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Technical Support Document (TSD): Preparation of
Emissions Inventories for the 2022vl North American
Emissions Modeling Platform
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EPA-454/B-25-001
May 2025
Technical Support Document (TSD) Preparation of Emissions Inventories for the 2022vl North American
Emissions Modeling Platform
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Authors:
Alison Eyth (EPA/OAR)
Jeff Vukovich (EPA/OAR)
Caroline Farkas (EPA/OAR)
Janice Godfrey (EPA/OAR)
Karl Seltzer (EPA/OAR)
Lindsay Dayton (EPA/OAR)
Yijia Dietrich (EPA/OAR)
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TABLE OF CONTENTS
LIST OF TABLES VIII
LIST OF FIGURES XI
ACRONYMS XII
1 INTRODUCTION 15
2 BASE YEAR EMISSIONS INVENTORIES AND APPROACHES 17
2.1 Point sources (ptegu, pt_oilgas, ptnonipm, airports) 22
2.1.1 EGU sector (ptegu) 24
2.1.2 Point source oil and gas sector (pt_oilgas) 26
2.1.3 Aircraft and ground support equipment (airports) 28
2.1.4 Non-IPM sector (ptnonipm) 29
2.2 NonpointSOURCES (afdust, fertilizer, livestock, np_oilgas, rwc, np_solvents, nonpt) 29
2.2.1 Area fugitive dust sector (afdust) 30
2.2.2 Agricultural Livestock (livestock) 36
2.2.3 Agricultural Fertilizer (fertilizer) 36
2.2.4 Nonpoint Oil and Gas Sector (np_oilgas) 39
2.2.5 Residential Wood Combustion (rwc) 43
2.2.6 Solvents (np_solvents) 45
2.2.7 Open burning (openburn) 45
2.2.8 Nonpoint (nonpt) 45
2.3 Onroad Mobile sources (onroad) 47
2.3.1 Inventory Development using SMOKE-MOVES 47
2.3.2 Onroad Activity Data Development 50
2.3.3 MOVES Emission Factor Table Development 52
2.3.4 Onroad California Inventory Development (onroad_ca_adj) 55
2.4 Nonroad Mobile sources (cmv, rail, nonroad) 56
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 56
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3) 62
2.4.3 Railway Locomotives (rail) 67
2.4.4 Nonroad Mobile Equipment (nonroad) 73
2.5 Fires (ptfire-rx, ptfire-wild, ptagfire) 75
2.5.1 Wild and Prescribed Fires (ptfire-rx, ptfire-wild) 76
2.5.2 Point source Agriculture Fires (ptagfire) 83
2.6 Biogenic Sources (beis) 84
2.7 Sources Outside ofthe UnitedStates 87
2.7.1 Point Sources in Canada and Mexico (canmex_point) 88
2.7.2 Fugitive Dust Sources in Canada (canada_afdust, canada_ptdust) 88
2.7.3 Agricultural Sources in Canada and Mexico (canmex_ag) 89
2.7.4 Surface-level Oil and Gas Sources in Canada (canada_og2D) 89
2.7.5 Nonpoint and Nonroad Sources in Canada and Mexico (canmex_area) 89
2.7.6 Onroad Sources in Canada and Mexico (canada_onroad, mexico_onroad) 89
2.7.7 Fires in Canada and Mexico (ptfire_othna) 89
2.7.8 Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury 90
3 EMISSIONS MODELING 91
3.1 Emissions Modeling Overview 91
3.2 Chemical Speciation 95
3.2.1 VOC speciation 100
3.2.2 PM speciation 105
3.2.2.1 Diesel PM 105
3.2.3 NOx speciation 105
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3.2.4 Sulfuric Acid Vapor (SULF) 106
3.2.5 Speciation of Metals and Mercury 107
3.3 Temporal Allocation 108
3.3.1 Use of FF10 format for finer than annual emissions 110
3.3.2 Temporal allocation for non-EGU sources (ptnonipm) 110
3.3.3 Electric Generating Utility temporal allocation (ptegu) Ill
3.3.4 Airport Temporal allocation (airports) 115
3.3.5 Residential Wood Combustion Temporal allocation (rwc) 118
3.3.6 Agricultural Ammonia Temporal Profiles (livestock) 122
3.3.7 Oil and gas temporal allocation (np_oilgas) 124
3.3.8 Onroad mobile temporal allocation (onroad) 124
3.3.9 Nonroad mobile temporal allocation (nonroad) 129
3.3.10 Fugitive dust temporal profiles (afdust) 130
3.3.11 Additional sector specific details (beis, cmv, rail, nonpt, np_solvents, ptfire-rx, ptfire-wild) 131
3.4 Spatial Allocation 133
3.4.1 Spatial Surrogates for U.S. emissions 133
3.4.2 Allocation method for airport-related sources in the U.S 148
3.4.3 Surrogates for Canada and Mexico emission inventories 148
4 ANALYTIC YEAR EMISSIONS INVENTORIES AND APPROACHES 159
4.1 EGU PointSource Projections (ptegu) 163
4.2 Sectors with Projections Computed using CoST 165
4.2.1 Background on the Control Strategy Tool (CoST) 166
4.2.2 CoST CLOSURE Packet (ptnonipm, pt_oilgas) 170
4.2.3 CoST PROJECTION Packets (afdust, airports, cmv, livestock, nonpt, np_oilgas, np_solvents, ptnonipm, pt_oilgas,
rail) 171
4.2.3.1 Fugitive dust growth (afdust) 171
4.2.3.2 Airport sources (airports) 173
4.2.3.3 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 174
4.2.3.4 Category 3 Commercial Marine Vessels (cmv_c3) 176
4.2.3.5 Livestock population growth (livestock) 177
4.2.3.6 Nonpoint Sources (nonpt) 178
4.2.3.7 Solvents (np_solvents) 189
4.2.3.8 Oil and Gas Sources (np_oilgas, pt_oilgas) 190
4.2.3.9 Non-EGU point sources (ptnonipm) 193
4.2.3.10 Railroads (rail) 194
4.2.3.11 Residential Wood Combustion (rwc) 195
4.2.4 CoST CONTROL Packets (nonpt, np_oilgas, ptnonipm, pt_oilgas, np_solvents) 195
4.2.4.1 Oil and Gas NSPS (np_oilgas, pt_oilgas) 197
4.2.4.2 RICE NSPS (nonpt, ptnonipm, np_oilgas, pt_oilgas) 201
4.2.4.3 Organic Liquids Distribution NESHAP (ptnonipm) 204
4.2.4.4 Natural Gas Turbines NOx NSPS (ptnonipm, pt_oilgas) 204
4.2.4.5 Process Heaters NOx NSPS (ptnonipm, pt_oilgas) 206
4.2.4.6 State-specific controls (nonpt, np_solvents, ptnonipm) 209
4.3 Sectors with Projections Computed Outside of CoST 210
4.3.1 Nonroad Mobile Equipment Sources (nonroad) 210
4.3.2 Onroad Mobile Sources (onroad) 211
4.3.3 Sources Outside of the United States (canada_onroad, mexico_onroad, canmex_point, canmex_ag, canada_og2D,
ptfire_othna, canmex_area, canada_afdust, canada_ptdust) 213
4.3.3.1 Canadian fugitive dust sources (canada_afdust, canada_ptdust) 213
4.3.3.2 Point Sources in Canada and Mexico (canmex_point, canada_og2D) 213
4.3.3.3 Nonpoint sources in Canada and Mexico (canmex_area, canmex_ag) 213
4.3.3.4 Onroad sources in Canada and Mexico (canada_onroad, canada_onroad) 214
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5 EMISSION SUMMARIES 215
6 REFERENCES 220
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List of Tables
Table 2-1. Platform sectors used in the Emissions Modeling Process 18
Table 2-2. Point source oil and gas sector NAICS Codes 26
Table 2-3. Point source oil and gas sector emissions for 2022 27
Table 2-4. SCCs for the airports sector 28
Table 2-5. Afdust sector SCCs 30
Table 2-6. Total impact of 2022 fugitive dust adjustments to the unadjusted inventory 31
Table 2-7. SCCs for the livestock sector 36
Table 2-8. Source of input variables for EPIC 38
Table 2-9. Nonpoint oil and gas emissions for 2022 39
Table 2-10. State emissions totals for year 2022 for Pipeline Blowdowns and Pigging sources 41
Table 2-11. State emissions totals for year 2022 for Abandoned Wells sources 42
Table 2-12. SCCs for the residential wood combustion sector 44
Table 2-13. SCCs in the openburn sector 45
Table 2-14. Datasets used to Develop Factors to Adjust Nonpoint Emissions from 2020 to 2022 46
Table 2-15. MOVES vehicle (source) types 48
Table 2-16. The fraction of IHS vehicle populations retained for 2020 NEI and 2022 emissions modeling
platform by model year 54
Table 2-17. SCCs for the cmv_clc2 sector 57
Table 2-18. Vessel groups in the cmv_clc2 sector 61
Table 2-19. SCCs for cmv_c3 sector 62
Table 2-20. SCCs for the Rail Sector 68
Table 2-21. 2020 and 2022 R-l Reported Locomotive Fuel Use for Class I Railroads 69
Table 2-22. 2020 Class ll/lll Line Haul Fleet by Tier Level 70
Table 2-23. Rail Freight Values by year (quadrillion BTU) 71
Table 2-24. SCCs included in the ptfire sector for the 2022 platform 76
Table 2-25. Types of State-provided Fire Activity Data 77
Table 2-26. SCCs included in the ptagfire sector 83
Table 2-27. Meteorological variables required by BEIS4 85
Table 3-1. Key emissions modeling steps by sector 92
Table 3-2. Descriptions of the platform grids 94
Table 3-3. Emission model species produced for CB6R5_AE7 for CMAQ 95
Table 3-4. Additional HAP gaseous model species generated for toxics modeling 97
Table 3-5. Additional HAP particulate model species generated for toxics modeling 98
Table 3-6. PAH/POM pollutant groups 98
Table 3-7. Integration status for each platform sector 101
Table 3-8. Integrated species from MOVES sources 102
Table 3-9. Mobile Speciation Profile Updates 103
Table 3-10. Mobile NOx and HONO fractions 104
Table 3-11. NOx speciation profiles 106
Table 3-12. Sulfate Split Factor Computation 106
Table 3-13. SO2 speciation profiles 107
Table 3-14. Particle Size Speciation of Metals 107
Table 3-15. Mercury Speciation Profiles 108
Table 3-16. Temporal settings used for the platform sectors in SMOKE 109
Table 3-17. U.S. Surrogates available for the 2022 modeling platforms 136
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Table 3-18. Shapefiles used to develop U.S. Surrogates 137
Table 3-19. Surrogates used to gapfill U.S. Surrogates 141
Table 3-20. Off-Network Mobile Source Surrogates 144
Table 3-21. Spatial Surrogates for Oil and Gas Sources 144
Table 3-22. Selected 2022 CAP emissions by sector for U.S. Surrogates (short tons in 12US1) 146
Table 3-23. Canadian Spatial Surrogates 149
Table 3-24. Shapefiles and Attributes used to Compute Canadian Spatial Surrogates 150
Table 3-25. Shapefiles and Attributes used to Compute Mexican Spatial Surrogates 155
Table 3-26. 2022 CAP Emissions Allocated to Mexican and Canadian Spatial Surrogates for 12US1 (short
tons) 155
Table 4-1. Overview of projection methods by sector for the analytic years 159
Table 4-2. EGU sector NOx emissions by State for the 2022vl cases 164
Table 4-3. Subset of CoST Packet Matching Hierarchy 167
Table 4-4. Summary of non-EGU projections subsections 169
Table 4-5. Tons reduced from all facility/unit/stack-level closures in 2026 from 2022 emissions levels 170
Table 4-6. Growth Indicators used to grow SCCs in the afdust sector 172
Table 4-7. Increase in afdust PM2.5 emissions from projections 173
Table 4-8. TAF 2023 growth factors for major airports, 2022 to 2026 173
Table 4-9. Impact of growth factors on 2022 airport emissions for 2026 174
Table 4-10. Resulting C1C2 Emissions for 2026 Compared to 2022 (tons/yr) 175
Table 4-11. Resulting C3 Emissions for 2026 Compared to 2022 (tons/yr) 176
Table 4-12. Impact of 2026 projection factors on livestock 177
Table 4-13. Impact of 2022-2026 projection factors on nonpt emissions 178
Table 4-14. SCCs in nonpt that were held constant 178
Table 4-15. SCCs in nonpt that use Human Population Growth for Projections 180
Table 4-16. Human population projections by state 182
Table 4-17. SCCs in nonpt that use ElA's AEO for Projections 183
Table 4-18. SCCs in np_solvents that use Human Population Growth for Projections 189
Table 4-19. Impact of projection factors on np_solvents emissions 190
Table 4-20. Impact of projections on pt_oilgas emissions 192
Table 4-21. Three year average of national oil and gas exploration emissions 193
Table 4-22. Impact of projections on np_oilgas emissions 193
Table 4-23. Annual Energy Outlook (AEO) 2023 tables used to project industrial sources 194
Table 4-24. Impact of projections other than refinery adjustments on ptnonipm emissions 194
Table 4-25. Projection factors for Rail SCCs from the 2022 Base Year 195
Table 4-26. Assumed new source emission factor ratios for NSPS rules 196
Table 4-27. Emissions reductions for the oil and gas sectors due to applying the Oil and Gas NSPS 198
Table 4-28. SCCs in np_oilgas for which the Oil and Gas NSPS controls were applied 198
Table 4-29. SCCs in pt_oilgas for which the Oil and Gas NSPS controls were applied 199
Table 4-30. Emissions reductions in nonpt due to RICE NSPS 202
Table 4-31. Emissions reductions in ptnonipm due to the RICE NSPS 202
Table 4-32. Emissions reductions in np_oilgas due to the RICE NSPS 202
Table 4-33. Emissions reductions in pt_oilgas due to the RICE NSPS 202
Table 4-34. SCCs and Engine Types where RICE NSPS controls applied for nonpt and ptnonipm 202
Table 4-35. Non-point Oil and Gas SCCs where RICE NSPS controls are applied 203
Table 4-36. Point source SCCs in pt_oilgas sector where RICE NSPS controls applied 203
Table 4-37. Summary of Organic Liquids Distribution NESHAP controls on ptnonipm emissions 204
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Table 4-38. Stationary gas turbines NSPS analysis and RACT regulations in selected states 205
Table 4-39. Emissions reductions due to the Natural Gas Turbines NSPS 206
Table 4-40. SCCs in ptnonipm for which Natural Gas Turbines NSPS controls were applied 206
Table 4-41. SCCs in pt_oilgas for which Natural Gas Turbines NSPS controls were applied 206
Table 4-42. Process Heaters NSPS analysis emission rates used to estimate controls 207
Table 4-43. Emissions reductions due to the application of the Process Heaters NSPS 207
Table 4-44. SCCs in ptnonipm for which Process Heaters NSPS controls were applied 208
Table 4-45. SCCs in pt_oilgas for which Process Heaters NSPS controls were applied 208
Table 4-46. SCCs in nonpt, np_solvents, and ptnonipm for which state-specific controls were applied 209
Table 4-47. Summary of SLT-provided controls on 2022 emissions 210
Table 4-48. Projection factors for VMT by Fuel and Vehicle Class 212
Table 5-1. National by-sector CAP emissions for the 2022 platform, year 2022, 12US1 grid (tons/yr) 216
Table 5-2. National by-sector VOC HAP emissions for the 2022 platform, year 2022, 12US1 grid (tons/yr) 217
Table 5-3. National by-sector CAP emissions for the 2022 platform, year 2026, 12US1 grid (tons/yr) 218
Table 5-4. National by-sector VOC HAP emissions for the 2022 platform, year 2026, 12US1 grid (tons/yr) 219
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List of Figures
Figure 2-1. Fugitive dust emissions and impact of adjustments due to transportable fraction, precipitation,
and cumulative 34
Figure 2-2. "Bidi" modeling system used to compute emissions from fertilizer application 37
Figure 2-3. Map of 2022 Representative Counties 53
Figure 2-4. NEI Commercial Marine Vessel Boundaries and Automatic Identification System Request Boxes
for the 2022 Emissions Modeling Platform 59
Figure 2-5. 2019 Class I Railroad Line Haul Activity 69
Figure 2-6. Class II and III Railroads in the United States 71
Figure 2-7. Amtrak National Rail Network 72
Figure 2-8 Amtrak Diesel Fuel Use 2020-2022 73
Figure 2-9. Processing flow for fire emission estimates in the 2022 inventory 79
Figure 2-10. Default fire type assignment by state and month where data are only from satellites 80
Figure 2-11. Blue Sky Modeling Pipeline 81
Figure 2-12. Flint Hills Acreage Burned in 2022 82
Figure 2-13. Annual biogenic VOC BEIS4 emissions forthe 12US1 domain 87
Figure 3-1. Air quality modeling domains 94
Figure 3-2. Process of integrating HAPs and speciating VOC in a modeling platform 101
Figure 3-3. Eliminating unmeasured spikes in CEMS data Ill
Figure 3-4. Regions used to Compute Temporal non-CEMS EGU Temporal Profiles 113
Figure 3-5. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type 114
Figure 3-6. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type 114
Figure 3-7. 2022 Airport Diurnal Profiles for PHX and state of Texas 116
Figure 3-8. 2022 Wisconsin and Atlanta annual-to-month profile for airport emissions 117
Figure 3-9. Alaska seaplane profile 118
Figure 3-10. Example of RWC temporal allocation using a 50 versus 60 °F threshold 119
Figure 3-11. Example of Annual-to-day temporal pattern of recreational wood burning emissions 120
Figure 3-12. RWC diurnal temporal profile 120
Figure 3-13. Data used to produce a diurnal profile for hydronic heaters 121
Figure 3-14. Monthly temporal profile for hydronic heaters 122
Figure 3-15. Examples of livestock temporal profiles in several parts of the country 123
Figure 3-16. Example of animal NH3 emissions temporal allocation approach (daily total emissions) 123
Figure 3-17. TMAS Data: VMT Fraction by Hour of Day and Day of Week 125
Figure 3-18. Example temporal variability of VMT compared to onroad NOx emissions 128
Figure 3-19. Example Nonroad Day-of-week Temporal Profiles 129
Figure 3-20. Example Nonroad Diurnal Temporal Profiles 130
Figure 3-21. Agricultural burning diurnal temporal profile 132
Figure 3-22. Prescribed and Wildfire diurnal temporal profiles 133
Figure 3-23. 2020 Residential Wood Combustion Emissions using NLCD Low Intensity Surrogate 135
Figure 3-24. 2020 Residential Wood Combustion Emissions using ACS-based Surrogate 135
Figure 4-1. EIA Oil and Gas Supply Regions as of AEO2023 191
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Acronyms
AADT Annual average daily traffic
AE6 CMAQ Aerosol Module, version 6, introduced inCMAQv5.0
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
BSP Blue Sky Pipeline
BTP BulkTerminal (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 Energy Information Administration
EIS 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
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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/lnstitutional (boilers and process heaters)
l/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
ONI Off network idling
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
pcSOA Potential combustion Secondary Organic Aerosol
PFC Portable Fuel Container
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PM2.5 Particulate matter less than or equal to 2.5 microns
PM 10 Particulate matter less than or equal to 10 microns
POA Primary Organic Aerosol
ppm Parts 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 for hoteling (emission mode used in SMOKE-MOVES)
RPHO Rate-per-hour for off-network idling (emission mode used in SMOKE-MOVES)
RPP Rate-per-profile (emission mode used in SMOKE-MOVES)
RPS Rate-per-start (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 Hourly Speed Profiles for weekday versus weekend
S/L/T state, local, and tribal
TAF Terminal Area Forecast
TCEQ Texas Commission on Environmental Quality
TOG Total Organic Gas
TSD Technical support document
USDA United States Department of Agriculture
VIIRS 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
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1 Introduction
The U.S. Environmental Protection Agency (EPA), in conjunction with the National Emissions
Collaborative, developed an air quality modeling platform for criteria air pollutants that represents the
year 2022. The platform is based on the 2020 National Emissions Inventory (2020 NEI) published in April
2023 (EPA, 2023), with many sectors adjusted to better reflect 2022 and/or using data specific to the
year 2022. 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
component of the 2022 air quality modeling platform, including the emission inventories, the ancillary
data files, and the approaches used to transform inventories for use in air quality modeling.
The emissions data in the modeling platform include criteria air pollutants and their precursors (CAPs),
two groups of hazardous air pollutants (HAPs), and diesel particulate matter. The first group of HAPs are
those explicitly used by the chemical mechanism in the Community Multiscale Air Quality (CMAQ) model
(Appel, 2018) for ozone/particulate matter (PM): chlorine (CI), hydrogen chloride (HCI), naphthalene,
benzene, acetaldehyde, formaldehyde, and methanol (the last five are abbreviated as NBAFM in
subsequent sections of the document). The second group of HAPs consists of over 50 HAPs or HAP
groups (such as polycyclic aromatic hydrocarbon groups) that are included in the emissions inventories
for the purposes of air quality modeling for a HAP+CAP platform, although HAP+CAP modeling is not
planned with version 1 of the 2022 platform.
Emissions were prepared for the Community Multiscale Air Quality (CMAQ) model version 5.4,2 which is
used to model ozone (O3) particulate matter (PM), and HAP concentrations. CMAQ requires hourly and
gridded emissions of the following inventory pollutants: carbon monoxide (CO), nitrogen oxides (NOx),
volatile organic compounds (VOC), sulfur dioxide (SO2), ammonia (NH3), primary particulate matter less
than or equal to 10 microns (PM10), and individual component species for primary particulate matter
less than or equal to 2.5 microns (PM2.5). In addition, the Carbon Bond mechanism version 6 (CB6) with
chlorine chemistry within CMAQ allows for explicit treatment of the VOC HAPs naphthalene, benzene,
acetaldehyde, formaldehyde and methanol (NBAFM), includes anthropogenic HAP emissions of HCI and
CI, and can model additional HAPs as described in Section 3. The short abbreviation for the modeling
case name was "2022hc", where 2022 is the year modeled, 'h' represents that it was based on the 2020
NEI, and 'c' represents that it was the third version of a 2020 NEI-based platform.
This TSD discusses the application of the emissions modeling platform for which CMAQ and the
Comprehensive Air Quality Model with Extensions (CAMx) were run. The effort to create the emissions
inputs for this study included development of emission inventories to represent emissions during the
year of 2022, along with application of emissions modeling tools to convert the inventories into the
format and resolution needed by CMAQ and CAMx, although this platform is not designed to be used for
analyses with the American Meteorological Society/Environmental Protection Agency Regulatory Model
(AERMOD).
2 CMAQ version 5.4: https://zenodo.org/record/7218076. CMAQ is also available from https://www.epa.gov/cmaq and the
Community Modeling and Analysis System (CMAS) Center at: https://www.cmascenter.org.
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In addition to the base year emissions representing 2022, emissions were projected to the year 2026.
The year 2026 emissions are needed by states to develop State Implementation Plans (SIPs) for
nonattainment areas classified as serious for the 2015 National Ambient Air Quality Standards (NAAQS)
for ozone. The analytic year emissions reflect on-the-books Federal and some state regulations that
were effective as of April, 2024.
The emissions modeling platform includes point sources, nonpoint sources, onroad mobile sources,
nonroad mobile sources, biogenic emissions 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, road type and process, while the NEI emissions are aggregated to vehicle type/fuel type totals and
annual temporal resolution. Emissions used in the CMAQ modeling from Canada are provided by
Environment and Climate Change Canada (ECCC) and Mexico are mostly provided by SEMARNAT and are
not part of the NEI. Year-specific emissions were used for fires, biogenic sources, fertilizer, point
sources, and onroad and nonroad mobile sources. Where available, hourly continuous emission
monitoring system (CEMS) data were used for electric generating unit (EGU) emissions.
The primary emissions modeling tool used to create the CMAQ model-ready emissions was the Sparse
Matrix Operator Kernel Emissions (SMOKE) modeling system. SMOKE version 5.1 was used to create
CMAQ-ready emissions files for a 12-km grid covering the continental U.S. Additional information about
SMOKE is available from http://www.cmascenter.org/smoke.
The gridded meteorological model used to provide input data for the emissions modeling was developed
using the Weather Research and Forecasting Model (WRF, https://github.com/wrf-
model/WRF/releases) version 4.2, 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 model was run for 2022 over a domain covering the
continental U.S. (CONUS) at both 12km resolution and 36km resolution with 35 vertical layers, and also
for domains that cover Alaska, Hawaii, and Puerto Rico plus the Virgin Islands. 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
abbreviation "22m." The full case abbreviation includes this suffix following the emissions portion of the
case name to fully specify the abbreviation of the case as "2022hc_cb6_22m."
Data files and summaries for this platform are available from this section of the air emissions modeling
website https://www.epa.gov/air-emissions-modeling/2022vl-emissions-modeling-platform.
This document contains five additional sections. Section 2 describes the emission inventories input to
SMOKE. Section 3 describes the emissions modeling and the ancillary files used to process the emission
inventories into air quality model-ready inputs. The analytic year emissions are described in Section 4.
Data summaries are provided in Section 5, and Section 6 provides references.
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2 Base Year Emissions Inventories and Approaches
This section describes the emissions inventories created for input to SMOKE, which are based on the
April 2023 version of the 2020 NEI with updates to reflect emissions in the year 2022. The NEI includes
four main data categories: a) nonpoint sources (which now include fires); b) point sources; c) nonroad
mobile sources; and d) onroad mobile sources. For CAPs, the NEI data are largely compiled from data
submitted by state, local and tribal (S/L/T) agencies. HAP emissions data are often augmented
(generated through speciation of relevant CAPs, e.g., VOC and PM2.5) by EPA when they are not
voluntarily submitted to the NEI by S/L/T agencies. The NEI was compiled using the Emissions Inventory
System (EIS). EIS collects and stores facility inventory and emissions data for the NEI and includes
hundreds of automated QA checks to improve data quality, and it also supports release point (stack)
coordinates separately from facility coordinates. EPA collaboration with S/L/T agencies helped prevent
duplication between point and nonpoint source categories such as industrial boilers. The 2020 NEI
Technical Support Document describes in detail the development of the 2020 emission inventories and
is available at https://www.epa.gov/air-emissions-inventories/2020-national-emissions-inventory-nei-
technical-support-document-tsd (EPA, 2023).
A complete set of emissions for all source categories is developed for the NEI every three years, with
2020 being the most recent year represented with a full "triennial" NEI. S/L/T agencies are required to
submit all applicable point sources to the NEI in triennial years, including the year 2020. Because only
point source emissions were submitted by S/L/T agencies for 2022, emissions for any point sources not
submitted for 2022, and not marked as shutdown, were pulled forward from the 2020 NEI. The
SMARTFIRE2 system and the BlueSky Pipeline (https://github.com/pnwairfire/bluesky) emissions
modeling system were used to develop the fire emissions. SMARTFIRE2 categorizes all fires as either
prescribed burning or wildfire, and the BlueSky Pipeline system includes fuel loading, consumption and
emission factor estimates for both types of fires. Onroad and nonroad mobile source emissions were
developed for this project using MOVES4 (https://www.epa.gov/moves).
With the exception of fire emissions, Canadian emissions were provided by Environment Canada and
Climate Change (ECCC) for the years 2020 and 2023 and most 2022 emissions were developed by
interpolating between 2020 and 2023. For point EGUs, instead of interpolating from 2020 and 2023
(which unlike other point sources, has different sources in 2020 vs 2023), the provided 2023 emissions
were used as is to represent 2022. For Mexico, year 2016-based inventories from the 2019 emissions
modeling platform (EPA, 2022b) were used as the starting point with area, nonroad, and point data for
border states (i.e., Baja California, Chihuahua, Coahuila, Nuevo Leon, Sonora, and Tamaulipas)
supplemented with data for calendar year 2018, which is newer than the data used in the 2019
platform, developed by SEMARNAT in collaboration with U.S. EPA.
The emissions modeling process was performed using SMOKE v5.1. Through this process, the emissions
inventories were apportioned into the grid cells used by CMAQ and temporally allocated into hourly
values. In addition, the pollutants in the inventories (e.g., NOx, PM and VOC) were split into the chemical
species needed by CMAQ. For the purposes of preparing the CMAQ- ready emissions, the NEI emissions
inventories by data category were split into emissions modeling platform "sectors"; and emissions from
sources other than the NEI are added, such as the Canadian, Mexican, and offshore inventories.
Emissions within the emissions modeling platform were separated into sectors for groups of related
emissions source categories that were run through the appropriate SMOKE programs, except the final
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merge, independently from emissions categories in the other sectors. The final merge program called
Mrggrid combines low-level sector-specific gridded, speciated and temporalized emissions to create the
final CMAQ-ready emissions inputs. For biogenic and fertilizer emissions, the CMAQ model allows for
these emissions to be included in the CMAQ-ready emissions inputs, or to be computed within CMAQ
itself (the "inline" option). This study used the option to compute biogenic emissions within the model
and the CMAQ bidirectional ammonia process to compute the fertilizer emissions.
Following the compilation of the initial draft of the base year emission inventories within the 2022vl
Emissions Modeling Platform, the inventories were posted to the 2022vl EPA website and to the Data
Retrieval Tool associated with the platform. Stakeholders were then given the opportunity to comment
on the inventory during an approximate 30-day period, with comments submitted to the 2022vl
Sharepoint site setup by the EPA. Following the comment period, where possible, EPA incorporated the
comments into the inventories prior to finalization. In total, 30 individual organizations submitted 127
comments during the base-year review. A similar process was followed when the inventories for 2026
were completed.
Table 2-1 presents the sectors in the emissions modeling platform used to develop the year 2022
emissions for this project. The sector abbreviations are provided in italics and start with lower case
letters; these abbreviations are used in the SMOKE modeling scripts, the inventory file names, and
throughout the remainder of this section. Note that while the fires sectors are in nonpoint NEI data
category, in the modeling platform they are treated as day-specific point sources.
Table 2-1. Platform sectors used in the Emissions Modeling Process
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
EGU units:
ptegu
Point
2022 NEI point source EGUs, replaced with hourly Continuous
Emissions Monitoring System (CEMS) values for NOx and S02,
and the remaining pollutants temporally allocated according to
CEMS heat input where the units are matched to the NEI.
Emissions for all sources not matched to CEMS data come from
the 2022 NEI point inventory. EGUs closed in 2022 are not part
of the inventory. Annual resolution for sources not matched to
CEMS data, hourly for CEMS sources.
Point source oil and gas:
ptjoiigas
Point
2022 NEI point sources that include oil and gas production
emissions processes for facilities with North American Industry
Classification System (NAICS) codes related to Oil and Gas
Extraction, Natural Gas Distribution, Drilling Oil and Gas Wells,
Support Activities for Oil and Gas Operations, Pipeline
Transportation of Crude Oil, and Pipeline Transportation of
Natural Gas. Includes U.S. offshore oil production. Annual
resolution.
Aircraft and ground
support equipment:
airports
Point
EPA estimated 2022 emissions, including aircraft and airport
ground support for the top 51 airports. Smaller airports,
including aircraft and airport ground support were projected
from 2020 NEI to 2022 based on the 2023 Terminal Area
Forecast (TAF). Georgia provided emissions for HJAIA. Annual
resolution.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Remaining non-EGU
point: ptnonipm
Point
All 2022 NEI point source records not matched to the airports,
ptegu, or pt_oilgas sectors. Includes 2020 NEI rail yard emissions
projected to 2022 using updated R-l reported yard fuel usage.
Annual resolution.
Livestock:
livestock
Nonpoint
2022 nonpoint livestock emissions developed using a similar
method to 2020 NEI but with adjusted animal counts and using
2022 meteorology. Livestock includes ammonia and other
pollutants (except PM2.5). County and annual resolution.
Agricultural Fertilizer:
fertilizer
Nonpoint
2022 agricultural fertilizer ammonia emissions based on
bidirectional flux calculations computed inline within CMAQ.
Area fugitive dust:
afdustjadj
Nonpoint
PM10 and PM2.5 nonpoint fugitive dust sources including building
construction, road construction, agricultural dust from crops,
and mining and quarrying which were all held constant.
Additional dust sources not held constant include paved road
dust and agricultural dust from livestock, where paved road dust
emissions were adjusted to 2022 based on VMT and dust from
livestock based on animal count differences. The emissions
modeling system applies a transportable fraction reduction and
zero-out adjustments based on the year-specific gridded hourly
meteorology (precipitation and snow/ice cover). County and
annual resolution.
Biogenic:
beis
Nonpoint
Year 2022 emissions from biogenic sources. These were left out
of the CMAQ-ready merged emissions, in favor of inline biogenic
emissions produced during the CMAQ model run itself. Version 4
of the Biogenic Emissions Inventory System (BEIS) was used with
Version 6 of the Biogenic Emissions Landuse Database (BELD6).
The CMAQ-generated emissions are similar to the biogenic
emissions generated through running SMOKE, but they are not
exactly the same. Gridded and hourly resolution.
Category 1, 2 CMV:
cmv_clc2
Nonpoint
2022 Category 1 (CI) and Category 2 (C2), commercial marine
vessel (CMV) emissions based on 2022 Automatic Identification
System (AIS) data categorized using SCCs specific to ship type.
Point and hourly resolution.
Category 3 CMV:
cmv_c3
Nonpoint
2022 Category 3 (C3) commercial marine vessel (CMV) emissions
based on 2022 AIS data categorized using SCCs specific to ship
type. Point and hourly resolution.
Locomotives :
rail
Nonpoint
Class 1 line haul rail locomotives emissions from 2020 NEI
projected to 2022 using R-l reported fuel usage. County and
annual resolution. Class II and III locomotive emissions were
projected from 2020 based on the 2021 U.S. Energy Information
Administration's Annual Energy Outlook. Commuter rail was
projected from 2020 using fuel use per company from the
Federal Transit Administration's (FTA) 2022 National Transit
Database. Amtrak emissions were adjusted down based on 2020
fuel use reported in Amtrak's FY22 AMTRAK Sustainability
Report. County and annual resolution.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Nonpoint source oil and
gas:
np_oilgas
Nonpoint
2022 well activity data (production and exploration of oil, gas,
etc.) run through Oil and Gas tool. Abandoned wells based on
2022, plus other state-specific inputs. County and annual
resolution. County and annual resolution.
Open Burning:
openburn
Nonpoint
This new sector for the 2022vl platform was split out from the
prior nonpt sector and includes emissions from yard waste, land
clearing, and residential household waste burning. These are
SCCs starting with 261. County and annual resolution.
Residential Wood
Combustion:
rwc
Nonpoint
2020 NEI nonpoint sources with residential wood combustion
(RWC) processes, projected to 2022 with state-level adjustment
factors derived from the State Energy Data System (SEDS) plus
specific adjustments for California and Idaho. County and annual
resolution.
Solvents:
np_solvents
Nonpoint
Emissions of solvents based on methods used for the 2020 NEI.
2021 emissions are used to represent 2022. Includes household
cleaners, personal care products, adhesives, architectural and
aerosol coatings, printing inks, and pesticides. Annual and
county resolution.
Remaining nonpoint:
nonpt
Nonpoint
Nonpoint sources not included in other platform sectors. Mostly
held constant at 2020 levels, but with some SCCs adjusted to
2022 based on population, energy consumption ratios and
employment data. County and annual resolution.
Nonroad:
nonroad
Nonroad
2022 nonroad equipment emissions developed with MOVES4,
including the updates made to spatial apportionment that were
developed with the 2016vl platform. MOVES4 was used for all
states except California, which submitted their own emissions
for 2020 and 2023 that were then interpolated to 2022. County
and monthly resolution.
Onroad:
on road
Onroad
Onroad mobile source gasoline and diesel vehicles from parking
lots and moving vehicles for 2022 developed using VMT from
many states, along with VMT data from 2020 NEI projected to
2022 using factors based on FHWA VM-2 data. Includes the
following emission processes: exhaust, extended idle, auxiliary
power units, evaporative, permeation, refueling, vehicle starts,
off network idling, long-haul truck hoteling, and brake and tire
wear. MOVES4 was run for 2022 to generate year-specific
emission factors. County/gridded and hourly resolution.
Onroad California:
onroad_ca_adj
Onroad
California-provided 2022 emissions for CAPs. VOC HAPs were
projected from California-provided 2020 NEI HAP emissions
using CAP trends. Onroad mobile source gasoline and diesel
vehicles from parking lots and moving vehicles based on
Emission Factor (EMFAC), gridded and temporalized based on
outputs from MOVES4. County/gridded and hourly resolution.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Point source agricultural
fires:
ptagfire
Nonpoint
Agricultural fire sources for 2022 developed by EPA as point and
day-specific emissions.3 Includes 2022 satellite data and land
use. Florida, Georgia, Idaho, and North Carolina have separate
datasets and are removed from the national datasets.
