urban versus rural. For those states, a single state-wide projection factor based on total FHWA VMT across all road types was applied to all VMT independent of road type. The following states used a single state-wide projection factor to adjust the VMT to 2016 levels: AK, GA, IN, ME, MA, NE, NM, NY, ND, TN, and WV. Also, state-wide projection factors in Texas and Utah were developed from alternative VMT datasets provided by their respective Departments of Transportation. The VMT projection factors for all states are provided in Table 2-19.

Table 2-19. Factors applied to project VMT from 2014 to 2016 to prepare default activity data
State
                                  Rural roads
                                  Urban roads
                           Projection Factor Source
Alabama
                                     5.36%
                                     5.47%
                             FHWA VM-2 urban/rural
Alaska
                                     8.27%
                                     8.27%
                                FHWA VM-2 total
Arizona
                                     1.07%
                                     6.35%
                             FHWA VM-2 urban/rural
Arkansas
                                     4.80%
                                     5.36%
                             FHWA VM-2 urban/rural
California
                                     1.06%
                                     2.39%
                             FHWA VM-2 urban/rural
Colorado
                                     5.97%
                                     6.67%
                             FHWA VM-2 urban/rural
Connecticut
                                     1.33%
                                     1.45%
                             FHWA VM-2 urban/rural
Delaware
                                     4.42%
                                     6.75%
                             FHWA VM-2 urban/rural
District of Columbia
                                     0.00%
                                     2.68%
                             FHWA VM-2 urban/rural
Florida
                                    10.27%
                                     6.64%
                             FHWA VM-2 urban/rural
Georgia
                                    10.10%
                                    10.10%
                                FHWA VM-2 total
Hawaii
                                     6.14%
                                     4.21%
                             FHWA VM-2 urban/rural
Idaho
                                     5.51%
                                     7.80%
                             FHWA VM-2 urban/rural
Illinois
                                     3.40%
                                     1.96%
                             FHWA VM-2 urban/rural
Indiana
                                     5.02%
                                     5.02%
                                FHWA VM-2 total
Iowa
                                     6.17%
                                     6.05%
                             FHWA VM-2 urban/rural
Kansas
                                     2.42%
                                     6.52%
                             FHWA VM-2 urban/rural
Kentucky
                                     2.52%
                                     3.26%
                             FHWA VM-2 urban/rural
Louisiana
                                    -5.49%
                                     7.10%
                             FHWA VM-2 urban/rural
Maine
                                     3.75%
                                     3.75%
                                FHWA VM-2 total
Maryland
                                     4.98%
                                     4.75%
                             FHWA VM-2 urban/rural
Massachusetts
                                     7.42%
                                     7.42%
                                FHWA VM-2 total
Michigan
                                     5.62%
                                     0.66%
                             FHWA VM-2 urban/rural
Minnesota
                                     2.66%
                                     2.97%
                             FHWA VM-2 urban/rural
Mississippi
                                     1.83%
                                     4.96%
                             FHWA VM-2 urban/rural
Missouri
                                     4.70%
                                     4.17%
                             FHWA VM-2 urban/rural
Montana
                                     3.32%
                                     4.34%
                             FHWA VM-2 urban/rural
Nebraska
                                     5.54%
                                     5.54%
                                FHWA VM-2 total
Nevada
                                     8.30%
                                     5.30%
                             FHWA VM-2 urban/rural
New Hampshire
                                     5.00%
                                     3.65%
                             FHWA VM-2 urban/rural
New Jersey
                                     5.41%
                                     2.83%
                             FHWA VM-2 urban/rural
New Mexico
                                    10.01%
                                    10.01%
                                FHWA VM-2 total
New York
                                    -4.90%
                                    -4.90%
                                FHWA VM-2 total
North Carolina
                                     7.47%
                                     8.41%
                             FHWA VM-2 urban/rural
North Dakota
                                    -7.35%
                                    -7.35%
                                FHWA VM-2 total
Ohio
                                     4.61%
                                     5.42%
                             FHWA VM-2 urban/rural
Oklahoma
                                     4.72%
                                     1.23%
                             FHWA VM-2 urban/rural
Oregon
                                     8.05%
                                     4.84%
                             FHWA VM-2 urban/rural
Pennsylvania
                                    -4.30%
                                     4.73%
                             FHWA VM-2 urban/rural
Rhode Island
                                     3.26%
                                     3.26%
                             FHWA VM-2 urban/rural
South Carolina
                                     9.70%
                                     8.89%
                             FHWA VM-2 urban/rural
South Dakota
                                     3.23%
                                     2.64%
                             FHWA VM-2 urban/rural
Tennessee
                                     6.29%
                                     6.29%
                                FHWA VM-2 total
Texas
                                     7.82%
                                     7.82%
                                     TxDOT
Utah
                                    11.62%
                                    11.62%
                                     UDOT
Vermont
                                     5.55%
                                     2.24%
                             FHWA VM-2 urban/rural
Virginia
                                    -4.93%
                                     9.78%
                             FHWA VM-2 urban/rural
Washington
                                     6.86%
                                     4.43%
                             FHWA VM-2 urban/rural
West Virginia
                                     2.21%
                                     2.21%
                                FHWA VM-2 total
Wisconsin
                                     4.15%
                                     9.32%
                             FHWA VM-2 urban/rural
Wyoming
                                    -1.38%
                                    -1.53%
                             FHWA VM-2 urban/rural
Puerto Rico
                                     0.00%
                                     0.00%
                               No FHWA VM-2 data
Virgin Islands
                                     0.00%
                                     0.00%
                               No FHWA VM-2 data

For the 2016v1 platform, VMT data submitted by state and local agencies were incorporated and used in place of EPA defaults, as described below.  Note that VMT data need to be provided to SMOKE for each county and SCC.  The onroad SCCs characterize vehicles by MOVES fuel type, vehicle (aka source) type, emissions process, and road type.  Any VMT provided at a different resolution than this were converted to a full county-SCC resolution to prepare the data for processing by SMOKE.

Air agencies from CO, CT, GA, IL, MD, NJ, NC, VA, WI, and Pima County (AZ) provided 2016 VMT data by county and Highway Performance Monitoring Systems (HPMS) vehicle type to be used for the 2016beta and 2016v1 platforms. That level of detail is sufficient for MOVES, but SMOKE also needs VMT broken out by MOVES vehicle type (which is more detailed than HPMS vehicle type), and by fuel type, and road type. To get VMT at the resolution needed by SMOKE, the county-HPMS VMT data provided by the states were loaded into the county databases (CDBs) that are used to run MOVES. MOVES CDBs include fuel type splits, road type splits, and VPOP by MOVES vehicle type. Using those tables, county-HPMS VMT data were converted into the county-SCC VMT data that are needed by SMOKE. One exception to the use of local data in these states was for North Carolina, where EPA default VMT for buses was used along with state-submitted VMT for other vehicle types.

South Carolina and Massachusetts submitted VMT by county-HPMS using the same HPMS splits in every county in the state. Unlike Massachusetts, South Carolina did not provide county-specific road type splits. Instead, a new set of county-specific HPMS splits was developed from the EPA default VMT. For all HPMS types except 25 (light cars and trucks), county-HPMS ratios were calculated from the EPA default VMT, and then scaled up or down so that the overall state-HPMS ratio would match South Carolina's state-HPMS ratio. For HPMS type 25, the county-HPMS ratios were set equal to the remainder within each county so that all ratios within each county sum to 1.0. The new VMT by county-HPMS varies by county while respecting the state-wide HPMS splits in South Carolina's original VMT dataset. The VMT was then split to full SCC level using a similar procedure as other states that submitted VMT at the county-HPMS level.

Pennsylvania and New Hampshire submitted VMT for the 2016beta platform at the full county-SCC level, already in the FF10 format needed by SMOKE. These data were used directly for the 2016v1 platform, except for the redistribution of light duty VMT (see last item in this subsection). 

Michigan and Minnesota submitted 2016 VMT by county and by road type for the 2016beta platform. Fuel type and vehicle type distributions from the EPA default VMT were used to convert these data to full SCC.

West Virginia submitted county total VMT only for the 2016beta platform. Fuel, vehicle, and road type distributions from the EPA default VMT were used to convert their data to full SCC.

For the 2016beta platform, Clark County, NV, submitted VMT by county and MOVES vehicle type, which is more detailed than HPMS vehicle type, but nevertheless cannot be imported into MOVES CDBs as easily to facilitate the creation of VMT at the full SCC detail. Fuel type and road type distributions from the EPA default VMT were used to convert these data to full SCC.

For the 2016v1 platform, VMT was provided by: 
 Massachusetts (by HPMS, to override what was provided for beta)
 Chicago area (8 counties, by HPMS/road; excluded motorcycles)
 Louisville area (5 counties, county totals restricted/unrestricted)
 Pima County AZ (by HPMS)

Some of the provided data were adjusted following quality assurance, as described below in the VPOP section. 

A final step was performed on all state-submitted VMT. The distinction between a "passenger car" (MOVES vehicle type 21) versus a "passenger truck" (MOVES vehicle type 31) versus a "light commercial truck" (MOVES vehicle type 32) is not always consistent between different datasets. This distinction can have a noticeable effect on the resulting emissions, since MOVES emission factors for passenger cars are quite different than those for passenger trucks and light commercial trucks. 

To ensure consistency in the 21/31/32 splits across the country, all state-submitted VMT for MOVES vehicle types 21, 31, and 32 (all of which are part of HPMS vehicle type 25) was summed, and then re-split using the 21/31/32 splits from the EPA default VMT. VMT for each source type as a percentage of total 21/31/32 VMT was calculated by county from the EPA default VMT. Then, state-submitted VMT for 21/31/32 was summed and then resplit according to those percentages. 

This was done for all states and counties listed above which submitted VMT for 2016. Most of the states listed above did not provide VMT down to the source type, so splitting the light-duty vehicle VMT does not create an inconsistency with state-provided data in those states. Exceptions are New Hampshire and Pennsylvania: those two states provided SCC-level VMT, but these were reallocated to 21/31/32 so that the splits are performed in a consistent way across the country. The 21/31/32 splits in the EPA default VMT can be traced back to the 2014NEIv2 VPOP data obtained from IHS-Polk.

Speed Activity (SPEED/SPDIST)

In SMOKE 4.7, SMOKE-MOVES was updated to use speed distributions similarly to how they are used when running MOVES in inventory mode. This new speed distribution file, called SPDIST, specifies the amount of time spent in each MOVES speed bin for each county, vehicle (aka source) type, road type, weekday/weekend, and hour of day.  This file contains the same information at the same resolution as the Speed Distribution table used by MOVES but is reformatted for SMOKE.  Using the SPDIST file results in a SMOKE emissions calculation that is more consistent with MOVES than the old hourly speed profile (SPDPRO) approach, because emission factors from all speed bins can be used, rather than interpolating between the two bins surrounding the single average speed value for each hour as is done with the SPDPRO approach.  

As was the case with the previous SPDPRO approach, the SPEED inventory that includes a single overall average speed for each county, SCC, and month, must still be read in by the SMOKE program Smkinven.  SMOKE requires the SPEED dataset to exist even when speed distribution data are available, even though only the speed distribution data affects the selection of emission factors. The SPEED dataset is carried over from 2014NEIv2, while the SPDIST dataset is new for the 2016v1 platform. Both are based on a combination of the Coordinating Research Council (CRC) A-100 data and MOVES CDBs.

Vehicle Population (VPOP)

The EPA default VPOP dataset was based on the EPA default VMT dataset described above. For each county, fuel type, and vehicle type, a VMT/VPOP ratio (miles per vehicle per year) was calculated based on the 2014NEIv2 VMT and VPOP datasets. That ratio was applied to the 2016 EPA default VMT, to produce an EPA default VPOP projection.  

As with VMT, several state and local agencies submitted VPOP data for the beta and v1 platforms, and those data were used in place of the EPA default VPOP. The VPOP SCCs used by SMOKE are similar to the VMT SCCs, except the emissions process is represented as "00" because it is not relevant to vehicle population data.

For the 2016 beta platform, GA, MD, MA, NJ, NC, WI, and Pima County AZ provided VPOP data for the year 2016 by county and MOVES vehicle type. That level of detail is sufficient for MOVES, but SMOKE also needs VPOP broken out by fuel type. To get VPOP by full SCC, the county-vehicle VPOP data provided by the states were loaded into the MOVES CDBs. Using fuel type tables in the CDBs, it is possible to take county-vehicle VPOP data and create county-SCC VPOP data at the resolution needed by SMOKE. For Massachusetts, based on quality assurance checks, modifications to their VPOP like those done for their VMT were not needed. Wisconsin provided VPOP for 2016 by county and HPMS vehicle type instead of by MOVES vehicle type, but the same procedure was applied as for other states in this group. For North Carolina, EPA default VPOP data were used for buses along with the state-submitted VPOP for other vehicle types, consistent with the VMT.

West Virginia and Clark County, Nevada also provided VPOP for the 2016 beta platform by county and MOVES vehicle type. Because they did not provide VMT by county-HPMS, these data were not put into MOVES databases for splitting.  Instead, the VPOP data were split to full SCC using county-vehicle to county-SCC ratios calculated from the 2016 beta VMT - not the EPA default VMT, but the final VMT incorporating state data and split to full SCC within MOVES CDBs. So effectively, MOVES CDBs were used to split their VPOP to full SCC, but only indirectly. West Virginia's VPOP dataset did not include any intercity buses (MOVES vehicle type 41), thus intercity bus VPOP data were taken from the EPA default VPOP.

The FF10-formatted county-SCC VPOP data provided by Pennsylvania and New Hampshire for the 2016 beta platform were used for the 2016v1 platform.

EPA default VPOP data were used for the states that submitted VMT but did not submit VPOP (CT, IL, MI, MN, and VA). The new VMT that South Carolina provided, in addition to the recalculation of HPMS splits between counties, introduced some issues with VMT/VPOP ratios when comparing the 2016beta VMT with EPA default beta VPOP. The largest VMT/VPOP ratio issues were for HD vehicles. Because the light-duty (LD) VPOP data are based on the IHS-Polk registration data, only the heavy-duty (HD) VPOP data were modified for South Carolina using the EPA defaults. For HD VPOP in South Carolina: new VPOP = EPA default VPOP * (SC-submitted VMT / EPA default VMT). In other words, the same changes that were made to the VMT as a result of the new state data were also made to the VPOP on a percentage basis. This preserves VMT/VPOP ratios for HD vehicles in South Carolina compared to the EPA default data. This procedure resulted in some changes to the overall HD VPOP total in South Carolina, both at the county level and state level.

VPOP by source type was not re-split among the LD types 21/31/32. This is consistent with the 2016beta platform, in which all state-submitted VMT was re-split, but state-submitted VPOP at the source type level or better was not.

For 2016v1, VPOP data were provided for:
 Massachusetts (by HPMS)
 Chicago area (8 counties, by source type)
 Colorado (by source type)
 New Jersey (by source type)
 Pima County, AZ (by source type)

The state-submitted VMT and VPOP data underwent several modifications based on quality assurance:

Colorado: 

 There was a lot of inconsistency between the VMT and VPOP when it was broken down into individual vehicle types. Colorado indicated that we shouldn't put too much stock into the HPMS->vehicle breakdowns in their VPOP data. So, we summed their VPOP to HPMS type and re-split to vehicle type based on splits from beta VPOP.
 Due to concerns about VMT/VPOP ratios for long haul source types (41, 53, 62), we recalculated the VPOP from VMT using average national VMT/VPOP ratios from 2014v2: 53,000 for 41s; 18,600 for 53s, and 68,000 for 62s. We also recalculated the 52 VPOP as old 52+53 VPOP minus new 53 VPOP. In one county (08019), 52 VPOP ended up negative, so we increased the 53 VMT/VPOP ratio (which decreased the VPOP) for that county only.
 There were also some VMT/VPOP ratios at the county level for HPMS vehicle types 42, 43, and 61 that were greater than 150,000 miles/year. For these, we increased the VPOP for these county-vehicle combinations so that the VMT/VPOP ratio would never exceed 150,000. This affected 6 county-vehicle combinations, mostly with small VPOP. 

Chicago area: 

 Chicago provided separate VMT for HPMS vehicle types 20 and 30, which were summed and re-split based on 2016beta platform VMT to keep LD vehicle type distributions consistent. 
 Motorcycles VMT and VPOP were taken from the 2016beta platform.
 Based on email communication and number comparison, the provided Chicago area bus VMT (submitted as total buses), appear to include only data for bust types 41 and 42 only and not 43 (school). So, the bus VMT were allocated to the 41and 42 types and school bus VMT (43) were carried forward from 2016beta. 
 For bus VPOP, Chicago did not provide intercity buses, so those were carried forward from 2016beta, but their transit and school bus VPOP values were retained.
 The provided 50/60 VPOP appeared to be much too low, so we recalculated it based on their VMT combined with average VMT/VPOP ratios: 24,000 for 51s; 10,000 for 52s; 18,600 for 53s; 4,000 for 54s; 57,000 for 61s and 68,000 for 62s.
 Counties 17063 and 17093 had VPOP for 41/42 but no VMT. We added VMT from the 2016beta platform for these county-vehicle combinations. The VMT for 41 was carried forward from 2016beta to 2016v1. For 42, the 2016v1 VMT = beta VMT * (v1 VPOP / beta VPOP).

Pima County: The provided 50/60 VPOP was not based on vehicle registrations, so we recalculated based on their VMT combined with average VMT/VPOP ratios (as was done for Chicago).

Hoteling Hours (HOTELING)

Hoteling hours activity is used to calculate emissions from extended idling and auxiliary power units (APUs) for heavy duty diesel vehicles. Many states have commented that EPA estimates of hoteling hours, and therefore emissions resulting from hoteling are higher than they could realistically be in reality given the available parking spaces. Therefore, recent hoteling activity datasets, including the 2014NEIv2, 2016 beta, and 2016v1 platforms, incorporate reductions to hoteling activity data based on the availability of truck stop parking spaces in each county, as described below. For 2016v1, hoteling hours were recomputed using a new factor identified by EPA's Office of Transportation and Air Quality as more appropriate based on recent studies.  

The method used in 2016v1 is the following: 
 Start with 2016v1 VMT for 62 on restricted roads, by county. 
 Multiply that by 0.007248 hours/mile (Sonntag, 2018). This results in about 73.5% less hoteling hours as compared to the 2014v2 approach. 
 Apply parking space reductions as has been done for 2016beta, except for states that requested we not do that (CO, ME, NJ, NY). 

Hoteling hours were adjusted down in counties for which there were more hoteling hours assigned to the county than could be supported by the known parking spaces.  To compute the adjustment, we started with the hoteling hours for the county as computed by the above method, and then we applied reductions directly to the 2016 hoteling hours based on known parking space availability so that there were not more hours assigned to the county than the available parking spaces could support if they were full every hour of every day.

A dataset of truck stop parking space availability with the total number of parking spaces per county was used in the computation of the adjustment factors. This same dataset is used to develop the spatial surrogate for hoteling emissions. For the 2016v1 platform, the parking space dataset includes several updates compared to 2016beta platform, based on information provided by some states (e.g., MD). Since there are 8,784 hours in the year 2016; the maximum number of possible hoteling hours in a particular county is equal to 8,784 * the number of parking spaces in that county. Hoteling hours for each county were capped at that theoretical maximum value for 2016 in that county, with some exceptions as outlined below.

Because the truck stop parking space dataset may be incomplete in some areas, and trucks may sometimes idle in areas other than designated spaces, it was assumed that every county has at least 12 parking spaces, even if fewer parking spaces are found in the parking space dataset. Therefore, hoteling hours are never reduced below 105,408 hours for the year in any county. If the unreduced hoteling hours were already below that maximum, the hours were left unchanged; in other words, hoteling activity are never increased as a result of this analysis.

A handful of high activity counties that would otherwise be subject to a large reduction were analyzed individually to see if their parking space count seemed unreasonably low. In the following counties, the parking space count and/or the reduction factor was manually adjusted:

 17043 / DuPage IL (instead of reducing hoteling by 89%, applied no adjustment)
 39061 / Hamilton OH (parking spot count increased to 20 instead of the minimum 12)
 47147 / Robertson TN (parking spot count increased to 52 instead of just 26)
 51015 / Augusta VA (parking space count increased to 48 instead of the minimum 12)
 51059 / Fairfax VA (parking spot count increased to 20 instead of the minimum 12)

Georgia and New Jersey submitted hoteling activity for the 2016v1 platform. For these states, the EPA default projection was replaced with their state data. New Jersey provided their hoteling activity in a series of HotellingHours MOVES-formatted tables, which include separate activity for weekdays and weekends and for each month and which have units of hours-per-week. These data first needed to be converted to annual totals by county.

For Georgia we were going to bring forward their beta HOTELING but found it was now much too large compared to other states once the new hoteling factor was implemented. After discussion with Georgia Department of Natural Resources staff, we agreed to recalculate from VMT for all counties except for those where parking > 0 and restricted VMT = 0. In those counties, Georgia's 2016beta hoteling were reduced by 73.5% (the same reduction factor applied to the rest of the country).

Alaska Department of Natural Resources staff requested that we zero out hoteling activity in several counties due to the nature of driving patterns in their region.  In addition, there are no hoteling hours or other emissions from long-haul combination trucks in Hawaii, Puerto Rico, or the Virgin Islands.

All parking space counts are the same as 2016beta except Maryland, which submitted an update for 2016v1.

The states of Colorado, Maine, New Jersey, and New York requested that no reductions be applied to the hoteling activity based on parking space availability. For these states, we did not apply any reductions based on parking space availability and left the hours that were computed using the updated method for 2016v1; or in the case of New Jersey, their submitted activity; unchanged. Otherwise, the submitted data from New Jersey would have been subject to reductions. The submitted data from Georgia did not exceed the maximum value in any county, so their submitted data did not need to be reduced. 

Finally, the county total hoteling must be split into separate values for extended idling (SCC 2202620153) and APUs (SCC 2202620191). New Jersey's submittal of hoteling activity specified a 30% APU split, and this was used for all New Jersey counties. For the rest of the country, a 12.4% APU split was used for the year 2016, meaning that APUs are used for 12.4% of the hoteling hours.

Onroad Emission Factor Table Development

MOVES2014b was run in emission rate mode to create emission factor tables using CB6 speciation for the years 2016, 2020, 2023, and 2028, for all representative counties and fuel months. MOVES was run for all counties in Alaska, Hawaii, and Virgin Islands, and for a single representative county in Puerto Rico.  The county databases (CDBs) used to run MOVES to develop the emission factor tables were updated from those used in the 2016beta platform.  

Age distributions are a key input to MOVES in determining emission rates. The age distributions for 2016v1 were updated based on vehicle registration data obtained from the CRC A-115 project, subject to reductions for older vehicles determined according to CRC A-115 methods but using additional age distribution data that became available as part of the 2017 NEI submitted input data.  One of the findings of CRC project A-115 is that IHS data contain higher vehicle populations than state agency analyses of the same Department of Motor Vehicles data, and the discrepancies tend to increase with increasing vehicle age (i.e., there are more older vehicles in the IHS data). The CRC project dealt with the discrepancy by releasing datasets based on raw (unadjusted) information and adjusted sets of age distributions, where the adjustments reflected the differences in population by model year of 2014 IHS data and 2014 submitted data from a single state.  

For the 2016 platform and 2017 NEI, EPA repeated the CRC's assessment of IHS vs. state discrepancies but with updated 2017 information and for more states.  The 2017 light-duty vehicle (LDV) populations from the CRC A-115 project were compared by model year to the populations submitted by state/local (S/L) agencies for the 2017 NEI.  The comparisons by model year were used to develop adjustment factors that remove older age LDVs from the IHS dataset. Out of 31 S/L agencies that provided data, 16 provided LDV population and age distributions with snapshot dates of January 2017, July 2017, or 2018.  The other 15 had either unknown or older (back to 2013) data pull dates, so were not a fair comparison to the 2017 IHS data.  

We reviewed the population by model year comparisons for each of the 16 geographic areas vs. IHS separately for source type 21 and for source type 31 plus 32 together. We reallocated the S/L agency populations of cars (source type 21) and light trucks (source types 31 and 32) to match IHS car and light-duty truck splits by county for consistent VIN decoding.  We also removed the state of Georgia from the pool of S/L agencies used to calculate the adjustment factors to avoid its influence on a pooled geographic adjustment.  Georgia already works closely with IHS on VIN decoding, and as a result, their submittal matched IHS.  The IHS data are higher than the pooled state data by 6.5 percent for cars and 5.9 percent for light trucks.

We calculated the vehicle age distribution adjustment factors as one minus the fraction of vehicles to remove from IHS to equal the state data, with two exceptions.  The model year range 2006/2007 to 2017 receives no adjustment and the model year 1987 receives a capped adjustment that equals the adjustment to 1988.  Table 2-20 below shows the fraction of vehicles to keep by model year based on this analysis.  The adjustments were applied to the 2016 IHS-based age distributions from CRC project A-115 prior to use in 2016v1.  In addition, we removed the county-specific fractions of antique license plate vehicles present in the registration summary from IHS.  Nationally, the prevalence of antique plates is only 0.8 percent, but as high as 6 percent in some states (e.g., Mississippi).

Table 2-20. Older Vehicle Adjustments Showing the Fraction of IHS Vehicle Populations to Retain for 2016v1 and 2017 NEI
Model Year
Cars
Light Trucks
pre-1989
                                                                          0.675
                                                                          0.769
                                                                           1989
                                                                          0.730
                                                                          0.801
                                                                           1990
                                                                          0.732
                                                                          0.839
                                                                           1991
                                                                          0.740
                                                                          0.868
                                                                           1992
                                                                          0.742
                                                                          0.867
                                                                           1993
                                                                          0.763
                                                                          0.867
                                                                           1994
                                                                          0.787
                                                                          0.842
                                                                           1995
                                                                          0.776
                                                                          0.865
                                                                           1996
                                                                          0.790
                                                                          0.881
                                                                           1997
                                                                          0.808
                                                                          0.871
                                                                           1998
                                                                          0.819
                                                                          0.870
                                                                           1999
                                                                          0.840
                                                                          0.874
                                                                           2000
                                                                          0.838
                                                                          0.896
                                                                           2001
                                                                          0.839
                                                                          0.925
                                                                           2002
                                                                          0.864
                                                                          0.921
                                                                           2003
                                                                          0.887
                                                                          0.942
                                                                           2004
                                                                          0.926
                                                                          0.953
                                                                           2005
                                                                          0.941
                                                                          0.966
                                                                           2006
                                                                              1
                                                                          0.987
2007-2017
                                                                              1
                                                                              1

In addition to removing the older and antique plate vehicles from the IHS data, we accounted for 25 counties that were outliers because their fleet age was significantly younger than typical. We limited our outlier identification to LDV source types 21, 31, and 32, because they're the most important. Many rural counties also have outliers for low-population source types such as Transit Bus and Refuse Truck; these do not have much of an impact on the inventory overall and reflect sparse data in low-population areas and therefore do not require correction.  

The most extreme examples of LDV outliers were Light Commercial Truck age distributions where over 50 percent of the population in the entire county is 0 and 1 years old. These sorts of young fleets can happen if the headquarters of a leasing or rental company is the owner/entity of a relatively large number of vehicles relative to the county-wide population.  While the business owner of thousands of new vehicles may reside in a single county, the vehicles likely operate in broader areas without being registered where they drive. To avoid creating artificial low spots of LDV emissions in these outlier counties, we flagged all counties above a 0.35 fraction of new vehicles and excluded their age distribution from the final set of grouped age distributions that went into the 2016v1 CDBs.

The 2016 age distributions were then grouped using a population-weighted average of the source type populations of each county in the representative county group.  The end-product was age distributions for each of the 13 source types in each of the 315 representative counties for 2016v1.  It should be noted that the long-haul truck source types 53 (Single Unit) and 62 (Combination Unit) are a nationwide average due to the long-haul nature of their operation. 

Input data tables provided by states were reviewed before they were used.  Some submitted data tables were found to be from previous emissions modeling platforms, primarily NEI 2014v2, 2016 alpha, or 2016 beta, and these were not explicitly used as most were already incorporated into the CDBs.  All average speed distributions in 2016v1 came from the CRC A-100 study, and most age distributions (other than accepted submittals for New Jersey, Pima County, Arizona, and Wisconsin) came from methods described above for 2016 v1.  The following submitted MOVES input data (other than the activity data discussed above) were incorporated into the 2016v1 base year MOVES CDBs:

 Chicago (IL) Metropolitan Agency for Planning: FF10 VMT, FF10 VPOP, Month/Day VMT Fraction, Ramp Fractions
 Georgia Department of Natural Resources:  Fuel Supply (county assignments to fuel type groups)
 Louisville (KY) Metro Air Pollution Control District:  Road Type Distributions, Ramp Fractions
 Maryland Department of the Environment:	Truck Stop Locations (these affect the spatial surrogate but not the MOVES run)
 New Jersey Department of Environmental Protection: Age Distribution
 Pima (AZ) Association of Governments: Age Distribution, I/M Coverage, Day VMT Fraction, Road Type Distribution
 Wisconsin Department of Natural Resources:  Age Distribution, I/M Coverage

Once the input data were incorporated into the CDBs, a new set of representative counties was developed.  Each county in the continental U.S. was classified according to its state, altitude (high or low), fuel region, the presence of inspection and maintenance programs, the mean light-duty age, and the fraction of ramps.  A binning algorithm was executed to identify "like counties", and then specific requests for representative county groups by states were honored from the states of Maryland, New York, New Jersey, Wisconsin, Michigan, and Georgia.  The final result was 315 representative counties (up from 304 in 2016 beta) as shown in Figure 2-3.  The representative counties themselves changed substantially; of the 315 representative counties, 145 were not representative counties in 2016 beta.  The CDBs for these 145 counties were developed from the 2014NEIv2 counties and updated to represent the year 2016. For more information on the development of the 2016 age distributions and representative counties and the review of the input data, see the memoranda "Onroad 2016v1 documentation_20191007" and "RepCountiesFor2016v1-2017_13jun2019" (ERG, 2019).

Figure 2-3. Representative Counties in 2016v1


To create the 2016v1 emission factors, MOVES was run separately for each representative county and fuel month for each temperature bin needed for calendar year 2016.  The CDBs used to run MOVES include the state-specific control measures such as the California low emission vehicle (LEV) program, except that fuels were updated to represent calendar year 2016.  In addition, the range of temperatures run along with the average humidities used were specific to the year 2016. The MOVES results were post-processed into CSV-formatted emission factor tables that can be read by SMOKE-MOVES.

Onroad California Inventory Development

The California Air Resources Board (CARB) provided their own onroad emissions inventories based on their EMFAC2017 model. EMFAC2017 was run by CARB for model years 2016, 2023, 2028, and 2035. Details on how SMOKE-MOVES emissions were adjusted to match the CARB-based 2016 inventory are provided in the Emissions Processing Requirements section of this document.
2016 Nonroad Mobile sources (cmv, rail, nonroad)
The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions (nonroad), locomotive (rail) and CMV emissions.

Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
The cmv_c1c2 inventory sector contains small to medium-size engine CMV emissions. Category 1 and Category 2 (C1C2) marine diesel engines typically range in size from about 700 to 11,000 hp. These engines are used to provide propulsion power on many kinds of vessels including tugboats, towboats, supply vessels, fishing vessels, and other commercial vessels in and around ports. They are also used as stand-alone generators for auxiliary electrical power on many types of vessels. Category 1 represents engines up to 7 liters per cylinder displacement. Category 2 includes engines from 7 to 30 liters per cylinder. 

The cmv_c1c2 inventory sector contains sources that traverse state and federal waters that are in the 2017NEI along with emissions from surrounding areas of Canada, Mexico, and international waters.  The cmv_c1c2 sources are modeled as point sources but using plume rise parameters that cause the emissions to be released in the ground layer of the air quality model.

The cmv_c1c2 sources within state waters are identified in the inventory with the Federal Information Processing Standard (FIPS) county code for the state and county in which the vessel is registered. The cmv_c1c2 sources that operate outside of state waters but within the Emissions Control Area (ECA) are encoded with a state FIPS code of 85.  The ECA areas include parts of the Gulf of Mexico, and parts of the Atlantic and Pacific coasts.  The cmv_c1c2 sources in the 2016v1 inventory are categorized as operating either in-port or underway and as main and auxiliary engines are encoded using the SCCs listed in Table 2-21.
Table 2-21. 2016v1 platform SCCs for cmv_c1c2 sector
SCC
Tier 1 Description
Tier 2 Description
Tier 3 Description
Tier 4 Description
                                                                     2280002101
C1/C2
Diesel
Port
Main
                                                                     2280002102
C1/C2
Diesel
Port
Auxiliary
                                                                     2280002201
C1/C2
Diesel
Underway
Main
                                                                     2280002202
C1/C2
Diesel
Underway
Auxiliary
                                       
Category 1 and 2 CMV emissions were developed for the 2017 NEI, The 2017 NEI emissions were developed based signals from Automated Identification System (AIS) transmitters. AIS is a tracking system used by vessels to enhance navigation and avoid collision with other AIS transmitting vessels.  The USEPA Office of Transportation and Air Quality received AIS data from the U.S. Coast Guard (USCG) in order to quantify all ship activity which occurred between January 1 and December 31, 2017. The provided AIS data extends beyond 200 nautical miles from the U.S. coast (Figure 2-4). This boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the North American ECA, although some non-ECA activity are captured as well.
Figure 2-4. 2017NEI/2016 platform geographical extent (solid) and U.S. ECA (dashed)
                                       

The AIS data were compiled into five-minute intervals by the USCG, providing a reasonably refined assessment of a vessel's movement. For example, using a five-minute average, a vessel traveling at 25 knots would be captured every two nautical miles that the vessel travels. For slower moving vessels, the distance between transmissions would be less. The ability to track vessel movements through AIS data and link them to attribute data, has allowed for the development of an inventory of very accurate emission estimates. These AIS data were used to define the locations of individual vessel movements, estimate hours of operation, and quantify propulsion engine loads. The compiled AIS data also included the vessel's International Marine Organization (IMO) number and Maritime Mobile Service Identifier (MMSI); which allowed each vessel to be matched to their characteristics obtained from the Clarksons ship registry (Clarksons, 2018). 

USEPA used the engine bore and stroke data to calculate cylinder volume. Any vessel that had a calculated cylinder volume greater than 30 liters was incorporated into the USEPA's new Category 3 Commercial Marine Vessel (C3CMV) model. The remaining records were assumed to represent Category 1 and 2 (C1C2) or non-ship activity.  The C1C2 AIS data were quality assured including the removal of duplicate messages, signals from pleasure craft, and signals that were not from CMV vessels (e.g., buoys, helicopters, and vessels that are not self-propelled).  Following this, there were 422 million records remaining.

The emissions were calculated for each time interval between consecutive AIS messages for each vessel and allocated to the location of the message following to the interval. Emissions were calculated according to Equation 1.

      Emissionsinterval=Time (hr)intervalx Power(kW)xEF(gkWh)xLLAF 	(1)

Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF represents the low load adjustment factor, a unitless factor which reflects increasing propulsive emissions during low load operations. Time indicates the activity duration time between consecutive intervals.

Next, vessels were identified in order determine their vessel type, and thus their vessel group, power rating, and engine tier information which are required for the emissions calculations. See the 2017 NEI documentation for more details on this process.  Following the identification, 108 different vessel types were matched to the C1C2 vessels. Vessel attribute data was not available for all these vessel types, so the vessel types were aggregated into 13 different vessel groups for which surrogate data were available as shown in Table 2-22.  11,302 vessels were directly identified by their ship and cargo number. The remaining group of miscellaneous ships represent 13 percent of the AIS vessels (excluding recreational vessels) for which a specific vessel type could not be assigned.
Table 2-22. Vessel groups in the cmv_c1c2 sector
                                 Vessel Group
                              NEI Area Ship Count
Bulk Carrier
                                                                             37
Commercial Fishing
                                                                          1,147
Container Ship
                                                                              7
Ferry Excursion
                                                                            441
General Cargo
                                                                          1,498
Government
                                                                          1,338
Miscellaneous
                                                                          1,475
Offshore support
                                                                          1,149
Reefer
                                                                             13
Ro Ro
                                                                             26
Tanker
                                                                            100
Tug
                                                                          3,994
Work Boat
                                                                             77
Total in Inventory:
                                                                         11,302

As shown in Equation (1), power is an important component of the emissions computation. Vessel-specific installed propulsive power ratings and service speeds were pulled from Clarkson's ship registry and adopted from the Global Fishing Watch (GFW) dataset when available. However, there is limited vessel specific attribute data for most of the C1C2 fleet. This necessitated the use of surrogate engine power and load factors, which were computed for each vessel group shown in Table 2.  In addition to the power required by propulsive engines, power needs for auxiliary engines were also computed for each vessel group.  Emissions from main and auxiliary engines are inventoried with different SCCs as shown in Table 2-21.

The final components of the emissions computation equation are the emission factors and the low load adjustment factor.  The emission factors used in this inventory take into consideration the EPA's marine vessel fuel regulations as well as exhaust standards that are based on the year that the vessel was manufactured to determine the appropriate regulatory tier. Emission factors in g/kWhr by tier for NOx, PM10, PM2.5, CO, CO2, SO2 and VOC were developed using Tables 3-7 through 3-10 in USEPA's (2008) Regulatory Impact Analysis on engines less than 30 liters per cylinder. To compile these emissions factors, population-weighted average emission factor were calculated per tier based on C1C2 population distributions grouped by engine displacement. Boiler emission factors were obtained from an earlier Entec study (Entec, 2004).  If the year of manufacture was unknown then it was assumed that the vessel was Tier 0, such that actual emissions may be less than those estimated in this inventory. Without more specific data, the magnitude of this emissions difference cannot be estimated.

Propulsive emissions from low-load operations were adjusted to account for elevated emission rates associated with activities outside the engines' optimal operating range. The emission factor adjustments were applied by load and pollutant, based on the data compiled for the Port Everglades 2015 Emission Inventory. Hazardous air pollutants and ammonia were added to the inventory according to multiplicative factors applied either to VOC or PM2.5. 

For more information on the emission computations for 2017, see the supporting documentation for the 2017 NEI C1C2 CMV emissions.  The emissions from the 2017 NEI were adjusted to represent 2016 in the cmv_c1c2 sector using factors derived from U.S. Army Corps of Engineers national vessel Entrance and Clearance data by applying a factor of 0.98 to all pollutants. For consistency, the same methods were used for California, Canadian, and other non-U.S. emissions.
Category 3 Commercial Marine Vessels (cmv_c3)
The cmv_c3 inventory is brand new for the 2016v1 platform.  It was developed in conjunction with the CMV inventory for the 2017 NEI.  This sector contains large engine CMV emissions. Category 3 (C3) marine diesel engines are those at or above 30 liters per cylinder, typically these are the largest engines rated at 3,000 to 100,000 hp. C3 engines are typically used for propulsion on ocean-going vessels including container ships, oil tankers, bulk carriers, and cruise ships. Emissions control technologies for C3 CMV sources are limited due to the nature of the residual fuel used by these vessels.  The cmv_c3 sector contains sources that traverse state and federal waters; along with sources in waters not covered by the NEI in surrounding areas of Canada, Mexico, and international waters.  

The cmv_c3 sources that operate outside of state waters but within the federal Emissions Control Area (ECA) are encoded with a FIPS state code of 85, with the "county code" digits representing broad regions such as the Atlantic, Gulf of Mexico, and Pacific.  The ECA areas include parts of the Gulf of Mexico, and parts of the Atlantic and Pacific coasts.  CMV C3 sources around Puerto Rico, Hawaii and Alaska, which are outside the ECA areas, are included in the 2016v1 inventory but are in separate files from the emissions around the continental United States (CONUS). The cmv_c3 sources in the 2016v1 inventory are categorized as operating either in-port or underway and are encoded using the SCCs listed in Table 2-23 and distinguish between diesel and residual fuel, in port areas versus underway, and main and auxiliary engines.  In addition to C3 sources in state and federal waters, the cmv_c3 sector includes emissions in waters not covered by the NEI (FIPS = 98) and taken from the "ECA-IMO-based" C3 CMV inventory. The ECA-IMO inventory is also used for allocating the FIPS-level emissions to geographic locations for regions within the domain not covered by the AIS selection boxes as described in the next section. 
Table 2-23. 2016v1 platform SCCs for cmv_c3 sector
                                      SCC
Tier 1 Description
Tier 2 Description
Tier 3 Description
Tier 4 Description
                                  2280002103
C3
Diesel
Port
Main
                                  2280002104
C3
Diesel
Port
Auxiliary
                                  2280002203
C3
Diesel
Underway
Main
                                  2280002204
C3
Diesel
Underway
Auxiliary
                                  2280003103
C3
Residual
Port
Main
                                  2280003104
C3
Residual
Port
Auxiliary
                                  2280003203
C3
Residual
Underway
Main
                                  2280003204
C3
Residual
Underway
Auxiliary

Prior to creation of the 2017 NEI, "The EPA received Automated Identification System (AIS) data from United States Coast Guard (USCG) in order to quantify all ship activity which occurred between January 1 and December 31, 2017. The International Maritime Organization's (IMO's) International Convention for the Safety of Life at Sea (SOLAS) requires AIS to be fitted aboard all international voyaging ships with gross tonnage of 300 or more, and all passenger ships regardless of size (IMO, 2002). In addition, the USCG has mandated that all commercial marine vessels continuously transmit AIS signals while transiting U.S. navigable waters. As the vast majority of C3 vessels meet these requirements, any omitted from the inventory due to lack of AIS adoption are deemed to have a negligible impact on national C3 emissions estimates. The activity described by this inventory reflects ship operations within 200 nautical miles of the official U.S. baseline. This boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the North American ECA, although some non-ECA activity is captured as well (Figure 2-4).

The 2017 NEI data were computed based on the AIS data from the USGS for the year of 2017.  The AIS data were coupled with ship registry data that contained engine parameters, vessel power parameters, and other factors such as tonnage and year of manufacture which helped to separate the C3 vessels from the C1C2 vessels.  Where specific ship parameters were not available, they were gap-filled. The types of vessels that remain in the C3 data set include: bulk carrier, chemical tanker, liquified gas tanker, oil tanker, other tanker, container ship, cruise, ferry, general cargo, fishing, refrigerated vessel, roll-on/roll-off, tug, and yacht.

Prior to use, the AIS data were reviewed - data deemed to be erroneous were removed, and data found to be at intervals greater than 5 minutes were interpolated to ensure that each ship had data every five minutes. The five-minute average data provide a reasonably refined assessment of a vessel's movement. For example, using a five-minute average, a vessel traveling at 25 knots would be captured every two nautical miles that the vessel travels. For slower moving vessels, the distance between transmissions would be less. 

The emissions were calculated for each C3 vessel in the dataset for each 5-minute time range and allocated to the location of the message following to the interval. Emissions were calculated according to Equation 1.

      Emissionsinterval=Time (hr)intervalx Power(kW)xEF(gkWh)xLLAF 	(1)

Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF represents the low load adjustment factor, a unitless factor which reflects increasing propulsive emissions during low load operations. Time indicates the activity duration time between consecutive intervals.

Emissions were computed according to a computed power need (kW) multiplied by the time (hr) and by an engine-specific emission factor (g/kWh) and finally by a low load adjustment factor that reflects increasing propulsive emissions during low load operations.  

The resulting emissions were available at 5-minute intervals.  Code was developed to aggregate these emissions to modeling grid cells and up to hourly levels so that the emissions data could be input to SMOKE for emissions modeling with SMOKE.  Within SMOKE, the data were speciated into the pollutants needed by the air quality model, but since the data were already in the form of point sources at the center of each grid cell, and they were already hourly, no other processing was needed within SMOKE.  SMOKE requires an annual inventory file to go along with the hourly data, so those files were also generated for each year.  

On January 1st, 2015, the ECA initiated a fuel sulfur standard which regulated large marine vessels to use fuel with 1,000 ppm sulfur or less. These standards are reflected in the cmv_c3 inventories.

There were some areas needed for modeling that the AIS request boxes did not cover (see Figure 2-4).  These include a portion of the St. Lawrence Seaway transit to the Great Lakes, a small portion of the Pacific Ocean far offshore of Washington State, portions of the southern Pacific Ocean around off the coast of Mexico, and the southern portion of the Gulf of Mexico that is within the 36-km domain used for air quality modeling. In addition, a determination had to be made regarding whether to use the existing Canadian CMV inventory or the more detailed AIS-based inventory.  In 2016v1, the AIS-based inventory was used in the areas for which data were available, and the areas not covered were gap-filled with inventory data from the 2016beta platform, which included data from Environment Canada and the 2011 ECA-IMO C3 inventory.

For the gap-filled areas not covered by AIS selections or the Environment Canada inventory, the 2016beta nonpoint C3 inventory was converted to a point inventory to support plume rise calculations for C3 vessels. The nonpoint emissions were allocated to point sources using a multi-step allocation process because not all of the inventory components had a complete set of county-SCC combinations. In the first step, the county-SCC sources from the nonpoint file were matched to the county-SCC points in the 2011 ECA-IMO C3 inventory. The ECA-IMO inventory contains multiple point locations for each county-SCC. The nonpoint emissions were allocated to those points using the PM2.5 emissions at each point as a weighting factor. 

Cmv_c3 underway emissions that did not have a matching FIPS in the ECA-IMO inventory were allocated using the 12 km 2014 offshore shipping activity spatial surrogate (surrogate code 806). Each county with underway emissions in the area inventory was allocated to the centroids of the cells associated with the respective county in the surrogate. The emissions were allocated using the weighting factors in the surrogate.

The resulting point emissions centered on each grid cell were converted to an annual point 2010 flat file format (FF10). Pictures of the emissions are shown in Section 7 of this document. A set of standard stack parameters were assigned to each release point in the cmv_c3 inventory. The assigned stack height was 65.62 ft, the stack diameter was 2.625 ft, the stack temperature was 539.6 °F, and the velocity was 82.02 ft/s. Emissions were computed for each grid cell needed for modeling.

Adjustment of the 2017 NEI CMV C3 to 2016 

Because the NEI emissions data were for 2017, an analysis was performed of 2016 versus 2017 entrance and clearance data (ERG, 2019a). Annual, monthly, and daily level data were reviewed. Annual ratios of entrance and clearance activity were developed for each ship type as shown in Table 2-24.  For vessel types with low populations (C3 Yacht, tug, barge, and fishing vessels), an annual ratio of 0.98 was applied.  
Table 2-24. 2017 to 2016 projection factors for C3 CMV
Ship Type
                                Annual Ratio[a]
Barge
                                     1.551
Bulk Carrier
                                     1.067
Chemical Tanker
                                     1.031
Container Ship
                                    1.0345
Cruise
                                     1.008
Ferry Ro Pax
                                     1.429
General Cargo
                                     0.888
Liquified Gas Tanker
                                     1.192
Miscellaneous Fishing
                                     0.932
Miscellaneous Other
                                     1.015
Offshore
                                     0.860
Oil Tanker
                                     1.101
Other Tanker
                                     1.037
Reefer
                                     0.868
Ro Ro
                                     1.007
Service Tug
                                     1.074
	[a] Above ratios are applied to the 2017 emission values to estimate 2016 values

The cmv_c3 projection factors were pollutant-specific and region-specific. Most states are mapped to a single region with a few exceptions.  Pennsylvania and New York were split between the East Coast and Great Lakes, Florida was split between the Gulf Coast and East Coast, and Alaska was split between Alaska East and Alaska West. The non-federal factors listed in this table were applied to sources outside of U.S. federal waters (FIPS 98). Volatile Organic Compound (VOC) Hazardous Air Pollutant (HAP) emissions were projected using the VOC factors. NH3 emissions were held constant at 2014 levels.
Rail Sources (rail)
The rail sector includes all locomotives in the NEI nonpoint data category. The 2016v1 inventory SCCs are shown in Table 2-25.  This sector excludes railway maintenance activities.  Railway maintenance emissions are included in the nonroad sector.  The point source yard locomotives are included in the ptnonipm sector.  In 2014NEIv2, rail yard locomotive emissions were present in both the nonpoint (rail sector) and point (ptnonipm sector) inventories.  For the 2016v1 platform, rail yard locomotive emissions are only in the point inventory / ptnonipm sector.  Therefore, SCC 2285002010 is not present in the 2016v1 platform rail sector, except in three California counties. The California Air Resources Board (CARB) submitted rail emissions, including rail yards, for 2016v1 platform. In three counties, CARB's rail yard emissions could not be mapped to point source rail yards, and so those counties' emissions were included in the rail sector.
Table 2-25. 2016v1 SCCs for the Rail Sector
SCC
Sector
Description: Mobile Sources prefix for all
                                  2285002006
                                     rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
                                  2285002007
                                     rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations 
                                  2285002008
                                     rail
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak) 
                                  2285002009
                                     rail
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines 
                                  2285002010
                                     rail
Railroad Equipment; Diesel; Yard Locomotives (nonpoint)
                                   28500201
                                     rail
Railroad Equipment; Diesel; Yard Locomotives (point)

Class I Line-haul Methodology

In 2008 air quality planners in the eastern US formed the Eastern Technical Advisory Committee (ERTAC) for solving persistent emissions inventory issues. This work is the fourth inventory created by the ERTAC rail group. For the 2016 inventory, the Class I railroads granted ERTAC Rail permission to use the confidential link-level line-haul activity GIS data layer maintained by the Federal Railroad Administration (FRA).  In addition, the Association of American Railroads (AAR) provided national emission tier fleet mix information.  This allowed ERTAC Rail to calculate weighted emission factors for each pollutant based on the percentage of the Class I line-haul locomotives in each USEPA Tier level category.  These two datasets, along with 2016 Class I line-haul fuel use data reported to the Surface Transportation Board (Table 2-26), were used to create a link-level Class I emissions inventory, based on a methodology recommended by Sierra Research. Rail Fuel Consumption Index (RFCI) is a measure of fuel use per ton mile of freight.  This link-level inventory is nationwide in extent, but it can be aggregated at either the state or county level. 
Table 2-26. Class I Railroad Reported Locomotive Fuel Use Statistics for 2016
                               Class I Railroads
               2016 R-1 Reported Locomotive 
Fuel Use (gal/year)
                                     RFCI
                                (ton-miles/gal)
                                 Adjusted RFCI
                                (ton-miles/gal)

                                  Line-Haul*
                                   Switcher 
                                       
                                       
                                     BNSF
                                 1,243,366,255
                                  40,279,454
                                      972
                                      904
                               Canadian National
                                 102,019,995 
                                   6,570,898
                                     1,164
                                     1,081
                               Canadian Pacific
                                  56,163,697 
                                   1,311,135
                                     1,123
                                     1,445
                              CSX Transportation
                                 404,147,932 
                                  39,364,896
                                     1,072
                                     1,044
                             Kansas City Southern
                                  60,634,689 
                                   3,211,538
                                      989
                                      995
                               Norfolk Southern
                                 437,110,632 
                                  28,595,955
                                      920
                                      906
                                 Union Pacific
                                 900,151,933 
                                  85,057,080
                                     1,042
                                     1,095
                                    Totals:
                                3,203,595,133 
                                  204,390,956
                                     1,006
                                      993
* Includes work trains; Adjusted RFCI values calculated from FRA gross ton-mile data as described on page 7.   RFCI total is ton-mile weighted mean. 

Annual default emission factors for locomotives based on operating patterns ("duty cycles") and the estimated nationwide fleet mixes for both switcher and line-haul locomotives are available.   However, Tier level fleet mixes vary significantly between the Class I and Class II/III railroads.  As can be seen in Figure 2-5 and Figure 2-6, Class I railroad activity is highly regionalized in nature and is subject to variations in terrain across the country which can have a significant impact on fuel efficiency and overall fuel consumption.
                                       
Figure 2-5. 2016 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT)
                                       
Figure 2-6. Class I Railroads in the United States[5]
                                       
For the 2016 inventory, the AAR provided a national line-haul Tier fleet mix profile representing the entire Class I locomotive fleet.  A locomotive's Tier level determines its allowable emission rates based on the year when it was built and/or re-manufactured.  The national fleet mix data was then used to calculate weighted average in-use emissions factors for the line-haul locomotives operated by the Class I railroads as shown in Table 2-27. 
Table 2-27. 2016 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal)
                                  Tier Level
                              AAR Fleet Mix Ratio
                                     PM10
                                      HC
                                      NOx
                                      CO
Uncontrolled (pre-1973)
                                   0.047494
                                     6.656
                                     9.984
                                     270.4
                                    26.624
Tier 0 (1973-2001)
                                   0.188077
                                     6.656
                                     9.984
                                    178.88
                                    26.624
Tier 0+ (Tier 0 rebuilds)
                                   0.141662
                                     4.16
                                     6.24
                                    149.76
                                    26.624
Tier 1 (2002-2004)
                                   0.029376
                                     6.656
                                     9.776
                                    139.36
                                    26.624
Tier 1+ (Tier 1 rebuilds)
                                   0.223147
                                     4.16
                                     6.032
                                    139.36
                                    26.624
Tier 2 (2005-2011)
                                   0.124536
                                     3.744
                                     5.408
                                    102.96
                                    26.624
Tier 2+ (Tier 2 rebuilds)
                                   0.093607
                                     1.664
                                     2.704
                                    102.96
                                    26.624
Tier 3 (2012-2014)
                                   0.123113
                                     1.664
                                     2.704
                                    102.96
                                    26.624
Tier 4 (2015 and later)
                                   0.028988
                                     0.312
                                     0.832
                                     20.8
                                    26.624
                             2016 Weighted EF's
                                   1.000000
                                     4.117
                                     6.153
                                    138.631
                                    26.624
Based on values in EPA Technical Highlights:  Emission Factors for Locomotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April 2009.
Weighted Emission Factors (EF) per pollutant for each gallon of fuel used (grams/gal or lbs/gal) were calculated for the US Class I locomotive fleet based on the percentage of line-haul locomotives certified at each regulated Tier level (Equation 1).   
                                 Equation (1)	
where:
	EFi	= 	Weighted Emission Factor for pollutant i for Class I locomotive fleet (g/gal). 
    	EFiT	=	Emission Factor for pollutant i for locomotives in Tier T (g/gal) (Table 4).
	fT	=		Percentage of the Class I locomotive fleet in Tier T expressed as a ratio.

While actual engine emissions will vary within Tier level categories, the approach described above likely provides reasonable emission estimates, as locomotive diesel engines are certified to meet the emission standards for each Tier.  It should be noted that actual emission rates may increase over time due to engine wear and degradation of the emissions control systems.  In addition, locomotives may be operated in a manner that differs significantly from the conditions used to derive line-haul duty-cycle estimates.  

Emission factors for other pollutants are not Tier-specific because these pollutants are not directly regulated by USEPA's locomotive emission standards.  PM2.5 was assumed to be 97% of PM10 [4], the ratio of volatile organic carbon (VOC) to (hydrocarbon) HC was assumed to be 1.053, and the emission factors used for sulfur dioxide (SO2) and ammonia (NH3)were 0.0939 g/gal[4] and 83.3 mg/gal[6], respectively.  The 2016 SO2 emission factor is based on the nationwide adoption of 15 ppm ultra-low sulfur diesel (ULSD) fuel by the rail industry.  

The remaining steps to compute the Class 1 rail emissions involved calculating class I railroad-specific rail fuel consumption index values and calculating emissions per link. The final 
link-level emissions for each pollutant were then aggregated by state/county FIPS code and then converted into an FF10 format used by SMOKE.  More detail on these steps is described in the specification sheet for the 2016v1 rail sector emissions.

Rail yard Methodology

Rail yard emissions were computed based on fuel use and/or yard switcher locomotive counts for the class I rail companies for all of the rail yards on their systems.  Three railroads provided complete rail yard datasets: BNSF, UP, and KCS.  CSX provided switcher counts for its 14 largest rail yards. This reported activity data was matched to existing yard locations and data stored in USEPA's Emissions Inventory System (EIS) database.  All existing EIS yards that had activity data assigned for prior years, but no reported activity data for 2016 were zeroed out.  New yard data records were generated for reported locations that were not found in EIS.  Special care was made to ensure that the new yards added to EIS did not duplicate existing data records.  Data for non-Class I yards was carried forward from the 2014 NEI.  

Since the railroads only supplied switcher counts, average fuel use per switcher values were calculated for each railroad.  This was done by dividing each company's 2016 R-1 yard fuel use total by the number of switchers reported for each railroad.  These values were then used to allocate fuel use to each yard based on the number of switchers reported for that location.  Table 2-28 summarizes the 2016 yard fuel use and switcher data for each Class I railroad.  The emission factors used for rail yard switcher engines are shown in Table 2-29. 
Table 2-28. Surface Transportation Board R-1 Fuel Use Data  -  2016
                                   Railroad
                                2016 R-1 Yard 
                                Fuel Use (gal)
                        ERTAC calculated Fuel Use (gal)
                             Identified Switchers
                       ERTAC per Switcher Fuel Use (gal)
                                     BNSF
                                  40,279,454
                                  40,740,317
                                      442
                                    92,173
                                     CSXT
                                  39,364,896
                                  43,054,795
                                      455
                                    94,626
                                      CN
                                   6,570,898
                                   6,570,898
                                      103
                                    63,795
                                      KCS
                                   3,211,538
                                   3,211,538
                                      176
                                    18,247
                                      NS
                                  28,595,955
                                  28,658,528
                                      458
                                    62,573
                                     CPRS
                                   1,311,135
                                   1,311,135
                                      70
                                    18,731
                                      UP
                                  85,057,080
                                  85,057,080
                                     1286
                                    66,141
                                 All Class I's
                                  204,390,956
                                  208,604,291
                                     2,990
                                    69,767

Table 2-29. 2016 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)[4]
                                  Tier Level
                                  AAR Fleet 
                                   Mix Ratio
                                     PM10
                                      HC
                                      NOx
                                      CO
Uncontrolled (pre-1973)
                                    0.2601
                                     6.688
                                    15.352
                                    264.48
                                    27.816
Tier 0 (1973-2001)
                                    0.2361
                                     6.688
                                    15.352
                                    191.52
                                    27.816
Tier 0+ (Tier 0 rebuilds)
                                    0.2599
                                     3.496
                                     8.664
                                    161.12
                                    27.816
Tier 1 (2002-2004)
                                    0.0000
                                     6.536
                                    15.352
                                    150.48
                                    27.816
Tier 1+ (Tier 1 rebuilds)
                                    0.0476
                                     3.496
                                     8.664
                                    150.48
                                    27.816
Tier 2 (2005-2011)
                                    0.0233
                                     2.888
                                     7.752
                                    110.96
                                    27.816
Tier 2+ (Tier 2 rebuilds)
                                    0.0464
                                     1.672
                                     3.952
                                    110.96
                                    27.816
Tier 3 (2012-2014)
                                    0.1018
                                     1.216
                                     3.952
                                     68.4
                                    27.816
Tier 4 (2015 and later)
                                    0.0247
                                     0.228
                                     1.216
                                     15.2
                                    27.816
                             2016 Weighted EF's
                                    0.9999
                                     4.668
                                    11.078
                                   178.1195
                                    27.813
Based on values in EPA Technical Highlights:  Emission Factors for Locomotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April 2009.  AAR fleet mix ratios did not add up to 1.0000, which caused a small error for the CO weighted emission factor as shown above.   

In addition to the Class I rail yards, Emission estimates were calculated for four large Class III railroad hump yards which are among the largest classification facilities in the United States.  These four yards are located in Chicago (Belt Railway of Chicago-Clearing and Indiana Harbor Belt-Blue Island) and Metro-East St. Louis (Alton & Southern-Gateway and Terminal Railroad Association of St. Louis-Madison).  Figure 2-7 shows the spatial distribution of active yards in the 2016v1 and 2017 NEI inventories.
Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States
                                       

Class II and III Methodology

There are approximately 560 Class II and III Railroads operating in the United States, most of which are members of the American Short Line and Regional Railroad Association (ASLRRA).  While there is a lot of information about individual Class II and III railroads available online, a significant amount of effort would be required to convert this data into a usable format for the creation of emission inventories.  In addition, the Class II and III rail sector has been in a constant state of flux ever since the railroad industry was deregulated under the Staggers Act in 1980.  Some states have conducted independent surveys of their Class II and III railroads and produced emission estimates, but no national level emissions inventory existed for this sector of the railroad industry prior to ERTAC Rail's work for the 2008 NEI.

Class II and III railroad activities account for nearly 4 percent of the total locomotive fuel use in the combined ERTAC Rail emission inventories and for approximately 35 percent of the industry's national freight rail track mileage.  These railroads are widely dispersed across the country and often utilize older, higher emitting locomotives than their Class I counterparts.  Class II and III railroads provide transportation services to a wide range of industries.  Individual railroads in this sector range from small switching operations serving a single industrial plant to large regional railroads that operate hundreds of miles of track. Figure 2-8 shows the distribution of Class II and III railroads and commuter railroads across the country.  This inventory will be useful for regional and local modeling, helps identify where Class II and III railroads may need to be better characterized, and provides a strong foundation for the future development of a more accurate nationwide short line and regional railroad emissions inventory.  A picture of the locations of class II and III railroads is shown in Figure 2-8. The data sources, calculations, and assumptions used to develop the Class II and III inventory are described in the 2016v1 rail specification sheet. 
Figure 2-8. Class II and III Railroads in the United States[5]
                                       
                                       
Commuter Rail Methodology
Commuter rail emissions were calculated in the same way as the Class II and III railroads. The primary difference is that the fuel use estimates were based on data collected by the Federal Transit Administration (FTA) for the National Transit Database.  2016 fuel use was then estimated for each of the commuter railroads shown in Table 2-30 by multiplying the fuel and lube cost total by 0.95, then dividing the result by Metra's average diesel fuel cost of $1.93/gallon.  These fuel use estimates were replaced with reported fuel use statistics for MARC (Maryland), MBTA (Massachusetts), Metra (Illinois), and NJT (New Jersey). The commuter railroads were separated from the Class II and III railroads so that the appropriate SCC codes could be entered into the emissions calculation sheet.  
Table 2-30. Expenditures and fuel use for commuter rail
                                   FRA Code
                                    System
                                 Cities Served
                                Propulsion Type
                                DOT Fuel &
                                  Lube Costs
                          Reported/Estimated Fuel Use
                                     ACEX
Altamont Corridor Express
San Jose / Stockton
                                    Diesel
                                   $889,828
                                  437,998.24
                                     CMRX
Capital MetroRail
Austin
                                    Diesel
                                    No data
                                      n/a
                                     DART
A-Train
Denton
                                    Diesel
                                      $0
                                     0.00
                                     DRTD
Denver RTD: A&B Lines
Denver
                                   Electric
                                      $0
                                     0.00
                                     JPBX
Caltrain
San Francisco / San Jose
                                    Diesel
                                  $7,002,612
                                 3,446,881.55
                                      LI
MTA Long Island Rail Road
New York
                              Electric and Diesel
                                  $13,072,158
                                 6,434,481.92
                                     MARC
MARC Train
Baltimore / Washington, D.C.
                              Diesel and Electric
                                  $4,648,060
                                 4,235,297.57
                                     MBTA
MBTA Commuter Rail
Boston / Worcester / Providence
                                    Diesel
                                  $37,653,001
                                 12,142,826.00
                                     MNCW
MTA Metro-North Railroad
New York / Yonkers / Stamford
                              Electric and Diesel
                                  $13,714,839
                                 6,750,827.49
                                     NICD
NICTD South Shore Line
Chicago / South Bend
                                   Electric
                                   $181,264
                                     0.00
                                     NIRC
Metra
Chicago
                              Diesel and Electric
                                  $52,460,705
                                 25,757,673.57
                                      NJT
New Jersey Transit
New York / Newark / Trenton / Philadelphia
                              Electric and Diesel
                                  $38,400,031
                                 16,991,164.00
                                     NMRX
New Mexico Rail Runner
Albuquerque / Santa Fe
                                    Diesel
                                  $1,597,302
                                  786,236.74
                                     CFCR
SunRail
Orlando
                                    Diesel
                                   $856,202
                                  421,446.58
                                     MNRX
Northstar Line
Minneapolis
                                    Diesel
                                   $708,855
                                  348,918.26
                                   Not Coded
SMART
San Rafael-Santa Rosa (Opened 2017)
                                    Diesel
                                      n/a
                                     0.00
                                     NRTX
Music City Star
Nashville
                                    Diesel
                                   $456,099
                                  224,504.69
                                     SCAX
Metrolink
Los Angeles / San Bernardino
                                    Diesel
                                  $19,245,255
                                 9,473,052.98
                                     SDNR
NCTD Coaster
San Diego / Oceanside
                                    Diesel
                                  $1,489,990
                                  733,414.77
                                     SDRX
Sounder Commuter Rail
Seattle / Tacoma
                                    Diesel
                                  $1,868,019
                                  919,491.22
                                     SEPA
SEPTA Regional Rail
Philadelphia
                                   Electric
                                   $483,965
                                     0.00
                                      SLE
Shore Line East
New Haven
                                    Diesel
                                    No data
                                      n/a
                                     TCCX
Tri-Rail
Miami / Fort Lauderdale / West Palm Beach
                                    Diesel
                                  $5,166,685
                                 2,543,186.92
                                     TREX
Trinity Railway Express
Dallas / Fort Worth
                                    Diesel
                                    No data
                                      n/a
                                      UTF
UTA FrontRunner
Salt Lake City / Provo
                                    Diesel
                                  $4,044,265
                                 1,990,700.39
                                     VREX
Virginia Railway Express
Washington, D.C.
                                    Diesel
                                  $3,125,912
                                 1,538,661.35
                                     WSTX
Westside Express Service
Beaverton
                                    Diesel
                                    No data
                                      n/a
*Reported fuel use values were used for MARC, MBTA, Metra, and New Jersey Transit.

Intercity Passenger Methodology (Amtrak)

2016 marked the first time that a nationwide intercity passenger rail emissions inventory was created for Amtrak.  The calculation methodology mimics that used for the Class II and III and commuter railroads with a few modifications. Since link-level activity data for Amtrak was unavailable, the default assumption was made to evenly distribute Amtrak's 2016 reported fuel use across all of it diesel-powered route-miles shown in Figure 2-9.  Participating states were instructed that they could alter the fuel use distribution within their jurisdictions by analyzing Amtrak's 2016 national timetable and calculating passenger train-miles for each affected route. Illinois and Connecticut chose to do this and were able to derive activity-based fuel use numbers for their states based on Amtrak's 2016 reported average fuel use of 2.2 gallons per passenger train-mile.  In addition, Connecticut provided supplemental data for selected counties in Massachusetts, New Hampshire, and Vermont.  Amtrak also submitted company-specific fleet mix information and company-specific weighted emission factors were derived.  Amtrak's emission rates were 25% lower than the default Class II and III and commuter railroad emission rate. Details on the computation of the Amtrak emissions are available in the rail specification sheet.
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains
                                       

Other Data Sources

The California Air Resources Board (CARB) provided rail inventories for inclusion in the 2016v1 platform. CARB's rail inventories were used in California, in place of the national dataset described above. For rail yards, the national point source rail yard dataset was used to allocate CARB-submitted rail yard emissions to point sources where possible. That is, for each California county with at least one rail yard in the national dataset, the emissions in the national rail yard dataset were adjusted so that county total rail yard emissions matched the CARB dataset. In other words, 2016v1 platform includes county total rail yard emissions from CARB, but the locations of rail yards are based on the national methodology. There are three counties with CARB-submitted rail yard emissions, but no rail yard locations in the national dataset; for those counties, the rail yard emissions were included in the rail sector using SCC 2285002010. 

North Carolina separately provided passenger train (SCC 2285002008) emissions for use in the platform. We used NC's passenger train emissions instead of the corresponding emissions from the Lake Michigan Air Directors Consortium (LADCO) dataset.

None of these rail inventory sources included HAPs. For VOC speciation, the EPA preferred augmenting the inventory with HAPs and using those HAPs for integration, rather than running the sector as a no-integrate sector. So, Naphthalene, Benzene, Acetaldehyde, Formaldehyde, and Methanol (NBAFM) emissions were added to all rail inventories, including the California inventory, using the same augmentation factors as are used to augment HAPs in the NEI.
Nonroad Mobile Equipment Sources (nonroad)
The mobile nonroad equipment sector includes all mobile source emissions that do not operate on roads, excluding commercial marine vehicles, railways, and aircraft. Types of nonroad equipment include recreational vehicles, pleasure craft, and construction, agricultural, mining, and lawn and garden equipment. Nonroad equipment emissions were computed by running the MOVES2014b model, which incorporates the NONROAD2008 model. MOVES2014b replaced MOVES2014a in August 2018, and incorporates updated nonroad engine population growth rates, nonroad Tier 4 engine emission rates, and sulfur levels of nonroad diesel fuels. MOVES2014b provides a complete set of HAPs and incorporates updated nonroad emission factors for HAPs. MOVES2014b was used for all states other than California and Texas, which developed their own emissions using their own tools. VOC and PM speciation profile assignments are determined by MOVES and applied by SMOKE.

MOVES2014b provides estimates of NONHAPTOG along with the speciation profile code for the NONHAPTOG emission source. This was accomplished by using NHTOG#### as the pollutant code in the Flat File 2010 (FF10) inventory file that can be read into SMOKE, where #### is a speciation profile code. One of the speciation profile codes is `95335a' (lowercase `a'); the corresponding inventory pollutant is NONHAPTOG95335A (uppercase `A') because SMOKE does not support inventory pollutant names with lowercase letters. Since speciation profiles are applied by SCC and pollutant, no changes to SMOKE were needed to use the inventory file with this profile information. This approach was not used for California or Texas, because the datasets in those states included VOC.  

MOVES2014b, unlike MOVES2014a, also provides estimates of PM2.5 by speciation profile code for the PM2.5 emission source, using PM25_#### as the pollutant code in the FF10 inventory file, where #### is a speciation profile code. To facilitate calculation of coarse particulate matter (PMC) within SMOKE, and to help create emissions summaries, an additional pollutant representing total PM2.5 called PM25TOTAL was added to the inventory. As with VOC / TOG, this approach is not used for California or Texas.

MOVES2014b outputs emissions data in county-specific databases, and a post-processing script converts the data into FF10 format. Additional post-processing steps were performed as follows:
 County-specific FF10s were combined into a single FF10 file.
 Emissions were aggregated from the more detailed SCCs modeled in MOVES to the SCCs modeled in SMOKE. A list of the aggregated SMOKE SCCs is in Appendix A of the 2016v1 nonroad specification sheet.
 To reduce the size of the inventory, HAPs that are not needed for air quality modeling, such as dioxins and furans, were removed from the inventory.
 To reduce the size of the inventory further, all emissions for sources (identified by county/SCC) for which total CAP emissions are less than 1*10[-10] were removed from the inventory. The MOVES model attributes a very tiny amount of emissions to sources that are actually zero, for example, snowmobile emissions in Florida. Removing these sources from the inventory reduces the total size of the inventory by about 7%.
 Gas and particulate components of HAPs that come out of MOVES separately, such as naphthalene, were combined.
 VOC was renamed VOC_INV so that SMOKE does not speciate both VOC and NONHAPTOG, which would result in a double count.
 PM25TOTAL, referenced above, was also created at this stage of the process.
 California and Texas emissions from MOVES were deleted and replaced with the CARB- and TCEQ-supplied emissions, respectively.
Emissions for airport ground support vehicles (SCCs ending in -8005), and oil field equipment (SCCs ending in -10010), were removed from the mobile nonroad inventory, to prevent a double count with the ptnonipm and np_oilgas sectors, respectively.

National Updates: Agricultural and Construction Equipment Allocation

The methodology for developing Agricultural equipment allocation data for the 2016v1 platform was developed by the North Carolina Department of Environmental Quality (NCDEQ). EPA updated the Construction equipment allocation data for the v1 platform.

NCDEQ compiled regional and state-level Agricultural sector fuel expenditure data for 2016 from the US Department of Agriculture, National Agricultural Statistics Service (NASS), August 2018 publication, "Farm Production Expenditures 2017 Summary." This resource provides expenditures for each of 5 major regions that cover the Continental U.S., as well as state-level data for 15 major farm producing states. Because of the limited coverage of the NASS source relative to that in MOVES, it was necessary to identify a means for estimating the 2016 Agricultural sector allocation data for the following States and Territories from a different source:  Alaska, Hawaii, Puerto Rico, and U.S. Virgin Islands. The approach for these areas is described below.

For the Continental U.S., NCDEQ first allocated the remainder of the regional fuel expenditures to states in each region for which state-level data are not reported. For this allocation, NCDEQ relied on 2012 fuel expenditure data from NASS' 2012 Census of Agriculture (note that 2017 data were not yet available at the time of this effort). The next step to developing county-level allocation data for agricultural equipment was to multiply the state-level fuel expenditure estimates by county-level allocation ratios. These allocation ratios were computed from county-level fuel expenditure data from the NASS' 2012 Census of Agriculture. There were 17 counties for which fuel expenditure data were withheld in the Census of Agriculture. For these counties, NDEQ allocated the fuel expenditures that were not accounted for in the applicable state via a surrogate indicator of fuel expenditures. For most states, the 2012 Census of Agriculture's total machinery asset value was the surrogate indicator used to perform the allocation. This indicator was found to have the strongest correlation to agricultural sector fuel expenditures based on analysis of 2012 state-level Census of Agriculture values for variables analyzed (correlation coefficient of 0.87). Because the analyzed surrogate variables were not available for the two counties in New York without fuel expenditure data, farm sales data from the 2012 Census of Agriculture were used in the allocation procedure for these counties.

For Alaska and Hawaii, NCDEQ estimated 2016 state-level fuel production expenditures by first applying the national change in fuel expenditures between 2012 and 2016 from NASS' "Farm Production Expenditures" summary publications to 2012 state expenditure data from the 2012 Census of Agriculture. Next, NCDEQ applied an adjustment factor to account for the relationship between national 2012 fuel expenditures as reported by the Census of Agriculture and those reported in the Farm Production Expenditures Summary. Hawaii's state-level fuel expenditures were allocated to counties using the same approach as the states in the Continental U.S. (i.e., county-level fuel expenditure data from the NASS' 2012 Census of Agriculture). Alaska's fuel expenditures total was allocated to counties using a different approach because the 2012 Census of Agriculture reports fuel expenditures data for a different list of counties than the one included in MOVES. To ensure consistency with MOVES, NCDEQ allocated Alaska's fuel expenditures based on the current allocation data in MOVES, which reflect 2002 harvested acreage data from the Census of Agriculture.

Because NCDEQ did not identify any source of fuel expenditures data for Puerto Rico or the U.S. Virgin Islands, the county allocation percentages that are represented by the 2002 MOVES allocation data were used for these territories.

For the Construction sector, MOVES2014b uses estimates of 2003 total dollar value of construction by county to allocate national Construction equipment populations to the state and local levels. However, the 2016 Nonroad Collaborative Work Group sought to update the surrogate data used to geographically allocate Construction equipment with a more recent data source thought to be more reflective of emissions-generating Construction equipment activity at the county level: acres disturbed by residential, non-residential, and road construction activity.

The nonpoint sector of the 2014 National Emissions Inventory (NEI) includes estimates of Construction Dust (PM2.5), for which acreage disturbed by residential, non-residential, and road construction activity is a function. The 2014 NEI Technical Support Document includes a description of the methods used to estimate acreage disturbed at the county level by residential, non-residential, and road construction activity, for the 50 states. 

Acreage disturbed by residential, non-residential, and road construction were summed together to arrive at a single value of acreage disturbed by Construction activities at the county level. County-level acreage disturbed were then summed together to arrive at acreage disturbed at the state level. State totals were then summed to arrive at a national total of acreage disturbed by Construction activities.  

Puerto Rico and the U.S. Virgin Islands are not included in the Construction equipment geographic allocation update, so their relative share of the national population of Construction equipment remains the same as MOVES2014b defaults.

For both the Agricultural and Construction equipment sectors, the surrogatequant and surrogateyearID fields in the model's nrstatesurrogate table, which allocates equipment from the state- to the county-level, were populated with the county-level surrogates described above (fuel expenditures in 2016 for Agricultural equipment; acreage disturbed by construction activity in 2014 for Construction equipment). In addition, the nrbaseyearequippopulation table, which apportions the model's national equipment populations to the state level, was adjusted so that each state's share of the MOVES2014b base-year national populations of Agricultural and Construction equipment is proportional to each state's share of national acreage disturbed by construction activity (Construction equipment) and agricultural fuel expenditures (Agricultural equipment). Additionally, the model's nrsurrogate table, which defines the surrogate data used in the nrstatesurrogate table, was updated to reflect the 2016v1 changes to the Agricultural and Construction equipment sectors.

Updated nrsurrogate, nrstatesurrogate, and nrbaseyearequippopulation tables, along with instructions for utilizing these tables in MOVES runs, are available for download from EPA's ftp site: ftp://newftp.epa.gov/air/emismod/2016/v1/reports/nonroad/.

State-Supplied Nonroad Data

As shown Table 2-31 several state and local agencies provided nonroad inputs for use in the 2016v1 platform. Additionally, per the table footnotes, EPA reviewed data submitted by state and local agencies for the 2014 and 2017 National Emissions Inventories and utilized that information where appropriate (data specific to calendar years 2014 and 2017 were not used in 2016v1).

Table 2-31. Submitted nonroad input tables by agency
stateid
State or County(ies) in the Agency
                nrbaseyearequippopulation
(source populations)
                   nrdayallocation
(allocation to day type)
                      nrfuelsupply
(allocation of fuels)
                       nrgrowthindex
(population growth)
               nrhourallocation
(allocation to diurnal pattern)
                    nrmonthallocation
(seasonal allocation)
                       nrsourceusetype
(yearly activity)
                  nrstatesurrogate
(allocations to counties)
                      countyyear 
(Stage II information)
                     nrequipmenttype
(surrogate selection)
                    nrsurrogate
(surrogate identification)
                                       4
ARIZONA - Maricopa Co.
                                       A
                                       
                                       
                                       
                                       
                                       
                                       D
                                       D
                                       D
                                       D
                                       D
                                       9
CONNECTICUT
                                       A
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                      13
GEORGIA
                                       
                                       
                                       D
                                       
                                       
                                       
                                       
                                       D
                                       
                                       
                                       
                                      16
IDAHO
                                       
                                       C
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                      17
ILLINOIS
                                       
                                       
                                       
                                       
                                       
                                       E
                                       
                                       
                                       
                                       
                                       
                                      18
INDIANA
                                       
                                       C
                                       
                                       
                                       
                                       E
                                       
                                       
                                       
                                       
                                       
                                      19
IOWA
                                       
                                       C
                                       
                                       
                                       
                                       E
                                       
                                       
                                       
                                       
                                       
                                      26
MICHIGAN
                                       
                                       C
                                       
                                       
                                       
                                       E
                                       
                                       
                                       
                                       
                                       
                                      27
MINNESOTA
                                       
                                       C
                                       
                                       
                                       
                                       E
                                       
                                       
                                       
                                       
                                       
                                      29
MISSOURI
                                       
                                       
                                       
                                       
                                       
                                       E
                                       
                                       
                                       
                                       
                                       
                                      36
NEW YORK
                                       D
                                       D
                                       
                                       D
                                       D
                                       D
                                       D
                                       D
                                       
                                       
                                       
                                      39
OHIO
                                       
                                       C
                                       
                                       
                                       
                                       E
                                       
                                       
                                       
                                       
                                       
                                      49
UTAH
                                       B
                                       D
                                       
                                       D
                                       D
                                       
                                       
                                       F
                                       
                                       
                                       
                                      53
WASHINGTON
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       D
                                       
                                       D
                                       D
                                      55
WISCONSIN
                                       
                                       
                                       
                                       
                                       
                                       E
                                       
                                       
                                       
                                       
                                       
[A] Submitted data with modification: updated the year ID to 2016.
[B] Submitted data with modification: deleted records that were not snowmobile source types 1002-1010.
[C] NEI 2014v2 data used for 2016v1 platform.
[D] Submitted data.
[E] Spreadsheet "ladco_nei2017_nrmonthallocation.xlsx."
[F] Submitted data with modification: deleted records that were not the snowmobile surrogate ID 14.


Emissions Inside California and Texas

California nonroad emissions were provided by CARB for the years 2016, 2023, and 2028. 

All California nonroad inventories are annual, with monthly temporalization applied in SMOKE. Emissions for oil field equipment (SCCs ending in -10010) were removed from the California inventory in order to prevent a double count with the np_oilgas sector.

Texas nonroad emissions were provided by the Texas Commission on Environmental Quality for the years 2016, 2023, and 2028, using TCEQ's TexN2 tool. This tool facilitates the use of detailed Texas-specific nonroad equipment population, activity, fuels, and related data as inputs for MOVES2014b, and accounts for Texas-specific emission adjustments such as the Texas Low Emission Diesel (TxLED) program. 

Nonroad Updates from State Comments
The 2016 Nonroad Collaborative Work group received a small number of comments on the 2016beta inventory, all of which were addressed and implemented in the 2016v1 nonroad inventory:
       Georgia Department of Natural Resources: incorporate updated fuel supply (nrfuelsupply table) for 45 Georgia counties, to reflect the removal of summer Reid Vapor Pressure restrictions in 2016; utilize updated geographic allocation factors (nrstatesurrogate table) for the Commercial, Lawn & Garden (commercial, public, and residential), Logging, Manufacturing, Golf Carts, Recreational, Railroad Maintenance Equipment and A/C/Refrigeration sectors, using data from the U.S. Census Bureau and U.S. Forest Service.
       Lake Michigan Air Directors Consortium (LADCO): update seasonal allocation of agricultural equipment activity (nrmonthallocation table) for Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, and Wisconsin.
       Texas Commission on Environmental Quality: replace MOVES2014b nonroad emissions for Texas with emissions calculated with TCEQ's TexN2 model.
 Alaska Department of Environmental Conservation: remove emissions as calculated by MOVES2014b for several equipment sector-county/census areas combinations in Alaska, due to an absence of nonroad activity (see Table 2-32).
       
       Table 2-32. Alaska counties/census areas for which nonroad equipment sector-specific emissions are removed in 2016v1
Nonroad Equipment Sector

Counties/Census Areas (FIPS) for which equipment sector emissions are removed in 2016v1
Agricultural

Aleutians East (02013), Aleutians West (02016), Bethel Census Area (02050), Bristol Bay Borough (02060), Dillingham Census Area (02070), Haines Borough (02100), Hoonah-Angoon Census Area (02105), Ketchikan Gateway (02130), Kodiak Island Borough (02150), Lake and Peninsula (02164), Nome (02180), North Slope Borough (02185), Northwest Arctic (02188), Petersburg Borough (02195), Pr of Wales-Hyder Census Area (02198), Sitka Borough (02220), Skagway Borough (02230), Valdez-Cordova Census Area (02261), Wade Hampton Census Area (02270), Wrangell City + Borough (02275), Yakutat City + Borough (02282), Yukon-Koyukuk Census Area (02290)
Logging

Aleutians East (02013), Aleutians West (02016), Nome (02180), North Slope Borough (02185), Northwest Arctic (02188), Wade Hampton Census Area (02270)
Railway Maintenance

Aleutians East (02013), Aleutians West (02016), Bethel Census Area (02050), Bristol Bay Borough (02060), Dillingham Census Area (02070), Haines Borough (02100), Hoonah-Angoon Census Area (02105), Juneau City + Borough (02110), Ketchikan Gateway (02130), Kodiak Island Borough (02150), Lake and Peninsula (02164), Nome (02180), ), North Slope Borough (02185), Northwest Arctic (02188), Petersburg Borough (02195), Pr of Wales-Hyder Census Area (02198), Sitka Borough (02220), Southeast Fairbanks (02240), Wade Hampton Census Area (02270), Wrangell City + Borough (02275), Yakutat City + Borough (02282), Yukon-Koyukuk Census Area (02290)

2016 Fires (ptfire, ptagfire)
Multiple types of fires are represented in the modeling platform.  These include wild and prescribed fires that are grouped into the ptfire sector, and agricultural fires that comprise the ptagfire sector.  All ptfire and ptagfire fires are in the United States.  Fires outside of the United States are described in the ptfire_othna sector later in this document.
Wild and Prescribed Fires (ptfire)
Wildfires and prescribed burns that occurred during the inventory year are included in the year 2016 version 1 (2016v1) inventory as event and point sources. The point agricultural fires inventory (ptagfire) is described in a separate section. For purposes of emission inventory preparation, wildland fire (WLF) is defined as any non-structure fire that occurs in the wildland.  The wildland is defined an area in which human activity and development are essentially non-existent, except for roads, railroads, power lines, and similar transportation facilities.  Wildland fire activity is categorized by the conditions under which the fire occurs. These conditions influence important aspects of fire behavior, including smoke emissions. In the 2016v1 inventory, data processing was conducted differently depending on the fire type, as defined below: 
 Wildfire (WF): any fire started by an unplanned ignition caused by lightning; volcanoes; other acts of nature; unauthorized activity; or accidental, human-caused actions, or a prescribed fire that has developed into a wildfire.
 Prescribed (Rx) fire: any fire intentionally ignited by management actions in accordance with applicable laws, policies, and regulations to meet specific land or resource management objectives.  Prescribed fire is one type of fire fuels treatment. Fire fuels treatments are vegetation management activities intended to modify or reduce hazardous fuels. Fuels treatments include prescribed fires, wildland fire use, and mechanical treatment.
The SCCs used for the ptfire sources are shown in Table 2-33. The ptfire inventory includes separate SCCs for the flaming and smoldering combustion phases for wildfire and prescribed burns.  Note that prescribed grassland fires or Flint Hills, Kansas have their own SCC in the 2016v1 inventory.  The year 2016 fire season also included some major wild grassland fires. These wild grassland fires were assigned the standard wildfire SCCs shown in Table 2-33.
Table 2-33. SCCs included in the ptfire sector for the 2016v1 inventory
                                      SCC
                                  Description
2801500170
Grassland fires; prescribed
2810001001
Forest Wildfires; Smoldering; Residual smoldering only (includes grassland wildfires)
2810001002
Forest Wildfires; Flaming (includes grassland wildfires)
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming


National Fire Information Data

Numerous fire information databases are available from U.S. national government agencies.  Some of the databases are available via the internet while others must be obtained directly from agency staff.  Table 2-34 provides the national fire information databases that were used for the 2016v1 ptfire inventory, including the website where the 2016 data were downloaded.  
Table 2-34. National fire information databases used in 2016v1 ptfire inventory
Dataset Name
Fire Types
Format
Agency
Coverage
Source
Hazard Mapping System (HMS)
WF/RX
CSV
NOAA
North America
https://www.ospo.noaa.gov/Products/land/hms.html
Geospatial Multi-Agency Coordination(GeoMAC)
WF
SHP
USGS
Entire US
https://www.geomac.gov/GeoMACTransition.shtml
Incident Command System Form 209: Incident Status Summary (ICS-209)
WF/RX
CSV
Multi
Entire US
https://fam.nwcg.gov/fam-web/
National Association of State Foresters (NASF)
WF
CSV
Multi
Participating US states
https://fam.nwcg.gov/fam-web/  (see Public Access Reports, Free Data Extract, then NASF State Data Extract)
Monitoring Trends in Burn Severity (MTBS)
WF/RX
SHP
USGS, USFS
Entire US
https://www.mtbs.gov/direct-download
Forest Service Activity Tracking System (FACTS)
RX
SHP
USFS
Entire US
Hazardous Fuel Treatment Reduction: Polygon at https://data.fs.usda.gov/geodata/edw/ datasets.php
US Fish and Wildland Service (USFWS) fire database
WF/RX
CSV
USFWS
Entire US
Direct communication with USFWS

The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and 
Atmospheric Administration's (NOAA) National Environmental Satellite and Data Information 
Service (NESDIS) as a tool to identify fires over North America in an operational environment. The system utilizes geostationary and polar orbiting environmental satellites.  Automated fire detection algorithms are employed for each of the sensors. When possible, HMS data analysts apply quality control procedures for the automated fire detections by eliminating those that are deemed to be false and adding hotspots that the algorithms have not detected via a thorough examination of the satellite imagery. 

The HMS product used for the 2016v1 inventory consisted of daily comma-delimited files containing fire detect information including latitude-longitude, satellite used, time detected, and other information.  The Visible Infrared Imaging Radiometer Suite (VIIRS) satellite fire detects were introduced into the HMS in late 2016.  Since it was only available for a small portion of the year, the VIIRS fire detects were removed for the entire year for consistency. In the 2016alpha inventory, the grassland fire detects were put in the point agricultural fire sector (ptagfire). As there were a few significant grassland wildfires in Kansas and Oklahoma in year 2016, all grassland fire detects were included in the ptfire sector for the 2016v1 inventory. These grassland fires were processed through Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Framework.
GeoMAC (Geospatial Multi-Agency Coordination) is an online wildfire mapping application designed for fire managers to access maps of current U.S. fire locations and perimeters. The wildfire perimeter data is based upon input from incident intelligence sources from multiple agencies, GPS data, and infrared (IR) imagery from fixed wing and satellite platforms.
The Incident Status Summary, also known as the "ICS-209" is used for reporting specific information on significant fire incidents. The ICS-209 report is a critical interagency incident reporting tool giving daily `snapshots' of the wildland fire management situation and individual incident information which include fire behavior, size, location, cost, and other information.  Data from two tables in the ICS-209 database were merged and used for the 2016v1 ptfire inventory: the SIT209_HISTORY_INCIDENT_209_REPORTS table contained daily 209 data records for large fires, and the SIT209_HISTORY_INCIDENTS table contained summary data for additional smaller fires.
The National Association of State Foresters (NASF) is a non-profit organization composed of the directors of forestry agencies in the states, U.S. territories, and District of Columbia to manage and protect state and private forests, which encompass nearly two-thirds of the nation's forests. The NASF compiles fire incident reports from agencies in the organization and makes them publicly available. The NASF fire information includes dates of fire activity, acres burned, and fire location information.  
Monitoring Trends in Burn Severity (MTBS) is an interagency program whose goal is to consistently map the burn severity and extent of large fires across the U.S. from 1984 to present. The MTBS data includes all fires 1,000 acres or greater in the western United States and 500 acres or greater in the eastern United States. The extent of coverage includes the continental U.S., Alaska, Hawaii, and Puerto Rico. Fire occurrence and satellite data from various sources are compiled to create numerous MTBS fire products. The MTBS Burned Areas Boundaries Dataset shapefiles include year 2016 fires and that are classified as either wildfires, prescribed burns or unknown fire types. The unknown fire type shapes were omitted in the 2016v1 inventory development due to temporal and spatial problems found when trying to use these data.
The US Forest Service (USFS) compiles a variety of fire information every year. Year 2016 data from the USFS Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and used for 2016v1 emissions inventory development. This database includes information about activities related to fire/fuels, silviculture, and invasive species. The FACTS database consists of shapefiles for prescribed burns that provide acres burned,and start and ending time information.
The US Fish and Wildland Service (USFWS) also compiles wildfire and prescribed burn activity on their federal lands every year. Year 2016 data were acquired from USFWS through direct communication with USFWS staff and were used for 2016v1 emissions inventory development.   The USFWS fire information provided fire type, acres burned, latitude-longitude, and start and ending times.

State/Local/Tribal Fire Information
During the 2016 emissions modeling platform development process, S/L/T agencies were invited by EPA and 2016 Inventory Collaborative Fire Workgroup to submit all fire occurrence data for use in developing the 2016v1 fire inventory.  A template form containing the desired format for data submittals was provided to S/L/T air agencies. The list of S/L/T agencies that submitted fire data is provided in Table 2-35.  Data from nine individual states and one Indian Tribe were used for the 2016v1 ptfire inventory. 
Table 2-35. List of S/L/T agencies that submitted fire data for 2016v1 with types and formats. 
S/L/T agency name
Fire Types
Format
NCDEQ
WF/RX
CSV
KDHE
RX/AG
CSV
CO Smoke Mgmt Program
RX
CSV
Idaho DEQ
AG
CSV
Nez Perce Tribe
AG
CSV
GA DNR
ALL
EIS
MN
RX/AG
CSV
WA ECY
AG
CSV
NJ DEP
WF/RX
CSV
Alaska DEC
WF/RX
CSV
The data provided by S/L/T agencies were evaluated by EPA and further feedback on the data submitted by the state was requested at times. Table 2-36 provides a summary of the type of data submitted by each S/L/T agency and includes spatial, temporal, acres burned and other information provided by the agencies.  
Table 2-36. Brief description of fire information submitted for 2016v1 inventory use. 
S/L/T agency name
Fire Types
Description
NCDEQ
WF/RX
Fire type, period-specific, latitude-longitude and acres burned information. Technical direction was to remove all fire detects that were not reconciled with any other national or state agency database.   
Kansas DHE
RX/AG
Day-specific, county-centroid located, acres burned for Flint Hills prescribed burns for Feb 27-May 4 time period. Reclassified fuels for some agricultural burns.  A grassland gridding surrogate was used to spatially allocate the day-specific grassland fire emissions.
Colorado Smoke Mgmt Program
RX
Day-specific, latitude-longitude, and acres burned for prescribed burns
Idaho DEQ
AG
Day-specific, latitude-longitude, acres burned for agricultural burns. Total replacement of 2016 alpha fire inventory for Idaho.
Nez Perce Tribe
AG
Day-specific, latitude-longitude, acres burned for agricultural burns. Total replacement of 2016 alpha fire inventory within the tribal area boundary.
Georgia DNR
ALL
Data submitted included all fires types via EIS. The wildfire and prescribed burn data were provided as daily, point emissions sources. The agricultural burns were provided as day-specific point emissions sources.
Minnesota
RX/AG
Corrected latitude-longitude, day-specific and acres burned for some prescribed and agricultural burns.
Washington ECY
AG
Month-specific, latitude-longitude, acres burned, fuel loading and emissions for agricultural burns. Not day-specific so allocation to daily implemented by EPA. WA state direction included to continue to use the 2014NEIv2 pile burns that were included in the non-point sector for 2016v1.
New Jersey DEP
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and prescribed burns.
Alaska DEC
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and prescribed burns.


Fire Emissions Estimation Methodology
The national and S/L/T data mentioned earlier were used to estimate daily wildfire and prescribed burn emissions from flaming combustion and smoldering combustion phases for the 2016v1 inventory. Flaming combustion is more complete combustion than smoldering and is more prevalent with fuels that have a high surface-to-volume ratio, a low bulk density, and low moisture content. Smoldering combustion occurs without a flame, is a less complete burn, and produces some pollutants, such as PM2.5, VOCs, and CO, at higher rates than flaming combustion. Smoldering combustion is more prevalent with fuels that have low surface-to-volume ratios, high bulk density, and high moisture content. Models sometimes differentiate between smoldering emissions that are lofted with a smoke plume and those that remain near the ground (residual emissions), but for the purposes of the 2016v1 inventory the residual smoldering emissions were allocated to the smoldering SCCs listed in Table 2-33. The lofted smoldering emissions were assigned to the flaming emissions SCCs in Table 2-33.  
Figure 2-10 is a schematic of the data processing stream for the 2016v1 inventory for wildfire and prescribe burn sources. The ptfire inventory sources were estimated using Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and Blue Sky Framework. SMARTFIRE2 is an algorithm and database system that operate within a geographic information system (GIS). SMARTFIRE2 combines multiple sources of fire information and reconciles them into a unified GIS database. It reconciles fire data from space-borne sensors and ground-based reports, thus drawing on the strengths of both data types while avoiding double-counting of fire events. At its core, SMARTFIRE2 is an association engine that links reports covering the same fire in any number of multiple databases. In this process, all input information is preserved, and no attempt is made to reconcile conflicting or potentially contradictory information (for example, the existence of a fire in one database but not another). 
For the 2016v1 inventory, the national and S/L/T fire information was input into SMARTFIRE2 and then merged and associated based on user-defined weights for each fire information dataset. The output from SMARTFIRE2 was daily acres burned by fire type, and latitude-longitude coordinates for each fire. The fire type assignments were made using the fire information datasets. If the only information for a fire was a satellite detect for fire activity, then the flow described in Figure 2-11 was used to make fire type assignment by state and by month.
Figure 2-10. Processing flow for fire emission estimates in the 2016v1 inventory
                                       

                                       
Figure 2-11. Default fire type assignment by state and month in cases where a satellite detect is only source of fire information.
                                       

The BlueSky Modeling Framework version 3.5 (revision #38169) was used to calculate fuel loading and consumption, and emissions using various models depending on the available inputs as well as the desired results. The contiguous United States and Alaska, where Fuel Characteristic Classification System (FCCS) fuel loading data are available, were processed using the modeling chain described in 	Figure 2-12. The Fire Emissions Production Simulator (FEPS) in the Bluesky Framework generated all of the CAP emission factors for wildland fires used in the 2016v1 inventory.    The HAPs were derived from regional emissions factors from Urbanski (2014).
	Figure 2-12.  Blue Sky Modeling Framework
                                       

For the 2016v1 inventory, the FCCSv2 spatial vegetation cover was upgraded to the LANDFIRE v1.4 fuel vegetation cover (See: https://www.landfire.gov/fccs.php). The FCCSv3 fuel bed characteristics were implemented along with LANDFIREv1.4 to provide better fuel classification for the BlueSky Framework. The LANDFIREv1.4 raster data were aggregated from the native resolution and projection to 200 meter resolution using a nearest-neighbor methodology. Aggregation and reprojection was required to allow these data to work in the BlueSky Framework.
Point source Agriculture Fires (ptagfire)
The point source agricultural fire (ptagfire) inventory sector contains daily agricultural burning emissions. Daily fire activity was derived from the NOAA Hazard Mapping System (HMS) fire activity data.  The agricultural fires sector includes SCCs starting with `28015'. The first three levels of descriptions for these SCCs are: 1) Fires - Agricultural Field Burning; Miscellaneous Area Sources; 2) Agriculture Production - Crops - as nonpoint; and 3) Agricultural Field Burning - whole field set on fire.  The SCC 2801500000 does not specify the crop type or burn method, while the more specific SCCs specify field or orchard crops and, in some cases, the specific crop being grown. The SCCs for this sector listed are in Table 2-37.
Table 2-37. SCCs included in the ptagfire sector for the 2016v1 inventory
                                      SCC
                                  Description
                                  2801500000
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Unspecified crop type and Burn Method
                                  2801500100
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crops Unspecified
                                  2801500112
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Alfalfa: Backfire Burning
                                  2801500130
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Barley: Burning Techniques Not Significant
                                  2801500141
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Bean (red): Headfire Burning
                                  2801500150
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Corn: Burning Techniques Not Important
                                  2801500151
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Double Crop Winter Wheat and Corn
                                  2801500152
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;DoubleCrop Corn and Soybeans
                                  2801500160
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Cotton: Burning Techniques Not Important
                                  2801500170
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Grasses: Burning Techniques Not Important
                                  2801500171
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Fallow
                                  2801500182
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Hay (wild): Backfire Burning
                                  2801500202
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Pea: Backfire Burning
                                  2801500220
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Rice: Burning Techniques Not Significant
                                  2801500250
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Sugar Cane: Burning Techniques Not Significant
                                  2801500262
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Wheat: Backfire Burning
                                  2801500263
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;DoubleCrop Winter Wheat and Cotton
                                  2801500264
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;DoubleCrop Winter Wheat and Soybeans
                                  2801500300
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Orchard Crop Unspecified
                                  2801500320
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Orchard Crop is Apple
                                  2801500350
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Orchard Crop is Cherry
                                  2801500410
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Orchard Crop is Peach
                                  2801500420
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Orchard Crop is Pear
                                  2801500500
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Vine Crop Unspecified
                                  2801500600
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Forest Residues Unspecified


The EPA estimated biomass burning emissions using remote sensing data. These estimates were then reviewed by the states and revised as resources allowed. As many states did not have the resources to estimate emissions for this sector, remote sensing was necessary to fill in the gaps for regions where there was no other source of data. Crop residue emissions result from either pre-harvest or post-harvest burning of agricultural fields. The crop residue emission inventory for 2016 is day-specific and includes geolocation information by crop type. The method employed and described here is based on the same methods employed in the 2014 NEI with a few minor updates. It should be noted that grassland fires were moved from the agricultural burning inventory sector to the prescribed and wildland fire sector for 2016beta and 2016v1 inventories. This was done to prevent double-counting of fires and because the largest fire (acres burned) in 2016 was a wild grassland fire in Kansas.

Daily, year-specific agricultural burning emissions were derived from HMS fire activity data, which contains the date and location of remote-sensed anomalies. As point source inventories, the locations of the fires are identified with latitude-longitude coordinates for specific fire events. The HMS activity data were filtered using 2016 USDA cropland data layer (CDL). Satellite fire detects over agricultural lands were assumed to be agricultural burns and assigned a crop type. Detects that were not over agricultural lands were output to a separate file for use in the point source wildfire (ptfire) inventory sector. Each detect was assigned an average size of between 40 and 80 acres based on crop type. The assumed field sizes are found in Table 2-38.

                                       
Table 2-38. Assumed field size of agricultural fires per state(acres)
State
                                  Field Size
Alabama
                                      40
Arizona
                                      80
Arkansas
                                      40
California
                                      120
Colorado
                                      80
Connecticut
                                      40
Delaware
                                      40
Florida
                                      60
Georgia
                                      40
Idaho
                                      120
Illinois
                                      60
Indiana
                                      60
Iowa
                                      60
Kansas
                                      80
Kentucky
                                      40
Louisiana
                                      40
Maine
                                      40
Maryland
                                      40
Massachusetts
                                      40
Michigan
                                      40
Minnesota
                                      60
Mississippi
                                      40
Missouri
                                      60
Montana
                                      120
Nebraska
                                      60
Nevada
                                      40
New Hampshire
                                      40
New Jersey
                                      40
New Mexico
                                      80
New York
                                      40
North Carolina
                                      40
North Dakota
                                      60
Ohio
                                      40
Oklahoma
                                      80
Oregon
                                      120
Pennsylvania
                                      40
Rhode Island
                                      40
South Carolina
                                      40
South Dakota
                                      60
Tennessee
                                      40
Texas
                                      80
Utah
                                      40
Vermont
                                      40
Virginia
                                      40
Washington
                                      120
West Virginia
                                      40
Wisconsin
                                      40
Wyoming
                                      80


Another feature of the ptagfire database is that the satellite detections for 2016 were filtered out to exclude areas covered by snow during the winter months.  To do this, the daily snow cover fraction per grid cell was extracted from a 2016 meteorological Weather Research Forecast (WRF) model simulation. The locations of fire detections were then compared with this daily snow cover file. For any day in which a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the inventory.   Due to the inconsistent reporting of fire detections for year 2016 from the Visible Infrared Imaging Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as VIIRS or Suomi National Polar-orbiting Partnership satellite were excluded.  In addition, certain crop types (corn and soybeans) were excluded from the following states: Iowa, Kansas, Indiana, Illinois, Michigan, Missouri, Minnesota, Wisconsin, and Ohio. Kansas was not included in this list in the 2014NEI but added for 2016.   The reason for these crop types being excluded is because states have indicated that these crop types are not burned.

Crop type-specific emissions factors were applied to each daily fire to calculate criteria and hazardous pollutant emissions. In all prior NEIs for this sector, the HAP emission factors and the VOC emission factors were known to be inconsistent. The HAP emission factors were copied from the HAP emission factors for wildfires in the 2014 NEI and in the 2016 beta and version 1 modeling platforms. The VOC emission factors were scaled from the CO emission factors in the 2014 NEI and the 2016 beta and version 1 modeling platforms.  See Pouliot et al, 2017 for a complete table of emission factors and fuel loading by crop type.

Heat flux values for computing fire plume rise were calculated using the size and assumed fuel loading of each daily fire.  Emission factors and fuel loading by crop type are available in Table 1 of Pouliot et al. (2017).  This information is needed for a plume rise calculation within a chemical transport modeling system. In prior NEIs including the 2014 NEI, all the emissions were placed into layer 1 (i.e. ground level).

The daily agricultural and open burning emissions were converted from a tabular format into the SMOKE-ready daily point Flat File 2010 (FF10) format. The daily emissions were also aggregated into annual values by location and converted into the annual point flat file format.
2016 Biogenic Sources (beis)
Biogenic emissions for the entire year 2016 were developed using the Biogenic Emission Inventory System version 3.61 (BEIS3.61) within SMOKE.  The landuse input into BEIS3.61 is the Biogenic Emissions Landuse Dataset (BELD) version 4.1 which is based on an updated version of the USDA-USFS Forest Inventory and Analysis (FIA) vegetation speciation-based data from 2001 to 2014 from the FIA version 5.1.  