Washington has supplemental datasets, to be used along with
WA from the national datasets. Agricultural fires are in the
nonpoint data category of the NEI, but in the modeling platform,
they are treated as day-specific point sources. Point and daily
resolution.
Point source prescribed
fires:
ptfire-rx
Nonpoint
Point source day-specific prescribed fires for 2022 computed
using SMARTFIRE 2 and BlueSky Pipeline. The ptfire emissions
were run as two separate sectors: ptfire-rx (prescribed, including
Flint Hills / grasslands) and ptfire-wild. Point and daily resolution
Point source wildfires:
ptfire-wild
Nonpoint
Point source day-specific wildfires for 2022 computed using
SMARTFIRE 2 and BlueSky Pipeline. Point and daily resolution
Non-US. Fires:
ptfire_othna
N/A
Point source day-specific wildfires and agricultural fires outside
of the U.S. for 2022. Canadian fires were computed using
SMARTFIRE 2 and BlueSky Pipeline. Mexico, Caribbean, Central
American, and other international fires, are from v2.5 of the Fire
INventory (FINN) from National Center for Atmospheric
Research (Wiedinmyer, C., 2023). Point and daily resolution.
Canada Area Fugitive
dust sources:
canada_afdust
N/A
Area fugitive dust sources from ECCCfor 2022 (interpolated
between provided 2020 and 2023 emissions) with transport
fraction and snow/ice adjustments based on 2022
meteorological data. Annual and province resolution.
Canada Point Fugitive
dust sources:
canada_ptdust
N/A
Point source fugitive dust sources from ECCC for 2022
(interpolated between provided 2020 and 2023 emissions) with
transport fraction and snow/ice adjustments based on 2022
meteorological data. Monthly and province resolution.
Canada and Mexico
stationary point sources:
canmex_point
N/A
Canada and Mexico point source emissions not included in other
sectors. Canada point sources were provided by ECCCfor 2020
and 2023 and interpolated to 2022. Mexico point source
emissions for six border states represent 2018 and were
developed by SEMARNAT in collaboration with EPA, while
emissions for all other states were carried forward from 2019ge
(EPA, 2022b). Annual and monthly point resolution.
Canada and Mexico
agricultural sources:
canmex_ag
N/A
Canada and Mexico agricultural emissions. Canada emissions
were provided by ECCC for 2020 and 2023; EGUs for 2023 were
used directly, and other point inventories were interpolated to
2022. Mexico agricultural emissions were provided by
SEMARNAT and include updated emissions for six border states
representing 2018 developed by SEMARNAT in collaboration
with EPA, while emissions for all other states were carried
forward from 2019ge. Annual municipio and province resolution.
3 Only EPA-developed agricultural fire data were included in this study; data submitted by states to the NEI were excluded.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Canada low-level oil and
gas sources:
canada_og2D
N/A
Canada emissions from upstream oil and gas, provided by ECCC
for 2020 and 2023 and interpolated to 2022. This sector contains
the portion of oil and gas emissions which are not subject to
plume rise. The rest of the Canada oil and gas emissions are in
the canmex_point sector. Annual province resolution.
Canada and Mexico
nonpoint and nonroad
sources:
canmex_area
N/A
Canada and Mexico nonpoint source emissions not included in
other sectors. Canada: ECCC provided surrogates and 2020 and
2023 inventories, that were interpolated to 2022. Mexico:
included updated emissions for six border states representing
2018 developed by SEMARNAT in collaboration with EPA, while
emissions for all other states were carried forward from 2019ge.
Annual and monthly municipio and province resolution.
Canada onroad sources:
canada_onroad
N/A
Canada onroad emissions. 2020 and 2023 Canada inventories
provided by ECCC and interpolated to 2022; processed using
updated surrogates. Province monthly resolution.
Mexico onroad sources:
mexico_onroad
N/A
Mexico onroad emissions. 2020 and 2023 emissions output from
MOVES-Mexico were interpolated to 2022. Municipio monthly
resolution.
Ocean chlorine emissions were also merged in with the above sectors. The ocean chlorine gas emission
estimates are based on the build-up of molecular chlorine (Cb) concentrations in oceanic air masses
(Bullock and Brehme, 2002). Ocean chlorine data at 12 km resolution were available from earlier studies
and were not modified other than the name "CHLORINE" was changed to "CL2" because that is the
name required by the CMAQ model.
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/2022vl-emissions-modeling-platform.
The platform informational text file indicates the zipped files associated with each platform sector.
Some emissions data summaries are available with the data files for the 2022vl 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.
2.1Point sources (ptegu, pt_oilgas, ptnonipm, airports)
Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude and
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, waste
piles, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and
sometimes burns natural gas). With a couple of minor exceptions, this section describes only NEI point
sources within the contiguous U.S. The offshore oil platform (pt_oilgas sector) and CMV emissions
(cmv_clc2 and cmv_c3 sectors) are processed by SMOKE as point source inventories and are discussed
later in this section. A complete NEI is developed every three years. At the time of this writing, 2020 is
the most recently finished complete NEI. A comprehensive description about the development of the
2020 NEI is available in the 2020 NEI TSD (EPA, 2023). Point inventories are also available in EIS for non-
triennial NEI years such as 2019 and 2021. In the interim year point inventories, states are required to
update large sources with the emissions that occurred in that year, while sources not updated by states
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for the interim year were either carried forward from the most recent triennial NEI or marked as closed
and removed.
In preparation for modeling, the complete set of point sources in the NEI was exported from EIS for the
year 2022 into the Flat File 2010 (FF10) format that is compatible with SMOKE (see
https://cmascenter.Org/smoke/documentation/5.l/html/ch06s02s08.html) and was then split into
several sectors for modeling. Any sources without specific locations (i.e., the FIPS code ends in 777) were
dropped and inventories for the other point source sectors were created from the remaining point
sources. The point sectors are: EGUs (ptegu), point source oil and gas extraction-related sources
(pt_oilgas), airport emissions (airports), and the remaining non-EGUs (ptnonipm). The EGU emissions
were 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) and airport emissions
(airports) were processed separately for the purposes of developing emissions summaries and due to
distinct projection techniques from the remaining non-EGU emissions (ptnonipm).
In some cases, data about facility or unit closures are entered into EIS after the inventory modeling
inventory flat files have been extracted. Prior to processing through SMOKE, submitted facility and unit
closures were reviewed and where closed sources were found in the inventory, those were removed.
For this platform, an analysis of point source stack parameters (e.g., stack height, diameter,
temperature, and velocity) was performed due to the presence of unrealistic and repeated stack
parameters. The defaulted values were noticed in data submissions for the states of Illinois, Louisiana,
Michigan, Pennsylvania, Texas, and Wisconsin. Where these defaults were detected and deemed to be
unreasonable for the specific process, the affected stack parameters were replaced by values from the
PSTK file that is input to SMOKE. PSTK contains default stack parameters by source classification code
(SCC). These updates impacted the ptnonipm and pt_oilgas inventories.
The inventory pollutants processed through SMOKE for input to CMAQ for the ptegu, pt_oilgas,
ptnonipm, and airports sectors included: CO, NOx, VOC, SO2, NH3, PM10, and PM2.5 and the following
HAPs: HCI (pollutant code = 7647010), CI (code = 7782505), and several dozen other HAPs listed in
Section 3. NBAFM pollutants from the point sectors were utilized.
The ptnonipm, pt_oilgas, and airports sector emissions were provided to SMOKE as annual emissions.
For sources in the ptegu sector that could be matched to 2022 CEMS data, hourly CEMS NOx and SO2
emissions for 2022 from EPA's Acid Rain Program were used rather than annual inventory emissions. For
all other pollutants (e.g., VOC, PM2.5, HCI), annual emissions were used as-is from the annual inventory
but were allocated to hourly values using heat input from the CEMS data. For the unmatched units in
the ptegu sector, annual emissions were allocated to daily values using IPM region- and pollutant-
specific profiles, and similarly, region- and pollutant-specific diurnal profiles were applied to create
hourly emissions.
The non-EGU stationary point source (ptnonipm) emissions were used as inputs to SMOKE as annual
emissions. The full description of how the NEI emissions were developed is provided in the NEI
documentation - a brief summary of their development follows:
a. CAP and HAP data were provided by States, locals and tribes under the Air Emissions Reporting
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Rule (AERR) [the reporting size threshold is larger for inventory years between the triennial inventory
years of 2011, 2014, 2017, 2020,...].
b. EPA corrected known issues and filled PM data gaps.
c. EPA added HAP data from the Toxic Release Inventory (TRI) where corresponding data was not
already provided by states/locals.
d. EPA stored and applied matches of the point source units to units with CEMS data and also for all
EGU units modeled by EPA's Integrated Planning Model (IPM).
e. Data for airports and rail yards were incorporated.
f. Off-shore platform data were added from the Bureau of Ocean Energy Management (BOEM).
The changes made to the NEI point sources prior to modeling with SMOKE are as follows:
• The tribal data, which do not use state/county Federal Information Processing Standards (FIPS)
codes in the NEI, but rather use the tribal code, were assigned a state/county FIPS code of 88XXX,
where XXX is the 3-digit tribal code in the NEI. This change was made because SMOKE requires all
sources to have a state/county FIPS code.
• Sources that did not have specific counties assigned (i.e., the county code ends in 777) were not
included in the modeling because it was only possible to know the state in which the sources
resided, but no more specific details related to the location of the sources were available.
Each of the point sectors is processed separately through SMOKE as described in the following
subsections.
2.1.1 EGU sector (ptegu)
The ptegu sector contains emissions from EGUs in the 2022 point source inventory that could be
matched to units found in the National Electric Energy Database System (NEEDS) v6 that is used by the
Integrated Planning Model (IPM) to develop projected EGU emissions. It was necessary to put these
EGUs into a separate sector in the platform because EGUs use different temporal profiles than other
sources in the point sector and it is useful to segregate these emissions from the rest of the point
sources to facilitate summaries of the data. Sources not matched to units found in NEEDS were placed
into the pt_oilgas or ptnonipm sectors. For studies that include analytic years, the sources in the ptegu
sector are fully replaced with analytic year emissions computed by IPM or through engineering analysis.
It is therefore important that the matching between the NEI and NEEDS database be as complete as
possible because there can be double-counting of emissions in analytic year modeling scenarios if
emissions for units projected by IPM are not properly matched to the units in the base year point source
inventory.
The 2022 ptegu emissions inventory is a subset of the point source flat file exported from the Emissions
Inventory System (EIS). In the point source 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. Thus, unit-level emissions were split into a separate EGU flat file for units that have a populated
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(non-null) ipm_yn field. A populated ipm_yn field indicates that a match was found for the EIS unit in the
NEEDS v6 database. Updates were made to the flat file output from EIS as follows:
• ORIS facility and unit identifiers were updated based on additional matches in a cross-platform
spreadsheet, based on state comments, and using the EIS alternate identifiers table as described
later in this section.
Some units in the ptegu sector are matched to Continuous Emissions Monitoring System (CEMS) data via
Office of Regulatory Information System (ORIS) facility codes and boiler IDs. For the matched units, the
annual emissions of NOx and SO2 in the flat file were replaced with the hourly CEMS emissions in base
year modeling. For other pollutants at matched units, the hourly CEMS heat input data were used to
allocate the NEI annual emissions to hourly values. All stack parameters, stack locations, and Source
Classification Codes (SCCs) for these sources come from the flat file. If CEMS data exists for a unit, but
the unit is not matched to the NEI, the CEMS data for that unit were 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.
EIS stores many matches from NEI units to the ORIS facility codes and boiler IDs used to reference the
CEMS data. In the 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 are available at
https://campd.epa.gov/data. Many smaller emitters in the CEMS program cannot be matched to the
NEI due to differences 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. In
addition, 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 ptegu sector than have CEMS data.
Matches from the NEI to ORIS codes and the NEEDS database were improved in the platform where
applicable. In some cases, NEI units in EIS match to many CAMD units. In these cases, a new entry was
made in the flat file with a "_M_" in the ipm_yn field of the flat file to indicate that there are "multiple"
ORIS IDs that match that unit. This helps facilitate appropriate temporal allocation of the emissions by
SMOKE. Temporal allocation for EGUs is discussed in more detail in the Ancillary Data section below.
The EGU flat file was split into two flat files: those that have unit-level matches to CEMS data using the
oris_facility_code and oris_boiler_id fields (egu_cems) and those that do not (egu_noncems) so that
different temporal profiles could be applied. In addition, the hourly CEMS data were processed through
v2.1 of the CEMCorrect tool to mitigate the impact of unmeasured values in the data.
Some comments were received on the base year EGU inventories and were addressed as follows:
• Many units in the engineering analysis had NOX and/or S02 but did not have the other CAPs, and
that those pollutants needed to be gapfilled. The gapfilling process added 1,500 tpy of PM2.5
nationally, across several states. Most increases outside of NOX and S02 can be attributed to the
gapfilling process.
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• The drop in Iowa S02, and the increases in Wisconsin N0X/S02, are based on corrections
provided by Michael Cohen. I believe the Iowa change was from a state comment, and Wisconsin
concerned unit(s) that were previously zero but shouldn't have been.
• Facilities in CT, MA, Ml, MN, VA, and WA were closed between draft and final, based mostly on
state comments, and also based on the NEEDS DB showing some of these were dropped. Most of
these were non-engineering-analysis facilities that had previously been carried forward from
2022.
• Kentucky emissions decreased because some units moved from ptegu to ptnonipm.
2.1.2 Point source oil and gas sector (pt_oilgas)
The pt_oilgas sector was separated from the ptnonipm sector by selecting sources with specific North
American Industry Classification System (NAICS) codes shown in Table 2-2. The emissions and other
source characteristics in the pt_oilgas sector are submitted by states, while EPA developed a dataset of
nonpoint oil and gas emissions for each county in the U.S. with oil and gas activity that was available for
states to use. Nonpoint oil and gas emissions can be found in the np_oilgas sector. The pt_oilgas sector
includes emissions from offshore oil platforms. Where available, the point source emissions submitted
as part of the 2022 NEI process with refinements based on the Collaborative data review process were
used. Sources without data submitted for 2022 were projected to 2022 from 2020 NEI emissions, or
where applicable, from 2021 NEI emissions.
Table 2-2. Point source oil and gas sector NAICS Codes
NAICS
NAICS description
2111
Oil and Gas Extraction
Natural Gas Liquid Extraction (although no emissions for this
211112
NAICS code exist in the 2022 inventory)
21112
Crude Petroleum Extraction
211120
Crude Petroleum Extraction
21113
Natural Gas Extraction
211130
Natural Gas Extraction
213111
Drilling Oil and Gas Wells
213112
Support Activities for Oil and Gas Operations
2212
Natural Gas Distribution
22121
Natural Gas Distribution
221210
Natural Gas Distribution
237120
Oil and Gas Pipeline and Related Structures Construction
4861
Pipeline Transportation of Crude Oil
48611
Pipeline Transportation of Crude Oil
486110
Pipeline Transportation of Crude Oil
4862
Pipeline Transportation of Natural Gas
48621
Pipeline Transportation of Natural Gas
486210
Pipeline Transportation of Natural Gas
26
-------
Information on the development of the 2020 NEI oil and gas emissions can be found in Section 13 of the
2020 NEI TSD. The point oil and gas emissions for 2022 by state are shown in Table 2-3.
Table 2-3. Point source oil and gas sector emissions for 2022
State
2022 NOx
2022 VOC
Alabama
10,608
1,209
Alaska
38,698
1,730
Arizona
2,374
180
Arkansas
4,029
320
California
2,564
2,430
Colorado
13,642
11,074
Connecticut
59
35
Delaware
6
1
Florida
6,192
696
Georgia
3,114
526
Idaho
1,291
38
Illinois
4,567
1,039
Indiana
949
136
Iowa
3,962
223
Kansas
17,741
3,009
Kentucky
9,201
1,125
Louisiana
27,882
8,160
Maine
32
64
Maryland
188
164
Massachusetts
235
69
Michigan
9,134
990
Minnesota
2,377
172
Mississippi
22,452
1,930
Missouri
2,342
92
Montana
812
1,027
Nebraska
2,757
266
Nevada
236
22
New Jersey
95
94
New Mexico
34,981
63,796
New York
1,072
256
North Carolina
1,681
237
North Dakota
4,197
2,736
Ohio
8,828
1,584
Oklahoma
33,870
26,113
Oregon
1,019
94
Pennsylvania
3,027
918
Puerto Rico
39
25
27
-------
State
2022 NOx
2022 VOC
Rhode Island
315
121
South Carolina
358
10
South Dakota
6,452
532
Tennessee
46,513
20,607
Texas
2,453
652
Utah
95
94
Virginia
2,725
428
Washington
874
56
West Virginia
8,335
3,263
Wisconsin
429
205
Wyoming
13,283
50,751
Offshore
34,660
31,406
Tribal Data
7,813
2,213
2.1.3 Aircraft and ground support equipment (airports)
Emissions at airports were separated from other sources in the point inventory based on sources that
have the facility source type of 100 (airports). The airports sector includes all aircraft types used for
public, private, and military purposes and aircraft ground support equipment. The Federal Aviation
Administration's (FAA) Aviation Environmental Design Tool (AEDT) is used to estimate emissions for this
sector. Additional information about aircraft emission estimates can be found in section 3 of the 2020
NEI TSD (EPA, 2023). EPA ran AEDT for 2022 for the largest (51) airports in the United States. For more
information on the estimation of emissions from larger airports, please see, 2022 National Emissions
Inventory: Aviation Component (ERG, 2024a). Smaller airport emissions were projected from the 2020
NEI to 2022 using factors derived from the 2023 Terminal Area Forecast (TAF)4 data. EPA used airport-
specific factors where available. Emissions for Hartsfield-Jackson (ATL) airport were provided by Georgia
EPD. Airport emissions were spread out into multiple 12km grid cells when the airport runways were
determined to overlap multiple grid cells. Otherwise, airport emissions for a specific airport are confined
to one air quality model grid cell. The SCCs included in the airport sector are shown in Table 2-4.
Table 2-4. SCCs for the airports sector
see
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
Total
2270008005
Mobile Sources
Off-highway Vehicle
Diesel
Airport Ground Support
Equipment
Total
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
4 See https://www.faa.gov/data research/aviation/taf for 2023 TAF released in January 2024.
28
-------
see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4
description
2275060011
Mobile Sources
Aircraft
Air Taxi
Piston
2275060012
Mobile Sources
Aircraft
Air Taxi
Turbine
2275070000
Mobile Sources
Aircraft
Auxiliary Power Units
Total
2.1.4 Non-IPM sector (ptnonipm)
With some exceptions, the ptnonipm sector contains the point sources that are not in the ptegu,
pt_oilgas, or airports sectors. For the most part, the ptnonipm sector reflects non-EGU emissions
sources and rail yards. However, it is possible that some low-emitting EGUs not matched to units in the
NEEDS database or to CEMS data are in the ptnonipm sector.
The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on
paved or unpaved roads at industrial facilities, coal handling at coal mines, and grain elevators. Sources
with state/county FIPS code ending with "111" are in the NEI but are not included in any modeling
sectors. These sources typically represent mobile (temporary) asphalt plants that are only reported for
some states and are generally in a fixed location for only a part of the year and are therefore difficult to
allocate to specific places and days as is needed for modeling. Therefore, these sources are dropped
from the point-based sectors in the modeling platform.
The ptnonipm sources (i.e., not EGUs and non -oil and gas sources) were used as-is from the 2022 NEI
point inventory following updates from the Collaborative review. Solvent emissions from point sources
were removed from the np_solvents sector to prevent double-counting, so that all point sources can be
retained in the modeling as point sources rather than as area sources. The modeling was based on the
point flat file exported from EIS on June 15, 2024, and included updates from the Collaborative review
process for the 2022 base year, and updates specific to ethylene oxide. The np_solvents sector is
described in more detail in Section 2.2.6.
Emissions from rail yards are included in the ptnonipm sector. Railyards are from the 2020 NEI railyard
inventory with a projection factor applied. Additional information about railyard estimates can be found
in section 3 of the 2020 NEI TSD.
2.2 Nonpoint sources (afdust, fertilizer, livestock, np_oilgas, rwc, np_solvents, nonpt)
This section describes the stationary nonpoint sources in the NEI nonpoint data category. Locomotives,
CI 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 2020 NEI TSD includes documentation for the nonpoint data.
Nonpoint tribal emissions submitted to the NEI 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.
29
-------
2.2.1 Area fugitive dust sector (afdust)
The area-source fugitive dust (afdust) sector contains PMio 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 2-5 is a listing of the Source Classification Codes (SCCs)
in the afdust sector.
Table 2-5. Afdust sector SCCs
see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2294000000
Mobile Sources
Paved Roads
All Paved Roads
Total: Fugitives
2296000000
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
2311010000
Industrial Processes
Construction: SIC 15 -17
Residential
Total
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
2325020000
Industrial Processes
Mining and Quarrying: SIC
14
Crushed and Broken
Stone
Total
2325030000
Industrial Processes
Mining and Quarrying: SIC
14
Sand and Gravel
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
2801000008
Miscellaneous Area
Sources
Ag. Production - Crops
Agriculture - Crops
Transport
2805100010
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Beef cattle -
finishing
operations on
feedlots (drylots)
2805100020
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Dairy Cattle
2805100030
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Broilers
2805100040
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Layers
2805100050
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Swine
2805100060
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Turkeys
30
-------
Area Fugitive Dust Transportable Fraction Adjustments
The afdust sector was separated from other nonpoint sectors to allow for the application of a
"transportable fraction" and meteorological/precipitation reductions. These adjustments were applied
using a script that applies land use-based gridded transport fractions based on landscape roughness,
followed by another script that performs meteorological adjustments 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. For example, less dust would be transported on a forest floor, than would be on an open
plain. This methodology is discussed in Pouliot, et al., 2010, and in "Fugitive Dust Modeling for the 2008
Emissions Modeling Platform" (Adelman, 2012). Both the transportable 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 transportable fraction approach is the lack of monthly variability that would be expected with
seasonal changes in vegetative cover. While wind speed and direction were not accounted for in the
emissions processing, the hourly variability due to soil moisture, snow cover and precipitation were
accounted for in the subsequent meteorological adjustment. The factor is treated as a multiplicative
factor for the emissions. Thus, if the factor is 1 (i.e., water), the dust emissions are not reduced at all,
and if the factor is near 0, the emissions are substantially reduced.
Area Fugitive Dust 2020-2022 Projection Factors
Paved road dust emissions were from the 2020 NEI adjusted to 2022 levels based on changes between
2020 and 2022 VMT. Dust from livestock hooves were also adjusted based on ratios of 2022 to 2020
livestock counts but all other types of dust emissions were held constant from 2020 to 2022. For the
fugitive dust emissions compiled into the 2020 NEI, meteorological adjustments were applied to paved
and unpaved road SCCs but not transport-related adjustments. This is because the modeling platform
applies meteorological adjustments and transportable fraction adjustments based on unadjusted NEI
values. For the 2022 platform, the meteorological adjustments that were applied in the NEI for the
paved and unpaved road SCCs were backed out and reapplied in SMOKE at an hourly resolution for each
grid cell. The FF10 that is run through SMOKE consists of 100% unadjusted emissions, and after SMOKE
all afdust sources have both transportable and meteorological adjustments applied according to year
2022 meteorology. The total impacts of the transportable fraction and meteorological adjustments are
shown in Table 2-6.
Table 2-6. Total impact of 2022 fugitive dust adjustments to the unadjusted inventory
State
Unadjusted
PMio
Unadjusted
PM2.5
Change in
PM10
Change in
PM2.5
PM10
Reduction
PM2.5
Reduction
Alabama
274,336
35,494
-202,367
-25,972
73.4%
72.8%
Arizona
153,731
20,858
-56,262
-7,470
36.0%
35.3%
Arkansas
398,457
55,506
-276,216
-37,439
69.1%
67.2%
California
336,443
43,093
-140,763
-17,470
41.3%
40.0%
Colorado
276,997
39,377
-145,222
-19,463
52.1%
49.1%
Connecticut
21,526
3,333
-15,568
-2,400
71.6%
71.3%
Delaware
16,535
2,554
-9,619
-1,483
57.3%
57.2%
31
-------
State
Unadjusted
PMio
Unadjusted
PM2.5
Change in
PM10
Change in
PM2.5
PM10
Reduction
PM2.5
Reduction
District of
Columbia
3,494
477
-2,325
-318
65.5%
65.7%
Florida
215,212
34,456
-117,305
-18,353
53.9%
52.7%
Georgia
296,225
41,844
-218,924
-30,614
73.5%
72.8%
Idaho
496,108
58,552
-288,420
-32,354
57.8%
55.0%
Illinois
702,578
90,846
-423,470
-53,837
60.0%
59.0%
Indiana
160,577
29,875
-98,398
-18,297
60.8%
60.8%
Iowa
370,922
54,793
-207,369
-29,999
55.7%
54.6%
Kansas
583,732
79,848
-238,573
-31,989
40.6%
39.9%
Kentucky
179,629
29,151
-127,894
-20,583
70.9%
70.3%
Louisiana
196,181
29,769
-125,867
-18,850
63.8%
63.0%
Maine
41,717
5,878
-33,149
-4,674
79.1%
79.1%
Maryland
60,743
8,821
-39,070
-5,688
63.6%
63.8%
Massachusetts
63,722
8,640
-46,310
-6,151
72.1%
70.6%
Michigan
293,285
38,837
-199,924
-26,154
67.8%
67.0%
Minnesota
537,979
72,776
-331,407
-43,413
61.4%
59.4%
Mississippi
439,287
52,963
-320,342
-37,933
72.6%
71.4%
Missouri
1,439,199
165,014
-960,853
-108,931
66.5%
65.7%
Montana
498,406
66,114
-321,080
-40,509
64.2%
61.1%
Nebraska
507,702
69,197
-194,215
-25,960
38.0%
37.3%
Nevada
125,368
16,303
-43,279
-5,635
33.8%
33.9%
New Hampshire
16,102
3,307
-12,859
-2,634
79.2%
79.0%
New Jersey
36,477
7,100
-23,617
-4,520
64.2%
63.0%
New Mexico
176,997
22,719
-73,934
-9,313
41.4%
40.6%
New York
264,168
37,984
-196,292
-27,753
73.8%
72.6%
North Carolina
257,146
35,016
-183,428
-24,779
70.9%
70.4%
North Dakota
360,358
55,646
-197,013
-29,403
54.5%
52.7%
Ohio
276,882
43,091
-188,841
-29,167
67.7%
67.2%
Oklahoma
562,803
77,603
-279,078
-37,504
49.3%
48.1%
Oregon
731,384
81,811
-548,493
-59,487
74.7%
72.4%
Pennsylvania
149,280
26,152
-106,519
-18,934
70.7%
71.8%
Rhode Island
6,003
1,006
-4,056
-674
66.7%
66.2%
South Carolina
190,577
25,236
-137,314
-18,038
71.7%
71.1%
South Dakota
210,669
37,092
-95,147
-16,442
45.0%
44.2%
Tennessee
141,443
26,022
-98,397
-18,111
69.2%
69.2%
Texas
1,540,940
214,891
-691,078
-94,837
44.5%
43.8%
Utah
142,084
18,020
-80,959
-9,976
56.5%
54.9%
Vermont
58,010
6,495
-50,078
-5,574
86.0%
85.5%
Virginia
138,872
22,095
-106,664
-17,031
76.3%
76.6%
32
-------
State
Unadjusted
PMio
Unadjusted
PM2.5
Change in
PM10
Change in
PM2.5
PM10
Reduction
PM2.5
Reduction
Washington
174,558
21,778
-101,076
-12,665
57.3%
57.6%
West Virginia
70,339
9,842
-62,535
-8,718
88.5%
88.2%
Wisconsin
202,901
34,398
-135,251
-22,889
66.2%
66.2%
Wyoming
588,124
62,948
-332,653
-35,219
56.3%
55.7%
Domain Total
(12km CONUS)
14,986,209
2,024,623
-8,889,472
-1,175,604
59.0%
57.8%
For categories other than paved roads, where states submitted afdust data to the NEI it was assumed
that the state-submitted data were not met-adjusted and therefore the meteorological adjustments
were applied. Thus, if states submitted data that were met-adjusted for sources other than paved and
unpaved roads, these sources would have been adjusted for meteorology twice. Even with that
possibility, air quality modeling shows that, in general, dust is frequently overestimated in the air quality
modeling results.
Figure 2-1 illustrates the impact of each step of the adjustment. The reductions due to the transportable
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.
33
-------
Figure 2-1. Fugitive dust emissions and impact of adjustments due to transportable fraction,
precipitation, and cumulative
2022hc afdust annual : PM2 5,
>81
60
40
20
0
-20
-40
-60
<-81
Max: 883.7736 Min:
2022hc afdust annual : PM2 5, xportfrac reduction
Max: 0.0 Min: -1726.271
34
-------
2022hc afdust annual : PM2 5, precip reduction
Max: 0.0
2022hc afdust annual : PM2 5. xportfrac and precip —
>28
21
14
c
o
-7
-14
-21
<-28
>104
78
52
26
0
-26
-52
-78
<-104
c
0)
u
v_
O)
Q.
35
-------
2.2.2 Agricultural Livestock (livestock)
The livestock SCCs are shown in Table 2-7. The livestock emissions are related to beef and dairy cattle,
poultry production and waste, swine production, waste from horses and ponies, and production and
waste for sheep, lambs, and goats. The sector does not include quite all of the livestock NH3 emissions,
as there is a very small amount of NH3 emissions from livestock in the ptnonipm inventory (as point
sources). In addition to NH3, the sector includes livestock emissions for all pollutants other than PM2.5,
since PM2.5from dust kicked up from livestock hooves are included in the afdust sector.
Agricultural livestock emissions in the 2022 platform were developed using methods similar to those
used to develop the 2020 NEI, which is a mix of state-submitted data and EPA estimates. The 2020 NEI
approach for estimating livestock emissions utilizes daily emission factors by animal and county from a
model developed by Carnegie Mellon University (CMU) (Pinder, 2004, McQuilling, 2015) and 2020 U.S.
Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) survey. Details on the
approach used to develop livestock emissions for the 2020 NEI are provided in Section 10 of the 2020
NEI TSD. Animal populations used for estimating livestock emissions came from 2022 USDA survey data
(see QuickStats at https://quickstats.nass.usda.gov) for the available counties. The FEM model was run
for 2022 using the 2022 animal counts and meteorological data for 2022 to create the emission
inventories for the livestock sector.
Table 2-7. SCCs for the livestock sector
see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
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
2.2.3 Agricultural Fertilizer (fertilizer)
As described in the 2020 NEI TSD, fertilizer emissions were based on the FEST-C model
(https://www.cmascenter.org/fest-c/). Unlike most of the other emissions input to the CMAQ model,
fertilizer emissions are computed during a run of CMAQ in bi-directional mode and are output during the
36
-------
model run. The bidirectional version of CMAQ (v5.4) and the Fertilizer Emissions Scenario Tool for CMAQ
FEST-C (vl.3) were used to estimate ammonia (NHb) emissions from agricultural soils. The computed
emissions were saved during the CMAQ, run so they can be included in emissions summaries and in
other model runs that do not use the bidirectional method.
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 (WRF) model for the year to be modeled, and nitrogen deposition data from a previous or
historical average CMAQ simulation. FEST-C, then uses the Environmental Policy Integrated Climate
(EPIC) modeling system (https://ej3icapex.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. First, fertilizer application by crop
type was estimated using FEST-C modeled data. To develop the emissions for this platform, CMAQ v5.4
was run with the Surface Tiled Aerosol and Gaseous Exchange (STAGE) deposition option along with
bidirectional exchange to estimate fertilizer and biogenic NHS emissions. However, for this study, the
M3DRY option was used to develop the fertilizer emissions. Figure 2-2 shows a schematic of the
bidirectional modeling system.
Figure 2-2. "Bidi" modeling system used to compute emissions from fertilizer application
The Fertilizer Emission Scenario Tool for CMAQ
(FEST-C)
37
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Fertilizer Activity Data
The following activity parameters were input into the EPIC model:
• Grid cell meteorological variables from WRF
• 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 the year
2022 using a national 12-km rectangular grid covering the continental U.S. The meteorological
parameters in Table 2-8 were used as EPIC model inputs.
Table 2-8. Source of input variables for EPIC
EPIC input variable
Variable Source
Daily Total Radiation (MJ/m2)
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 2017 EPIC/WRF/CMAQ simulation.
The presence of crops in each model grid cell was determined using 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.
38
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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 was estimated using the modeled
crop demand. These data were useful in making a reasonable assignment of what kind of fertilizer was
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.ers.usda.gov/data-
products/arms-farm-financial-and-crop-production-practices /) 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, cotton, oats, peanuts,
potatoes, rice, rye, grain sorghum, silage sorghum, soybeans, spring wheat, winter wheat, canola, and
other crops (e.g., lettuce, tomatoes, etc.).
2.2.4 Nonpoint Oil and Gas Sector (np_oilgas)
The nonpoint oil and gas (np_oilgas) sector includes onshore and offshore oil and gas emissions. The EPA
estimated emissions for all counties with 2022 oil and gas activity data using the Oil and Gas Tool. The
types of sources covered include drill rigs, workover rigs, artificial lift, hydraulic fracturing engines,
pneumatic pumps and other devices, storage tanks, flares, truck loading, compressor engines, and
dehydrators. Because of the importance of emissions from this sector, special consideration was given
to the speciation, spatial allocation, and monthly temporalization of nonpoint oil and gas emissions,
instead of relying on older, more generalized profiles.
The 2020 NEI version of the Nonpoint Oil and Gas Emission Estimation Tool (i.e., the "NEl oil and gas
tool") populated with 2022-specific activity data and updated with Subpart W data was used to estimate
2022. Year 2022 oil and gas activity data were obtained from Enverus' activity database
(www.enverus.com) and supplied by some state air agencies. The NEI oil and gas 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 was
used to create a CSV-formatted emissions dataset covering all national nonpoint oil and gas emissions.
This dataset was converted to the FF10 format for use in SMOKE modeling. More details on the inputs
for and running of the tool for 2020 are provided in the 2020 NEI TSD. Table 2-9 shows the nonpoint oil
and gas NOx and VOC emissions for 2022 by state. The Colorado emissions in this table include updated
emissions for the state developed from the Oil and Gas Tool and state-submitted emissions, along with
emissions submitted to the 2020 NEI within the Southern Ute reservation that are still used in this 2022
platform. For spatial allocation purposes, the Southern Ute oil and gas emissions - totaling 11,663
tons/yr of NOx and 879 tons/yr of VOC - were allocated to Colorado counties, with 95% of the emissions
in La Plata County (FIPS 08067) and 5% of the emissions in Archuleta County (FIPS 08007).
Table 2-9. Nonpoint oil and gas emissions for 2022
State
2022 NOx
2022 VOC
Alabama
3,914
11,545
Alaska
2,815
9,665
Arizona
12
137
39
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State
2022 NOx
2022 VOC
Arkansas
4,586
8,526
California
1,298
28,206
Colorado
29,542
56,625
Florida
19
1,123
Georgia
0
0
Idaho
12
99
Illinois
13,887
49,502
Indiana
2,741
13,492
Iowa
0
0
Kansas
22,927
62,638
Kentucky
16,116
42,631
Louisiana
17,099
52,799
Maryland
1
2
Michigan
10,435
13,227
Minnesota
0
0
Mississippi
1,807
17,404
Missouri
232
554
Montana
1,815
31,980
Nebraska
247
1,778
Nevada
4
160
New Mexico
99,096
282,137
New York
887
7,131
North Carolina
0
0
North Dakota
43,681
226,680
Ohio
2,996
30,890
Oklahoma
44,446
170,335
Oregon
6
20
Pennsylvania
58,718
139,865
South Dakota
196
1,291
Tennessee
1,057
3,272
Texas
258,865
1,339,498
Utah
8,442
69,862
Virginia
3,826
7,883
Washington
0
3
West Virginia
25,351
77,700
Wyoming
1,943
8,571
A new source was added to the oil and gas sector for the 2020 NEI. Pipeline Blowdowns and Pigging
(SCC= 2310021801) emissions were estimated using US EPA Greenhouse Gas Reporting Program
(GHGRP) data. These Pipeline Blowdowns and Pigging emissions for year 2022 included county-level
estimates of VOC, benzene, toluene, ethylbenzene, and xylene (BTEX). These emissions estimates were
calculated outside of the Oil and GasTool and submitted to EIS separately from the Oil and GasTool
emissions. These emissions were considered EPA default emissions and SLTs had the opportunity to
40
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submit their own Pipeline Blowdowns and Pigging (e.g., Utah) emissions and/or accept/omit these
emissions using the Nonpoint Survey. Unfortunately, these EPA default Pipeline Blowdowns and Pigging
emissions did not get into the 2020 NEI release for the states that accepted these emissions due to EIS
tagging issues. These emissions were included in this 2022 Emissions Modeling Platform. Table 2-10
shows the emissions totals by state for Pipeline Blowdowns and Pigging sources.