BEIS3.61 has some important updates from BEIS 3.14.  These include the incorporation of Version 4.1 of the Biogenic Emissions Landuse Database (BELD4), and the incorporation of a canopy model to estimate leaf-level temperatures (Pouliot and Bash, 2015).  BEIS3.61 includes a two-layer canopy model. Layer structure varies with light intensity and solar zenith angle.  Both layers of the canopy model include estimates of sunlit and shaded leaf area based on solar zenith angle and light intensity, direct and diffuse solar radiation, and leaf temperature (Bash et al., 2016).  The new algorithm requires additional meteorological variables over previous versions of BEIS.  The variables output from the Meteorology-Chemistry Interface Processor (MCIP) that are used for BEIS3.61 processing are shown in Table 2-39.   The 2016 version 1 of the BEIS3 modeling for year 2016 included processing for both a 36km (36US3) and 12km domain (12US1) (see Figure 3-1Error! Reference source not found.).    The 12US2 modeling domain can also be supported by taking a subset or window of the 12US1 BEIS3 emissions dataset.
Table 2-39. Hourly Meteorological variables required by BEIS 3.61
                                   Variable
                                       
                                       
                                  Description
LAI


leaf-area index 
PRSFC


surface pressure
Q2 


mixing ratio at 2 m
RC


convective precipitation 
RGRND


solar rad reaching sfc
RN


nonconvective precipitation 
RSTOMI


inverse of bulk stomatal resistance 
SLYTP


soil texture type by USDA category
SOIM1


volumetric soil moisture in top cm 
SOIT1


soil temperature in top cm
TEMPG


skin temperature at ground
USTAR


cell averaged friction velocity
RADYNI


inverse of aerodynamic resistance
TEMP2


temperature at 2 m

SMOKE-BEIS3 modeling system consists of two programs named: 1) Normbeis3 and 2) Tmpbeis3.   Normbeis3 uses emissions factors and BELD4 landuse to compute gridded normalized emissions for chosen model domain (see Figure 2-13).  The emissions factor file (B360FAC) contains leaf-area-indices (LAI), dry leaf biomass, winter biomass factor, indicator of specific leaf weight, and normalized emission fluxes for 35 different species/compounds.    The BELD4 file is the gridded landuse for 276 different landuse types.    The output gridded domain is the same as the input domain for the land use data.   Output emission fluxes (B3GRD) are normalized to 30 °C, and isoprene and methyl-butenol fluxes are also normalized to a photosynthetic active radiation of 1000 umol/m[2]s.   
Figure 2-13. Normbeis3 data flows
                                       
The normalized emissions output from Normbeis3 (B3GRD) are input into Tmpbeis3 along with the MCIP meteorological data, chemical speciation profile to use for desired chemical mechanism, and BIOSEASON file used to indicate how each day in year 2016 should be treated, either as summer or winter.   Figure 2-14 illustrates the data flows for the Tmpbeis3 program.  The output from Tmpbeis includes gridded, speciated, hourly emissions both in moles/second (B3GTS_L) and tons/hour (B3GTS_S).    
Figure 2-14. Tmpbeis3 data flow diagram.
                                       
Biogenic emissions do not use an emissions inventory and do not have SCCs.   The gridded land use data, gridded meteorology, an emissions factor file, and a speciation profile are further described in the next section.
Sources Outside of the United States
The emissions from Canada and Mexico and other areas outside of the U.S. are included in these emissions modeling sectors:  othpt, othar, othafdust, othptdust, onroad_can, onroad_mex, and ptfire_othna.  The "oth" refers to the fact that these emissions are usually "other" than those in the NEI, and the remaining characters provide the SMOKE source types: "pt" for point, "ar" for "area and nonroad mobile," "afdust" for area fugitive dust (Canada only), and "ptdust" for point fugitive dust. Because Canada and Mexico onroad mobile emissions are modeled differently from each other, they are separated into two sectors: onroad_can and onroad_mex.  Emissions for Mexico are based on the Inventario Nacional de Emisiones de Mexico, 2008 projected to year 2016 (ERG, 2014a). Additional details for these sectors can be found in the 2016v1 platform specification sheets.
Point Sources in Canada and Mexico (othpt)
Canadian point sources were taken from the ECCC 2015 emission inventory, including upstream oil and gas emissions,  agricultural ammonia and VOC, along with point source emissions from Mexico's 2008 inventory projected to 2014 and 2018 and then interpolated to 2016. The Canadian point source inventory is pre-speciated for the CB6 chemical mechanicsm.  Also for Canada, agricultural data were originally provided on a rotated 10-km grid for the 2016beta platform.  These were smoothed out so as to avoid the artifact of grid lines in the processed emissions.  The data were monthly resolution for Canadian agricultural and airport emissions, along with some Canadian point sources, and annual resolution for the remainder of Canada and all of Mexico.  
Fugitive Dust Sources in Canada (othafdust, othptdust)
Fugitive dust sources of particulate matter emissions excluding land tilling from agricultural activities, were provided by Environment and Climate Change Canada (ECCC) as part of their 2015 emission inventory.  Different source categories were provided as gridded point sources and area (nonpoint) source inventories.  Following consultation with ECCC, construction dust emissions in the othafdust inventory were reduced to levels compatible with their 2010 inventory. 

Gridded point source emissions resulting from land tilling due to agricultural activities were provided as part of the ECCC 2015 emission inventory.  The provided wind erosion emissions were removed.  The data were originally provided on a rotated 10-km grid for the 2016 beta platform, but these were smoothed so as to avoid the artifact of grid lines appearing in the emissions output from SMOKE. The othptdust emissions have a monthly resolution.  
A transport fraction adjustment that reduces dust emissions based on land cover types was applied to both point and nonpoint dust emissions, along with a meteorology-based (precipitation and snow/ice cover) zero-out of emissions when the ground is snow covered or wet.
Nonpoint and Nonroad Sources in Canada and Mexico (othar)
ECCC provided year 2015 Canada province, and in some cases sub-province, resolution emissions from for nonpoint and nonroad sources. The nonroad sources were monthly while the nonpoint and rail emissions were annual.  For Mexico, year 2016 Mexico nonpoint and nonroad inventories at the municipio resolution were interpolated from 2014 and 2018 inventories that were projected from their 2008 inventory.  All Mexico inventories were annual resolution.  Canadian CMV inventories that had been included in this sector in past modeling platforms are now included in the cmv_c1c2 and cmv_c3 sectors as point sources.
Onroad Sources in Canada and Mexico (onroad_can, onroad_mex)
ECCC provided monthly year 2015 onroad emissions for Canada at the province resolution or sub-province resolution depending on the province.  For Mexico, monthly year 2016 onroad inventories at the municipio resolution were used.  The Mexico onroad emissions are based on MOVES-Mexico runs for 2014 and 2018 that were then interpolated to 2016
 Fires in Canada and Mexico (ptfire_othna)
Annual point source 2016 day-specific wildland emissions for Mexico, Canada, Central America, and Caribbean nations were developed from a combination of the Fire Inventory from NCAR (FINN) daily fire emissions and fire data provided by Environment Canada when available.  Environment Canada emissions were used for Canada wildland fire emissions for April through November and FINN fire emissions were used to fill in the annual gaps from January through March and December.  Only CAP emissions are provided in the ptfire_othna sector inventories.

For FINN fires, listed vegetation type codes of 1 and 9 are defined as agricultural burning, all other fire detections and assumed to be wildfires.  All wildland fires that are not defined as agricultural are assumed to be wild fires rather than prescribed.  FINN fire detects less than 50 square meters (0.012 acres) are removed from the inventory.  The locations of FINN fires are geocoded from latitude and longitude to FIPS code.
 Ocean Chlorine
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cl2) concentrations in oceanic air masses (Bullock and Brehme, 2002).  Data at 36 km and 12 km resolution were available and were not modified other than the model-species name "CHLORINE" was changed to "CL2" to support CMAQ modeling.

Emissions Modeling 
The CMAQ and CAMx air quality models require hourly emissions of specific gas and particle species for the horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain).  To provide emissions in the form and format required by the model, it is necessary to "pre-process" the "raw" emissions (i.e., emissions input to SMOKE) for the sectors described above in Section 2.  In brief, the process of emissions modeling transforms the emissions inventories from their original temporal resolution, pollutant resolution, and spatial resolution into the hourly, speciated, gridded resolution required by the air quality model.  Emissions modeling includes temporal allocation, spatial allocation, and pollutant speciation.  Emissions modeling sometimes includes the vertical allocation of point sources, but many air quality models also perform this task because it greatly reduces the size of the input emissions files if the vertical layers of the sources are not included. 

As seen in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary across sectors and may be hourly, daily, monthly, or annual total emissions.  The spatial resolution may be individual point sources; totals by county (U.S.), province (Canada), or municipio (Mexico); or gridded emissions.  This section provides some basic information about the tools and data files used for emissions modeling as part of the modeling platform.  For additional details that may not be covered in this section, see the specification sheets provided with the 2016v1platform as many will contain additional sector-specific information.  
 Emissions modeling Overview
SMOKE version 4.7 was used to process the raw emissions inventories into emissions inputs for each modeling sector into a format compatible with CMAQ, which were then converted to CAMx.  For sectors that have plume rise, the in-line plume rise capability allows for the use of emissions files that are much smaller than full three-dimensional gridded emissions files.  For quality assurance of the emissions modeling steps, emissions totals by specie for the entire model domain are output as reports that are then compared to reports generated by SMOKE on the input inventories to ensure that mass is not lost or gained during the emissions modeling process.  

When preparing emissions for the air quality model, emissions for each sector are processed separately through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sector-specific 2-D gridded emissions across sectors.  The SMOKE settings in the run scripts and the data in the SMOKE ancillary files control the approaches used by the individual SMOKE programs for each sector.  Table 3-1 summarizes the major processing steps of each platform sector with the columns as follows.

The "Spatial" column shows the spatial approach used: "point" indicates that SMOKE maps the source from a point location (i.e., latitude and longitude) to a grid cell; "surrogates" indicates that some or all of the sources use spatial surrogates to allocate county emissions to grid cells; and "area-to-point" indicates that some of the sources use the SMOKE area-to-point feature to grid the emissions (further described in Section 3.4.2).  

The "Speciation" column indicates that all sectors use the SMOKE speciation step, though biogenics speciation is done within the Tmpbeis3 program and not as a separate SMOKE step.  

The "Inventory resolution" column shows the inventory temporal resolution from which SMOKE needs to calculate hourly emissions.  Note that for some sectors (e.g., onroad, beis), there is no input inventory; instead, activity data and emission factors are used in combination with meteorological data to compute hourly emissions. 

Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used.  These sectors are the only ones with emissions in aloft layers based on plume rise.  The term "in-line" means that the plume rise calculations are done inside of the air quality model instead of being computed by SMOKE.  The air quality model computes the plume rise using stack parameters and the hourly emissions in the SMOKE output files for each emissions sector.  The height of the plume rise determines the model layer into which the emissions are placed.  The othpt sector has only "in-line" emissions, meaning that all of the emissions are treated as elevated sources and there are no emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE.  Other inline-only sectors are: cmv_c3, ptegu, ptfire, ptfire_othna, ptagfire. Day-specific point fire emissions are treated differently in CMAQ.  After plume rise is applied, there are emissions in every layer from the ground up to the top of the plume.
   Table 3-1.  Key emissions modeling steps by sector.
                                Platform sector
                                    Spatial
                                  Speciation
                             Inventory
resolution
                                  Plume rise
afdust_adj
                                  Surrogates
                                      Yes
                                    annual
                                       
afdust_ak_adj (36US3 only)
                                  Surrogates
                                      Yes
                                    annual
                                       
ag
                                  Surrogates
                                      Yes
                                    monthly
                                       
airports
                                     Point
                                      Yes
                                    annual
                                     None
beis
                             Pre-gridded land use
                                  in BEIS3.61
                                computed hourly
                                       
cmv_c1c2
                                  Surrogates
                                      Yes
                                    annual
                                       
cmv_c3
                                     Point
                                      Yes
                                    annual
                                    in-line
nonpt
                        Surrogates &
area-to-point
                                      Yes
                                    annual
                                       
nonroad
                        Surrogates &
area-to-point
                                      Yes
                                    monthly
                                       
np_oilgas
                                  Surrogates
                                      Yes
                                    annual
                                       
onroad
                                  Surrogates
                                      Yes
                       monthly activity, computed hourly
                                       
onroad_ca_adj
                                  Surrogates
                                      Yes
                       monthly activity, computed hourly
                                       
onroad_nonconus (36US3 only)
                                  Surrogates
                                      Yes
                       monthly activity, computed hourly
                                       
onroad_can
                                  Surrogates
                                      Yes
                                    monthly
                                       
onroad_mex
                                  Surrogates
                                      Yes
                                    monthly
                                       
othafdust_adj
                                  Surrogates
                                      Yes
                                    annual
                                       
othar
                                  Surrogates
                                      Yes
                             annual & monthly
                                       
othpt
                                     Point
                                      Yes
                             annual & monthly
                                    in-line
othptdust_adj
                                     Point
                                      Yes
                                    monthly
                                     None
ptagfire
                                     Point
                                      Yes
                                     daily
                                    in-line
pt_oilgas
                                     Point
                                      Yes
                                    annual
                                    in-line
ptegu
                                     Point
                                      Yes
                              daily & hourly
                                    in-line
ptfire
                                     Point
                                      Yes
                                     daily
                                    in-line
ptfire_othna
                                     Point
                                      Yes
                                     daily
                                    in-line
ptnonipm
                                     Point
                                      Yes
                                    annual
                                    in-line
rail
                                  Surrogates
                                      Yes
                                    annual
                                       
rwc
                                  Surrogates
                                      Yes
                                    annual
                                       

Biogenic emissions can be modeled two different ways in the CMAQ model. The BEIS model in SMOKE can produce gridded biogenic emissions that are then included in the gridded CMAQ-ready emissions inputs, or alternatively, CMAQ can be configured to create "in-line" biogenic emissions within CMAQ itself. For this platform, biogenic emissions were processed in SMOKE and included in the gridded CMAQ-ready emissions.  When CAMx is the targeted air quality modeling, BEIS is run within SMOKE and the resulting emissions are included with the ground-level emissions input to CAMx. 
SMOKE has the option of grouping sources so that they are treated as a single stack when computing plume rise.  For this platform, no grouping was performed because grouping combined with "in-line" processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-dimensional files).  This occurs when stacks with different stack parameters or latitudes/longitudes are grouped, thereby changing the parameters of one or more sources.  The most straightforward way to get the same results between in-line and offline is to avoid the use of grouping.  
SMOKE was run for two modeling domains: a 36-km resolution CONtinental United States "CONUS" modeling domain (36US3), and the 12-km resolution domain. 12US2. More specifically, SMOKE was run on the 12US1 domain and emissions were extracted from 12US1 data files to create 12US2 emission. The domains are shown in Figure 3-1. All grids use a Lambert-Conformal projection, with Alpha = 33º, Beta = 45º and Gamma = -97º, with a center of X = -97º and Y = 40º.  Table 3-2 describes the grids for the three domains.
Table 3-2.  Descriptions of the platform grids
                                  Common Name
                                Grid Cell Size
                         Description 
(see Figure 3-1)
                                   Grid name
Parameters listed in SMOKE grid description (GRIDDESC) file: projection name, xorig, yorig, xcell, ycell, ncols, nrows, nthik
Continental 36km grid
                                     36 km
 Entire conterminous US, almost all of Mexico, most of Canada (south of 60°N)
36US3
'LAM_40N97W', -2952000, -2772000, 36.D3, 36.D3, 172, 148, 1
Continental 12km grid
                                     12 km
               Entire conterminous US plus some of Mexico/Canada
12US1_459X299
`LAM_40N97W', -2556000, -1728000, 12.D3, 12.D3, 459, 299, 1
US 12 km or "smaller" CONUS-12
                                     12 km
                 Smaller 12km CONUS plus some of Mexico/Canada
12US2
`LAM_40N97W', -2412000 , -1620000, 12.D3, 12.D3, 396, 246, 1

Figure 3-1. Air quality modeling domains

 Chemical Speciation
The emissions modeling step for chemical speciation creates the "model species" needed by the air quality model for a specific chemical mechanism.  These model species are either individual chemical compounds (i.e., "explicit species") or groups of species (i.e., "lumped species").  The chemical mechanism used for the 2016 platform is the CB6 mechanism (Yarwood, 2010).  We used a particular version of CB6 that we refer to as "CMAQ CB6" that breaks out naphthalene from model species XYL, resulting in explicit model species NAPH and XYLMN instead of XYL and uses SOAALK.  This platform generates the PM2.5 model species associated with the CMAQ Aerosol Module version 6 (AE6). Table 3-3 lists the model species produced by SMOKE in the platform used for this study.  Updates to species assignments for CB05 and CB6 were made for the 2014v7.1 platform and are described in Appendix A.

Table 3-3. Emission model species produced for CB6 for CMAQ
Inventory Pollutant
Model Species
Model species description
Cl2
CL2
Atomic gas-phase chlorine
HCl
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOX
NO 
Nitrogen oxide

NO2 
Nitrogen dioxide

HONO
Nitrous acid
SO2
SO2 
Sulfur dioxide

SULF  
Sulfuric acid vapor
NH3
NH3
Ammonia

NH3_FERT   
Ammonia from fertilizer
VOC
ACET
Acetone

ALD2  
Acetaldehyde

ALDX  
Propionaldehyde and higher aldehydes

BENZ
Benzene (not part of CB05)

CH4
Methane

ETH   
Ethene

ETHA  
Ethane

ETHY
Ethyne

ETOH  
Ethanol

FORM  
Formaldehyde

IOLE  
Internal olefin carbon bond (R-C=C-R)

ISOP  
Isoprene

KET
Ketone Groups

MEOH  
Methanol

NAPH
Naphthalene

NVOL
Non-volatile compounds

OLE   
Terminal olefin carbon bond (R-C=C)

PAR   
Paraffin carbon bond

PRPA
Propane

SESQ
Sequiterpenes (from biogenics only)

SOAALK
Secondary Organic Aerosol (SOA) tracer

TERP
Terpenes (from biogenics only)

TOL   
Toluene and other monoalkyl aromatics

UNR
Unreactive 

XYLMN   
Xylene and other polyalkyl aromatics, minus naphthalene
Naphthalene
NAPH
Naphthalene from inventory
Benzene
BENZ
Benzene from the inventory
Acetaldehyde
ALD2  
Acetaldehyde from inventory
Formaldehyde
FORM  
Formaldehyde from inventory
Methanol
MEOH
Methanol from inventory
PM10
PMC
Coarse PM > 2.5 microns and  10 microns
PM2.5
PEC   
Particulate elemental carbon  2.5 microns

PNO3  
Particulate nitrate  2.5 microns

POC
Particulate organic carbon (carbon only)  2.5 microns

PSO4  
Particulate Sulfate  2.5 microns

PAL
 Aluminum

PCA
Calcium

PCL
Chloride

PFE
Iron

PK
Potassium

PH2O
Water

PMG
Magnesium

PMN
Manganese

PMOTHR
PM2.5 not in other AE6 species

PNA
Sodium

PNCOM
Non-carbon organic matter

PNH4
Ammonium

PSI
Silica

PTI
Titanium
Sea-salt species (non  - anthropogenic) 
PCL
Particulate chloride

PNA
Particulate sodium

The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were developed from the SPECIATE 4.5 database (https://www.epa.gov/air-emissions-modeling/speciate-2), which is the EPA's repository of TOG and PM speciation profiles of air pollution sources.  The SPECIATE database development and maintenance is a collaboration involving the EPA's Office of Research and Development (ORD), Office of Transportation and Air Quality (OTAQ), and the Office of Air Quality Planning and Standards (OAQPS), in cooperation with Environment Canada (EPA, 2016).  The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation profiles for PM2.5.  
Some key features and recent updates to speciation from previous platforms include the following:
 VOC speciation profile cross reference assignments for point and nonpoint oil and gas sources were updated to (1) make corrections to the 2011v6.3 cross references, (2) use new and revised profiles that were added to SPECIATE4.5 and (3) account for the portion of VOC estimated to come from flares, based on data from the Oil and Gas estimation tool used to estimate emissions for the NEI. The new/revised profiles included oil and gas operations in specific regions of the country and a national profile for natural gas flares;
 the Western Regional Air Partnership (WRAP) speciation profiles used for the np_oilgas sector are the SPECIATE4.5 revised versions (profiles with "_R" in the profile code);
 the VOC and PM speciation process for nonroad mobile has been updated - profiles are now assigned within MOVES2014b which outputs the emissions with those assignments; also the nonroad profiles themselves were updated;
 VOC and PM speciation for onroad mobile sources occurs within MOVES2014a except for brake and tirewear PM speciation which occurs in SMOKE;
 speciation for onroad mobile sources in Mexico is done within MOVES and is more consistent with that used in the United States; 
 the PM speciation profile for C3 ships in the US and Canada was updated to a new profile, 5675AE6; and
 As with previous platforms, some Canadian point source inventories are provided from Environment Canada as pre-speciated emissions; however for the 2015 inventory, not all CB6-CMAQ species were provided; missing species were supplemented by speciating VOC which was provided separately.

Speciation profiles and cross-references for this study platform are available in the SMOKE input files for the 2016 platform.  Emissions of VOC and PM2.5 emissions by county, sector and profile for all sectors other than onroad mobile can be found in the sector summaries for the case.  Totals of each model species by state and sector can be found in the state-sector totals workbook for this case.  
 VOC speciation
The speciation of VOC includes HAP emissions from the 2014NEIv2 in the speciation process.  Instead of speciating VOC to generate all of the species listed in Table 3-3, emissions of five specific HAPs: naphthalene, benzene, acetaldehyde, formaldehyde and methanol (collectively known as "NBAFM") from the NEI were "integrated" with the NEI VOC.  The integration combines these HAPs with the VOC in a way that does not double count emissions and uses the HAP inventory directly in the speciation process.  The basic process is to subtract the specified HAPs emissions mass from the VOC emissions mass, and to then use a special "integrated" profile to speciate the remainder of VOC to the model species excluding the specific HAPs.  The EPA believes that the HAP emissions in the NEI are often more representative of emissions than HAP emissions generated via VOC speciation, although this varies by sector.

The NBAFM HAPs were chosen for integration because they are the only explicit VOC HAPs in the CMAQ version 5.2.  Explicit means that they are not lumped chemical groups like PAR, IOLE and several other CB6 model species.  These "explicit VOC HAPs" are model species that participate in the modeled chemistry using the CB6 chemical mechanism.  The use of inventory HAP emissions along with VOC is called "HAP-CAP integration."  

The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats, including PTDAY (the format used for the ptfire and ptagfire sectors).  The ability to use integration with the PTDAY format was made available in the version of SMOKE used for the 2014v7.1 platform, but this new feature is not used for the 2016 platform because the ptfire and ptagfire inventories for 2016 do not include HAPs.  SMOKE allows the user to specify the particular HAPs to integrate via the INVTABLE.  This is done by setting the "VOC or TOG component" field to "V" for all HAP pollutants chosen for integration.  SMOKE allows the user to also choose the particular sources to integrate via the NHAPEXCLUDE file (which actually provides the sources to be excluded from integration).  For the "integrated" sources, SMOKE subtracts the "integrated" HAPs from the VOC (at the source level) to compute emissions for the new pollutant "NONHAPVOC."  The user provides NONHAPVOC-to-NONHAPTOG factors and NONHAPTOG speciation profiles.  SMOKE computes NONHAPTOG and then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC species not including the integrated HAPs.  After determining if a sector is to be integrated, if all sources have the appropriate HAP emissions, then the sector is considered fully integrated and does not need a NHAPEXCLUDE file.  If, on the other hand, certain sources do not have the necessary HAPs, then an NHAPEXCLUDE file must be provided based on the evaluation of each source's pollutant mix.  The EPA considered CAP-HAP integration for all sectors in determining whether sectors would have full, no or partial integration (see Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation).  For sectors with partial integration, all sources are integrated other than those that have either the sum of NBAFM > VOC or the sum of NBAFM = 0.  

In this platform, we create NBAFM species from the no-integrate source VOC emissions using speciation profiles.  Figure 3-2 illustrates the integrate and no-integrate processes for U.S. Sources.  Since Canada and Mexico inventories do not contain HAPs, we use the approach of generating the HAPs via speciation, except for Mexico onroad mobile sources where emissions for integrate HAPs were available.

It should be noted that even though NBAFM were removed from the SPECIATE profiles used to create the GSPRO for both the NONHAPTOG and no-integrate TOG profiles, there still may be small fractions for "BENZ", "FORM", "ALD2", and "MEOH" present.  This is because these model species may have come from species in SPECIATE that are mixtures.  The quantity of these model species is expected to be very small compared to the BAFM in the NEI.  There are no NONHAPTOG profiles that produce "NAPH."

In SMOKE, the INVTABLE allows the user to specify the particular HAPs to integrate. Two different INVTABLE files are used for different sectors of the platform.  For sectors that had no integration across the entire sector (see Table 3-4), EPA created a "no HAP use" INVTABLE in which the "KEEP" flag is set to "N" for NBAFM pollutants.  Thus, any NBAFM pollutants in the inventory input into SMOKE are automatically dropped.  This approach both avoids double-counting of these species and assumes that the VOC speciation is the best available approach for these species for sectors using this approach.  The second INVTABLE, used for sectors in which one or more sources are integrated, causes SMOKE to keep the inventory NBAFM pollutants and indicates that they are to be integrated with VOC. This is done by setting the "VOC or TOG component" field to "V" for all five HAP pollutants.  Note for the onroad sector, "full integration" includes the integration of benzene, 1,3 butadiene, formaldehyde, acetaldehyde, naphthalene, acrolein, ethyl benzene, 2,2,4-Trimethylpentane, hexane, propionaldehyde, styrene, toluene, xylene, and methyl tert-butyl ether (MTBE).

                                       
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation
                                        


Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM) for each platform sector

Platform Sector 
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B), Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
ptegu
No integration, create NBAFM from VOC speciation 
ptnonipm
No integration, create NBAFM from VOC speciation 
ptfire 
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptfire_othna
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptagfire
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
airport
No integration, create NBAFM from VOC speciation 
ag
Partial integration (NBAFM)
afdust
N/A  -  sector contains no VOC
beis
N/A  -  sector contains no inventory pollutant "VOC"; but rather specific VOC species
cmv_c1c2
Full integration (NBAFM)
cmv_c3
Full integration (NBAFM)
rail
Partial integration (NBAFM)
nonpt
Partial integration (NBAFM)
nonroad 
Full integration (NBAFM in California, internal to MOVES elsewhere) 
np_oilgas
Partial integration (NBAFM)
othpt
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
pt_oilgas
No integration, create NBAFM from VOC speciation
rwc
Partial integration (NBAFM)
onroad
Full integration (internal to MOVES); however, MOVES2014a speciation was CB6-CAMx, not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-CMAQ
onroad_can
No integration, no NBAFM in inventory, create NBAFM from speciation 
onroad_mex
Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation was CB6-CAMx, not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-CMAQ
othafdust
N/A  -  sector contains no VOC
othptdust
N/A  -  sector contains no VOC
othar
No integration, no NBAFM in inventory, create NBAFM from VOC speciation

Integration for the mobile sources estimated from MOVES (onroad and nonroad sectors, other than for California) is done differently.  Briefly there are three major differences: 1) for these sources integration is done using more than just NBAFM, 2) all sources from the MOVES model are integrated, and 3) integration is done fully or partially within MOVES.  For onroad mobile, speciation is done fully within MOVES2014a such that the MOVES model outputs emission factors for individual VOC model species along with the HAPs.  This requires MOVES to be run for a specific chemical mechanism.  MOVES was run for the CB6-CAMx mechanism rather than CB6-CMAQ, so post-SMOKE onroad emissions were converted to CB6-CMAQ.  More specifically, the CB6-CAMx mechanism excludes XYLMN, NAPH, and SOAALK. After SMOKE processing, we converted the onroad and onroad_mex emissions to CB6-CMAQ as follows:
 XYLMN = XYL[1]-0.966*NAPHTHALENE[1]
 PAR = PAR[1]-0.00001*NAPHTHALENE[1]
 SOAALK = 0.108*PAR[1]

For nonroad mobile, speciation is partially done within MOVES such that it does not need to be run for a specific chemical mechanism.  For nonroad, MOVES outputs emissions of HAPs and NONHAPTOG are +split by speciation profile.  Taking into account that integrated species were subtracted out by MOVES already, the appropriate speciation profiles are then applied in SMOKE to get the VOC model species.  HAP integration for nonroad uses the same additional HAPs and ethanol as for onroad. 
 County specific profile combinations 
SMOKE can compute speciation profiles from mixtures of other profiles in user-specified proportions via two different methods.  The first method, which uses a GSPRO_COMBO file, has been in use since the 2005 platform; the second method (GSPRO with fraction) was used for the first time in the 2014v7.0 platform.  The GSPRO_COMBO method uses profile combinations specified in the GSPRO_COMBO ancillary file by pollutant (which can include emissions mode, e.g., EXH__VOC), state and county (i.e., state/county FIPS code) and time period (i.e., month).  Different GSPRO_COMBO files can be used by sector, allowing for different combinations to be used for different sectors; but within a sector, different profiles cannot be applied based on SCC.  The GSREF file indicates that a specific source uses a combination file with the profile code "COMBO."  SMOKE computes the resultant profile using the fraction of each specific profile assigned by county, month and pollutant.
 
In previous platforms, the GSPRO_COMBO feature was used to speciate nonroad mobile and gasoline-related stationary sources that use fuels with varying ethanol content.  In these cases, the speciation profiles require different combinations of gasoline profiles, e.g., 0% ethanol (E0) and 10% ethanol (E10) profiles. Since the ethanol content varied spatially (e.g., by state or county), temporally (e.g., by month), and by modeling year (future years have more ethanol), the GSPRO_COMBO feature allowed combinations to be specified at various levels for different years. The GSPRO_COMBO is no longer needed for nonroad sources outside of California because nonroad emissions within MOVES have the speciation profiles built into the results, so there is no need to assign them via the GSREF or GSPRO_COMBO feature.  For the 2016 alpha platform, GSPRO_COMBO is still used for nonroad sources in California and for certain gasoline-related stationary sources nationwide.  The fractions combining the E0 and E10 profiles are based on year 2010 regional fuels and do not vary by month.  GSPRO_COMBO is not needed for inventory years after 2016, because the vast majority of fuel is projected to be E10 in future years.

Starting with the 2016v7.2 beta and regional haze platforms, a GSPRO_COMBO is used to specify a mix of E0 and E10 fuels in Canada. ECCC provided percentages of ethanol use by province, and these were converted into E0 and E10 splits. For example, Alberta has 4.91% ethanol in its fuel, so we applied a mix of 49.1% E10 profiles (4.91% times 10, since 10% ethanol would mean 100% E10), and 50.9% E0 fuel. Ethanol splits for all provinces in Canada are listed in Table 3-5. The Canadian onroad inventory includes four distinct FIPS codes in Ontario, allowing for application of different E0/E10 splits in Southern Ontario versus Northern Ontario. In Mexico, only E0 profiles are used.
 Table 3-5. Ethanol percentages by volume by Canadian province
Province
                        Ethanol % by volume (E10 = 10%)
Alberta
                                     4.91%
British Columbia
                                     5.57%
Manitoba
                                     9.12%
New Brunswick
                                     4.75%
Newfoundland & Labrador
                                     0.00%
Nova Scotia
                                     0.00%
NW Territories
                                     0.00%
Nunavut
                                     0.00%
Ontario (Northern)
                                     0.00%
Ontario (Southern)
                                     7.93%
Prince Edward Island
                                     0.00%
Québec
                                     3.36%
Saskatchewan
                                     7.73%
Yukon
                                     0.00%

A new method to combine multiple profiles became available in SMOKE4.5.  It allows multiple profiles to be combined by pollutant, state and county (i.e., state/county FIPS code) and SCC.  This was used specifically for the oil and gas sectors (pt_oilgas and np_oilgas) because SCCs include both controlled and uncontrolled oil and gas operations which use different profiles.
 Additional sector specific considerations for integrating HAP emissions from inventories into speciation
The decision to integrate HAPs into the speciation was made on a sector by sector basis.  For some sectors, there is no integration and VOC is speciated directly; for some sectors, there is full integration meaning all sources are integrated; and for other sectors, there is partial integration, meaning some sources are not integrated and other sources are integrated.  The integrated HAPs are either NBAFM or, in the case of MOVES (onroad, nonroad, and MOVES-Mexico), a larger set of HAPs plus ethanol are integrated.  Table 3-4 above summarizes the integration method for each platform sector.

For the rail sector, the EPA integrated NBAFM for most sources.  Some SCCs had zero BAFM and, therefore, they were not integrated.  These were SCCs provided by states for which EPA did not do HAP augmentation (2285002008, 2285002009 and 2285002010) because EPA does not create emissions for these SCCs.  The VOC for these sources sum to 272 tons, and most of the mass is in California (189 tons) and Washington state (62 tons).

Speciation for the onroad sector is unique.  First, SMOKE-MOVES is used to create emissions for these sectors and both the MEPROC and INVTABLE files are involved in controlling which pollutants are processed.  Second, the speciation occurs within MOVES itself, not within SMOKE.  The advantage of using MOVES to speciate VOC is that during the internal calculation of MOVES, the model has complete information on the characteristics of the fleet and fuels (e.g., model year, ethanol content, process, etc.), thereby allowing it to more accurately make use of specific speciation profiles.  This means that MOVES produces emission factor tables that include inventory pollutants (e.g., TOG) and model-ready species (e.g., PAR, OLE, etc).  SMOKE essentially calculates the model-ready species by using the appropriate emission factor without further speciation.  Third, MOVES' internal speciation uses full integration of an extended list of HAPs beyond NBAFM (called "M-profiles").  The M-profiles integration is very similar to NBAFM integration explained above except that the integration calculation (see Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation) is performed on emissions factors instead of on emissions, and a much larger set of pollutants are integrated besides NBAFM.  The list of integrated pollutants is described in Table 3-6.  An additional run of the Speciation Tool was necessary to create the M-profiles that were then loaded into the MOVES default database.  Fourth, for California, the EPA applied adjustment factors to SMOKE-MOVES to produce California adjusted model-ready files.  By applying the ratios through SMOKE-MOVES, the CARB inventories are essentially speciated to match EPA estimated speciation.  This resulted in changes to the VOC HAPs from what CARB submitted to the EPA.  Finally, MOVES speciation used the CAMx version of CB6 which does not split out naphthalene.  
Table 3-6.  MOVES integrated species in M-profiles
                                   MOVES ID
Pollutant Name
                                       5
Methane (CH4)
                                      20
Benzene
                                      21
Ethanol
                                      22
MTBE
                                      24
1,3-Butadiene
                                      25
Formaldehyde
                                      26
Acetaldehyde
                                      27
Acrolein
                                      40
2,2,4-Trimethylpentane
                                      41
Ethyl Benzene
                                      42
Hexane
                                      43
Propionaldehyde
                                      44
Styrene
                                      45
Toluene
                                      46
Xylene
                                      185
Naphthalene gas

For the nonroad sector, all sources are integrated using the same list of integrated pollutants as shown in Table 3-6.  Outside of California, the integration calculations are performed within MOVES.  For California, integration calculations are handled by SMOKE.  The CARB-based nonroad inventory includes VOC HAP estimates for all sources, so every source in California was integrated as well.  Some sources in the original CARB inventory had lower VOC emissions compared to sum of all VOC HAPs.  For those sources, VOC was augmented to be equal to the VOC HAP sum, ensuring that every source in California could be integrated.  The CARB-based nonroad data includes exhaust and evaporative mode-specific data for VOC, but, does not contain refueling.


MOVES-MEXICO for onroad used the same speciation approach as for the U.S. in that the larger list of species shown in Table 3-6 was used.  However, MOVES-MEXICO used CB6-CAMx, not CB6-CMAQ, so post-SMOKE we converted the emissions to CB6-CMAQ as follows:
 XYLMN = XYL[1]-0.966*NAPHTHALENE[1]
 PAR = PAR[1]-0.00001*NAPHTHALENE[1]
 SOAALK = 0.108*PAR[1]

For most sources in the rwc sector, the VOC emissions were greater than or equal to NBAFM, and NBAFM was not zero, so those sources were integrated, although a few specific sources that did not meet these criteria could not be integrated.  In all cases, these sources have SCC= 2104008400 (pellet stoves), and NBAFM > VOC, but not by a significant amount.  This results from the sum of NBAFM emission factors exceeding the VOC emission factor.  In total, the no-integrate rwc sector sources sum to 4.4 tons VOC and 66 tons of NBAFM.  Since for the NATA case the NBAFM are used from the inventory, these no-integrate NBAFM emissions were used in the speciation.

For the nonpt sector, sources for which VOC emissions were greater than or equal to NBAFM, and NBAFM was not zero, were integrated.  There is a substantial amount of mass in the nonpt sector that is not integrated: 731,000 tons which is about 20% of the VOC in that sector.  It is likely that there would be sources in nonpt that are not integrated because the emission source is not expected to have NBAFM.  In fact, 390,000 tons of the no-integrate VOC have no NBAFM in the speciation profiles used for these no-integrate sources. Of the portion of no-integrate VOC with NBAFM there is 3,900 tons NBAFM in the profiles (that are dropped from the profiles per the procedure in Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation) for these no-integrate sources.

For the biog sector, the speciation profiles used by BEIS are not included in SPECIATE.  BEIS3.61 includes the species (SESQ) that is mapped to the BEIS model species SESQT (Sesquiterpenes).  The profile code associated with BEIS3.61 for use with CB05 is "B10C5," while the profile for use with CB6 is "B10C6."  The main difference between the profiles is the explicit treatment of acetone emissions in B10C6.
 Oil and gas related speciation profiles
Most of the recently added VOC profiles from SPECIATE4.5 (listed in Appendix B) are in the oil and gas sector.  A new national flare profile, FLR99, Natural Gas Flare Profile with DRE >98% was developed from a Flare Test study and used in the v7.0 platform. For the oil and gas sources in the np_oilgas and pt_oilgas sectors, several counties were assigned to newly available basin or area-specific profiles in SPECIATE4.5 that account for measured or modeled, from measured compositions specific to a particular region of the country.  In the 2011 platform, the only county-specific profiles were for the WRAP, but in the 2014 and 2016 platforms, several new profiles were added for other parts of the country.  The 2016 platform uses the latest version of the WRAP profiles. These profiles are denoted with an _R suffix, and reflect newer data and corrections to older WRAP profiles.  All WRAP profile codes were renamed to include an "_R" to distinguish between the previous set of profiles (even those that did not change).  For the Uintah basin and Denver-Julesburg Basin, Colorado, more updated profiles were used instead of the WRAP profiles. Table 3-7 lists the region-specific profiles assigned to particular counties or groups of counties. Although this platform increases the use of regional profiles, many counties still rely on the national profiles. A minor change in 2016v1 was to use county-specific profile assignments from SCC 2310121700 for the SCCs 2310021500, 2310421700 in Pennsylvania.

In addition to region-specific assignments, multiple profiles were assigned to particular county/SCC combinations using the SMOKE feature discussed in 3.2.1.1. Oil and gas SCCs for associated gas, condensate tanks, crude oil tanks, dehydrators, liquids unloading and well completions represent the total VOC from the process, including the portions of process that may be flared or directed to a reboiler.  For example, SCC 2310021400 (gas well dehydrators) consists of process, reboiler, and/or flaring emissions.  There are not separate SCCs for the flared portion of the process or the reboiler.  However, the VOC associated with these three portions can have very different speciation profiles.  Therefore, it is necessary to have an estimate of the amount of VOC from each of the portions (process, flare, reboiler) so that the appropriate speciation profiles can be applied to each portion.  The Nonpoint Oil and Gas Emission Estimation Tool generates an intermediate file which provides flare, non-flare (process), and reboiler (for dehydrators) emissions for six source categories that have flare emissions: by county FIPS and SCC code for the U.S.  From these emissions we can compute the fraction of the emissions to assign to each profile.  These fractions can vary by county FIPS, because they depend on the level of controls, which is an input to the Speciation Tool.
Table 3-7.  Basin/Region-specific profiles for oil and gas
Profile Code
Description
Region (if not in the profile name)
DJVNT_R
Denver-Julesburg Basin Produced Gas Composition from Non-CBM Gas Wells

PNC01_R
Piceance Basin Produced Gas Composition from Non-CBM Gas Wells

PNC02_R
Piceance Basin Produced Gas Composition from Oil Wells

PNC03_R
Piceance Basin Flash Gas Composition for Condensate Tank

PNCDH
Piceance Basin, Glycol Dehydrator

PRBCB_R
Powder River Basin Produced Gas Composition from CBM Wells

PRBCO_R
Powder River Basin Produced Gas Composition from Non-CBM Wells

PRM01_R
Permian Basin Produced Gas Composition for Non-CBM Wells

SSJCB_R
South San Juan Basin Produced Gas Composition from CBM Wells

SSJCO_R
South San Juan Basin Produced Gas Composition from Non-CBM Gas Wells

SWFLA_R
SW Wyoming Basin Flash Gas Composition for Condensate Tanks

SWVNT_R
SW Wyoming Basin Produced Gas Composition from Non-CBM Wells

UNT01_R
Uinta Basin Produced Gas Composition from CBM Wells

WRBCO_R
Wind River Basin Produced Gagres Composition from Non-CBM Gas Wells

95087a
Oil and Gas - Composite - Oil Field - Oil Tank Battery Vent Gas
East Texas
95109a
Oil and Gas - Composite - Oil Field - Condensate Tank Battery Vent Gas
East Texas
95417
Uinta Basin, Untreated Natural Gas 

95418
Uinta Basin, Condensate Tank Natural Gas

95419
Uinta Basin, Oil Tank Natural Gas

95420
Uinta Basin, Glycol Dehydrator

95398
Composite Profile - Oil and Natural Gas Production - Condensate Tanks
Denver-Julesburg Basin
95399
Composite Profile - Oil Field  -  Wells
State of California
95400
Composite Profile - Oil Field  -  Tanks
State of California
95403
Composite Profile - Gas Wells
San Joaquin Basin

 Mobile source related VOC speciation profiles
The VOC speciation approach for mobile source and mobile source-related source categories is customized to account for the impact of fuels and engine type and technologies.  The impact of fuels also affects the parts of the nonpt and ptnonipm sectors that are related to mobile sources such as portable fuel containers and gasoline distribution.

The VOC speciation profiles for the nonroad sector other than for California are listed in Table 3-8. They include new profiles (i.e., those that begin with "953") for 2-stroke and 4-stroke gasoline engines running on E0 and E10 and compression ignition engines with different technologies developed from recent EPA test programs, which also supported the updated toxics emission factor in MOVES2014a (Reichle, 2015 and EPA, 2015b).  California nonroad source profiles are presented in Table 3-9.
Table 3-8.  TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a used for the 2016 Platform
Profile
Profile Description
Engine Type
Engine Technology
Engine
 Size
Horse-power category
Fuel
Fuel Sub-type
Emission Process
                                                                          95327
SI 2-stroke E0
SI 2-stroke
all
All
all
Gasoline
E0
exhaust
                                                                          95328
SI 2-stroke E10
SI 2-stroke
all
All
all
Gasoline
E10
exhaust
                                                                          95329
SI 4-stroke E0
SI 4-stroke
all
All
all
Gasoline
E0
exhaust
                                                                          95330
SI 4-stroke E10
SI 4-stroke
all
All
all
Gasoline
E10
exhaust
                                                                          95331
CI Pre-Tier 1
CI
Pre-Tier 1
All
all
Diesel
all
exhaust
                                                                          95332
CI Tier 1
CI
Tier 1
All
all
Diesel
all
exhaust
                                                                          95333
CI Tier 2
CI
Tier 2 and 3
all
all
Diesel
all
exhaust
                                                                          95333
CI Tier 2
CI
Tier 4
<56 kW (75 hp)
S
Diesel
all
exhaust
                                                                           8775
ACES Phase 1 Diesel Onroad
CI Tier 4
Tier 4
>=56 kW (75 hp)
L
Diesel
all
exhaust
                                                                           8753
E0 Evap
SI
all
all
all
Gasoline
E0
evaporative
                                                                           8754
E10 Evap
SI
all
all
all
Gasoline
E10
evaporative
                                                                           8766
E0 evap permeation
SI
all
all
all
Gasoline
E0
permeation
                                                                           8769
E10 evap permeation
SI
all
all
all
Gasoline
E10
permeation
                                                                           8869
E0 Headspace
SI
all
all
all
Gasoline
E0
headspace
                                                                           8870
E10 Headspace
SI
all
all
all
Gasoline
E10
headspace
                                                                           1001
CNG Exhaust
All
all
all
all
CNG
all
exhaust
                                                                           8860
LPG exhaust
All
all
all
all
LPG
all
exhaust

Speciation profiles for VOC in the nonroad sector account for the ethanol content of fuels across years.  A description of the actual fuel formulations for 2014 can be found in the 2014NEIv2 TSD.  For previous platforms, the EPA used "COMBO" profiles to model combinations of profiles for E0 and E10 fuel use, but beginning with 2014v7.0 platform, the appropriate allocation of E0 and E10 fuels is done by MOVES.

Combination profiles reflecting a combination of E10 and E0 fuel use are still used for sources upstream of mobile sources such as portable fuel containers (PFCs) and other fuel distribution operations associated with the transfer of fuel from bulk terminals to pumps (BTP), which are in the nonpt sector.  They are also used for California nonroad sources.  For these sources, ethanol may be mixed into the fuels, in which case speciation would change across years.  The speciation changes from fuels in the ptnonipm sector include BTP distribution operations inventoried as point sources.  Refinery-to-bulk terminal (RBT) fuel distribution and bulk plant storage (BPS) speciation does not change across the modeling cases because this is considered upstream from the introduction of ethanol into the fuel.  The mapping of fuel distribution SCCs to PFC, BTP, BPS, and RBT emissions categories can be found in Appendix C.

Table 3-9 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for 2016.  The term "COMBO" indicates that a combination of the profiles listed was used to speciate that subcategory using the GSPRO_COMBO file.  
Table 3-9.  Select mobile-related VOC profiles 2016 
Sector
Sub-category
                                     2014
                       Nonroad- California & non US
                               gasoline exhaust
COMBO
 


8750a
Pre-Tier 2 E0 exhaust


8751a
Pre-Tier 2 E10 exhaust
                              Nonroad-California
                             gasoline evaporative
COMBO
 


8753
E0 evap


8754
E10 evap
                              Nonroad-California
                              gasoline refueling
COMBO
 


8869
E0 Headspace


8870
E10 Headspace
                              Nonroad-California
                                diesel exhaust
8774
Pre-2007 MY HDD exhaust
                              Nonroad-California
                   diesel evap-
orative and diesel refueling
4547
Diesel Headspace
                                nonpt/
ptnonipm
                                       
                                  PFC and BTP
                                       
COMBO
 
                                       
                                       
8869
E0 Headspace
                                       
                                       
8870
E10 Headspace
                                nonpt/
ptnonipm
      Bulk plant storage (BPS) and refine-to-bulk terminal (RBT) sources
8869
E0 Headspace
The speciation of onroad VOC occurs completely within MOVES.  MOVES  accounts for fuel type and properties, emission standards as they affect different vehicle types and model years, and specific emission processes.  Table 3-10 describes all of the M-profiles available to MOVES depending on the model year range, MOVES process (processID), fuel sub-type (fuelSubTypeID), and regulatory class (regClassID).  Table 3-11 through Table 3-13 describe the meaning of these MOVES codes.  For a specific representative county and future year, there will be a different mix of these profiles.  For example, for HD diesel exhaust, the emissions will use a combination of profiles 8774M and 8775M depending on the proportion of HD vehicles that are pre-2007 model years (MY) in that particular county.  As that county is projected farther into the future, the proportion of pre-2007 MY vehicles will decrease.  A second example, for gasoline exhaust (not including E-85), the emissions will use a combination of profiles 8756M, 8757M, 8758M, 8750aM, and 8751aM.  Each representative county has a different mix of these key properties and, therefore, has a unique combination of the specific M-profiles.  More detailed information on how MOVES speciates VOC and the profiles used is provided in the technical document, "Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles in MOVES2014" (EPA, 2015c).
Table 3-10.  Onroad M-profiles
Profile
Profile Description
Model Years
ProcessID
FuelSubTypeID
RegClassID
1001M
CNG Exhaust
1940-2050
1,2,15,16
30
48
4547M
Diesel Headspace
1940-2050
11
20,21,22
0
4547M
Diesel Headspace
1940-2050
12,13,18,19
20,21,22
10,20,30,40,41, 42,46,47,48
8753M
E0 Evap
1940-2050
12,13,19
10
10,20,30,40,41,42, 46,47,48
8754M
E10 Evap
1940-2050
12,13,19
12,13,14
10,20,30,40,41, 42,46,47,48
8756M
Tier 2 E0 Exhaust
2001-2050
1,2,15,16
10
20,30
8757M
Tier 2 E10 Exhaust
2001-2050
1,2,15,16
12,13,14
20,30
8758M
Tier 2 E15 Exhaust
1940-2050
1,2,15,16
15,18
10,20,30,40,41, 42,46,47,48
8766M
E0 evap permeation
1940-2050
11
10
0
8769M
E10 evap permeation
1940-2050
11
12,13,14
0
8770M
E15 evap permeation
1940-2050
11
15,18
0
8774M
Pre-2007 MY HDD exhaust 
1940-2006
1,2,15,16,17,90
20, 21, 22
40,41,42,46,47, 48
8774M
Pre-2007 MY HDD exhaust 
1940-2050
91
20, 21, 22
46,47
8774M
Pre-2007 MY HDD exhaust 
1940-2006
1,2,15,16
20, 21, 22
20,30
8775M
2007+ MY HDD exhaust
2007-2050
1,2,15,16
20, 21, 22
20,30
8775M
2007+ MY HDD exhaust
2007-2050
1,2,15,16,17,90
20, 21, 22
40,41,42,46,47,48
8855M
Tier 2 E85 Exhaust
1940-2050
1,2,15,16
50, 51, 52
10,20,30,40,41, 42,46,47,48
8869M
E0 Headspace
1940-2050
18
10
10,20,30,40,41, 42,46,47,48
8870M
E10 Headspace
1940-2050
18
12,13,14
10,20,30,40,41, 42,46,47,48
8871M
E15 Headspace
1940-2050
18
15,18
10,20,30,40,41, 42,46,47,48
8872M
E15 Evap
1940-2050
12,13,19
15,18
10,20,30,40,41, 42,46,47,48
8934M
E85 Evap
1940-2050
11
50,51,52
0
8934M
E85 Evap
1940-2050
12,13,18,19
50,51,52
10,20,30,40,41, 42,46,47,48
8750aM
Pre-Tier 2 E0 exhaust
1940-2000
1,2,15,16
10
20,30
8750aM
Pre-Tier 2 E0 exhaust
1940-2050
1,2,15,16
10
10,40,41,42,46,47,48
8751aM
Pre-Tier 2 E10 exhaust
1940-2000
1,2,15,16
11,12,13,14
20,30
8751aM
Pre-Tier 2 E10 exhaust
1940-2050
1,2,15,16
11,12,13,14,15, 18
10,40,41,42,46,47,48
Table 3-11.  MOVES process IDs
Process ID
Process Name
                                                                              1
Running Exhaust
                                                                              2
Start Exhaust
                                                                              9
Brakewear
                                                                             10
Tirewear
                                                                             11
Evap Permeation
                                                                             12
Evap Fuel Vapor Venting
                                                                             13
Evap Fuel Leaks
                                                                             15
Crankcase Running Exhaust
                                                                             16
Crankcase Start Exhaust
                                                                             17
Crankcase Extended Idle Exhaust
                                                                             18
Refueling Displacement Vapor Loss
                                                                             19
Refueling Spillage Loss
                                                                             20
Evap Tank Permeation
                                                                             21
Evap Hose Permeation
                                                                             22
Evap RecMar Neck Hose Permeation
                                                                             23
Evap RecMar Supply/Ret Hose Permeation
                                                                             24
Evap RecMar Vent Hose Permeation
                                                                             30
Diurnal Fuel Vapor Venting
                                                                             31
HotSoak Fuel Vapor Venting
                                                                             32
RunningLoss Fuel Vapor Venting
                                                                             40
Nonroad
                                                                             90
Extended Idle Exhaust
                                                                             91
Auxiliary Power Exhaust
Table 3-12.  MOVES Fuel subtype IDs
Fuel Subtype ID
Fuel Subtype Descriptions
                                                                             10
Conventional Gasoline
                                                                             11
Reformulated Gasoline (RFG)
                                                                             12
Gasohol (E10)
                                                                             13
Gasohol (E8)
                                                                             14
Gasohol (E5)
                                                                             15
Gasohol (E15)
                                                                             18
Ethanol (E20)
                                                                             20
Conventional Diesel Fuel
                                                                             21
Biodiesel (BD20)
                                                                             22
Fischer-Tropsch Diesel (FTD100)
                                                                             30
Compressed Natural Gas (CNG)
                                                                             50
Ethanol
                                                                             51
Ethanol (E85)
                                                                             52
Ethanol (E70)
Table 3-13.  MOVES regclass IDs
Reg. Class ID
Regulatory Class Description
                                                                              0
Doesn't Matter
                                                                             10
Motorcycles
                                                                             20
Light Duty Vehicles
                                                                             30
Light Duty Trucks
                                                                             40
Class 2b Trucks with 2 Axles and 4 Tires (8,500 lbs < GVWR <= 10,000 lbs)
                                                                             41
Class 2b Trucks with 2 Axles and at least 6 Tires or Class 3 Trucks (8,500 lbs < GVWR <= 14,000 lbs)
                                                                             42
Class 4 and 5 Trucks (14,000 lbs < GVWR <= 19,500 lbs)
                                                                             46
Class 6 and 7 Trucks (19,500 lbs < GVWR <= 33,000 lbs)
                                                                             47
Class 8a and 8b Trucks (GVWR > 33,000 lbs)
                                                                             48
Urban Bus (see CFR Sec 86.091_2)
For portable fuel containers (PFCs) and fuel distribution operations associated with the bulk-plant-to-pump (BTP) distribution, ethanol may be mixed into the fuels; therefore, county- and month-specific COMBO speciation was used (via the GSPRO_COMBO file).  Refinery to bulk terminal (RBT) fuel distribution and bulk plant storage (BPS) speciation are considered upstream from the introduction of ethanol into the fuel; therefore, a single profile is sufficient for these sources.  No refined information on potential VOC speciation differences between cellulosic diesel and cellulosic ethanol sources was available; therefore, cellulosic diesel and cellulosic ethanol sources used the same SCC (30125010: Industrial Chemical Manufacturing, Ethanol by Fermentation production) for VOC speciation as was used for corn ethanol plants.  
 PM speciation
In addition to VOC profiles, the SPECIATE database also contains profiles for speciating PM2.5.  PM2.5 was speciated into the AE6 species associated with CMAQ 5.0.1 and later versions.  Of particular note for the 2016v7.2 beta and regional haze platforms, the nonroad PM2.5 speciation was updated as discussed later in this section. Most of the PM profiles come from the 911XX series (Reff et. al, 2009), which include updated AE6 speciation.  Starting with the 2014v7.1 platform, we replaced profile 91112 (Natural Gas Combustion  -  Composite) with 95475 (Composite -Refinery Fuel Gas and Natural Gas Combustion).  This updated profile is an AE6-ready profile based on the median of 3 SPECIATE4.5 profiles from which AE6 versions were made (to be added to SPECIATE5.0):  boilers (95125a), process heaters (95126a) and internal combustion combined cycle/cogen plant exhaust (95127a).  As with profile 91112, these profiles are based on tests using natural gas and refinery fuel gas (England et al., 2007).  Profile 91112 which is also based on refinery gas and natural gas is thought to overestimate EC.  