An additional new source was added to the oil and gas sector for this 2021 and 2022 modeling
platforms. This new source was abandoned oil and gas wells in the USA. The term "abandoned wells"
encompasses various types of wells:
• Wells with no recent production, and not plugged. Common terms (such as those used in state
databases) might include: inactive, temporarily abandoned, shut-in, dormant, and idle.
• Wells with no recent production and no responsible operator. Common terms might include:
orphaned, deserted, long-term idle, and abandoned.
• Wells that have been plugged to prevent migration of gas or fluids.
As of year 2022, there were approximately 3.7 million abandoned wells in the U.S., with around 2.3
million abandoned oil wells, 0.6 million abandoned gas wells, and 0.8 million abandoned dry wells (may
be oil or gas wells). Abandoned wells may emit CH4, C02, VOC, and various HAP.
Estimates of greenhouse gas (GHG) emissions (CH4 and C02) from abandoned wells have been
estimated as part of the Inventory of U.S. Greenhouse Gas Emissions and Sinks since 2018. Currently,
the inventory from 1990 - 2022 is available5. The GHG inventory (GHGI) methodology and estimates of
emissions from abandoned wells served as the starting point for development of the VOC and HAP
emissions inventory for abandoned wells used in this year 2022 modeling platform. Year 2022
estimates of VOC and BTEX were estimated and used in this 2022 modeling platform. Table 2-11 shows
the emissions totals by state for Pipeline Blowdowns and Pigging sources. The inventories for
blowdowns and pigging and abandoned wells are separate from the emissions output from the oil and
gas tool.
Table 2-10. State emissions totals for year 2022 for Pipeline Blowdowns and Pigging sources
State
VOC (tpy)
Benzene (tpy)
Ethylbenzene (tpy)
Toluene (tpy)
Xylene (tpy)
Alabama
329
1.35
0.074
1.17
0.35
Alaska
14
0.06
0.004
0.06
0.02
Arizona
97
0.44
0.025
0.39
0.11
Arkansas
22
0.01
0.000
0.00
0.00
California
146
0.67
0.038
0.59
0.17
Colorado
2,137
5.47
0.273
6.86
2.14
Florida
2
0.00
0.000
0.00
0.00
Illinois
210
0.77
0.043
0.68
0.19
Indiana
180
0.73
0.042
0.65
0.19
Kansas
1,326
2.34
0.273
1.98
0.86
Kentucky
531
2.40
0.136
2.14
0.61
Louisiana
365
3.01
0.000
0.30
0.51
Maryland
0
0.00
0.000
0.00
0.00
5 Inventory of U.S. Greenhouse Gas Emissions and Sinks I US EPA
41
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State
VOC (tpy)
Benzene (tpy)
Ethylbenzene (tpy)
Toluene (tpy)
Xylene (tpy)
Michigan
239
1.08
0.061
0.97
0.27
Mississippi
2,183
3.35
0.072
1.29
1.08
Missouri
4
0.00
0.000
0.00
0.00
Montana
147
0.67
0.038
0.59
0.17
Nebraska
57
0.14
0.007
0.17
0.05
New Mexico
1,044
0.00
0.000
0.00
0.00
New York
140
0.63
0.036
0.57
0.16
North Dakota
9
0.04
0.002
0.04
0.01
Ohio
391
1.77
0.100
1.58
0.45
Oklahoma
2,004
1.47
0.090
1.16
0.89
Oregon
8
0.04
0.002
0.03
0.01
Pennsylvania
66
0.30
0.017
0.27
0.08
South Dakota
2
0.01
0.001
0.01
0.00
Tennessee
13
0.06
0.003
0.05
0.01
Texas
9,599
9.05
0.236
3.82
3.23
Utah
18
0.09
0.005
0.08
0.04
Virginia
189
0.86
0.049
0.77
0.22
West Virginia
859
3.89
0.221
3.47
0.99
Wyoming
680
4.19
0.327
2.04
1.34
US Total
23,010
44.92
2.172
31.77
14.15
Table 2-11. State emissions totals for year 2022 for Abandoned Wells sources
State
2022 VOC (tpy)
Alabama
198
Alaska
64
Arizona
10
Arkansas
794
California
5,357
Colorado
451
Florida
32
Georgia
0
Idaho
0
Illinois
6,738
Indiana
3,326
Iowa
0
Kansas
6,663
Kentucky
12,817
Louisiana
3,195
Maryland
1
Michigan
487
Minnesota
0
Mississippi
749
Missouri
118
42
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State
2022 VOC (tpy)
Montana
740
Nebraska
141
Nevada
34
New Mexico
348
New York
596
North Carolina
0
North Dakota
401
Ohio
22,286
Oklahoma
8,944
Oregon
3
Pennsylvania
69,730
South Dakota
31
Tennessee
1,329
Texas
31,588
Utah
178
Virginia
69
Washington
3
West Virginia
2,723
Wyoming
552
US Total
180,694
Lastly, EPA and the state of Oklahoma, New Mexico and Kansas worked together to exercise the point
source subtraction step in the Oil and Gas Tool during the 2022 platform development period. This point
source subtraction step is a process used to eliminate possible double counting of sources in the Oil and
Gas Tool that are already defined in the point source inventory.
2.2.5 Residential Wood Combustion (rwc)
The residential wood combustion (rwc) sector includes residential wood burning devices such as
fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor hydronic
heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepots and
chimeneas. 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 speaking, the conventional units were constructed prior to 1988. Units constructed after 1988
have to meet EPA emission standards and they are either catalytic or non-catalytic. As with the other
nonpoint categories, a mix of S/L and EPA estimates were used. The EPA's estimates use updated
methodologies for activity data and some changes to emission factors. The source classification codes
(SCCs) in the rwc sector are listed in Table 2-12.
The 2022 platform RWC emissions are adjusted from 2020 NEI using SEDS data for 2021. Additionally,
Idaho provided new 2022 RWC emissions data, and California (CARB) requested two updates: use EPA
estimates for the SCC 2104008700, and remove emissions other than NH3 from the SCCs 2104008210,
2104008200, and 2104008230. Some improvements to RWC emissions estimates were developed as
part of the 2020 NEI process. The EPA, along with the Commission on Environmental Cooperation (CEC),
43
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the Northeast States for Coordinated Air Use Management (NESCAUM), and Abt Associates, conducted a
national survey of wood-burning activity in 2018. The results of this survey were used to estimate
county-level burning activity data. The activity data for RWC processes is the amount of wood burned in
each county, which is based on data from the CEC survey on the fraction of homes in each county that
use each wood-burning appliance and the average amount of wood burned in each appliance. These
assumptions were used with the number of occupied homes in each county to estimate the total
amount of wood burned in each county, in cords for cordwood appliances and tons for pellet appliances.
Cords of wood were converted to tons using county-level density factors from the U.S. Forest Service.
RWC emissions were calculated by multiplying the tons of wood burned by emissions factors. For more
information on the development of the residential wood combustion emissions, see Section 27 of the
2020 NEITSD.
Table 2-12. SCCs for the residential wood combustion sector
see
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
2104008300
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: freestanding, general
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
2104008530
Stationary Source Fuel
Combustion
Residential
Wood
Furnace: Indoor, pellet-fired,
general
2104008610
Stationary Source Fuel
Combustion
Residential
Wood
Flydronic heater: outdoor
2104008620
Stationary Source Fuel
Combustion
Residential
Wood
Flydronic heater: indoor
2104008630
Stationary Source Fuel
Combustion
Residential
Wood
Flydronic heater: pellet-fired
2104008700
Stationary Source Fuel
Combustion
Residential
Wood
Outdoor wood burning device,
NEC (fire-pits, chimeneas, etc)
2104009000
Stationary Source Fuel
Combustion
Residential
Firelog
Total: All Combustor Types
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2.2.6 Solvents (np_solvents)
The np_solvents sector is a diverse collection of emission sources for which emissions are driven by
evaporation. Included in this sector are everyday items, such as cleaners, personal care products,
adhesives, architectural and aerosol coatings, printing inks, and pesticides. These sources exclusively
emit organic gases and feature origins spanning residential, commercial, institutional, and industrial
settings. The organic gases that evaporate from these sources often fulfill other functions than acting as
a traditional solvent (e.g., propellants, fragrances, emollients). For this reason, the solvents sector is
often referred to as "volatile chemical products." The base methodology used to estimate these
emissions are unchanged from the 2020 NEI, which is described in Section 32 of the 2020 NEI TSD.
including the SCCs that are included in the sector.
For 2022, all np_solvent emissions, except asphalt paving, are projected using the 2020 NEI as a base
year. This includes State, Locality, and Tribal emissions submissions. Here, the model used to estimate a
majority of the nonpoint solvent emissions in the NEI (VCPy) was used to estimate 2021 emissions (2022
usage data were not available). From there a SCC-specific ratio (of 2021 / 2020) was applied to the 2020
NEI. This method ensures that state-submitted emissions magnitudes are preserved. In addition, some
updates were made based on comments provided by New Jersey, and asphalt-related SCCs featured
temporal profile updates using ElA-based monthly profiles for "asphalt and road oil" by PADD region.
2.2.7 Open burning (openburn)
This new sector for 2022vl platform was split out from the nonpt sector and includes emissions from
@yard waste, land clearing, and residential household waste burning (SCCs starting with 261). For
2022vl, these emissions were held constant at 2020 NEI levels.
Table 2-13. SCCs in the openburn sector
see
Description
2610000100
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Yard Waste -
Leaf Species Unspecified
2610000400
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Yard Waste -
Brush Species Unspecified
2610000500
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Land Clearing
Debris
2610030000
Waste Disposal, Treatment, and Recovery; Open Burning; Residential; Household Waste
2610000300
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Yard Waste -
Weed Species Unspecified (incl Grass)
2.2.8 Nonpoint (nonpt)
The 2022 platform nonpt sector inventory is based on the April 2023 version of the 2020 NEI but
adjusted to better reflect 2022 emissions levels as described below. Stationary nonpoint sources that
were not subdivided into the afdust, livestock, fertilizer, np_oilgas, rwc or np_solvents sectors were
assigned to the "nonpt" sector. Locomotives and CMV mobile sources from the 2020 NEI nonpoint
inventory are described with the mobile sources. The types of sources in the nonpt sector include:
• stationary source fuel combustion, including industrial, commercial, and residential and orchard
heaters;
45
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• chemical manufacturing;
• industrial processes such as commercial cooking, metal production, mineral processes,
petroleum refining, wood products, fabricated metals, and refrigeration;
• 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; and
• miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair
shops.
The nonpt sector includes emission estimates for Portable Fuel Containers (PFCs), also known as "gas
cans." The PFC inventory consists of three distinct sources of PFC emissions, further distinguished by
residential or commercial use. The three sources are: (1) displacement of the vapor within the can; (2)
emissions due to evaporation (i.e., diurnal emissions); and (3) emissions due to permeation. Note that
spillage and vapor displacement associated with using PFCs to refuel nonroad equipment are included in
the nonroad inventory.
The factors used to adjust the emissions were developed using the datasets as described in Table 2-14.
Emissions for SCC groups other than those listed in this table (e.g., waste disposal, treatment and
recovery) were held constant at 2020 NEI levels in the 2022 base year inventory.
Table 2-14. Datasets used to Develop Factors to Adjust Nonpoint Emissions from 2020 to 2022
Source Category Group
2020-2022 Projection Method
All Other Nonpoint Source Fuel
Combustion
Apply EIA State Energy Data System energy consumption ratios.
Note that 2021 SEDS data are available for all fuels and 2022 data
are available for some fuels.
Stage 1 Gasoline Unloading at
Service Stations
Apply EIA State Energy Data System Transportation Sector/Motor
Gasoline consumption ratios
Stage 1 Gasoline Unloading at
Bulk Terminals/Plants
Apply EIA State Energy Data System Total Motor Gasoline
consumption ratios
Aviation Gasoline Stage 1 and II
Apply EIA State Energy Data System Aviation Gasoline consumption
ratios
Pipeline Gasoline
Apply EIA State Energy Data System Total Motor Gasoline
consumption ratios
Human Cremation
Estimate 2022 county-level number of cremations from 2022 actual
county-level deaths from CDC's Wonder Database and 2022 state-
level (projected) cremation rates from National Funeral Directors
Association's "Cremation and Burial Report" and apply 2022/2020
county-level cremation ratios to 2020 NEI cremation emissions to
compute 2022 cremation emissions
Commercial Cooking
Hold constant
Portable Fuel Containers
Hold constant
Asphalt Paving
Hold constant
46
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Source Category Group
2020-2022 Projection Method
Landfills/POTWs
Hold constant
Charcoal Grilling
Hold constant
2.3 Onroad Mobile sources (onroad)
Onroad mobile sources 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,
compressed natural gas (CNG), or electric 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). For more details on the approach and for a summary of
the MOVES inputs submitted by states, see Section 5 of the 2020 NEI TSD (EPA, 2023).
For the 2022 emissions modeling platform activity data (i.e., vehicle miles traveled (VMT) and vehicle
population (VPOP)) were based on data submitted by state and local agencies for the 2020 NEI and for
the 2022 platform, as well as data from Federal Highway Administration (FHWA) annual VMT at the
county level. VMT were based on county-level VM-2 data from FHWA. VPOP was mostly held constant at
2020 levels. A new MOVES run for 2022 was done using MOVES4 to obtain year-specific emission
factors.
Except for California, all onroad emissions were generated using the SMOKE-MOVES emissions modeling
framework that leverages MOVES-generated emission factors https://www.epa.gov/moves). county and
SCC-specific activity data, and hourly 2022 meteorological data. Specifically, EPA used vehicle miles
traveled (VMT) and other 2022-specific activity data, along with tools that interface between the MOVES
model and SMOKE. In this way, it was possible to take advantage of the gridded hourly temperature
data available from meteorological modeling that are also used for air quality modeling. The onroad
source classification codes (SCCs) in the emissions modeling platform are more finely resolved than
those in the National Emissions Inventory (NEI). The NEI SCCs distinguish vehicles and fuels, while the
SCCs used in the model platform also distinguish between emissions processes (i.e., off-network, on-
network, and extended idle), and road types. EPA mostly elected to keep 2020 NEI fuel splits (derived
from MOVES3) and not upgrade to MOVES4 fuels.
MOVES4 includes the following updates from MOVES3 that impacted the development of the emissions
modeling platform:
• Incorporates updates to fuel supply, inspection and maintenance programs, and emission rates.
• Accounts for the emission impacts of the EPA heavy-duty low NOx rule for model years 2027 and
later and the light-duty greenhouse gas rule for model years 2023 and later.
• Adds the ability to model heavy-duty battery-electric and fuel-cell vehicles, as well as
compressed natural gas (CNG) long-haul combination trucks.
• Improves the modeling of light-duty electric vehicles.
2.3.1 Inventory Development using SMOKE-MOVES
Except for California, 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
47
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activity data and the emission factor development. The vehicles (aka source types) for which MOVES
computes emissions are shown in Table 2-15. 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 non-
Conus sector. In some summary reports these non-CONUS emissions are aggregated with emissions
from the onroad sector.
Table 2-15. 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
Other 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
SMOKE-MOVES makes use of emission rate "lookup" tables generated by MOVES that differentiate
emissions by process (i.e., running, start, vapor venting, etc.), vehicle type, road type, temperature,
speed, hour of day, etc., to generate the MOVES emission rates that could be applied across the U.S.,
EPA used an automated process to run MOVES to produce year 2022-specific emission factors by
temperature and speed for a series of "representative counties," to which every other county was
mapped. The representative counties for which emission factors were generated were selected
according to their state, elevation, fuels, age distribution, ramp fraction, and inspection and
maintenance programs. Each county was then mapped to a representative county based on its
similarity to the representative county with respect to those attributes. For this study, there are 259
representative counties in the continental U.S. and a total of 298 including the non-CONUS areas. The
only differences between 2020 and 2022 being a change in Alaska county equivalents which removed
one borough (county ID 2261, Valdez-Cordova Census Area) which in 2019 split into two areas (county ID
2063, Chugach Census Area; and county ID 2066, Copper River Census Area), as well as some updates
recommended by Texas.
Once representative counties were identified, emission factors were generated with MOVES for each
representative county and for two "fuel months" - January to represent winter months, and July to
represent summer months - due to the different types of fuels used. SMOKE selected the appropriate
MOVES emissions rates for each county, hourly temperature, SCC, and speed bin and then multiplied the
emission rate by appropriate activity data. For on-roadway emissions, vehicle miles traveled (VMT) is the
activity data; off-network processes use vehicle population (VPOP), vehicle starts, and hours of off-
48
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network idling (ONI); and hoteling hours are used to develop emissions for extended idling of
combination long-haul trucks. These calculations were done for every county and grid cell in the
continental U.S. for each hour of the year.
The SMOKE-MOVES process for creating the model-ready emissions consists of the following steps:
1) Determine which counties will be used to represent other counties in the MOVES runs.
2) Determine which months will be used to represent other month's fuel characteristics.
3) Create inputs needed only by MOVES. MOVES requires county-specific information on vehicle
populations, age distributions, and inspection-maintenance programs for each of the
representative counties.
4) Create inputs needed both by MOVES and by SMOKE, including temperatures and activity data.
5) Run MOVES to create emission factor tables for the temperatures found in each county.
6) Run SMOKE to apply the emission factors to activity data (VMT, VPOP, STARTS, off-network
idling, and HOTELING) to calculate emissions based on the gridded hourly temperatures in the
meteorological data.
7) Aggregate the results to the county-SCC level for summaries and quality assurance.
The onroad emissions were processed in six processing streams that were then merged together into
the onroad sector emissions after each of the six streams have been processed:
• rate-per-distance (RPD) uses VMT as the activity data plus speed and speed profile information
to compute on-network emissions from exhaust, evaporative, permeation, refueling, and brake
and tire wear processes;
• rate-per-vehicle (RPV) uses VPOP activity data to compute off-network emissions from exhaust,
evaporative, permeation, and refueling processes;
• rate-per-profile (RPS) uses STARTS activity data to compute off-network emissions from vehicles
starts;
• rate-per-profile (RPP) uses VPOP activity data to compute off-network emissions from
evaporative fuel vapor venting, including hot soak (immediately after a trip) and diurnal (vehicle
parked for a long period) emissions;
• rate-per-hour (RPH) uses hoteling hours activity data to compute off-network emissions for idling
of long-haul trucks from extended idling and auxiliary power unit process; and
• rate-per-hour off-network idling (RPHO) uses off network idling hours activity data to compute
off-network idling emissions for all types of vehicles.
The onroad emissions inputs to MOVES for the 2022 emissions modeling platform are based on the 2020
NEI, described in more detail in Section 5 of the 2020 NEI TSD. These inputs include:
• Key parameters in the MOVES County databases (CDBs) including Low Emission Vehicle (LEV)
table
• Fuel months
• Activity data (e.g., VMT, VPOP, speed, HOTELING)
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Fuel months, age distributions, and other inputs were consistent with those used to compute the 2020
NEI. Activity data submitted by states and development of the EPA default activity data sets for VMT,
VPOP, hoteling hours, starts, and off-network idling (ONI) hours follows a similar process to the 2020
NEI, but based on 2022-specific VMT. These methods are described in detail in the 2020 NEI TSD and
supporting documents. Details specific to 2022 activity data development are described below.
2.3.2 Onroad Activity Data Development
SMOKE-MOVES uses vehicle miles traveled (VMT), vehicle population (VPOP), vehicle starts, hours of off-
network idling (ONI), 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. The default was supplemented with data submitted
by state and local agencies. In the EPA default dataset, VMT was derived from FHWA's county-level VM-
2 data for 2022. EPA default VPOP was held constant at 2020 levels, as were the starts and fuel splits.
Hours of hoteling and off-network idling were computed from 2022 VMT. EPA default activity was used
for California, but the emissions were scaled to California-supplied values during the emissions
processing.
Vehicle Miles Traveled (VMT) and Vehicle Population (VPOP)
Activity data submitted by states and development of the EPA default activity data sets for VMT, VPOP,
and hoteling hours are described in detail in the 2020 NEI TSD (EPA, 2023) and supporting documents.
The process for developing VMT for 2022 is similar to the 2020 NEI process, except starting with 2022-
specific VMT from the FHWA VM-2 (county-level and by road type) and VM-4 (distributions of VMT by
state and HPMS vehicle type). The VM-2 and VM-4 data were combined to create a 2022 VMT dataset
by county, HPMS vehicle type, and road type. 2020 NEI VMT was then used to allocate VMT from HPMS
vehicle type to MOVES vehicle type, and to different fuel types. New monthly profiles for 2022 VMT
were also used, based on FHWA's Travel Monitoring and Analysis System (TMAS) data. See Section 3.3.8
for more information on the use of TMAS data.
The following states submitted VMT for the 2022 platform base year: AK, CO, CT, DE, GA, KS, MA, Ml,
MD, ME, NC, NH, NJ, NY, OR, PA, SC, TN, TX, UT, VA, WA, Wl, WV, and Jefferson Co. KY. In the final base
year data, VMT for Colorado are based on EPA default data, and other activity based on VMT was
adjusted as a result of this change. VPOP was mostly held constant with the 2020 NEI VPOP except for a
few states that supplied VPOP data: DE, GA, NY, and Wl.
Speed Activity (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. The 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.
Speeds are based on data for January 2020 as speed data were not available for 2021 or 2022 in time for
the 2022vl platform. Speed data from the StreetLight dataset were used to generate hourly speed
profiles by county, SCC, and month. The SPDIST files for the 2022 emissions modeling platform are based
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on a combination of the StreetLight project data and 2020 NEI MOVES CDBs. More information can be
found in the 2020 NEI TSD (EPA, 2023) and supporting documents.
Hoteling Hours (HOTELING)
Hoteling hours were computed from the 2022 VMT, using a factor of 0.007248 hoteling hours pet VMT
for combination long haul trucks on restricted highways. This is the same approach as in 2020 NEI,
except based on 2022 VMT. Hoteling hours were capped by county at a theoretical maximum and any
excess hours of the maximum were reduced. For calculating reductions, a dataset of truck stop parking
space availability was used, which includes a total number of parking spaces per county. This same
dataset is used to develop the spatial surrogate for allocating county-total hoteling emissions to model
grid cells. The parking space dataset was last updated during the development of the 2016 platforms.
There are 8,760 hours in the year 2022; therefore, the maximum number of possible hoteling hours in a
particular county is equal to 8,760 * the number of parking spaces in that county. Hoteling hours were
capped at that theoretical maximum value for 2022 in all counties, with some exceptions. Also, Texas
submitted hoteling activity for 2020 NEI, and their 2020 hoteling activity was projected to 2022 using
ratios of 2022 VMT / 2020 VMT for combination long haul trucks.
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 were never reduced below 105,120 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 were never increased in this analysis. For recent NEIs, four states requested that no reductions
be applied to the hoteling activity based on parking space availability: CO, ME, NJ, and NY. For these
states, reductions based on parking space availability were not applied. Reductions were also not
applied in Texas, because the hoteling activity in that state are based on state-submitted data.
The final step related to hoteling activity is to split county totals into separate values for extended idling
(SCC 2202620153) and Auxiliary Power Units (APUs) (SCC 2202620191). For 2022 modeling with
MOVES4, an 9.8% APU split is used nationwide, meaning that during 9.8% of the hoteling hours auxiliary
power units are assumed to be running.
Starts
Onroad "start" emissions are the instantaneous exhaust emissions that occur at the engine start (e.g.,
due to the fuel rich conditions in the cylinder to initiate combustion) as well as the additional running
exhaust emissions that occur because the engine and emission control systems have not yet stabilized at
the running operating temperature. Operationally, start emissions are defined as the difference in
emissions between an exhaust emissions test with an ambient temperature start and the same test with
the engine and emission control systems already at operating temperature. As such, the units for start
emission rates are instantaneous grams/start.
MOVES4 uses vehicle population information to sort the vehicle population into source bins defined by
vehicle source type, fuel type (gas, diesel, etc.), regulatory class, model year and age. The model uses
default data from instrumented vehicles (or user-provided values) to estimate the number of starts for
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each source bin and to allocate them among eight operating mode bins defined by the amount of time
parked ("soak time") prior to the start. Thus, MOVES4 accounts for different amounts of cooling of the
engine and emission control systems. Each source bin and operating mode has an associated g/start
emission rate. Start emissions are also adjusted to account for fuel characteristics, light duty inspection
and maintenance (l/M) programs, and ambient temperatures. Starts were mostly held constant from
2020 to 2022, except where the VPOP changed and thus starts were changed in proportion to the
change in VPOP. Additionally, new monthly profiles were applied for 2022.
Off-network Idling Hours
After creating VMT inputs for SMOKE-MOVES, Off-network idle (ONI) activity data were also needed.
ONI is defined in MOVES as time during which a vehicle engine is running idle and the vehicle is
somewhere other than on the road, such as in a parking lot, a driveway, or at the side of the road. This
engine activity contributes to total mobile source emissions but does not take place on the road
network. Examples of ONI activity include:
• light duty passenger vehicles idling while waiting to pick up children at school or to pick up
passengers at the airport or train station,
• single unit and combination trucks idling while loading or unloading cargo or making
deliveries, and
• vehicles idling at drive-through restaurants.
Note that ONI does not include idling that occurs on the road, such as idling at traffic signals, stop signs,
and in traffic—these emissions are included as part of the running and crankcase running exhaust
processes on the other road types. ONI also does not include long-duration idling by long-haul
combination trucks (hoteling/extended idle), as that type of long duration idling is accounted for in other
MOVES processes.
ONI activity hours were calculated based on VMT. For each representative county, the ratio of ONI hours
to onroad VMT (on all road types) was calculated using the MOVES ONI Tool by source type, fuel type,
and month. These ratios are then multiplied by each county's total VMT (aggregated by source type, fuel
type, and month) to get hours of ONI activity.
2.3.3 MOVES Emission Factor Table Development
MOVES4 was run in emission rate mode to create emission factor tables for 2022, for all representative
counties and fuel months. The county databases used to run MOVES to develop the emission factor
tables included the state-specific control measures such as the California LEV program, and fuels
representing the year 2022. The range of temperatures run along with the average humidities used
were specific to the year 2022. The remaining settings for the CDBs are documented in the 2020 NEI
TSD. To create the emission factors, MOVES was run separately for each representative county and fuel
month for each temperature bin needed for the calendar year 2022. The MOVES results were post-
processed into CSV-formatted emission factor tables that can be read by SMOKE-MOVES. Additionally,
MOVES was run for all counties in Alaska, Hawaii, and Virgin Islands, and for a single representative
county in Puerto Rico.
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The county databases (CDBs) used to run MOVES to develop the emission factor tables were based on
those used for the 2020 NEI. The 2022 emissions modeling platform development included an extensive
review of the various tables including speed distributions. Each county in the continental U.S. was
classified according to its state, altitude (high or low), fuel region, the presence of l/M programs, and the
mean light-duty age. A binning algorithm was executed to identify "like counties." The result was 259
representative counties for the CONUS shown in Figure 2-3 along with 39 for Alaska, Hawaii, Puerto
Rico, and the US Virgin Islands. The CONUS representation counties for 2022 are the same as those used
for 2020 NEI with the exception of Alaska, which, in 2019, removed one borough (county ID 2261,
Valdez-Cordova Census Area) and split that into two areas (county ID 2063, Chugach Census Area; and
county ID 2066, Copper River Census Area); as well as some updates recommended by Texas.
Figure 2-3. Map of 2022 Representative Counties
Age distributions are a key input to MOVES in determining emission rates. Age distributions were held
constant from 2020 for the 2022 emissions modeling platform; with the exception of Georgia, who
supplied their own age distribution. The age distributions for 2020 were updated based on vehicle
registration data obtained from IMS Markit, subject to reductions for older vehicles. For more
information on how age distributions were developed for the 2020 NEI, please see Section 5 of the 2020
NEITSD.
EPA calculated the adjustment factors representing the fraction of population remaining in every model
year, with two exceptions. Model years from 2011 to 2020 received no adjustment and the model year
1990 received a capped adjustment that equals the adjustment for model year 1991. The adjustment
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factors in Table 2-16 were applied to the 2020 IHS data to create the EPA Default set of population and
age distributions for the NEI.
Table 2-16. The fraction of IHS vehicle populations retained for 2020 NEI and 2022 emissions modeling
platform by model year
Model Year
LDV Adjustment Factor
pre-1991
0.722
1991
0.722
1992
0.728
1993
0.742
1994
0.754
1995
0.766
1996
0.774
1997
0.790
1998
0.787
1999
0.798
2000
0.796
2001
0.806
2002
0.808
2003
0.828
2004
0.844
2005
0.857
2006
0.874
2007
0.892
2008
0.905
2009
0.919
2010
0.929
2011-2021
1
EPA also removed the county-specific fractions of antique license plate vehicles present in the
registration data from IHS, based on the assumption that antique vehicles are operated significantly less
than average. States without any CDB submittals received EPA default populations and age distributions
based on the adjusted IHS data, and some states with submittals were overridden, decided on a case-by-
case basis.
In addition to removing the older and antique plate vehicles from the IHS data, 28 counties found to be
outliers because their fleet age was significantly younger than in typical counties. The outlier review was
limited to LDV source types 21, 31, and 32. Many rural counties have outliers for low-population source
types such as Transit Bus and Refuse Truck due to small sample sizes, but 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
85 percent of the population in the entire county is 0 and 1 years old. These sorts of young fleets can
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happen if the headquarters of a leasing or rental company is the owner/entity of a relatively large
number of newer 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.
In areas where submitted vehicle population data were accepted for the 2020 NEI, the relative
populations of cars vs. light-duty trucks were reapportioned (while retaining the magnitude of the light-
duty vehicles from the submittals) using the county-specific percentages from the IHS data. In this way,
the categorization of cars versus light trucks is consistent from state to state. The county total light-duty
vehicle populations were preserved through this process.
To create the emission factors, MOVES was run separately for each representative county and fuel
month and for each temperature bin needed for calendar year 2022. The CDBs used to run MOVES
include the state-specific control measures such as the California low emission vehicle (LEV) program. In
addition, the range of temperatures and the average humidities used in the CDBs were specific to the
year 2022. The MOVES results were post-processed into CSV-formatted emission factor tables that can
be read by SMOKE-MOVES.
2.3.4 Onroad California Inventory Development (onroad_ca_adj)
California uses their own emission model, EMFAC, to develop onroad emissions inventories and provides
those inventories to EPA. EMFAC uses emission inventory codes (EICs) to characterize the emission
processes instead of SCCs. The EPA and California worked together to develop a code mapping to better
match EMFAC's EICs to EPA MOVES' detailed set of SCCs that distinguish between off-network and on-
network and brake and tire wear emissions. This detail is needed for modeling but not for the NEI.
California submitted onroad emissions for all 2022vl platform years, including 2022. Since California's
2022 inventory did not contain HAPs, VOC-based speciation factors were used to estimate VOC HAPs for
2022. Other HAPs such as PAHs and metals are not needed for this platform. The EPA added NH3 to the
CARB inventory by using the state total NH3 from MOVES and allocating it at the county level based on
CO. Refueling emissions were taken from MOVES for California.
California onroad mobile source emissions were created through a hybrid approach of combining state-
supplied annual emissions with EPA-developed SMOKE-MOVES runs. Through this approach, the
platform was able to reflect the California-developed emissions, while leveraging the more detailed SCCs
and the highly resolved spatial patterns, temporal patterns, and speciation from SMOKE-MOVES. The
basic steps involved in temporally allocating onroad emissions from California based on SMOKE-MOVES
results were:
1) Run CA using EPA inputs through SMOKE-MOVES to produce hourly emissions hereafter
known as "EPA estimates." These EPA estimates for CA were run in a separate sector called
"onroad_ca."
2) Calculate ratios between state-supplied emissions and EPA estimates. The ratios were
calculated for each county/SCC/pollutant combination based on the California onroad
emissions inventory. The 2020 California data did not separate off and on-network emissions
or extended idling, and also did not include information for vehicles fueled by E-85, so these
differentiations were obtained using MOVES.
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3) Create an adjustment factor file (CFPRO) that includes EPA-to-state estimate ratios.
4) Rerun CA through SMOKE-MOVES using EPA inputs and the new adjustment factor file.
Through this process, adjusted model-ready files were created that sum to annual totals from California,
but have the temporal and spatial patterns reflecting the highly resolved meteorology and SMOKE-
MOVES. After adjusting the emissions, this sector is called "onroad_ca_adj." Note that in emission
summaries, the emissions from the "onroad" and "onroad_ca_adj" sectors were summed and
designated as the emissions for the onroad sector.
2.4 Nonroad Mobile sources (cmv, rail, nonroad)
The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions
(nonroad), locomotive (rail), and commercial marine CMV emissions (cmv_clc2 and cmv_c3).
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2)
The cmv_clc2 sector contains Category 1 and 2 (C1C2) CMV emissions. Category 1 and 2 vessels use
diesel fuel. All emissions in this sector are annual and at county-SCC resolution; however, in the
emissions modeling platform they are provided at the sub-county level (i.e., port shape ids) and by SCC
and emission type (e.g., hoteling, maneuvering). For the 2021 emissions modeling platform EPA
expanded the list of SCCs. SCCs are now further resolved based on ship type than they were for the 2020
NEI. A list of SCCs for the C1C2 sector can be seen in Table 2-17 For more information on the 2022 CMV
C1C2 emissions development, see the supplemental documentation (ERG, 2024b). C1C2 emissions that
occur outside of state waters are not assigned to states. For this modeling platform, all CMV emissions in
the cmv_clc2 sector are treated as hourly gridded point sources with stack parameters that should
result in them being placed in layer 1.
Sulfur dioxide (S02) emissions reflect rules that reduced sulfur emissions for CMV that took effect in the
year 2015. The cmv_clc2 inventory sector contains small to medium-size engine CMV emissions.
Category 1 and Category 2 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_clc2 inventory sector contains sources that traverse state and federal waters along with
emissions from surrounding areas of Canada, Mexico, and international waters. The cmv_clc2 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_clc2 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_clc2 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_clc2 sources are categorized as operating either in-port or
underway and as main and auxiliary engines are encoded using the SCCs listed in Table 2-17. Level 1 and
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Level 2 descriptions for all of the entries are "Mobile Sources", and "Marine Vessels, Commercial",
respectively.
Table 2-17. SCCs for the cmv clc2 sector
see
Level 3 Description
Level 4 Description
2280201113
D
esel Barge
C1C2 Port Emissions
Main Engine
2280202113
D
esel Offshore support
C1C2 Port Emissions
Main Engine
2280203113
D
esel Bulk Carrier
C1C2 Port Emissions
Main Engine
2280204113
D
esel Commercial Fishing
C1C2 Port Emissions
Main Engine
2280205113
D
esel Container Ship
C1C2 Port Emissions
Main Engine
2280206113
D
esel Ferry
C1C2 Port Emissions
Main Engine
2280207113
D
esel General Cargo
C1C2 Port Emissions
Main Engine
2280208113
D
esel Government
C1C2 Port Emissions
Main Engine
2280209113
D
esel Miscellaneous
C1C2 Port Emissions
Main Engine
2280210113
D
esel RollOn RollOff
C1C2 Port Emissions
Main Engine
2280211113
D
eselTanker
C1C2 Port Emissions
Main Engine
2280212113
D
esel Tour Boat
C1C2 Port Emissions
Main Engine
2280213113
D
esel Tug
C1C2 Port Emissions
Main Engine
2280214113
D
esel Refrigerated
C1C2 Port Emissions
Main Engine
2280215113
D
esel Cruise
C1C2 Port Emissions
Main Engine
2280216113
D
esel Passenger Other
C1C2 Port Emissions
Main Engine
2280201114
D
esel Barge
C1C2 Port Emissions
Auxil
ary Engine
2280202114
D
esel Offshore support
C1C2 Port Emissions
Auxil
ary Engine
2280203114
D
esel Bulk Carrier
C1C2 Port Emissions
Auxil
ary Engine
2280204114
D
esel Commercial Fishing
C1C2 Port Emissions
Auxil
ary Engine
2280205114
D
esel Container Ship
C1C2 Port Emissions
Auxil
ary Engine
2280206114
D
esel Ferry
C1C2 Port Emissions
Auxil
ary Engine
2280207114
D
esel General Cargo
C1C2 Port Emissions
Auxil
ary Engine
2280208114
D
esel Government
C1C2 Port Emissions
Auxil
ary Engine
2280209114
D
esel Miscellaneous
C1C2 Port Emissions
Auxil
ary Engine
2280210114
D
esel RollOn RollOff
C1C2 Port Emissions
Auxil
ary Engine
2280211114
D
eselTanker
C1C2 Port Emissions
Auxil
ary Engine
2280212114
D
esel Tour Boat
C1C2 Port Emissions
Auxil
ary Engine
2280213114
D
esel Tug
C1C2 Port Emissions
Auxil
ary Engine
2280214114
D
esel Refrigerated
C1C2 Port Emissions
Auxil
ary Engine
2280215114
D
esel Cruise
C1C2 Port Emissions
Auxil
ary Engine
2280216114
D
esel Passenger Other
C1C2 Port Emissions
Auxil
ary Engine
2280201123
D
esel Barge
C1C2 Underway emissions
Main Engine
2280202123
D
esel Offshore support
C1C2 Underway emissions
Main Engine
2280203123
D
esel Bulk Carrier
C1C2 Underway emissions
Main Engine
2280204123
D
esel Commercial Fishing
C1C2 Underway emissions
Main Engine
2280205123
D
esel Container Ship
C1C2 Underway emissions
Main Engine
2280206123
D
esel Ferry
C1C2 Underway emissions
Main Engine
2280207123
D
esel General Cargo
C1C2 Underway emissions
Main Engine
57
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see
Level 3 Description
Level 4 Description
2280208123
D
esel Government
C1C2 Underway emissions
Main Engine
2280209123
D
esel Miscellaneous
C1C2 Underway emissions
Main Engine
2280210123
D
esel RollOn RollOff
C1C2 Underway emissions
Main Engine
2280211123
D
eselTanker
C1C2 Underway emissions
Main Engine
2280212123
D
esel Tour Boat
C1C2 Underway emissions
Main Engine
2280213123
D
esel Tug
C1C2 Underway emissions
Main Engine
2280214123
D
esel Refrigerated
C1C2 Underway emissions
Main Engine
2280215123
D
esel Cruise
C1C2 Underway emissions
Main Engine
2280216123
D
esel Passenger Other
C1C2 Underway emissions
Main Engine
2280201124
D
esel Barge
C1C2 Underway emissions
Auxiliary Engine
2280202124
D
esel Offshore support
C1C2 Underway emissions
Auxiliary Engine
2280203124
D
esel Bulk Carrier
C1C2 Underway emissions
Auxiliary Engine
2280204124
D
esel Commercial Fishing
C1C2 Underway emissions
Auxiliary Engine
2280205124
D
esel Container Ship
C1C2 Underway emissions
Auxiliary Engine
2280206124
D
esel Ferry
C1C2 Underway emissions
Auxiliary Engine
2280207124
D
esel General Cargo
C1C2 Underway emissions
Auxiliary Engine
2280208124
D
esel Government
C1C2 Underway emissions
Auxiliary Engine
2280209124
D
esel Miscellaneous
C1C2 Underway emissions
Auxiliary Engine
2280210124
D
esel RollOn RollOff
C1C2 Underway emissions
Auxiliary Engine
2280211124
D
eselTanker
C1C2 Underway emissions
Auxiliary Engine
2280212124
D
esel Tour Boat
C1C2 Underway emissions
Auxiliary Engine
2280213124
D
esel Tug
C1C2 Underway emissions
Auxiliary Engine
2280214124
D
esel Refrigerated
C1C2 Underway emissions
Auxiliary Engine
2280215124
D
esel Cruise
C1C2 Underway emissions
Auxiliary Engine
2280216124
D
esel Passenger Other
C1C2 Underway emissions
Auxiliary Engine
Category 1 and 2 CMV emissions were developed for the 2022 platform and were not based on 2020 NEI
although the methods used to develop the emissions were similar. The emissions were developed based
on 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) to quantify all ship
activity which occurred between January 1 and December 31, 2022. During the acquisition of the 2022
AIS data from the U.S. Coast Guard, EPA was made aware of a data quality issue that started in late
March and continued through late June of 2022. To address this, emissions were substituted in from the
2021 CMV C1C2 inventory for this period. To ensure coverage for all of the areas needed by the NEI, the
requested and provided AIS data extend beyond 200 nautical miles from the U.S. coast. The area
covered by the AIS Area, 2022 Modeling Platform Geographical Extent, and U.S. ECA is shown in Figure
2-4 (a). 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. Two types of AIS data were
received: satellite (S-AIS) and terrestrial (T-AIS). The distribution of terrestrial and satellite AIS data for
the 2022 emissions modeling platform are shown in Figure 2-4 (b). An additional enhancement for the
2022 C1C2 CMV inventory was the development and application of a mask that was applied to remove
any emissions over land due to stray AIS signals.
58
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Figure 2-4. NEI Commercial Marine Vessel Boundaries and Automatic Identification System Request
Boxes for the 2022 Emissions Modeling Platform
a) Entire AIS Area (Transparent Gray), 2022 Modeling Platform Geographical Extent (Black Outline),
and U.S. ECA (White Outline)
b) Distribution of Terrestrial and Satellite AIS Data
Num. Rows in S-AIS 2022
< 10,000,000
100,00,001 - 50,000,000
50,000,001 - 100,000,000
100,000,001 - 200,000,000
> 200,000,000
Num. Rows in T-AIS 2022
< 10,000,000
10,000,001 - 50,000,000
50,000,001 - 250,000,000
250,000,001 - 500,000,000
> 500,000,000
59
-------
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, 2021).
The engine bore and stroke data were used 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).
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 2-1.
g
Emissionsinterval = Time (hr)interval x Power(kW) x x LLAF Equation 2-l
Power was 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 to determine their vessel type, and thus their vessel group, power rating,
and engine tier information which are required for the emissions calculations. See the 2022 CI C2 CMV
development documentation for more details on this process. Following the identification, 236 different
vessel types were matched to the C1C2 vessels. Vessel attribute data were 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 2-18. 19,322 vessels were directly identified by their ship and
cargo number. The remaining group of miscellaneous ships represent 1.6 percent of the AIS vessels
(excluding recreational vessels) for which a specific vessel type could not be assigned.
60
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Table 2-18. Vessel groups in the cmv_clc2 sector
2017 Entire
2020 Entire
Area Ship
Area Ship
2021 Entire
2022 Entire
Vessel Group
Count
Count
Area Ship Count
Area Ship Count
Bulk Carrier
45
44
46
47
Commercial Fishing
1,686
4,262
5,826
5,859
Container Ship
8
16
11
15
Ferry Excursion
482
724
849
997
General Cargo
1,555
3,451
3,190
3,122
Government
1,368
1,192
1,179
1,216
Miscellaneous
1,810
269
291
300
Offshore support
1,203
1,337
1,416
1,377
Pilot
NA
17
15
15
Reefer
15
13
12
28
Ro Ro
27
218
219
212
Tanker
144
555
591
677
Tug
4,203
5,661
5,299
5,289
Work Boat
83
151
162
168
Total in Inventory:
12,629
17,910
19,106
19,322
As shown in Equation 2-1, power is an important component of the emissions computation. Vessel-
specific installed propulsive power ratings and service speeds were pulled from Clarksons 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. 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 2-17.
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,
PMio, 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 factors were calculated per tier based on C1C2
population distributions grouped by engine displacement. Boiler emission factors were obtained from an
earlier Swedish Environmental Protection Agency study (Swedish EPA, 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
61
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Inventory.6 Hazardous air pollutants and ammonia were added to the inventory according to
multiplicative factors applied either to VOC or PM2.5.
The stack parameters used for cmv_clc2 are a stack height of 1 ft, stack diameter of 1 ft, stack
temperature of 70°F, and a stack velocity of 0.1 ft/s. These parameters force emissions into layer 1.
For more information on the emission computations for 2022, see the supporting documentation for the
development of the 2022 C1C2 CMV emissions (ERG, 2024). The cmv_clc2 emissions were aggregated
to total hourly values in each grid cell and run through SMOKE as point sources. SMOKE requires an
annual inventory file to go along with the hourly data and this file was generated for 2022.
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3)
The cmv_c3 sector contains large engine CMV emissions. Category 3 (C3) marine diesel engines at or
above 30 liters per cylinder. Typically, these are the largest CMV engines and are 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.7 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 inventory but are in separate files from the emissions
around the continental United States (CONUS). The cmv_c3 sources in the inventory are categorized as
operating either in-port or underway and are encoded using the SCCs listed in Table 2-19 and distinguish
between diesel and residual fuel, in port areas versus underway, and main and auxiliary engines. The
Level 1 and Level 2 descriptions for each of the SCCs are "Mobile Sources" and "Marine Vessels,
Commercial", respectively.
Table 2-19. SCCs for cmv c3 sector
see
Level 3 Description
Level 4 Description
2280201313
Diesel Barge
C3 Port Emissions: Main Engine
2280202313
Diesel Offshore support
C3 Port Emissions: Main Engine
2280203313
Diesel Bulk Carrier
C3 Port Emissions: Main Engine
2280204313
Diesel Commercial Fishing
C3 Port Emissions: Main Engine
2280205313
Diesel Container Ship
C3 Port Emissions: Main Engine
2280206313
Diesel Ferry
C3 Port Emissions: Main Engine
2280207313
Diesel General Cargo
C3 Port Emissions: Main Engine
6 USEPA. EPA and Port Everglades Partnership: Emission Inventories and Reduction Strategies. US Environmental Protection
Agency, Office of Transportation and Air Quality, June 2018. https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P100UKV8.pdf.
7 https://www.epa.gov/regulations-emissions-vehicles-and-engines/regulations-emissions-marine-vessels.
62
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see
Level 3 Description
Level 4 Description
2280208313
Diesel Government
C3 Port Emissions: Main Engine
2280209313
Diesel Miscellaneous
C3 Port Emissions: Main Engine
2280210313
Diesel RollOn RollOff
C3 Port Emissions: Main Engine
2280211313
Diesel Tanker
C3 Port Emissions: Main Engine
2280212313
Diesel Tour Boat
C3 Port Emissions: Main Engine
2280213313
Diesel Tug
C3 Port Emissions: Main Engine
2280214313
Diesel Refrigerated
C3 Port Emissions: Main Engine
2280215313
Diesel Cruise
C3 Port Emissions: Main Engine
2280216313
Diesel Passenger Other
C3 Port Emissions: Main Engine
2280201314
Diesel Barge
C3 Port Emissions: Auxiliary Engine
2280202314
Diesel Offshore support
C3 Port Emissions: Auxiliary Engine
2280203314
Diesel Bulk Carrier
C3 Port Emissions: Auxiliary Engine
2280204314
Diesel Commercial Fishing
C3 Port Emissions: Auxiliary Engine
2280205314
Diesel Container Ship
C3 Port Emissions: Auxiliary Engine
2280206314
Diesel Ferry
C3 Port Emissions: Auxiliary Engine
2280207314
Diesel General Cargo
C3 Port Emissions: Auxiliary Engine
2280208314
Diesel Government
C3 Port Emissions: Auxiliary Engine
2280209314
Diesel Miscellaneous
C3 Port Emissions: Auxiliary Engine
2280210314
Diesel RollOn RollOff
C3 Port Emissions: Auxiliary Engine
2280211314
Diesel Tanker
C3 Port Emissions: Auxiliary Engine
2280212314
Diesel Tour Boat
C3 Port Emissions: Auxiliary Engine
2280213314
Diesel Tug
C3 Port Emissions: Auxiliary Engine
2280214314
Diesel Refrigerated
C3 Port Emissions: Auxiliary Engine
2280215314
Diesel Cruise
C3 Port Emissions: Auxiliary Engine
2280216314
Diesel Passenger Other
C3 Port Emissions: Auxiliary Engine
2280201323
Diesel Barge
C3 Underway emissions: Main Engine
2280202323
Diesel Offshore support
C3 Underway emissions: Main Engine
2280203323
Diesel Bulk Carrier
C3 Underway emissions: Main Engine
2280204323
Diesel Commercial Fishing
C3 Underway emissions: Main Engine
2280205323
Diesel Container Ship
C3 Underway emissions: Main Engine
2280206323
Diesel Ferry
C3 Underway emissions: Main Engine
2280207323
Diesel General Cargo
C3 Underway emissions: Main Engine
2280208323
Diesel Government
C3 Underway emissions: Main Engine
2280209323
Diesel Miscellaneous
C3 Underway emissions: Main Engine
2280210323
Diesel RollOn RollOff
C3 Underway emissions: Main Engine
2280211323
Diesel Tanker
C3 Underway emissions: Main Engine
2280212323
Diesel Tour Boat
C3 Underway emissions: Main Engine
2280213323
Diesel Tug
C3 Underway emissions: Main Engine
2280214323
Diesel Refrigerated
C3 Underway emissions: Main Engine
63
-------
see
Level 3 Description
Level 4 Description
2280215323
Diesel Cruise
C3 Underway emissions: Main Engine
2280216323
Diesel Passenger Other
C3 Underway emissions: Main Engine
2280201324
Diesel Barge
C3 Underway emissions: Auxiliary Engine
2280202324
Diesel Offshore support
C3 Underway emissions: Auxiliary Engine
2280203324
Diesel Bulk Carrier
C3 Underway emissions: Auxiliary Engine
2280204324
Diesel Commercial Fishing
C3 Underway emissions: Auxiliary Engine
2280205324
Diesel Container Ship
C3 Underway emissions: Auxiliary Engine
2280206324
Diesel Ferry
C3 Underway emissions: Auxiliary Engine
2280207324
Diesel General Cargo
C3 Underway emissions: Auxiliary Engine
2280208324
Diesel Government
C3 Underway emissions: Auxiliary Engine
2280209324
Diesel Miscellaneous
C3 Underway emissions: Auxiliary Engine
2280210324
Diesel RollOn RollOff
C3 Underway emissions: Auxiliary Engine
2280211324
Diesel Tanker
C3 Underway emissions: Auxiliary Engine
2280212324
Diesel Tour Boat
C3 Underway emissions: Auxiliary Engine
2280213324
Diesel Tug
C3 Underway emissions: Auxiliary Engine
2280214324
Diesel Refrigerated
C3 Underway emissions: Auxiliary Engine
2280215324
Diesel Cruise
C3 Underway emissions: Auxiliary Engine
2280216324
Diesel Passenger Other
C3 Underway emissions: Auxiliary Engine
2280301313
Residual Barge
C3 Port Emissions: Main Engine
2280302313
Residual Offshore support
C3 Port Emissions: Main Engine
2280303313
Residual Bulk Carrier
C3 Port Emissions: Main Engine
2280304313
Residual Commercial Fishing
C3 Port Emissions: Main Engine
2280305313
Residual Container Ship
C3 Port Emissions: Main Engine
2280306313
Residual Ferry
C3 Port Emissions: Main Engine
2280307313
Residual General Cargo
C3 Port Emissions: Main Engine
2280308313
Residual Government
C3 Port Emissions: Main Engine
2280309313
Residual Miscellaneous
C3 Port Emissions: Main Engine
2280310313
Residual RollOn RollOff
C3 Port Emissions: Main Engine
2280311313
Residual Tanker
C3 Port Emissions: Main Engine
2280312313
Residual Tour Boat
C3 Port Emissions: Main Engine
2280313313
Residual Tug
C3 Port Emissions: Main Engine
2280314313
Residual Refrigerated
C3 Port Emissions: Main Engine
2280315313
Residual Cruise
C3 Port Emissions: Main Engine
2280316313
Residual Passenger Other
C3 Port Emissions: Main Engine
2280301314
Residual Barge
C3 Port Emissions: Auxiliary Engine
2280302314
Residual Offshore support
C3 Port Emissions: Auxiliary Engine
2280303314
Residual Bulk Carrier
C3 Port Emissions: Auxiliary Engine
2280304314
Residual Commercial Fishing
C3 Port Emissions: Auxiliary Engine
2280305314
Residual Container Ship
C3 Port Emissions: Auxiliary Engine
64
-------
see
Level 3 Description
Level 4 Description
2280306314
Residual Ferry
C3 Port Emissions: Auxiliary Engine
2280307314
Residual General Cargo
C3 Port Emissions: Auxiliary Engine
2280308314
Residual Government
C3 Port Emissions: Auxiliary Engine
2280309314
Residual Miscellaneous
C3 Port Emissions: Auxiliary Engine
2280310314
Residual RollOn RollOff
C3 Port Emissions: Auxiliary Engine
2280311314
Residual Tanker
C3 Port Emissions: Auxiliary Engine
2280312314
Residual Tour Boat
C3 Port Emissions: Auxiliary Engine
2280313314
Residual Tug
C3 Port Emissions: Auxiliary Engine
2280314314
Residual Refrigerated
C3 Port Emissions: Auxiliary Engine
2280315314
Residual Cruise
C3 Port Emissions: Auxiliary Engine
2280316314
Residual Passenger Other
C3 Port Emissions: Auxiliary Engine
2280301323
Residual Barge
C3 Underway emissions: Main Engine
2280302323
Residual Offshore support
C3 Underway emissions: Main Engine
2280303323
Residual Bulk Carrier
C3 Underway emissions: Main Engine
2280304323
Residual Commercial Fishing
C3 Underway emissions: Main Engine
2280305323
Residual Container Ship
C3 Underway emissions: Main Engine
2280306323
Residual Ferry
C3 Underway emissions: Main Engine
2280307323
Residual General Cargo
C3 Underway emissions: Main Engine
2280308323
Residual Government
C3 Underway emissions: Main Engine
2280309323
Residual Miscellaneous
C3 Underway emissions: Main Engine
2280310323
Residual RollOn RollOff
C3 Underway emissions: Main Engine
2280311323
Residual Tanker
C3 Underway emissions: Main Engine
2280312323
Residual Tour Boat
C3 Underway emissions: Main Engine
2280313323
Residual Tug
C3 Underway emissions: Main Engine
2280314323
Residual Refrigerated
C3 Underway emissions: Main Engine
2280315323
Residual Cruise
C3 Underway emissions: Main Engine
2280316323
Residual Passenger Other
C3 Underway emissions: Main Engine
2280301324
Residual Barge
C3 Underway emissions: Auxiliary Engine
2280302324
Residual Offshore support
C3 Underway emissions: Auxiliary Engine
2280303324
Residual Bulk Carrier
C3 Underway emissions: Auxiliary Engine
2280304324
Residual Commercial Fishing
C3 Underway emissions: Auxiliary Engine
2280305324
Residual Container Ship
C3 Underway emissions: Auxiliary Engine
2280306324
Residual Ferry
C3 Underway emissions: Auxiliary Engine
2280307324
Residual General Cargo
C3 Underway emissions: Auxiliary Engine
2280308324
Residual Government
C3 Underway emissions: Auxiliary Engine
2280309324
Residual Miscellaneous
C3 Underway emissions: Auxiliary Engine
2280310324
Residual RollOn RollOff
C3 Underway emissions: Auxiliary Engine
2280311324
Residual Tanker
C3 Underway emissions: Auxiliary Engine
2280312324
Residual Tour Boat
C3 Underway emissions: Auxiliary Engine
65
-------
see
Level 3 Description
Level 4 Description
2280313324
Residual Tug
C3 Underway emissions: Auxiliary Engine
2280314324
Residual Refrigerated
C3 Underway emissions: Auxiliary Engine
2280315324
Residual Cruise
C3 Underway emissions: Auxiliary Engine
2280316324
Residual Passenger Other
C3 Underway emissions: Auxiliary Engine
The EPA received Automated Identification System (AIS) data from United States Coast Guard (USCG) to
quantify all ship activity which occurred between January 1 and December 31, 2022. 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.8 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 data incorporated
into this inventory reflect ship operations within 200 nautical miles of the official U.S. baseline and
beyond. Activity data within the border of the U.S Exclusive Economic Zone and the North American ECA
are included as well as some activity data outside of the ECA.
The 2022 CMV emissions modeling platform data were computed based on the AIS data from the USGS
for the year of 2022. This process is described in more detail in the Category 3 Commercial Marine
Vessel 2022 Emissions Inventory (EPA, 2024a). During the acquisition of the 2022 AIS data from the U.S.
Coast Guard, EPA was made aware of a data quality issue that started in late March and continued
through late June of 2022. To address this, emissions were substituted in from the 2021 CMV C3
inventory for this period. 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. An additional enhancement for the 2022 C3 CMV inventory was the development and
application of a mask that was applied to remove any emissions over land due to stray AIS signals and
interpolated values.
8 International Maritime Organization (IMO) Resolution MSC.99(73) adopted December 12th. 2000 and entered into force
July 1st, 2002; as amended by SOLAS Resolution CONF.5/32 adopted December 13th, 2002.
66
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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 2-2.
g
Emissionsinterval = Time (hr)interval x Power(kW) x EFtj^) x LLAF Equation 2-2
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,9 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 this annual file was
generated for 2022.
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.
The resulting point emissions centered on each grid cell were converted to an annual point 2010 flat file
format (FF10). 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.
2.4.3 Railway Locomotives (rail)
The rail sector includes all locomotives in the NEI nonpoint data category including line haul locomotives
on Class 1, 2, and 3 railroads along with emissions from commuter rail lines and Amtrak. The rail sector
excludes railway maintenance locomotives and point source yard locomotives. Railway maintenance
emissions are included in the nonroad sector. The point source yard locomotives are included in the
ptnonipm sector. The development of the 2022 rail inventory is summarized here but is described in
more detail in the 2022 National Emissions Inventory Locomotive Methodology documentation (ERG,
2024c).
International Maritime Organization (IMO) Resolution MSC.99(73).
67
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The rail sector emissions for the 2022 emissions modeling platform are based on the 2020 NEI.
Projection factors were applied based on fuel use data for Class I locomotives and rail yards. For Class
ll/lll locomotives, activity data for the years 2012, 2017, 2020, and 2022 from the U.S. Energy
Information Administration's Annual Energy Outlook was examined. Based on these data, the fuel data
used in 2020 was increased across the rail system by 11.6% for the 2022 effort. The 2020 NEI is based on
methods developed during the development of the 2017 NEI rail inventory by the Lake Michigan Air
Directors Consortium (LADCO) and the State of Illinois with support from various other states. Class I
railroad emissions are based on 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
preliminary 2023 national emission tier fleet mix information for Class I railroads. Class II and III railroad
emissions are based on a comprehensive nationwide GIS database of locations where short line and
regional railroads operate. Passenger rail (Amtrak) emissions follow a similar procedure as Class II and III,
except using a database of Amtrak rail lines. Yard locomotive emissions are based on a combination of
yard data provided by individual rail companies, and by using Google Earth and other tools to identify
rail yard locations for rail companies which did not provide yard data. Information on specific yards
were combined with fuel use data and emission factors to create an emissions inventory for rail yards.
Pollutant-specific factors were applied on top of the activity-based changes for the Class I rail. The
inventory SCCs are shown in Table 2-20. More detailed information on the development of the 2022
emission modeling platform rail inventory is available in the 2020 NEI TSD and in the Rail 2020 National
Emissions Inventory Supplementary Document on the 2020 NEI supporting data FTP site.
Table 2-20. SCCs for the Rail Sector
see
Sector
Description: Mobile Sources prefix for all
2285002006
Rail
Railroad Equipment
Diesel; Line Haul Locomotives: Class 1 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
For the 2020 inventory, the Class I railroads granted EPA permission to use the confidential link-level line
haul activity geographic information system (GIS) data layer maintained and updated annually by the
Federal Railroad Administration (FRA). At the time of inventory development, 2019 million gross ton
(MGT) data was the most recent and complete data available. A map of the Class I railroad lines is shown
in Figure 2-5. The dataset contains three columns indicating railroad ownership and nine columns
indicating trackage rights for each rail segment. While most rail links have a single owner, some links
have up to six different Class 1 railroad companies operating on it. To prepare the FRA data for use in
the Class I line haul calculations, all segments associated with a railroad company were extracted to
identify the full network for each company. This involved iterating through each of those twelve columns
to identify all segments within each railroad company's network. This process was conducted seven
68
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times, one for each Class i railroad company. This resulted in a complete inventory of rail links trafficked
by each Class I railroads with a record for each link/railroad company combination.
Figure 2-5. 2019 Class I Railroad Line Haul Activity
EPA collected 2020 and 2022 Class I line haul fuel use data from the most recent R-l submittals from the
Surface Transportation Board.10 Consistent with previous inventory efforts, EPA summed line haul and
work train fuel usage, Table 2-21. Projection factors were developed based on the increased fuel use in
2022 and applied to the 2020 emissions.
Table 2-21. 2020 and 2022 R-l Reported Locomotive Fuel Use for Class I Railroads
Class 1 Railroad
2020 Line Haul Fuel Use (gal)*
2022 Line Haul Fuel Use (gal)*
BNSF
1,137,598,007
1,175,184,806
Canadian National (CN)
96,337,392
107,012,486
Canadian Pacific (CPRS)
57,664,407
64,138,533
CSX Transportation (CSXT)
327,917,859
356,002,171
Kansas City Southern (KCS)
55,763,748
64,185,774
Norfolk Southern (NS)
342,470,779
354,139,306
10 Surface Transportation Board. Available at https://www.stb.gov/reports-data/economic-data/annual-report-financial-data/
Retrieved 22 June 2021.
69
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Class 1 Railroad
2020 Line Haul Fuel Use (gal)*
2022 Line Haul Fuel Use (gal)*
Union Pacific (UP)
773,476,896
839,457,293
* Includes work train fuel usage
The Association of American Railroads (AAR) provided national Class I locomotive tier fleet mix
information that reflects engine turnover in the nation. Given the impact of the pandemic in 2020, AAR
provided a fleet mix that reflected active locomotives and excluded those that were held in storage. A
locomotive's Tier level determines its allowable emission rates based on the year when it was built
and/or re-manufactured. More accurate emission factors for each pollutant were calculated based on
the percentage of the operating Class I line haul locomotives for each USEPA Tier-level category.
Class II and III Methodology
There are approximately 630 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). Data on Class II and III
locomotive operations is publicly available from Bureau of Transportation Statistics' National
Transportation Atlas Database (NTAD), along with related data including reporting mark, railroad name,
route miles owned or operated, and total route miles of links.
Class II and III railroads are widely dispersed across the country (see Figure 2-6), often utilizing older,
higher emitting locomotives than their Class I counterparts. AAR provided a national line-haul tier fleet
mix profile for 2020 which reflects the trend toward older engines in this sector as shown in Table 2-22.
These data continue to be used for the 2022 platform. The national fleet mix data was then used to
calculate weighted average in-use emissions factors for the locomotives operated by the Class II and III
railroads. Note that to be consistent with the 2020 inventory, the unweighted emission factors were the
same as the Class I line haul due to the conservative use of the EPA's large locomotive conversion factor
of 20.8 bhp-hr/gal. Emission factors for PM2.5, S02, NH3, VOC, and GHGs were calculated in the same
manner as those used for Class I line-haul inventory described above.
Table 2-22. 2020 Class ll/lll Line Haul Fleet by Tier Level
Tier
2020 Class ll/lll Locomotive Count
Percent of Total Fleet
0
1,664
48%
1
31
1%
2
169
5%
3
160
5%
4
64
2%
Not
Classified
1,359
39%
Total
3,447
100%
70
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Figure 2-6. Class li and ill Railroads in the United States
Sarr* Fodtial SstawS Admn«ir»l«>n Juw2S18
For the 2022 inventory, EPA considered activity data for the years 2012, 2017, 2020, and 2022 from the
U.S. Energy Information Administration's Annual Energy Outlook, shown in Table 2-23 below.11 Based on
these data, the fuel data used in 2020 was increased across the rail system by 11.6% for the 2022 effort.
Table 2-23. Rail Freight Values by year (quadrillion BTU)
2012
2017
2020
2022
0.43
0.52
0.44
0.48
Commuter Rail Methodology
11 USEIA, Annual Energy Outlook 2021. Accessed 3 Apr 2024. Available at
https://www.eia.gov/outlooks/aeo/data/browser/#/?id=7-AE02021&cases=ref2021&sourcekev=0
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Commuter rail emissions were calculated in the same way as the Class II and HI railroads. The primary
difference is that the fuel use estimates for 2020 and 2022 were based on data collected by the Federal
Transit Administration (FTA) for the National Transit Database and projection factors calculated. These
fuel use estimates were replaced with reported fuel use statistics for MBTA (Massachusetts) and Metra
(Illinois). 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.
Intercity Passenger Methodology (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 2020 reported fuel use across all of it diesel-powered route-miles
shown in Figure 2-7.
Figure 2-7. Amtrak National Rail Network
For 2022 platform, the 2020 fuel use and emissions were adjusted down based on the fuel use reported
in Amtrak's FY22 AMTRAK Sustainability Report as shown in Figure 2-8. The adjustment was applied
uniformly, so the spatial representation of the emissions did not change.
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Figure 2-8 Amtrak Diesel Fuel Use 2020-2022
DIESEL FUEL USE
FY22 GojI
Progmt throughout FY22
ACHIEVED
- 59.3 million gallons
(Milltont
of gjllo«)
Upon receipt of state-provided comments, two adjustments were made to Amtrak emissions. First,
Delaware verified that all Amtrak passenger service in/through the state utilize electric locomotives only,
so fuel usage and emissions for Delaware SCC 2285002008 were removed. Second, Connecticut
confirmed that AMTRAK trains operating on electrified lines do not have diesel emissions. The state
provided emissions estimates which were used to replace the previously calculated emissions.
Other Data Sources
The 2020 NEI locomotives sector includes data from SLT agency-provided emissions data, and an EPA
dataset of locomotive emissions. The following agencies also submitted emissions to locomotive SCCs:
Alaska Department of Environmental Conservation; California; Connecticut; District of Columbia;
Maricopa County, AZ; Minnesota; North Carolina; Texas; Virginia; Washington; and Washoe County, NV.
2.4.4 Nonroad Mobile Equipment (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 for 2022 were computed by running MOVES4 which
incorporates the NONROAD model. MOVES3 incorporated updated nonroad engine population growth
rates, nonroad Tier 4 engine emission rates, and sulfur levels of nonroad diesel fuels. The only change
made for nonroad from MOVES3 to MOVES4 was a change to fuel properties. Additionally, MOVES4 was
run using 2022 meteorological data. MOVES provides a complete set of HAPs and incorporates updated
nonroad emission factors for HAPs. MOVES4 was used for all states other than California, which uses
their own model. California nonroad emissions were provided by the California Air Resources Board
(CARB) for the 2020 NEI, as well as 2023. For the 2022 emissions modeling platform CARB nonroad
emissions were interpolated between 2020 and 2023. CARB emissions were used in California for all
pollutants except PAHs and C02, which were taken from MOVES.
MOVES creates a monthly emissions inventory for criteria air pollutants (CAPs) and a full set of HAPs,
plus additional pollutants such as NONHAPTOG and ETHANOL, which are not included in the NEI but are
73
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used for speciation. MOVES 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. For California, NHTOG####-VOC and HAP-VOC ratios from MOVES-based emissions were applied
to VOC emissions so that VOC emissions can be speciated consistently with other states.
MOVES 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 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, PM25_####-PM25TOTAL ratios were calculated and applied to PM2.5 emissions in California so
that PM2.5 emissions in California can be speciated consistently with other states.
MOVES4 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 FFlOs 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 2016vl platform
nonroad specification sheet (NEIC, 2019).
• To reduce the size of the inventory, HAPs 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 CAP emissions totaling 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 VOCJNV 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.
• Emissions for airport ground support vehicles (SCCs ending in -8005), and oil field equipment
(SCCs ending in -10010), were removed from the inventory at this stage, to prevent a double
count with the airports and np_oilgas sectors, respectively.
• California emissions from MOVES were deleted and replaced with the CARB-supplied emissions.
National Updates: Agricultural and Construction Equipment Allocation
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The modified MOVES default database for that included the refinements made to construction and
agricultural sectors in the 2016 platform process (movesdb20220105_nrupdates) and state-submitted
inputs in CDBs from the most recent NEI were used to run MOVES-Nonroad to produce emissions for all
states other than California. California-submitted emissions were used. 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:
https://gaftp.epa.gov/air/emismod/2016/vl/reports/nonroad/).
Emissions Inside California
California nonroad emissions were provided by CARB for the 2020 NEI and 2023. The 2022 emissions
were interpolated between 2020 and 2023 where pollutants were available in both data sets. 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. VOC HAPs from California were incorporated into
speciation similarly to VOC HAPs from MOVES elsewhere, e.g., model species BENZ is equal to HAP
emissions for benzene as submitted by CARB. VOC and PM2.5 emissions were allocated to speciation
profiles. Ratios of VOC (PM2.5) by speciation profile to total VOC (PM2.5) were calculated by county and
SCC from the MOVES run in California, and then applied CARB-provided VOC (PM2.5) in the inventory so
that California nonroad emissions could be speciated consistently with the rest of the country.
State Submitted Data
The CDBs used to run MOVES-Nonroad to produce emissions for all states other than California were
consistent with those used to develop the 2020 NEI. The following states submitted CDBs for the 2020
NEI: Arizona - Maricopa Co.; Connecticut; Georgia; Illinois; Indiana; Michigan; Minnesota; Ohio; Oregon;
Texas; Utah; Washington; and Wisconsin.
Following the completion of the MOVES runs, railway maintenance emissions were removed from
specific counties / census areas in Alaska because Alaska DEC specified that this type of activity does not
happen in those areas. Specifically, emissions from SCCs 2285002015, 2285004015, and 2285006015
were removed from the following counties / census areas: 02013, 02016, 02050, 02060, 02063, 02066,
02070, 02100, 02105, 02110, 02130, 02150, 02158, 02164, 02180, 02185, 02188, 02195, 02198, 02220,
02240, 02275, and 02282. Alaska DEC also specified some counties / census areas in which logging and
agricultural emissions do not happen, but the emissions for the specified SCCs were already zero in the
specified areas.
For more information on the development of the nonroad emissions inputs for the 2020 NEI see Section
4 of the 2020 NEI TSD.
2.5 Fires (ptfire-rx, ptfire-wild, ptagfire)
Multiple types of fires are represented in the modeling platform. These include wild and prescribed fires
that are grouped into the ptfire-wild and ptfire-rx sectors, respectively, 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.
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2.5.1 Wild and Prescribed Fires (ptfire-rx, ptfire-wild)
Wildfire and prescribed burning emissions are contained in the ptfire-wild and ptfire-rx sectors, respectively. The
ptfire sector has emissions provided at geographic coordinates (point locations) and has daily emissions values.
The ptfire-rx sector excludes agricultural burning and other open burning sources that are included in the ptagfire
and nonpt sectors. The ptfire-rx sector includes a new methodology for calculating pile burn emissions with this
year 2022 emissions modeling platform. Emissions are day-specific and include satellite-derived
latitude/longitude of the fire's origin and other parameters associated with the emissions such as acres burned
and fuel load, which allow estimation of plume rise. The SCCs used for the ptfire-rx and ptfire-wild sources
are shown in Table 2-24. The ptfire-rx and ptfire-wild inventories include separate SCCs for the flaming
and smoldering combustion phases for wildfire and prescribed burns. Note that prescribed grassland
fires or Flint Hills, Kansas has their own SCC (2801500171) in the inventory. Any wild grassland fires
were assigned the standard wildfire SCCs shown in Table 2-24. A new source was added to wildland fires
for the 2022 platform. This new source was Pile Burns with a SCC = 2810005001. Pile burns has been a
burn method used for prescribed burns for many years, but no methodology for estimating the
emissions from these burns had been used in previous NEIs or Emissions Modeling Platforms.
Table 2-24. SCCs included in the ptfire sector for the 2022 platform
SCC
Description
2801500171
Agricultural Field Burning - whole field set on fire; Fallow
2810001001
Forest Wildfires; Smoldering; Residual smoldering only (includes grassland
wildfires)
2810001002
Forest Wildfires; Flaming (includes grassland wildfires)
2810005001
Prescribed burning; pile burns
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming
2811020002
Prescribed Rangeland Burning
Fire Information Data
Inputs to SMARTFIRE2 for 2022 include:
• The National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping System
(HMS) fire location information
• Wildland Fire Interagency Geospatial Services (WFIGS) wildland fire perimeter polygons
• The Incident Status Summary, also known as the "ICS-209", used for reporting specific
information on fire incidents of significance
• Hazardous fuel treatment reduction polygons for prescribed burns from the Forest Service
Activity Tracking System (FACTS)
• Fire activity on federal lands from the United States Department of Interior agencies
The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and
Atmospheric Administration's (NOAA) National Environmental Satellite and Data Information
76
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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 2022 inventory consisted of daily comma-delimited files containing fire
detect information including latitude-longitude, satellite used, time detected, and other information.
These detects were processed through a modified, python-based, Satellite Mapping Automated
Reanalysis Tool for Fire Incident Reconciliation version 2 SmartFire2/BlueSky Pipeline (SF2/BSP).