Profile 95475 (Composite -Refinery Fuel Gas and Natural Gas Combustion) is shown along with the underlying profiles composited in Figure 3-3.  Figure 3-4 shows a comparison of the new profile as of the 2014v7.1 platform with the one that we had been using in the 2014v7.0 and earlier platforms.
Figure 3-3.  Profiles composited for the new PM gas combustion related sources


Figure 3-4.  Comparison of PM profiles used for Natural gas combustion related sources
                                       

 Mobile source related PM2.5 speciation profiles
 For the onroad sector, for all processes except brake and tire wear, PM speciation occurs within MOVES itself, not within SMOKE (similar to the VOC speciation described above).  The advantage of using MOVES to speciate PM is that during the internal calculation of MOVES, the model has complete information on the characteristics of the fleet and fuels (e.g., model year, sulfur content, process, etc.) to accurately match to specific profiles.  This means that MOVES produces EF tables that include total PM (e.g., PM10 and PM2.5) and speciated PM (e.g., PEC, PFE, etc).  SMOKE essentially calculates the PM components by using the appropriate EF without further speciation.  The specific profiles used within MOVES include two CNG profiles, 45219 and 45220, which were added to SPECIATE4.5.  A list of profiles is provided in the technical document, "Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles in MOVES2014" (EPA, 2015c).

For onroad brake and tire wear, the PM is speciated in the moves2smk postprocessor that prepares the emission factors for processing in SMOKE.  The formulas for this are based on the standard speciation factors from brake and tire wear profiles, which were updated from the v6.3 platform based on data from a Health Effects Institute report (Schauer, 2006).  Table 3-14 shows the differences in the v7.1 and v6.3 profiles.
Table 3-14.  SPECIATE4.5 brake and tire profiles compared to those used in the 2011v6.3 Platform
                              Inventory Pollutant
                                     Model
                                    Species
                    V6.3 platform brakewear profile:  91134
           SPECIATE4.5 brakewear profile: 95462 from Schauer (2006)
                     V6.3 platform tirewear profile: 91150
            SPECIATE4.5 tirewear profile: 95460 from Schauer (2006)
PM2_5
PAL
                                                                        0.00124
                                                                    0.000793208
                                                                       6.05E-04
                                                                    3.32401E-05
PM2_5
PCA
                                                                           0.01
                                                                    0.001692177
                                                                        0.00112
 
PM2_5
PCL
                                                                       0.001475
 
                                                                         0.0078
 
PM2_5
PEC
                                                                         0.0261
                                                                    0.012797085
                                                                           0.22
                                                                    0.003585907
PM2_5
PFE
                                                                          0.115
                                                                    0.213901692
                                                                         0.0046
                                                                     0.00024779
PM2_5
PH2O
                                                                      0.0080232
 
                                                                       0.007506
 
PM2_5
PK
                                                                       1.90E-04
                                                                    0.000687447
                                                                       3.80E-04
                                                                    4.33129E-05
PM2_5
PMG
                                                                         0.1105
                                                                    0.002961309
                                                                       3.75E-04
                                                                    0.000018131
PM2_5
PMN
                                                                       0.001065
                                                                    0.001373836
                                                                       1.00E-04
                                                                       1.41E-06
PM2_5
PMOTHR
                                                                         0.4498
                                                                    0.691704999
                                                                         0.0625
                                                                    0.100663209
PM2_5
PNA
                                                                       1.60E-04
                                                                    0.002749787
                                                                       6.10E-04
                                                                    7.35312E-05
PM2_5
PNCOM
                                                                         0.0428
                                                                    0.020115749
                                                                         0.1886
                                                                    0.255808124
PM2_5
PNH4
                                                                       3.00E-05
 
                                                                       1.90E-04
 
PM2_5
PNO3
                                                                         0.0016
 
                                                                         0.0015
 
PM2_5
POC
                                                                          0.107
                                                                    0.050289372
                                                                         0.4715
                                                                    0.639520309
PM2_5
PSI
                                                                          0.088
 
                                                                        0.00115
 
PM2_5
PSO4
                                                                         0.0334
 
                                                                         0.0311
 
PM2_5
PTI
                                                                         0.0036
                                                                    0.000933341
                                                                       3.60E-04
                                                                       5.04E-06

 The formulas used based on brake wear profile 95462 and tire wear profile 95460 are as follows:

      POC = 0.6395 * PM25TIRE + 0.0503 * PM25BRAKE
      PEC = 0.0036 * PM25TIRE + 0.0128 * PM25BRAKE
      PNO3 = 0.000 * PM25TIRE + 0.000 * PM25BRAKE
      PSO4 = 0.0 * PM25TIRE + 0.0 * PM25BRAKE
      PNH4 = 0.000 * PM25TIRE + 0.0000 * PM25BRAKE
      PNCOM = 0.2558 * PM25TIRE + 0.0201 * PM25BRAKE

For California onroad emissions, adjustment factors were applied to SMOKE-MOVES to produce California adjusted model-ready files.  California did not supply speciated PM, therefore, the adjustment factors applied to PM2.5 were also applied to the speciated PM components.  By applying the ratios through SMOKE-MOVES, the CARB inventories are essentially speciated to match EPA estimated speciation.

For nonroad PM2.5, speciation is partially done within MOVES such that it does not need to be run for a specific chemical mechanism.  For nonroad, MOVES outputs emissions of PM2.5 split by speciation profile.  Similar to how VOC and NONHAPTOG are speciated, PM2.5 is now also speciated this way starting with MOVES2014b. California nonroad emissions, which are not from MOVES, continue to be speciated the traditional way with speciation profiles assigned by SMOKE using the GSREF cross-reference.  The PM2.5 profiles assigned to nonroad sources are listed in Table 3-15.  
Table 3-15.  Nonroad PM2.5 profiles 
                                                       SPECIATE4.5 Profile Code
SPECIATE4.5 Profile Name
Assigned to Nonroad sources based on Fuel Type
                                                                           8996
Diesel Exhaust - Heavy-heavy duty truck - 2007 model year with NCOM
Diesel
                                                                          91106
HDDV Exhaust  -  Composite
Diesel 
                                                                          91113
Nonroad Gasoline Exhaust  -  Composite
Gasoline 
                                                                          91156
Residential Natural Gas Combustion
CNG and LPG (California only)
                                                                          95219
CNG Transit Bus Exhaust
CNG and LPG

 NOX speciation
NOx emission factors and therefore NOx inventories are developed on a NO2 weight basis. For air quality modeling, NOX is speciated into NO, NO2, and/or HONO.  For the non-mobile sources, the EPA used a single profile "NHONO" to split NOX into NO and NO2. 

The importance of HONO chemistry, identification of its presence in ambient air and the measurements of HONO from mobile sources have prompted the inclusion of HONO in NOx speciation for mobile sources.  Based on tunnel studies, a HONO to NOx ratio of 0.008 was chosen (Sarwar, 2008).  For the mobile sources, except for onroad (including nonroad, cmv, rail, othon sectors), and for specific SCCs in othar and ptnonipm, the profile "HONO" is used.  Table 3-16 gives the split factor for these two profiles.  The onroad sector does not use the "HONO" profile to speciate NOX.  MOVES2014 produces speciated NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables used by SMOKE-MOVES.  Within MOVES, the HONO fraction is a constant 0.008 of NOX.  The NO fraction varies by heavy duty versus light duty, fuel type, and model year.  
The NO2 fraction = 1  -  NO  -  HONO.  For more details on the NOX fractions within MOVES, see EPA report "Use of data from `Development of Emission Rates for the MOVES Model,'
Sierra Research, March 3, 2010" available at https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100F1A5.pdf.
Table 3-16.  NOX speciation profiles
Profile
pollutant
species
split factor
HONO
NOX
NO2
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
NO2
0.1
NHONO
NOX
NO
0.9

 Creation of Sulfuric Acid Vapor (SULF)
Since at least the 2002 Platform, sulfuric acid vapor (SULF) has been estimated through the SMOKE speciation process for coal combustion and residual and distillate oil fuel combustion sources.  Profiles that compute SULF from SO2 are assigned to coal and oil combustion SCCs in the GSREF ancillary file.  The profiles were derived from information from AP-42 (EPA, 1998), which identifies the fractions of sulfur emitted as sulfate and SO2 and relates the sulfate as a function of SO2.

Sulfate is computed from SO2 assuming that gaseous sulfate, which is comprised of many components, is primarily H2SO4. The equation for calculating H2SO4 is given below.

Emissions of SULF as H2SO4=SO2 emissions xfraction of S emitted as sulfatefraction of S emitted as SO2xMW H2SO4MW SO2

In the above, MW is the molecular weight of the compound.  The molecular weights of H2SO4 and SO2 are 98 g/mol and 64 g/mol, respectively.

This method does not reduce SO2 emissions; it solely adds gaseous sulfate emissions as a function of SO2 emissions.  The derivation of the profiles is provided in Table 3-17; a summary of the profiles is provided in Table 3-18.
Table 3-17.  Sulfate split factor computation 
fuel
SCCs
Profile Code
Fraction as SO2
Fraction as 
sulfate
Split factor (mass fraction)
Bituminous
1-0X-002-YY, where X is 1, 2 or 3 and YY is 01 thru 19 and 21-ZZ-002-000 where ZZ is 02,03 or 04
95014
0.95
0.014
.014/.95 * 98/64 = 0.0226

Subbituminous
1-0X-002-YY, where X is 1, 2 or 3 and YY is 21 thru 38
87514
.875
0.014
.014/.875 * 98/64 = 0.0245 

Lignite
1-0X-003-YY, where X is 1, 2 or 3 and YY is 01 thru 18 and 21-ZZ-002-000 where ZZ is 02,03 or 04
75014
0.75
0.014
.014/.75 * 98/64 = 0.0286

Residual oil
1-0X-004-YY, where X is 1, 2 or 3 and YY is 01 thru 06 and 21-ZZ-005-000 where ZZ is 02,03 or 04
99010
0.99
0.01
.01/.99 * 98/64 = 0.0155

Distillate oil
1-0X-005-YY, where X is 1, 2 or 3 and YY is 01 thru 06 and 21-ZZ-004-000 where ZZ is 02,03 or 04
99010
0.99
0.01
Same as residual oil
Table 3-18.  SO2 speciation profiles
Profile
pollutant
species
split factor
95014
SO2
SULF
                                    0.0226
95014
SO2
SO2
                                       1
87514
SO2
SULF
                                     0.0245
87514
SO2
SO2
                                       1
75014
SO2
SULF
                                    0.0286
75014
SO2
SO2
                                       1
99010
SO2
SULF
                                    0.0155
99010
SO2
SO2
                                       1

 Temporal Allocation
Temporal allocation is the process of distributing aggregated emissions to a finer temporal resolution, thereby converting annual emissions to hourly emissions as is required by CMAQ.  While the total emissions are important, the timing of the occurrence of emissions is also essential for accurately simulating ozone, PM, and other pollutant concentrations in the atmosphere.  Many emissions inventories are annual or monthly in nature.  Temporal allocation takes these aggregated emissions and distributes the emissions to the hours of each day.  This process is typically done by applying temporal profiles to the inventories in this order: monthly, day of the week, and diurnal, with monthly and day-of-week profiles applied only if the inventory is not already at that level of detail.

The temporal factors applied to the inventory are selected using some combination of country, state, county, SCC, and pollutant.  Table 3-19 summarizes the temporal aspects of emissions modeling by comparing the key approaches used for temporal processing across the sectors.  In the table, "Daily temporal approach" refers to the temporal approach for getting daily emissions from the inventory using the SMOKE Temporal program.  The values given are the values of the SMOKE L_TYPE setting.  The "Merge processing approach" refers to the days used to represent other days in the month for the merge step.  If this is not "all," then the SMOKE merge step runs only for representative days, which could include holidays as indicated by the right-most column.  The values given are those used for the SMOKE M_TYPE setting (see below for more information).  
Table 3-19.  Temporal settings used for the platform sectors in SMOKE
Platform sector short name
Inventory resolutions
Monthly profiles used?
Daily temporal approach
Merge processing approach
Process holidays as separate days
afdust_adj
Annual
Yes
week
All
Yes
afdust_ak_adj
Annual
Yes
week
All
Yes
ag
Monthly
 No
all
All
No
airports
Annual
Yes
week
week
Yes
beis
Hourly
 No
n/a
All
No
cmv_c1c2
Annual
Yes 
aveday
aveday
No
cmv_c3
Annual
Yes
aveday
aveday
No
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
 No
mwdss
mwdss
Yes
np_oilgas
Annual
Yes
aveday
aveday
No
onroad
Annual & monthly[1]
 No
all
all
Yes
onroad_ca_adj
Annual & monthly[1]
 No
all
all
Yes
onroad_nonconus
Annual & monthly[1]
 No
all
all
Yes
othafdust_adj
Annual
Yes
week
all
No
othar
Annual & monthly
Yes
week
week
No
onroad_can
Monthly
No
week
week
No
onroad_mex
Monthly
No
week
week
No
othpt
Annual & monthly
Yes
mwdss
mwdss
No
othptdust_adj
Monthly
No
week
all
No
pt_oilgas
Annual
Yes
mwdss
mwdss
Yes
ptegu
Annual & hourly
Yes[2]
all
all
No
ptnonipm
Annual
Yes
mwdss
mwdss
Yes
ptagfire
Daily
No
all
all
No
ptfire
Daily
No
all
all
No
ptfire_othna
Daily
No
all
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No[3]
met-based[3]
all
No[3]
      [1]Note the annual and monthly "inventory" actually refers to the activity data (VMT, hoteling, and VPOP) for onroad. VMT and hoteling is monthly and VPOP is annual. The actual emissions are computed on an hourly basis.
      [2]Only units that do not have matching hourly CEMS data use monthly temporal profiles.
      [3]Except for 2 SCCs that do not use met-based speciation

The following values are used in the table.  The value "all" means that hourly emissions are computed for every day of the year and that emissions potentially have day-of-year variation.  The value "week" means that hourly emissions computed for all days in one "representative" week, representing all weeks for each month.  This means emissions have day-of-week variation, but not week-to-week variation within the month.  The value "mwdss" means hourly emissions for one representative Monday, representative weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each month. This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within the month, but not week-to-week variation within the month.  The value "aveday" means hourly emissions computed for one representative day of each month, meaning emissions for all days within a month are the same.  Special situations with respect to temporal allocation are described in the following subsections. 

In addition to the resolution, temporal processing includes a ramp-up period for several days prior to January 1, 2016, which is intended to mitigate the effects of initial condition concentrations.  The ramp-up period was 10 days (December 22-31, 2015).  For most sectors, emissions from December 2016 (representative days) were used to fill in emissions for the end of December 2015.  For biogenic emissions, December 2015 emissions were processed using 2015 meteorology.
 Use of FF10 format for finer than annual emissions
The FF10 inventory format for SMOKE provides a consolidated format for monthly, daily, and hourly emissions inventories.  With the FF10 format, a single inventory file can contain emissions for all 12 months and the annual emissions in a single record.  This helps simplify the management of numerous inventories.  Similarly, daily and hourly FF10 inventories contain individual records with data for all days in a month and all hours in a day, respectively. 

SMOKE prevents the application of temporal profiles on top of the "native" resolution of the inventory.  For example, a monthly inventory should not have annual-to-month temporal allocation applied to it; rather, it should only have month-to-day and diurnal temporal allocation.  This becomes particularly important when specific sectors have a mix of annual, monthly, daily, and/or hourly inventories.  The flags that control temporal allocation for a mixed set of inventories are discussed in the SMOKE documentation.  The modeling platform sectors that make use of monthly values in the FF10 files are ag, nonroad, onroad, onroad_can, onroad_mex, othar, and othpt. 
 Electric Generating Utility temporal allocation (ptegu)
 Base year temporal allocation of EGUs
The 2016 annual EGU emissions not matched to CEMS sources use region/fuel specific profiles based on average hourly emissions for the region and fuel.  Peaking units were removed during the averaging to minimize the spikes generated by those units.  The non-matched units are allocated to hourly emissions using the following three-step methodology: annual value to month, month to day, and day to hour.  First, the CEMS data were processed using a tool that reviewed the data quality flags that indicate the data were not measured.  Unmeasured data can be filled in with maximum values and thereby cause erroneously high values in the CEMS data.  The CEMCorrect tool identifies hours for which the data were not measured.  When those values are found to be more than three times the annual mean for that unit, the data for those hours are replaced with annual mean values (Adelman et al., 2012).  These adjusted CEMS data were then used for the remainder of the temporal allocation process described below (see Figure 3-5 for an example).  Winter and summer seasons are included in the development of the diurnal profiles as opposed to using data for the entire year because analysis of the hourly CEMS data revealed that there were different diurnal patterns in winter versus summer in many areas.  Typically, a single mid-day peak is visible in the summer, while there are morning and evening peaks in the winter as shown in Figure 3-6. 

The temporal allocation procedure is differentiated by whether or not the source could be directly matched to a CEMS unit via ORIS facility code and boiler ID.  Note that for units matched to CEMS data, annual totals of their emissions input to CMAQ may be different than the annual values in 2016 because the CEMS data replaces the NOx and SO2 inventory data for the seasons in which the CEMS are operating.  If a CEMS-matched unit is determined to be a partial year reporter, as can happen for sources that run CEMS only in the summer, emissions totaling the difference between the annual emissions and the total CEMS emissions are allocated to the non-summer months.
Figure 3-5.  Eliminating unmeasured spikes in CEMS data
                                       
                                       
Figure 3-6.  Seasonal diurnal profiles for EGU emissions in a Virginia Region
                                       
                                       

For sources not matched to CEMS units, temporal profiles are calculated that are used by SMOKE to allocate the annual emissions to hourly values.  For these units, the allocation of the inventory annual emissions to months is done using average fuel-specific annual-to-month factors generated for regions with similar climate.  These factors are based on 2016 CEMS data only.  In each region, separate factors were developed for the fuels:  coal, natural gas, and "other," where the types of fuels included in "other" vary by region.  Separate profiles were computed for NOX, SO2, and heat input.  An overall composite profile was also computed and used when there were no CEMS units with the specified fuel in the region containing the unit.  For both CEMS-matched units and units not matched to CEMS, NOX and SO2 CEMS data are used to allocate NOX and SO2 emissions to monthly emissions, respectively, while heat input data are used to allocate emissions of all pollutants from monthly to daily emissions.
Daily temporal allocation of units matched to CEMS was performed using a procedure similar to the approach to allocate emissions to months in that the CEMS data replaces the inventory data for each pollutant.  For units without CEMS data, emissions were allocated from month to day using IPM-region and fuel-specific average month-to-day factors based on the 2016 CEMS heat data.  Separate month-to-day allocation factors were computed for each month of the year using heat input for the fuels coal, natural gas, and "other" in each region.  For CEMS matched units, NOX and SO2 CEMS data are used to replace inventory NOX and SO2 emissions, while CEMS heat input data are used to allocate all other pollutants.  

For units matched to CEMS data, hourly emissions use the hourly CEMS values for NOX and SO2, while other pollutants are allocated according to heat input values.  For units not matched to CEMS data, temporal profiles from days to hours are computed based on the season-, region- and fuel-specific average day-to-hour factors derived from the CEMS data for those fuels and regions using the appropriate subset of data.  For the unmatched units, CEMS heat input data are used to allocate all pollutants (including NOX and SO2) because the heat input data was generally found to be more complete than the pollutant-specific data.  SMOKE then allocates the daily emissions data to hours using the temporal profiles obtained from the CEMS data for the analysis base year (i.e., 2016 in this case).

Certain sources without CEMS data, such as specific municipal waste combustors (MWCs) and cogeneration facilities (cogens), were assigned a flat temporal profile by source. The emissions for these sources have an equal value for each hour of the year.

For additional information on EGU temporal allocation, please see the Point-EGU-IPM specification sheet provided with the 2016v1 platform.
 Airport Temporal allocation (airports)
Airport temporal profiles were updated in 2014v7.0 and were kept the same for the 2016v1 platform.  All airport SCCs (i.e., 2275*, 2265008005, 2267008005, 2268008005 and 2270008005) were given the same hourly, weekly and monthly profile for all airports other than Alaska seaplanes (which are not in the CMAQ modeling domain).  Hourly airport operations data were obtained from the Aviation System Performance Metrics (ASPM) Airport Analysis website (https://aspm.faa.gov/apm/sys/AnalysisAP.asp).  A report of 2014 hourly Departures and Arrivals for Metric Computation was generated.  An overview of the ASPM metrics is at http://aspmhelp.faa.gov/index.php/Aviation_Performance_Metrics_%28APM%29.  Figure 3-7 shows the diurnal airport profile.

Weekly and monthly temporal profiles are based on 2014 data from the FAA Operations Network Air Traffic Activity System (http://aspm.faa.gov/opsnet/sys/Terminal.asp).  A report of all airport operations (takeoffs and landings) by day for 2014 was generated.  These data were then summed to month and day-of-week to derive the monthly and weekly temporal profiles shown in Figure 3-7, Figure 3-8, and Figure 3-9.  An overview of the Operations Network data system is at http://aspmhelp.faa.gov/index.php/Operations_Network_%28OPSNET%29. 

Alaska seaplanes, which are outside the CONUS domain use the same monthly profile as in the 2011 platform shown in Figure 3-10.  These were assigned based on the facility ID.

Figure 3-7.  Diurnal Profile for all Airport SCCs
                                       

Figure 3-8.  Weekly profile for all Airport SCCs
                                       
Figure 3-9.  Monthly Profile for all Airport SCCs
                                       

Figure 3-10.  Alaska Seaplane Profile 
                                       
 Residential Wood Combustion Temporal allocation (rwc)
There are many factors that impact the timing of when emissions occur, and for some sectors this includes meteorology.  The benefits of utilizing meteorology as a method for temporal allocation are: (1) a meteorological dataset consistent with that used by the AQ model is available (e.g., outputs from WRF); (2) the meteorological model data are highly resolved in terms of spatial resolution; and (3) the meteorological variables vary at hourly resolution and can, therefore, be translated into hour-specific temporal allocation.

The SMOKE program Gentpro provides a method for developing meteorology-based temporal allocation.  Currently, the program can utilize three types of temporal algorithms: annual-to-day temporal allocation for residential wood combustion (RWC); month-to-hour temporal allocation for agricultural livestock NH3; and a generic meteorology-based algorithm for other situations.  Meteorological-based temporal allocation was used for portions of the rwc sector and for the entire ag sector.
 
Gentpro reads in gridded meteorological data (output from MCIP) along with spatial surrogates and uses the specified algorithm to produce a new temporal profile that can be input into SMOKE.  The meteorological variables and the resolution of the generated temporal profile (hourly, daily, etc.) depend on the selected algorithm and the run parameters.  For more details on the development of these algorithms and running Gentpro, see the Gentpro documentation and the SMOKE documentation at http://www.cmascenter.org/smoke/documentation/3.1/GenTPRO_TechnicalSummary_Aug2012_Final.pdf and https://www.cmascenter.org/smoke/documentation/4.5/html/ch05s03s05.html, respectively.

For the RWC algorithm, Gentpro uses the daily minimum temperature to determine the temporal allocation of emissions to days.  Gentpro was used to create an annual-to-day temporal profile for the RWC sources.  These generated profiles distribute annual RWC emissions to the coldest days of the year.  On days where the minimum temperature does not drop below a user-defined threshold, RWC emissions for most sources in the sector are zero.  Conversely, the program temporally allocates the largest percentage of emissions to the coldest days.  Similar to other temporal allocation profiles, the total annual emissions do not change, only the distribution of the emissions within the year is affected.  The temperature threshold for RWC emissions was 50 ˚F for most of the country, and 60 ˚F for the following states:  Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and Texas.

Figure 3-11 illustrates the impact of changing the temperature threshold for a warm climate county.  The plot shows the temporal fraction by day for Duval County, Florida, for the first four months of 2007.  The default 50 ˚F threshold creates large spikes on a few days, while the 60 ˚F threshold dampens these spikes and distributes a small amount of emissions to the days that have a minimum temperature between 50 and 60 ˚F.
Figure 3-11.  Example of RWC temporal allocation in 2007 using a 50 versus 60 ˚F threshold
                                       

The diurnal profile used for most RWC sources (see Figure 3-12) places more of the RWC emissions in the morning and the evening when people are typically using these sources. This profile is based on a 2004 MANE-VU survey based temporal profiles (https://s3.amazonaws.com/marama.org/wp-content/uploads/2019/11/04184303/Open_Burning_Residential_Areas_Emissions_Report-2004.pdf). This profile was created by averaging three indoor and three RWC outdoor temporal profiles from counties in Delaware and aggregating them into a single RWC diurnal profile. This new profile was compared to a concentration-based analysis of aethalometer measurements in Rochester, New York (Wang et al. 2011) for various seasons and days of the week and was found that the new RWC profile generally tracked the concentration based temporal patterns.
Figure 3-12.  RWC diurnal temporal profile
                                       

The temporal allocation for "Outdoor Hydronic Heaters" (i.e., "OHH," SCC=2104008610) and "Outdoor wood burning device, NEC (fire-pits, chimneas, etc.)" (i.e., "recreational RWC," SCC=21040087000) is not based on temperature data, because the meteorologically-based temporal allocation used for the rest of the rwc sector did not agree with observations for how these appliances are used.  
For OHH, the annual-to-month, day-of-week and diurnal profiles were modified based on information in the New York State Energy Research and Development Authority's (NYSERDA) "Environmental, Energy Market, and Health Characterization of Wood-Fired Hydronic Heater Technologies, Final Report" (NYSERDA, 2012), as well as a Northeast States for Coordinated Air Use Management (NESCAUM) report "Assessment of Outdoor Wood-fired Boilers" (NESCAUM, 2006).  A Minnesota 2008 Residential Fuelwood Assessment Survey of individual household responses (MDNR, 2008) provided additional annual-to-month, day-of-week, and diurnal activity information for OHH as well as recreational RWC usage.
Data used to create the diurnal profile for OHH, shown in Figure 3-13, are based on a conventional single-stage heat load unit burning red oak in Syracuse, New York.  As shown in Figure 3-14, the NESCAUM report describes how for individual units, OHH are highly variable day-to-day but that in the aggregate, these emissions have no day-of-week variation.  In contrast, the day-of-week profile for recreational RWC follows a typical "recreational" profile with emissions peaked on weekends.
Annual-to-month temporal allocation for OHH as well as recreational RWC were computed from the MDNR 2008 survey and are illustrated in Figure 3-15.  The OHH emissions still exhibit strong seasonal variability, but do not drop to zero because many units operate year-round for water and pool heating.  In contrast to all other RWC appliances, recreational RWC emissions are used far more frequently during the warm season.
Figure 3-13.  Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr)
                                       
Figure 3-14.  Day-of-week temporal profiles for OHH and Recreational RWC
                                       

Figure 3-15.  Annual-to-month temporal profiles for OHH and recreational RWC
                                       
 Agricultural Ammonia Temporal Profiles (ag)
For the agricultural livestock NH3 algorithm, the GenTPRO algorithm is based on an equation derived by Jesse Bash of the EPA's ORD based on the Zhu, Henze, et al. (2013) empirical equation.  This equation is based on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to estimate diurnal NH3 emission variations from livestock as a function of ambient temperature, aerodynamic resistance, and wind speed.  The equations are:
      Ei,h = [161500/Ti,h x e[(-1380/T]i,h[)]] x ARi,h
      PEi,h = Ei,h / Sum(Ei,h) 
where
 PEi,h = Percentage of emissions in county i on hour h
 Ei,h = Emission rate in county i on hour h
 Ti,h = Ambient temperature (Kelvin) in county i on hour h
 ARi,h = Aerodynamic resistance in county i
GenTPRO was run using the "BASH_NH3" profile method to create month-to-hour temporal profiles for these sources.  Because these profiles distribute to the hour based on monthly emissions, the monthly emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized to the month.  Figure 3-16 compares the daily emissions for Minnesota from the "old" approach (uniform monthly profile) with the "new" approach (GenTPRO generated month-to-hour profiles) for 2014.  Although the GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the same between the two approaches.
Figure 3-16.  Example of animal NH3 emissions temporal allocation approach, summed to daily emissions
                                       

For the 2016 platform, the GenTPRO approach is applied to all sources in the ag sector, NH3 and non- NH3, livestock and fertilizer.  Monthly profiles are based on the daily-based EPA livestock emissions and are the same as were used in 2014v7.0.  Profiles are by state/SCC_category, where SCC_category is one of the following: beef, broilers, layers, dairy, swine. 
 Oil and gas temporal allocation (np_oilgas)

Monthly oil and gas temporal profiles by county and SCC were updated to use 2016 activity information for the 2016v1 platform. Weekly and diurnal profiles are flat and are based on comments received on a version of the 2011 platform.
 Onroad mobile temporal allocation (onroad)
For the onroad sector, the temporal distribution of emissions is a combination of traditional temporal profiles and the influence of meteorology.  This section will discuss both the meteorological influences and the development of the temporal profiles for this platform.
The "inventories" referred to in Table 3-19 consist of activity data for the onroad sector, not emissions.  For the off-network emissions from the rate-per-profile (RPP) and rate-per-vehicle (RPV) processes, the VPOP activity data is annual and does not need temporal allocation.  For rate-per-hour (RPH) processes that result from hoteling of combination trucks, the HOTELING inventory is annual and was temporalized to month, day of the week, and hour of the day through temporal profiles.
For on-roadway rate-per-distance (RPD) processes, the VMT activity data is annual for some sources and monthly for other sources, depending on the source of the data.  Sources without monthly VMT were temporalized from annual to month through temporal profiles.  VMT was also temporalized from month to day of the week, and then to hourly through temporal profiles.  The RPD processes require a speed profile (SPDPRO) that consists of vehicle speed by hour for a typical weekday and weekend day.  For onroad, the temporal profiles and SPDPRO will impact not only the distribution of emissions through time but also the total emissions.  Because SMOKE-MOVES (for RPD) calculates emissions based on the VMT, speed and meteorology, if one shifted the VMT or speed to different hours, it would align with different temperatures and hence different emission factors.  In other words, two SMOKE-MOVES runs with identical annual VMT, meteorology, and MOVES emission factors, will have different total emissions if the temporal allocation of VMT changes.  Figure 3-17 illustrates the temporal allocation of the onroad activity data (i.e., VMT) and the pattern of the emissions that result after running SMOKE-MOVES.  In this figure, it can be seen that the meteorologically varying emission factors add variation on top of the temporal allocation of the activity data.
Meteorology is not used in the development of the temporal profiles, but rather it impacts the calculation of the hourly emissions through the program Movesmrg.  The result is that the emissions vary at the hourly level by grid cell.  More specifically, the on-network (RPD) and the off-network parked vehicle (RPV, RPH, and RPP) processes use the gridded meteorology (MCIP) either directly or indirectly.  For RPD, RPV, and RPH, Movesmrg determines the temperature for each hour and grid cell and uses that information to select the appropriate emission factor for the specified SCC/pollutant/mode combination.  For RPP, instead of reading gridded hourly meteorology, Movesmrg reads gridded daily minimum and maximum temperatures.  The total of the emissions from the combination of these four processes (RPD, RPV, RPH, and RPP) comprise the onroad sector emissions.  The temporal patterns of emissions in the onroad sector are influenced by meteorology.
Figure 3-17.  Example of temporal variability of NOX emissions

New VMT day-of-week and hour-of-day temporal profiles were developed for use in the 2014NEIv2 and later platforms as part of the effort to update the inputs to MOVES and SMOKE-MOVES under CRC A-100 (Coordinating Research Council, 2017). CRC A-100 data includes profiles by region or county, road type, and broad vehicle category. There are three vehicle categories: passenger vehicles (11/21/31), commercial trucks (32/52), and combination trucks (53/61/62). CRC A-100 does not cover buses, refuse trucks, or motor homes, so those vehicle types were mapped to other vehicle types for which CRC A-100 did provide profiles as follows: 1) Intercity/transit buses were mapped to commercial trucks; 2) Motor homes were mapped to passenger vehicles for day-of-week and commercial trucks for hour-of-day; 3) School buses and refuse trucks were mapped to commercial trucks for hour-of-day and use a new custom day-of-week profile called LOWSATSUN that has a very low weekend allocation, since school buses and refuse trucks operate primarily on business days.  In addition to temporal profiles, CRC A-100 data were also used to develop the average hourly speed data (SPDPRO) used by SMOKE-MOVES.  In areas where CRC A-100 data does not exist, hourly speed data is based on MOVES county databases.
The CRC A-100 dataset includes temporal profiles for individual counties, Metropolitan Statistical Areas (MSAs), and entire regions (e.g. West, South).  For counties without county or MSA temporal profiles specific to itself, regional temporal profiles are used.  Temporal profiles also vary by each of the MOVES road types, and there are distinct hour-of-day profiles for each day of the week.  Plots of hour-of-day profiles for passenger vehicles in Fulton County, GA, are shown in Figure 3-18.  Separate plots are shown for Monday, Friday, Saturday, and Sunday, and each line corresponds to a particular MOVES road type (i.e., road type 2 = rural restricted, 3 = rural unrestricted, 4 = urban restricted, and 5 = urban unrestricted).  Figure 3-19 shows which counties have temporal profiles specific to that county, and which counties use regional average profiles. 
Figure 3-18.  Sample onroad diurnal profiles for Fulton County, GA
                                       
Figure 3-19.  Counties for which MOVES Speeds and Temporal Profiles could be Populated
                                       

For hoteling, day-of-week profiles are the same as non-hoteling for combination trucks, while hour-of-day non-hoteling profiles for combination trucks were inverted to create new hoteling profiles that peak overnight instead of during the day.  The combination truck profiles for Fulton County are shown in Figure 3-20.

The CRC A-100 temporal profiles were used in the entire contiguous United States, except in California.  All California temporal profiles were carried over from 2014v7.0, although California hoteling uses CRC A-100-based profiles just like the rest of the country, since CARB didn't have a hoteling-specific profile. Monthly profiles in all states (national profiles by broad vehicle type) were also carried over from 2014v7.0 and applied directly to the VMT.  For California, CARB supplied diurnal profiles that varied by vehicle type, day of the week, and air basin.  These CARB-specific profiles were used in developing EPA estimates for California.  Although the EPA adjusted the total emissions to match California-submitted emissions for 2016, the temporal allocation of these emissions took into account both the state-specific VMT profiles and the SMOKE-MOVES process of incorporating meteorology.

Figure 3-20.  Example of Temporal Profiles for Combination Trucks


 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire)
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to reduce the total emissions based on meteorological conditions.  These adjustments are applied through sector-specific scripts, beginning with the application of land use-based gridded transport fractions and then subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the ground.  The land use data used to reduce the NEI emissions explains the amount of emissions that are subject to transport.  This methodology is discussed in (Pouliot et al., 2010), and in "Fugitive Dust Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012).  The precipitation adjustment is applied to remove all emissions for hours where measurable rain occurs, or where there is snow cover.  Therefore, the afdust emissions vary day-to-day based on the precipitation and/or snow cover for each grid cell and hour.  Both the transport fraction and meteorological adjustments are based on the gridded resolution of the platform; therefore, somewhat different emissions will result from different grid resolutions.  For this reason, to ensure consistency between grid resolutions, afdust emissions for the 36US3 grid are aggregated from the 12US1 emissions. Application of the transport fraction and meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid modeling as compared to ambient samples.

Biogenic emissions in the beis sector vary by every day of the year because they are developed using meteorological data including temperature, surface pressure, and radiation/cloud data.  The emissions are computed using appropriate emission factors according to the vegetation in each model grid cell, while taking the meteorological data into account.

For the cmv sectors, most areas use hourly emission inventories derived from the 5-minute AIS data.  In some areas where AIS data are not available, such as in Canada between the St. Lawrence Seaway and the Great Lakes and in the southern Carribbean, the flat temporal profiles are used for hourly and day-of-week values. Most regions without AIS data also use a flat monthly profile, with some offshore areas using an average monthly profile derived from the 2008 ECA inventory monthly values. These areas without AIS data also use flat day of week and hour of day profiles.

For the rail sector, new monthly profiles were developed for the 2016 platform.  Monthly temporal allocation for rail freight emissions is based on AAR Rail Traffic Data, Total Carloads and Intermodal, for 2016.  For passenger trains, monthly temporal allocation is flat for all months.  Rail passenger miles data is available by month for 2016 but it is not known how closely rail emissions track with passenger activity since passenger trains run on a fixed schedule regardless of how many passengers are aboard, and so a flat profile is chosen for passenger trains.  Rail emissions are allocated with flat day of week profiles, and most emissions are allocated with flat hourly profiles. 

For the ptagfire sector, the inventories are in the daily point fire format FF10 PTDAY. The diurnal temporal profile for ag fires reflects the fact that burning occurs during the daylight hours - see Figure 3-21 (McCarty et al., 2009).  This puts most of the emissions during the work day and suppresses the emissions during the middle of the night.  
Figure 3-21.  Agricultural burning diurnal temporal profile
                                       

Industrial processes that are not likely to shut down on Sundays, such as those at cement plants, use profiles that include emissions on Sundays, while those that would shut down on Sundays use profiles that reflect Sunday shutdowns.

For the ptfire sectors, the inventories are in the daily point fire format FF10 PTDAY.  Separate hourly profiles for prescribed and wildfires were used.  Figure 3-22 below shows the profiles used for each state for the 2014v7.0 and 2014v7.1 modeling platforms. They are similar but not the same and vary according to the average meteorological conditions in each state. The 2016 alpha platform uses the ptfire diurnal profiles form 2014v7.1 platform.
Figure 3-22.  Prescribed and Wildfire diurnal temporal profiles
 




For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly inventories from output from MOVES.  For California, CARB's annual inventory was temporalized to monthly using monthly temporal profiles applied in SMOKE by SCC.  This is an improvement over the 2011 platform, which applied monthly temporal allocation in California at the broader SCC7 level.

 Spatial Allocation
The methods used to perform spatial allocation are summarized in this section.  For the modeling platform, spatial factors are typically applied by county and SCC.  As described in Section 3.1, spatial allocation was performed for national 36-km and 12-km domains.  To accomplish this, SMOKE used national 36-km and 12-km spatial surrogates and a SMOKE area-to-point data file.  For the U.S., the EPA updated surrogates to use circa 2014 data wherever possible.  For Mexico, updated spatial surrogates were used as described below.  For Canada, updated surrogates were provided by Environment Canada for the 2016v7.2 platform.  The U.S., Mexican, and Canadian 36-km and 12-km surrogates cover the entire CONUS domain 12US1 shown in Figure 3-1. The 36US3 domain includes a portion of Alaska, and since Alaska emissions are typically not included in air quality modeling, special considerations are taken to include Alaska emissions in 36-km modeling.

Documentation of the origin of the spatial surrogates for the platform is provided in the workbook US_SpatialSurrogate_Workbook_v07172018 which is available with the reports for the 2014v7.1 platform. The remainder of this subsection summarizes the data used for the spatial surrogates and the area-to-point data which is used for airport refueling.
 Spatial Surrogates for U.S. emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions to the 36-km and 12-km grid cells used by the air quality model.  As described in Section 3.4.2, an area-to-point approach overrides the use of surrogates for an airport refueling sources.  Table 3-20 lists the codes and descriptions of the surrogates.  Surrogate names and codes listed in italics are not directly assigned to any sources for the 2016 alpha platform, but they are sometimes used to gapfill other surrogates, or as an input for merging two surrogates to create a new surrogate that is used. 
Many surrogates were updated or newly developed for use in the 2014v7.0 platform (Adelman, 2016). They include the use of the 2011 National Land Cover Database (the previous platform used 2006) and development of various development density levels such as open, low, medium high and various combinations of these.  These landuse surrogates largely replaced the FEMA category surrogates that were used in the 2011 platform.  Additionally, onroad surrogates were developed using average annual daily traffic counts from the highway monitoring performance system (HPMS).  Previously, the "activity" for the onroad surrogates was length of road miles.  This and other surrogates are described in a reference (Adelman, 2016). 
Several surrogates were updated or developed as new surrogates for the 2016v7.1 (aka alpha) platform:
 Oil and gas surrogates were updated to correct errors found after they were used for 2014v7.0; 
 Onroad spatial allocation uses surrogates that do not distinguish between urban and rural road types, correcting the issue arising in some counties due to the inconsistent urban and rural definitions between MOVES and the surrogate data and were further updated for the 2016v1 platform; 
 Correction was made to the water surrogate to gap fill missing counties using the 2006 National Land Cover Database (NLCD).
In addition, spatial surrogates 201 through 244, which concern road miles, annual average daily traffic (AADT), and truck stops, were further updated for the 2016 beta and regional haze platforms. The surrogates for the U.S. were mostly generated using the Surrogate Tool to drive the Spatial Allocator, but a few surrogates were developed directly within ArcGIS or using scripts that manipulate spatial data in PostgreSQL.  The tool and documentation for the Surrogate Tool is available at https://www.cmascenter.org/sa-tools/documentation/4.2/SurrogateToolUserGuide_4_2.pdf.

Table 3-20.  U.S. Surrogates available for the 2016v1 modeling platforms
                                                                           Code
Surrogate Description
                                                                           Code
Surrogate Description
                                                                            N/A
Area-to-point approach (see 3.6.2)
                                                                            506
Education
                                                                            100
Population
                                                                            507
Heavy Light Construction Industrial Land
                                                                            110
Housing
                                                                            510
Commercial plus Industrial
                                                                            131
urban Housing
                                                                            515
Commercial plus Institutional Land
                                                                            132
Suburban Housing
                                                                            520
Commercial plus Industrial plus Institutional
                                                                            134
Rural Housing
                                                                            525
Golf Courses plus Institutional plus Industrial plus Commercial
                                                                            137
Housing Change
                                                                            526
Residential  -  Non-Institutional
                                                                            140
Housing Change and Population
                                                                            527
Single Family Residential
                                                                            150
Residential Heating  -  Natural Gas
                                                                            535
Residential + Commercial + Industrial + Institutional + Government
                                                                            160
Residential Heating  -  Wood
                                                                            540
Retail Trade (COM1)
                                                                            170
Residential Heating  -  Distillate Oil
                                                                            545
Personal Repair (COM3)
                                                                            180
Residential Heating  -  Coal
                                                                            555
Professional/Technical (COM4) plus General Government (GOV1)
                                                                            190
Residential Heating  -  LP Gas
                                                                            560
Hospital (COM6)
                                                                            201
Urban Restricted Road Miles
                                                                            575
Light and High Tech Industrial (IND2 + IND5)
                                                                            202
Urban Restricted AADT
                                                                            580
Food Drug Chemical Industrial (IND3)
                                                                            205
Extended Idle Locations
                                                                            585
Metals and Minerals Industrial (IND4)
                                                                            211
Rural Restricted Road Miles
                                                                            590
Heavy Industrial (IND1)
                                                                            212
Rural Restricted AADT
                                                                            595
Light Industrial (IND2)
                                                                            221
Urban Unrestricted Road Miles
                                                                            596
Industrial plus Institutional plus Hospitals
                                                                            222
Urban Unrestricted AADT
                                                                            650
Refineries and Tank Farms
                                                                            231
Rural Unrestricted Road Miles
                                                                            670
Spud Count  -  CBM Wells
                                                                            232
Rural Unrestricted AADT
                                                                            671
Spud Count  -  Gas Wells
                                                                            239
Total Road AADT
                                                                            672
Gas Production at Oil Wells
                                                                            240
Total Road Miles
                                                                            673
Oil Production at CBM Wells
                                                                            241
Total Restricted Road Miles
                                                                            674
Unconventional Well Completion Counts
                                                                            242
All Restricted AADT
                                                                            676
Well Count  -  All Producing
                                                                            243
Total Unrestricted Road Miles
                                                                            677
Well Count  -  All Exploratory
                                                                            244
All Unrestricted AADT
                                                                            678
Completions at Gas Wells
                                                                            258
Intercity Bus Terminals
                                                                            679
Completions at CBM Wells
                                                                            259
Transit Bus Terminals
                                                                            681
Spud Count  -  Oil Wells
                                                                            260
Total Railroad Miles
                                                                            683
Produced Water at All Wells
                                                                            261
NTAD Total Railroad Density
                                                                            685
Completions at Oil Wells
                                                                            271
NTAD Class 1 2 3 Railroad Density
                                                                            686
Completions at All Wells
                                                                            272
NTAD Amtrak Railroad Density
                                                                            687
Feet Drilled at All Wells
                                                                            273
NTAD Commuter Railroad Density
                                                                            691
Well Counts -  CBM Wells
                                                                            275
ERTAC Rail Yards
                                                                            692
Spud Count  -  All Wells
                                                                            280
Class 2 and 3 Railroad Miles
                                                                            693
Well Count  -  All Wells
                                                                            300
NLCD Low Intensity Development
                                                                            694
Oil Production at Oil Wells
                                                                            301
NLCD Med Intensity Development
                                                                            695
Well Count  -  Oil Wells
                                                                            302
NLCD High Intensity Development
                                                                            696
Gas Production at Gas Wells
                                                                            303
NLCD Open Space
                                                                            697
Oil Production at Gas Wells
                                                                            304
NLCD Open + Low
                                                                            698
Well Count  -  Gas Wells
                                                                            305
NLCD Low + Med
                                                                            699
Gas Production at CBM Wells
                                                                            306
NLCD Med + High
                                                                            710
Airport Points
                                                                            307
NLCD All Development
                                                                            711
Airport Areas
                                                                            308
NLCD Low + Med + High
                                                                            801
Port Areas
                                                                            309
NLCD Open + Low + Med
                                                                            802
Shipping Lanes
                                                                            310
NLCD Total Agriculture
                                                                            805
Offshore Shipping Area
                                                                            318
NLCD Pasture Land
                                                                            806
Offshore Shipping NEI2014 Activity
                                                                            319
NLCD Crop Land
                                                                            807
Navigable Waterway Miles
                                                                            320
NLCD Forest Land
                                                                            808
2013 Shipping Density
                                                                            321
NLCD Recreational Land
                                                                            820
Ports NEI2014 Activity
                                                                            340
NLCD Land
                                                                            850
Golf Courses
                                                                            350
NLCD Water
                                                                            860
Mines
                                                                            500
Commercial Land
                                                                            890
Commercial Timber
                                                                            505
Industrial Land
                                                                               


For the onroad sector, the on-network (RPD) emissions were allocated differently from the off-network (RPP and RPV).  On-network used AADT data and off network used land use surrogates as shown in Table 3-21. Emissions from the extended (i.e., overnight) idling of trucks were assigned to surrogate 205, which is based on locations of overnight truck parking spaces. This surrogate's underlying data were updated for use in the 2016 platforms to include additional data sources and corrections based on comments received.
Table 3-21.  Off-Network Mobile Source Surrogates
                                  Source type
Source Type name
                                 Surrogate ID
                                  Description
                                      11
Motorcycle
                                      307
                             NLCD All Development
                                      21
Passenger Car
                                      307
                             NLCD All Development
                                      31
Passenger Truck
                                      307
                             NLCD All Development
                                      32
Light Commercial Truck
                                      308
                             NLCD Low + Med + High
                                      41
Intercity Bus
                                      258
                            Intercity Bus Terminals
                                      42
Transit Bus
                                      259
                             Transit Bus Terminals
                                      43
School Bus
                                      506
                                   Education
                                      51
Refuse Truck
                                      306
                                NLCD Med + High
                                      52
Single Unit Short-haul Truck
                                      306
                                NLCD Med + High
                                      53
Single Unit Long-haul Truck
                                      306
                                NLCD Med + High
                                      54
Motor Home
                                      304
                                NLCD Open + Low
                                      61
Combination Short-haul Truck
                                      306
                                NLCD Med + High
                                      62
Combination Long-haul Truck
                                      306
                                NLCD Med + High

For the oil and gas sources in the np_oilgas sector, the spatial surrogates were updated to those shown in Table 3-22 using 2016 data consistent with what was used to develop the 2016 beta nonpoint oil and gas emissions.  The primary activity data source used for the development of the oil and gas spatial
surrogates was data from Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2017).  This
database contains well-level location, production, and exploration statistics at the monthly level.
Due to a proprietary agreement with DI Desktop, individual well locations and ancillary
production cannot be made publicly available, but aggregated statistics are allowed.  These data were supplemented with data from state Oil and Gas Commission (OGC) websites (Illinois, Idaho, Indiana, Kentucky, Missouri, Nevada, Oregon and Pennsylvania, Tennessee).  In many cases, the correct surrogate parameter was not available (e.g., feet drilled), but an alternative surrogate parameter was available (e.g., number of spudded wells) and downloaded.  Under that methodology, both completion date and date of first production from HPDI were used to identify wells completed during 2016.  In total, over 1.43 million unique wells were compiled from the above data sources.  The wells cover 34 states and 1,158 counties. (ERG, 2016b). Corrections to these data were made for the 2014v7.1 platform, and carried forward into the 2016 platforms, after errors were discovered in some counties.
Table 3-22.  Spatial Surrogates for Oil and Gas Sources
                                Surrogate Code
                             Surrogate Description
                                      670
Spud Count - CBM Wells
                                      671
Spud Count - Gas Wells
                                      672
Gas Production at Oil Wells
                                      673
Oil Production at CBM Wells
                                      674
Unconventional Well Completion Counts
                                      676
Well Count - All Producing
                                      677
Well Count - All Exploratory
                                      678
Completions at Gas Wells
                                      679
Completions at CBM Wells
                                      681
Spud Count - Oil Wells
                                      683
Produced Water at All Wells
                                      685
Completions at Oil Wells
                                      686
Completions at All Wells
                                      687
Feet Drilled at All Wells
                                      691
Well Counts -  CBM Wells
                                      692
Spud Count - All Wells
                                      693
Well Count - All Wells
                                      694
Oil Production at Oil Wells
                                      695
Well Count - Oil Wells
                                      696
Gas Production at Gas Wells
                                      697
Oil Production at Gas Wells
                                      698
Well Count - Gas Wells
                                      699
Gas Production at CBM Wells

Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is, some surrogates shown in Table 3-20 were not assigned to any SCCs, although many of the "unused" surrogates are actually used to "gap fill" other surrogates that are used.  When the source data for a surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the emission inventories. Table 3-23 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by sector assigned to each spatial surrogate.
Table 3-23. Selected 2016 CAP emissions by sector for U.S. Surrogates (short tons in 12US1)
Sector
ID
Description
 NH3 
 NOX 
 PM2_5 
 SO2 
 VOC
afdust
                                                                            240
Total Road Miles
                                                                               
                                                                               
                                                                        294,379
                                                                               
                                                                               
afdust
                                                                            304
NLCD Open + Low
                                                                               
                                                                               
                                                                      1,053,145
                                                                               
                                                                               
afdust
                                                                            306
NLCD Med + High
                                                                               
                                                                               
                                                                         43,633
                                                                               
                                                                               
afdust
                                                                            308
NLCD Low + Med + High
                                                                               
                                                                               
                                                                        123,524
                                                                               
                                                                               
afdust
                                                                            310
NLCD Total Agriculture
                                                                               
                                                                               
                                                                        988,012
                                                                               
                                                                               
ag
                                                                            310
NLCD Total Agriculture
                                                                      3,409,761
                                                                               
                                                                               
                                                                               