Wildland Fire Interagency Geospatial Services (WFIGS) 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 are based upon input data 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 2022 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 US Forest Service (USFS) compiles a variety of fire information every year. Year 2022 data from the
USFS Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and used
for 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 Department of Interior (DOI) also compiles wildfire and prescribed burn activity on their federal
lands every year. Year 2022 DOI data were acquired from National Fire Plan Operations and Reporting
System (NFPORS) and through direct communication with DOI staff and were used for 2022 platform
development. The DOI fire information provided fire type, acres burned, latitude-longitude, and start
and ending times.
About 30 different states provided fire activity that was used in developing the wildland fire inventory.
Table 2-25 below gives a listing of the type of fire activity data provided by each state that participated.
Table 2-25. Types of State-provided Fire Activity Data
SLT
Wildfire
Prescribed
burns
RX includes pile
burns
Ag burns
Arizona
No
Yes
Yes
No
Arkansas
Yes
Yes
Yes
Yes
California
Yes
Yes
Yes
No
Colorado
No
Yes
Yes
No
Connecticut
Yes
Yes
No
No
77
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SLT
Wildfire
Prescribed
burns
RX includes pile
burns
Ag burns
Delaware
No
Yes
No
Few
Florida
Yes
Yes
Yes
Yes
Georgia
Yes
Yes
No
Yes
Idaho
No
No
No
Yes
Iowa
Yes
Yes
Yes
No
Kansas
No
Yes
No
No
Maine
Yes
Few
No
No
Maryland
Yes
Yes
Yes
No
Minnesota
No
Yes
No
No
Missouri
No
Yes
No
Yes
Montana
No
Yes
Yes
No
Nevada
No
Yes
Yes
No
New Jersey
Yes
Yes
No
No
New Mexico
Yes
Yes
No
No
Nez Perce Tribe
No
Yes
Yes
Yes
North Carolina
Yes
Yes
No
No
North Dakota
No
Yes
No
No
Oklahoma
No
Yes
No
No
Oregon
Yes
Yes
Yes
No
Pennsylvania
Yes
Yes
No
No
South Carolina
Yes
Yes
Yes
Yes
Texas
Yes
Yes
No
No
Utah
No
Yes
Yes
No
Virginia
Yes
Yes
No
No
Washington
No
Yes
Yes
Yes
Wyoming
Yes
Yes
Yes
No
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 2022 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 inventory the residual
smoldering emissions were allocated to the smoldering SCCs listed in Table 2-24. The lofted smoldering
emissions were assigned to the flaming emissions SCCs in Table 2-24.
Figure 2-9 is a schematic of the data processing stream for the inventory of wildfire and prescribed burn
sources. The ptfire-rx and ptfire-wild inventory sources were estimated using Satellite Mapping
Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Pipeline.
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SMARTFIRE2 is an algorithm and database system that is 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, ali 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 2022 platform, 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 2-9 was used to make fire
type assignment by state and by month in conjunction with the default fire type assignments shown in
Figure 2-10.
Figure 2-9. Processing flow for fire emission estimates in the 2022 inventory
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Figure 2-10. Default fire type assignment by state and month where data are only from satellites
2022vl platform HMS default wildfire type months
Apr-Jul
May - Sep
May - Oct
Jun - Aug
Jun- Sep
Jun - Oct
The second system used to estimate emissions is the BlueSky Modeling Pipeline (BSP). The framework
supports the calculation of 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 2-11. The Smoke Emissions Reference
Application (SERA) in the BSP generates all the CAP emission factors for wildland fires used in the 2022
study. SERA factors can vary by phase, fire type, region, fuel type and more pollutants. SERA emission
factors are available here: https://depts.washington.edu/nwfire/sera/index.php. SERA consists of
existing peer-reviewed emission factors (EFs) of 276 known air pollutants. The SERA database enables
the analysis and summaries of existing EFs, and creation of average EFs to be used in decision support
tools for smoke management, including BSP. HAP emission factors were obtained from Urbanski's
(2014) work and applied by region and by fire type.
80
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Figure 2-11. Blue Sky Modeling Pipeline
The FCCSv4 cross-reference was implemented along with the LANDFIREv2 (at 120 meter resolution) to
provide better fuel bed information for the BlueSky Pipeline (BSP). The LANDFIREv2 was aggregated
from the native resolution and projection to 120 meter using a nearest-neighbor methodology.
Aggregation and reprojection were required for the proper function on BSP.
The Flint Hills grasslands typically have 1 to 2 million acres of prescribed burns each year usually
between late February to early May. Kansas provided county acres burned information for these
prescribed burns for 2022 that cover most of eastern Kansas and 4 additional counties in eastern
Oklahoma. As shown in Figure 2-12. Flint Hills Acreage Burned in 2022below, between February 15-April
30 about 2.1M acres were burned in the Flint Hills. The HMS detects for this time period and for these
counties (about 21000 detects) were used to temporally and spatially allocate these prescribed burns
and the associated estimated emissions. The emissions estimation process is done outside of BSP using
SERA emissions factors except for PM2.5 where a factor of 12.68 g/kg was used based on EPA ORD test
results. The Flint Hills emissions are assigned the SCC 2801500171.
81
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Figure 2-12. Flint Hills Acreage Burned in 2022
Flint Hills Acreage Burned (February 14-April 30, 2022)
•*£ v ¦
- \
a
"'Mb i
?r. i
r - <,
' ¦ £: 3*"$ • >*? *u"
j*.
V ,',W ' £'
f »
nr
Countv
Acres Burned
Butler
163,895
Chase
237,442
Chautauqua
57,901
Coffey
85,902
Cowley
88,095
Elk
109,933
Geary
17,035
Greenwood
315,605
Lyon
180,190
Marion
37,483
Morris
96,126
Osage (KS)
83,894
Pottawatomie
59,106
Riley
53,700
Wabaunsee
182,259
Wilson
33,592
Woodson
69,422
Nowata (OK)
43,507
Osage (OK)
156.297
Washington (OK)
30,842
Kay (OK)
10,533
Total
2,112,759
Vao Tang
Bureau of Air, KDHE
In 2022vl arid in the 2020NEI, HMS detects on or near corn and soybean fields in the Midwest were
assumed to be nearby irrigation ditch or other type of ditch burns. These emissions were also estimated
outside of BSP using the assumption of fuels being similar to grasses. These ditch burns were put into
the prescribed burn sector (ptfire-rx) and assigned a Rangeland burning SCC 2811020002.
The final products from this process were annual and daily FFlO-formatted emissions inventories. These
SMOKE-ready inventory files contain both CAPs and HAPs. The BAFM HAP emissions from the inventory
were used directly in modeling and were not overwritten with VOC speciation profiles (i.e., an "integrate
HAP" use case).
For the 2022 platform, pile burn (PB) emissions were estimated using a combination of federal, state,
local, and tribal activity data. This activity data was supplied in the form of daily estimates of area
treated, pile volume, pile dimensions, or mass piled by location, varying by data source. As with the RX
and WF S/L/T data, the pile burn data was imported into SF2 so that it could be reconciled with other
data sources to avoid duplication of activity and emissions. HMS satellite detects that reconciled only
with the location of the PB activity were removed from the BSP workflow as pile burns. The PB activity
data was then directly imported into a calculator script that estimates the amount of biomass consumed
at each location and the resulting emissions. The consumption calculations made are consistent with
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those used in the University of Washington pile burn calculator
(https://depts.washington.edu/nwfire/piles/). For activity data where only a treated area is provided a
default fuel loading of 4.5 tons per acre is used based on an analysis of California and Washington
historical pile burn permits. A consumption efficiency of 90% is assumed unless otherwise specified in
the activity data. Emissions factors averaged over pile burn studies in the SERA database were applied to
estimate CAPs from the consumed piled biomass.
The 2022 wildfire season was slightly below average with about 4.6M acres burned in the CONUS. The
2022vl EMP includes emissions from the 4.6M acres of wildfires plus an estimated 13.5M acres in
prescribed burns. The prescribed burns include the 829K acres estimated for the Midwest ditch burns.
It is important to note that using the activity data available mentioned early from federal and state
agencies about 8M prescribed burn acres were reconciled with or without HMS detects. The remaining
5.5M prescribed burn acres were estimated using a default acre burn assumption were not reconciled
with any federal or state agency fire activity data. The default acre burn assumption was applied to any
HMS detects that did not reconcile with any federal or state agency activity data.
2.5.2 Point source Agriculture Fires (ptagfire)
In the NEI, agricultural fires are stored as county-annual emissions and are part of the nonpoint data
category. For this 2022 platform, agricultural fires are modeled as day specific fires derived from
satellite data for the year 2022 in a similar way to the emissions in ptfire.
Daily year-specific agricultural burning emissions are derived from HMS fire activity data, which contains
the date and location of remote-sensed anomalies. The activity is filtered using the 2022 USDA cropland
data layer (CDL). Satellite fire detects over agricultural lands are assumed to be agricultural burns and
assigned a crop type. Detects that are not over agricultural lands are output to a separate file for use in
the ptfire sector. Each detect is assigned an average size of between 40 and 120 acres varying by state.
Grassland/pasture fires were moved to the ptfire sectors for this 2022 modeling platform. Depending on
their origin, grassland fires are in both ptfire-rx and ptfire-wild sectors because both fire types do involve
grassy fuels.
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 2-26.
Table 2-26. SCCs included in the ptagfire sector
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
2801500141
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Field Crop is Bean (red): Headfire Burning
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see
Description
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
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
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
2801500264
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; DoubleCrop Winter Wheat and Soybeans
2811020002
Miscellaneous Area Sources; Other Combustion - as Event;
Prescribed Rangeland Burning; Flaming
Another feature of the ptagfire database is that the satellite detections for 2022 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 2022 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 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) have been excluded from these specific midwestern states: Iowa, Kansas, Indiana, Illinois,
Michigan, Missouri, Minnesota, Wisconsin, and Ohio. The reason for these crop types being excluded is
because states have indicated that these crop types are not burned.
Heat flux for plume rise was calculated using the size and assumed fuel loading of each daily agricultural
fire. This information is needed for a plume rise calculation within a chemical transport modeling
system.
The daily agricultural emissions were converted from a tabular format into the SMOKE-ready daily point
flat file format. The daily emissions were also aggregated into annual values by location and converted
into the annual point flat file format.
For this modeling platform, a SMOKE update allows the use of HAP integration for speciation for PTDAY
inventories. The 2022 agricultural fire inventories include emissions for HAPs, so HAP integration was
used for this study.
2.6 Biogenic Sources (beis)
Biogenic emissions were computed based on the 2022 meteorology data used for the 2022 platform and
were developed using the Biogenic Emission Inventory System version 4 (BEIS4) within CMAQ. BEIS4
creates gridded, hourly, model-species emissions from vegetation and soils. It estimates CO, VOC (most
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notably isoprene, terpene, and sesquiterpene), and NO emissions for the contiguous U.S. and for
portions of Mexico and Canada. In the BEIS4 two-layer canopy model, the layer structure varies with
light intensity and solar zenith angle (Pouliot and Bash, 2015). Both layers 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., 2015). BEIS4 computes the seasonality of emissions using the 1-meter
soil temperature (SOIT2) instead of the BIOSEASON file, and canopy temperature and radiation
environments are now modeled using the driving meteorological model's (WRF) representation of leaf-
area index (LAI) rather than the estimated LAI values from BELD data alone. See these CMAQ Release
Notes for technical information on BEIS4: https://github.com/USEPA/CMAQ/wiki/CMAQ-Release-
Notes:-Emissions-Updates:-BEIS-Biogenic-Emissions. The variables output from the Meteorology-
Chemistry Interface Processor (MCIP) that are used to convert WRF outputs to CMAQ inputs are shown
in Table 2-27.
Table 2-27. Meteorological variables required by BEIS4
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2 m
RC
convective precipitation per met TSTEP
RGRND
solar rad reaching surface
RN
nonconvective precipitation per met TSTEP
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
WSAT_PX
soil saturation from (Pleim-Xiu Land Surface Model) PX-LSM
The Biogenic Emissions Landcover Database version 6 (BELD6) was used as the input gridded land use
information in generating the biogenic emissions estimates. There are now two different BELD6 datasets
that are input into BEIS4. The gridded landuse and the other is the gridded dry leaf biomass (grams/m2)
values for various vegetation types. The BELD6 includes the following datasets:
• High resolution tree species and biomass data from Wilson et al. 2013a, and Wilson et al.
2013b for which species names were changed from non-specific common names to scientific
names
• Tree species biogenic volatile organic carbon (BVOC) emission factors for tree species were
taken from the NCAR Enclosure database (Wiedinmyer, 2001)
o https://www.sciencedirect.com/science/article/pii/S135223100100429Q
• Agricultural land use from US Department of Agriculture (USDA) crop data layer
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• Global Moderate Resolution Imaging Spectroradiometer (MODIS) 20 category data with
enhanced lakes and Fraction of Photosynthetically Active Radiation (FPAR) for vegetation
coverage from National Center for Atmospheric Research (NCAR)
• Canadian BELD land use, updates to Version 4 of the Biogenic Emissions Landuse Database
(BELD4) for Canada and Impacts on Biogenic VOC Emissions
(https://www.epa.gov/sites/default/files/2019-08/documents/800am zhang 2 O.pdf).
Bug fixes included in BEIS4 included the following:
• Solar radiation attenuation in the shaded portion of the canopy was using the direct beam
photosynthetically active radiation (PAR) when the diffuse beam PAR attenuation coefficient
should have been used.
o This update had little impact on the total emissions but did result in slightly higher
emissions in the morning and evening transition periods for isoprene, methanol and
Methylbutenol (MBO).
• The fraction of solar radiation in the sunlit and shaded canopy layers, SOLSUN and SOLSHADE
respectively were estimated using a planar surface. These should have been estimated based on
the PAR intercepted by a hemispheric surface rather than a plane.
o This update can result in an earlier peak in leaf temperature, approximately up to an
hour.
• The quantum yield for isoprene emissions (ALPHA) was updated to the mean value in Niinemets
et al. 2010a and the integration coefficient (CL) was updated to yield 1 when PAR = 1000
following Niinemets et al 2010b.
o This updated resulted in a slight reduction in isoprene, methanol, and MBO emissions.
Biogenic emissions computed with BEIS were used to review and prepare summaries but were left out of
the CMAQ-ready merged emissions in favor of inline biogenic emissions produced during the CMAQ
model run itself using the same algorithm described above but with finer time steps within the air
quality model. Figure 2-13 provides an annual estimate of the biogenic VOC emissions in year 2022 from
BEIS4.
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Figure 2-13. Annual biogenic VOC BEIS4 emissions for the 12US1 domain
2022vl beis4 12US1 annual : VOC BEIS
Max: 3304.1482 Min:
>1795
1596
1396
1197
1997 £
o
798
598
399
<199
2.7 Sources Outside of the United States
The emissions from Canada and Mexico are included as part of the emissions modeling sectors:
canmex_point; canmex_area, canada_afdust, canada_ptdust, canada_onroad, mexico_onroad,
canmex_ag, and canada_og2D. The canmex_ag sector is processed as a separate sector for reporting
and tracking purposes, and unlike in other recent emissions platforms, the Canada ag sources are area
sources in this platform rather than pre-gridded point sources. As in prior platforms, Fugitive dust
emissions in Canada are represented as both area sources (canada_afdust sector, formerly "othafdust")
and point sources (canada_ptdust sector, formerly "othptdust"). Due to the large number of individual
points, low-level oil and gas emissions in Canada are processed separately from the canmex_point sector
to reduce the number of individual points to track within CMAQ, and also to reduce the size of the
model-ready emissions files.
Canadian emissions in these sectors were generally taken from 2020 and 2023 inventories provided by
Environment and Climate Change Canada (ECCC), interpolated to 2022. ECCC provided the following
inventories, the sectors in which they were incorporated are listed and the inventories are described in
more detail below:
Agricultural livestock and fertilizer, area source format (canmex_ag sector)
Surface-level oil and gas emissions in Canada (canada_og2D sector)
Agricultural fugitive dust, point source format (canada_ptdust sector)
Other area source dust (canada_afdust sector)
Onroad (canada_onroad sector)
Nonroad and rail (canmex_area sector)
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Airports (canmex_point sector)
Other area sources (canmex_area sector)
Other point sources (canmex_point sector)
The 2022 CMV data included coastal waters of Canada and Mexico with emissions derived from AIS data.
These emissions were used for all areas of Canada and Mexico and are included in the cmv_clc2 and
cmv_c3 sectors. Both the C1C2 and C3 emissions were developed in a point source format with point
locations at the center of the 12km grid cells.
Other than the CB6 species present in the speciated point source data, there are no explicit HAP
emissions in these Canadian inventories. In addition to emissions inventories, the ECCC 2020 dataset
also included shapefiles for creating spatial surrogates. These surrogates were used for this study.
While emissions in the 2020 platform were adjusted at the monthly level to reflect COVID pandemic
effects, no such adjustments were made for 2022 modeling.
2.7.1 Point Sources in Canada and Mexico (canmex_point)
Canadian point source inventories provided by ECCC include emissions for airports and other point
sources. The Canadian industrial point source inventory is pre-speciated for the CB6 chemical
mechanism. All Canada point source emissions were interpolated from 2020 and 2023 inventories to
2022 except for the point EGU inventory, for which the 2023 inventory was used directly. This is because
for point EGUs, the ECCC inventories contain different facilities in different years, making an
interpolation difficult.
Point sources in Mexico were compiled in two parts. New emissions inventories representing 2018
developed through a collaboration between EPA and SEMARNAT were used for the six Mexico border
states: Baja California, Sonora, Chihuahua, Coahuila, Nuevo Leon, and Tamaulipas. Mexico inventories
for other states were based on inventories projected from the Inventario Nacional de Emisiones de
Mexico, 2016 (Secretarfa de Medio Ambiente y Recursos Naturales (SEMARNAT)), projected to 2019 as
part of the 2019 emissions modeling platform. For the emissions carried forward from the 2019
platform, the point source emissions were converted to English units and into the FF10 format that
could be read by SMOKE, missing stack parameters were gapfilled using SCC-based defaults, latitude and
longitude coordinates were verified and adjusted if they were not consistent with the reported
municipality. Only CAPs are covered in the Mexico point source inventory.
2.7.2 Fugitive Dust Sources in Canada (canada_afdust, canada_ptdust)
Fugitive dust sources of particulate matter emissions excluding land tilling from agricultural activities,
were provided by Environment and Climate Change Canada (ECCC) for 2020 and 2023, and were
interpolated to 2022 for this study. Dust emissions resulting from land tilling due to agricultural activities
and livestock were provided as part of the ECCC area source dust inventory. The provided wind erosion
emissions were removed. The ECCC point source dust inventory includes emissions from road dust. 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.
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2.7.3 Agricultural Sources in Canada and Mexico (canmex_ag)
Agricultural emissions from Canada and Mexico, excluding fugitive dust, are included in the canmex_ag
sector. Canadian agricultural emissions were provided by Environment and Climate Change Canada
(ECCC) as part of their 2020 and 2023 emission inventories (interpolated to 2022). Unlike in recent
platforms, Canadian agricultural were not represented as point sources, instead they were represented
as area sources and gridded using spatial surrogates. In Mexico, agricultural sources were based on new
emissions inventories representing 2018 for the six Mexico border states (Baja California, Sonora,
Chihuahua, Coahuila, Nuevo Leon, and Tamaulipas), and emissions from the 2019 emissions platform
(SEMARNAT-provided 2016, projected to 2019) were carried forward for all other states.
2.7.4 Surface-level Oil and Gas Sources in Canada (canada_og2D)
Canadian point source inventories provided by ECCC included oil and gas emissions, and were
interpolated from 2020 and 2023 to 2022. A very large number of these oil and gas point sources are
surface level emissions, appropriate to be modeled in layer 1. Reducing the size of the canmex_point
sector improves air quality model run time because plume rise calculations are needed for fewer
sources, so these surface level oil and gas sources were placed into the canada_og2D sector for layer 1
modeling.
2.7.5 Nonpoint and Nonroad Sources in Canada and Mexico (canmex_area)
ECCC provided year 2020 and 2023 at the Canada province, and in some cases sub-province, resolution
emissions from for nonpoint and nonroad sources (canmex_area). 2022 was interpolated from the 2020
and 2023 emissions. The nonroad sources were monthly while the nonpoint and rail emissions were
annual.
In Mexico, nonroad and nonpoint sources were based on new emissions inventories representing 2018
for the six Mexico border states (Baja California, Sonora, Chihuahua, Coahuila, Nuevo Leon, and
Tamaulipas), and emissions from the 2019 emissions platform (SEMARNAT-provided 2016, projected to
2019) were carried forward for all other states.
2.7.6 Onroad Sources in Canada and Mexico (canada_onroad, mexico_onroad)
The onroad emissions for Canada and Mexico are in the canada_onroad and mexico_onroad sectors,
respectively. ECCC provided year 2020 and 2023 at the Canada province, and in some cases sub-province
resolution. 2022 was interpolated from the 2020 and 2023 emissions.
For Mexico onroad emissions, a version of the MOVES model for Mexico was run for 2020 and 2023.
2022 was then interpolated. This provided the same VOC HAPs and speciated VOCs as for the U.S.
MOVES model (ERG, 2016a). This includes NBAFM plus several other VOC HAPs such as toluene, xylene,
ethylbenzene and others. Except for VOC HAPs that are part of the speciation, no other HAPs are
included in the Mexico onroad inventory (such as particulate HAPs nor diesel particulate matter).
2.7.7 Fires in Canada and Mexico (ptfire_othna)
Annual 2022 wildland fire emissions for Mexico, Canada, Central America, and Caribbean nations are
included in the ptfire_othna sector. Canadian fire activity was developed by processing the Canadian
Wildland Fire Information System's National Burned Area Composite (NBAC) and NOAA's Hazard
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Mapping System (HMS) through SMARTFIRE 2.12 Emissions were estimated from the wildland fire
activity using BlueSky pipeline with Canadian Fire Behavior Prediction (FBP) fuel beds mapped to Fuel
Characteristic Classification System (FCCS) fuel beds. Fires in Mexico, Central America, and the
Caribbean, were developed from the Fire Inventory from NCAR (FINN) v2.5 daily fire emissions for 2022
(Wiedenmyer, 2023). 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 wildfires rather than prescribed. FINN fire detects of 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.
2.7.8 Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb)
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.
For mercury, the volcanic mercury emissions that were used in the recent modeling platforms were not
included in this 2022vl platform because no HAP+CAP modeling was performed. The emissions were
originally developed for a 2002 multipollutant modeling platform with coordination and data from
Christian Seigneur and Jerry Lin for 2001 (Seigneur et. al, 2004 and Seigneur et. al, 2001). The volcanic
emissions from the most recent eruption were not included in the because they have diminished by the
year 2019. Thus, no volcanic emissions were included.
Because of mercury bidirectional flux within the latest version of CMAQ, no natural mercury emissions
are included in the emissions merge step for HAP+CAP platforms.
12 See https://www.cmascenter.ora/conference/2023/slides/2023-10-18-1350-2021 -Canada-WF-Updates-CMAS.pptx.
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3 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 and vertical
resolution required by the air quality model. Emissions modeling includes temporal allocation, spatial
allocation, and pollutant speciation. Emissions modeling sometimes includes the vertical allocation (i.e.,
plume rise) 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.
3.1 Emissions Modeling Overview
SMOKE version 5.1 was used to process the raw emissions inventories into emissions inputs for each
modeling sector into a format compatible with CMAQ. SMOKE executables and source code are
available from the Community Multiscale Analysis System (CMAS) Center at
http://www.cmascenter.org. Additional information about SMOKE is available from http://www.smoke-
model.org. 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
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inventory; instead, activity data and emission factors are used in combination with meteorological data
to compute hourly emissions.
Table 3-1. Key emissions modeling steps by sector.
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust_adj
Surrogates
Yes
Annual
airports
Point
Yes
Annual
None
beis
Pre-gridded
land use
in BEIS4
computed
hourly in CMAQ
fertilizer
EPIC
No
computed
hourly in CMAQ
livestock
Surrogates
Yes
Daily
cmv_clc2
Point
Yes
hourly
in-line
cmv_c3
Point
Yes
hourly
in-line
nonpt
Surrogates &
area-to-point
Yes
Annual
nonroad
Surrogates
Yes
monthly
np_oilgas
Surrogates
Yes
Annual
onroad
Surrogates
Yes
monthly activity,
computed
hourly
onroad_ca_adj
Surrogates
Yes
monthly activity,
computed
hourly
canada_onroad
Surrogates
Yes
monthly
mexico_onroad
Surrogates
Yes
monthly
canada_afdust
Surrogates
Yes
annual &
monthly
canmex_area
Surrogates
Yes
monthly
canmex_point
Point
Yes
monthly
in-line
canada_ptdust
Point
Yes
annual
None
canada_og2D
Point
Yes
monthly
None
canmex_ag
Surrogates
Yes
annual
ptagfire
Point
Yes
daily
in-line
pt_oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire-rx
Point
Yes
daily
in-line
ptfire-wild
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
np_solvents
Surrogates
Yes
annual
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The "plume rise" column in 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. In
all of the "in-line" sectors, all sources are output by SMOKE into point source files which are subject to
plume rise calculations in the air quality model. In other words, no emissions are output to layer 1
gridded emissions files from those sectors as has been done in past platforms. The air quality model
computes the plume rise using stack parameters, the Briggs algorithm, and the hourly emissions in the
SMOKE output files for each emissions sector. The height of the plume rise determines the model layers
into which the emissions are placed. The plume top and bottom are computed, along with the plumes'
distributions into the vertical layers that the plumes intersect. The pressure difference across each layer
divided by the pressure difference across the entire plume is used as a weighting factor to assign the
emissions to layers. This approach gives plume fractions by layer and source. 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.
Note that SMOKE has the option of grouping sources so that they are treated as a single stack when
computing plume rise. For the modeling cases discussed in this document, 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 latitude and 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 stack grouping.
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 study, the in-line biogenic emissions option was used, and so biogenic emissions from BEIS
were not included in the gridded CMAQ-ready emissions.
For this study, SMOKE was run for the larger 12-km CONtinental United States "CONUS" modeling
domain (12US1) shown in Figure 3-1, but the air quality model was run on the smaller 12-km domain
(12US2). More specifically, SMOKE was run on the 12US1 domain and emissions were extracted from
12US1 data files to create 12US2 emissions. The grids used a Lambert-Conformal projection, with Alpha =
33, Beta = 45 and Gamma = -97, with a center of X = -97 and Y = 40. In addition, SMOKE was run for grids
over Alaska, Hawaii, and Puerto Rico plus the Virgin Islands. Later sections provide details on the spatial
surrogates and area-to-point data used to accomplish spatial allocation with SMOKE. Table 3-2 describes
the grids. WRF, SMOKE, and CMAQ all presume the Earth is a sphere with a radius of 6370000 m.
93
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Table 3-2. Descriptions of the platform grids
Common Name
Grid
Cell
Size
Description
Grid name
Parameters listed in SMOKE grid description
(GRIDDESC) file: projection name, xorig,
yorig, xcell, ycell, ncols, nrows, nthik
Continental
12km grid
12 km
Entire conterminous US
plus some of
Mexico/Canada
12U51_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
Alaska 9km
9 km
Small 9 km Alaska with
parts of Canada
9AK1
LAM_36N_155W', -1107000, -1134000,
9000, 9000, 312, 252, 1
Hawaii 3km
3 km
Small 3 km Hawaii
3HI1
LAM_21N_157W', -391500, -346500,
3000, 3000, 225, 201, 1
Puerto Rico &
Virgin Islands
3km
3 km
Small 3 km covering
Puerto Rico and the
Virgin Islands
3PR1
LAM_18N_66W', -274500, -202500,
3000, 3000, 150, 150, 1
Figure 3-1. Air quality modeling domains
a) 12US1 and 12US2
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3.2 Chemical Speciation
Chemical speciation involves the process of translating emissions from the inventory into the chemical
mechanism-specific "model species" needed by an air quality model. Using the CB6R5_AE7 chemical
mechanism as an example, these model species either represent explicit chemical compounds (e.g.,
acetone, benzene, ethanol) or groups of species (i.e., "lumped species;" e.g., PAR, OLE, KET). Table 3-3
lists the model species generated by SMOKE for this mechanism. Table 3-4 and Table 3-5 list additional
model species that are generated when performing toxics modeling, and Table 3-6 lists the mapping
between individual polycyclic aromatic hydrocarbons (PAHs) to the PAH groups used in toxics modeling.
Table 3-3. Emission model species produced for CB6R5_AE7 for CMAQ
Inventory Pollutant
Model Species
Model species description
Cl2
CL2
Atomic gas-phase chlorine
HCI
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOx
NO
Nitrogen oxide
NOx
N02
Nitrogen dioxide
NOx
HONO
Nitrous acid
S02
S02
Sulfur dioxide
S02
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
nh3
NH3_FERT
Ammonia from fertilizer
VOC
AACD
Acetic acid
VOC
ACET
Acetone
VOC
ALD2
Acetaldehyde
VOC
ALDX
Propionaldehyde and higher aldehydes
VOC
APIN
Alpha pinene
VOC
BENZ
Benzene
VOC
CAT1
Methyl-catechols
VOC
CH4
Methane
VOC
CRES
Cresols
VOC
CRON
Nitro-cresols
VOC
ETH
Ethene
VOC
ETHA
Ethane
VOC
ETHY
Ethyne
VOC
ETOH
Ethanol
VOC
FACD
Formic acid
VOC
FORM
Formaldehyde
VOC
GLY
Glyoxal
VOC
GLYD
Glycolaldehyde
VOC
IOLE
Internal olefin carbon bond (R-C=C-R)
VOC
ISOP
Isoprene
VOC
ISPD
Isoprene Product
VOC
IVOC
Intermediate volatility organic compounds
VOC
KET
Ketone Groups
VOC
MEOH
Methanol
95
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Inventory Pollutant
Model Species
Model species description
voc
MGLY
Methylglyoxal
voc
NAPH
Naphthalene
voc
NVOL
Non-volatile compounds
voc
OLE
Terminal olefin carbon bond (R-C=C)
voc
PACD
Peroxyacetic and higher peroxycarboxylic acids
voc
PAR
Paraffin carbon bond
voc
PRPA
Propane
voc
SESQ
Sesquiterpenes (from biogenics only)
voc
SOAALK
Secondary Organic Aerosol (SOA) tracer
voc
TERP
Terpenes (from biogenics only)
voc
TOL
Toluene and other monoalkyl aromatics
voc
UNR
Unreactive
voc
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
PMio
PMC
Coarse PM > 2.5 microns and 0 10 microns
PM2.5
PEC
Particulate elemental carbon 0 2.5 microns
PM2.5
PN03
Particulate nitrate 0 2.5 microns
PM2.5
POC
Particulate organic carbon (carbon only) 0 2.5 microns
PM2.5
PS04
Particulate Sulfate 0 2.5 microns
PM2.5
PAL
Aluminum
PM2.5
PCA
Calcium
PM2.5
PCL
Chloride
PM2.5
PFE
Iron
PM2.5
PK
Potassium
PM2.5
PH20
Water
PM2.5
PMG
Magnesium
PM2.5
PMN
Manganese
PM2.5
PMOTHR
PM2.5 not in other AE6 species
PM2.5
PNA
Sodium
PM2.5
PNCOM
Non-carbon organic matter
PM2.5
PNH4
Ammonium
PM2.5
PSI
Silica
PM2.5
PTI
Titanium
96
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Table 3-4. Additional HAP gaseous model species generated for toxics modeling
Inventory Pollutant
Model Species
Acetaldehyde
ALD2_PRIMARY
Formaldehyde
FORM_PRIMARY
Acetonitrile
ACETONITRILE
Acrolein
ACROLEIN
Acrylic acid
ACRYLICACID
Acrylonitrile
ACRYLONITRILE
Benzo[a]Pyrene
BENZOAPYRNE
1,3-Butadiene
BUTADIENE13
Carbon tetrachloride
CARBONTET
Carbonyl Sulfide
CARBSULFIDE
Chloroform
CHCL3
Chloroprene
CHLOROPRENE
l,4-Dichlorobenzene(p)
DICHLOROBENZENE
1,3-Dichloropropene
DICHLOROPROPENE
Ethylbenzene
ETHYLBENZ
Ethylene dibromide (Dibromoethane)
BR2_C2_12
Ethylene dichloride (1,2-Dichloroethane)
CL2_C2_12
Ethylene oxide
ETOX
Hexamethylene-l,6-diisocyanate
HEXAMETH_DIIS
Hexane
HEXANE
Hydrazine
HYDRAZINE
Maleic Anyhydride
MAL_ANYHYDRIDE
Methyl Chloride
METHCLORIDE
Methylene chloride (Dichloromethane)
CL2_ME
Specific PAHs assigned w
th URE =0
PAH_000E0
Specific PAHs assigned w
th URE =9.6E-06 (previously 1.76E-5)
PAH_176E5
Specific PAHs assigned w
th URE =4.8E-05 (previously 8.8E-5)
PAH_880E5
Specific PAHs assigned w
th URE =9.6E-05 (previously 1.76E-4)
PAH_176E4
Specific PAHs assigned w
th URE =9.6E-04 (previously 1.76E-3)
PAH_176E3
Specific PAHs assigned w
th URE =9.6E-03 (previously 1.76E-2)
PAH_176E2
Specific PAHs assigned w
th URE =0.01 (previously 1.01E-2)
PAH_101E2
Specific PAHs assigned w
th URE =1.14E-1
PAH_114E1
Specific PAHs assigned w
th URE =9.9E-04 (previously 1.92E-3)
PAH_192E3
Propylene dichloride (1,2-Dichloropropane)
PROPDICHLORIDE
Quinoline
QUINOLINE
Styrene
STYRENE
1,1,2,2-Tetrachloroethane
CL4 ETHANE1122
Tetrachloroethylene (Perchloroethylene)
CL4 ETHE
Toluene
TOLU
2,4-Toluene diisocyanate
TOL DIIS
Trichloroethylene
CL3 ETHE
Triethylamine
TRIETHYLAMINE
m-xylene, o-xylene, p-xylene, xylenes (mixed isomers)
XYLENES
Vinyl chloride
CL_ETHE
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Table 3-5. Additional HAP particulate model species generated for toxics modeling
Inventory Pollutant
Model Species
Arsenic
ARSENIC_C, ARSENIC_F
Beryllium
BERYLLIUM_C, BERYLLIUM_F
Cadmium
CADMIUM_C, CADMIUM_F
Chromium VI, Chromic Acid (VI), Chromium Trioxide
CHROMHEX_C, CHROMHEX_F
Chromium III
CHROMTRI_C, CHROMTRI_F
Lead
LEAD_C, LEAD_F
Manganese
MANGANESE_C, MANGANESE_F
Mercury1
HGIIGAS, HGNRVA, PHGI
Nickel, Nickel Oxide, Nickel Refinery Dust
NICKEL_C, NICKEL_F
Diesel-PMIO, Diesel-PM25
DIESEL_PMC, DIESEL_PMFINE,
DIESEL_PMEC, DIESEL_PMOC,
DIESEL_PMN03, DIESEL_PMS04
Mercury is multi-phase
Table 3-6. PAH/POM pollutant groups
PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(pg/m3)
PAH_000E0
120127
Anthracene
0
PAH_000E0
129000
Pyrene
0
PAH_000E0
85018
Phenanthrene
0
PAH_101E2
56495
3-Methylcholanthrene
0.01
PAH_114E1
57976
7,12-Dimethylbenz[a] Anthracene
0.114
PAH_176E2
189559
Dibenzo[a,i] Pyrene
9.6E-03
PAH_176E2
189640
Dibenzo[a,h]Pyrene
9.6E-03
PAH_176E2
191300
Dibenzo[a,l]Pyrene
9.6E-03
PAH_176E2
7496028
6-Nitrochrysene
9.6E-03
PAH_176E3
192654
Dibenzo[a,e] Pyrene
9.6E-04
PAH_176E3
194592
7H-Dibenzo[c,g]carbazole
9.6E-04
PAH_176E3
3697243
5-Methylchrysene
9.6E-04
PAH_176E3
41637905
Methylchrysene
9.6E-04
PAH_176E3
53703
Dibenzo[a,h] Anthracene
9.6E-04
PAH_176E4
193395
lndeno[l,2,3-c,d]Pyrene
9.6E-05
PAH_176E4
205823
Benzo[j]Fluoranthene
9.6E-05
PAH_176E4
205992
Benzo[b]Fluoranthene
9.6E-05
PAH_176E4
224420
Dibenzo[a,j]Acridine
9.6E-05
PAH_176E4
226368
Dibenz[a,h]acridine
9.6E-05
PAH_176E4
5522430
1-Nitropyrene
9.6E-05
PAH_176E4
56553
Benz[a] Anthracene
9.6E-05
PAH_176E5
207089
Benzo[k]Fluoranthene
9.6E-06
PAH_176E5
218019
Chrysene
9.6E-06
PAH_176E5
86748
Carbazole
9.6E-06
PAH_192E3
8007452
Coal Tar
9.9E-04
PAH_880E5
130498292
PAH, total
4.8E-05
PAH_880E5
191242
Benzo[g,h,i,]Perylene
4.8E-05
PAH_880E5
192972
Benzo[e]Pyrene
4.8E-05
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PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(ng/m3)
PAH_880E5
195197
Benzo(c)phenanthrene
4.8E-05
PAH_880E5
198550
Perylene
4.8E-05
PAH_880E5
203123
Benzo(g,h,i)Fluoranthene
4.8E-05
PAH_880E5
203338
Benzo(a)fluoranthene
4.8E-05
PAH_880E5
206440
Fluoranthene
4.8E-05
PAH_880E5
208968
Acenaphthylene
4.8E-05
PAH_880E5
2381217
1-Methylpyrene
4.8E-05
PAH_880E5
2422799
12-Methylbenz(a)Anthracene
4.8E-05
PAH_880E5
250
PAFI/POM - Unspecified
4.8E-05
PAH_880E5
2531842
2-Methylphenanthrene
4.8E-05
PAH_880E5
26914181
Methylanthracene
4.8E-05
PAH_880E5
284
Extractable Organic Matter (EOM)
4.8E-05
PAH_880E5
56832736
Benzofluoranthenes
4.8E-05
PAH_880E5
65357699
Methylbenzopyrene
4.8E-05
PAH_880E5
779022
9-Methyl Anthracene
4.8E-05
PAH_880E5
832699
1-Methylphenanthrene
4.8E-05
PAH_880E5
83329
Acenaphthene
4.8E-05
PAH_880E5
86737
Fluorene
4.8E-05
PAH_880E5
90120
1-Methylnaphthalene
4.8E-05
PAH_880E5
91576
2-Methylnaphthalene
4.8E-05
PAH_880E5
91587
2-Chloronaphthalene
4.8E-05
PAH_880E5
N590
Polycyclic aromatic compounds
(includes PAH/POM)
4.8E-05
The TOG and PM2.5 profiles used to speciate emissions are part of the SPECIATE v5.2 database
(https://www.epa.gov/air-emissions-modeling/speciate). The SPECIATE database is developed and
maintained by 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). These profiles are processed using the EPA's S2S-Tool
(https://github.com/USEPA/S2S-Tool) to generate the GSPRO and GSCNV files needed by SMOKE. As
with previous platforms, some Canadian point source inventories are provided from Environment
Canada as pre-speciated emissions.