                                                                        194,779
nonpt
                                                                            100
Population
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                      1,240,692
nonpt
                                                                            150
Residential Heating - Natural Gas
                                                                         42,973
                                                                        219,189
                                                                          3,632
                                                                          1,442
                                                                         13,296
nonpt
                                                                            170
Residential Heating - Distillate Oil
                                                                          1,563
                                                                         31,048
                                                                          3,356
                                                                         41,193
                                                                          1,051
nonpt
                                                                            180
Residential Heating - Coal
                                                                             20
                                                                            101
                                                                             53
                                                                          1,086
                                                                            111
nonpt
                                                                            190
Residential Heating - LP Gas
                                                                            111
                                                                         33,230
                                                                            175
                                                                            705
                                                                          1,292
nonpt
                                                                            239
Total Road AADT
                                                                              0
                                                                             25
                                                                            551
                                                                              0
                                                                        274,266
nonpt
                                                                            240
Total Road Miles
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         34,027
nonpt
                                                                            242
All Restricted AADT
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                          5,451
nonpt
                                                                            244
All Unrestricted AADT
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         96,232
nonpt
                                                                            271
NTAD Class 1 2 3 Railroad Density
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                          2,252
nonpt
                                                                            300
NLCD Low Intensity Development
                                                                          5,198
                                                                         27,727
                                                                        104,108
                                                                          3,722
                                                                         71,770
nonpt
                                                                            306
NLCD Med + High
                                                                         27,518
                                                                        180,692
                                                                        207,536
                                                                         62,698
                                                                        950,022
nonpt
                                                                            307
NLCD All Development
                                                                             25
                                                                         46,331
                                                                        126,722
                                                                         14,185
                                                                        601,828
nonpt
                                                                            308
NLCD Low + Med + High
                                                                          1,027
                                                                        171,603
                                                                         16,096
                                                                         13,527
                                                                         65,123
nonpt
                                                                            310
NLCD Total Agriculture
                                                                              0
                                                                              0
                                                                             37
                                                                              0
                                                                        204,819
nonpt
                                                                            319
NLCD Crop Land
                                                                              0
                                                                              0
                                                                             95
                                                                             71
                                                                            293
nonpt
                                                                            320
NLCD Forest Land
                                                                             69
                                                                            378
                                                                          1,289
                                                                              9
                                                                            474
nonpt
                                                                            505
Industrial Land
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                            174
nonpt
                                                                            535
Residential + Commercial + Industrial + Institutional + Government
                                                                              5
                                                                              2
                                                                            130
                                                                              0
                                                                             39
nonpt
                                                                            560
Hospital (COM6)
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                              0
nonpt
                                                                            650
Refineries and Tank Farms
                                                                              0
                                                                             22
                                                                              0
                                                                              0
                                                                         99,564
nonpt
                                                                            711
Airport Areas
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                            271
nonpt
                                                                            801
Port Areas
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                          8,194
nonroad
                                                                            261
NTAD Total Railroad Density
                                                                              3
                                                                          2,154
                                                                            227
                                                                              2
                                                                            425
nonroad
                                                                            304
NLCD Open + Low
                                                                              4
                                                                          1,824
                                                                            159
                                                                              5
                                                                          2,727
nonroad
                                                                            305
NLCD Low + Med
                                                                             94
                                                                         15,985
                                                                          3,832
                                                                            126
                                                                        114,513
nonroad
                                                                            306
NLCD Med + High
                                                                            305
                                                                        183,591
                                                                         11,873
                                                                            421
                                                                         93,596
nonroad
                                                                            307
NLCD All Development
                                                                             99
                                                                         31,526
                                                                         15,340
                                                                            125
                                                                        169,943
nonroad
                                                                            308
NLCD Low + Med + High
                                                                            498
                                                                        338,083
                                                                         28,585
                                                                            487
                                                                         51,865
nonroad
                                                                            309
NLCD Open + Low + Med
                                                                            119
                                                                         21,334
                                                                          1,257
                                                                            162
                                                                         45,498
nonroad
                                                                            310
NLCD Total Agriculture
                                                                            422
                                                                        378,388
                                                                         28,387
                                                                            425
                                                                         40,707
nonroad
                                                                            320
NLCD Forest Land
                                                                             15
                                                                          5,910
                                                                            703
                                                                             15
                                                                          3,939
nonroad
                                                                            321
NLCD Recreational Land
                                                                             83
                                                                         11,616
                                                                          6,517
                                                                            104
                                                                        246,154
nonroad
                                                                            350
NLCD Water
                                                                            188
                                                                        115,175
                                                                          5,952
                                                                            240
                                                                        353,189
nonroad
                                                                            850
Golf Courses
                                                                             13
                                                                          2,001
                                                                            117
                                                                             18
                                                                          5,613
nonroad
                                                                            860
Mines
                                                                              2
                                                                          2,691
                                                                            281
                                                                              3
                                                                            521
np_oilgas
                                                                            670
Spud Count - CBM Wells
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                            112
np_oilgas
                                                                            671
Spud Count - Gas Wells
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                          6,284
np_oilgas
                                                                            674
Unconventional Well Completion Counts
                                                                             12
                                                                         18,802
                                                                            720
                                                                              9
                                                                          1,264
np_oilgas
                                                                            678
Completions at Gas Wells
                                                                              0
                                                                          5,315
                                                                            136
                                                                          2,488
                                                                         16,615
np_oilgas
                                                                            679
Completions at CBM Wells
                                                                              0
                                                                              3
                                                                              0
                                                                             80
                                                                            395
np_oilgas
                                                                            681
Spud Count - Oil Wells
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         15,164
np_oilgas
                                                                            683
Produced Water at All Wells
                                                                              0
                                                                             11
                                                                              0
                                                                              0
                                                                         47,271
np_oilgas
                                                                            685
Completions at Oil Wells
                                                                              0
                                                                            255
                                                                              0
                                                                            769
                                                                         27,935
np_oilgas
                                                                            687
Feet Drilled at All Wells
                                                                              0
                                                                         36,162
                                                                          1,309
                                                                             22
                                                                          2,664
np_oilgas
                                                                            691
Well Counts -  CBM Wells
                                                                              0
                                                                         32,971
                                                                            490
                                                                             13
                                                                         27,566
np_oilgas
                                                                            693
Well Count - All Wells
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                            159
np_oilgas
                                                                            694
Oil Production at Oil Wells
                                                                              0
                                                                          4,165
                                                                              0
                                                                         15,385
                                                                      1,062,178
np_oilgas
                                                                            695
Well Count - Oil Wells
                                                                              0
                                                                        134,921
                                                                          2,953
                                                                             32
                                                                        566,235
np_oilgas
                                                                            696
Gas Production at Gas Wells
                                                                              0
                                                                         16,339
                                                                          1,847
                                                                            164
                                                                        428,206
np_oilgas
                                                                            698
Well Count - Gas Wells
                                                                              0
                                                                        320,688
                                                                          6,217
                                                                            258
                                                                        582,442
np_oilgas
                                                                            699
Gas Production at CBM Wells
                                                                              0
                                                                          2,413
                                                                            312
                                                                             25
                                                                          7,602
onroad
                                                                            205
Extended Idle Locations
                                                                            230
                                                                         78,126
                                                                            794
                                                                             36
                                                                         13,711
onroad
                                                                            239
Total Road AADT
                                                                               
                                                                               
                                                                               
                                                                               
                                                                          5,755
onroad
                                                                            242
All Restricted AADT
                                                                         34,545
                                                                      1,175,197
                                                                         38,140
                                                                          8,744
                                                                        194,836
onroad
                                                                            244
All Unrestricted AADT
                                                                         65,543
                                                                      1,773,993
                                                                         67,525
                                                                         17,788
                                                                        477,839
onroad
                                                                            258
Intercity Bus Terminals
                                                                               
                                                                            147
                                                                              2
                                                                              0
                                                                             34
onroad
                                                                            259
Transit Bus Terminals
                                                                               
                                                                             53
                                                                              3
                                                                              0
                                                                            149
onroad
                                                                            304
NLCD Open + Low
                                                                               
                                                                            829
                                                                             29
                                                                              1
                                                                          3,874
onroad
                                                                            306
NLCD Med + High
                                                                               
                                                                         15,209
                                                                            333
                                                                             17
                                                                         19,917
onroad
                                                                            307
NLCD All Development
                                                                               
                                                                        546,312
                                                                         10,195
                                                                            910
                                                                      1,073,380
onroad
                                                                            308
NLCD Low + Med + High
                                                                               
                                                                         40,054
                                                                            722
                                                                             62
                                                                         62,127
onroad
                                                                            506
Education
                                                                               
                                                                            629
                                                                             15
                                                                              1
                                                                            637
rail
                                                                            261
NTAD Total Railroad Density
                                                                             13
                                                                         33,389
                                                                            996
                                                                             15
                                                                          1,647
rail
                                                                            271
NTAD Class 1 2 3 Railroad Density
                                                                            313
                                                                        525,992
                                                                         14,823
                                                                            442
                                                                         24,435
rwc
                                                                            300
NLCD Low Intensity Development
                                                                         15,439
                                                                         31,282
                                                                        316,943
                                                                          7,703
                                                                        340,941

For 36US3 modeling in the 2016 platforms, most U.S. emissions sectors were processed using 36-km spatial surrogates, and if applicable, 36-km meteorology. Exceptions include:
 For the onroad and onroad_ca_adj sectors, 36US3 emissions were aggregated from 12US1 by summing emissions from a 3x3 group of 12-km cells into a single 36-km cell.  Differences in 12-km and 36-km meteorology can introduce differences in onroad emissions, and so this approach ensures that the 36-km and 12-km onroad emissions are consistent. However, this approach means that 36US3 onroad does not include emissions in Southeast Alaska; therefore, Alaska onroad emissions are included in a separate sector called onroad_nonconus that is processed for only the 36US3 domain. The 36US3 onroad_nonconus emissions are spatially allocated using 36-km surrogates and processed with 36-km meteorology.
 Similarly to onroad, because afdust emissions incorporate meteorologically-based adjustments, afdust_adj emissions for 36US3 were aggregated from 12US1 to ensure consistency in emissions between modeling domains. Again, similarly to onroad, this means 36US3 afdust does not include emissions in Southeast Alaska; therefore, Alaska afdust emissions are processed in a separate sector called afdust_ak_adj. The 36US3 afdust_ak_adj emissions are spatially allocated using 36-km surrogates and adjusted with 36-km meteorology.
 The ag and rwc sectors are processed using 36-km spatial surrogates, but using temporal profiles based on 12-km meteorology.

 Allocation method for airport-related sources in the U.S. 
There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground support equipment, and jet refueling.  The modeling platform includes the aircraft and airport ground support equipment emissions as point sources.  For the modeling platform, the EPA used the SMOKE "area-to-point" approach for only jet refueling in the nonpt sector.  The following SCCs use this approach: 2501080050 and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/rocket engine firing and testing).  The ARTOPNT approach is described in detail in the 2002 platform documentation:  http://www3.epa.gov/scram001/reports/Emissions%20TSD%20Vol1_02-28-08.pdf.  The ARTOPNT file that lists the nonpoint sources to locate using point data were unchanged from the 2005-based platform.  
 Surrogates for Canada and Mexico emission inventories
Spatial surrogates for allocating Mexico municipio level emissions have been updated in the 2014v7.1 platform and carried forward into the 2016 alpha platform. For the 2016v7.2 platform, a new set of Canada shapefiles were provided by Environment Canada along with cross references spatially allocate the year 2015 Canadian emissions. Gridded surrogates were generated using the Surrogate Tool (previously referenced); Table 3-24 provides a list.  Due to computational reasons, total roads (1263) were used instead of the unpaved rural road surrogate provided.  The population surrogate was recently updated for Mexico; surrogate code 11, which uses 2015 population data at 1 km resolution, replaces the previous population surrogate code 10.  The other surrogates for Mexico are circa 1999 and 2000 and were based on data obtained from the Sistema Municipal de Bases de Datos (SIMBAD) de INEGI and the Bases de datos del Censo Economico 1999. Most of the CAPs allocated to the Mexico and Canada surrogates are shown in Table 3-25.  
Table 3-24.  Canadian Spatial Surrogates 
Code
Canadian Surrogate Description
Code
Description
100
                                  Population
                                      923
                      TOTAL INSTITUTIONAL AND GOVERNEMNT
101
                                total dwelling
                                      924
                               Primary Industry
104
                             capped total dwelling
                                      925
                          Manufacturing and Assembly
106
                                  ALL_INDUST
                                      926
                    Distribution and Retail (no petroleum)
113
                             Forestry and logging
                                      927
                              Commercial Services
200
                           Urban Primary Road Miles
                                      932
                                    CANRAIL
210
                           Rural Primary Road Miles
                                      940
                                PAVED ROADS NEW
211
                            Oil and Gas Extraction
                                      945
                           Commercial Marine Vessels
212
                           Mining except oil and gas
                                      946
                            Construction and mining
220
                          Urban Secondary Road Miles
                                      948
                                    Forest
221
                                 Total Mining
                                      951
                          Wood Consumption Percentage
222
                                   Utilities
                                      955
                           UNPAVED_ROADS_AND_TRAILS
230
                          Rural Secondary Road Miles
                                      960
                                    TOTBEEF
233
                            Total Land Development
                                      970
                                    TOTPOUL
240
                               capped population
                                      980
                                    TOTSWIN
308
                              Food manufacturing
                                      990
                                    TOTFERT
321
                          Wood product manufacturing
                                      996
                                  urban_area
323
                    Printing and related support activities
                                     1251
                                 OFFR_TOTFERT
324
                   Petroleum and coal products manufacturing
                                     1252
                                  OFFR_MINES
326
                  Plastics and rubber products manufacturing
                                     1253
                       OFFR Other Construction not Urban
327
                  Non-metallic mineral product manufacturing
                                     1254
                           OFFR Commercial Services
331
                          Primary Metal Manufacturing
                                     1255
                             OFFR Oil Sands Mines
350
                                     Water
                                     1256
                          OFFR Wood industries CANVEC
412
                   Petroleum product wholesaler-distributors
                                     1257
                           OFFR UNPAVED ROADS RURAL
448
                   clothing and clothing accessories stores
                                     1258
                                OFFR_Utilities
482
                              Rail transportation
                                     1259
                              OFFR total dwelling
562
                   Waste management and remediation services
                                     1260
                                  OFFR_water
901
                                    AIRPORT
                                     1261
                                OFFR_ALL_INDUST
902
                                 Military LTO
                                     1262
                          OFFR Oil and Gas Extraction
903
                                Commercial LTO
                                     1263
                                 OFFR_ALLROADS
904
                             General Aviation LTO
                                     1265
                                 OFFR_CANRAIL
921
                          Commercial Fuel Combustion
                                     9450
                        Commercial Marine Vessel Ports

Table 3-25. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3)
Sector
Code
Mexican or Canadian Surrogate Description
                                      NH3
                                      NOX
                                    PM 2_5
                                      SO2
                                      VOC
                                                                      othafdust
                                                                            106
CAN ALL_INDUST
                                                                             --
                                                                             --
                                                                          5,632
                                                                             --
                                                                             --
                                                                      othafdust
                                                                            212
CAN Mining except oil and gas
                                                                             --
                                                                             --
                                                                            684
                                                                             --
                                                                             --
                                                                      othafdust
                                                                            221
CAN Total Mining
                                                                             --
                                                                             --
                                                                        142,940
                                                                             --
                                                                             --
                                                                      othafdust
                                                                            222
CAN Utilities
                                                                             --
                                                                             --
                                                                         23,640
                                                                             --
                                                                             --
                                                                      othafdust
                                                                            940
CAN Paved Roads New
                                                                             --
                                                                             --
                                                                        210,336
                                                                             --
                                                                             --
                                                                      othafdust
                                                                            955
CAN UNPAVED_ROADS_AND_TRAILS
                                                                             --
                                                                             --
                                                                        389,775
                                                                             --
                                                                             --
                                                                      othafdust
                                                                            960
CAN TOTBEEF
                                                                             --
                                                                             --
                                                                          1,289
                                                                             --
                                                                             --
                                                                      othafdust
                                                                            970
CAN TOTPOUL
                                                                             --
                                                                             --
                                                                            184
                                                                             --
                                                                             --
                                                                      othafdust
                                                                            980
CAN TOTSWIN
                                                                             --
                                                                             --
                                                                            792
                                                                             --
                                                                             --
                                                                      othafdust
                                                                            990
CAN TOTFERT
                                                                             --
                                                                             --
                                                                            321
                                                                             --
                                                                             --
                                                                      othafdust
                                                                            996
CAN urban_area
                                                                             --
                                                                             --
                                                                            617
                                                                             --
                                                                             --
                                                                          othar
                                                                             11
MEX 2015 Population
                                                                        164,464
                                                                        168,447
                                                                         13,521
                                                                          1,164
                                                                        291,178
                                                                          othar
                                                                             14
MEX Residential Heating - Wood
                                                                              0
                                                                         23,842
                                                                        305,597
                                                                          3,658
                                                                      2,101,033
                                                                          othar
                                                                             16
MEX Residential Heating - Distillate Oil
                                                                              2
                                                                             58
                                                                              1
                                                                             16
                                                                              2
                                                                          othar
                                                                             20
MEX Residential Heating - LP Gas
                                                                              0
                                                                         26,526
                                                                            838
                                                                              0
                                                                            505
                                                                          othar
                                                                             22
MEX Total Road Miles
                                                                              1
                                                                          1,046
                                                                              2
                                                                              7
                                                                          2,308
                                                                          othar
                                                                             24
MEX Total Railroads Miles
                                                                              0
                                                                         63,136
                                                                          1,407
                                                                            551
                                                                          2,494
                                                                          othar
                                                                             26
MEX Total Agriculture
                                                                        713,253
                                                                        399,070
                                                                         80,458
                                                                         18,650
                                                                         33,742
                                                                          othar
                                                                             32
MEX Commercial Land
                                                                              0
                                                                            457
                                                                          7,719
                                                                              0
                                                                        106,077
                                                                          othar
                                                                             34
MEX Industrial Land
                                                                              8
                                                                          3,383
                                                                          4,833
                                                                              1
                                                                        563,953
                                                                          othar
                                                                             36
MEX Commercial plus Industrial Land
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                        272,155
                                                                          othar
                                                                             38
MEX Commercial plus Institutional Land
                                                                              3
                                                                          6,740
                                                                            235
                                                                              3
                                                                            148
                                                                          othar
                                                                             40
MEX Residential (RES1-4)+Commercial+ Industrial+Institutional+Government
                                                                              0
                                                                             16
                                                                             39
                                                                              0
                                                                        331,216
                                                                          othar
                                                                             42
MEX Personal Repair (COM3)
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         26,261
                                                                          othar
                                                                             44
MEX Airports Area
                                                                              0
                                                                         13,429
                                                                            306
                                                                          1,561
                                                                          3,766
                                                                          othar
                                                                             50
MEX Mobile sources - Border Crossing
                                                                              5
                                                                            161
                                                                              1
                                                                              3
                                                                            293
                                                                          othar
                                                                            100
CAN Population
                                                                            761
                                                                             54
                                                                            669
                                                                             15
                                                                            241
                                                                          othar
                                                                            101
CAN total dwelling
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                        150,892
                                                                          othar
                                                                            104
CAN Capped Total Dwelling
                                                                            421
                                                                         37,205
                                                                          2,766
                                                                            206
                                                                          1,952
                                                                          othar
                                                                            113
CAN Forestry and logging
                                                                            185
                                                                          2,210
                                                                         11,310
                                                                             45
                                                                          6,246
                                                                          othar
                                                                            211
CAN Oil and Gas Extraction
                                                                              0
                                                                             31
                                                                             60
                                                                             22
                                                                            925
                                                                          othar
                                                                            212
CAN Mining except oil and gas
                                                                              0
                                                                              0
                                                                          3,079
                                                                              0
                                                                              0
                                                                          othar
                                                                            221
CAN Total Mining
                                                                              0
                                                                              0
                                                                             43
                                                                              0
                                                                              0
                                                                          othar
                                                                            222
CAN Utilities
                                                                             34
                                                                          1,858
                                                                              0
                                                                            386
                                                                             22
                                                                          othar
                                                                            308
CAN Food manufacturing
                                                                              0
                                                                              0
                                                                         20,185
                                                                              0
                                                                         10,324
                                                                          othar
                                                                            321
CAN Wood product manufacturing
                                                                            874
                                                                          4,822
                                                                          1,646
                                                                            383
                                                                         16,606
                                                                          othar
                                                                            323
CAN Printing and related support activities
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         11,770
                                                                          othar
                                                                            324
CAN Petroleum and coal products manufacturing
                                                                              0
                                                                          1,205
                                                                          1,542
                                                                            486
                                                                          9,304
                                                                          othar
                                                                            326
CAN Plastics and rubber products manufacturing
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         23,283
                                                                          othar
                                                                            327
CAN Non-metallic mineral product manufacturing
                                                                              0
                                                                              0
                                                                          6,695
                                                                              0
                                                                              0
                                                                          othar
                                                                            331
CAN Primary Metal Manufacturing
                                                                              0
                                                                            158
                                                                          5,595
                                                                             30
                                                                             72
                                                                          othar
                                                                            350
CAN Water
                                                                              0
                                                                            120
                                                                              2
                                                                              0
                                                                              4
                                                                          othar
                                                                            412
CAN Petroleum product wholesaler-distributors
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         45,257
                                                                          othar
                                                                            448
CAN clothing and clothing accessories stores
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                            149
                                                                          othar
                                                                            482
CAN Rail Transportation
                                                                              2
                                                                          4,980
                                                                            106
                                                                             12
                                                                            310
                                                                          othar
                                                                            562
CAN Waste management and remediation services
                                                                            271
                                                                          1,977
                                                                          2,710
                                                                          2,528
                                                                         13,138
                                                                          othar
                                                                            901
CAN Airport
                                                                              0
                                                                            109
                                                                             11
                                                                              0
                                                                             11
                                                                          othar
                                                                            921
CAN Commercial Fuel Combustion
                                                                            243
                                                                         23,628
                                                                          2,333
                                                                          2,821
                                                                          1,091
                                                                          othar
                                                                            923
CAN TOTAL INSTITUTIONAL AND GOVERNEMNT
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         14,859
                                                                          othar
                                                                            924
CAN Primary Industry
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         40,376
                                                                          othar
                                                                            925
CAN Manufacturing and Assembly
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         71,198
                                                                          othar
                                                                            926
CAN Distribution and Retail (no petroleum)
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                          7,461
                                                                          othar
                                                                            927
CAN Commercial Services
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                         32,167
                                                                          othar
                                                                            932
CAN CANRAIL
                                                                             61
                                                                        132,985
                                                                          3,107
                                                                            485
                                                                          6,567
                                                                          othar
                                                                            946
CAN Construction and Mining
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                          4,359
                                                                          othar
                                                                            951
CAN Wood Consumption Percentage
                                                                          1,950
                                                                         21,662
                                                                        179,087
                                                                          3,095
                                                                        253,523
                                                                          othar
                                                                            990
CAN TOTFERT
                                                                             48
                                                                          4,456
                                                                              0
                                                                          9,881
                                                                            164
                                                                          othar
                                                                           1251
CAN OFFR_TOTFERT
                                                                             81
                                                                         77,166
                                                                          5,671
                                                                             58
                                                                          7,176
                                                                          othar
                                                                           1252
CAN OFFR_MINES
                                                                              1
                                                                          1,004
                                                                             70
                                                                              1
                                                                            138
                                                                          othar
                                                                           1253
CAN OFFR Other Construction not Urban
                                                                             66
                                                                         53,671
                                                                          6,096
                                                                             47
                                                                         12,159
                                                                          othar
                                                                           1254
CAN OFFR Commercial Services
                                                                             40
                                                                         17,791
                                                                          2,552
                                                                             34
                                                                         44,338
                                                                          othar
                                                                           1255
CAN OFFR Oil Sands Mines
                                                                             18
                                                                          9,491
                                                                            311
                                                                             10
                                                                          1,025
                                                                          othar
                                                                           1256
CAN OFFR Wood industries CANVEC
                                                                              9
                                                                          5,856
                                                                            476
                                                                              7
                                                                          1,318
                                                                          othar
                                                                           1257
CAN OFFR Unpaved Roads Rural
                                                                             32
                                                                         11,866
                                                                          1,169
                                                                             28
                                                                         49,975
                                                                          othar
                                                                           1258
CAN OFFR_Utilities
                                                                              8
                                                                          5,579
                                                                            349
                                                                              7
                                                                          1,087
                                                                          othar
                                                                           1259
CAN OFFR total dwelling
                                                                             16
                                                                          5,768
                                                                            773
                                                                             14
                                                                         15,653
                                                                          othar
                                                                           1260
CAN OFFR_water
                                                                             15
                                                                          4,356
                                                                            451
                                                                             29
                                                                         28,411
                                                                          othar
                                                                           1261
CAN OFFR_ALL_INDUST
                                                                              4
                                                                          5,770
                                                                            253
                                                                              3
                                                                          1,049
                                                                          othar
                                                                           1262
CAN OFFR Oil and Gas Extraction
                                                                              0
                                                                            368
                                                                             29
                                                                              0
                                                                            143
                                                                          othar
                                                                           1263
CAN OFFR_ALLROADS
                                                                              3
                                                                          2,418
                                                                            244
                                                                              2
                                                                            582
                                                                          othar
                                                                           1265
CAN OFFR_CANRAIL
                                                                              0
                                                                             85
                                                                              9
                                                                              0
                                                                             15
                                                                    onroad_
can
                                                                            200
CAN Urban Primary Road Miles
                                                                          1,619
                                                                         85,558
                                                                          2,851
                                                                            329
                                                                          8,396
                                                                    onroad_ can
                                                                            210
CAN Rural Primary Road Miles
                                                                            683
                                                                         51,307
                                                                          1,673
                                                                            139
                                                                          3,807
                                                                    onroad_ can
                                                                            220
CAN Urban Secondary Road Miles
                                                                          3,021
                                                                        136,582
                                                                          5,708
                                                                            690
                                                                         22,374
                                                                    onroad_ can
                                                                            230
CAN Rural Secondary Road Miles
                                                                          1,769
                                                                         96,911
                                                                          3,238
                                                                            374
                                                                         10,370
                                                                    onroad_ can
                                                                            240
CAN Total Road Miles
                                                                             43
                                                                         57,401
                                                                          1,355
                                                                             77
                                                                        103,658
                                                                    onroad_ mex
                                                                             11
MEX 2015 Population
                                                                              0
                                                                        281,317
                                                                          1,873
                                                                            533
                                                                        291,992
                                                                    onroad_
mex
                                                                             22
MEX Total Road Miles
                                                                         10,321
                                                                      1,208,461
                                                                         54,823
                                                                         25,855
                                                                        251,931
                                                                    onroad_
mex
                                                                             36
MEX Commercial plus Industrial Land
                                                                              0
                                                                          7,975
                                                                            142
                                                                             29
                                                                          9,192

 Preparation of Emissions for the CAMx model
 Development of CAMx Emissions for Standard CAMx Runs
To perform air quality modeling with the Comprehensive Air Quality Model with Extensions (CAMx model), the gridded hourly emissions output by the SMOKE model are output in the format needed by the CMAQ model, but must be converted to the format required by CAMx. For "regular" CAMx modeling (i.e., without two-way nesting), the CAMx conversion process consists of the following:

 Convert all emissions file formats from the I/O API NetCDF format used by CMAQ to the UAM format used by CAMx, including the merged, gridded low-level emissions files that include biogenics
 Shift hourly emissions files from the 25 hour format used by CMAQ to the averaged 24 hour format used by CAMx
 Rename and aggregate model species for CAMx
 Convert 3D wildland and agricultural fire emissions into CAMx point format
 Merge all inline point source emissions files together for each day, including layered fire emissions originally from SMOKE
 Add sea salt aerosol emissions to the converted, gridded low-level emissions files

Conversion of file formats from I/O API to UAM is performed using a program called "cmaq2uam". In the CAMx conversion process, all SMOKE outputs are passed through this step first. Unlike CMAQ, the CAMx model does not have an inline biogenics option, and so for the purposes of CAMx modeling, emissions from SMOKE must include biogenic emissions.

One difference between CMAQ-ready emissions files and CAMx-ready emissions files involves hourly temporalization. A daily emissions file for CMAQ includes data for 25 hours, where the first hour is 0:00 GMT of a given day, and the last hour is 0:00 GMT of the following day. For the CAMx model, a daily emissions file must only include data for 24 hours, not 25. Furthermore, to match the hourly configuration expected by CAMx, each set of consecutive hourly timesteps from CMAQ-ready emissions files must be averaged. For example, the first hour of a CAMx-ready emissions file will equal the average of the first two hours from the corresponding CMAQ-ready emissions file, and the last (24[th]) hour of a CAMx-ready emissions file will equal the average of the last two hours (24[th] and 25[th]) from the corresponding CMAQ-ready emissions file. This time conversion is incorporated into each step of the CAMx-ready emissions conversion process.

The CAMx model uses a slightly different version of the CB6 speciation mechanism than does the CMAQ model. SMOKE prepares emissions files for the CB6 mechanism used by the CMAQ model ("CB6-CMAQ"), and therefore, the emissions must be converted to the CB6 mechanism used by the CAMx model ("CB6-CAMx") during the CAMx conversion process. In addition to the mechanism differences, CMAQ and CAMx also occasionally use different species naming conventions. For CAMx modeling, we also create additional tracer species. A summary of the differences between CMAQ input species and CAMx input species for CB6 (VOC), AE6 (PM2.5), and other model species, is provided in Table 3-26. Each step of the CAMx-ready emissions conversion process includes conversion of CMAQ species to CAMx species using a species mapping table which includes the mappings in Table 3-26.

Table 3-26. Emission model species mappings for CMAQ and CAMx
Inventory Pollutant
CMAQ Model Species
CAMx Model Species
Cl2
CL2
CL2
HCl
HCL
HCL
CO
CO
CO
NOX
NO 
NO

NO2 
NO2

HONO
HONO
SO2
SO2 
SO2

SULF  
SULF
NH3
NH3
NH3

NH3_FERT   
n/a (not used in CAMx)
VOC
ACET
ACET

ALD2  
ALD2

ALDX  
ALDX

BENZ
BENZ and BNZA (duplicate species)

CH4
CH4

ETH   
ETH

ETHA  
ETHA

ETHY
ETHY

ETOH  
ETOH

FORM  
FORM

IOLE  
IOLE

ISOP  
ISOP and ISP (duplicate species)

KET
KET

MEOH  
MEOH

NAPH + XYLMN (sum)
XYL

NVOL
n/a (not used in CAMx)

OLE   
OLE

PAR   
PAR

PRPA
PRPA

SESQ
SQT

SOAALK
n/a (not used in CAMx)

TERP
TERP and TRP (duplicate species)

TOL   
TOL and TOLA (duplicate species)

UNR + NR (sum)
NR
PM10
PMC
CPRM
PM2.5
PEC   
PEC

PNO3  
PNO3

POC
POC

PSO4  
PSO4

PAL
PAL

PCA
PCA

PCL
PCL

PFE
PFE

PK
PK

PH2O
PH2O

PMG
PMG

PMN
PMN

PMOTHR
PMOTHR and FPRM (duplicate species)

PNA
NA

PNCOM
PNCOM

PNH4
PNH4

PSI
PSI

PTI
PTI

POC + PNCOM (sum)
POA[1]

PAL + PCA + PFE + PMG + PK + PMN + PSI + PTI (sum)
FCRS[1]
[1] The POA species, which is the sum of POC and PNCOM, is passed to the CAMx model in addition to individual species POC and PNCOM. The FCRS species, which is also a sum of multiple PM species, is passed to CAMx in addition to each of the individual component species.

One feature which is part of CMAQ and is not part of CAMx involves plume rise for fires. For CMAQ modeling, we process fire emissions through SMOKE as inline point sources, and plume rise for fires is calculated within CMAQ using parameters from the inline emissions files (heat flux, etc). This is similar to how non-fire point sources are handled, except that the fire parameters are used to calculate plume rise instead of traditional stack parameters. The CAMx model supports inline plume rise calculations using traditional stack parameters, but, does not support inline plume rise for fire sources. Therefore, for the purposes of CAMx modeling, we must have SMOKE calculate plume rise for fires using the Laypoint program. In this modeling platform, this must be done for the ptfire, ptfire_othna, and ptagfire sectors. To distinguish these layered fire emissions from inline fire emissions, layered fire emissions are processed with the sector names "ptfire3D", "ptfire_othna3D", and "ptagfire3D". When converting layered fire emissions files to CAMx format, stack parameters are added to the CAMx-ready fire emissions files to force the correct amount of fire emissions into each layer for each fire location. 

CMAQ modeling uses one gridded low-level emissions file, plus multiple inline point source emissions files, per day. CAMx modeling also uses one gridded low-level emissions file per day - but instead of reading multiple inline point source emissions files at once, CAMx can only read a single point source file per day. Therefore, as part of the CAMx conversion process, all inline point source files are merged into a single "mrgpt" file per day. The mrgpt file includes the layered fire emissions described in the previous paragraph, in addition to all non-fire elevated point sources from the cmv_c3, othpt, ptegu, ptnonipm, and pt_oilgas sectors.

The remaining step in the CAMx emissions process is to generate sea salt aerosol emissions, which are distinct from ocean chlorine emissions. Sea salt emissions do not need to be included in CMAQ-ready emissions because they are calculated by the model, but, do need to be included in CAMx-ready emissions. After the merged low-level emissions are converted to CAMx format, sea salt emissions are generated using a program called "seasalt" and added to the low-level emissions. Sea salt emissions depend on meteorology, vary on a daily and hourly basis, and exist for model species PCL, NA, PSO4, and SS (i.e., sea salt).
 Development of CAMx Emissions for Source Apportionment CAMx Runs
The CAMx model supports source apportionment modeling for ozone and PM sources using techniques called Ozone Source Apportionment Technology (OSAT) and Particulate Matter Source Apportionment Technology (PSAT).  These source apportionment techniques allow emissions from different types of sources to be tracked through the CAMx model.  For the Revised CSAPR Update study, OSAT modeling was performed in CAMx for 2023 and 2028 using one-way nesting (i.e., the inner 12km grid takes boundary information from the outer 36km grid but the inner grid does not feed any concentration information back to the outer grid). The emissions developed specifically for OSAT modeling used the case names "2023fh1_ussa_16j" and "2028fh1_ussa_16j". 

Source Apportionment modeling involves assigning tags to different categories of emissions. These tags can be applied by region (e.g., state), by emissions type (e.g., SCC or sector), or a combination of the two. For the Revised CSAPR Update study, emissions tagging was applied by state. All emissions from US states, except for biogenics, fires, and fugitive dust (afdust), were assigned a state-specific tag. Emissions from tribal lands were also assigned a separate tag, as well as offshore emissions. Other tags include a tag for biogenics and afdust; a tag for all fires, both inside and outside the US; and a tag for all anthropogenic emissions from Canada and Mexico. A full list of tags is provided in Table 3-27. State-level tags 2 through 51 exclude emissions from biogenics, fugitive dust, and fires, which are included in other tags. 

Table 3-27. State tags for 2023fh1, 2028fh1 USSA modeling
                                      Tag
                           Emissions applied to tag
                                       1
       All biogenics (beis sector) and US fugitive dust (afdust sector)
                                       2
                                    Alabama
                                       3
                                    Arizona
                                       4
                                   Arkansas 
                                       5
                                  California 
                                       6
                                   Colorado 
                                       7
                                 Connecticut 
                                       8
                                   Delaware 
                                       9
                             District of Columbia 
                                      10
                                   Florida 
                                      11
                                   Georgia 
                                      12
                                    Idaho 
                                      13
                                   Illinois 
                                      14
                                   Indiana 
                                      15
                                     Iowa 
                                      16
                                    Kansas 
                                      17
                                   Kentucky 
                                      18
                                  Louisiana 
                                      19
                                    Maine 
                                      20
                                   Maryland 
                                      21
                                Massachusetts 
                                      22
                                   Michigan 
                                      23
                                  Minnesota 
                                      24
                                 Mississippi 
                                      25
                                   Missouri 
                                      26
                                   Montana 
                                      27
                                   Nebraska 
                                      28
                                    Nevada 
                                      29
                                New Hampshire 
                                      30
                                  New Jersey 
                                      31
                                  New Mexico
                                      32
                                   New York
                                      33
                                North Carolina
                                      34
                                 North Dakota
                                      35
                                     Ohio
                                      36
                                   Oklahoma
                                      37
                                    Oregon
                                      38
                                 Pennsylvania
                                      39
                                 Rhode Island
                                      40
                                South Carolina
                                      41
                                 South Dakota
                                      42
                                   Tennessee
                                      43
                                     Texas
                                      44
                                     Utah
                                      45
                                    Vermont
                                      46
                                   Virginia
                                      47
                                  Washington
                                      48
                                 West Virginia
                                      49
                                   Wisconsin
                                      50
                                    Wyoming
                                      51
                                  Tribal Data
                                      52
                       Canada and Mexico (except fires)
                                      53
                                   Offshore
                                      54
           All fires from US, Canada, and Mexico, including ag fires

For OSAT and PSAT modeling, all emissions must be input to CAMx in the form of a point source (mrgpt) file, including low level sources that are found in gridded files for regular CAMx runs. In addition, for two-way nested modeling, all emissions must be input in a single mrgpt file, rather than separate mrgpt files for each of the two domains (36US3 and 12US2). Note that fire emissions require special consideration in two-way nested model runs and for PSAT and OSAT modeling.  That same consideration must be given to any sector in which emissions are being gridded by SMOKE.

There are two main approaches for tagging emissions for CAMx modeling. One approach is to tag emissions within SMOKE.  Here, SMOKE will output tagged point source files (SGINLN files), which can then be converted to CAMx point source format with the tags applied by SMOKE carried forward into the CAMx inputs. The second approach is to, if necessary, depending on the nature of the tags, split sectors into multiple components by tag so that each sector corresponds to a single tag. Then, the gridded and/or point source format SMOKE outputs from those split sectors are converted to CAMx point source format, and then merged into the full mrgpt file, with the tags applied at that last step.  In some situtations, a mix of the two approaches is appropriate.

For the Revised CSAPR Update study the first approach was used for most sectors, meaning tags were applied in SMOKE. The exceptions were sectors where the entire sector receives only one tag: afdust, beis, onroad_ca_adj, ptfire, ptagfire, ptfire_othna, and all Canada and Mexico sectors. Afdust emissions are not tagged by state because the current tagging methodology does not support applying transportable fraction and meteorological adjustments to tagged emissions. 

Once the individual sector tagging is complete, the point source files for all of the sectors are merged together to create the mrgpt file which includes all emissions, with the desired tags and appropriate resolution throughout the domain for OSAT or PSAT modeling.
Development of 2023 and 2028 Emissions
The emission inventories for future years of 2023 and 2028 have been developed using projection methods that are specific to the type of emissions source. Future emissions are projected from the 2016 base case either by running models to estimate future year emissions from specific types of emission sources (e.g., EGUs, and onroad and nonroad mobile sources), or for other types of sources by adjusting the base year emissions according to the best estimate of changes expected to occur in the intervening years (e.g., non-EGU point and nonpoint sources). For some sectors, the same emissions are used in the base and future years, such as biogenic and fire. For the remaining sectors, rules and specific legal obligations that go into effect in the intervening years, along with changes in activity for the sector, are considered when possible. These sectors have been projected to 2023 and 2028 as summarized in Table 4-1. 
Table 4-1.  Overview of projection methods for the 2023 and 2028 regional cases
                         Platform Sector: abbreviation
              Description of Projection Methods for 2023 and 2028
EGU units:
Ptegu
The Integrated Planning Model (IPM) was run to create the 2023 and 2028 emissions. IPM outputs from the January, 2020 version of the IPM platform were used (https://www.epa.gov/airmarkets/epas-power-sector-modeling-platform-v6-using-ipm-january-2020-reference-case). For 2023, the 2023 IPM output year was used and for 2028 the 2030 output year was used because the year 2028 maps to the 2030 output year. Emission inventory Flat Files for input to SMOKE were generated using post-processed IPM output data. Temporal allocation for future year emissions is discussed in the EGU-IPM specification sheet for the 2016v1 platform.
Point source oil and gas: 
pt_oilgas
First, known closures were applied to the 2016 pt_oilgas sources. Production-related sources were then grown from 2016 to 2017 using historic production data. The production-related sources were then grown to 2023 and 2028 based on growth factors derived from the Annual Energy Outlook (AEO) 2019 data for oil, natural gas, or a combination thereof.  The grown emissions were then controlled to account for the impacts of relevant New Source Performance Standards (NSPS). 
Remaining non-EGU point:
Ptnonipm
First, known closures were applied to the 2016 ptnonipm sources.  Closures were obtained from the Emission Inventory System (EIS) and also submitted by the states of Alabama, North Carolina, Ohio, Pennsylvania, and Virginia. Industrial sources were grown using factors derived from the AEO 2019. Rail yard emissions were grown using the same factors as line haul locomotives in the rail sector. Controls were then applied to account for relevant NSPS for reciprocating internal combustion engines (RICE), gas turbines, and process heaters. Reductions due to consent decrees that had not been fully implemented by 2016 were also applied, along with specific comments received by S/L/T agencies.
Airports
Starts with 2017 NEI. Airport emissions were grown using factors derived from the Terminal Area Forecast (TAF) (see https://www.faa.gov/data_research/aviation/taf/).
Agricultural:
Ag
Livestock were projected based on factors created from USDA National livestock inventory projections published in February 2018 (https://www.ers.usda.gov/webdocs/outlooks/87459/oce-2018-1.pdf?v=7587).  Fertilizer emissions were held constant at year 2016 levels.
Area fugitive dust:
afdust, afdust_ak
Paved road dust was grown to 2023 and 2028 levels based on the growth in VMT from 2016 to 2023 and 2028. The remainder of the sector including building construction, road construction, agricultural dust, and unpaved road dust was held constant, except in the MARAMA region where some factors were provided for categories other than paved roads.  The projected emissions are reduced during modeling according to a transport fraction (newly computed for the beta platform) and a meteorology-based (precipitation and snow/ice cover) zero-out as they are for the base year.
Category 1, 2 CMV:
cmv_c1c2
Category 1 and category 2 (C1C2) CMV emissions sources outside of California were projected to 2023 and 2028 based on factors from the Regulatory Impact Analysis (RIA) Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters per Cylinder. California emissions were projected based on factors provided by the state.
Category 3 CMV:
cmv_c3
Category 3 (C3) CMV emissions were projected using a forthcoming EPA report on projected bunker fuel demand. The report projects bunker fuel consumption by region out to the year 2030. Bunker fuel usage was used as a surrogate for marine vessel activity. Factors based on the report were used for all pollutants except NOx. Growth factors for NOx emissions were handled separately to account for the phase in of Tier 3 vessel engines. The NOx growth rates from the EPA C3 Regulatory Impact Assessment (RIA) were refactored to use the new bunker fuel usage growth rates. The assumptions of changes in fleet composition and emissions rates from the C3 RIA were preserved and applied to the new bunker fuel demand growth rates for 2023 and 2028 to arrive at the final growth rates.
Locomotives: 
rail
Passenger and freight were projected using separate factors. Freight emissions were computed for future years based on future year fuel use values for 2020, 2023, and 2028. Specifically, they were based on 2018 AEO freight rail energy use growth rate projections and emission factors, which are based on historic emissions trends that reflect the rate of market penetration of new locomotive engines.
Remaining nonpoint:
nonpt
Industrial emissions were grown according to factors derived from AEO2019. Portions of the nonpt sector were grown using factors based on expected growth in human population. Controls were applied to reflect relevant NSPS rules (i.e., reciprocating internal combustion engines (RICE), natural gas turbines, and process heaters).  Emissions were also reduced to account for fuel sulfur rules in the mid-Atlantic and northeast.
Nonpoint source oil and gas: 
np_oilgas
Production-related sources were grown starting from an average of 2014 and 2016 production data. Emissions were initially projected to 2017 using historical data and then grown to 2023 and 2028 based on factors generatedfrom AEO2019. Based on the SCC, factors related to oil, gas, or combined growth were used. Coalbed methane SCCs were projected independently. Controls were then applied to account for NSPS for oil and gas and RICE.
Residential Wood Combustion:
rwc
RWC emissions were projected from 2016 to 2023 and 2028 based on growth and control assumptions compatible with EPA's 2011v6.3 platform, which accounts for growth, retirements, and NSPS, although implemented in the Mid-Atlantic Regional Air Management Association (MARAMA)'s growth tool.  RWC emissions in California, Oregon, and Washington were held constant.
Nonroad:
nonroad
Outside California, the MOVES2014b model was run to create nonroad emissions for 2023 and 2028 without any state inputs. The fuels used are specific to the future year, but the meteorological data represented the year 2016. For California, datasets provided by the California Air Resources Board (CARB) circa 2017 were used. 
Onroad:
onroad, onroad_nonconus
Activity data were projected from 2016 to 2023 and 2028 based on factors derived from AEO2019. Where S/Ls provided activity data, those data were used. To create the emission factors, MOVES2014b was run for the years 2023 and 2028, with 2016 meteorological data and fuels, but with age distributions projected to represent future years, and the remaining inputs consistent with those used in 2014NEIv2.  The future year activity data and emission factors were then combined using SMOKE-MOVES to produce the 2023 and 2028 emissions. Section 4.3.2 describes the applicable rules that were considered when projecting onroad emissions.
Onroad California:
onroad_ca_adj 
CARB-provided emissions were used for California, but they were gridded and temporalized using MOVES2014b-based data output from SMOKE-MOVES.  Volatile organic compound (VOC) HAP emissions derived from California-provided VOC emissions and MOVES-based speciation.
Other Area Fugitive dust sources not from the NEI:
othafdust
Othafdust emissions for future years were provided by ECCC. The emissions were extracted from a broader nonpoint source inventory. Adjustments to construction dust were made to make those more consistent with the 2016 and ECCC 2010 inventories. Mexico emissions are not included in this sector.
Other Point Fugitive dust sources not from the NEI:
othptdust
Wind erosion emissions were removed from the point fugitive dust inventory prior to regional haze modeling. Base year 2015 inventories with the rotated grid pattern removed were projected to 2023 and 2028 based on factors provided by ECCC. A transport fraction adjustment is applied to the projected inventories along with a meteorology-based (precipitation and snow/ice cover) zero-out.
Other point sources not from the NEI:
othpt
For agricultural sources that were originally developed on the rotated 10-km grid, the reallocated base year emissions were projected to 2023 and 2028 using projection factors based on data provided by ECCC and applied by province, pollutant, and ECCC sub-class code. Airports were also projected from 2016 using ECCC-based factors. For the remaining sources in this sector, ECCC provided future year inventories. For Mexico sources, inventories projected from Mexico's 2008 inventory to 2018, 2025, and 2030 were interpolated to the years 2023 and 2028.
Other non-NEI nonpoint and nonroad:
othar
Future year nonpoint inventories for many parts of this sector were provided by ECCC and were split into sectors to match those in the base year inventory. For Canadian nonroad sources, factors were provided from which the future year inventories could be derived.  For Mexico nonpoint and nonroad sources, inventories projected to 2018, 2025, and 2030 from their 2008 inventory were interpolated to 2023 and 2028.
Other non-NEI onroad sources:
onroad_can
For Canadian mobile onroad sources, future year inventories were derived from the base year 2015 inventory and data provided by ECCC. Projection factors were applied by province, sub-class code, and pollutant.
Other non-NEI onroad sources:
onroad_mex
Monthly year Mexico (municipio resolution) onroad mobile inventories were developed based runs of MOVES-Mexico for 2023 and 2028.


 EGU Point Source Projections (ptegu)
The original 2023fh and 2028fh EGU emissions inventories were developed from the output of the v6 platform using the May 2019 reference case run, while the 2023fh1 and 2028fh1 emissions are based on the January 2020 reference case run of the Integrated Planning Model (IPM). IPM is a linear programming model that accounts for variables and information such as energy demand, planned unit retirements, and planned rules to forecast unit-level energy production and configurations. The following specific rules and regulations are included in IPM v6 platform run from May 2019:

 The Cross-State Air Pollution Rule (CSAPR) Update, a federal regulatory measure to address transport of ozone and its precursors under the 1997 and 2008 National Ambient Air Quality Standards (NAAQS) for ozone. 
 The Standards of Performance for Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary Sources: Electric Utility Generating Units. 
 The Mercury and Air Toxics Rule (MATS), which was initially finalized in 2011 and later revised (https://www.epa.gov/mats/regulatory-actions-final-mercury-and-air-toxics-standards-mats-power-plants). MATS establishes National Emissions Standards for Hazardous Air Pollutants (NESHAP) for the "electric utility steam generating unit" source category. 
 Current and existing state regulations.
 The final actions EPA has taken to implement the Regional Haze Regulations and Guidelines for Best Available Retrofit Technology (BART) Determinations Final Rule. This regulation requires states to submit revised State Implementation Plans (SIPs) that include (1) goals for improving visibility in Class I areas on the 20% worst days and allowing no degradation on the 20% best days and (2) assessments and plans for achieving BART emission targets for sources placed in operation between 1962 and 1977. Since 2010, EPA has approved SIPs or, in a very few cases, put in place regional haze Federal Implementation Plans for several states. The BART limits approved in these plans (as of summer 2017) that will be in place for EGUs are represented in the EPA Platform v6. 
 Three non-air federal rules affecting EGUs: National Pollutant Discharge Elimination System-Final Regulations to Establish Requirements for Cooling Water Intake Structures at Existing Facilities and Amend Requirements at Phase I Facilities, Hazardous and Solid Waste Management System; Disposal of Coal Combustion Residuals From Electric Utilities; and the Effluent Limitation Guidelines and Standards for the Steam Electric Power Generating Point Source Category. 

Some additional updates were made to IPM for the January 2020 case:

 Updated NEEDS to the December 2019 version.  This included more than 10 GW of retirements, 4 GW of which were coal plants, along with some unit-level rate changes in Utah, Nebraska, Kentucky, and New York.
 Updated (i.e., lowered) storage and renewal energy technology costs based on the National Renewable Energy Laboratory (NREL) Annual Technology Baseline 2019 mid case. 
 Implemented offshore wind power mandates in Maryland, New Jersey, Connecticut, Massachusetts, and New York .
 Incorporated clean energy standards in California, New Mexico, Nevada, New York, and Washington.
 Implemented renewable portfolio standard updates in California, Washington D.C., Maryland, Maine, New Mexico, Nevada, New York, Ohio, and Washington.
 Reflected the Affordable Clean Energy (ACE) rule (June 19, 2019).
 Incorporated the 26 U.S. Code § 45Q. Credit for carbon oxide sequestration (https://www.energy.gov/sites/prod/files/2019/10/f67/Internal%20Revenue%20Code%20Tax%20Fact%20Sheet.pdf). 

IPM is run for a set of years, including the 2023 and 2028 future years used in the 2016v1 platform. Further documentation of the IPM model and the v6 platform can be found on the CAMD website (https://www.epa.gov/airmarkets/documentation-epas-power-sector-modeling-platform-v6-january-2020-reference-case).

The EGU missions are calculated for the inventory using the output of the IPM model for the forecast year. Units that are identified to have a primary fuel of landfill gas, fossil waste, non-fossil waste, residual fuel oil, or distillate fuel oil may be missing emissions values for certain pollutants in the generated inventory flat file. Units with missing emissions values are gapfilled using projected base year values. The projections are calculated using the ratio of the future year seasonal generation in the IPM parsed file and the base year seasonal generation at each unit for each fuel type in the unit as derived from the 2016 EIA-923 tables. New controls identified at a unit in the IPM parsed file are accounted for with appropriate emissions reductions in the gapfill projection values. When base year unit-level generation data cannot be obtained no gapfill value is calculated for that unit. Additionally, some units, such as landfill gas, may not be assigned a valid SCC in the initial flat file. The SCCs for these units are updated based on the base year SCC for the unit-fuel type.

Combined cycle units produce some of their energy from process steam that turns a steam turbine. The IPM model assigns a fraction of the total combined cycle production to the steam turbine. When the emissions are calculated these steam units are assigned emissions values that come from the combustion portion of the process. In the base year NEI steam turbines are usually implicit to the total combined cycle unit. To achieve the proper plume rise for the total combined cycle emissions, the stack parameters for the steam turbine units are updated with the parameters from the combustion release point.

Large EGUs in the IPM-derived flat file inventory are associated with hourly CEMS data for NOX and SO2 emissions values in the base year. To maintain a temporal pattern consistent with the 2016 base year, the NOX and SO2 values in the hourly CEMS inventories are projected to match the total seasonal emissions values in the future years.