Speciation profiles (GSPRO files) and cross-references (GSREF files) for this platform are available in the
SMOKE input files for the 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. Total emissions for each
model species by state and sector can be found in the state-sector totals workbook.
The following updates to profile assignments were made to this modeling platform and vary from prior
years:
• For PM2.5:
o All GSPRO files were generated by the S2S-Tool, dated 09-11-2023, and utilized SPECIATE
v5.3.
o Update of the CMV speciation cross-reference files to utilize the SCC updates for this
sector and use the new CROC profiles introduced in SPECIATE v5.3.
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o Update onroad and nonroad mobile cross-reference files to utilize the CROC profiles
introduced in SPECIATE v5.3.
• ForVOC:
o All GSPRO and GSCNV files were generated by the S2S-Tool, dated 09-11-2023, and
utilized SPECIATE v5.3.
o All oil and gas well completion and abandoned wells emissions were updated (or added in
the case of abandoned wells) from 1101 and 8949, respectively, to 95404 and 95403,
respectively. However, this update was not performed for basin-specific profiles that
were output by the O&G Tool,
o Update of the CMV speciation cross-reference files to utilize the SCC updates for this
sector and use the new GROC profiles introduced in SPECIATE v5.3.
o Update usage of 95120a to 95120c.
o Update onroad and nonroad mobile cross-reference files to utilize the GROC profiles
introduced in SPECIATE v5.3.
3.2.1 VOC speciation
The base emissions inventory for this modeling platform includes total VOC and individual HAP
emissions. Often, individual HAPs are components of VOC (HAP-VOC), and these HAP-VOCs are included
("integrated") in the speciation process. This HAP integration is performed in a way to ensure double
counting of emitted mass does not occur and requires specific data processing by the S2S-Tool and user
input in SMOKE.
To incorporate HAP emissions from the base inventory into the modeling platform, one of two methods
are performed. (1) Integrate, HAP-use is a method where the mass of integrated HAP-VOCs is summed
and subtracted from VOC, and the residual mass (NONHAPVOC) is speciated using a renormalized
speciation profile that does not include the integrated HAP-VOCs (they are subtracted from the profile
and then the profile is renormalized to 100%). (2) No-Integrate, HAP-use is a method where the mass of
VOC is speciated using a speciation profile that does not include the integrated HAP-VOCs (they are
subtracted from the profile and the profile is not renormalized to 100%). In this scenario, the HAP-VOC
and VOC portions of the inventory are difficult to harmonize, and it is assumed that the proportions of
HAPs from these sources are adequately captured in the speciation profile used to speciate the VOC
emissions (which is why there is no renormalization). In addition, HAPs can be introduced into a
modeling platform using speciation profiles. In this scenario, HAP-VOC emissions are "generated"
through VOC speciation and are not incorporated from the base inventory. This method is called
"Criteria" speciation. An illustration of these methods is shown in Figure 3-2 and the integration
methods used for this platform for each sector are shown in Table 3-7.
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Figure 3-2. Process of integrating HAPs and speciating VOC in a modeling platform
Table 3-7. Integration status for each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B), Acetaldehyde
(A), Formaldehyde (F) and Methanol (M)
afdust
N/A - sector contains no VOC
airports
No integration, use NBAFM in inventory
beis
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
cmv clc2
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
cmv c3
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
fertilizer
N/A - sector contains no VOC
livestock
Full integration (NBAFM)
nonpt
Partial integration (NBAFM)
nonroad
Full integration (internal to MOVES)
np_oilgas
Partial integration (NBAFM)
onroad
Full integration (internal to MOVES)
Canada onroad
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
mexico_onroad
Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation was
older CB6, so post-SMOKE emissions were converted to CB6R3AE6
Canada afdust
N/A - sector contains no VOC
canmex area
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
canmex_point
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
canada_ptdust
N/A - sector contains no VOC
canada_og2D
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
canmex_ag
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
pt_oilgas
No integration, use NBAFM in inventory
ptagfire
Full integration (NBAFM)
ptegu
No integration, use NBAFM in inventory
ptfire-rx
Full integration (NBAFM)
ptfi re-wild
Partial integration (NBAFM)
ptfire_othna
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptnonipm
No integration, use NBAFM in inventory
rail
Full integration (NBAFM)
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Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B), Acetaldehyde
(A), Formaldehyde (F) and Methanol (M)
rwc
Full integration (NBAFM)
np_solvents
Partial integration (NBAFM)
The HAPs integrated from the base inventory into the modeling platform are sector and chemical
mechanism specific. In recent years, CB6R3_AE7 has been the primary chemical mechanism used at the
EPA. Within that mechanism, naphthalene (NAPH), benzene (BENZ), acetaldehyde (ALD2), formaldehyde
(FORM), and methanol (MEOH) are explicit HAP-VOCs, and these compounds are collectively referred to
as NBAFM. Since NBAFM are explicitly modeled in CB6R3_AE7, these species have become the default
collection of integrated HAP species at the EPA. MOVES, the EPA's mobile emissions model, features
additional species that are explicitly modeled (e.g., ethanol). These species (Table 3-8) are also
incorporated directly into modeling platforms. To incorporate these species, additional files from the
S2S-Tool are required. For California, speciation of NONHAPTOG is performed on CARB's VOC
submissions using the county-specific speciation profile assignments generated by MOVES in California.
Table 3-8. Integrated species from MOVES sources
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
Flexane
43
Propionaldehyde
44
Styrene
45
Toluene
46
Xylene
185
Naphthalene gas
Several sectors require VOC speciation to occur at the county-level and consistent speciation profiles
cannot be applied across the nation. To accomplish this, the GSREF is setup to provide profiles that are
"blended" at the county/SCC-level using proportions included in the input file. These variable VOC
speciation methods are year-specific and applied in the oil and gas sector and for various mobile
emissions sources. In both the np_oilgas and pt_oilgas sector, VOC speciation profiles are weighted to
reflect region-specific application of controls, differences in gas composition, and variable sources of
emissions (e.g., varying proportions of emissions from associated gas, condensate tanks, crude oil tanks,
dehydrators, liquids unloading and well completions). The Nonpoint Oil and Gas Emissions Estimation
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Tool generates an intermediate file that provides SCC and county-specific emissions proportions, which
are subsequently incorporated into the modeling platform.
For onroad and nonroad mobile sources, the speciation of total organic gas and particulate matter
emissions has historically been performed within MOVES. However, this is now performed outside of
MOVES as a post-processing step. This has the advantages of making MOVES simpler, faster to run, and
making it easier to change or update chemical mechanisms and speciation profiles used in the emissions
modeling process. Some speciation is still performed inside MOVES (i.e., for "integrated species"). In
many cases, these integrated species have effects like temperature or fuel effects which are not always
well captured by external speciation profiles. For total organic gases, MOVES calculates 15 integrated
species, such as methane and benzene, and the remainder is called NONHAPTOG and speciated outside
MOVES.
In MOVES, speciation profiles are assigned by emission process, fuel subtype, regulatory class, and
model year. Each of these dimensions are available in MOVES output except for fuel subtype, which is
aggregated as part of each fuel type. To apply speciation outside of MOVES and make it compatible with
the needs of SMOKE, we need to determine the speciation profile mapping by SMOKE process
(aggregation of MOVES emission processes) and SMOKE Source Classification Code (SCC), which are
defined by fuel type, source type, and road type. To support use of new ROC-based speciation profiles
for mobile sources, during nonroad inventory post-MOVES processing, speciation profile assignments
were updated to both NONHAPTOG and PM2.5 in a one-to-one manner. As well, to support use of these
new profiles, PM2.5 was split into four parts: PEC and PS04 (based on the new speciation profiles); total
organic matter, or TOM (PNCOM plus PEC); and residual_PM, is RESID_PM (all other PM species). These
profile updates are included in the tables below.
Table 3-9. Mobile Speciation Profile Updates
Pollutant
Old profile
New profile
PM2.5
8992
100CROC
PM2.5
8993
101CROC
PM2.5
8994
103CROC (starts)
102CROC (other)
PM2.5
8995
103CROC
PM2.5
8996
104CROC
PM2.5
95219a
105CROC
PM2.5
95220a
106CROC
NONHAPTOG
1001
107GROC
NONHAPTOG
8757
101GROC (starts)
103GROC (other)
NONHAPTOG
8774
104GROC
NONHAPTOG
8775
105GROC
NONHAPTOG
8855
108GROC (starts)
109GROC (other)
NONHAPTOG
8751a
100GROC (starts)
102GROC (other)
NONHAPTOG
95335a
106GROC
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NONHAPTOG
95335a
106GROC
NONHAPTOG
95327
110GROC
NONHAPTOG
95328
111GROC
NONHAPTOG
95329
112GROC
NONHAPTOG
95330
113GROC
NONHAPTOG
95331
114GROC
NONHAPTOG
95332
115GROC
NONHAPTOG
95333
116GROC
NONHAPTOG
8775
105GROC
NONHAPTOG
1001
107GROC
NONHAPTOG
8860
117GROC
PM2_5
8996
109CROC
PM2_5
91106
108CROC
PM2_5
91113
107CROC
PM2_5
95219
105CROC
For this platform, MOVES runs were performed in inventory mode13 for each representative county and
season (i.e., winter and summer) to compute NONHAPTOG output by emission process, fuel type,
regulatory class, and model year. Emissions were then disaggregated by fuel subtype using the market
share of each fuel blend in each county. Then, emissions were normalized and aggregated to calculate
the percentage of total NONHAPTOG emissions that should be speciated by each profile for each SMOKE
SCC and process. Finally, these percentages were applied in SMOKE-MOVES to all counties based on
their representative county. A MOVES post-processing tool was used to generate the speciation cross-
references (GSREFs) for SMOKE from the outputs of the inventory mode runs.
To generate onroad emissions and to perform the subsequent speciation, SMOKE-MOVES was first run
to estimate emissions and both the MEPROC and INVTABLE files were used to control which pollutants
are processed and eventually integrated. From there, the NONHAPTOG emission factor tables produced
by MOVES were speciated within SMOKE using the GSREF files and the NONHAPTOG GSPRO files
generated by the S2S-Tool. Further details on speciation methods involving MOVES can be found in
Table 3-10 and in the associated technical reports (EPA-420-R-22-017, EPA-420-R-23-006).
Table 3-10. Mobile NOx and HONO fractions
Fuel
Model Years
Process
NO
NOx
HONO
Gasoline
1960-1980
Running Exhaust
0.975
0.017
0.008
Gasoline
1981-1990
Running Exhaust
0.932
0.06
0.008
Gasoline
1991-1995
Running Exhaust
0.954
0.038
0.008
Gasoline
1996-2050
Running Exhaust
0.836
0.156
0.008
Gasoline
1960-1980
Start Exhaust
0.975
0.017
0.008
13 Inventory mode was run rather than rates mode because: 1) MOVES inventory mode is faster than rates mode, 2) there are
several dimensions of rates mode output which are not relevant to the assigning of speciation profiles, such as speed bin and
temperature profile and 3) weighting speciation profiles by their emissions inventory is both easier and more accurate than
by MOVES output activity or emission rates.
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Fuel
Model Years
Process
NO
NOx
HONO
Gasoline
1981-1990
Start Exhaust
0.961
0.031
0.008
Gasoline
1991-1995
Start Exhaust
0.987
0.005
0.008
Gasoline
1996-2050
Start Exhaust
0.951
0.041
0.008
Diesel
1960-2003
Exhaust
0.9622
0.0298
0.008
Diesel
2004-2006
Exhaust
0.9325
0.0595
0.008
Diesel
2007-2009
Exhaust
0.7529
0.2381
0.008
Diesel
2010-2060
Exhaust
0.8035
0.1885
0.008
In Canada, a GSPRO_COMBO file is used to generate speciated gasoline emissions that account for
various ethanol mixes. In Mexico, onroad emissions are pre-speciated from the MOVES-Mexico model,
thus eliminating the need for a GSPRO_COMBO file. For both Canada and Mexico, nonroad VOC
emissions are not defined by mode (e.g., exhaust versus evaporative), which necessitates the need for a
GSPRO_COMBO file that splits total VOC into exhaust and evaporative components. In addition, MOVES-
Mexico uses an older version of MOVES that is hardcoded for an older version of the CB6 chemical
mechanism ("CB6-CAMx"). This version does not generate the model species XYLMN or SOAALK, so
additional post-processing is performed to generate those emissions:
• XYLMN = XYL[1]-0.966*NAPHTHALENE[1]
• PAR = PAR[1]-0.00001*NAPHTHALENE[1]
• SOAALK = 0.108*PAR[1]
3.2.2 PM speciation
Like VOC speciation, PM2.5 speciation does feature integrated species from the base inventory, though
there are far fewer (only BC and SO4). The remaining mass is either TOM (total organic matter) or
RESID_PM (residual PM = PM2.5 - BC - SO4 - TOM), which is speciated using SPECIATE profiles that were
post-processed using the S2S-Tool. Small adjustments to the methods were needed to accommodate
the reporting by California. Since California does not provide speciated PM2.5 emissions, total PM2.5
emissions for onroad and nonroad sources in California were speciated using the profile proportions
estimated by MOVES in California. Finally, onroad brake and tire wear PM2.5 emissions were speciated in
the moves2smk postprocessor using the SPECIATE profiles 95462 and 95460, respectively.
3.2.2.1 Diesel PM
Diesel PM emissions are explicitly included in the NEI using the pollutant names DIESEL-PM10 and
DIESEL-PM25 for select mobile sources whose engines burn diesel or residual oil fuels. This includes
sources in onroad, nonroad, point airport ground support equipment, point locomotives, nonpoint
locomotives, and all PM from diesel or residual oil fueled nonpoint CMV. These emissions are equal to
their primary PM10-PRI and PM25-PRI counterparts, are exclusively from exhaust (i.e., do not include
brake/tire wear), and are exclusively used in toxics modeling. Diesel PM is then speciated in SMOKE
using the same speciation profiles and methods as primary PM, except that diesel PM is mapped to
model species that feature "DIESEL_PM" in their species name.
3.2.3 NOx speciation
In the NEI, NOx emissions are inventoried on a NO2 weighted basis, but must be speciated into NO, NO2,
and HONO. Table 3-11 provides the NOx speciation profiles used in EPA's modeling platforms. The only
difference between the two profiles is the allocation of some NO2 mass to HONO in the "HONO" profile.
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HONO emissions from mobile sources have been identified in tunnel studies and its inclusion in
emissions inventories is important for urban chemistry. Here, a HONO to NOx ratio of 0.008 was selected
(Sarwar, 2008). In this modeling platform, all non-mobile sources use the "NHONO" profile, all non-
onroad mobile sources (including nonroad, cmv, and rail) use the "HONO" profile, and all onroad NOx
speciation occurs within MOVES. For further details on NOx speciation within MOVES, please see the
associated technical report.
Table 3-11. NOx speciation profiles
Profile
Pollutant
Species
Mass Split Factor
HONO
NOX
N02
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
N02
0.1
NHONO
NOX
NO
0.9
3.2.4 Sulfuric Acid Vapor (SULF)
Sulfuric acid vapor (SULF) is added for coal and distillate oil fuel combustion sources to the emissions
files using SO2 emissions from the base inventory. This process utilizes profiles assignments in the GSREF
file and the profiles were derived using data from AP-42 (EPA, 1998). The weight fraction of added
sulfuric acid vapor is fuel specific, assumes that gaseous sulfate is primarily H2SO4, and is calculated as
follows:
fraction of S emitted as sulfate MW H2S04
SULF emissions = S02 emissions x — : — : - —— x ¦
fraction of S emitted as S02 MW S02
In the above, the molecular weight {MW) of sulfate and sulfur dioxide are 98 g/mol and 64 g/mol,
respectively. The fractions of sulfur emissions emitted as sulfate and sulfur dioxide, as well as the
resulting sulfuric acid vapor split factors, by fuel, are summarized in Table 3-12 and Table 3-13 below.
Table 3-12. Sulfate Split Factor Computation
Fuel
SCCs
Profile
Fraction
Fraction
Split Factor (Mass
Code
as S02
as Sulfate
Fraction)
Bituminous
1-0X-002-YY
X is 1, 2, or 3
YY is 01-19
21-0Z-002-000
Z is 2, 3, or 4
95014
0.95
0.014
.014/.95 * 98/64 =
0.0226
Subbituminous
1-0X-002-YY
X is 1, 2, or 3
YY is 21-38
87514
0.875
0.014
.014/.875 * 98/64
= 0.0245
Lignite
1-0X-003-YY
X is 1, 2, or 3
YY is 01-18
75014
0.75
0.014
.014/.75 * 98/64 =
0.0286
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Fuel
SCCs
Profile
Code
Fraction
as S02
Fraction
as Sulfate
Split Factor (Mass
Fraction)
Residual oil
1-0X-004-YY
X is 1, 2, or 3
YY is 01-06
21-0Z-005-000
Z is 2, 3, or 4
99010
0.99
0.01
.01/.99 * 98/64 =
0.0155
Distillate oil
1-0X-005-YY
X is 1, 2, or 3
YY is 01-06
21-0Z-004-000
Z is 2, 3, or 4
99010
0.99
0.01
Same as residual
oil
Table 3-13. SO2 speciation profiles
Profile
pollutant
species
split factor
95014
S02
SULF
0.0226
95014
S02
S02
1
87514
S02
SULF
0.0245
87514
S02
S02
1
75014
S02
SULF
0.0286
75014
S02
S02
1
99010
S02
SULF
0.0155
99010
S02
S02
1
3.2.5 Speciation of Metals and Mercury
Metals and mercury emissions from the base inventory require speciation for use in modeling. Non-
mercury metals must be speciated into coarse and fine size ranges for use in CMAQ, and Table 3-14,
summarizes the particle size profiles used for each data category.
Table 3-14. Particle Size Speciation of Metals
Source Type
Profile
Pollutant
Fine
Coarse
Onroad
OARS
Arsenic
0.95
0.05
Onroad
ONMN
Manganese
0.4375
0.5625
Onroad
ONNI
Nickel
0.83
0.17
Onroad
CRON
Chromhex
0.86
0.14
Nonroad
NOARS
Arsenic
0.83
0.17
Nonroad
NONMN
Manganese
0.67
0.33
Nonroad
NONNI
Nickel
0.49
0.51
Nonroad
CRNR
Chromhex
0.80
0.20
Stationary
STANI
Nickel
0.59
0.41
Stationary
STACD
Cadmium
0.76
0.24
Stationary
STAMN
Manganese
0.67
0.33
Stationary
STAPB
Lead
0.74
0.26
Stationary
STABE
Beryllium
0.68
0.32
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Source Type
Profile
Pollutant
Fine
Coarse
Stationary
CRSTA
Chromhex
0.71
0.29
Stationary
STARS
Arsenic
0.59
0.41
Mercury is speciated into one of the three forms used by CMAQ; elemental, divalent gaseous, and
divalent particulate. Table 3-15 provides the mercury speciation profiles used in the modeling platform
All relevant SCCs were mapped to these profiles within the GSREF. A caveat is the onroad and nonroad
sectors, where mercury emissions are pre-speciated in MOVES, nonroad emissions from California,
which use the appropriate profiles below, and onroad emissions from California, where MOVES-based
speciation is applied.
Table 3-15. Mercury Speciation Profiles
Profile Code
Description
Elemental
Divalent Gas
Particulate
HGCEM
Cement kiln exhaust
0.66
0.34
0
HGCLI
Cement clinker cooler
0
0
1
HBCMB
Fuel combustion
0.5
0.4
0.1
HGCRE
Human cremation
0.8
0.15
0.05
HGELE
Elemental only (used?)
1
0
0
HGGEO
Geothermal power plants
0.87
0.13
0
HGGLD
Gold mining
0.8
0.15
0.05
HGHCL
Chlor-Alkali plants
0.972
0.028
0
HGINC
Waste incineration
0.2
0.6
0.2
HGIND
Industrial average
0.73
0.22
0.05
HGMD
Mobile diesel
0.56
0.29
0.15
HGMG
Mobile gas
0.915
0.082
0.003
HGMET
Metal production
0.8
0.15
0.005
HGMWI
Medical waste incineration
0.2
0.6
0.2
HGPETCOKE
Petroleum coke
0.6
0.3
0.1
3.3 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 were selected using some combination of country, state,
county, SCC, and pollutant. Table 3-16 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
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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 3-16. 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
airports
Annual
Yes
all
all
No
beis
Hourly
n/a
all
No
cmv_clc2
Annual & hourly
All
all
No
cmv_c3
Annual & hourly
All
all
No
fertilizer
Monthly
met-based
All
Yes
livestock
Daily
No
met-based
All
No
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
mwdss
mwdss
Yes
np_oilgas
Annual
Yes
aveday
aveday
No
onroad
Annual &
monthly1
all
all
Yes
onroad_ca_adj
Annual &
monthly1
all
all
Yes
canada_afdust
Annual & monthly
Yes
week
all
No
canmex_area
Monthly
week
week
No
canada_onroad
Monthly
week
week
No
mexico_onroad
Monthly
week
week
No
canmex_point
Monthly
Yes
mwdss
mwdss
No
canada_ptdust
Annual
Yes
week
all
No
canmex_ag
Annual
Yes
mwdss
mwdss
No
canada_og2D
Monthly
mwdss
mwdss
No
pt_oilgas
Annual
Yes
mwdss
mwdss
Yes
Ptegu
Annual & hourly
Yes2
all
All
No
ptnonipm
Annual
Yes
mwdss
mwdss
Yes
ptagfire
Daily
all
all
No
ptfire-rx
Daily
all
all
No
ptfire-wild
Daily
all
all
No
ptfire_othna
Daily
all
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No3
met-based3
all
No3
np_solvents
Annual
Yes
aveday
aveday
No
1Note 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.
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20nly units that do not have matching hourly CEMS data use monthly temporal profiles.
3Except for 3 SCCs that do not use met-based speciation.
The following values are used in the table. The value "all" means that hourly emissions were computed
for every day of the year and that emissions potentially have day-of-year variation. The value "week"
means that hourly emissions were 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, 2022, which is intended to mitigate the effects of initial condition concentrations. The ramp-
up period was 10 days (December 22-31, 2021). For all anthropogenic sectors, emissions from
December 2022 were used to fill in surrogate emissions for the end of December 2021. For biogenic
emissions, December 2021 emissions were computed using year meteorology.
3.3.1 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
nonroad, onroad (for activity data), and all Canada and Mexico inventories except for agriculture.
Commercial marine vessels in cmv_c3 and cmv_clc2 use hourly data in the FF10 files.
3.3.2 Temporal allocation for non-EGU sources (ptnonipm)
Most temporal profiles in ptnonipm result in primarily constant emissions for each day of the year,
although some have lower emissions on Sundays. An update in the 2018 platform was an analysis of
monthly temporal profiles for non-EGU point sources in the ptnonipm sector. A number of profiles were
found to be not quite flat over the months but were so close to flat that the difference was not
meaningful. These profiles were replaced in the cross reference to point instead to the flat monthly
profile. The codes for the profiles that were replaced were: 202, 214, 220, 221, 222, 223, 227, 257, 263,
264, 265, 266, 267, 269, 271, 272, 279, 280, 295, 302, 303, 304, 305, 306, 309, 310, 327, 329, 332, and
333. For the 2022vl platform, temporal profiles for SCC 40202501 emissions for which are related to
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surface coating for metals were changed to use hourly profiles number 11 that reflects operations from
7AM to 5PM local time.
3.3.3 Electric Generating Utility temporal allocation (ptegu)
Electric generating unit (EGU) sources matched to ORIS units were temporally allocated to hourly
emissions needed for modeling using the hourly CEMS data for units that could be matched to the CEMS
emissions. Those hourly data were processed through v2.1 of the CEMCorrect tool to mitigate the
impact of unmeasured values in the data.
The temporal allocation procedure for EGUs in the base year is differentiated by whether or not the unit
could be directly matched to a unit with CEMS data via its 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
values in the annual inventory because the CEMS data replace the NOx and SO2 annual 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. Prior to use of the CEMS data in SMOKE it is processed through the CEMCorrect tool.
The CEMCorrect tool identifies hours for which the data were not measured as indicated by the data
quality flags in the CEMS data files. Unmeasured data can be filled in with maximum values and thereby
cause erroneously high values in the CEMS data. When data were flagged as unmeasured and the
values were found to be more than three times the annual mean for that unit, the data for those hours
were 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 3-3 for an
example).
Figure 3-3. Eliminating unmeasured spikes in CEMS data
2017 January Unit 469_5
2000
1800
1600
1400
1200
1000
800
600
400
200
0
p>.rocr)Ln^HP>.rocr)Ln^HP>.rocr)Ln^HP>.rocr)Ln^HP>.rocr)Ln\-ihNrocr)
rMLnr^oroi-ncoorotDco^HrotDcn^H^rtDcnrM^r-vcnrMi-nr^ocN
January 2017 Hour
•Raw CEM
•Corrected
The region, fuel, and type (peaking or non-peaking) must be identified for each input EGU with CEMS
data so the data can be used to generate profiles. The identification of peaking units was done using
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hourly heat input data from 2022 and the two previous years (2020 and 2021). The heat input was
summed for each year. Equation 1 shows how the annual heat input value is converted from heat units
(BTU/year) to power units (MW) using the NEEDS v6 derived unit-level heat rate (BTU/kWh). In equation
2 a capacity factor is calculated by dividing the annual unit MW value by the NEEDS v6 unit capacity
value (MW) multiplied by the hours in the year. A peaking unit was defined as any unit that had a
maximum capacity factor of less than 0.2 for every year (2020, 2021, and 2022) and a 3-year average
capacity factor of less than 0.1.
Equation 1. Annual unit power output
v8760 Hourly HI ,mw\
Li = 0 *1000 ( , J
Annual Unit Output (MW) =
NEEDS Heat Rate ; T<7;
XkWhJ
Equation 2. Unit capacity factor
_ . „ Annual Unit Output (MW)
Capacity Factor = :—jww-
NEEDS Unit Capacity ^—J*8760 Qi)
Input regions were determined from one of the eight EGU modeling regions based on MJO and climate
regions. Regions were used to group units with similar climate-based load demands. Region assignment
is made on a state level, where all units within a state were assigned to the appropriate region. Unit fuel
assignments were made using the primary NEEDS v6 fuel. Units fueled by bituminous, subbituminous, or
lignite were assigned to the coal fuel type. Natural gas units were assigned to the gas fuel type. Distillate
and residual fuel oil were assigned to the oil fuel type. Units with any other primary fuel were assigned
the "other" fuel type. Figure 3-4 shows the regions used to generate the profiles. Currently there are 64
unique profiles available based on 8 regions, 4 fuels, and 2 for peaking unit status (peaking and non-
peaking).
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Figure 3-4. Regions used to Compute Temporal non-CEMS EGU Temporal Profiles
EGU Regions
| LADCO
~ MANE-VU
J Northwest
~ SESARM
| | South
¦ West
| Southwest
¦ West North Central
The daily and diurnal profiles were calculated for each region, fuel, and peaking type group from the
year 2022 CEMS heat input values. The heat input values were summed for each input group to the
annual level at each level of temporal resolution: monthly, month-of-day, and diurnal. The sum by
temporal resolution value was then divided by the sum of annual heat input in that group to get a set of
temporalization factors. Diurnal factors were created for both the summer and winter seasons to
account for the variation in hourly load demands between the seasons. For example, the sum of all hour
1 heat input values in the group was divided by the sum of all heat inputs over all hours to get the hour 1
factor. Each grouping contained 12 monthly factors, up to 31 daily factors per month, and two sets of 24
hourly factors. The profiles were weighted by unit size where the units with more heat input have more
influence on the shape of the profile. Composite profiles were created for each region and type across
ali fuels as a way to provide profiles for a fuel type that does not have hourly CEMS data in that region.
Figure 3-5 shows peaking and non-peaking daily temporal profiles for the gas fuel type in the LADCO
region. Figure 3-6 shows the diurnal profiles for the coal fuel type in the Mid-Atlantic Northeast Visibility
Union (MANE-VU) region.
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Figure 3-5. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type
Daily Small EGU Profile for LADCO gas
0.040
0.035 ¦
Nonpeaking
Peaking
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2017
Figure 3-6. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type
Diurnal Small EGU Profile for MANE-VU coal
SMOKE uses a cross-reference file to select a monthly, daily, and diurnal profile for each source. For the
2022 platform, the temporal profiles were assigned in the cross-reference at the unit level to EGU
sources without hourly CEMS data. An inventory of all EGU sources without CEMS data was used to
identify the region, fuel type, and type (peaking/non-peaking) of each source. The region used to select
the temporal profile is assigned based on the state from the unit FIPS. The fuel was assigned by SCC to
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one of the four fuel types: coal, gas, oil, and other. A fuel type unit assignment is made by summing the
VOC, NOX, PM2.5, and S02 for all SCCs in the unit. The SCC that contributed the highest total emissions
to the unit for selected pollutants was used to assign the unit fuel type. Peaking units were identified as
any unit with an oil, gas, or oil fuel type with a NAICS of 22111 or 221112. Some units may be assigned
to a fuel type within a region that does not have an available input unit with a matching fuel type in that
region. These units without an available profile for their group were assigned to use the regional
composite profile. Municipal waste combustor and cogeneration units were identified using the NEEDS
primary fuel type and cogeneration flag, respectively, from the NEEDS v6 database. Assignments for
each unit needed a profile were made using the regions shown in Figure 3-4.
3.3.4 Airport Temporal allocation (airports)
Airport temporal profiles were updated to 2022-specific temporal profiles for all airports other than
Alaska seaplanes. Hourly airport operations data were obtained from the Aviation System Performance
Metrics (ASPM) Airport Analysis website (https://aspm.faa.gov/apm/svs/AnalysisAP.asp). A report of
2022 hourly Departures and Arrivals for Metric Computation by airport was generated. An overview of
the ASPM metrics is at
https://aspmhelp.faa.gov/index/Aviation System Performance Metrics (ASPM).html. Figure 3-7
shows examples of diurnal airport profiles for the Phoenix airport (PHX) and the default profile for Texas.
Month-to-day and Annual-to month temporal profiles were developed based on a separate query of the
2022 Aviation System Performance Metrics (ASPM) Airport Analysis
(https://aspm.faa.gov/apm/svs/AnalysisAP.asp). A report of all airport operations (takeoffs and
landings) by day for 2022 was generated. Annual-to-month profiles were derived directly from the daily
airport operations report and examples are shown for Wisconsin and Atlanta in Figure 3-8.
For 2022, all airport SCCs (i.e., 2275*, 2265008005, 2267008005, 2268008005 and 2270008005) were
assigned to individual commercial airports where a match could be made between the inventory facility
and the FAA identifier in the ASPM derived data. State average profiles were calculated as the average
of the temporal fractions for all airports within a state. The state average profiles were assigned by
state to all airports in the inventory that did not have an airport specific match in the ASPM data.
Package processing hubs at the Memphis (MEM), Indianapolis (IND), Louisville (SDF), and Chicago
Rockford (RFD) airports produced peaks in the average state profiles at times not typical for activity in
smaller commercial airports. These packaging hubs were removed from the state averages. Airports
that required state-defaults in states lacking ASPM data use national average profiles calculated from
the average of the state temporal profiles.
Alaska seaplanes, which are outside the CONUS domain use the monthly profile in Figure 3-9. These
were assigned based on the facility ID.
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Figure 3-7. 2022 Airport Diurnal Profiles for PHX and state of Texas
2022 FAA Airport Diurnal Profile: PHX
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Figure 3-8. 2022 Wisconsin and Atlanta annual-to-month profile for airport emissions
2022 FAA State Monthly Profile: Wl default
2022 FAA Airport Monthly Profile: ATL
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Figure 3-9. Alaska seaplane profile
0.14
0.12
0.10
0.08
0.06
0.04
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0.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3.3.5 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.l/GenTPRQ TechnicalSummary Aug2012 Final.p
df and https://www.cmascenter.Org/smoke/documentation/4.5/html/ch05s03s05.html. respectively.
For the RWC sector, two different algorithms for calculating temporal allocation are used. For most SCCs
in the sector, in which wood burning is more prominent on colder days, Gentpro was used to compute
annual to day-of-year temporal profiles based on the daily minimum temperature. These 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
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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. The algorithm is as
follows:
If Td >= Tt: no emissions that day
If Td < Tt: daily factor = 0.79*(Tt -Td)
where (Td = minimum daily temperature; Tt = threshold temperature, which is 60 degrees F in southern states and
50 degrees F elsewhere).
Once computed, the factors were normalized to sum to 1 to ensure that the total annual emissions are
unchanged (or minimally changed) during the temporal allocation process.
Figure 3-10 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 3-10. Example of RWC temporal allocation using a 50 versus 60 °F threshold
RWC temporal profile, Duval County, FL, Jan - Apr
0.035
For the 2022 emissions modeling platform, a separate algorithm is used to determine temporal
allocation of recreational wood burning, e.g., fire pits (SCC 2104008700) and is applied by Gentpro.
Recreational wood burning depends on both minimum and maximum daily temperatures by county, and
also uses a day-of-week temporal profile (61500) in which emissions are much higher on weekends than
on weekdays. According to the recreational wood burning algorithm, only days in which the
temperature falls within a range of 50°F and 80°F at some point during the day receive emissions. On
days when the maximum temperature is less than 50°F or the minimum temperature is above 80°F, the
daily temporal factor is zero. For all other days, the day-of-week profile 61500 is applied, which has 33%
of the emissions on each weekend day and lower emissions on weekdays. An example is shown in Figure
3-11. As a result of applying this algorithm, northern states have more recreational wood burning in
summer months while southern states show a flatter pattern with emissions distributed more evenly
throughout the months.
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Figure 3-11. Example of Annual-to-day temporal pattern of recreational wood burning emissions
•Ukkn
FjJJUwuw
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 (MANE-VU, 2004). 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
Comparison of RWC diurnal profile
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The temporal profiles for hydronic heaters" (i.e., SCCs=2104008610 [outdoor], 2104008620 [indoor],
and 2104008620 [pellet-fired]) are 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 hydronic heaters, 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 hydronic heaters, shown in Figure 3-13, are based on a
conventional single-stage heat load unit burning red oak in Syracuse, New York.
Annual-to-month temporal allocation for OHH was computed from the MDNR 2008 survey and is
illustrated in Figure 3-14. The hydronic heater emissions still exhibit strong seasonal variability, but do
not drop to zero because many units operate year-round for water and pool heating.
Figure 3-13. Data used to produce a diurnal profile for hydronic heaters
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Figure 3-14. Monthly temporal profile for hydronic heaters
3.3.6 Agricultural Ammonia Temporal Profiles (livestock)
For the ag sector, agricultural GenTPRO temporal allocation was applied to livestock emissions and to all
pollutants within the sector, not just NH3. The GenTPRO algorithm is based on an equation derived by
Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2014) 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:
Equation 3-1
Ehh = [161500/T,^ x e<-1380V] * ARhh
PEi,h = Ei,h / Sum(E/,b) Equation 3-2
where
• PEi,h = Percentage of emissions in county /' on hour h
• Ei,h = Emission rate in county /' on hour h
• Ti,h = Ambient temperature (Kelvin) in county /' on hour h
• ARi,h = Aerodynamic resistance in county /'
Some examples plots of the profiles by animal type in different parts of the country are shown in Figure
3-15.
To develop month-to-hour temporal profiles of livestock emissions, GenTPRO was run using the
"BASH_NH3" profile method to create for these sources. Because these profiles distribute to the hour
based on monthly emissions, the monthly emissions were 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.
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Figure 3-15. Examples of livestock temporal profiles in several parts of the country
Tulare County, CA Duplin County, NC
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
» Be^ ¦ Broiler > Dairy » Layer i Swine | » Beef m Broiler m Dairy 9 Layer 9 Swine
0.2
0.15
0.1
0.05
Sioux County, IA
Lancaster County, PA
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
m Beef a Broiler m Dairy » Layer m Swine
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
a Beef m Broiler m Dairy » Layer m Swine
0.2
0.15
0.1
0.05
Figure 3-16. Example of animal IMH3 emissions temporal allocation approach (daily total emissions)
MN ag NH3 livestock temporal profiles
u . w . . . ..n
0.0 I- ^
1/1/2008 2/1/2008 3/1/2008 4/1/2008 5/1/2008 6/1/2008 7/1/2008 8/1/2008 9/1/2008 10/1/2008 11/1/2008 12/1/2008
-old
-new
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3.3.7 Oil and gas temporal allocation (np_oilgas)
Monthly temporalization of np_oilgas emissions is based primarily on year-specific monthly factors from
the Oil and Gas Tool (OGT). Factors were specific to each county and SCC. For use in SMOKE, each
unique set of factors was assigned a label (OG20M_0001 through OG20M_6306), and then a SMOKE-
formatted ATPRO_MONTHLY and an ATREF were developed. This dataset of monthly temporal factors
included profiles for all counties and SCCs in the Oil and Gas Tool inventory. Because we are using non-
tool datasets in some states, this monthly temporalization dataset did not cover all counties and SCCs in
the entire inventory used for this study. To fill in the gaps in those states, state average monthly profiles
for oil, natural gas, and combination sources were calculated from Energy Information Administration
(EIA) data and assigned to each county/SCC combination not already covered by the OGT monthly
temporal profile dataset. Coal bed methane (CBM) and natural gas liquid sources were assigned flat
monthly profiles where there was not already a profile assignment in the dataset.
3.3.8 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. For the 2022 platform EPA utilized the FHWA's Travel
Monitoring and Analysis System (TMAS). This system measures monthly traffic volume, by class and
weight. The primary purpose for using TMAS in 2022 platform was to replace the month VMT
distribution from 2020, which were marked by the COVID pandemic shutdowns in mid-March/April. The
2022 TMAS month VMT distribution looks more like a typical nonpandemic year. We also used day/hour
distributions from the same dataset because they were available and for the correct year (2022). TMAS
data was processed for each state, for each month, and vehicle class. Figure 3-17 shows TMAS data. The
first plot shows hour of the day for the state of Maryland. Note that you can see the rush hour in the
morning and the evening. The second plot shows the state of Minnesota for the month of June. Notice
that motorcycles come out in the spring (winter months show less VMT for motorcycles) and are driven
more on Saturday. The third plot shows an annual, by month plot of Montana. Note that there is an
increase in passenger cars and light duty trucks during the month of July. This may be due to an increase
in tourism during the warmer months.
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Figure 3-17. TMAS Data: VMT Fraction by Hour of Day and Day of Week
TMAS Data: VMT Fraction v. Hour by State
state=Maryland dayType=Weekday
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61 - Combination Short-haul True
11 - Motorcycles
40s - Buses
62 - Combination Long-haul Truck
21 - Passenger Cars
50s - Single Unit Trucks
/proj1/EPA_2022_Platform/TMAS_2022/p!ot_TMAS_hour.sas 30JAN24 17:32
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TMAS Data: VMT Fraction v. Day of Week by State
state=Minnesota monthlD=6
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/proj 1 /EPA_2022_PIatform/TMAS_2022fplot_TMA5_day.sas 30JAN24 1732
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TMAS Data: VMT Fraction v. Month by State
state=Montana
Total Vehicles
30s - Light-duty Trucks
61 - Combination Short-haul True
11 - Motorcycles
40s - Buses
62 - Combination Long-haul Truck
21 - Passenger Cars
50s - Single Unit Trucks
/proj 1 /EPA_2022_PIatform/TMAS_2022/plot_TMAS.sas 09FEB24 12:53
The "inventories" referred to in Table 3-16 consist of activity data for the onroad sector, not emissions.
VMT is the activity data used for on-network rate-per-distance (RPD) processes. The off-network
emissions from the rate-per-profile (RPP) and rate-per-vehicle (RPV) processes use the VPOP activity
data, which are annual and do 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. Day-of-week and hour-of-day
temporal profiles are also used to temporalize the starts activity used for rate-per-start (RPS) processes,
and the off-network idling (ONI) hours activity used for rate-per-hour-ONI (RPHO) processes. The
inventories for starts and ONI activity contain monthly activity so that monthly temporal profiles are not
needed.
For on-roadway RPD processes, the VMT activity data are 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 also use hourly speed
distributions (SPDIST) as discussed in Section 2.3. For onroad, the temporal profiles and SPDIST will
impact not only the distribution of emissions through time but also the total emissions. SMOKE-MOVES
calculates emissions for RPD processed based on the VMT, speed and meteorology. Thus, if the VMT or
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speed data were shifted 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 3-18 (from 2021) 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.
Figure 3-18. Example temporal variability of VMT compared to onroad NOx emissions
Wake County, NC 2021 VMT and Onroad NOx emissions
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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 and
stationary vehicle (RPV, RPH, RPHO, RPS, and RPP) processes use the gridded meteorology (MCIP) either
directly or indirectly. For RPD, RPV, RPH, RPHO, and RPS, 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 six processes (RPD, RPV, RPH, RPHO, RPS, and RPP) comprise the onroad sector
emissions. In summary, the temporal patterns of emissions in the onroad sector are influenced by
meteorology.
Day-of-week, hour-of-day, and month-of-year temporal profiles for VMT were developed fromTMAS
data. Data were provided for motorcycles (11), passenger vehicles (21), light duty trucks (30s), buses
(40s), single unit trucks (50s), and combination short-haul trucks (61), and combination long-haul trucks
(62). The dataset includes temporal profiles for individual states.
The StreetLight temporal profiles were used in areas of the contiguous United States that did not submit
temporal profiles of sufficient detail for the 2020 NEI. For this platform, the data selection hierarchy
favored local input data over EPA-developed information, with the exception of the three MOVES tables
"hourVMTFraction", dayVMTFraction", and "avgSpeedDistribution" where county-level, telematics-based
EPA Defaults were adopted for the NEI universally. For hoteling, day-of-week profiles are the same as
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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.
Temporal profiles for RPHO are based on the same temporal profiles as the on-network processes in
RPD, but since the on-network profiles are road-type-specific and ONI is not road-type-specific, the
RPHO profiles were assigned to use rural unrestricted profiles for counties considered "rural" and urban
unrestricted profiles for counties considered "urban." RPS uses the same day-of-week profiles as on-
network processes in RPD, but uses a separate set of diurnal temporal profiles specifically for starts
activity. For starts, there are two hour-of-day temporal profiles for each source type, one for weekdays
and one for weekends. The starts diurnal temporal profiles are applied nationally and are based on the
default starts-hour-fraction tables from MOVES.
3.3.9 Nonroad mobile temporal allocation (nonroad)
For nonroad mobile sources, temporal allocation is performed differently for different SCCs. Beginning
with the final 2011 platform, improvements to temporal allocation of nonroad mobile sources were
made to make the temporal profiles more realistically reflect real-world practices. The specific updates
were made for agricultural sources (e.g., tractors), construction, and commercial residential lawn and
garden sources. In the 2022vl platform, temporal profiles for residential and commercial snowblowers
were changed to be flat for each day of the week since snowfall is not influenced by the day of the week.
Figure 3-19 shows two previously existing temporal profiles (9 and 18) and a newer temporal profile (19)
which has lower emissions on weekends. In this platform, construction and commercial lawn and garden
sources use the new profile 19 which has lower emissions on weekends. Residental lawn and garden
sources continue to use profile 9 and agricultural sources continue to use profile 19.
Figure 3-19. Example Nonroad Day-of-week Temporal Profiles
Day of Week Profiles
0.24
0.22
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.0G
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monda^ tuesday Wednesday thursday friday Saturday sunda/
9 18 19
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Figure 3-20 shows the previously existing temporal profiles 26 and 27 along with newer temporal
profiles (25a and 26a) which have lower emissions overnight. In this platform, construction sources use
profile 26a. Commercial lawn and garden and agriculture sources use the profiles 26a and 25a,
respectively. Residental lawn and garden sources use profile 27.
Figure 3-20. Example Nonroad Diurnal Temporal Profiles
Hour of Day Profiles
0.11
26a- New 27 25 a-New 26
Additionally, the temporal profile for residential and commercial snowblowers were changed to flat day-
of-week, since snow falls when it falls regardless of weekday/weekend. 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.
3.3.10 Fugitive dust temporal profiles (afdust)
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 explain 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. Application of the transport fraction and meteorological adjustments
prevents the overestimation of fugitive dust impacts in the grid modeling as compared to ambient
samples.
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In the 2022vl platform, some changes were made to temporal profiles in the afdust sector as follows:
• New temporal profiles (monthly, weekly, hourly) were created for paved and unpaved road dust.
The monthly profile is based on monthly emissions from the 2022hc onroad PM2.5 brake and tire
wear, since that has less temperature dependence than other pollutants and process. Weekly
and hourly profiles are based on averages of the TMAS profiles used in SMOKE-MOVES. Unpaved
road dust profiles use averages of passenger trucks only; paved road dust profiles use weighted
averages of 3/4 light-duty vehicle emissions excluding motorcycles, and 1/4 heavy duty emissions
excluding buses. There are separate hourly profiles for weekdays vs weekends.
• For agricultural tilling, flat day-of-week profiles are now being used along with new monthly
profiles mostly based on nonroad ag emissions. The monthly nonroad ag profiles are based on
LADCO-provided MOVES data and more accurately reflect tilling activities, peaking in spring and
fall.
• For dust from livestock, the monthly profiles for 2805100010 and 2805100050 (beef cattle and
swine) were updated to the 2022 data from https://u.osu.edu/beef/2023/10/25/more-heifers-
supporting-feedlot-inventory/. Profiles for other livestock dust are not changed from the 2020
platform.
3.3.11 Additional sector specific details (beis, cmv, rail, nonpt, np solvents,
ptfire-rx, ptfire-wild)
In the 2022vl platform, some changes were made to temporal profiles in the nonpt sector:
• Evaporative SCCs starting with 250105 and 250106 were updated to use monthly temporal
profiles based on monthly total VOC emissions computed from the 2022hc onroad evaporative
off-network processes in the RPP and RPV subsectors contained in the final 2022 onroad
emissions.
• Residential natural gas (SCC 2104006000) monthly temporal profiles were derived for each state
based on Energy Information Administration (EIA) data for 2022.
In the 2022vl platform, some changes were made to temporal profiles in the np_solvents sector:
• All asphalt SCCs (paving and roofing) are using new ElA-based monthly profiles for "asphalt and
road oil" by PADD region. The data source is
https://www.eia.gov/dnav/pet/PET CONS PSUP A EPPA VPP MBBL A.htm.
• For interior painting, a.k.a. architectural coating (2401001000): created a new monthly profile
PAINT22 based on 2022 data from https://fred.stlouisfed.org/series/MRTSSM44412USN/.
• For pesticides (SCCs 2461850000, 2461800001, and 2460800000), monthly profiles were
changed as follows: AZ/CA/FL/HI/TX (the warmest states) are flat annual. Other moderately
warm southeast states from North Carolina south and west to Oklahoma are flat from March
through October, and zero in other months. All other states are flat from April through
September and zero in other months.
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Biogenic emissions from the BEIS model vary each 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.
For the rail sector, monthly profiles from the 2016 platform were used. 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 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
Comparison of Agricultural Burning Temporal Profiles
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, so temporal profiles
are only used to go from day-specific to hourly emissions. Separate hourly profiles for prescribed and
wildfires were used. For ptfire, state-specific hourly profiles were used, with distinct profiles for
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prescribed fires and wildfires. Figure 3-22 below shows the profiles used for each state for the platform
The wildfire diurnal profiles are similar but vary according to the average meteorological conditions in
each state. For all agricultural burning, the diurnal temporal profile used reflects the fact that burning
occurs during the daylight. This puts most of the emissions during the workday and suppresses the
emissions during the middle of the night. This diurnal profile was used for each day of the week for all
agricultural burning emissions in all states.
Figure 3-22. Prescribed and Wildfire diurnal temporal profiles
3.4 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. Spatial allocation was performed for
each of the modeling grids shown in Section 3.1. To accomplish this, SMOKE used national 12-km spatial
surrogates and a SMOKE area-to-point data file. For the U.S., the EPA updated surrogates to use circa
2020 data. The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain 12US1
shown in Figure 3-1. While highlights of information are provided below, the file
Surrogate_specifications_2022_platform_US_Can_Mex.xlsx documents the complete configuration for
generating the surrogates and can be referenced for more details.
3.4.1 Spatial Surrogates for U.S. emissions
There are more than 90 spatial surrogates available for spatially allocating U.S. county-level emissions to
the 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 airport refueling sources.
The surrogates for the platform are based on a variety of geospatial data sources, including the
American Community Survey (ACS) for census-related data, the National Land Cover Database (NLCD)
Onroad surrogates are based on average annual daily traffic counts (AADT) from the highway monitoring
performance system (HPMS).
U.S. Surrogate datasets used for this platform include:
County boundaries used for all surrogates use the 2020 TIGER boundaries.
Oil and gas surrogates represent activity from the year 2022.
ACS-based surrogates use the 2020 ACS.
NLCD-based surrogates use NLCD 2019.
133
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Animal specific livestock waste surrogates were derived from National Pollutant Discharge
Elimination System (NPDES) animal operation water permits and Food and Agriculture
Organization (FAO) gridded livestock count data.
Surrogates for fuel stations, asphalt surfaces, and unpaved roads are based on data from the
OpenStreetMap database.
Gravel and lead mines use separate surrogates based on the more general United States
Geological Survey (USGS) mining surrogate.
Residential wood combustion surrogates are based on ACS data.
When developing modeling platforms, EPA routinely updates surrogates to utilize updated versions of
the underlying surrogate databases or to use a different source of data when it is deemed more
representative for a particular source category. In the 2020 platform, NLCD-based surrogates were
updated from using the 2011 National Land Cover Database (NLCD) to use the 2019 National Land Cover
Database. During these updates, EPA also examined the Residential Wood Combustion (RWC)
surrogates that were based on the NLCD. This was done to see if there are other sources of spatial data
that could improve the geographic representation of the RWC sector when disaggregating the county-
level emissions provided by the emissions inventory to grid cells. For the RWC sector prior to the 2020
platform, the spatial surrogate used was #300 computed from "NLCD Low Intensity development" (i.e.,
land areas with 20-49% impervious surface). This surrogate was initially selected for RWC to capture
geographic areas where there may be houses but generally in less developed spaces. However, this
surrogate does not differentiate by development or structure type. The result is that RWC emissions can
end up concentrated around roads, commercial, and other low to moderately developed grid cells.
In the 2020 platform, housing data provided by the American Community Survey (ACS) were used. The
particular attributes used are: single family detached, single family attached, dual family and mobile
home and combinations of these, depending on the particular RWC specific source category. Using
types of housing seemed more reflective of where RWC emissions would be located. However, a
downside of using the ACS housing data is that the census shapes are broad (particularly in rural areas),
so the emissions can appear more spread out in some areas than when using the NLCD-based
surrogates. When comparing the two approaches for RWC surrogates (NLCD vs ACS), the ACS-based
surrogates looked reasonable, and in fact better than the NLCD Low intensity development surrogate. A
comparison of the PM2.5 emissions gridded with each of these approaches is shown in Figure 3-23 and
Figure 3-24. In the future, a goal is to further improve the resolution surrogates, as such, the use of
building structure data weighted by the ACS will be examined for future platform updates.
134
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Figure 3-23. 2020 Residential Wood Combustion Emissions using NLCD Low Intensity Surrogate
2020ha RWC PM2 5 - old surrogates, total emissions
Max: 315.8044 Min: 0.
>14.4
12.8
11.2
9.6
8.0
6.4
4.8
3.2
<1.6
Figure 3-24. 2020 Residential Wood Combustion Emissions using ACS-based Surrogate
>14.4
12.8
11.2
9.6
k_
>.
8.0 £
o
6.4
4.8
3.2
<1.6
Surrogates for the U.S. were generated using the Surrogate Tools DB with the Java-based Surrogate tools
used to perform gapfilling and normalization where needed. The tool and documentation for the
original Surrogate Tool are available at https://www.cmascenter.org/sa-
135
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tools/documentation/4.2/SurrogateToolUserGuide 4 2.pdf. and the tool and documentation for the
Surrogate Tools DB is available from https://www.cmascenter.org/surrogate tools db/. Table 3-17 lists
the codes and descriptions of the surrogates. Surrogate names and codes listed in italics are not directly
assigned to any sources in the platform, but they are sometimes used to gapfill other surrogates. When
the source data for a surrogate have no values for a particular county, gap filling is used to provide
values for the spatial surrogate in those counties to ensure that no emissions are dropped when the
spatial surrogates are applied to the emission inventories. The Shapefiles used to develop the US
surrogates along with the attributes and filters used are shown in Table 3-18.
Table 3-17. U.S. Surrogates available for the 2022 modeling platforms
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2)
650
Refineries and Tank Farms
100
Population
669
All Abandoned Wells
110
Housing
6691
All Abandoned Oil Wells
135
Detached Housing
6692
All Abandoned Gas Wells
136
Single and Dual Unit Housing
6693
All Abandoned CBM Wells
137
Single + Dual Unit+ Manufactured Housing
6694
All Abandoned Oil Wells - Plugged
150
Residential Heating - Natural Gas
6695
All Abandoned Gas Wells - Plugged
170
Residential Heating - Distillate Oil
6696
All Abandoned CBM Wells - Plugged
180
Residential Heating - Coal
6697
All Abandoned Oil Wells - Unplugged
190
Residential Heating - LP Gas
6698
All Abandoned Gas Wells - Unplugged
205
Extended Idle Locations
670
Spud Count - CBM Wells
239
Total Road AADT
671
Spud Count - Gas Wells
240
Total Road Miles
672
Gas Production at Oil Wells
242
All Restricted AADT
673
Oil Production at CBM Wells
244
All Unrestricted AADT
674
Unconventional Well Completion Counts
258
Intercity Bus Terminals
676
Well Count - All Producing
259
Transit Bus Terminals
677
Well Count - All Exploratory
260
Total Railroad Miles
678
Completions at Gas Wells
261
NTAD Total Railroad Density
679
Completions at CBM Wells
271
NTAD Class 12 3 Railroad Density
681
Spud Count - Oil Wells
300
NLCD Low Intensity Development
683
Produced Water at All Wells
304
NLCD Open + Low
6831
Produced Water at CBM Wells
305
NLCD Low + Med
6832
Produced Water at Gas Wells
306
NLCD Med + High
6833
Produced Water at Oil Wells
307
NLCD All Development
685
Completions at Oil Wells
308
NLCD Low + Med + High
686
Completions at All Wells
309
NLCD Open + Low + Med
687
Feet Drilled at All Wells
310
NLCD Total Agriculture
689
Gas Produced - Total
318
NLCD Pasture Land
691
Well Counts-CBM Wells
319
NLCD Crop Land
692
Spud Count - All Wells
320
NLCD Forest Land
693
Well Count - All Wells
136
-------
321
NLCD Recreational Land
694
Oil Production at Oil Wells
340
NLCD Land
695
Well Count - Oil Wells
350
NLCD Water
696
Gas Production at Gas Wells
401
FAO 2010 Cattle
697
Oil Production at Gas Wells
4011
FAO 2010 Large Cattle Operations
698
Well Count - Gas Wells
4012
NPDES 2020 Beef Cattle
699
Gas Production at CBM Wells
4013
NPDES 2020 Dairy Cattle
711
Airport Areas
402
FAO 2010 Pig
801
Port Areas
4021
NPDES 2020 Swine
850
Golf Courses
403
FAO 2010 Chicken
860
Mines
4031
NPDES 2020 Chicken
861
Sand and Gravel Mines
404
FAO 2010 Goat
862
Lead Mines
4041
NPDES 2020 Goat
863
Crushed Stone Mines
405
FAO 2010 Horse
900
OSM Fuel
406
FAO 2010 Sheep
901
OSM Asphalt Surfaces
4071
NPDES2020 Turkey
902
OSM Unpaved Roads
508
Public Schools
Table 3-18. Shapefiles used to develop U.S. Surrogates
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
100
Population
ACS_2020_5YR_BG_pop_hu
POP2020
110
Housing
ACS_2020_5YR_BG_pop_hu
HU2020
135
Detached Housing
ACS_2020_5YR_BG_pop_hu
detachedh
136
Single and Dual Unit Housing
ACS_2020_5YR_BG_pop_hu
Ittriunit
137
Single + Dual Unit +
Manufactured Housing
ACS 2020 5YR BG pop hu mobile
sngdlmobl
150
Residential Heating - Natural
Gas
ACS_2020_5YR_BG_pop_hu
UTIL GAS
170
Residential Heating - Distillate
Oil
ACS_2020_5YR_BG_pop_hu
FUEL OIL
180
Residential Heating - Coal
ACS_2020_5YR_BG_pop_hu
COAL
190
Residential Heating - LP Gas
ACS_2020_5YR_BG_pop_hu
LP GAS
205
Extended Idle Locations
pil_2019_06_24
rev truck
rev truck>0
239
Total Road AADT
hpms2017_v3_04052020
aadt
moves2014 IN
('02703704705')
240
Total Road Miles
hpms2017_v3_04052020
NONE
moves2014 IN
('02703704705')
242
All Restricted AADT
hpms2017_v3_04052020
aadt
moves2014 IN
('02704')
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
137
-------
244
All Unrestricted AADT
hpms2017_v3_04052020
aadt
moves2014 IN
('03705')
259
Transit Bus Terminals
ntad_2016_ipcd
NONE
bus t=l
260
Total Railroad Miles
tiger_2014_rail
NONE
261
NTAD Total Railroad Density
ntad 2014 rail fixed
dens
RAILTYPE IN
(1,2,3)
271
NTAD Class 12 3 Railroad
Density
ntad 2014 rail fixed
dens
RAILTYPE=1
300
NLCD Low Intensity
Development
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE=22
304
NLCD Open + Low
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(21,22)
305
NLCD Low + Med
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(22,23)
306
NLCD Med + High
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(23,24)
307
NLCD All Development
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(21,22,23,24)
308
NLCD Low + Med + High
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(22,23,24)
309
NLCD Open + Low + Med
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(21,22,23)
310
NLCD Total Agriculture
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(81,82)
318
NLCD Pasture Land
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE=81
319
NLCD Crop Land
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE=82
320
NLCD Forest Land
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(41,42,43)
321
NLCD Recreational Land
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(21,31,41,42,43,5
2,71)
340
NLCD Land
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE != 11
350
NLCD Water
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE=ll
401
FAO 2010 Cattle
fao_Cattle_2010_Da_nlcdproj_masked
DN
4011
FAO 2010 Large Cattle
Operations
f ao_La rgeCattl e_2010_Da_n 1 cd proj_m as
ked
DN
4012
NPDES 2020 Beef Cattle
livestock_npdes_state_permits_subset
Population
Animal = 'Beef'
4013
NPDES 2020 Dairy Cattle
livestock_npdes_state_permits_subset
Population
Animal = 'Dairy'
402
FAO 2010 Pig
fao_Pig_2010_Da_nlcdproj_masked
DN
4021
NPDES 2020 Swine
livestock_npdes_state_permits_subset
Population
Animal = 'Swine'
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
403
FAO 2010 Chicken
fao_Chicken_2010_Da_nlcdproj_masked
DN
138
-------
Animal =
4031
NPDES 2020 Chicken
livestock_npdes_state_permits_subset
Population
'Chicken'
404
FAO 2010 Goat
fao_Goat_2010_Da_nlcdproj_masked
DN
4041
NPDES 2020 Goat
livestock_npdes_state_permits_subset
Population
Animal = 'Goat'
405
FAO 2010 Horse
fao_Horse_2010_Da_nlcdproj_masked
DN
406
FAO 2010 Sheep
fao_Sheep_2010_Da_nlcdproj_masked
DN
4071
NPDES 2020 Turkey
livestock_npdes_state_permits_subset
Population
Animal = 'Turkey'
508
Public Schools
public_schools_2018_2019
TOTAL
650
Refineries and Tank Farms
eia 2015 us oil
NONE
669
All Abandoned Wells
AW ALL COUNTS 669 2022
ACTIVITY
All Abandoned CBM Wells -
6696
Plugged
AW CBM PLUGGED 6696 2022
ACTIVITY
6693
All Abandoned CBM Wells
AW_CBM_PLUGGED_UNPLUGGED_6693_202
2
ACTIVITY
All Abandoned Gas Wells -
6695
Plugged
AW GAS PLUGGED 6695 2022
ACTIVITY
6692
All Abandoned Gas Wells
AW_GAS_PLUGGED_UNPLUGGED_6692_202
2
ACTIVITY
All Abandoned Gas Wells -
6698
Unplugged
AW GAS UNPLUGGED 6698 2022
ACTIVITY
All Abandoned Oil Wells -
6694
Plugged
AW OIL PLUGGED 6694 2022
ACTIVITY
6691
All Abandoned Oil Wells
AW OIL PLUGGED UNPLUGGED 6691 2022
ACTIVITY
All Abandoned Oil Wells -
6697
Unplugged
AW OIL UNPLUGGED 6697 2022
ACTIVITY
670
Spud Count - CBM Wells
SPUD CBM 670 2022
ACTIVITY
671
Spud Count - Gas Wells
SPUD GAS 671 2022
ACTIVITY
ASSOCIATED GAS PRODUCTION 672 20
672
Gas Production at Oil Wells
22
ACTIVITY
CONDENSATE CBM PRODUCTION 673
673
Oil Production at CBM Wells
2022
ACTIVITY
Unconventional Well
COMPLETIONS UNCONVENTIONAL 674
674
Completion Counts
2022
ACTIVITY
676
Well Count - All Producing
TOTAL PROD WELL 676 2022
ACTIVITY
677
Well Count - All Exploratory
TOTAL EXPL WELL 677 2022
ACTIVITY
678
Completions at Gas Wells
COMPLETIONS GAS 678 2022
ACTIVITY
679
Completions at CBM Wells
COMPLETIONS CBM 679 2022
ACTIVITY
681
Spud Count - Oil Wells
SPUD OIL 681 2022
ACTIVITY
683
Produced Water at All Wells
PRODUCED WATER ALL 683 2022
ACTIVITY
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
6831
Produced Water at CBM Wells
PRODUCED WATER CBM 6831 2022
ACTIVITY
6832
Produced Water at Gas Wells
P RO D UCED_WATE R_GAS_683 2_20 22
ACTIVITY
139
-------
6833
Produced Water at Oil Wells
PRODUCED WATER OIL 6833 2022
ACTIVITY
685
Completions at Oil Wells
COMPLETIONS OIL 685 2022
ACTIVITY
686
Completions at All Wells
COMPLETIONS ALL 686 2022
ACTIVITY
687
Feet Drilled at All Wells
FEET DRILLED 687 2022
ACTIVITY
689
Gas Produced - Total
TOTAL GAS PRODUCTION 689 2022
ACTIVITY
691
Well Counts - CBM Wells
CBM WELLS 691 2022
ACTIVITY
692
Spud Count - All Wells
SPUD ALL 692 2022
ACTIVITY
693
Well Count - All Wells
TOTAL WELL 693 2022
ACTIVITY
694
Oil Production at Oil Wells
OIL PRODUCTION 694 2022
ACTIVITY
695
Well Count - Oil Wells
OIL WELLS 695 2022
ACTIVITY
696
Gas Production at Gas Wells
GAS PRODUCTION 696 2022
ACTIVITY
697
Oil Production at Gas Wells
CO N D E N SATE_GAS_PRO D UCTIO N_697_2
022
ACTIVITY
698
Well Count - Gas Wells
GAS WELLS 698 2022
ACTIVITY
699
Gas Production at CBM Wells
CBM PRODUCTION 699 2022
ACTIVITY
711
Airport Areas
airport_area
area
801
Port Areas
Ports 2014NEI
area_sqmi
850
Golf Courses
u sa_go lf_co u rses_2019_10
NONE
860
Mines
usgs_mrds_active_mines
NONE
861
Sand and Gravel Mines
usgs_mrds_active_mines
NONE
CAT='Gravel'
862
Lead Mines
usgs_mrds_active_mines
NONE
CAT='Lead'
863
Crushed Stone Mines
usgs_mrds_active_mines
NONE
CAT='Stone'
900
OSM Fuel
osm_fuel_points_us_mar2023
NONE
901
OSM Asphalt Surfaces
osm_asphalt_surfaces_us_mar2023
NONE
902
OSM Unpaved Roads
osm_unpaved_roads_us_mar2023
NONE
The 'Data Shapefile' used for all of the U.S. surrogates except for those based on HPMS data is
cb_2020_us_county_500k, while the HPMS-based surrogates use hpms2017_v3_04052020. Similarly,
most surrogates use the GEOID as the Data attribute while the HPMS surrogates use FIPS. The gapfilling
configuration for the surrogates is shown in Table 3-19. If there are no entries for a county for the
primary surrogate, the values for the county from the secondary surrogate are used. If there are also no
entries for the secondary surrogate, the values for the tertiary surrogate are used, with the quarternary
surrogate being the final fallback. Typically, only surrogates that should have values for all counties are
selected as the quarternary surrogate. This process is used to limit any emissions that could be dropped
if there are emissions in the inventory in a county for which the primary surrogate does not have values.
It is important to note that once gapfilling is performed, SMOKE does not know that emissions for that
county were from a secondary, tertiary or quarternary surrogate and any reports will assign the
emissions in gapfilled counties to the primary surrogate.
140
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Table 3-19. Surrogates used to gapfill U.S. Surrogates
SURROGATE
CODE
SURROGATE
SECONDARY
SURROGATE
TERTIARY
SURROGATE
QUARTERNARY
SURROGATE
100
Population
110
Housing
Population
135
Detached Housing
NLCD Low Intensity
Development
136
Single and Dual Unit Housing
NLCD Low Intensity
Development
137
Single + Dual Unit +
Manufactured Housing
NLCD Low Intensity
Development
NLCD Land
150
Residential Heating - Natural
Gas
Population
170
Residential Heating -
Distillate Oil
Housing
180
Residential Heating-Coal
Housing
190
Residential Heating - LP Gas
Housing
205
Extended Idle Locations
Total Road Miles
239
Total Road AADT
Total Road Miles
240
Total Road Miles
242
All Restricted AADT
Total Road Miles
244
All Unrestricted AADT
Total Road Miles
259
Transit Bus Terminals
Population
NLCD Land
260
Total Railroad Miles
Total Road Miles
Population
261
NTAD Total Railroad Density
Total Railroad Miles
Total Road Miles
Population
271
NTAD Class 12 3 Railroad
Density
NTAD Total Railroad
Density
Total Railroad Miles
Total Road Miles
300
NLCD Low Intensity
Development
Housing
Population
NLCD Land
304
NLCD Open + Low
Housing
Population
NLCD Land
305
NLCD Low + Med
Housing
Population
NLCD Land
306
NLCD Med + High
Housing
Population
NLCD Land
307
NLCD All Development
Housing
Population
NLCD Land
308
NLCD Low + Med + High
Housing
Population
NLCD Land
309
NLCD Open + Low + Med
Housing
Population
NLCD Land
310
NLCD Total Agriculture
NLCD Open + Low
NLCD Land
318
NLCD Pasture Land
Housing
NLCD Land
319
NLCD Crop Land
Housing
NLCD Land
320
NLCD Forest Land
Housing
NLCD Land
321
NLCD Recreational Land
Housing
NLCD Land
340
NLCD Land
350
NLCD Water
401
FAO 2010 Cattle
NLCD Total Agriculture
NLCD Open + Low
4011
FAO 2010 Large Cattle
Operations
FAO 2010 Cattle
NLCD Total
Agriculture
NLCD Open + Low
4012
NPDES 2020 Beef Cattle
FAO 2010 Cattle
NLCD Total
Agriculture
NLCD Open + Low
141
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SURROGATE
CODE
SURROGATE
SECONDARY
SURROGATE
TERTIARY
SURROGATE
QUARTERNARY
SURROGATE
4013
NPDES 2020 Dairy Cattle
FAO 2010 Large Cattle
Operations
NLCD Total
Agriculture
NLCD Open + Low
402
FAO 2010 Pig
NLCD Total Agriculture
NLCD Open + Low
4021
NPDES 2020 Swine
FAO 2010 Pig
NLCD Total
Agriculture
NLCD Open + Low
403
FAO 2010 Chicken
NLCD Total Agriculture
NLCD Open + Low
4031
NPDES 2020 Chicken
FAO 2010 Chicken
NLCD Total
Agriculture
NLCD Open + Low
404
FAO 2010 Goat
NLCD Total Agriculture
NLCD Open + Low
4041
NPDES 2020 Goat
FAO 2010 Goat
NLCD Total
Agriculture
NLCD Open + Low
405
FAO 2010 Horse
NLCD Total Agriculture
NLCD Open + Low
406
FAO 2010 Sheep
NLCD Total Agriculture
NLCD Open + Low
4071
NPDES2020 Turkey
NLCD Total Agriculture
NLCD Open + Low
508
Public Schools
Population
NLCD Land
650
Refineries and Tank Farms
NLCD Low + Med
Population
NLCD Land
669
All Abandoned Wells
Well Count - All Wells
NLCD Open + Low
6696
All Abandoned CBM Wells -
Plugged
All Abandoned CBM
Wells
Well Count - All
Wells
NLCD Open + Low
6693
All Abandoned CBM Wells
Well Count - All Wells
NLCD Open + Low
6695
All Abandoned Gas Wells -
Plugged
All Abandoned Gas
Wells
Well Count - All
Wells
NLCD Open + Low
6692
All Abandoned Gas Wells
Well Count - All Wells
NLCD Open + Low
6698
All Abandoned Gas Wells -
Unplugged
All Abandoned Gas
Wells
Well Count - All
Wells
NLCD Open + Low
6694
All Abandoned Oil Wells -
Plugged
All Abandoned Oil
Wells
Well Count - All
Wells
NLCD Open + Low
6691
All Abandoned Oil Wells
Well Count - All Wells
NLCD Open + Low
6697
All Abandoned Oil Wells -
Unplugged
All Abandoned Oil
Wells
Well Count - All
Wells
NLCD Open + Low
670
Spud Count - CBM Wells
Spud Count - All Wells
Well Count - All
Wells
671
Spud Count - Gas Wells
Well Count - Gas Wells
Well Count - All
Wells
672
Gas Production at Oil Wells
NLCD Open + Low
Well Count - Oil
Wells
Well Count - All
Wells
673
Oil Production at CBM Wells
Well Count-CBM
Wells
Well Count - All
Wells
NLCD Open + Low
674
Unconventional Well
Completion Counts
Completions at All
Wells
Well Count - All
Wells
NLCD Open + Low
676
Well Count - All Producing
Well Count - All Wells
NLCD Open + Low
677
Well Count - All Exploratory
Well Count - All Wells
NLCD Open + Low
678
Completions at Gas Wells
Spud Count - All Wells
Well Count - All
Wells
NLCD Open + Low
679
Completions at CBM Wells
Spud Count - All Wells
Well Count - All
Wells
NLCD Open + Low
681
Spud Count - Oil Wells
Well Count - Oil Wells
Well Count - All
Wells
NLCD Open + Low
142
-------
SURROGATE
CODE
SURROGATE
SECONDARY
SURROGATE
TERTIARY
SURROGATE
QUARTERNARY
SURROGATE
683
Produced Water at All Wells
Completions at All
Wells
Well Count - All
Wells
NLCD Open + Low
6831
Produced Water at CBM
Wells
Well Counts - CBM
Wells
Well Count - All
Wells
NLCD Open + Low
6832
Produced Water at Gas Wells
Well Count - Gas Wells
Well Count - All
Wells
NLCD Open + Low
6833
Produced Water at Oil Wells
Well Count - Oil Wells
Well Count - All
Wells
NLCD Open + Low
685
Completions at Oil Wells
Spud Count - All Wells
Well Count - All
Wells
NLCD Open + Low
686
Completions at All Wells
Well Count - All
Exploratory
Well Count - All
Wells
NLCD Open + Low
687
Feet Drilled at All Wells
Well Count - All
Exploratory
Well Count - All
Wells
NLCD Open + Low
689
Gas Produced - Total
Well Count - All Wells
NLCD Open + Low
691
Well Counts - CBM Wells
Completions at CBM
Wells
Well Count - All
Wells
NLCD Open + Low
692
Spud Count - All Wells
Completions at All
Wells
Well Count - All
Wells
NLCD Open + Low
693
Well Count - All Wells
NLCD Open + Low
694
Oil Production at Oil Wells
Completions at Oil
Wells
Well Count - All
Wells
NLCD Open + Low
695
Well Count - Oil Wells
Completions at Oil
Wells
Well Count - All
Wells
NLCD Open + Low
696
Gas Production at Gas Wells
Completions at Gas
Wells
Well Count - All
Wells
NLCD Open + Low
697
Oil Production at Gas Wells
Well Count - Gas Wells
Well Count - All
Wells
NLCD Open + Low
698
Well Count - Gas Wells
Completions at Gas
Wells
Well Count - All
Wells
NLCD Open + Low
699
Gas Production at CBM Wells
Well Counts - CBM
Wells
Well Count - All
Wells
NLCD Open + Low
711
Airport Areas
Population
NLCD Land
801
Port Areas
NLCD Water
850
Golf Courses
Housing
Population
NLCD Land
860
Mines
NLCD Open + Low
NLCD Land
861
Sand and Gravel Mines
Mines
NLCD Open + Low
NLCD Land
862
Lead Mines
Mines
NLCD Open + Low
NLCD Land
863
Crushed Stone Mines
Mines
NLCD Open + Low
NLCD Land
900
OSM Fuel
Total Road AADT
Total Road Miles
901
OSM Asphalt Surfaces
NLCD All Development
902
OSM Unpaved Roads
NLCD Open + Low
For the onroad sector, the on-network (RPD) emissions were spatially allocated differently from other
off-network processes (i.e., RPV, RPP, RPHO, RPS, RPH). Surrogates for on-network processes are based
on AADT data and off network processes (including the off-network idling included in RPHO) are based
on land use surrogates as shown in Table 3-20. 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.