The EGU sector NOx emissions by state are listed in Table 4-2 for 2023 and 2028 regional cases. The designation "fh" here refers to the May 2019 IPM case and "fh1" refers to the January 2020 IPM case.
Table 4-2.  EGU sector NOx emissions by State for the 2023 and 2028 regional cases
State
                                    2016fh
                                    2023fh
                                    2023fh1
                                    2028fh
                                    2028fh1
Alabama
                                    28,596
                                     9,545
                                     9,954
                                    11,812
                                    12,376
Arizona
                                    18,786
                                    10,909
                                    11,175
                                     9,259
                                     9,011
Arkansas
                                    26,808
                                    11,579
                                    17,461
                                    15,318
                                    17,074
California
                                     6,908
                                     7,501
                                     5,808
                                     2,707
                                     1,719
Colorado
                                    30,152
                                    17,965
                                    16,561
                                    18,616
                                    15,448
Connecticut
                                     4,088
                                     4,359
                                     4,365
                                     4,249
                                     4,202
Delaware
                                     1,487
                                      367
                                      488
                                      407
                                      544
District of Columbia
                                      NA
                                       1
                                       1
                                       1
                                       1
Florida
                                    65,059
                                    32,327
                                    32,684
                                    33,282
                                    31,488
Georgia
                                    29,384
                                    14,292
                                    13,760
                                    15,950
                                    15,666
Idaho
                                     1,369
                                      469
                                      469
                                      949
                                      419
Illinois
                                    30,250
                                    31,189
                                    21,321
                                    32,474
                                    21,668
Indiana
                                    83,425
                                    44,029
                                    45,169
                                    44,971
                                    45,328
Iowa
                                    22,971
                                    23,069
                                    24,264
                                    22,976
                                    23,379
Kansas
                                    14,959
                                    15,669
                                    15,725
                                    15,684
                                    14,528
Kentucky
                                    57,342
                                    14,411
                                    14,316
                                    11,761
                                    14,495
Louisiana
                                    47,931
                                    17,223
                                    18,145
                                    16,179
                                    16,909
Maine
                                     4,935
                                     3,016
                                     3,005
                                     2,557
                                     2,945
Maryland
                                    10,448
                                     5,387
                                     5,436
                                     5,115
                                     5,599
Massachusetts
                                     8,121
                                     5,851
                                     5,819
                                     5,626
                                     5,683
Michigan
                                    37,149
                                    30,141
                                    28,344
                                    31,948
                                    32,895
Minnesota
                                    21,737
                                    15,565
                                    17,497
                                    15,364
                                    12,665
Mississippi
                                    16,414
                                     5,749
                                     5,604
                                     6,248
                                     6,135
Missouri
                                    57,647
                                    46,714
                                    48,809
                                    46,528
                                    45,433
Montana
                                    15,819
                                     9,186
                                     9,186
                                     9,193
                                     9,018
Nebraska
                                    20,734
                                    21,428
                                    21,451
                                    21,508
                                    21,468
Nevada
                                     3,949
                                     2,215
                                     2,368
                                     1,458
                                     1,531
New Hampshire
                                     2,158
                                      601
                                      590
                                      533
                                      529
New Jersey
                                     5,723
                                     5,771
                                     5,889
                                     6,135
                                     6,582
New Mexico
                                    20,222
                                     8,246
                                     9,332
                                     6,532
                                     6,542
New York
                                    13,770
                                    14,740
                                    14,552
                                    13,699
                                    13,707
North Carolina
                                    27,892
                                    30,088
                                    29,482
                                    21,685
                                    24,320
North Dakota
                                    38,400
                                    25,458
                                    25,772
                                    25,314
                                    24,151
Ohio
                                    55,581
                                    40,029
                                    45,211
                                    38,572
                                    43,345
Oklahoma
                                    25,084
                                    17,877
                                    17,396
                                    17,342
                                    16,375
Oregon
                                     4,067
                                     1,560
                                     1,827
                                     1,665
                                     1,791
Pennsylvania
                                    84,086
                                    33,301
                                    31,707
                                    31,326
                                    28,769
Rhode Island
                                      261
                                      769
                                      764
                                      739
                                      737
South Carolina
                                    13,734
                                    13,460
                                    13,474
                                    13,053
                                    13,048
South Dakota
                                     1,095
                                      692
                                      756
                                      832
                                      776
Tennessee
                                    18,752
                                     4,285
                                     5,896
                                     4,753
                                     5,958
Texas
                                    111,612
                                    81,051
                                    82,699
                                    80,579
                                    77,506
Tribal Data
                                    35,057
                                     6,897
                                     6,907
                                     6,902
                                     6,854
Utah
                                    27,450
                                    21,063
                                    14,455
                                    20,991
                                    13,986
Vermont
                                      302
                                      21
                                      21
                                      20
                                      20
Virginia
                                    26,387
                                    10,183
                                    10,050
                                    11,217
                                    11,899
Washington
                                     8,860
                                     1,760
                                     1,909
                                     1,809
                                     1,875
West Virginia
                                    50,984
                                    41,891
                                    41,992
                                    39,495
                                    39,601
Wisconsin
                                    16,148
                                    10,238
                                    10,467
                                    10,048
                                     9,293
Wyoming
                                    36,095
                                    15,216
                                    17,463
                                    13,300
                                    13,371

 Non-EGU Point and Nonpoint Sector Projections
To project all U.S. non-EGU stationary sources, facility/unit closures information and growth (PROJECTION) factors and/or controls were applied to certain categories within the afdust, ag, cmv, rail, nonpt, np_oilgas, ptnonipm, pt_oilgas and rwc platform sectors.  Some facility or sub-facility-level closure information was also applied to the point sources.  There are also a handful of situations where new inventories were generated for sources that did not exist in the 2014v2 NEI (e.g., biodiesel and cellulosic plants, yet-to-be constructed cement kilns).  This subsection provides details on the data and projection methods used for these sectors. 

Because much of the projections and controls data are developed independently from how the EPA defines its emissions modeling sectors, this section is organized primarily by the type of projections data, with secondary consideration given to the emissions modeling sector (e.g., industrial source growth factors are applicable to four emissions modeling sectors).  The rest of this section is organized in the order that the EPA uses the Control Strategy Tool (CoST) in combination with other methods to produce future year inventories: 1) for point sources, apply plant (facility or sub-facility-level) closure information via CoST; 2) apply all PROJECTION packets via CoST (multiplicative factors that could cause increases or decreases); 3) apply all percent reduction-based CONTROL packets via CoST; and 4) append all other future-year inventories not generated via CoST.  This organization allows consolidation of the discussion of the emissions categories that are contained in multiple sectors, because the data and approaches used across the sectors are consistent and do not need to be repeated.  Sector names associated with the CoST packets are provided in parentheses.
 Background on the Control Strategy Tool (CoST)
CoST is used to apply most non-EGU projection/growth factors, controls and facility/unit/stack-level closures to the 2016-based emissions modeling inventories to create future year inventories for the following sectors:  afdust, ag, cmv, rail, nonpt, np_oilgas, ptnonipm, pt_oilgas and rwc.  Information about CoST and related data sets is available from https://www.epa.gov/economic-and-cost-analysis-air-pollution-regulations/cost-analysis-modelstools-air-pollution. 

CoST allows the user to apply projection (growth) factors, controls and closures at various geographic and inventory key field resolutions.  Each of these CoST datasets, also called "packets" or "programs," provides the user with the ability to perform numerous quality assurance assessments as well as create SMOKE-ready future year inventories.  Future year inventories are created for each emissions modeling sector via a CoST "strategy" and each strategy includes all base year 2016 inventories and applicable CoST packets.  For reasons discussed later, some emissions modeling sectors require multiple CoST strategies to account for the compounding of control programs that impact the same type of sources.  There are also available linkages to existing and user-defined control measures databases and it is up to the user to determine how control strategies are developed and applied.  The EPA typically creates individual CoST packets that represent specific intended purposes (e.g., aircraft projections for airports are in a separate PROJECTION packet from residential wood combustion sales/appliance turnover-based projections).  CoST uses three packet types as described below:
 CLOSURE: Applied first in CoST.  This packet can be used to zero-out (close) point source emissions at resolutions as broad as a facility to as specific as a stack.  The EPA uses these types of packets for known post-2016 controls as well as information on closures provided by states on specific facilities, units or stacks.  This packet type is only used in the ptnonipm and pt_oilgas sectors.
 PROJECTION: This packet allows the user to increase or decrease emissions for virtually any geographic and/or inventory source level.  Projection factors are applied as multiplicative factors to the 2011 emissions inventories prior to the application of any possible subsequent CONTROLs.  A PROJECTION packet is necessary whenever emissions increase from 2011 and is also desirable when information is based more on activity assumptions rather than known control measures.  The EPA uses PROJECTION packet(s) in every non-EGU modeling sector.
 CONTROL: These packets are applied after any/all CLOSURE and PROJECTION packet entries.  The user has similar level of control as PROJECTION packets regarding specificity of geographic and/or inventory source level application.  Control factors are expressed as a percent reduction (0 to 100) and can be applied in addition to any pre-existing inventory control, or as a replacement control where inventory controls are first backed out prior to the application of a more-stringent replacement control.  

All of these packets are stored as data sets within the Emissions Modeling Framework and use comma-delimited formats.  As mentioned above, CoST first applies any/all CLOSURE information for point sources, then applies PROJECTION packet information, followed by CONTROL packets.  A hierarchy is used by CoST to separately apply PROJECTION and CONTROL packets.  In short, in a separate process for PROJECTION and CONTROL packets, more specific information is applied in lieu of less-specific information in ANY other packets.  For example, a facility-level PROJECTION factor will be replaced by a unit-level, or facility and pollutant-level PROJECTION factor.  It is important to note that this hierarchy does not apply between packet types (e.g., CONTROL packet entries are applied irrespective of PROJECTION packet hierarchies).  A more specific example: a state/SCC-level PROJECTION factor will be applied before a stack/pollutant-level CONTROL factor that impacts the same inventory record.  However, an inventory source that is subject to a CLOSURE packet record is removed from consideration of subsequent PROJECTION and CONTROL packets. 

The implication for this hierarchy and intra-packet independence is important to understand and quality assure when creating future year strategies.  For example, with consent decrees, settlements and state comments, the goal is typically to achieve a targeted reduction (from the 2011NEI) or a targeted future-year emissions value. Therefore, as encountered with this future year base case, consent decrees and state comments for specific cement kilns (expressed as CONTROL packet entries) needed to be applied instead of (not in addition to) the more general approach of the PROJECTION packet entries for cement manufacturing.  By processing CoST control strategies with PROJECTION and CONTROL packets separated by the type of broad measure/program, it is possible to show actual changes from the base year inventory to the future year inventory as a result of applying each packet.

Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a hierarchal matching approach to assign PROJECTION factors to the inventory.  For example, a packet entry with Ranking=1 will supersede all other potential inventory matches from other packets.  CoST then computes the projected emissions from all PROJECTION packet matches and then performs a similar routine for all CONTROL packets.  Therefore, when summarizing "emissions reduced" from CONTROL packets, it is important to note that these reductions are not relative to the 2011 inventory, but rather to the intermediate inventory after application of any/all PROJECTION packet matches (and CLOSURES).  A subset of the more than 70 hierarchy options is shown in Table 4-3, although the fields in the table are not necessarily named the same in CoST, but rather are similar to those in the SMOKE FF10 inventories.  For example, "REGION_CD" is the county-state-county FIPS code (e.g., Harris county Texas is 48201) and "STATE" would be the 2-digit state FIPS code with three trailing zeroes (e.g., Texas is 48000).  
Table 4-3. Subset of CoST Packet Matching Hierarchy
Rank
Matching Hierarchy
Inventory Type
                                       1
REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID, SCC, POLL
point
                                       2
REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID, POLL
point
                                       3
REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, POLL
point
                                       4
REGION_CD, FACILITY_ID, UNIT_ID, POLL
point
                                       5
REGION_CD, FACILITY_ID, SCC, POLL
point
                                       6
REGION_CD, FACILITY_ID, POLL
point
                                       7
REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID, SCC
point
                                       8
REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID
point
                                       9
REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID
point
                                      10
REGION_CD, FACILITY_ID, UNIT_ID
point
                                      11
REGION_CD, FACILITY_ID, SCC
point
                                      12
REGION_CD, FACILITY_ID
point
                                      13
REGION_CD, NAICS, SCC, POLL
point, nonpoint
                                      14
REGION_CD, NAICS, POLL
point, nonpoint
                                      15
STATE, NAICS, SCC, POLL
point, nonpoint
                                      16
STATE, NAICS, POLL
point, nonpoint
                                      17
NAICS, SCC, POLL
point, nonpoint
                                      18
NAICS, POLL
point, nonpoint
                                      19
REGION_CD, NAICS, SCC
point, nonpoint
                                      20
REGION_CD, NAICS
point, nonpoint
                                      21
STATE, NAICS, SCC
point, nonpoint
                                      22
STATE, NAICS
point, nonpoint
                                      23
NAICS, SCC
point, nonpoint
                                      24
NAICS
point, nonpoint
                                      25
REGION_CD, SCC, POLL
point, nonpoint
                                      26
STATE, SCC, POLL
point, nonpoint
                                      27
SCC, POLL
point, nonpoint
                                      28
REGION_CD, SCC
point, nonpoint
                                      29
STATE, SCC
point, nonpoint
                                      30
SCC
point, nonpoint
                                      31
REGION_CD, POLL
point, nonpoint
                                      32
REGION_CD
point, nonpoint
                                      33
STATE, POLL
point, nonpoint
                                      34
STATE
point, nonpoint
                                      35
POLL
point, nonpoint

The contents of the controls, local adjustments and closures for the future year base case are described in the following subsections.  Year-specific projection factors (PROJECTION packets) for the future year were used to create the future year base case, unless noted otherwise in the specific subsections.  The contents of a few of these projection packets (and control reductions) are provided in the following subsections where feasible.  However, most sectors used growth or control factors that varied geographically and their contents could not be provided in the following sections (e.g., facilities and units subject to the Boiler MACT reconsideration has thousands of records).  The remainder of Section 4.2 is divided into several subsections that are summarized in Table 4-4.  Note that future year inventories were used rather than projection or control packets for some sources.
Table 4-4. Summary of non-EGU stationary projections subsections
Subsection
Title
Sector(s)
Brief Description
4.2.2
CoST Plant CLOSURE packet
ptnonipm, pt_oilgas
All facility/unit/stack closures information, primarily from Emissions Inventory System (EIS), but also includes information from states and other organizations.
4.2.3
CoST PROJECTION packets
All
Introduces and summarizes national impacts of all CoST PROJECTION packets to the future year.
4.2.3.1
Fugitive dust growth
afdust
PROJECTION packet: county-level resolution, primarily based on VMT growth.
4.2.3.2
Livestock population growth
ag
PROJECTION packet: national, by-animal type resolution, based on animal population projections.
4.2.3.3
Category 1, 2, and 3 commercial marine vessels
cmv
PROJECTION packet: Category 1 & 2: CMV uses SCC/poll for all states except Calif.

4.2.3.4
Category 3 commercial marine vessels
cmv
PROJECTION packet: Category 3: region-level by-pollutant, based on cumulative growth and control impacts from rulemaking.
4.2.3.5
Oil and gas and industrial source growth
nonpt, np_oilgas, ptnonipm, pt_oilgas
Several PROJECTION packets: varying geographic resolutions from state, county, to oil/gas play-level and by-process/fuel-type applications.  Data derived from AEO2016 with several modifications.
4.2.3.6
Non-IPM Point Sources
ptnonipm
Several PROJECTION packets: specific projections from MARAMA region and states, EIA-based projection factors for industrial sources for non-MARAMA states.
4.2.3.7
Nonpoint sources
nonpt
Several PROJECTION packets: MARAMA states projection for Portable Fuel Containers and for all other nonpt sources. Non-MARAMA states projected with EIA-based factors for industrial sources. Evaporative Emissions from Finished Fuels projected using EIA-based factors. Human population used as growth for applicable sources.
4.2.3.8
Airport Sources
ptnonipm
PROJECTION packet: by-airport for all direct matches to FAA Terminal Area Forecast data, with state-level factors for non-matching NEI airports.
4.2.3.9
Residential wood combustion
rwc
PROJECTION packet: national with exceptions, based on appliance type sales growth estimates and retirement assumptions and impacts of recent NSPS.
4.2.4
CoST CONTROL packets
ptnonipm, nonpt, np_oilgas, pt_oilgas 
Introduces and summarizes national impacts of all CoST CONTROL packets to the future year.
4.2.4.1
Oil and Gas NSPS
np_oil gas, pt_oilgas

4.2.4.2
RICE NSPS
ptnonipm, nonpt, np_oilgas, pt_oilgas
CONTROL packet: applies reductions for lean burn, rich burn, and combined engines for identified SCCs.
4.2.4.3
Fuel Sulfur Rules
ptnonipm, nonpt
CONTROL packet: updated by MARAMA, applies reductions to specific units in ten states.
4.2.4.4
Natural Gas Turbines NOx NSPS
ptnonipm
CONTROL packet: applies NOx emission reductions established by the NSPS.
4.2.4.5
Process Heaters NOx NSPS
ptnonipm
CONTROL packet: applies NOx emission limits established by the NSPS.
4.2.4.6
CISWI
ptnonipm
CONTROL packet: applies controls to specific CISWI units in 11 states.
4.2.4.7
Petroleum Refineries NSPS Subpart JA
ptnonipm
CONTROL packet: control efficiencies are applied to identified delayed coking and storage tank units.
4.2.4.8
State-Specific Controls
ptnonipm
CONTROL packets and comments submitted by individual states for rules that may only impact their state or corrections noted from previous review.


 CoST Plant CLOSURE Packet (ptnonipm, pt_oilgas)
Packet: CLOSURES_2016_beta_platform_04oct2019_v1

The CLOSURES packet contains facility, unit and stack-level closure information derived from an Emissions Inventory System (EIS) unit-level report from March 5, 2019, with closure status equal to "PS" (permanent shutdown; i.e., post-2016 permanent facility/unit shutdowns known in EIS as of the date of the report). In addition, comments on past modeling platforms received by states and other agencies specified additional closures, as well as some previously specified closures which should remain open, in the following states: Alabama, North Carolina, Ohio, Pennsylvania, and Virginia.  Ultimately, all data were updated to match the SMOKE FF10 inventory key fields, with all duplicates removed, and a single CoST packet was generated.  These changes impact sources in the ptnonipm and pt_oilgas sectors.  The cumulative reduction in emissions for ptnonipm are shown in Table 4-5.
Table 4-5. Reductions from all facility/unit/stack-level closures in 2016v1
                                   Pollutant
                                   ptnonipm
                                   pt_oilgas
CO
                                                                          1,010
                                                                            187
NH3
                                                                             59
                                                                              0
NOX
                                                                          1,373
                                                                            284
PM10
                                                                            447
                                                                              9
PM2.5
                                                                            358
                                                                              9
SO2
                                                                            727
                                                                            178
VOC
                                                                          2,211
                                                                            106

 CoST PROJECTION Packets (afdust, ag, cmv, rail, nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc)
As previously discussed, for point inventories, after application of any/all CLOSURE packet information, the next step in running a CoST control strategy is the application of all CoST PROJECTION packets.  Regardless of inventory type (point or nonpoint), the PROJECTION packets applied prior to the CoST packets.  For several emissions modeling sectors (i.e., afdust, ag, cmv, rail and rwc), there is only one CoST PROJECTION packet. For all other sectors, there are several different sources of PROJECTIONS data and, therefore, there are multiple PROJECTION packets that are concatenated and quality-assured for duplicates and applicability to the inventories in the CoST strategy.  The PROJECTION (and CONTROL) packets were separated into a few "key" control program types to allow for quick summaries of these distinct control programs.  The remainder of this section is broken out by CoST packet, with the exception of discussion of the various packets used for oil and gas and industrial source projections; these packets are a mix of different sources of data that target similar sources.

MARAMA provided PROJECTION and CONTROL packets for years 2023 and 2028 for states including: Connecticut, Delaware, Maryland, Massachusetts, New Hampshire, New York, New Jersey, North Carolina, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia, Maine, and the District of Columbia.   MARAMA only provided pt_oilgas and np_oilgas packets for Rhode Island, Maryland and Massachusetts. For states not covered by the MARAMA packets, projection factors were developed using nationally available data and methods

 Fugitive dust growth (afdust)
Packets: 
      Projection_2016_2023_afdust_version1_platform_MARAMA_04oct2019_v1 Projection_2016_2023_afdust_version1_platform_NJ_13sep2019_v0 Projection_2016_2023_afdust_version1_platform_national_04oct2019_v1 Projection_2016_2023_all_nonpoint_version1_platform_NC_04oct2019_v2 Projection_2016_2028_afdust_version1_platform_MARAMA_04oct2019_v1 Projection_2016_2028_afdust_version1_platform_NJ_13sep2019_v0 Projection_2016_2028_afdust_version1_platform_national_04oct2019_v1 Projection_2016_2028_all_nonpoint_version1_platform_NC_04oct2019_v2 

MARAMA States
MARAMA submitted projection factors for their states to project 2016 afdust emissions to future years 2023 and 2028. These county-specific projection factors impacted paved roads (SCC 2294000000), residential construction dust (SCC 2311010000), industrial/commercial/institutional construction dust (SCC 2311020000), road construction dust (SCC 2311030000), dust from mining and quarrying (SCC 2325000000), agricultural crop tilling dust (SCC 2801000003), and agricultural dust kick-up from beef cattle hooves (SCC 2805001000). Other afdust emissions, including unpaved road dust emissions, were held constant in future year projections. Note that North Carolina and New Jersey provided their own packets for this sector.

Non-MARAMA States
For paved roads (SCC 2294000000), the 2016 afdust emissions were projected to future years 2023 and 2028 based on differences in county total VMT:
Future year afdust paved roads = 2016 afdust paved roads * (Future year county total VMT) / (2016 county total VMT)
The VMT projections are described in the onroad section.
All emissions other than paved roads are held constant in future year projections.  The impacts of the projections are shown in Table 4-6.
Table 4-6. Increase in total afdust PM2.5 emissions from projections in 2016v1
2016 Emissions
2023 Emissions
percent Increase 2023
2028 Emissions
percent Increase 2028
                                   2,530,625
                                   2,557,970
                                     1.09%
                                   2,570,714
                                     1.60%

 Livestock population growth (ag)
Packet: 
      Projection_2016_2023_all_nonpoint_version1_platform_NC_04oct2019_v2
      Projection_2016_2028_all_nonpoint_version1_platform_NC_04oct2019_v2
      Projection_2017_2023_ag_version1_platform_11sep2019_v0
      Projection_2017_2023_ag_version1_platform_NJ_11sep2019_v0
      Projection_2017_2028_ag_version1_platform_11sep2019_v0
      Projection_2017_2028_ag_version1_platform_NJ_11sep2019_v0
The 2017NEI livestock emissions were projected to year 2023 and 2028 using projection factors created from USDA National livestock inventory projections published in March 2019 (https://www.ers.usda.gov/publications/pub-details/?pubid=92599) and are shown in Table 4-7. For emission projections to 2023, a ratio was created between animal inventory counts for 2023 and 2017 to create a projection factor. This process was completed for the animal categories of beef, dairy, broilers, layers, turkeys, and swine. The projection factor was then applied to the 2017NEI base emissions for the specific animal type to estimate 2023 NH3 and VOC emissions. For emission projections to 2028, the same projection method was used. New Jersey (NJ) provided NJ-specific projection factors that were used to grow livestock waste emissions from 2017 to 2023 and 2028. North Carolina (NC) provided NC-specific projection factors that used a 2016-based projection, therefore, NC's livestock waste emissions are projected from the 2016 back-casted base year emissions to 2023 and 2028.
Table 4-7. National projection factors for livestock: 2016 to 2023 and 2028
                                    Animal
                                     2023
                                     2028
beef
-0.02%
-2.87%
swine
+7.47%
+10.36%
broilers
+8.60%
+12.50%
turkeys
-0.03%
+1.57%
layers
+9.28%
+15.93%
dairy
+0.92%
+1.24%
 Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
The cmv_c1c2 emissions outside of California were projected from 2016 to 2023 and 2028 using factors derived from the Regulatory Impact Analysis (RIA) Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters per Cylinder (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-emissions-air-pollution-locomotive). Table 4-8 lists the pollutant-specific projection factors to 2023, and 2028 that were used for cmv_c1c2 sources outside of California. California sources were projected to 2023 and 2028 using the factors in Table 4-9, which are based on data provided by CARB.

Table 4-8. National projection factors for cmv_c1c2
Pollutant
2016-to-2023
2016-to-2028
CO
-2.67%
-1.11%
NOX
-34.6%
-48.7%
PM10
-36.2%
-49.6%
PM2.5
-36.2%
-49.6%
SO2
-86.2%
-86.5%
VOC
-37.0%
-51.4%

Table 4-9. California projection factors for cmv_c1c2
Pollutant
2016-to-2023
2016-to-2028
CO
20.1%
25.3%
NOX
-29.3%
-17.7%
PM10
-29.9%
-33.5%
PM2.5
-29.9%
-33.5%
SO2
24.1%
48.7%
VOC
1.5%
1.9%

 Category 3 Commercial Marine Vessels (cmv_c3)
Growth rates for cmv_c3 emissions from 2016 to 2023 and 2028 were developed using a forthcoming EPA report on projected bunker fuel demand. The report projects bunker fuel consumption by region out to the year 2030. Bunker fuel usage was used as a surrogate for marine vessel activity. To estimate future year emissions of CO, CO2, hydrocarbons, PM10, and PM2.5, the bunker fuel growth rate from 2016 to 2023, and 2028 were directly applied to the estimated 2016 emissions. 

Growth factors for NOx emissions were handled separately to account for the phase in of Tier 3 vessel engines. To estimate these emissions, the NOx growth rates from the EPA C3 Regulatory Impact Assessment (RIA) were refactored to use the new bunker fuel usage growth rates. The assumptions of changes in fleet composition and emissions rates from the C3 RIA were preserved and applied to the new bunker fuel demand growth rates for 2023, and 2028 to arrive at the final growth rates. The Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards from April, 2010 (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-emissions-new-marine-compression-0) were also considered for emission estimates.

The 2023 and 2028 projection factors are shown in Table 4-10. Some regions for which 2016 projection factors were available did not have 2023 or 2028 projection factors specific to that region, so factors from another region were used as follows:
 Alaska was projected using North Pacific factors. 
 Hawaii was projected using South Pacific factors. 
 Puerto Rico and Virgin Islands were projected using Gulf Coast factors.
 Emissions outside Federal Waters (FIPS 98) were projected using the factors given in Table 4-10 for the region "Other".
 California was projected using a separate set of state-wide projection factors based on CMV emissions data provided by the California Air Resources Board (CARB). These factors are shown in Table 4-11
Table 4-10. 2016-to-2023 and 2016-2028 CMV C3 projection factors outside of California
Region
                               2016-to-2023
NOX
                         2016-to-2023
other pollutants
                               2016-to-2028
NOX
                         2016-to-2028
other pollutants
US East Coast
                                    -6.05%
                                    27.71%
                                    -7.54%
                                    49.71%
US South Pacific
(ex. California)
                                    -24.79%
                                    20.89%
                                    -33.97%
                                    45.86%
US North Pacific
                                    -3.37%
                                    22.57%
                                    -4.07%
                                    41.31%
US Gulf
                                    -6.88%
                                    20.82%
                                    -12.40%
                                    36.41%
US Great Lakes
                                     8.71%
                                    14.55%
                                    19.80%
                                    28.29%
Other
                                    23.09%
                                    23.09%
                                    42.58%
                                    42.58%

 Non-Federal Waters
                                 2016-to-2023
                                 2016-to-2028
SO2
                                    -77.21%
                                    -73.60%
PM (main engines)
                                    -36.06%
                                    -25.93%
PM (aux. engines)
                                    -39.69%
                                    -30.14%
Other pollutants
                                    +23.09%
                                    +42.58%

Table 4-11. 2016-to-2023 and 2016-2028 CMV C3 projection factors for California
Pollutant
                                 2016-to-2023
                                 2016-to-2028
CO
                                     18.0%
                                     34.0%
Nox
                                     15.6%
                                     32.7%
PM10 / PM2.5
                                     20.5%
                                     38.1%
SO2
                                     18.3%
                                     33.2%
VOC
                                     24.2%
                                     46.1%
 Oil and Gas Sources (pt_oilgas, np_oilgas)
Future year projections for the 2016v1 platform were generated for point oil and gas sources for years 2023 and 2028.  These projections consisted of three components: (1) applying facility closures to the pt_oilgas sector using the CoST CLOSURE packet; (2) using historical and/or forecast activity data to generate future-year emissions before applicable control technologies are applied using the CoST PROJECTION packet; and (3) estimating impacts of applicable control technologies on future-year emissions using the CoST CONTROL packet. Applying the CLOSURE packet to the pt_oilgas sector resulted in small emissions changes to the national summary shown inTable 4-5.  Note the closures for years 2023 and 2028 are the same. 

For pt_oilgas growth to 2023 and 2028, the oil and gas sources were separated into production-related and exploration-related sources by SCC. These sources were further subdivided by fuel-type by SCC into either OIL, natural gas (NGAS), BOTH oil-natural gas fuels possible, or coal-bed methane (CBM).  The next two subsections describe the growth component process. 

For np_oilgas growth to 2023 and 2028, oil and gas sources were separated into production-related, transmission-related, and all other point sources by NAICS.  These sources are further subdivided by fuel-type by SCC into either OIL, natural gas (NGAS), or BOTH oil-natural gas fuels possible.

Production-related Sources (pt_oilgas, np_oilgas)

The growth factors for the production-related NAICS-SCC combinations were generated in a two-step process.   The first step used historical production data at the state-level to get state-level short-term trends or factors from 2016 to year 2017. In some cases, historical data for year 2018 were available for a state, in these cases a 2016 to 2018 factor was calculated. These historical data were acquired from EIA from the following links:

 Historical Natural Gas: http://www.eia.gov/dnav/ng/ng_sum_lsum_a_epg0_fgw_mmcf_a.htm
 Historical Crude Oil: http://www.eia.gov/dnav/pet/pet_crd_crpdn_adc_mbbl_a.htm
 Historical CBM: https://www.eia.gov/dnav/ng/ng_prod_coalbed_s1_a.htm

The second step involved using the Annual Energy Outlook (AEO) 2019 reference case for the Lower 48 forecast production tables to project from year 2017 to the years of 2023 and 2028.   Specifically, AEO 2019 Table 60 "Lower 48 Crude Oil Production and Wellhead Prices by Supply Region" and AEO 2019 Table 61 "Lower 48 Natural Gas Production and Supply Prices by Supply Region" were used in this projection process.  The AEO2019 forecast production is supplied for each EIA Oil and Gas Supply region shown in Figure 4-1.   

Figure 4-1.  EIA Oil and Gas Supply Regions as of AEO2019
                                       


The result of this second step is a growth factor for each Supply Region from 2017 (or 2018) to 2023 and from 2017 (or 2018) to 2028. A Supply Region mapping to FIPS cross-walk was developed so the regional growth factors could be applied for each FIPS (for pt_oilgas) or to the county-level np_oilgas inventories. Note that portions of Texas are in three different Supply Regions and portions of New Mexico are in two different supply regions. The state-level historical factor (2016 to 2017 or 2018) was then multiplied by the Supply Region factor (2017 or 2018 to future years) to produce a state-level or FIPS-level factor to grow from 2016 to 2023 and from 2016 to 2028. This process was done using crude production forecast information to generate a factor to apply to oil-production related SCCs or NAICS-SCC combinations and it was also done using natural gas production forecast information to generate a factor to apply to natural gas-production related NAICS-SCC combinations. For the NAICS-SCC combinations that are designated "BOTH" the average of the oil-production and natural-gas production factors was calculated and applied to these specific combinations.   

The state of Texas provided specific technical direction for growth of production-related point sources. Texas provided updated basin specific production for 2016 and 2017 to allow for a better calculation of the estimated growth for this one-year period. The AEO2019 was used as described above for the three AEO Oil and Gas Supply Regions that include Texas counties to grow from 2017 to 2023 and 2028 years. However, Texas only wanted these growth factors applied to sources in the Permian and Eagle Ford basins. The oil and gas production point sources in the other basins in Texas were not grown (i.e., 2016v1=2023=2028 emissions).

Transmission-related Sources (pt_oilgas)

Projection factors were generated using the same AEO2019 tables used for production sources.  The growth factors for transmission sources were developed solely using AEO 2019 data by Oil and Gas Supply Regions shown in Figure 4-1. Additionally, limits were put on these regional factors where the minimum factor was set to 1.0 and the maximum factor was set to 1.5. The states of Virginia and Pennsylvania provided source specific growth factors for natural gas transmission sources to be used in place of the AEO regional factors.

Exploration-related Sources (np_oilgas)
Due to Year 2016 being a low exploration activity year when compared to exploration activity in other recent years, Years 2014 through 2017 exploration activity data were averaged and the average activity input into EPA's Oil and Gas Tool to produce "averaged" emissions for exploration sources (Table 4-12). This four-year average (2014-2017) activity data were used because they were readily available for use with the 2016v1 platform. These averaged emissions were used for both the 2023 and 2028 future years in the 2016v1 emissions modeling platform. Colorado, Pennsylvania, California, and Oklahoma submitted inventories for use. Note CoST was not used for this step for exploration sources.    

Table 4-12.  Year 2014-2017 high-level summary of national oil and gas exploration activity
                           Parameter (all US states)
                                   Year2014
                                   Year2015
                                   Year2016
                                   Year2017
                                4-year average
Total Well Completions
                                                                         40,306
                                                                         22,754
                                                                         15,605
                                                                         21,850
                                                                         25,129
Unconventional Well Completions
                                                                         20,896
                                                                         11,673
                                                                          7,610
                                                                         11,617
                                                                         12,949
Total Oil Spuds
                                                                         36,104
                                                                         17,240
                                                                          7,014
                                                                         14,322
                                                                         18,670
Total Natural Gas Spuds
                                                                          4,750
                                                                          3,168
                                                                          4,244
                                                                          4,025
                                                                          4,047
Total Coalbed Methane Spuds
                                                                            239
                                                                            130
                                                                            141
                                                                            222
                                                                            183
Total Spuds
                                                                         41,093
                                                                         20,538
                                                                         11,399
                                                                         18,569
                                                                         22,900
Total Feet Drilled
                                                                    327,832,580
                                                                    178,297,779
                                                                    106,468,774
                                                                    181,164,800
                                                                    198,440,983


 Non-EGU point sources (ptnonipm)
The 2023 and 2028 ptnonipm projections involved several growth and projection methods described here. The projection of all oil and gas sources is explained in the oil and gas specification sheet and will not be discussed in these methods. 
2023 and 2028 Point Inventory - inside MARAMA region

2016-to-2023 and 2016-to-2028 projection packets for point sources were provided by MARAMA for the following states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV. 

The MARAMA projection packets were used throughout the MARAMA region, except in North Carolina, New Jersey, and Virginia. Those three states provided their own projection packets for the ptnonipm sector, and those projection packets were used instead of the MARAMA packets in those states. The Virginia growth factors for one facility were edited to incorporate emissions limits provided by MARAMA for that facility.

2023 and 2028 Point Inventory - outside MARAMA region

The Energy Information Administration's (EIA) AEO for year 2019 was used as a starting point for projecting industrial sources in this sector. SCC's were mapped to AEO categories and projection factors were created using a ratio between the base year and projection year estimates from each specific AEO category. Table 4-13 below details the 2019 AEO tables used to map SCCs to AEO categories for the projections of industrial sources. Depending on the category, a projection factor may be national or regional. The maximum projection factor was capped at 1.25 and the minimum projection factor was capped at 0.5. MARAMA states were not projected using this method, nor were aircraft and rail sources.
An SCC-NAICS projection was also developed using AEO2019. SCC/NAICS combinations with emissions >100tons/year for any CAP were mapped to AEO sector and fuel. Projection factors for this method were capped at a maximum of 2.5 and a minimum of 0.5.
Table 4-13. EIA's 2019 Annual Energy Outlook (AEO) tables used to project industrial sources
Table #
Table name
                                       2
Energy Consumption by Sector and Source
                                      25
Refining Industry Energy Consumption
                                      26
Food Industry Energy Consumption
                                      27
Paper Industry Energy Consumption
                                      28
Bulk Chemical Industry Energy Consumption
                                      29
Glass Industry Energy Consumption
                                      30
Cement Industry Energy Consumption
                                      31
Iron and Steel Industries Energy Consumption
                                      32
Aluminum Industry Energy Consumption
                                      33
Metal Based Durables Energy Consumption
                                      34
Other Manufacturing Sector Energy Consumption
                                      35
Nonmanufacturing Sector Energy Consumption
The state of Wisconsin provided source-specific growth factors for four facilities in the state. For those facilities, the growth factors provided by Wisconsin were used instead of those derived from the AEO.
 Nonpoint Sources (nonpt)

Inside MARAMA region

2016-to-2023 and 2016-to-2028 projection packets for all nonpoint sources were provided by MARAMA for the following states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV. MARAMA provided one projection packet per year for portable fuel containers (PFCs), and a second projection packet per year for all other nonpt sources.

The MARAMA projection packets were used throughout the MARAMA region, except in North Carolina and New Jersey. Both NC and NJ provided separate projection packets for the nonpt sector, and those projection packets were used instead of the MARAMA packets in those two states. New Jersey did not provide projection factors for PFCs, and so NJ PFCs were projected using the MARAMA PFC growth packet.

Industrial Sources outside MARAMA region

The EIA's AEO for year 2019 was used as a starting point for projecting industrial sources in this sector. SCC's were mapped to AEO categories and projection factors were created using a ratio between the base year and projection year estimates from each specific AEO category. For the nonpoint sector, only 2018 AEO Table 2 was used to map SCCs to AEO categories for the projections of industrial sources. Depending on the category, a projection factor may be national or regional. The maximum projection factor was capped at a factor of 1.25 and the minimum projection factor was capped at 0.5. Aircraft and rail sources were not projected using this method. Sources within the MARAMA region were not projected with these factors, but with the MARAMA-provided growth factors.
Evaporative Emissions from Transport of Finished Fuels outside MARAMA region

Estimates on growth of evaporative emissions from transporting finished fuels are partially covered in the nonpoint and point oil and gas projection packets.  However, there are some processes with evaporative emissions from storing and transporting finished fuels which are not included in the nonpoint and point oil and gas projection packets, e.g., withdrawing fuel from tanks at bulk plants, filling tanks at service stations, etc., and those processes are included in nonpoint other.  The EIA's AEO for year 2018 was used as a starting point for projecting volumes of finished fuel that would be transported in future years, i.e., 2023 and 2028.  Then these volumes were used to calculate inventories associated with evaporative emissions in 2016, 2023, and 2028 using the upstream modules.  Those emission inventories were mapped to the appropriate SCCs and projection packets were generated from 2016 to 2023 and 2016 to 2028 using the upstream modules.  Sources within the MARAMA region were not projected with these factors, but with the MARAMA-provided growth factors.
Human Population Growth outside MARAMA region
For SCCs that are projected based on human population growth, population projection data were available from the Benefits Mapping and Analysis Program (BenMAP) model by county for several years, including 2017, 2023, and 2028.  These human population data were used to create modified county-specific projection factors. Note that 2017 is being used as the base year since 2016 human population is not available in this dataset. A newer human population dataset was assessed but it did not have trustworthy near-term (e.g., 2023/2028) projections, and was not used; for example, rural areas of NC were projected to have more growth than urban areas, which is the opposite of what one would expect. Growth factors were limited to a range of 0.9-1.35 for 2023 and 0.85-1.6 for 2028, but none of the factors fell outside that range. (The 1.35 and 1.6 caps are based on 5% annual growth.) Sources within the MARAMA region were not projected with these factors, but with the MARAMA-provided growth factors.
 Airport sources (airports)
Airports emissions were projected to 2023 and 2028 mostly using 2018 Terminal Area Forecast (TAF) data available from the Federal Aviation Administration (https://www.faa.gov/data_research/aviation/taf/). Projection factors were computed using the ratio of the itinerant (ITN) data between the base and projection year. For airports not matching a unit in the TAF data, state default growth factors by itinerant class (commercial, air taxi, and general) were created from the collection of airports unmatched. Emission growth for facilities is capped at 500% and the state default growth is capped at 200%. Military state default projection values were kept flat (i.e., equal to 1.0) to reflect uncertainly in the data regarding these sources.
 Residential Wood Combustion (rwc)
For states other than California, Oregon, and Washington, RWC emissions from 2016 were projected to 2023 and 2028 using projection factors derived using the MARAMA tool that is based on the projection methodology from EPA's 2011v6.3 platform.  Table 4-14 contains the factors to adjust the emissions from 2016 to 2023 and 2028. California, Oregon, and Washington RWC were held constant at NEI2014v2 levels for 2016, 2023, and 2028 due to the unique control programs those states have in place.
Table 4-14. Projection factors for RWC
SCC

SCC description
Pollutant*

                                 2016-to-2023
                                 2016-to-2028
2104008100
Fireplace: general

                                     7.19%
                                    12.36%
2104008210
Woodstove: fireplace inserts; non-EPA certified

                                    -13.92%
                                    -17.97%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic
PM10-PRI
                                     4.09%
                                     5.08%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic
PM25-PRI
                                     4.09%
                                     5.08%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic

                                     8.34%
                                    10.28%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic
PM10-PRI
                                     6.06%
                                     7.68%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic
PM25-PRI
                                     6.06%
                                     7.68%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic

                                    12.08%
                                    15.27%
2104008310
Woodstove: freestanding, non-EPA certified
CO
                                    -12.09%
                                    -15.72%
2104008310
Woodstove: freestanding, non-EPA certified
PM10-PRI
                                    -12.67%
                                    -16.52%
2104008310
Woodstove: freestanding, non-EPA certified
PM25-PRI
                                    -12.67%
                                    -16.52%
2104008310
Woodstove: freestanding, non-EPA certified
VOC
                                    -11.40%
                                    -14.84%
2104008310
Woodstove: freestanding, non-EPA certified

                                    -12.09%
                                    -15.72%
2104008320
Woodstove: freestanding, EPA certified, non-catalytic
PM10-PRI
                                     4.09%
                                     5.08%
2104008320
Woodstove: freestanding, EPA certified, non-catalytic
PM25-PRI
                                     4.09%
                                     5.08%
2104008320
Woodstove: freestanding, EPA certified, non-catalytic

                                     8.34%
                                    10.28%
2104008330
Woodstove: freestanding, EPA certified, catalytic
PM10-PRI
                                     6.07%
                                     7.69%
2104008330
Woodstove: freestanding, EPA certified, catalytic
PM25-PRI
                                     6.07%
                                     7.69%
2104008330
Woodstove: freestanding, EPA certified, catalytic

                                    12.08%
                                    15.27%
2104008400
Woodstove: pellet-fired, general (freestanding or FP insert)
PM10-PRI
                                    30.09%
                                    38.02%
2104008400
Woodstove: pellet-fired, general (freestanding or FP insert)
PM25-PRI
                                    30.09%
                                    38.02%
2104008400
Woodstove: pellet-fired, general (freestanding or FP insert)

                                    26.96%
                                    33.85%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
CO
                                    -64.93%
                                    -84.78%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM10-PRI
                                    -62.99%
                                    -82.89%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM25-PRI
                                    -62.99%
                                    -82.89%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
VOC
                                    -65.02%
                                    -84.89%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified

                                    -64.93%
                                    -84.78%
2104008610
Hydronic heater: outdoor
PM10-PRI
                                     0.06%
                                    -0.40%
2104008610
Hydronic heater: outdoor
PM25-PRI
                                     0.06%
                                    -0.40%
2104008610
Hydronic heater: outdoor

                                    -0.73%
                                    -1.30%
2104008700
Outdoor wood burning device, NEC (fire-pits, chimineas, etc)

                                     7.19%
                                     9.25%
2104009000
Fire log total

                                     7.19%
                                     9.25%
  * If no pollutant is specified, facture is used for any pollutants that do not have a pollutant-specific factor
 CoST CONTROL Packets (nonpt, np_oilgas, ptnonipm, pt_oilgas)
The final step in the projection of emissions to a future year is the application of any control technologies or programs. For future-year New Source Performance Standards (NSPS) controls (e.g., oil and gas, Reciprocating Internal Combustion Engines (RICE), Natural Gas Turbines, and Process Heaters), we attempted to control only new sources/equipment using the following equation to account for growth and retirement of existing sources and the differences between the new and existing source emission rates.

                                       
Qn  =   Qo { [ (1 + Pf ) t  -  1 ] Fn + ( 1 - Ri ) t  Fe + [ 1 - ( 1 - Ri ) t ] Fn ] }
Eq. 4-1
where:
      Qn  =  emissions in projection year
      Qo  =  emissions in base year
      Pf  =  growth rate expressed as ratio (e.g., 1.5=50 percent cumulative growth)
      t  =  number of years between base and future years
      Fn  =  emission factor ratio for new sources
      Ri  =  retirement rate, expressed as whole number (e.g., 3.3 percent=0.033)
      Fe  =  emission factor ratio for existing sources
The first term in Eq. 4-1 represents new source growth and controls, the second term accounts for retirement and controls for existing sources, and the third term accounts for replacement source controls.   For computing the CoST % reductions (Control Efficiency), the simplified Eq. 4-2 was used for 2028 projections:
                                       
    Control Efficiency2028%=100x 1-Pf2028-1xFn+1-Ri12+1-1-Ri12xFnPf2028 
Eq. 4-2

Here, the existing source emissions factor (Fe) is set to 1.0, 2028 (future year) minus 2016 (base year) is 12, and new source emission factor (Fn) is the ratio of the NSPS emission factor to the existing emission factor.  Table 4-15 shows the values for Retirement rate and new source emission factors (Fn) for new sources with respect to each NSPS regulation and other conditions within. For the nonpt sector, the RICE NSPS control program was applied when estimating year 2023 and 2028 emissions for the 2016v1 modeling platform.  Further information about the application of NSPS controls can be found in Section 4 of the Additional Updates to Emissions Inventories for the Version 6.3, 2011 Emissions Modeling Platform for the Year 2023 technical support document (https://www.epa.gov/sites/production/files/2017-11/documents/2011v6.3_2023en_update_emismod_tsd_oct2017.pdf).

Table 4-15. Assumed retirement rates and new source emission factor ratios for NSPS rules
NSPS Rule
Sector(s)
Retirement Rate years (%/year)
Pollutant Impacted
Applied where?
New Source Emission Factor (Fn)
Oil and Gas


np_oilgas, pt_oilgas
No assumption
VOC
Storage Tanks: 70.3% reduction in growth-only (>1.0)
0.297




Gas Well Completions: 95% control (regardless)
0.05




Pneumatic controllers, not high-bleed >6scfm or low-bleed: 77% reduction in growth-only (>1.0)
0.23




Pneumatic controllers, high-bleed >6scfm or low-bleed: 100% reduction in growth-only (>1.0)
0.00




Compressor Seals: 79.9% reduction in growth-only (>1.0)
0.201




Fugitive Emissions: 60% Valves, flanges, connections, pumps, open-ended lines, and other
0.40	




Pneumatic Pumps: 71.3%; Oil and Gas
0.287
RICE
np_oilgas, pt_oilgas, nonpt, ptnonipm
40, (2.5%)
NOX
Lean burn: PA, all other states
0.25, 0.606




Rich Burn: PA, all other states
0.1, 0.069




Combined (average) LB/RB: PA, other states
0.175, 0.338



CO
Lean burn: PA, all other states
1.0 (n/a), 0.889




Rich Burn: PA, all other states
0.15, 0.25




Combined (average) LB/RB: PA, other states
0.575, 0.569



VOC
Lean burn: PA, all other states
0.125, n/a




Rich Burn: PA, all other states
0.1, n/a




Combined (average) LB/RB: PA, other states
0.1125, n/a
Gas Turbines
pt_oilgas, ptnonipm
45 (2.2%)
NOX
California and NOX SIP Call states
0.595




All other states
0.238
Process Heaters
pt_oilgas, ptnonipm
30 (3.3%)
NOX
Nationally to Process Heater SCCs
0.41

 Oil and Gas NSPS (np_oilgas, pt_oilgas)
For oil and gas NSPS controls, except for gas well completions (a 95 percent control), the assumption of no equipment retirements through year 2028 dictates that NSPS controls are applied to the growth component only of any PROJECTION factors.  For example, if a growth factor is 1.5 for storage tanks (indicating a 50 percent increase activity), then, using Table 4-15, the 70.3 percent VOC NSPS control to this new growth will result in a 23.4 percent control: 100 *(70.3 * (1.5 -1) / 1.5); this yields an "effective" growth rate (combined PROJECTION and CONTROL) of 1.1485, or a 70.3 percent reduction from 1.5 to 1.0.  The impacts of all non-drilling completion VOC NSPS controls are therefore greater where growth in oil and gas production is assumed highest.  Conversely, for oil and gas basins with assumed negative growth in activity/production, VOC NSPS controls will be limited to well completions only.  These reductions are year-specific because projection factors for these sources are year-specific.    Table 4-16 (np_oilgas) and Table 4-18 (pt_oilgas) list the SCCs where Oil and Gas NSPS controls were applied; note controls are applied to production and exploration-related SCCs.  Table 4-17 (np_oilgas) and Table 4-19 (pt_oilgas) shows the reduction in VOC emissions after the application of the Oil and Gas NSPS CONTROL packet for both future years 2023 and 2028.
Table 4-16. Non-point (np_oilgas) SCCs in 2016v1 modeling platform where Oil and Gas NSPS controls applied
                                      SCC
                                   SRC_TYPE
OILGAS NSPS CATEGORY
TOOL OR STATE SCC
SRC CAT TYPE
SCCDESC
                                  2310010200
                                      OIL
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; Crude Petroleum; Oil Well Tanks - Flashing & Standing/Working/Breathing
                                  2310010300
                                      OIL
3. Pnuematic controllers: not high or low bleed
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; Crude Petroleum; Oil Well Pneumatic Devices
                                  2310011500
                                      OIL
5. Fugitives
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Oil Production; Fugitives: All Processes
                                  2310011501
                                      OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Oil Production; Fugitives: Connectors
                                  2310011502
                                      OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Oil Production; Fugitives: Flanges
                                  2310011503
                                      OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Oil Production; Fugitives: Open Ended Lines
                                  2310011505
                                      OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Oil Production; Fugitives:  Valves
                                  2310021010
                                     NGAS
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Storage Tanks: Condensate
                                  2310021300
                                     NGAS
3. Pnuematic controllers: not high or low bleed
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Gas Well Pneumatic Devices
                                  2310021310
                                     NGAS
6. Pneumatic Pumps
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Gas Well Pneumatic Pumps
                                  2310021501
                                     NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Fugitives: Connectors
                                  2310021502
                                     NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Fugitives: Flanges
                                  2310021503
                                     NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Fugitives: Open Ended Lines
                                  2310021505
                                     NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Fugitives:  Valves
                                  2310021506
                                     NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Fugitives:  Other
                                  2310021509
                                     NGAS
5. Fugitives
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Fugitives: All Processes
                                  2310021601
                                     NGAS
2. Well Completions
STATE
EXPLORATION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Gas Well Venting - Initial Completions
                                  2310030300
                                     NGAS
1. Storage Tanks
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; Natural Gas Liquids; Gas Well Water Tank Losses
                                  2310111401
                                      OIL
6. Pneumatic Pumps
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Oil Exploration; Oil Well Pneumatic Pumps
                                  2310111700
                                      OIL
2. Well Completions
TOOL
EXPLORATION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Oil Exploration; Oil Well Completion: All Processes
                                  2310121401
                                     NGAS
6. Pneumatic Pumps
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Exploration; Gas Well Pneumatic Pumps
                                  2310121700
                                     NGAS
2. Well Completions
TOOL
EXPLORATION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Exploration; Gas Well Completion: All Processes
                                  2310421010
                                     NGAS
1. Storage Tanks
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production - Unconventional; Storage Tanks: Condensate
                                  2310421700
                                     NGAS
2. Well Completions
STATE
EXPLORATION
Gas Well Completion: All Processes Unconventional

Table 4-17. Emissions reductions for np_oilgas sector due to application of Oil and Gas NSPS
year
poll
2016v1
2016
pre-CoST
emissions
emissions
change from 2016
% change
                                                                           2023
VOC
                                                                        2817303
                                                                        2881217
                                                                        -863524
                                                                         -30.0%
                                                                           2028
VOC
                                                                        2817303
                                                                        2881217
                                                                       -1077514
                                                                         -37.4%

Table 4-18. Point source SCCs in pt_oilgas sector where Oil and Gas NSPS controls were applied.
SCC
                                 FUEL PRODUCED
                             OILGAS NSPS CATEGORY
SCCDESC
                                                                       31000101
                                      Oil
                              2. Well Completions
Industrial Processes; Oil and Gas Production; Crude Oil Production; Well Completion
                                                                       31000130
                                      Oil
                              4. Compressor Seals
Industrial Processes; Oil and Gas Production; Crude Oil Production; Fugitives: Compressor Seals
                                                                       31000133
                                      Oil
                               1. Storage Tanks
Industrial Processes; Oil and Gas Production; Crude Oil Production; Storage Tank
                                                                       31000151
                                      Oil
                  3. Pnuematic controllers: high or low bleed
Industrial Processes; Oil and Gas Production; Crude Oil Production; Pneumatic Controllers, Low Bleed
                                                                       31000152
                                      Oil
                  3. Pnuematic controllers: high or low bleed
Industrial Processes; Oil and Gas Production; Crude Oil Production; Pneumatic Controllers High Bleed >6 scfh
                                                                       31000207
                                      Gas
                                 5. Fugitives
Industrial Processes; Oil and Gas Production; Natural Gas Production; Valves: Fugitive Emissions
                                                                       31000220
                                      Gas
                                 5. Fugitives
Industrial Processes; Oil and Gas Production; Natural Gas Production; All Equipt Leak Fugitives (Valves, Flanges, Connections, Seals, Drains
                                                                       31000222
                                      Gas
                              2. Well Completions
Industrial Processes; Oil and Gas Production; Natural Gas Production; Well Completions
                                                                       31000225
                                      Gas
                              4. Compressor Seals
Industrial Processes; Oil and Gas Production; Natural Gas Production; Compressor Seals
                                                                       31000233
                                      Gas
                  3. Pnuematic controllers: high or low bleed
Industrial Processes; Oil and Gas Production; Natural Gas Production; Pneumatic Controllers, Low Bleed
                                                                       31000309
                                      Gas
                              4. Compressor Seals
Industrial Processes; Oil and Gas Production; Natural Gas Processing; Compressor Seals
                                                                       31000324
                                      Gas
                  3. Pnuematic controllers: high or low bleed
Industrial Processes; Oil and Gas Production; Natural Gas Processing; Pneumatic Controllers Low Bleed
                                                                       31000325
                                      Gas
                  3. Pnuematic controllers: high or low bleed
Industrial Processes; Oil and Gas Production; Natural Gas Processing; Pneumatic Controllers, High Bleed >6 scfh
                                                                       31088811
                                     Both
                                 5. Fugitives
Industrial Processes; Oil and Gas Production; Fugitive Emissions; Fugitive Emissions
                                       
Table 4-19. VOC reductions (tons/year) for the pt_oilgas sector after application of the Oil and Gas NSPS CONTROL packet for both future years 2023 and 2028.
Year
Pollutant
2016v1
Emissions Reductions
% change
                                                                           2023
VOC
                                                                        129,253
                                                                         -2,523
                                                                          -2.0%
                                                                           2028
VOC
                                                                        129,253
                                                                         -2,808
                                                                          -2.2%

 RICE NSPS (nonpt, ptnonipm, np_oilgas, pt_oilgas)

For RICE NSPS controls, the EPA emission requirements for stationary engines differ according to whether the engine is new or existing, whether the engine is located at an area source or major source, and whether the engine is a compression ignition or a spark ignition engine.  Spark ignition engines are further subdivided by power cycle, two-stroke versus four-stroke, and whether the engine is rich burn or lean burn.  We applied NSPS reduction for lean burn, rich burn and "combined" engines using Eq. 4-2 and information listed in Table 4-15. Table 4-20, Table 4-21 and Table 4-25 list the SCCs where RICE NSPS controls were applied for the 2016v1 platform. Table 4-22, Table 4-23, Table 4-24 and Table 4-26 show the reductions in emissions in the nonpoint, ptnonipm, and nonpoint oil and gas sectors after the application of the RICE NSPS CONTROL packet for both future years 2023 and 2028. Note that for nonpoint oil and gas, VOC reductions were only appropriate in the state of Pennsylvania.

Table 4-20. SCCs and Engine Type in 2016v1 modeling platform where RICE NSPS controls applied for nonpt and ptnonipm sectors.
SCC
Lean, Rich, or Combined
SCCDESC
                                                                       20200202
Combined
Internal Combustion Engines; Industrial; Natural Gas; Reciprocating
                                                                       20200253
Rich
Internal Combustion Engines; Industrial; Natural Gas; 4-cycle Rich Burn
                                                                       20200254
Lean
Internal Combustion Engines; Industrial; Natural Gas; 4-cycle Lean Burn
                                                                       20200256
Lean
Internal Combustion Engines; Industrial; Natural Gas; 4-cycle Clean Burn
                                                                       20300201
Combined
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Reciprocating
                                                                     2102006000
Combined
Stationary Source Fuel Combustion; Industrial; Natural Gas; Total: Boilers and IC Engines
                                                                     2102006002
Combined
Stationary Source Fuel Combustion; Industrial; Natural Gas; All IC Engine Types
                                                                     2103006000
Combined
Stationary Source Fuel Combustion; Commercial/Institutional; Natural Gas; Total: Boilers and IC Engines

Table 4-21. Non-point Oil and Gas SCCs in 2016v1 modeling platform where RICE NSPS controls applied
SCC
Lean, Rich, or Combined category
SRC_TYPE
TOOL OR STATE SCC
SRC CAT TYPE
SCCDESC
2310000220
Combined
BOTH
TOOL
EXPLORATION
Industrial Processes; Oil and Gas Exploration and Production; All Processes; Drill Rigs
2310000660
Combined
BOTH
TOOL
EXPLORATION
Industrial Processes; Oil and Gas Exploration and Production; All Processes; Hydraulic Fracturing Engines
2310020600
Combined
NGAS
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; Natural Gas; Compressor Engines
2310021202
Lean
NGAS
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Natural Gas Fired 4Cycle Lean Burn Compressor Engines 50 To 499 HP
2310021251
Lean
NGAS
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Lateral Compressors 4 Cycle Lean Burn
2310021302
Rich
NGAS
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Natural Gas Fired 4Cycle Rich Burn Compressor Engines 50 To 499 HP
2310021351
Rich
NGAS
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production; Lateral Compressors 4 Cycle Rich Burn
2310023202
Lean
CBM
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; Coal Bed Methane Natural Gas; CBM Fired 4Cycle Lean Burn Compressor Engines 50 To 499 HP
2310023251
Lean
CBM
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; Coal Bed Methane Natural Gas; Lateral Compressors 4 Cycle Lean Burn
2310023302
Rich
CBM
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; Coal Bed Methane Natural Gas; CBM Fired 4Cycle Rich Burn Compressor Engines 50 To 499 HP
2310023351
Rich
CBM
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and Production; Coal Bed Methane Natural Gas; Lateral Compressors 4 Cycle Rich Burn
2310400220
Combined
BOTH
STATE
EXPLORATION
Industrial Processes; Oil and Gas Exploration and Production; All Processes - Unconventional; Drill Rigs

Table 4-22. Nonpoint Emissions reductions after the application of the RICE NSPS
year
poll
2016v1 (tons)
emissions reductions (tons)
% change
                                                                           2023
CO
                                                                      2,688,250
                                                                        -16,982
                                                                          -0.6%
                                                                           2023
NOX
                                                                        718,766
                                                                        -23,704
                                                                          -3.3%
                                                                           2028
CO
                                                                      2,688,250
                                                                        -23,145
                                                                          -0.9%
                                                                           2028
NOX
                                                                        718,766
                                                                        -33,621
                                                                          -4.7%

Table 4-23. Ptnonipm Emissions reductions after the application of the RICE NSPS
year
poll
2016v1 (tons)
emissions reductions (tons)
% change
                                                                           2023
CO
                                                                      1,446,353
                                                                         -2,756
                                                                          -0.2%
                                                                           2023
NOX
                                                                        952,181
                                                                         -3,400
                                                                          -0.4%
                                                                           2023
VOC
                                                                        774,289
                                                                             -2
                                                                           0.0%
                                                                           2028
CO
                                                                      1,446,353
                                                                         -3,295
                                                                          -0.2%
                                                                           2028
NOX
                                                                        952,181
                                                                         -4,232
                                                                          -0.4%
                                                                           2028
VOC
                                                                        774,289
                                                                             -3
                                                                           0.0%

Table 4-24. Oil and Gas Emissions reductions for np_oilgas sector due to application of RICE NSPS
year
poll
2016v1
2016
pre-CoST
 emissions
emissions 
reduction
% change
                                                                           2023
CO
                                                                         762706
                                                                         767414
                                                                        -106005
                                                                         -13.8%
                                                                           2023
NOX
                                                                         574133
                                                                         598738
                                                                         -93806
                                                                         -15.7%
                                                                           2023
VOC
                                                                        2817303
                                                                        2881217
                                                                           -525
                                                                         -0.02%
                                                                           2028
CO
                                                                         762706
                                                                         767414
                                                                        -145622
                                                                         -19.0%
                                                                           2028
NOX
                                                                         574133
                                                                         598738
                                                                        -134144
                                                                         -22.4%
                                                                           2028
VOC
                                                                        2817303
                                                                        2881217
                                                                           -785
                                                                         -0.03%

Table 4-25. Point source SCCs in pt_oilgas sector where RICE NSPS controls applied.
SCC
Lean, Rich, or Combined
SCCDESC
                                                                       20200202
Combined
Internal Combustion Engines; Industrial; Natural Gas; Reciprocating
                                                                       20200253
Rich
Internal Combustion Engines; Industrial; Natural Gas;4-cycle Rich Burn
                                                                       20200254
Lean
Internal Combustion Engines; Industrial; Natural Gas;4-cycle Lean Burn
                                                                       20200256
Combined
Internal Combustion Engines; Industrial; Natural Gas;4-cycle Clean Burn
                                                                       20300201
Combined
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Reciprocating
                                                                       31000203
Combined
Industrial Processes; Oil and Gas Production; Natural Gas Production; Compressors (See also 310003-12 and -13)

Table 4-26. Emissions reductions (tons/year) in pt_oilgas sector after the application of the RICE NSPS CONTROL packet for future years 2023 and 2028.
Year
Pollutant
2016v1 
Emissions Reductions 
% change
                                                                           2023
CO
                                                                        177,690
                                                                        -20,258
                                                                         -11.4%
                                                                           2023
NOX
                                                                        379,866
                                                                        -53,694
                                                                         -14.1%
                                                                           2023
VOC
                                                                        129,253
                                                                           -436
                                                                          -0.3%
                                                                           2028
CO
                                                                        177,690
                                                                        -26,095
                                                                         -14.7%
                                                                           2028
NOX
                                                                        379,866
                                                                        -70,659
                                                                         -18.6%
                                                                           2028
VOC
                                                                        129,253
                                                                           -512
                                                                          -0.4%

 Fuel Sulfur Rules (nonpt, ptnonipm)
Fuel sulfur rules, based on web searching and the 2011 emissions modeling notice of data availability (NODA) comments, are currently limited to the following states: Connecticut, Delaware, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. The fuel limits for these states are incremental starting after year 2012, but are fully implemented by July 1, 2018, in all of these states. The control packet representing these controls was updated by MARAMA for version 1 platform.

Summaries of the sulfur rules by state, with emissions reductions are provided in Table 4-27 and Table 4-28. These tables reflect the impacts of the MARAMA packet only, as these reductions are not estimated in non-MARAMA states. Most of these reductions occur in the nonpt sector; a small amount of reductions occurs in the ptnonipm sector, and a negligible amount of reductions occur in the pt_oilgas sector.

Table 4-27. Summary of fuel sulfur rule impacts on nonpoint SO2 emissions for 2023 and 2028
year
poll
2016v1 (tons)
emissions reductions (tons)
% change
                                                                           2023
SO2
                                                                        140,469
                                                                        -28,137
                                                                         -20.0%
                                                                           2028
SO2
                                                                        140,469
                                                                        -24,200
                                                                         -17.2%

Table 4-28. Summary of fuel sulfur rule impacts on ptnonipm SO2 emissions for 2023 and 2028
year
poll
2016v1 (tons)
emissions reductions (tons)
% change
                                                                           2023
SO2
                                                                        658,204
                                                                         -1,183
                                                                          -0.2%
                                                                           2028
SO2
                                                                        658,204
                                                                         -1,241
                                                                          -0.2%

 Natural Gas Turbines NOx NSPS (ptnonipm, pt_oilgas)
Natural Gas Turbines NSPS controls were generated based on examination of emission limits for stationary combustion turbines that are not in the power sector.  In 2006, the EPA promulgated standards of performance for new stationary combustion turbines in 40 CFR part 60, subpart KKKK.  The standards reflect changes in NOx emission control technologies and turbine design since standards for these units were originally promulgated in 40 CFR part 60, subpart GG.  The 2006 NSPSs affecting NOx and SO2 were established at levels that bring the emission limits up-to-date with the performance of current combustion turbines.  Stationary combustion turbines were also regulated by the NOx State Implementation Plan (SIP) Call, which required affected gas turbines to reduce their NOx emissions by 60 percent.  Table 4-29 compares the 2006 NSPS emission limits with the NOx Reasonably Available Control Technology (RACT) regulations in selected states within the NOx SIP Call region.  The map showing the states and partial-states in the NOx SIP Call Program can be found at: http://www3.epa.gov/airmarkets/progress/reports/program_basics.html. The state NOx RACT regulations summary (Pechan, 2001) is from a year 2001 analysis, so some states may have updated their rules since that time.

Table 4-29. Stationary gas turbines NSPS analysis and resulting emission rates used to compute controls
NOx Emission Limits for New Stationary Combustion Turbines
Firing Natural Gas
<50 MMBTU/hr
50-850 MMBTU/hr
>850 MMBTU/hr
 
Federal NSPS
100
25
15
ppm
 
 
 
 
 
State RACT Regulations
5-100 MMBTU/hr
100-250 MMBTU/hr
>250 MMBTU/hr
 
Connecticut
225
75
75
ppm
Delaware
42
42
42
ppm
Massachusetts
65*
65
65
ppm
New Jersey
50*
50
50
ppm
New York
50
50
50
ppm
New Hampshire
55
55
55
ppm
* Only applies to 25-100 MMBTU/hr
Notes: The above state RACT table is from a 2001 analysis. The current NY State regulations have the same emission limits.
New source emission rate (Fn)
NOX ratio (Fn)
Control (%)
NOx SIP Call states plus CA
= 25 / 42 = 
0.595
40.5%
Other states
= 25 / 105 = 
0.238
76.2%

For control factor development, the existing source emission ratio was set to 1.0 for combustion turbines. The new source emission ratio for the NOx SIP Call states and California is the ratio of state NOx emission limit to the Federal NSPS.  A complicating factor in the above is the lack of size information in the stationary source SCCs.  Plus, the size classifications in the NSPS do not match the size differentiation used in state air emission regulations.  We accepted a simplifying assumption that most industrial applications of combustion turbines are in the 100-250 MMBtu/hr size range and computed the new source emission rates as the NSPS emission limit for 50-850 MMBtu/hr units divided by the state emission limits.  We used a conservative new source emission ratio by using the lowest state emission limit of 42 ppmv (Delaware).  This yields a new source emission ratio of 25/42, or 0.595 (40.5 percent reduction) for states with existing combustion turbine emission limits.  States without existing turbine NOx limits would have a lower new source emission ratio -the uncontrolled emission rate (105 ppmv via AP-42) divided into 25 ppmv = 0.238 (76.2 percent reduction).  This control was then plugged into Eq. 4-2 as a function of the year-specific projection factor.  Also, Natural Gas Turbines control factors supplied by MARAMA were used within the MARAMA region.

Table 4-30 and Table 4-32 list the point source SCCs where Natural Gas Turbines NSPS controls were applied for the 2016v1 platform. Table 4-31 and Table 4-33 show the reduction in NOx emissions after the application of the Natural Gas Turbines NSPS CONTROL packet for both future years 2023 and 2028. The values in Table 4-31 and Table 4-33 include emissions both inside and outside the MARAMA region.

Table 4-30. Ptnonipm SCCs in 2016v1 modeling platform where Natural Gas Turbines NSPS controls applied
SCC
SCC description
20200201
Internal Combustion Engines; Industrial; Natural Gas; Turbine
20200203
Internal Combustion Engines; Industrial; Natural Gas; Turbine: Cogeneration
20200209
Internal Combustion Engines; Industrial; Natural Gas; Turbine: Exhaust
20200701
Internal Combustion Engines; Industrial; Process Gas; Turbine
20200714
Internal Combustion Engines; Industrial; Process Gas; Turbine: Exhaust
20300202
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine
20300203
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine: Cogeneration

Table 4-31. Ptnonipm emissions reductions after the application of the Natural Gas Turbines NSPS
year
poll
2016v1 (tons)
emissions reduction (tons)
% change
                                                                           2023
NOX
                                                                        952,181
                                                                         -2,531
                                                                          -0.3%
                                                                           2028
NOX
                                                                        952,181
                                                                         -3,346
                                                                          -0.4%
Table 4-32. Point source SCCs in pt_oilgas sector where Natural Gas Turbines NSPS control applied.
SCC
SCC description
20200201
Internal Combustion Engines; Industrial; Natural Gas; Turbine
20200209
Internal Combustion Engines; Industrial; Natural Gas; Turbine: Exhaust
20300202
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine
20300209
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine: Exhaust
20200203
Internal Combustion Engines; Industrial; Natural Gas; Turbine: Cogeneration
20200714
Internal Combustion Engines; Industrial; Process Gas; Turbine: Exhaust
20300203
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine: Cogeneration 
Table 4-33. Emissions reductions (tons/year) for pt_oilgas after the application of the Natural Gas Turbines NSPS CONTROL packet for future years 2023 and 2028.
Year
Pollutant
2016v1
Emissions Reduction
% change
                                                                           2023
NOX
                                                                        379,866
                                                                         -8,079
                                                                          -2.1%
                                                                           2028
NOX
                                                                        379,866
                                                                        -11,282
                                                                          -3.0%

 Process Heaters NOx NSPS (ptnonipm, pt_oilgas)
Process heaters are used throughout refineries and chemical plants to raise the temperature of feed materials to meet reaction or distillation requirements.  Fuels are typically residual oil, distillate oil, refinery gas, or natural gas.  In some sense, process heaters can be considered as emission control devices because they can be used to control process streams by recovering the fuel value while destroying the VOC.  The criteria pollutants of most concern for process heaters are NOx and SO2. 
In 2016, it is assumed that process heaters have not been subject to regional control programs like the NOx SIP Call, so most of the emission controls put in-place at refineries and chemical plants have resulted from RACT regulations that were implemented as part of SIPs to achieve ozone NAAQS in specific areas, and refinery consent decrees. The boiler/process heater NSPS established NOx emission limits for new and modified process heaters. These emission limits are displayed in Table 4-41.

Table 4-34. Process Heaters NSPS analysis and 2016v1 new emission rates used to estimate controls
NOX emission rate Existing (Fe)
Fraction at this rate
Average
PPMV
Natural Draft
Forced Draft

80
0.4
0
 
100
0.4
0.5
 
150
0.15
0.35
 
200
0.05
0.1
 
240
0
0.05
 
Cumulative, weighted: Fe
104.5
134.5
119.5
NSPS Standard
40
60
 
New Source NOX ratio (Fn)
0.383
0.446
0.414
NSPS Control (%)
61.7
55.4
58.6

For computations, the existing source emission ratio (Fe) was set to 1.0. The computed (average) NOx emission factor ratio for new sources (Fn) is 0.41 (58.6 percent control). The retirement rate is the inverse of the expected unit lifetime.  There is limited information in the literature about process heater lifetimes. This information was reviewed at the time that the Western Regional Air Partnership (WRAP) developed its initial regional haze program emission projections, and energy technology models used a 20-year lifetime for most refinery equipment.  However, it was noted that in practice, heaters would probably have a lifetime that was on the order of 50 percent above that estimate.  Therefore, a 30-year lifetime was used to estimate the effects of process heater growth and retirement.  This yields a 3.3 percent retirement rate. This control was then plugged into Eq. 4-2 as a function of the year-specific projection factor. Table 4-35 and Table 4-37 list the point source SCCs where Process Heaters NSPS controls were applied for the 2016v1 platform.  Table 4-36 and Table 4-38 show the reduction in NOx emissions after the application of the Process Heaters NSPS CONTROL packet for both future years 2023 and 2028.

Table 4-35. Ptnonipm SCCs in 2016v1 modeling platform where Process Heaters NSPS controls applied.
scc
sccdesc
30190003
Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Process Heater: Natural Gas
30190004
Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Process Heater: Process Gas
30590002
Industrial Processes; Mineral Products; Fuel Fired Equipment; Residual Oil: Process Heaters
30590003
Industrial Processes; Mineral Products; Fuel Fired Equipment; Natural Gas: Process Heaters
30600101
Industrial Processes; Petroleum Industry; Process Heaters; Oil-fired
30600102
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600103
Industrial Processes; Petroleum Industry; Process Heaters; Oil
30600104
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600105
Industrial Processes; Petroleum Industry; Process Heaters; Natural Gas-fired
30600106
Industrial Processes; Petroleum Industry; Process Heaters; Process Gas-fired
30600107
Industrial Processes; Petroleum Industry; Process Heaters; Liquified Petroleum Gas (LPG)
30600199
Industrial Processes; Petroleum Industry; Process Heaters; Other Not Classified
30990003
Industrial Processes; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas: Process Heaters
31000401
Industrial Processes; Oil and Gas Production; Process Heaters; Distillate Oil (No. 2)
31000402
Industrial Processes; Oil and Gas Production; Process Heaters; Residual Oil
31000403
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil
31000404
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas
31000405
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas
31000406
Industrial Processes; Oil and Gas Production; Process Heaters; Propane/Butane
31000413
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil: Steam Generators
31000414
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas: Steam Generators
31000415
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas: Steam Generators
39900501
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace; Distillate Oil
39900601
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace; Natural Gas
39990003
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Manufacturing Industries; Natural Gas: Process Heaters

Table 4-36. Ptnonipm emissions reductions after the application of the Process Heaters NSPS 
year
poll
2016v1 (tons)
emissions reductions (tons)
% change
                                                                           2023
NOX
                                                                        952,181
                                                                         -9,511
                                                                          -1.0%
                                                                           2028
NOX
                                                                        952,181
                                                                        -12,692
                                                                          -1.3%
Table 4-37. Point source SCCs in pt_oilgas sector where Process Heaters NSPS controls were applied
SCC
SCC Description
30190003
Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Process Heater: Natural Gas
30600102
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired 
30600104
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600105
Industrial Processes; Petroleum Industry; Process Heaters; Natural Gas-fired
30600106
Industrial Processes; Petroleum Industry; Process Heaters; Process Gas-fired
30600199
Industrial Processes; Petroleum Industry; Process Heaters; Other Not Classified
30990003
Industrial Processes; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas: Process Heaters
31000401
Industrial Processes; Oil and Gas Production; Process Heaters; Distillate Oil (No. 2)
31000402
Industrial Processes; Oil and Gas Production; Process Heaters; Residual Oil
31000403
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil
31000404
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas
31000405
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas
31000413
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil: Steam Generators
31000414
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas: Steam Generators
31000415
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas: Steam Generators
39900501
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace; Distillate Oil
39900601
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace; Natural Gas
Table 4-38.  NOx emissions reductions (tons/year) in pt_oilgas sector after the application of the Process Heaters NSPS CONTROL packet for futures years 2023 and 2028. 
Year
Poll
2016v1
Emissions Reductions 
% change
                                                                           2023
NOX
                                                                        379,866
                                                                         -1,698
                                                                          -0.4%
                                                                           2028
NOX
                                                                        379,866
                                                                         -2,376
                                                                          -0.6%

 CISWI (ptnonipm)
On March 21, 2011, the EPA promulgated the revised NSPS and emission guidelines for Commercial and Industrial Solid Waste Incineration (CISWI) units. This was a response to the voluntary remand that was granted in 2001 and the vacatur and remand of the CISWI definition rule in 2007. In addition, the standards redevelopment included the 5-year technology review of the new source performance standards and emission guidelines required under Section 129 of the Clean Air Act. The history of the CISWI implementation is documented here: https://www.epa.gov/stationary-sources-air-pollution/commercial-and-industrial-solid-waste-incineration-units-ciswi-new. Baseline and CISWI rule impacts associated with the CISWI rule are documented here: https://www.regulations.gov/document?D=EPA-HQ-OAR-2003-0119-2559. The EPA mapped the units from the CISWI baseline and controlled dataset to the 2014 NEI inventory and computed percent reductions such that our future year emissions matched the CISWI controlled dataset values. Table 4-39 summarizes the total impact of CISWI controls for 2023 and 2028. Note that this rule applies to specific units in 11 states: Alaska, Arkansas, Illinois, Iowa, Louisiana, Maine, Oklahoma, Oregon, Pennsylvania, Tennessee, and Texas for CO, SO2, and NOX.

Table 4-39. Summary of CISWI rule impacts on ptnonipm emissions for 2023 and 2028
year
poll
2016v1 (tons)
emissions reductions (tons)
% change
                                                                           2023
CO
                                                                      1,446,353
                                                                         -2,745
                                                                          -0.2%
                                                                           2023
NOX
                                                                        952,181
                                                                         -1,711
                                                                          -0.2%
                                                                           2023
SO2
                                                                        658,204
                                                                         -1,807
                                                                          -0.3%
                                                                           2028
CO
                                                                      1,446,353
                                                                         -2,937
                                                                          -0.2%
                                                                           2028
NOX
                                                                        952,181
                                                                         -1,722
                                                                          -0.2%
                                                                           2028
SO2
                                                                        658,204
                                                                         -1,933
                                                                          -0.3%

 Petroleum Refineries NSPS Subpart JA (ptnonipm)
On June 24, 2008, EPA issued final amendments to the Standards of Performance for Petroleum Refineries. This action also promulgated separate standards of performance for new, modified, or reconstructed process units after May 14, 2007 at petroleum refineries. The final standards for new process units included emissions limitations and work practice standards for fluid catalytic cracking units, fluid coking units, delayed coking units, fuel gas combustion devices, and sulfur recovery plants. In 2012, EPA finalized the rule after some amendments and technical corrections. See https://www.epa.gov/stationary-sources-air-pollution/petroleum-refineries-new-source-performance-standards-nsps-40-cfr for more details on NSPS  -  40 CFR 60 Subpart Ja. These NSPS controls were applied to petroleum refineries in the ptnonipm sector for years 2023 and 2028. Units impacted by this rule were identified in the 2016v1 inventory. For delayed coking units, an 84% control efficiency was applied and for storage tanks, a 49% control efficiency was applied. The analysis of applicable units was completed prior to the 2014v2 NEI and the 2016v1 platform. Therefore, to ensure that a control was not applied to a unit that was already in compliance with this rule, we compared emissions from the 2016v1 inventory and the 2011en inventory (the time period of the original analysis). Any unit that demonstrated a 55+% reduction in VOC emissions from 2011en to 2016v1 would be considered compliant with the rule and therefore not subject to this control. Table 4-40 below reflects the impacts of these NSPS controls on the ptnonipm sector. This control is applied to all pollutants; Table 4-40 summarizes reductions for the years 2023 and 2028 for NOX, SO2, and VOC.

Table 4-40. Summary of NSPS Subpart JA rule impacts on ptnonipm emissions for 2023 and 2028
year
poll
2016v1 (tons)
emissions reductions (tons)
% change
                                                                           2023
NOX
                                                                        952,181
                                                                             -1
                                                                           0.0%
                                                                           2023
SO2
                                                                        658,204
                                                                             -3
                                                                           0.0%
                                                                           2023
VOC
                                                                        774,289
                                                                         -5,269
                                                                          -0.7%
                                                                           2028
NOX
                                                                        952,181
                                                                             -1
                                                                           0.0%
                                                                           2028
SO2
                                                                        658,204
                                                                             -3
                                                                           0.0%
                                                                           2028
VOC
                                                                        774,289
                                                                         -5,233
                                                                          -0.7%

 State-Specific Controls (ptnonipm)
ICI Boilers  -  North Carolina
The Industrial/Commercial/Institutional Boilers and Process Heaters MACT Rule, hereafter simply referred to as the "Boiler MACT," was promulgated on January 31, 2013, based on reconsideration. Background information on the Boiler MACT can be found at: https://www.epa.gov/stationary-sources-air-pollution/industrial-commercial-and-institutional-boilers-and-process-heaters. The Boiler MACT promulgates national emission standards for the control of HAPs (NESHAP) for new and existing industrial, commercial, and institutional (ICI) boilers and process heaters at major sources of HAPs. The expected cobenefit for CAPs at these facilities is significant and greatest for SO2 with lesser impacts for direct PM, CO and VOC. This control addresses only the expected cobenefits to existing ICI boilers in the State of North Carolina. All other states previously considered for this rule are assumed to be in compliance with the rule and therefore the emissions need no further estimated controls applied. The control factors applied here were provided by North Carolina.

Arizona Regional Haze Controls
U.S. EPA Region 9 provided regional haze FIP controls for a few industrial facilities. Information on these controls are available in the docket for this rulemaking at https://www.regulations.gov/document?D=EPA-R09-OAR-2013-0588-0072. These non-EGU controls have implementation dates between September 2016 and December 2018.

Consent Decrees
MARAMA provided a list of controls relating to consent decrees to be applied to specific units within the MARAMA region. This list includes sources in North Carolina that were subject to controls in the beta version of this emission modeling platform. Outside of the MARAMA region, controls related to consent decrees were applied to several sources, including the LaFarge facility in Michigan (8127411), for which NOX emissions must be reduced by 18.633% to meet the decree; and the Cabot facilities in Louisiana and Texas, which had been subject to consent decree controls in the 2011 platforms, and 2016 emissions values suggest controls have not yet taken effect. Other facilities subject to a consent decree were determined to already be in compliance based on 2016 emissions values.

State Comments
A comment from the State of Illinois that was included in the 2011 platform was carried over for the 2016v1 platform. The data accounts for three coal boilers being replaced by two gas boilers not in the inventory and results in a large SO2 reduction.

The State of Ohio reported that the P. H. Glatfelter Company facility (8131111) has switched fuels after 2016, and so controls related to the fuel switch were applied. This is a new control for version 1 platform.

Comments relating to Regional Haze in the 2011 platform were analyzed for potential use in the 2016v1 platform. For those comments that are still applicable, control efficiencies were recalculated so that 2016v1 post-control emissions (without any projections) would equal post-control emissions for the 2011 platform (without any projections). This is to ensure that controls which may already be applied are accounted for. Some facilities' emissions were already less than the 2011 post-control value in 2016v1 and therefore did not need further controls here. For facility 3982311 (Eastman Chemical in Tennessee), one unit has a control efficiency of 90 in 2016v1 and the others have no control; a replacement control of 91.675 was applied for this facility so that the unit with control efficiency=90 is not double controlled.

Wisconsin provided alternate emissions to use as input to 2023v1/2028v1 CoST. Wisconsin provided new emissions totals for three facilities and requested that these new totals be used as the basis for 2023v1 and 2028v1 projections, instead of 2016v1. The provided emissions were facility-level only, therefore 2016v1 emissions were scaled at these facilities to match the new provided totals.

The District of Columbia provided a control packet to be applied to three ptnonipm facilities in all 2016v1 platform projections.

 Projections Computed Outside of CoST
Projections for some sectors are not calculated using CoST.  These are discussed in this section.
 Nonroad Mobile Equipment Sources (nonroad)
Outside California and Texas, the MOVES2014b model was run separately for each future year, including 2023 and 2028, resulting in a separate inventory for each year. The fuels used are specific to each future year, but the meteorological data represented the year 2016. The 2023 and 2028 nonroad emission factors account for regulations such the Emissions Standards for New Nonroad Spark-Ignition Engines, Equipment, and Vessels (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-emissions-nonroad-spark-ignition), Locomotives and Marine Compression-Ignition Engines Less than 30 Liters per Cylinder (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-emissions-air-pollution-locomotive), and Clean Air Nonroad Diesel Final Rule  -  Tier 4 (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-emissions-air-pollution-nonroad-diesel). The resulting future year inventories were processed into the format needed by SMOKE in the same way as the base year emissions. Inside California and Texas, CARB and TCEQ provided separate datasets for each future year. Because the CARB and TCEQ inventories already reflect future year emissions, no additional work related to projections was required except to include them as SMOKE input files.
 Onroad Mobile Sources (onroad)
The MOVES2014b model was run separately for each future year, including 2023 and 2028, resulting in separate emission factors for each year. The 2023 and 2028 onroad emission factors account for changes in activity data and the impact of on-the-books rules that are implemented into MOVES2014b.  These include regulations such as the Light Duty Vehicle GHG Rule for Model-Year 2017-2025, and the Tier 3 Motor Vehicle Emission and Fuel Standards Rule (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-air-pollution-motor-vehicles-tier-3).  Local inspection and maintenance (I/M) and other onroad mobile programs are included such as California LEVIII, the National Low Emissions Vehicle (LEV) and Ozone Transport Commission (OTC) LEV regulations (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-air-pollution-new-motor-vehicles-and-2), local fuel programs, and Stage II refueling control programs. Regulations finalized after the year 2014 are not included, such as the Safer Affordable Fuel Efficient (SAFE) Vehicles Final Rule for Model Years 2021-2026 and the Final Rule for Phase 2 Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles.   

The fuels used are specific to each future year, the age distributions were projected to the future year, and the meteorological data represented the year 2016. The resulting emission factors were combined with future year activity data using SMOKE-MOVES run in a similar way as the base year.  The development of the future year activity data is described later in this section. CARB provided separate emissions datasets for each future year. The CARB-provided emissions were adjusted to match the temporal and spatial patterns of the SMOKE-MOVES based emissions. Additional information about the development of future year onroad emission and on how SMOKE was run to develop the emissions can be found in the 2016v1 platform onroad sector specification sheet.

Where state and local agencies did not provide future year activity data, future year VMT were computed based on annual VMT data from the AEO2019 reference case for VMT by fuel and vehicle type. Specifically, the following two AEO2019 tables were used:

 Light Duty (LD): Light-Duty VMT by Technology Type (table #51: https://www.eia.gov/outlooks/aeo/data/browser/#/?id=51-AEO2019&cases=ref2019&sourcekey=0)
 Heavy Duty (HD): Freight Transportation Energy Use (table #58: https://www.eia.gov/outlooks/aeo/data/browser/#/?id=58-AEO2019&cases=ref2019&sourcekey=0)
 
Total VMT for each MOVES fuel and vehicle grouping was calculated for the years 2016, 2020, 2023, and 2028 based on the AEO-to-MOVES mappings above. From these totals, 2016-2023 and 2016-2028 VMT trends were calculated for each fuel and vehicle grouping. Those trends became the national VMT projection factors. The AEO2019 tables include data starting from the year 2017. Since we were projecting from 2016, 2016-to-2017 projection factors were calculated from AEO2018, and then multiplied by 2017-to-future projection factors from AEO2019. MOVES fuel and vehicle types were mapped to AEO fuel and vehicle classes.  The resulting 2016-to-future year national VMT projection factors used for the 2016v1 platform are provided in Table 4-41.  These factors were adjusted to prepare county-specific projection factors for light duty vehicles based on human population data available from the BenMAP model by county for the years 2017, 2023, and 2028 (https://www.woodsandpoole.com/ circa 2015).  The purpose of this adjustment based on population changes helps account for areas of the country that are growing more than others.  Where agencies provided future year VMT data, those data were used.
Table 4-41. Factors used to Project 2016 VMT to 2023 and 2028
SCC6
description
                                  2023 factor
                                  2028 factor
220111
LD gas
                                     5.99%
                                     6.99%
220121
LD gas
                                     5.99%
                                     6.99%
220131
LD gas
                                     5.99%
                                     6.99%
220132
LD gas
                                     5.99%
                                     6.99%
220142
Buses gas
                                     8.43%
                                    19.86%
220143
Buses gas
                                     8.43%
                                    19.86%
220151
MHD gas
                                     8.43%
                                    19.86%
220152
MHD gas
                                     8.43%
                                    19.86%
220153
MHD gas
                                     8.43%
                                    19.86%
220154
MHD gas
                                     8.43%
                                    19.86%
220161
HHD gas
                                    -51.15%
                                    -64.99%
220221
LD diesel
                                    86.79%
                                    177.3%
220231
LD diesel
                                    86.79%
                                    177.3%
220232
LD diesel
                                    86.79%
                                    177.3%
220241
Buses diesel
                                    14.30%
                                    21.23%
220242
Buses diesel
                                    14.30%
                                    21.23%
220243
Buses diesel
                                    14.30%
                                    21.23%
220251
MHD diesel
                                    14.30%
                                    21.23%
220252
MHD diesel
                                    14.30%
                                    21.23%
220253
MHD diesel
                                    14.30%
                                    21.23%
220254
MHD diesel
                                    14.30%
                                    21.23%
220261
HHD diesel
                                    12.91%
                                    17.85%
220262
HHD diesel
                                    12.91%
                                    17.85%
220342
Buses CNG
                                    65.57%
                                    88.00%
220521
LD E-85
                                    -0.70%
                                    -10.03%
220531
LD E-85
                                    -0.70%
                                    -10.03%
220532
LD E-85
                                    -0.70%
                                    -10.03%
220921
LD Electric
                                     1258%
                                     2695%
220931
LD Electric
                                     1258%
                                     2695%
220932
LD Electric
                                     1258%
                                     2695%

Future year VPOP data were projected using calculations of VMT/VPOP ratios for each county, fuel, and vehicle type from the 2016 VMT and VPOP data. Those ratios were then applied to the future year projected VMT to estimate future year VPOP. Future year VPOP data submitted by state and local agencies were then incorporated into the VPOP projections. Future year VPOP data were provided by state and local agencies in NH, NJ, NC, WI, Pima County, AZ, and Clark County, NV. All of these submissions were the same as for the 2016beta platform except for New Jersey, which provided a new submission for the 2016v1 platform. For Pima County, just like with the VMT, future year VPOP was only provided for 2022 (used directly for 2023) and not for 2028. Where necessary, VPOP was split to SCC (full FF10) using SCC distributions from the EPA projection.  Both VMT and VPOP were redistributed between the LD car and truck vehicle types (21/31/32) based on splits from the EPA projection, and used the EPA projection for buses in North Carolina and state-provided VPOP for all other vehicles in North Carolina.

Hoteling hours were projected to the future years by calculating 2016 inventory HOTELING/VMT ratios for each county for combination long-haul trucks on restricted roads only.  Those ratios were then applied to the future year projected VMT for combination long-haul trucks on restricted roads to calculate future year hoteling. Some counties had hoteling activity but did not have combination long-haul truck restricted road VMT in 2016; in those counties, the national AEO2018-based projection factor for diesel combination trucks was used to project 2016 hoteling to the future years. This procedure gives county-total hoteling for the future years. Each future year also has a distinct APU percentage based on MOVES input data that was used to split county total hoteling to each SCC: 22.6% APU for 2023, and 25.9% APU for 2028.
 Locomotives (rail)
Rail emissions were computed for future years based on future year fuel use values for 2020, 2023, and 2028 were based on the Energy Information Administration's 2018 Annual Energy Outlook (AEO) freight rail energy use growth rate projections for 2016 thru 2028 (see Table 4-42) and emission factors based on historic emissions trends that reflect the rate of market penetration of new locomotive engines.

A correction factor was added to adjust the AEO projected fuel use for 2017 to match the actual 2017 R-1 fuel use data.  The additive effect of this correction factor was carried forward for each subsequent year from 2018 thru 2028. The modified AEO growth rates were used to calculate future year Class I line-haul fuel use totals for 2020, 2023, and 2028. As shown in Table 4-42 the future year fuel use values ranged between 3.2 and 3.4 billion gallons, which matched up well with the long-term line-haul fuel use trend between 2005 and 2018. The emission factors for NOx, PM10 and VOC were derived from trend lines based on historic line-haul emission factors from the period of 2007 through 2017.  
Table 4-42. Class I Line-haul Fuel Projections based on 2018 AEO Data
                                     Year
                              AEO Freight Factor
                               Projection Factor
                              Corrected AEO Fuel
                                 Raw AEO Fuel 
                                     2016
                                       1
                                       1
                                 3,203,595,133
                                 3,203,595,133
                                     2017
                                    1.0212
                                    1.0346
                                 3,314,384,605
                                 3,271,393,249
                                     2018
                                    1.0177
                                    1.0311
                                 3,303,215,591
                                 3,260,224,235
                                     2019
                                    1.0092
                                    1.0226
                                 3,275,939,538
                                 3,232,948,182
                                     2020
                                    1.0128
                                    1.0262
                                 3,287,479,935
                                 3,244,488,580
                                     2021
                                    1.0100
                                    1.0235
                                 3,278,759,301
                                 3,235,767,945
                                     2022
                                    0.9955
                                    1.0090
                                 3,232,267,591
                                 3,189,276,235
                                     2023
                                    0.9969
                                    1.0103
                                 3,236,531,624
                                 3,193,540,268
                                     2024
                                    1.0221
                                    1.0355
                                 3,317,383,183
                                 3,274,391,827
                                     2025
                                    1.0355
                                    1.0489
                                 3,360,367,382
                                 3,317,376,026
                                     2026
                                    1.0410
                                    1.0544
                                 3,377,946,201
                                 3,334,954,845
                                     2027
                                    1.0419
                                    1.0553
                                 3,380,697,189
                                 3,337,705,833
                                     2028
                                    1.0356
                                    1.0490
                                 3,360,491,175
                                 3,317,499,820

The projected fuel use data was combined with the emission factor estimates to create future year link-level emission inventories based on the MGT traffic density values contained in the FRA's 2016 shapefile. The link-level data created for 2020, 2023, and 2028 was aggregated to create county, state, and national emissions estimates (see Table 4-43) which were then converted into FF10 format for use in the 2016v1 emissions platform.
Table 4-43. Class I Line-haul Historic and Future Year Projected Emissions  
                                   Inventory
                                      CO
                                      HC
                                      NH3
                                      NOx
                                     PM10
                                     PM2.5
                                      SO2
                                2007 (2008 NEI)
                                    110,969
                                    37,941
                                      347
                                    754,433
                                    25,477
                                    23,439
                                     7,836
                                   2014 NEI
                                    107,995
                                    29,264
                                      338
                                    609,295
                                    19,675
                                    18,101
                                      381
                                    2016 v1
                                    94,020
                                    21,727
                                      294
                                    489,562
                                    14,538
                                    14,102
                                      332
                                   2017 NEI
                                    97,272
                                    21,560
                                      304
                                    492,385
                                    14,411
                                    13,979
                                      343
                                2020 Projected
                                    96,482
                                    19,133
                                      302
                                    448,924
                                    12,800
                                    12,415
                                      340
                                2023 Projected
                                    94,987
                                    16,550
                                      297
                                    404,329
                                    11,059
                                    10,728
                                      335
                                2028 Projected
                                    98,625
                                    13,847
                                      309
                                    361,914
                                     9,236
                                     8,959
                                      348
                                 2016 vs 2028
                                     4.90%
                                    -36.27%
                                     4.90%
                                    -26.07%
                                    -36.47%
                                    -36.47%
                                     4.90%

Other rail emissions were projected based on AEO growth rates as shown in Table 4-44. See the 2016v1 rail specification sheet for additional information on rail projections.
 Table 4-44. AEO growth rates for rail sub-groups
                                    Sector
                                     2016
                                     2020
                                     2023
                                     2028
                                  Rail Yards
                                      1.0
                                    0.97513
                                   0.947802
                                   0.952483
                            Class II/III Railroads
                                      1.0
                                    0.97513
                                   0.947802
                                   0.952483
                              Commuter/Passenger
                                      1.0
                                   1.033858
                                   1.071348
                                   1.136023

 Sources Outside of the United States (onroad_can, onroad_mex, othpt, ptfire_othna, othar, othafdust, othptdust)
This section discusses the projection of emissions from Canada and Mexico and other areas outside of the U.S. Information about the base inventory used for these projections or the the naming conventions can be found in Section 2.7.  Emissions for Mexico are based on the Inventario Nacional de Emisiones de Mexico, 2008 projected to years 2023 and 2028 (ERG, 2014a). Additional details for these sectors can be found in the 2016v1 platform specification sheets.
 Canadian fugitive dust sources (othafdust, othptdust)
Canadian area source dust (othafdust)
ECCC provided area stationary source inventories for the years 2023 and 2028. Unlike in their 2015 inventories in which area dust emissions were grouped into a separate file, these sources were not provided as separate inventories for the future years, and so othafdust sector emissions were extracted from that single area source inventory. As with 2015, the future year dust emissions are pre-adjusted, so future year othafdust follows the same emissions processing methodology as the base year. To make the future year emissions consistent with the base year, the same 2015->2010 adjustment factors for construction dust that were applied to the base year inventory were also applied to the future year projected inventories.
Canadian point source dust (othptdust)

ECCC had provided their own future year projections of the harvest and tillage point ag dust inventories, but those inventories exhibited the same waffle pattern as 2015, so we instead decided to project the improved 2015 inventories. ECCC separately provided data from which future year projections could be derived in a file called "Projected_CAN2015_2023_2028.xlsx", which includes emissions data for 2015, 2023, and 2028 by pollutant, province, ECCC sub-class code, and other source categories. This data was used to calculate 2015-to-2023 and 2015-to2028 projection factors, which were then applied to the improved 2015 Canada point ag dust inventories to create projections for 2023 and 2028. Emissions values from these in-house projections were found to be close in magnitude to ECCC's own projections. Projection factors were applied by province, sub-class code, and pollutant. The ECCC projection workbook included additional source information which provides more detail than do the subclass codes, but that more detailed information could not be easily mapped to the inventory, and the level of detail offered by the sub-class codes was considered sufficient for projection purposes. For the othptdust sector, there are separate sub-class codes for each of the two inventories (harvest and tillage).
 Point Sources in Canada and Mexico (othpt)
Canada point airport and agriculture emissions
Future year airport and agriculture emission inventories from ECCC were not available in time for inclusion in the platform. Instead, ECCC provided data from which future year projections of these inventories could be derived. This data, provided by ECCC in a file called "Projected_CAN2015_2023_2028.xlsx", includes emissions data for 2015, 2023, and 2028 by pollutant, province, ECCC sub-class code, and other source categories. This data was used to calculate 2015-to-2023 and 2015-to-2028 projection factors, which were then applied to the improved 2015 point airport and ag inventories to create projections of Canadian emissions for 2023 and 2028. Projection factors were applied by province, sub-class code, and pollutant. The ECCC projection workbook included additional source information which provides more detail than do the subclass codes, but that more detailed information could not be easily mapped to the inventory, and the level of detail offered by the sub-class codes was considered sufficient for projection purposes. For the ag inventories, the sub-class codes are similar in detail to SCCs: fertilizer has a single sub-class code, and animal emissions categories (broilers, dairy, horses, sheep, etc) each have a separate sub-class code. Sub-class codes for airport emissions are similar in detail to SCCs, with separate codes for piston and turbine emissions from military aircraft, commercial aircraft, and general aviation.
Other Canada point sources
Future year projections for stationary point sources (excluding ag) were provided by ECCC for 2023 and 2028. ECCC provided emissions inventories for upstream oil and gas sources (UOG) and for all other stationary point sources, including electric power generation. These inventories were generally used as-is, with the following exceptions. The 2015 non-UOG stationary point source inventories included monthly emissions as well as annual emissions. In the future year projected inventories provided by ECCC, monthly emissions were included not included for EPG (electric power generation) sources, but were for the rest of the non-UOG sources. For consistency with the base year, monthly emissions were added to the EPG sources in the inventory, using facility-specific monthly temporal profiles derived from the 2015 inventory. For new facilities that were not in 2015, monthly emissions were left blank in the inventory, and monthly temporalization is applied SMOKE using profiles assigned by SCC. For 2015, ECCC provided a pre-speciated point source inventory including species for the CB6 mechanism. For the future years, ECCC did not provide a pre-speciated inventory, but advised that speciation for the future years is unchanged from the base year. Because the baseline VOC emissions are different in the future year projections, it was necessary to develop a prespeciated CB6 inventory for the future years which is consistent with the 2015 inventory but is based on future year projections of VOC. For this, speciation profiles for each facility-SCC in 2015 were calculated using the 2015 CB6 inventory, and these profiles were applied to future year VOC to create a CB6 future year inventory. Speciation profiles were also developed by SCC from 2015, for application to future year facility-SCC combinations which could not be matched to 2015. The future year inventories also include SCCs which were not in the 2015 inventory all; for those sources, we apply standard speciation profiles in SMOKE. To prevent double counting of VOC speciated within SMOKE with pre-speciated VOC, the point source inventory has VOC emissions represented as VOC_INV for sources that are in the pre-speciated CB6 inventory, and as VOC for sources that are not pre-speciated. Only the VOC and not the VOC_INV is speciated within SMOKE. Changes to point source IDs in the stationary source inventory were necessary for the PMC calculation, which is based on inventory PM10 and PM2.5. This SMOKE calculation requires that PM10 and PM2.5 emissions are assigned to the same point source IDs, but that was not always the case with respect to the rel_point_id and process_id fields for each unit. This was also an issue with the 2015 inventory, but the procedure that was used to fix 2015 did not help resolve this issue in the future year inventories, and so a more robust fix was implemented for 2023 and 2028. All rel_point_id and process_id values in the 2023 and 2028 Canada stationary point inventories were redefined, such that all records with the same FIPS code, latitude, longitude, and stack parameters (implying emissions from the same stack) were assigned the same rel_point_id and process_id for all pollutants. This fixed all instances in which PM10 and PM2.5 from the same source were assigned different point source IDs, but there are still sources in the future year inventories in which PM10 emissions are less than the PM2.5 emissions from the same source.
Mexico
The othpt sector includes a general point source inventory in Mexico. This inventory is based on projections of a 2008 inventory. The inventory was originally projected to years 2018, 2025, and 2030 by ERG1 . For the beta and v1 platform future year projections, emissions values from 2018 and 2025 were interpolated to 2023, and values from 2025 and 2030 were interpolated to 2028. These inventories are unchanged from the 2011 platform.
 Nonpoint sources in Canada and Mexico (othar)
Canadian stationary sources
ECCC provided area stationary source inventories for the years 2023 and 2028. Unlike in their 2015 inventories in which dust and agricultural emissions were grouped into separate files, these sources were not provided as separate inventories for the future years. Therefore, dust emissions from the othafdust and othptdust sectors, and ag emissions from the othpt sector, needed to be removed from the future year area source inventory to prevent a double count. PM emissions for all SCCs in the othafdust inventory (see othafdust sector document) were moved to a separate inventory. Then, most emissions from agricultural SCCs (2801- and 2805-) were removed, since the NH3 and VOC emissions overlap the point format ag inventories which are part of the othpt sector, and the PM emissions were either already moved to the othafdust sector, overlap the othptdust sector, or were not present in 2015 (see note about fertilizer below). One ag SCC was partially retained in the area source inventory according to both the SCC and ECCC's 5-digit "sub-class codes". SCC 2805000000 for sub-class code 80104, which represents agricultural fuel combustion, was not removed from the area source inventory, since these emissions were part of the othar sector in 2016ff and are not included in any of the other inventories. PM emissions from fertilizer were not present in any 2015 ECCC inventory, but did appear in the future year area source inventory. According to ECCC, this was an error in 2015, and the 2015 inventories should have included approximately 7,000 tons per year of PM emissions from fertilizer. Fertilizer PM emissions were also excluded from in future year modeling to preserve consistency between modeling years. ECCC provided an additional stationary area source inventory for 2023 and 2028 representing electric power generation (EPG). According to ECCC, this inventory's emissions were covered by the point source EPG inventory in 2015 and does not double count the 2023 and 2028 point source inventories, and it is appropriate to include this new area source EPG inventory in the othar sector.
Canadian mobile sources
For mobile nonroad sources, including rail and CMV, future year inventories from ECCC were not available in time for inclusion in beta platform. Instead, ECCC provided data from which future year projections of these inventories could be derived. This data, provided by ECCC in a file called "Projected_CAN2015_2023_2028.xlsx", includes emissions data for 2015, 2023, and 2028 by pollutant, province, ECCC sub-class code, and other source categories. This data was used to calculate 2015-to-2023 and 2015-to-2028 projection factors, which were then applied to the 2015 mobile source inventories to create projections of Canadian mobile source emissions for 2023 and 2028. Projection factors were applied by province, sub-class code, and pollutant. The ECCC projection workbook included additional source information which provides more detail than do the subclass codes, but that more detailed information could not be easily mapped to the inventory, and the level of detail offered by the sub-class codes was considered sufficient for projection purposes. For the nonroad inventory, the sub-class code is analogous to the SCC7 level in U.S. inventories. For example, there are separate sub-class codes for fuels (e.g. 2-stroke gasoline, diesel, LPG) and category (e.g. construction, lawn and garden) but not for individual vehicle types within each category (e.g. snowmobiles, tractors). For CMV and rail, the sub-class code is closer to full SCC, because there are separate codes for port and underway emissions, and for freight and passenger rail emissions.
Mexico
The othar sector includes two Mexico inventories, an area inventory and a nonroad inventory. Similar to 2016, the future year Mexico inventories are based on projections of a 2008 inventory, but are based on different interpolations. In addition to the 2014 and 2018 projections that were the basis for 2016, these inventories were also originally projected to years 2025 and 2030.  For future year projections, emissions values from 2018 and 2025 were interpolated to 2023, and emissions values from 2025 and 2030 were interpolated to 2028. These emissions are unchanged from the 2011 platform, except that CMV emissions were removed from the nonroad inventory to prevent a double count with the Mexico CMV inventory, which was not part of the 2011 platform.
 Onroad sources in Canada and Mexico (onroad_can, onroad_mex)
For Canadian mobile onroad sources, future year inventories from ECCC were not available in time for inclusion in the v1 platform. Instead, ECCC provided data from which future year projections of these inventories could be derived. This data, provided by ECCC in a file called "Projected_CAN2015_2023_2028.xlsx", includes emissions data for 2015, 2023, and 2028 by pollutant, province, ECCC sub-class code, and other source categories. This data was used to calculate 2015-to-2023 and 2015-to-2028 projection factors, which were then applied to the 2015 mobile source inventories to create projections of Canadian mobile source emissions for 2023 and 2028. Projection factors were applied by province, sub-class code, and pollutant. The ECCC projection workbook included additional source information which provides more detail than do the subclass codes, but that more detailed information could not be easily mapped to the inventory, and the level of detail offered by the sub-class codes was considered sufficient for projection purposes. For the onroad inventory, the sub-class code is analogous to the SCC6+process level in U.S. inventories, in that it specifies fuel type, vehicle type, and process (e.g. brake, tire, exhaust, refueling), but not road type.

For Mexican mobile onroad sources, MOVES-Mexico was run to create emissions inventories for years 2023 and 2028. Results from those runs are used in future year emissions processing for the v1 platform. These emissions are unchanged from the 2011 platform.

Emission Summaries
Tables 5-1 through 5-4 summarize emissions by sector for the 2016fh, 2023fh1, and 2028fh1 cases. These summaries are provided at the national level by sector for the contiguous U.S. and for the portions of Canada and Mexico inside the larger 12km domain (12US1) discussed in Section 3.1 and for the 36-km domain (36US3).  Note that totals for the 12US2 domain are not available here, but the sum of the U.S. sectors would be essentially the same, only the Canadian and Mexican emissions would change according to how far north/south the grids go.  Note that the afdust sector emissions here represent the emissions after application of both the land use (transport fraction) and meteorological adjustments; therefore, this sector is called "afdust_adj" in these summaries.  The afdust emissions in the 36km domain are smaller than those in the 12km domain due to how the adjustment factors are computed and the size of the grid cells. The onroad sector totals are post-SMOKE-MOVES totals, representing air quality model-ready emission totals, and include CARB emissions for California. The cmv sectors include U.S. emissions within state waters only; these extend to roughly 3-5 miles offshore and includes CMV emissions at U.S. ports.  "Offshore" represents CMV emissions that are outside of U.S. state waters. Canadian CMV emissions are included in the other sector. The total of all US sectors is listed as "Con U.S. Total."  State totals and other summaries are available in the reports area on the web and FTP site for the 2016v1 platform (ftp://newftp.epa.gov/air/emismod/2016/v1/).  

Table 5-1. National by-sector CAP emissions summaries for the 2016fh case, 12US1 grid (tons)
Sector
                                      CO
                                      NH3
                                      NOX
                                     PM10
                                     PM2_5
                                      SO2
                                      VOC
afdust_adj
                                                                              
                                                                              
                                                                              
                                                                      7,203,692
                                                                      1,006,446
                                                                              
                                                                              
ag
                                                                              
                                                                      3,409,761
                                                                              
                                                                              
                                                                              
                                                                              
                                                                        194,779
airports
                                                                        674,176
                                                                              0
                                                                        185,454
                                                                         11,068
                                                                          9,805
                                                                         25,412
                                                                         85,768
cmv_c1c2
                                                                         23,548
                                                                             83
                                                                        162,502
                                                                          4,457
                                                                          4,320
                                                                            634
                                                                          6,436
cmv_c3
                                                                         13,956
                                                                             39
                                                                        110,462
                                                                          2,201
                                                                          2,025
                                                                          4,528
                                                                          8,600
nonpt
                                                                      2,629,755
                                                                         78,509
                                                                        710,918
                                                                        570,314
                                                                        463,807
                                                                        138,650
                                                                      3,695,093
nonroad
                                                                     10,593,274
                                                                          1,845
                                                                      1,110,277
                                                                        109,196
                                                                        103,230
                                                                          2,133
                                                                      1,128,691
np_oilgas
                                                                        759,771
                                                                             12
                                                                        572,043
                                                                         14,050
                                                                         13,984
                                                                         19,243
                                                                      2,792,092
onroad
                                                                     19,889,617
                                                                        100,318
                                                                      3,630,693
                                                                        239,997
                                                                        117,758
                                                                         27,559
                                                                      1,852,260
ptagfire
                                                                        262,645
                                                                         51,276
                                                                         10,240
                                                                         38,688
                                                                         26,951
                                                                          3,694
                                                                         17,181
ptegu
                                                                        658,346
                                                                         23,976
                                                                      1,290,190
                                                                        163,981
                                                                        133,517
                                                                      1,540,589
                                                                         33,739
ptfire
                                                                     13,717,466
                                                                        239,605
                                                                        227,337
                                                                      1,461,693
                                                                      1,234,062
                                                                        111,291
                                                                      3,109,465
ptnonipm
                                                                      1,439,081
                                                                         63,731
                                                                        940,031
                                                                        396,884
                                                                        254,386
                                                                        654,527
                                                                        770,204
pt_oilgas
                                                                        167,531
                                                                          4,338
                                                                        339,280
                                                                         11,301
                                                                         10,784
                                                                         33,227
                                                                        127,565
rail
                                                                        104,551
                                                                            326
                                                                        559,381
                                                                         16,344
                                                                         15,819
                                                                            457
                                                                         26,082
rwc
                                                                      2,119,402
                                                                         15,439
                                                                         31,282
                                                                        317,469
                                                                        316,943
                                                                          7,703
                                                                        340,941
                                                                              
 
 
 
 
 
 
 
Con. U.S. Total
                                                                     53,053,119
                                                                      3,989,258
                                                                      9,880,090
                                                                     10,561,336
                                                                      3,713,836
                                                                      2,569,647
                                                                     14,188,893
                                                                              
 
 
 
 
 
 
 
beis
                                                                      7,167,921
                                                                              
                                                                        965,761
                                                                              
 
 
                                                                     42,133,700
CONUS + beis
                                                                     60,221,040
                                                                      3,989,258
                                                                     10,845,852
                                                                     10,561,336
                                                                      3,713,836
                                                                      2,569,647
                                                                     56,322,592
                                                                               







Can./Mex./Offshore







Sector
CO
NH3
NOX
PM10
PM2_5
SO2
VOC
Canada othafdust
 
 
 
                                                                      1,060,979
                                                                        187,228
                                                                              
 
Canada othar
                                                                      2,727,917
                                                                          4,842
                                                                        397,394
                                                                        313,494
                                                                        248,467
                                                                         19,939
                                                                        832,491
Canada onroad_can
                                                                      1,665,792
                                                                          6,877
                                                                        404,856
                                                                         25,204
                                                                         14,076
                                                                          1,556
                                                                        143,213
Canada othpt
                                                                      1,081,673
                                                                        503,214
                                                                        657,348
                                                                        115,280
                                                                         46,765
                                                                        993,944
                                                                        797,611
Canada othptdust
 
 
 
                                                                        150,832
                                                                         55,539
                                                                              
 
Canada ptfire_othna
                                                                        761,402
                                                                         13,032
                                                                         16,359
                                                                         84,476
                                                                         71,745
                                                                          6,731
                                                                        185,476
Canada CMV
                                                                         10,741
                                                                             37
                                                                         93,456
                                                                          1,682
                                                                          1,563
                                                                          2,984
                                                                          5,184
Mexico othar
                                                                        241,571
                                                                        201,994
                                                                        220,491
                                                                        115,460
                                                                         54,294
                                                                          7,717
                                                                        522,236
Mexico onroad_mex
                                                                      1,828,101
                                                                          2,789
                                                                        442,410
                                                                         15,151
                                                                         10,836
                                                                          6,247
                                                                        158,812
Mexico othpt
                                                                        171,065
                                                                          5,049
                                                                        371,671
                                                                         67,173
                                                                         51,791
                                                                        436,802
                                                                         67,343
Mexico ptfire_othna
                                                                        383,162
                                                                          7,436
                                                                         16,604
                                                                         44,992
                                                                         38,176
                                                                          2,785
                                                                        131,499
Mexico CMV
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                              0
Offshore cmv in Federal waters
                                                                         33,224
                                                                            128
                                                                        293,102
                                                                          7,188
                                                                          6,658
                                                                         28,060
                                                                         16,209
Offshore cmv outside Federal waters
                                                                         23,338
                                                                            440
                                                                        257,615
                                                                         24,828
                                                                         22,848
                                                                        181,941
                                                                         11,083
Offshore pt_oilgas
                                                                         50,052
                                                                             15
                                                                         48,691
                                                                            668
                                                                            667
                                                                            502
                                                                         48,210
Non-US Total
                                                                      8,978,039
                                                                        745,854
                                                                      3,219,997
                                                                      2,027,409
                                                                        810,652
                                                                      1,689,208
                                                                      2,919,366


Table 5-2. National by-sector CAP emissions summaries for the 2023fh1 case, 12US1 grid (tons)
Sector
                                      CO
                                      NH3
                                      NOX
                                     PM10
                                     PM2_5
                                      SO2
                                      VOC
afdust_adj
                                                                              
                                                                              
                                                                              
                                                                      7,255,011
                                                                      1,016,777
                                                                              
                                                                              
ag
                                                                              
                                                                      3,543,157
                                                                              
                                                                              
                                                                              
                                                                              
                                                                        205,451
airports
                                                                        738,835
                                                                              0
                                                                        219,766
                                                                         11,358
                                                                         10,127
                                                                         30,208
                                                                         92,473
cmv_c1c2
                                                                         23,570
                                                                             59
                                                                        116,344
                                                                          3,191
                                                                          3,093
                                                                            242
                                                                          4,527
cmv_c3
                                                                         16,709
                                                                             48
                                                                        104,555
                                                                          2,623
                                                                          2,413
                                                                          5,380
                                                                         10,397
nonpt
                                                                      2,644,789
                                                                         79,342
                                                                        709,268
                                                                        579,169
                                                                        472,935
                                                                        106,355
                                                                      3,756,888
nonroad
                                                                     10,581,376
                                                                          2,032
                                                                        737,625
                                                                         71,457
                                                                         66,940
                                                                          1,527
                                                                        856,474
np_oilgas
                                                                        788,072
                                                                             20
                                                                        585,230
                                                                         16,221
                                                                         16,102
                                                                         31,269
                                                                      3,203,738
onroad
                                                                     13,773,993
                                                                         89,285
                                                                      1,751,007
                                                                        199,979
                                                                         72,468
                                                                         12,484
                                                                      1,098,966
ptagfire
                                                                        262,645
                                                                         51,276
                                                                         10,240
                                                                         38,688
                                                                         26,951
                                                                          3,694
                                                                         17,181
ptegu
                                                                        659,538
                                                                         36,544
                                                                            996
                                                                        144,758
                                                                        124,433
                                                                         18,820
                                                                         35,922
ptfire
                                                                     13,717,466
                                                                        239,605
                                                                        227,337
                                                                      1,461,693
                                                                      1,234,062
                                                                        111,291
                                                                      3,109,465
ptnonipm
                                                                      1,448,566
                                                                         63,739
                                                                        928,896
                                                                        400,192
                                                                        257,145
                                                                        572,494
                                                                        771,838
pt_oilgas
                                                                        186,242
                                                                          4,377
                                                                        361,166
                                                                         13,602
                                                                         12,973
                                                                         38,125
                                                                        156,725
rail
                                                                        105,988
                                                                            330
                                                                        469,157
                                                                         12,778
                                                                         12,376
                                                                            460
                                                                         20,436
rwc
                                                                      2,046,853
                                                                         14,793
                                                                         31,902
                                                                        304,464
                                                                        303,920
                                                                          7,010
                                                                        329,017
                                                                              
 
 
 
 
 
 
 
Con. U.S. Total
                                                                     46,994,644
                                                                      4,124,607
                                                                      6,253,489
                                                                     10,515,185
                                                                      3,632,716
                                                                        939,358
                                                                     13,669,497
                                                                              
 
 
 
 
 
 
 
beis
                                                                      7,167,921
                                                                              
                                                                        965,761
                                                                              
 
 
                                                                     42,133,700
CONUS + beis
                                                                     54,162,565
                                                                      4,124,607
                                                                      7,219,250
                                                                     10,515,185
                                                                      3,632,716
                                                                        939,358
                                                                     55,803,196
                                                                               







Can./Mex./Offshore







Sector
CO
NH3
NOX
PM10
PM2_5
SO2
VOC
Canada othafdust
 
 
 
                                                                      1,178,439
                                                                        207,111
                                                                              
 
Canada othar
                                                                      2,689,047
                                                                          4,702
                                                                        310,393
                                                                        303,854
                                                                        228,992
                                                                         19,477
                                                                        823,199
Canada onroad_can
                                                                      1,418,143
                                                                          6,043
                                                                        234,813
                                                                         25,849
                                                                         10,996
                                                                            752
                                                                         87,466
Canada othpt
                                                                      1,094,900
                                                                        610,668
                                                                        541,448
                                                                         87,726
                                                                         46,205
                                                                        868,739
                                                                        684,095
Canada othptdust
 
 
 
                                                                        150,854
                                                                         55,547
                                                                              
 
Canada ptfire_othna
                                                                        760,345
                                                                         13,015
                                                                         16,337
                                                                         84,366
                                                                         71,652
                                                                          6,721
                                                                        185,224
Canada CMV
                                                                         11,597
                                                                             40
                                                                         67,837
                                                                          1,819
                                                                          1,690
                                                                          3,158
                                                                          5,525
Mexico othar
                                                                        263,826
                                                                        198,635
                                                                        240,372
                                                                        118,422
                                                                         56,685
                                                                          7,993
                                                                        583,403
Mexico onroad_mex
                                                                      1,772,026
                                                                          3,266
                                                                        427,900
                                                                         17,023
                                                                         11,764
                                                                          7,556
                                                                        161,115
Mexico othpt
                                                                        200,105
                                                                          6,273
                                                                        380,429
                                                                         75,143
                                                                         57,034
                                                                        365,518
                                                                         84,277
Mexico ptfire_othna
                                                                        384,764
                                                                          7,466
                                                                         16,665
                                                                         45,198
                                                                         38,354
                                                                          2,798
                                                                        131,980
Mexico CMV
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                              0
Offshore cmv in Federal waters
                                                                         39,846
                                                                            150
                                                                        257,244
                                                                          8,460
                                                                          7,815
                                                                         34,951
                                                                         19,345
Offshore cmv outside Federal waters
                                                                         28,551
                                                                            277
                                                                        314,614
                                                                         15,644
                                                                         14,397
                                                                         41,490
                                                                         13,542
Offshore pt_oilgas
                                                                         50,052
                                                                             15
                                                                         48,691
                                                                            668
                                                                            667
                                                                            502
                                                                         48,210
Non-US Total
                                                                      8,713,201
                                                                        850,550
                                                                      2,856,743
                                                                      2,113,463
                                                                        808,909
                                                                      1,359,655
                                                                      2,827,380


Table 5-3. National by-sector CAP emissions summaries for the 2028fh1 case, 12US1 grid (tons)
Sector
                                      CO
                                      NH3
                                      NOX
                                     PM10
                                     PM2_5
                                      SO2
                                      VOC
afdust_adj
                                                                              
                                                                              
                                                                              
                                                                      7,279,406
                                                                      1,021,715
                                                                              
                                                                              
ag
                                                                              
                                                                      3,564,066
                                                                              
                                                                              
                                                                              
                                                                              
                                                                        207,123
airports
                                                                        803,407
                                                                              0
                                                                        245,192
                                                                         11,871
                                                                         10,622
                                                                         33,866
                                                                        100,258
cmv_c1c2
                                                                         24,002
                                                                      47.404946
                                                                         92,763
                                                                          2,549
                                                                          2,471
                                                                      243.87567
                                                                          3,574
cmv_c3
                                                                         19,175
                                                                      53.299262
                                                                        104,503
                                                                          3,010
                                                                          2,770
                                                                          6,160
                                                                         11,990
nonpt
                                                                      2,665,492
                                                                         79,603
                                                                        708,891
                                                                        593,878
                                                                        485,092
                                                                        106,954
                                                                      3,800,741
nonroad
                                                                     10,892,398
                                                                          2,104
                                                                        611,510
                                                                         58,356
                                                                         54,323
                                                                          1,545
                                                                        801,819
np_oilgas
                                                                        774,404
                                                                      20.377326
                                                                        560,267
                                                                         16,462
                                                                         16,343
                                                                         33,574
                                                                      3,331,524
onroad
                                                                     10,308,234
                                                                         87,913
                                                                      1,246,069
                                                                        189,838
                                                                         58,925
                                                                         11,703
                                                                        836,112
ptagfire
                                                                        262,645
                                                                         51,276
                                                                         10,240
                                                                         38,688
                                                                         26,951
                                                                          3,694
                                                                         17,181
ptegu
                                                                        648,829
                                                                         35,883
                                                                        748,663
                                                                        140,100
                                                                        120,420
                                                                        781,397
                                                                         33,831
ptfire
                                                                     13,717,466
                                                                        239,605
                                                                        227,337
                                                                      1,461,693
                                                                      1,234,062
                                                                        111,291
                                                                      3,109,465
ptnonipm
                                                                      1,460,891
                                                                         63,990
                                                                        933,843
                                                                        402,471
                                                                        258,983
                                                                        575,210
                                                                        772,997
pt_oilgas
                                                                        186,008
                                                                          4,383
                                                                        355,109
                                                                         14,119
                                                                         13,477
                                                                         40,437
                                                                        160,295
rail
                                                                        110,026
                                                                      342.97954
                                                                        423,103
                                                                         10,953
                                                                         10,611
                                                                       472.9168
                                                                         17,558
rwc
                                                                      2,023,977
                                                                         14,612
                                                                         32,049
                                                                        300,378
                                                                        299,829
                                                                          6,788
                                                                        325,390
                                                                              
 
 
 
 
 
 
 
Con. U.S. Total
                                                                     43,896,953
                                                                      4,143,899
                                                                      6,299,537
                                                                     10,523,775
                                                                      3,616,594
                                                                      1,713,335
                                                                     13,529,856
                                                                              
 
 
 
 
 
 
 
beis
                                                                      7,167,921
                                                                              
                                                                        965,761
                                                                              
 
 
                                                                     42,133,700
CONUS + beis
                                                                     51,064,874
                                                                      4,143,899
                                                                      7,265,298
                                                                     10,523,775
                                                                      3,616,594
                                                                      1,713,335
                                                                     55,663,555
                                                                               







Can./Mex./Offshore







Sector
CO
NH3
NOX
PM10
PM2_5
SO2
VOC
Canada othafdust
 
 
 
                                                                      1,267,025
                                                                        222,026
                                                                              
 
Canada othar
                                                                      2,687,318
                                                                          4,670
                                                                        282,912
                                                                        301,578
                                                                        221,810
                                                                         19,502
                                                                        849,301
Canada onroad_can
                                                                      1,303,551
                                                                          5,492
                                                                        168,631
                                                                         26,129
                                                                          9,498
                                                                            698
                                                                         60,932
Canada othpt
                                                                      1,133,173
                                                                        695,896
                                                                        443,884
                                                                         93,439
                                                                         49,576
                                                                        855,167
                                                                        752,057
Canada othptdust
 
 
 
                                                                        151,228
                                                                         55,685
                                                                              
 
Canada ptfire_othna
                                                                        760,345
                                                                         13,015
                                                                         16,337
                                                                         84,366
                                                                         71,652
                                                                          6,721
                                                                        185,224
Canada CMV
                                                                         12,247
                                                                             42
                                                                         73,084
                                                                          1,921
                                                                          1,785
                                                                          3,361
                                                                          5,832
Mexico othar
                                                                        277,263
                                                                        200,038
                                                                        252,523
                                                                        120,590
                                                                         58,294
                                                                          8,206
                                                                        628,715
Mexico onroad_mex
                                                                      1,615,412
                                                                          3,732
                                                                        393,339
                                                                         18,728
                                                                         12,667
                                                                          8,530
                                                                        164,793
Mexico othpt
                                                                        215,237
                                                                          7,273
                                                                        423,250
                                                                         85,626
                                                                         64,575
                                                                        394,409
                                                                         98,420
Mexico ptfire_othna
                                                                        384,764
                                                                          7,466
                                                                         16,665
                                                                         45,198
                                                                         38,354
                                                                          2,798
                                                                        131,980
Mexico CMV
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                              0
Offshore cmv in Federal waters
                                                                         45,623
                                                                            171
                                                                        240,686
                                                                          9,623
                                                                          8,879
                                                                         40,870
                                                                         22,153
Offshore cmv outside Federal waters
                                                                         32,972
                                                                            320
                                                                        363,173
                                                                         18,088
                                                                         16,645
                                                                         48,061
                                                                         15,638
Offshore pt_oilgas
                                                                         50,052
                                                                             15
                                                                         48,691
                                                                            668
                                                                            667
                                                                            502
                                                                         48,210
Non-US Total
                                                                      8,517,957
                                                                        938,131
                                                                      2,723,176
                                                                      2,224,208
                                                                        832,112
                                                                      1,388,825
                                                                      2,963,253


Table 5-4. National by-sector CAP emissions summaries for the 2016fh case, 36US3 grid (tons)
Sector
                                      CO
                                      NH3
                                      NOX
                                     PM10
                                     PM2_5
                                      SO2
                                      VOC
afdust_adj
                                                                              
                                                                              
                                                                              
                                                                      7,205,579
                                                                      1,006,637
                                                                              
                                                                              
ag
                                                                              
                                                                      3,409,762
                                                                              
                                                                              
                                                                              
                                                                              
                                                                        194,779
airports
                                                                        675,321
                                                                              0
                                                                        185,708
                                                                         11,097
                                                                          9,832
                                                                         25,452
                                                                         85,912
cmv_c1c2
                                                                         23,786
                                                                             84
                                                                        164,075
                                                                          4,498
                                                                          4,360
                                                                            636
                                                                          6,489
cmv_c3
                                                                         14,296
                                                                             40
                                                                        113,795
                                                                          2,260
                                                                          2,080
                                                                          4,666
                                                                          8,743
nonpt
                                                                      2,631,492
                                                                         78,565
                                                                        711,375
                                                                        570,526
                                                                        463,960
                                                                        138,883
                                                                      3,695,797
nonroad
                                                                     10,596,610
                                                                          1,846
                                                                      1,110,476
                                                                        109,228
                                                                        103,260
                                                                          2,134
                                                                      1,129,520
np_oilgas
                                                                        759,771
                                                                             12
                                                                        572,043
                                                                         14,050
                                                                         13,984
                                                                         19,243
                                                                      2,792,092
onroad
                                                                     19,894,976
                                                                        100,332
                                                                      3,631,843
                                                                        240,071
                                                                        117,803
                                                                         27,562
                                                                      1,853,073
ptagfire
                                                                        262,645
                                                                         51,276
                                                                         10,240
                                                                         38,688
                                                                         26,951
                                                                          3,694
                                                                         17,181
ptegu
                                                                        658,346
                                                                         23,976
                                                                      1,290,190
                                                                        163,981
                                                                        133,517
                                                                      1,540,589
                                                                         33,739
ptfire
                                                                     13,717,466
                                                                        239,605
                                                                        227,337
                                                                      1,461,693
                                                                      1,234,062
                                                                        111,291
                                                                      3,109,465
ptnonipm
                                                                      1,439,095
                                                                         63,731
                                                                        940,048
                                                                        396,913
                                                                        254,394
                                                                        654,527
                                                                        770,205
pt_oilgas
                                                                        167,531
                                                                          4,338
                                                                        339,280
                                                                         11,301
                                                                         10,784
                                                                         33,227
                                                                        127,565
rail
                                                                        104,551
                                                                            326
                                                                        559,381
                                                                         16,344
                                                                         15,819
                                                                            457
                                                                         26,082
rwc
                                                                      2,119,890
                                                                         15,442
                                                                         31,291
                                                                        317,537
                                                                        317,011
                                                                          7,704
                                                                        341,020
                                                                              
 
 
 
 
 
 
 
36US3 U.S. Total
                                                                     53,065,776
                                                                      3,989,335
                                                                      9,887,082
                                                                     10,563,766
                                                                      3,714,454
                                                                      2,570,065
                                                                     14,191,662
                                                                              
 
 
 
 
 
 
 
beis
                                                                      7,232,588
                                                                              
                                                                        968,624
                                                                              
 
 
                                                                     42,374,150
36US3 U.S. Total + beis
                                                                     60,298,364
                                                                      3,989,335
                                                                     10,855,706
                                                                     10,563,766
                                                                      3,714,454
                                                                      2,570,065
                                                                     56,565,812
                                                                               







Can./Mex./Offshore







Sector
CO
NH3
NOX
PM10
PM2_5
SO2
VOC
Canada othafdust
 
 
 
                                                                      1,101,762
                                                                        194,352
                                                                              
 
Canada othar
                                                                      2,933,979
                                                                          5,152
                                                                        437,979
                                                                        327,343
                                                                        260,341
                                                                         20,590
                                                                        885,639
Canada onroad_can
                                                                      1,730,052
                                                                          7,125
                                                                        425,462
                                                                         26,286
                                                                         14,757
                                                                          1,606
                                                                        148,376
Canada othpt
                                                                      1,312,748
                                                                        521,088
                                                                        826,476
                                                                        149,520
                                                                         56,407
                                                                      1,116,771
                                                                        979,359
Canada othptdust
 
 
 
                                                                        150,320
                                                                         54,747
                                                                              
 
Canada ptfire_othna
                                                                      6,282,821
                                                                        104,683
                                                                        134,301
                                                                        685,135
                                                                        580,928
                                                                         60,914
                                                                      1,501,988
Canada CMV
                                                                         13,802
                                                                             49
                                                                        121,859
                                                                          2,292
                                                                          2,126
                                                                          5,172
                                                                          6,760
Mexico othar
                                                                      2,684,115
                                                                        878,370
                                                                        707,975
                                                                        585,933
                                                                        415,474
                                                                         25,671
                                                                      3,739,965
Mexico onroad_mex
                                                                      6,273,194
                                                                         10,319
                                                                      1,497,028
                                                                         74,169
                                                                         56,782
                                                                         26,400
                                                                        552,952
Mexico othpt
                                                                        743,265
                                                                         36,318
                                                                        698,064
                                                                        256,840
                                                                        179,384
                                                                      2,110,426
                                                                        340,352
Mexico ptfire_othna
                                                                      7,133,496
                                                                        120,584
                                                                        346,990
                                                                      1,155,522
                                                                        745,819
                                                                         45,208
                                                                      2,259,747
Mexico CMV
                                                                         64,730
                                                                              0
                                                                        204,997
                                                                         16,286
                                                                         15,087
                                                                        109,778
                                                                          8,817
Offshore cmv in Federal waters
                                                                         36,317
                                                                            163
                                                                        322,293
                                                                          9,143
                                                                          8,466
                                                                         40,888
                                                                         17,404
Offshore cmv outside Federal waters
                                                                         88,556
                                                                          1,178
                                                                      1,008,678
                                                                         92,681
                                                                         85,293
                                                                        685,101
                                                                         40,344
Offshore pt_oilgas
                                                                         50,052
                                                                             15
                                                                         48,691
                                                                            668
                                                                            667
                                                                            502
                                                                         48,210
Non-US Total
                                                                     29,347,127
                                                                      1,685,043
                                                                      6,780,791
                                                                      4,633,898
                                                                      2,670,630
                                                                      4,249,027
                                                                     10,529,914



Table 5-5. National by-sector CAP emissions summaries for the 2023fh1 case, 36US3 grid (tons)
Sector
                                      CO
                                      NH3
                                      NOX
                                     PM10
                                     PM2_5
                                      SO2
                                      VOC
afdust_adj
                                                                              
                                                                              
                                                                              
                                                                      7,256,900
                                                                      1,016,968
                                                                              
                                                                              
ag
                                                                              
                                                                      3,543,158
                                                                              
                                                                              
                                                                              
                                                                              
                                                                        205,451
airports
                                                                        740,248
                                                                              0
                                                                        220,047
                                                                         11,394
                                                                         10,161
                                                                         30,253
                                                                         92,649
cmv_c1c2
                                                                         23,806
                                                                             60
                                                                        117,456
                                                                          3,220
                                                                          3,122
                                                                            243
                                                                          4,563
cmv_c3
                                                                         17,126
                                                                             49
                                                                        107,776
                                                                          2,696
                                                                          2,480
                                                                          5,549
                                                                         10,572
nonpt
                                                                      2,646,550
                                                                         79,408
                                                                        709,732
                                                                        579,371
                                                                        473,087
                                                                        106,585
                                                                      3,757,585
nonroad
                                                                     10,584,399
                                                                          2,033
                                                                        737,782
                                                                         71,479
                                                                         66,960
                                                                          1,527
                                                                        857,041
np_oilgas
                                                                        788,072
                                                                             20
                                                                        585,230
                                                                         16,221
                                                                         16,102
                                                                         31,269
                                                                      3,203,738
onroad
                                                                     13,777,542
                                                                         89,297
                                                                      1,751,649
                                                                        200,035
                                                                         72,495
                                                                         12,486
                                                                      1,099,467
ptagfire
                                                                        262,645
                                                                         51,276
                                                                         10,240
                                                                         38,688
                                                                         26,951
                                                                          3,694
                                                                         17,181
ptegu
                                                                        659,538
                                                                         36,544
                                                                            996
                                                                        144,758
                                                                        124,433
                                                                         18,820
                                                                         35,922
ptfire
                                                                     13,717,466
                                                                        239,605
                                                                        227,337
                                                                      1,461,693
                                                                      1,234,062
                                                                        111,291
                                                                      3,109,465
ptnonipm
                                                                      1,448,583
                                                                         63,739
                                                                        928,917
                                                                        400,219
                                                                        257,153
                                                                        572,494
                                                                        771,839
pt_oilgas
                                                                        186,242
                                                                          4,377
                                                                        361,166
                                                                         13,602
                                                                         12,973
                                                                         38,125
                                                                        156,725
rail
                                                                        105,988
                                                                            330
                                                                        469,157
                                                                         12,778
                                                                         12,376
                                                                            460
                                                                         20,436
rwc
                                                                      2,047,318
                                                                         14,796
                                                                         31,911
                                                                        304,528
                                                                        303,984
                                                                          7,011
                                                                        329,092
                                                                              
 
 
 
 
 
 
 
36US3 U.S. Total
                                                                     47,005,523
                                                                      4,124,692
                                                                      6,259,396
                                                                     10,517,582
                                                                      3,633,307
                                                                        939,807
                                                                     13,671,726
                                                                              
 
 
 
 
 
 
 
beis
                                                                      7,232,588
                                                                              
                                                                        968,624
                                                                              
 
 
                                                                     42,374,150
36US3 U.S. Total + beis
                                                                     54,238,111
                                                                      4,124,692
                                                                      7,228,020
                                                                     10,517,582
                                                                      3,633,307
                                                                        939,807
                                                                     56,045,876
                                                                               







Can./Mex./Offshore







Sector
CO
NH3
NOX
PM10
PM2_5
SO2
VOC
Canada othafdust
 
 
 
                                                                      1,222,521
                                                                        214,760
                                                                              
 
Canada othar
                                                                      2,896,925
                                                                          5,004
                                                                        351,959
                                                                        316,554
                                                                        239,499
                                                                         20,395
                                                                        875,086
Canada onroad_can
                                                                      1,471,769
                                                                          6,260
                                                                        247,154
                                                                         26,948
                                                                         11,536
                                                                            778
                                                                         90,813
Canada othpt
                                                                      1,306,333
                                                                        631,845
                                                                        682,142
                                                                         99,818
                                                                         53,521
                                                                        977,647
                                                                        851,263
Canada othptdust
 
 
 
                                                                        150,273
                                                                         54,730
                                                                              
 
Canada ptfire_othna
                                                                      6,282,821
                                                                        104,683
                                                                        134,301
                                                                        685,165
                                                                        580,958
                                                                         60,914
                                                                      1,501,988
Canada CMV
                                                                         14,789
                                                                             52
                                                                         88,545
                                                                          2,463
                                                                          2,285
                                                                          5,507
                                                                          7,134
Mexico othar
                                                                      2,873,134
                                                                        864,397
                                                                        767,216
                                                                        610,423
                                                                        438,710
                                                                         26,588
                                                                      4,050,948
Mexico onroad_mex
                                                                      6,053,503
                                                                         12,083
                                                                      1,447,199
                                                                         94,407
                                                                         72,468
                                                                         31,838
                                                                        560,284
Mexico othpt
                                                                        930,547
                                                                         44,909
                                                                        777,407
                                                                        303,309
                                                                        210,038
                                                                      2,111,906
                                                                        427,407
Mexico ptfire_othna
                                                                      7,136,168
                                                                        120,627
                                                                        347,132
                                                                      1,155,991
                                                                        746,107
                                                                         45,222
                                                                      2,260,695
Mexico CMV
                                                                         79,677
                                                                              0
                                                                        252,331
                                                                         20,046
                                                                         18,571
                                                                         19,304
                                                                         10,853
Offshore cmv in Federal waters
                                                                         43,338
                                                                            191
                                                                        280,425
                                                                         10,740
                                                                          9,920
                                                                         50,540
                                                                         20,650
Offshore cmv outside Federal waters
                                                                        108,334
                                                                            741
                                                                      1,234,211
                                                                         58,177
                                                                         53,538
                                                                        155,668
                                                                         49,468
Offshore pt_oilgas
                                                                         50,052
                                                                             15
                                                                         48,691
                                                                            668
                                                                            667
                                                                            502
                                                                         48,210
Non-US Total
                                                                     29,247,390
                                                                      1,790,809
                                                                      6,658,712
                                                                      4,757,504
                                                                      2,707,306
                                                                      3,506,810
                                                                     10,754,799

Table 5-6. National by-sector CAP emissions summaries for the 2028fh1 case, 36US3 grid (tons)
Sector
                                      CO
                                      NH3
                                      NOX
                                     PM10
                                     PM2_5
                                      SO2
                                      VOC
afdust_adj
                                                                              
                                                                              
                                                                              
                                                                      7,281,296
                                                                      1,021,906
                                                                              
                                                                              
ag
                                                                              
                                                                      3,564,067
                                                                              
                                                                              
                                                                              
                                                                              
                                                                        207,123
airports
                                                                        804,754
                                                                              0
                                                                        245,466
                                                                         11,900
                                                                         10,649
                                                                         33,910
                                                                        100,417
cmv_c1c2
                                                                         24,241
                                                                             47
                                                                         93,634
                                                                          2,572
                                                                          2,494
                                                                            245
                                                                          3,602
cmv_c3
                                                                         19,655
                                                                             54
                                                                        107,701
                                                                          3,094
                                                                          2,847
                                                                          6,354
                                                                         12,192
nonpt
                                                                      2,667,254
                                                                         79,670
                                                                        709,358
                                                                        594,080
                                                                        485,244
                                                                        107,185
                                                                      3,801,426
nonroad
                                                                     10,895,363
                                                                          2,105
                                                                        611,654
                                                                         58,375
                                                                         54,340
                                                                          1,545
                                                                        802,328
np_oilgas
                                                                        774,404
                                                                             20
                                                                        560,267
                                                                         16,462
                                                                         16,343
                                                                         33,574
                                                                      3,331,524
onroad
                                                                     10,310,777
                                                                         87,925
                                                                      1,246,494
                                                                        189,887
                                                                         58,944
                                                                         11,705
                                                                        836,476
ptagfire
                                                                        262,645
                                                                         51,276
                                                                         10,240
                                                                         38,688
                                                                         26,951
                                                                          3,694
                                                                         17,181
ptegu
                                                                        648,829
                                                                         35,883
                                                                        748,663
                                                                        140,100
                                                                        120,420
                                                                        781,397
                                                                         33,831
ptfire
                                                                     13,717,466
                                                                        239,605
                                                                        227,337
                                                                      1,461,693
                                                                      1,234,062
                                                                        111,291
                                                                      3,109,465
ptnonipm
                                                                      1,460,908
                                                                         63,990
                                                                        933,863
                                                                        402,498
                                                                        258,991
                                                                        575,210
                                                                        772,998
pt_oilgas
                                                                        186,008
                                                                          4,383
                                                                        355,109
                                                                         14,119
                                                                         13,477
                                                                         40,437
                                                                        160,295
rail
                                                                        110,026
                                                                            343
                                                                        423,103
                                                                         10,953
                                                                         10,611
                                                                            473
                                                                         17,558
rwc
                                                                      2,024,434
                                                                         14,615
                                                                         32,058
                                                                        300,440
                                                                        299,891
                                                                          6,789
                                                                        325,463
                                                                              
 
 
 
 
 
 
 
36US3 U.S. Total
                                                                     43,906,764
                                                                      4,143,984
                                                                      6,304,947
                                                                     10,526,157
                                                                      3,617,170
                                                                      1,713,809
                                                                     13,531,879
                                                                              
 
 
 
 
 
 
 
beis
                                                                      7,232,588
                                                                              
                                                                        968,624
                                                                              
 
 
                                                                     42,374,150
36US3 U.S. Total + beis
                                                                     51,139,352
                                                                      4,143,984
                                                                      7,273,571
                                                                     10,526,157
                                                                      3,617,170
                                                                      1,713,809
                                                                     55,906,029
                                                                               







Can./Mex./Offshore







Sector
CO
NH3
NOX
PM10
PM2_5
SO2
VOC
Canada othafdust
 
 
 
                                                                      1,314,491
                                                                        230,228
                                                                              
 
Canada othar
                                                                      2,896,712
                                                                          4,968
                                                                        319,942
                                                                        313,751
                                                                        231,705
                                                                         20,393
                                                                        902,227
Canada onroad_can
                                                                      1,353,512
                                                                          5,692
                                                                        177,653
                                                                         27,234
                                                                          9,960
                                                                            723
                                                                         63,284
Canada othpt
                                                                      1,344,360
                                                                        719,520
                                                                        564,509
                                                                        106,041
                                                                         57,167
                                                                        965,763
                                                                        928,552
Canada othptdust
 
 
 
                                                                        150,646
                                                                         54,865
                                                                              
 
Canada ptfire_othna
                                                                      6,282,821
                                                                        104,683
                                                                        134,301
                                                                        685,165
                                                                        580,958
                                                                         60,914
                                                                      1,501,988
Canada CMV
                                                                         15,570
                                                                             55
                                                                         95,172
                                                                          2,598
                                                                          2,409
                                                                          5,866
                                                                          7,502
Mexico othar
                                                                      2,995,073
                                                                        871,163
                                                                        800,519
                                                                        627,824
                                                                        454,427
                                                                         27,308
                                                                      4,263,367
Mexico onroad_mex
                                                                      5,496,594
                                                                         13,807
                                                                      1,336,088
                                                                        108,810
                                                                         83,255
                                                                         36,064
                                                                        574,688
Mexico othpt
                                                                      1,007,430
                                                                         51,510
                                                                        870,465
                                                                        346,653
                                                                        239,665
                                                                      2,188,067
                                                                        495,677
Mexico ptfire_othna
                                                                      7,136,168
                                                                        120,627
                                                                        347,132
                                                                      1,155,991
                                                                        746,107
                                                                         45,222
                                                                      2,260,695
Mexico CMV
                                                                         92,295
                                                                              0
                                                                        292,291
                                                                         23,221
                                                                         21,512
                                                                         22,361
                                                                         12,572
Offshore cmv in Federal waters
                                                                         49,577
                                                                            218
                                                                        261,208
                                                                         12,259
                                                                         11,309
                                                                         59,247
                                                                         23,628
Offshore cmv outside Federal waters
                                                                        125,652
                                                                            858
                                                                      1,424,152
                                                                         67,233
                                                                         61,846
                                                                        180,627
                                                                         57,032
Offshore pt_oilgas
                                                                         50,052
                                                                             15
                                                                         48,691
                                                                            668
                                                                            667
                                                                            502
                                                                         48,210
Non-US Total
                                                                     28,845,814
                                                                      1,893,116
                                                                      6,672,122
                                                                      4,942,583
                                                                      2,786,081
                                                                      3,613,056
                                                                     11,139,423
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                   Appendix A:	CB6 Assignment for New Species
 



 

 

 Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE4.5 that were used in the 2016 alpha platform

Sector
Pollutant
Profile code
Profile description
SPECIATE version
comment
nonpt
VOC
G95223TOG
Poultry Production - Average of Production Cycle with gapfilled methane and ethane
5.0 (not yet released)
Replacement for v4.5 profile 95223; Used 70% methane, 20% ethane, and the 10% remaining VOC is from profile 95223 
Nonpt, ptnonipm
VOC
G95240TOG
Beef Cattle Farm and Animal Waste with gapfilled methane and ethane
5.0 (not yet released)
Replacement for v4.5 profile 95240. Used 70% methane, 20% ethane; the 10% remaining VOC is from profile 95240.
nonpt
VOC
G95241TOG
Swine Farm and Animal Waste
5.0 (not yet released)
Replacement for v4.5 profile 95241. Used 70% methane, 20% ethane; the 10% remaining VOC is from profile 95241
nonpt, ptnonipm, pt_oilgas, ptegu
PM2.5
95475
Composite -Refinery Fuel Gas and Natural Gas Combustion
5.0 (not yet released)
Composite of AE6-ready versions of SPECIATE4.5 profies 95125, 95126, and 95127 
nonroad
VOC
95328
Spark-Ignition Exhaust Emissions from 2-stroke off-road engines  - E10 ethanol gasoline
4.5

nonroad
VOC
95330
Spark-Ignition Exhaust Emissions from 4-stroke off-road engines - E10 ethanol gasoline
4.5

nonroad
VOC
95331
Diesel Exhaust Emissions from Pre-Tier 1 Off-road Engines
4.5

nonroad
VOC
95332
Diesel Exhaust Emissions from Tier 1 Off-road Engines
4.5

nonroad
VOC
95333
Diesel Exhaust Emissions from Tier 2 Off-road Engines
4.5

np_oilgas
VOC
95087a
Oil and Gas - Composite - Oil Field - Oil Tank Battery Vent Gas
4.5

np_oilgas
VOC
95109a
Oil and Gas - Composite - Oil Field - Condensate Tank Battery Vent Gas
4.5

np_oilgas
VOC
95398
Composite Profile - Oil and Natural Gas Production - Condensate Tanks
4.5

np_oilgas
VOC
95403
Composite Profile - Gas Wells
4.5

np_oilgas
VOC
95417
Oil and Gas Production - Composite Profile - Untreated Natural Gas, Uinta Basin
4.5

np_oilgas
VOC
95418
Oil and Gas Production - Composite Profile - Condensate Tank Vent Gas, Uinta Basin
4.5

np_oilgas
VOC
95419
Oil and Gas Production - Composite Profile - Oil Tank Vent Gas, Uinta Basin
4.5

np_oilgas
VOC
95420
Oil and Gas Production - Composite Profile - Glycol Dehydrator, Uinta Basin
4.5

np_oilgas
VOC
DJVNT_R
Oil and Gas -Denver-Julesburg Basin Produced Gas Composition from Non-CBM Gas Wells
4.5

np_oilgas
VOC
FLR99
Natural Gas Flare Profile with DRE >98%
4.5

np_oilgas
VOC
PNC01_R
Oil and Gas -Piceance Basin Produced Gas Composition from Non-CBM Gas Wells
4.5

np_oilgas
VOC
PNC02_R
Oil and Gas -Piceance Basin Produced Gas Composition from Oil Wells
4.5

np_oilgas
VOC
PNC03_R
Oil and Gas -Piceance Basin Flash Gas Composition for Condensate Tank
4.5

np_oilgas
VOC
PNCDH
Oil and Gas Production - Composite Profile - Glycol Dehydrator, Piceance Basin
4.5

np_oilgas
VOC
PRBCB_R
Oil and Gas -Powder River Basin Produced Gas Composition from CBM Wells
4.5

np_oilgas
VOC
PRBCO_R
Oil and Gas -Powder River Basin Produced Gas Composition from Non-CBM Wells
4.5

np_oilgas
VOC
PRM01_R
Oil and Gas -Permian Basin Produced Gas Composition for Non-CBM Wells
4.5

np_oilgas
VOC
SSJCB_R
Oil and Gas -South San Juan Basin Produced Gas Composition from CBM Wells
4.5

np_oilgas
VOC
SSJCO_R
Oil and Gas -South San Juan Basin Produced Gas Composition from Non-CBM Gas Wells
4.5

np_oilgas
VOC
SWFLA_R
Oil and Gas -SW Wyoming Basin Flash Gas Composition for Condensate Tanks
4.5

np_oilgas
VOC
SWVNT_R
Oil and Gas -SW Wyoming Basin Produced Gas Composition from Non-CBM Wells
4.5

np_oilgas
VOC
UNT01_R
Oil and Gas -Uinta Basin Produced Gas Composition from CBM Wells
4.5

np_oilgas
VOC
WRBCO_R
Oil and Gas -Wind River Basin Produced Gas Composition from Non-CBM Gas Wells
4.5

pt_oilgas
VOC
95325
Chemical Manufacturing Industry Wide Composite
4.5

pt_oilgas
VOC
95326
Pulp and Paper Industry Wide Composite
4.5

pt_oilgas, ptnonipm
VOC
95399
Composite Profile - Oil Field - Wells
4.5

pt_oilgas
VOC
95403
Composite Profile - Gas Wells
4.5

pt_oilgas
VOC
95417
Oil and Gas Production - Composite Profile - Untreated Natural Gas, Uinta Basin
4.5

pt_oilgas
VOC
DJVNT_R
Oil and Gas -Denver-Julesburg Basin Produced Gas Composition from Non-CBM Gas Wells
4.5

pt_oilgas, ptnonipm
VOC
FLR99
Natural Gas Flare Profile with DRE >98%
4.5

pt_oilgas
VOC
PNC01_R
Oil and Gas -Piceance Basin Produced Gas Composition from Non-CBM Gas Wells
4.5

pt_oilgas
VOC
PNC02_R
Oil and Gas -Piceance Basin Produced Gas Composition from Oil Wells
4.5

pt_oilgas
VOC
PNCDH
Oil and Gas Production - Composite Profile - Glycol Dehydrator, Piceance Basin
4.5

pt_oilgas, ptnonipm
VOC
PRBCO_R
Oil and Gas -Powder River Basin Produced Gas Composition from Non-CBM Wells
4.5

pt_oilgas, ptnoniom
VOC
PRM01_R
Oil and Gas -Permian Basin Produced Gas Composition for Non-CBM Wells
4.5

pt_oilgas, ptnonipm
VOC
SSJCO_R
Oil and Gas -South San Juan Basin Produced Gas Composition from Non-CBM Gas Wells
4.5

pt_oilgas, ptnonipm
VOC
SWVNT_R
Oil and Gas -SW Wyoming Basin Produced Gas Composition from Non-CBM Wells
4.5

ptfire
VOC
95421
Composite Profile - Prescribed fire southeast conifer forest
4.5

ptfire
VOC
95422
Composite Profile - Prescribed fire southwest conifer forest
4.5

ptfire
VOC
95423
Composite Profile - Prescribed fire northwest conifer forest
4.5

ptfire
VOC
95424
Composite Profile - Wildfire northwest conifer forest
4.5

ptfire
VOC
95425
Composite Profile - Wildfire boreal forest
4.5

ptnonipm
VOC
95325
Chemical Manufacturing Industry Wide Composite
4.5

ptnonipm
VOC
95326
Pulp and Paper Industry Wide Composite
4.5

onroad
PM2.5
95462 
Composite - Brake Wear
4.5
Used in SMOKE-MOVES
onroad
PM2.5
95460
Composite - Tire Dust
4.5
Used in SMOKE-MOVES


	
       Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT

The table below provides a crosswalk between fuel distribution SCCs and classification type for portable fuel containers (PFC), fuel distribution operations associated with the bulk-plant-to-pump (BTP), refinery to bulk terminal (RBT) and bulk plant storage (BPS). 

                                      SCC
                                     Type
Description
                                   40301001
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 13: Breathing Loss (67000 Bbl. Tank Size) 
                                   40301002
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 10: Breathing Loss (67000 Bbl. Tank Size) 
                                   40301003
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 7: Breathing Loss (67000 Bbl. Tank Size) 
                                   40301004
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 13: Breathing Loss (250000 Bbl. Tank Size) 
                                   40301006
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 7: Breathing Loss (250000 Bbl. Tank Size) 
                                   40301007
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 13: Working Loss (Tank Diameter Independent) 
                                   40301101
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 13: Standing Loss (67000 Bbl. Tank Size) 
                                   40301102
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 10: Standing Loss (67000 Bbl. Tank Size) 
                                   40301103
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 7: Standing Loss (67000 Bbl. Tank Size) 
                                   40301105
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 10: Standing Loss (250000 Bbl. Tank Size) 
                                   40301151
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline: Standing Loss - Internal 
                                   40301202
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor Space; Gasoline RVP 10: Filling Loss 
                                   40301203
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor Space; Gasoline RVP 7: Filling Loss 
                                   40400101
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank 
                                   40400102
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank 
                                   40400103
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank 
                                   40400104
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank 
                                   40400105
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank 
                                   40400106
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Breathing Loss (250000 Bbl Capacity) - Fixed Roof Tank 
                                   40400107
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Working Loss (Diam. Independent) - Fixed Roof Tank 
                                   40400108
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Working Loss (Diameter Independent) - Fixed Roof Tank 
                                   40400109
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Working Loss (Diameter Independent) - Fixed Roof Tank 
                                   40400110
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank 
                                   40400111
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank 
                                   40400112
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss (67000 Bbl Capacity)- Floating Roof Tank 
                                   40400113
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank 
                                   40400114
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank 
                                   40400115
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank 
                                   40400116
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float Rf Tnk 
                                   40400117
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss (250000 Bbl Cap.) - Float Rf Tnk 
                                   40400118
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space 
                                   40400119
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space 
                                   40400120
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space 
                                   40400130
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal 
                                   40400131
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal 
                                   40400132
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal 
                                   40400133
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal 
                                   40400140
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: Standing Loss - Ext. Float Roof Tank w/ Secondy Seal 
                                   40400141
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal 
                                   40400142
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Secondary Seal 
                                   40400143
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss - Ext. Floating Roof w/ Secondary Seal 
                                   40400148
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal) 
                                   40400149
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: External Floating Roof (Primary/Secondary Seal) 
                                   40400150
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Miscellaneous Losses/Leaks: Loading Racks 
                                   40400151
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Valves, Flanges, and Pumps 
                                   40400152
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Vapor Collection Losses 
                                   40400153
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Vapor Control Unit Losses 
                                   40400160
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal 
                                   40400161
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal 
                                   40400162
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal 
                                   40400163
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal 
                                   40400170
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal 
                                   40400171
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal 
                                   40400172
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal 
                                   40400173
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal 
                                   40400178
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal) 
                                   40400179
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: Internal Floating Roof (Primary/Secondary Seal) 
                                   40400199
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; 
                                   40400201
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank 
                                   40400202
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank 
                                   40400203
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank 
                                   40400204
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank 
                                   40400205
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank 
                                   40400206
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank 
                                   40400207
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank 
                                   40400208
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank 
                                   40400210
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float Rf Tnk 
                                   40400211
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space 
                                   40400212
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space 
                                   40400213
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space 
                                   40400230
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal 
                                   40400231
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal 
                                   40400232
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal 
                                   40400233
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal 
                                   40400240
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Standing Loss - Ext. Floating Roof w/ Secondary Seal 
                                   40400241
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal 
                                   40400248
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10/13/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal) 
                                   40400249
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: External Floating Roof (Primary/Secondary Seal) 
                                   40400250
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Loading Racks 
                                   40400251
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Valves, Flanges, and Pumps 
                                   40400252
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Miscellaneous Losses/Leaks: Vapor Collection Losses 
                                   40400253
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Miscellaneous Losses/Leaks: Vapor Control Unit Losses 
                                   40400260
                                      RBT
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal 
                                   40400261
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal 
                                   40400262
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal 
                                   40400263
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal 
                                   40400270
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal 
                                   40400271
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal 
                                   40400272
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal 
                                   40400273
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal 
                                   40400278
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10/13/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal) 
                                   40400279
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Internal Floating Roof (Primary/Secondary Seal) 
                                   40400401
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP 13: Breathing Loss 
                                   40400402
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP 13: Working Loss 
                                   40400403
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP 10: Breathing Loss 
                                   40400404
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP 10: Working Loss 
                                   40400405
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP 7: Breathing Loss 
                                   40400406
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP 7: Working Loss 
                                   40600101
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Splash Loading 
                                   40600126
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged Loading  
                                   40600131
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged Loading (Normal Service) 
                                   40600136
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Splash Loading (Normal Service) 
                                   40600141
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged Loading (Balanced Service) 
                                   40600144
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Splash Loading (Balanced Service) 
                                   40600147
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged Loading (Clean Tanks) 
                                   40600162
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Loaded with Fuel (Transit Losses) 
                                   40600163
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Return with Vapor (Transit Losses) 
                                   40600199
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Not Classified 
                                   40600231
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tankers: Cleaned and Vapor Free Tanks 
                                   40600232
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tankers 
                                   40600233
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Barges: Cleaned and Vapor Free Tanks 
                                   40600234
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tankers: Ballasted Tank 
                                   40600235
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Ocean Barges Loading - Ballasted Tank
                                   40600236
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tankers: Uncleaned Tanks 
                                   40600237
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Ocean Barges Loading - Uncleaned Tanks
                                   40600238
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Barges: Uncleaned Tanks 
                                   40600239
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Tankers: Ballasted Tank 
                                   40600240
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Barges: Average Tank Condition 
                                   40600241
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Tanker Ballasting 
                                   40600299
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Not Classified  
                                   40600301
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Splash Filling 
                                   40600302
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Submerged Filling w/o Controls 
                                   40600305
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Unloading 
                                   40600306
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Balanced Submerged Filling 
                                   40600307
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Underground Tank Breathing and Emptying 
                                   40600399
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Not Classified ** 
                                   40600401
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling Vehicle Gas Tanks - Stage II; Vapor Loss w/o Controls 
                                   40600501
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport - General - All Products; Pipeline Leaks 
                                   40600502
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport - General - All Products; Pipeline Venting 
                                   40600503
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport - General - All Products; Pump Station 
                                   40600504
                                      RBT
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport - General - All Products; Pump Station Leaks 
                                   40600602
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage II; Liquid Spill Loss w/o Controls 
                                   40600701
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage I; Splash Filling 
                                   40600702
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage I; Submerged Filling w/o Controls 
                                   40600706
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage I; Balanced Submerged Filling 
                                   40600707
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage I; Underground Tank Breathing and Emptying 
                                   40688801
                                    BTP/BPS
 Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Fugitive Emissions; Specify in Comments Field 
                                  2501050120
                                      RBT
 Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Terminals: All Evaporative Losses; Gasoline 
                                  2501055120
                                    BTP/BPS
 Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Plants: All Evaporative Losses; Gasoline 
                                  2501060050
                                    BTP/BPS
 Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Total 
                                  2501060051
                                    BTP/BPS
 Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Submerged Filling 
                                  2501060052
                                    BTP/BPS
 Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Splash Filling 
                                  2501060053
                                    BTP/BPS
 Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Balanced Submerged Filling 
                                  2501060200
                                    BTP/BPS
 Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Underground Tank: Total 
                                  2501060201
                                    BTP/BPS
 Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Underground Tank: Breathing and Emptying 
                                  2501995000
                                    BTP/BPS
 Storage and Transport; Petroleum and Petroleum Product Storage; All Storage Types: Working Loss; Total: All Products 
                                  2505000120
                                      RBT
 Storage and Transport; Petroleum and Petroleum Product Transport; All Transport Types; Gasoline 
                                  2505020120
                                      RBT
 Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline 
                                  2505020121
                                      RBT
 Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline - Barge 
                                  2505030120
                                    BTP/BPS
 Storage and Transport; Petroleum and Petroleum Product Transport; Truck; Gasoline 
                                  2505040120
                                      RBT
 Storage and Transport; Petroleum and Petroleum Product Transport; Pipeline; Gasoline 
                                  2660000000
                                    BTP/BPS
 Waste Disposal, Treatment, and Recovery; Leaking Underground Storage Tanks; Leaking Underground Storage Tanks; Total: All Storage Types 