143
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The underlying data for this surrogate were updated during the development of the various 2016
platforms to include additional data sources and corrections based on comments received and these
updates were carried into this platform.
Table 3-20. 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
Other Bus
306
NLCD Med + High
42
Transit Bus
259
Transit Bus Terminals
43
School Bus
508
Public Schools
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 3-21 using 2022 data consistent with what was used to develop the nonpoint oil and gas
emissions. The exploration and production of oil and gas have generally increased in terms of quantities
and locations over recent years, primarily due to the use of new technologies, such as hydraulic
fracturing. Census-tract, 2-km, and 4-km sub-county Shapefiles were developed, from which the 2020
oil and gas surrogates were generated. All spatial surrogates for np_oilgas are developed based on
known locations of oil and gas activity for year 2022.
The primary activity data source used for the development of the oil and gas spatial surrogates was data
from ENVERUS [formerly Drilling Info (Dl) Desktop's HPDI] database (ENVERUS, 2023). This database
contains well-level location, production, and exploration statistics at the monthly level. Due to a
proprietary agreement with ENVERUS, 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 (Alaska, Arizona, Idaho, Illinois, Indiana, Kentucky,
Louisiana, Michigan, Mississippi, Missouri, Nevada, Oregon, Pennsylvania, and Tennessee). In cases
when the desired surrogate parameter was not available (e.g., feet drilled), data for an alternative
surrogate parameter (e.g., number of spudded wells) were downloaded and used. Under that
methodology, both completion date and date of first production from HPDI were used to identify wells
completed during 2022. The spatial surrogates were gapfilled using fallback surrogates as shown in Table
3-19. All gapfilling was performed with the Surrogate Tool.
Table 3-21. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
669
All Abandoned Wells
144
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Surrogate Code
Surrogate Description
6691
All Abandoned Oil Wells
6692
All Abandoned Gas Wells
6693
All Abandoned CBM Wells
6694
All Abandoned Oil Wells - Plugged
6695
All Abandoned Gas Wells - Plugged
6696
All Abandoned CBM Wells - Plugged
6697
All Abandoned Oil Wells - Unplugged
6698
All Abandoned Gas Wells - Unplugged
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
689
Gas Produced - Total
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
6831
Produced water at CBM wells
6832
Produced water at gas wells
6833
Produced water at oil wells
Table 3-22 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by sector assigned to each
spatial surrogate.
145
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Table 3-22. Selected 2022 CAP emissions by sector for U.S. Surrogates (short tons in 12US1)
Sector
ID
Description
NH3
NOX
PM2_5
S02
voc
afdust
240
Total Road Miles
0
0
305,537
0
0
afdust
306
NLCD Med + High
0
0
41,167
0
0
afdust
308
NLCD Low + Med + High
0
0
122,726
0
0
afdust
310
NLCD Total Agriculture
0
0
502,702
0
0
afdust
861
Sand and Gravel Mines
0
0
271
0
0
afdust
863
Crushed Stone Mines
0
0
291
0
0
afdust
902
OSM Unpaved Roads
0
0
852,397
0
0
afdust
4012
NPDES 2020 Beef Cattle
0
0
185,956
0
0
afdust
4013
NPDES 2020 Dairy Cattle
0
0
12,408
0
0
afdust
4021
NPDES 2020 Swine
0
0
630
0
0
afdust
4031
NPDES 2020 Chicken
0
0
4,948
0
0
afdust
4071
NPDES2020 Turkey
0
0
1,948
0
0
fertilizer
310
NLCD Total Agriculture
1,671,401
0
0
0
0
livestock
405
FAO 2010 Horse
31,973
0
0
0
2,558
livestock
406
FAO 2010 Sheep
18,425
0
0
0
1,474
livestock
4012
NPDES 2020 Beef Cattle
775,290
0
0
0
62,023
livestock
4013
NPDES 2020 Dairy Cattle
350,829
0
0
0
28,066
livestock
4021
NPDES 2020 Swine
839,869
0
0
0
67,190
livestock
4031
NPDES 2020 Chicken
473,844
0
0
0
37,908
livestock
4041
NPDES 2020 Goat
17,609
0
0
0
1,409
livestock
4071
NPDES2020 Turkey
82,538
0
0
0
6,603
nonpt
100
Population
454
0
0
0
36
nonpt
150
Residential Heating - Natural Gas
47,317
228,596
2,638
1,522
13,491
nonpt
170
Residential Heating - Distillate Oil
1,718
29,360
3,626
738
1,246
nonpt
180
Residential Heating - Coal
0
2
1
7
2
nonpt
190
Residential Heating - LP Gas
136
39,187
156
175
1,539
nonpt
239
Total Road AADT
0
0
0
0
6,536
nonpt
244
All Unrestricted AADT
0
0
0
0
98,151
nonpt
271
NTAD Class 12 3 Railroad Density
0
0
0
0
2,074
nonpt
300
NLCD Low Intensity Development
155
2,315
12,856
180
21,920
nonpt
306
NLCD Med + High
17,744
245,613
372,811
66,676
131,535
nonpt
307
NLCD All Development
0
0
0
0
19
nonpt
308
NLCD Low + Med + High
1,066
176,213
18,723
5,179
10,910
nonpt
310
NLCD Total Agriculture
517
311
504
31
440
nonpt
319
NLCD Crop Land
0
0
95
70
292
nonpt
320
NLCD Forest Land
0
11
31
0
44
nonpt
650
Refineries and Tank Farms
0
0
0
0
98,366
nonpt
711
Airport Areas
0
0
0
0
414
nonpt
801
Port Areas
0
0
0
0
2,351
nonpt
900
OSM Fuel
0
0
0
0
221,575
nonpt
4011
FAO 2010 Large Cattle Operations
0
0
0
0
295,993
nonroad
136
Single and Dual Unit Housing
100
14,634
2,946
38
90,886
146
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Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nonroad
261
NTAD Total Railroad Density
3
1,484
146
1
314
nonroad
304
NLCD Open + Low
6
1,580
140
4
5,554
nonroad
305
NLCD Low + Med
5
869
1,028
2
21,946
nonroad
306
NLCD Med + High
387
155,659
8,689
256
99,729
nonroad
307
NLCD All Development
113
28,711
16,198
44
185,409
nonroad
308
NLCD Low + Med + High
597
202,020
16,431
231
41,323
nonroad
309
NLCD Open + Low + Med
134
21,959
1,310
51
50,916
nonroad
310
NLCD Total Agriculture
355
214,932
14,943
158
23,324
nonroad
320
NLCD Forest Land
15
1,614
379
7
3,423
nonroad
321
NLCD Recreational Land
79
13,629
4,747
28
173,733
nonroad
350
NLCD Water
203
111,936
3,865
95
220,708
nonroad
850
Golf Courses
13
2,143
123
5
6,017
nonroad
860
Mines
2
2,316
210
1
423
nP_oilgas
670
Spud Count - CBM Wells
0
0
0
0
43
nP_oilgas
671
Spud Count - Gas Wells
0
0
0
0
2,275
nP_oilgas
674
Unconventional Well Completion
Counts
51
41,657
742
19
1,877
nP_oilgas
678
Completions at Gas Wells
0
6,122
130
1,773
14,674
np_oilgas
679
Completions at CBM Wells
0
5
0
750
694
np_oilgas
681
Spud Count - Oil Wells
0
0
0
0
28,651
np_oilgas
683
Produced Water at All Wells
0
0
0
0
48
np_oilgas
685
Completions at Oil Wells
0
384
0
2,218
33,301
np_oilgas
687
Feet Drilled at All Wells
0
79,175
1,823
47
2,881
np_oilgas
689
Gas Produced - Total
0
232
26
2
58,012
np_oilgas
691
Well Counts - CBM Wells
0
19,717
469
10
15,442
np_oilgas
692
Spud Count - All Wells
0
15
1
1
1
np_oilgas
694
Oil Production at Oil Wells
0
3,428
0
31,148
801,395
np_oilgas
695
Well Count - Oil Wells
0
170,141
4,207
243,928
668,363
np_oilgas
696
Gas Production at Gas Wells
0
2,738
0
0
422,743
np_oilgas
698
Well Count - Gas Wells
3,771
352,214
4,846
142
471,083
np_oilgas
699
Gas Production at CBM Wells
0
32
4
0
3,816
np_oilgas
6694
All Abandoned Oil Wells - Plugged
0
0
0
0
115
np_oilgas
6695
All Abandoned Gas Wells - Plugged
0
0
0
0
64
np_oilgas
6697
All Abandoned Oil Wells - Unplugged
0
0
0
0
166,197
np_oilgas
6698
All Abandoned Gas Wells - Unplugged
0
0
0
0
14,255
np_oilgas
6831
Produced water at CBM wells
0
0
0
0
1,024
np_oilgas
6832
Produced water at gas wells
0
340
0
0
10,113
np_oilgas
6833
Produced water at oil wells
0
0
0
0
68,474
np_solvents
100
Population
0
0
0
0
1,376,197
np_solvents
240
Total Road Miles
0
0
0
0
43,466
np_solvents
306
NLCD Med + High
0
0
0
0
391,245
np_solvents
307
NLCD All Development
0
0
0
0
235,011
np_solvents
308
NLCD Low + Med + High
0
0
0
0
31,056
np_solvents
310
NLCD Total Agriculture
0
0
0
0
173,739
147
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Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
np_solvents
901
OSM Asphalt Surface
0
0
0
0
339,778
onroad
205
Extended Idle Locations
0
33,669
265
17
2,724
onroad
242
All Restricted AADT
58,506
724,836
18,562
2,918
110,498
onroad
244
All Unrestricted AADT
119,030
1,031,723
41,897
5,264
301,493
onroad
259
Transit Bus Terminals
20
1,458
30
1
468
onroad
304
NLCD Open + Low
0
467
13
0
2,532
onroad
306
NLCD Med + High
1,217
97,909
2,136
75
22,427
onroad
307
NLCD All Development
5,938
157,433
6,858
444
515,072
onroad
308
NLCD Low + Med + High
292
16,565
482
27
26,500
onroad
508
Public Schools
19
1,984
59
1
392
openburn
135
Detached Housing
0
16,359
81,108
2,724
18,946
openburn
300
NLCD Low Intensity Development
2,704
1,113
4,159
226
4,514
openburn
307
NLCD All Development
76,463
28,172
126,918
10,917
81,324
rail
261
NTAD Total Railroad Density
16
26,427
763
18
1,249
rail
271
NTAD Class 12 3 Railroad Density
287
430,178
10,685
324
17,539
rwc
135
Detached Housing
6,875
9,428
135,997
3,348
126,771
rwc
137
Single + Dual Unit+ Manufactured
Housing
15,722
35,166
312,817
8,545
324,110
3.4.2 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 file that lists the nonpoint sources to locate using point data was
unchanged from the 2005-based platform.
3.4.3 Surrogates for Canada and Mexico emission inventories
The surrogates for Canada to spatially allocate the Canadian emissions are based on the 2020 Canadian
inventories and associated data. The spatial surrogate data came from ECCC, along with cross
references. The shapefiles they provided were used in the Surrogate Tool (previously referenced) to
create spatial surrogates. The Canadian surrogates used for this platform are listed in Table 3-23. The
Shapefiles used to compute these surrogates and some configuration information are shown in Table
3-24. Note that the name of most Data Shapefiles have been abbreviated to shorten the table. The
complete names and additional details on surrogate computation for Canada and Mexico are available in
the file Surrogate_specifications_2022_platform_US_Can_Mex.xlsx that is posted in the reports folder
for this platform.
Mexico surrogates were updated for the 2021 EMP. The data source for the Mexico population
surrogate is the INEGI National Geostatistical Framework's Censo de Poblacion y Vivienda 2020 based on
the 2020 GPW v4 (see https://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=889463807469 ).
Other data sources used are Sistema Nacional de Informacion Estadistic y Geografica (SNIEG), US
Department of Transportation's (DOT) North American Rail Network Lines, and US DOT's Bureau of
148
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Transportation Statistics Border Crossing Data. The Shapefiles and some configuration information used
to develop the Mexico surrogates are shown in Table 3-25. The Data Shapefile for all Mexico surrogates
is areas_geoestadisticas_municipales_ll and the Data Attribute is FIPS. Most of the CAP emissions
allocated to the Mexico and Canada surrogates are shown in Table 3-26.
Table 3-23. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
100
Population
925
Manufacturing and Assembly
101
total dwelling
926
Distribution and Retail (no petroleum)
102
urban dwelling
927
Commercial Services
103
rural dwelling
933
Rail-Passenger
104
capped total dwelling
934
Rail-Freight
105
capped meat cooking dwelling
935
Rail-Yard
106
ALL INDUST
940
PAVED ROADS NEW
113
Forestry and logging
945
Commercial Marine Vessels
116
Total Resources
946
Construction and mining
200
Urban Primary Road Miles
948
Forest
210
Rural Primary Road Miles
949
Combination of Dwelling
211
Oil and Gas Extraction
951
Wood Consumption Percentage
212
Mining except oil and gas
952
Residential Fuel Wood Combustion (PIRD)
220
Urban Secondary Road Miles
955
UN PAVED ROADS AND TRAILS
221
Total Mining
960
TOTBEEF
222
Utilities
961
80110 Broilers
230
Rural Secondary Road Miles
962
80111_Cattle_dairy_and_Fleifer
233
Total Land Development
963
80112_Cattle_non-Dairy
240
capped population
964
80113_Laying_hens_and_Pullets
308
Food manufacturing
965
80114 Florses
321
Wood product manufacturing
966
80115_Sheep_and_Lamb
323
Printing and related support activities
967
80116 Swine
Petroleum and coal products
324
manufacturing
968
80117_Turkeys
Plastics and rubber products
326
manufacturing
969
80118 Goat
Non-metallic mineral product
327
manufacturing
970
TOTPOUL
331
Primary Metal Manufacturing
971
80119 Buffalo
340
Construction - Oil and Gas
972
80120_Llama_and_Alpacas
350
Water
973
80121 Deer
Petroleum product wholesaler-
412
distributors
974
80122 Elk
448
clothing and clothing accessories stores
975
80123 Wild boars
Waste management and remediation
562
services
976
80124 Rabbit
SCL:12003 Petroleum Liquids
601
Transportation (PIRD)
977
80125_Mink
149
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Code
Canadian Surrogate Description
Code
Description
SCL12007 Oil Sands In-Situ Extraction
602
and Processing (PIRD)
978
80126 Fox
SCL12010 Light Medium Crude Oil
603
Production (PIRD)
980
TOTSWIN
604
SCL12011 Well Drilling (PIRD)
981
Harvest Annual
605
SCL12012 Well Servicing (PIRD)
982
Harvest Perennial
606
SCL12013 Well Testing (PIRD)
983
Synthfert_Annual
607
SCL12014 Natural Gas Production (PIRD)
984
Syn thfert_ Perennial
608
SCL12015 Natural Gas Processing (PIRD)
985
Tillage_Annual
SCL12016 Heavy Crude Oil Cold
609
Production (PIRD)
990
TOTFERT
SCL12018 Disposal and Waste Treatment
610
(PIRD)
996
urban area
SCL12019 Accidents and Equipment
611
Failures (PIRD)
1251
OFFR TOTFERT
SCL12020 Natural Gas Transmission and
612
Storage(PIRD)
1252
OFFR MINES
651
MEIT C1C2 Anchored
1253
OFFR Other Construction not Urban
652
MEIT C1C2 Underway
1254
OFFR Commercial Services
653
MEIT C1C2 Berthed
1255
OFFR Oil Sands Mines
661
MEIT C3 Anchored
1256
OFFR Wood industries CANVEC
662
MEIT C3 Underway
1257
OFFR UNPAVED ROADS RURAL
663
MEIT C3 Berthed
1258
OFFR Utilities
901
AIRPORT
1259
OFFR total dwelling
902
Military LTO
1260
OFFR water
903
Commercial LTO
1261
OFFR ALL INDUST
904
General Aviation LTO
1262
OFFR Oil and Gas Extraction
905
Air Taxi LTO
1263
OFFR ALLROADS
921
Commercial Fuel Combustion
1264
OFFR AIRPORT
TOTAL INSTITUTIONAL AND
923
GOVERNEMNT
1265
OFFR RAILWAY
924
Primary Industry
Table 3-24. Shapefiles and Attributes used to Compute Canadian Spatial Surrogates
Code
Surrogate
Data Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
100
Population
gPr_gda
pruid
da_popdwell_100m_nolakes
lnovl7
Pop
101
total dwelling
gPr_gda
pruid
da_popdwell_100m_nolakes
lnovl7
Urdwell
102
urban dwelling
gPr_gda
pruid
da_popdwell_100m_nolakes
lnovl7
Uadwell
103
rural dwelling
gPr_gda
pruid
da_popdwell_100m_nolakes
lnovl7
Radwell
104
capped total dwelling
gPr_gda
pruid
da_popdwell_100m_nolakes
_lnovl7
CAP_URDWEL
150
-------
Code
Surrogate
Data Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
105
capped meat cooking dwelling
gpr
pruid
da_SimP_100m_pop_dwellJ
ul2014
Cap_Dwell
106
ALL INDUST
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
ALL INDUST
111
Farms
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
FARMS
113
Forestry and logging
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
FORLOG
116
Total Resources
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
TOTRESOURC
1251
OFFR TOTFERT
gcd
CDID
naesi fert
TOTFERT
1252
OFFR MINES
gcd
CDID
mine
MINES
1253
OFFR Other Construction not
Urban
gcd
CDID
construction other
TOTAL
1254
OFFR Commercial Services
gcd
CDID
da2006_pop_labour_SimP_
MaxOff 100m noLake
COMSER
1255
OFFR Oil Sands Mines
gcd
CDID
OS MinePit D v2
1256
OFFR Wood industries CANVEC
gcd
CDID
wood industries
WOOD
1257
OFFR UNPAVED ROADS RURAL
gcd
CDID
unpaved_ur
1258
OFFR Utilities
gcd
CDID
da2006_pop_labour_SimP_
MaxOff 100m noLake
UTILITIES
1259
OFFR total dwelling
gcd
CDID
da2006_pop_labour_SimP_
MaxOff 100m noLake
DATDWELL20
1260
OFFR water
gcd
CDID
lulOO valid
1261
OFFR ALL INDUST
gcd
CDID
da2006_pop_labour_SimP_
MaxOff 100m noLake
ALL INDUST
1262
OFFR Oil and Gas Extraction
gcd
CDID
da2006_pop_labour_SimP_
MaxOff 100m noLake
OILGASEXTR
1263
OFFR ALLROADS
gcd
CDID
allroads
1264
OFFR AIRPORT
gcd
CDID
offroad_osm_airport_locs_s
pring2017
Movements
1265
OFFR RAILWAY
gcd
CDID
sh p_ra i lway_ca n vec Ju 117_v
2
LENGTH
200
Urban Primary Road Miles
gcd_ON4
CDID
NRN_CA_Simp2_16Apr2016_
sphere
Classl
210
Rural Primary Road Miles
gcd_ON4
CDID
NRN_CA_Simp2_16Apr2016_
sphere
Class2
211
Oil and Gas Extraction
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
OILGASEXTR
212
Mining except oil and gas
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
MINING2
215
Oil Sands Mines
prov2006
pruid
OS MinePit D v2
216
Oil Sands Tailing Ponds
prov2006
pruid
OS_WetTailing_D_2015
217
Oil Sands Plants
prov2006
Pruid
OS PlantSite D 2015
220
Urban Secondary Road Miles
gcd_ON4
CDID
NRN_CA_Simp2_16Apr2016_
sphere
Class3
221
Total Mining
prov2006
Pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
TOTALMI3
222
Utilities
prov2006
Pruid
da2006_pop_labour_SimP_
MaxOff_100m_noLake
UTILITIES
151
-------
Data
Weight
Code
Surrogate
Data Shapefile
Attribute
Weight Shapefile
Attribute
NRN_CA_Simp2_16Apr2016_
230
Rural Secondary Road Miles
gcd_ON4
CDID
sphere
Class4
da2006_pop_labour_SimP_
233
Total Land Development
prov2006
Pruid
MaxOff 100m noLake
TOTLND
da_popdwell_100m_nolakes
240
capped population
gcd_ON4
CDID
lnovl7
CAPURPOP
da2006_pop_labour_SimP_
308
Food manufacturing
prov2006
Pruid
MaxOff 100m noLake
FOODMANU
da2006_SimplifyP_250m_sp
321
Wood product manufacturing
prov2006
Pruid
here_treesa_Clip
WOODMANU
Printing and related support
da2006_pop_labour_SimP_
323
activities
prov2006
pruid
MaxOff 100m noLake
PRINTSUPRT
Petroleum and coal products
da2006_pop_labour_SimP_
324
manufacturing
prov2006
pruid
MaxOff 100m noLake
PETCOLMANU
Plastics and rubber products
da2006_pop_labour_SimP_
326
manufacturing
prov2006
pruid
MaxOff 100m noLake
PLASTCMANU
Non-metallic mineral product
da2006_pop_labour_SimP_
327
manufacturing
prov2006
pruid
MaxOff 100m noLake
MINERLMANU
da2006_pop_labour_SimP_
331
Primary Metal Manufacturing
prov2006
pruid
MaxOff 100m noLake
METALMANU
loc land UOG2015 CO v3
340
Construction - Oil and Gas
gPr_gda
pruid
Que_NB_NS
350
Water
coast
pruid
CONT42_pop_water_Clip_b
Pop
Petroleum product wholesaler-
da2006_pop_labour_SimP_
412
distributors
prov2006
pruid
MaxOff 100m noLake
PETPRWSL
Building material and supplies
da2006_pop_labour_SimP_
416
wholesaler-distributors
prov2006
pruid
MaxOff 100m noLake
BUILDPRWSL
da2006_pop_labour_SimP_
447
Gasoline stations
prov2006
pruid
MaxOff 100m noLake
GASSTOR
clothing and clothing
da2006_pop_labour_SimP_
448
accessories stores
prov2006
pruid
MaxOff 100m noLake
CLOTHSTOR
da2006_pop_labour_SimP_
482
Rail transportation
prov2006
pruid
MaxOff 100m noLake
RAILTRANS
Waste management and
da2006_pop_labour_SimP_
562
remediation services
prov2006
pruid
MaxOff 100m noLake
WASTEMGMT
offroad_osm_airport_locs_s
901
AIRPORT
gcd
CDID
pring2017
Movements
aviation_runways_spring201
902
Military LTO
surg_2017
FAKEFIPS
7
Military
aviation_runways_spring201
903
Commercial LTO
surg_2017
FAKEFIPS
7
Commercial
aviation_runways_spring201
904
General Aviation LTO
surg_2017
FAKEFIPS
7
General Av
Airport_movements_2006_
905
Air Taxi LTO
prov2006
pruid
MultiRingBuffer
SCC2275060
da2006_pop_labour_SimP_
921
Commercial Fuel Combustion
prov2006
pruid
MaxOff 100m noLake
COMFUEL
TOTAL INSTITUTIONAL AND
da2006_pop_labour_SimP_
923
GOVERNEMNT
prov2006
pruid
MaxOff 100m noLake
TOTINSTGOV
da2006_pop_labour_SimP_
924
Primary Industry
prov2006
pruid
MaxOff_100m_noLake
PRIM1
152
-------
Data
Weight
Code
Surrogate
Data Shapefile
Attribute
Weight Shapefile
Attribute
da2006_pop_labour_SimP_
925
Manufacturing and Assembly
prov2006
pruid
MaxOff 100m noLake
MANASSEM
Distribution and Retail (no
da2006_pop_labour_SimP_
926
petroleum)
prov2006
pruid
MaxOff 100m noLake
DISRET
da2006_pop_labour_SimP_
927
Commercial Services
prov2006
pruid
MaxOff 100m noLake
COMSER
sh p_ra i lway_ca n vec Ju 117_v
933
Rail-Passenger
gPr_gda
pruid
2
Passenger
sh p_ra i lway_ca n vec Ju 117_v
934
Rail-Freight
gPr_gda
pruid
2
Fret
sh p_ra i lway_ca n vec Ju 117_v
935
Rail-Yard
gPr_gda
pruid
2
Yard
NRN_CA_Simp2_16Apr2016_
940
PAVED ROADS NEW
gpr
fips
sphere
PAVEDRD
942
UNPAVED ROADS
prov2006
pruid
unpaved4
945
Commercial Marine Vessels
lowmedjetjl
CLASS
marine
S02
MERGE: 0.5*Mining except
oil and gas+0.5*Total Land
946
Construction and mining
Development
MERGE 0.34*Total Resources
Agriculture Construction and
+ 0.66 * Construction and
947
mining
mining
948
Forest
prov2006
pruid
treesa valid
MERGE: 0.20*urban
dwelling+0.80* rural
949
Combination of Dwelling
dwelling
Wood Consumption
da2006_SimP_100m_WoodC
951
Percentage
gpr
fips
on_lAugl4
WoodComp
UNPAVED ROADS AND TRAIL
955
S
prov2006
pruid
unpaved5
960
TOTBEEF
prov2006
pruid
naesi livestk
TOTBEEF
970
TOTPOUL
prov2006
pruid
naesi livestk
TOTPOULT
980
TOTSWIN
prov2006
pruid
naesi livestk
TOTSWIN E
990
TOTFERT
prov2006
pruid
naesi fert
TOTFERT
996
urban area
prov2006
pruid
ua2001
animal nh3 to agri sic 801
961
80110 Broilers
gPr_gda
pruid
10 valid
QUANTITY
80111_Cattle_dairy_and_Heife
animal nh3 to agri sic 801
962
r
gPr_gda
pruid
11 valid
QUANTITY
animal nh3 to agri sic 801
963
80112_Cattle_non-Dairy
gPr_gda
pruid
12 valid
QUANTITY
80113_Laying_hens_and_Pulle
animal nh3 to agri sic 801
964
ts
gPr_gda
pruid
13 valid
QUANTITY
animal nh3 to agri sic 801
965
80114 Horses
gPr_gda
pruid
14 valid
QUANTITY
animal nh3 to agri sic 801
966
80115_S h ee p_a n d_La m b
gPr_gda
pruid
15 valid
QUANTITY
animal nh3 to agri sic 801
967
80116_Swine
gPr_gda
pruid
16_valid
QUANTITY
153
-------
Data
Weight
Code
Surrogate
Data Shapefile
Attribute
Weight Shapefile
Attribute
animal nh3
to
agri
sic
801
968
80117_Turkeys
gPr_gda
pruid
17 valid
QUANTITY
animal nh3
to
agri
sic
801
969
80118 Goat
gPr_gda
pruid
18 valid
QUANTITY
animal nh3
to
agri
sic
801
971
80119 Buffalo
gPr_gda
pruid
19 valid
QUANTITY
animal nh3
to
agri
sic
801
972
80120_Uama_and_Alpacas
gPr_gda
pruid
20 valid
QUANTITY
animal nh3
to
agri
sic
801
973
80121 Deer
gPr_gda
pruid
21 valid
QUANTITY
animal nh3
to
agri
sic
801
974
80122 Elk
gPr_gda
pruid
22 valid
QUANTITY
animal nh3
to
agri
sic
801
975
80123 Wild boars
gPr_gda
pruid
23 valid
QUANTITY
animal nh3
to
agri
sic
801
976
80124 Rabbit
gPr_gda
pruid
24 valid
QUANTITY
animal nh3
to
agri
sic
801
977
80125 Mink
gPr_gda
pruid
25 valid
QUANTITY
animal nh3
to
agri
sic
801
978
80126 Fox
gPr_gda
pruid
26 valid
QUANTITY
animal nh3
to
agri
sic
801
979
80127 Mules and Asses
gPr_gda
pruid
27 valid
QUANTITY
h a rvest_p m 10_An n u a l_to_a
981
Harvest Annual
gPr_gda
pruid
gri_slc_valid
QUANTITY
h a rvest_p m 10_Pe re n n i a l_to
982
Harvest Perennial
gPr_gda
pruid
_agri_slc_valid
QUANTITY
synth_fert_nh3_Annual_to_a
983
Synthfert_Annual
gPr_gda
pruid
gri_slc_valid
QUANTITY
synth_fert_nh3_Perennial_t
984
Synthfert_Perennial
gPr_gda
pruid
o_agri_slc_valic
QUANTITY
tillage_pmlO_Annual_to_agr
985
Tillage_Annual
gPr_gda
pruid
i sic valid
QUANTITY
SCL:12003 Petroleum Liquids
601
Transportation (PIRD)
gPr_gda
pruid
scl 12003 valid
SCL:12007 Oil Sands In-Situ
Extraction and Processing
602
(PIRD)
gPr_gda
pruid
scl 12007 valid
NONE
SCL:12010 Light Medium Crude
603
Oil Production (PIRD)
gPr_gda
pruid
scll2010 valid
NONE
604
SCL:12011 Well Drilling (PIRD)
gPr_gda
pruid
scll2011 valid
NONE
SCL:12012 Well Servicing
605
(PIRD)
gPr_gda
pruid
scll2012 valid
NONE
606
SCL:12013 Well Testing (PIRD)
gPr_gda
pruid
scll2013 valid
NONE
SCL:12014 Natural Gas
607
Production (PIRD)
gPr_gda
pruid
scll2014 valid
NONE
SCL:12015 Natural Gas
608
Processing (PIRD)
gPr_gda
pruid
scll2015 valid
NONE
SCL:12016 Heavy Crude Oil
609
Cold Production (PIRD)
gPr_gda
pruid
scll2016 valid
NONE
SCL:12018 Disposal and Waste
610
Treatment (PIRD)
gPr_gda
pruid
scll2018_valid
NONE
154
-------
Code
Surrogate
Data Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
611
SCL:12019 Accidents and
Equipment Failures (PIRD)
gPr_gda
pruid
scll2019 valid
NONE
612
SCL:12020 Natural Gas
Transmission and Storage
(PIRD)
gPr_gda
pruid
scll2020
NONE
952
Residential Fuel Wood
Combustion (PIRD)
gPr_gda
pruid
scl20401 valid
NONE
651
MEITC1C2 Anchored
lowmedjetjl
CLASS
MEIT 2280002101 2018
Fuel
652
MEITC1C2 Underway
lowmedjetjl
CLASS
MEIT 2280002202 2018
Fuel
653
MEITC1C2 Berthed
lowmedjetjl
CLASS
MEIT 2280002301 2018
Fuel
661
MEITC3 Anchored
lowmedjetjl
CLASS
MEIT 2280003101 2018
Fuel
662
MEIT C3 Underway
lowmedjetjl
CLASS
MEIT 2280003200 2018
Fuel
663
MEITC3 Berthed
lowmedjetjl
CLASS
MEIT_2280003301_2018
Fuel
Table 3-25. Shapefiles and Attributes used to Compute Mexican Spatial Surrogates
Code
SURROGATE
WEIGHT SHAPEFILE
WEIGHT
ATTRIBUTE
10
MEX Population
mex_population_2020
gridcode_Y
22
MEXTotal Road Miles
mex roads
NONE
24
MEX Total Railroads Miles
mex railroads
NONE
26
MEX Total Agriculture
mex_agriculture
NONE
36
MEX Commercial plus Industrial Land
mex com ind land
NONE
44
MEX Airports Area
m ex_a i rports_a rea
NONE
45
MEX Airports Point
mex_airports_point
NONE
48
MEX Brick Kilns
mex brick kilns
NONE
50
MEX Border Crossings
mex_border_crossings
SUM_Value
Table 3-26. 2022 CAP Emissions Allocated to Mexican and Canadian Spatial Surrogates for 12US1
(short tons)
Code
Mexican or Canadian Surrogate
Description
NH3
NO*
PM2.5
SO2
voc
11
MEX Population
26,149
93,951
8,245
7,833
178,980
22
MEX Total Road Miles
2,887
310,214
14,588
6,483
76,211
24
MEX Total Railroads Miles
0
22,455
498
198
900
26
MEX Total Agriculture
137,457
11,648
13,703
13,570
2,370
36
MEX Commercial plus Industrial Land
44
5,532
2,531
26
295,777
44
MEX Airports Area
0
2,955
61
315
1,832
48
MEX Brick Kilns
0
227
3,692
151
182
50
MEX Mobile sources - Border Crossing
4
86
3
0
65
100
CAN Population
710
57
225
17
4,025
101
CAN total dwelling
0
0
0
0
109,016
104
CAN Capped Total Dwelling
305
31,578
2,383
1,928
1,620
106
CAN ALLJNDUST
596
155
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NOx
PM2.5
SO2
voc
113
CAN Forestry and logging
83
627
2,934
15
2,715
200
CAN Urban Primary Road Miles
1,590
75,668
2,697
209
7,406
210
CAN Rural Primary Road Miles
608
40,578
1,422
89
2,995
212
CAN Mining except oil and gas
0
0
1,785
0
0
220
CAN Urban Secondary Road Miles
2,985
120,376
5,476
406
19,742
221
CAN Total Mining
0
0
13,564
0
0
222
CAN Utilities
0
1,998
2,751
32
89
230
CAN Rural Secondary Road Miles
1,613
75,161
2,728
211
7,997
240
CAN Total Road Miles
345
45,969
1,175
41
82,324
308
CAN Food manufacturing
0
0
17,199
0
5,233
321
CAN Wood product manufacturing
513
1,677
591
213
8,464
323
CAN Printing and related support
activities
0
0
0
0
20,852
324
CAN Petroleum and coal products
manufacturing
0
1,056
1,481
439
6,751
326
CAN Plastics and rubber products
manufacturing
0
0
0
0
21,858
327
CAN Non-metallic mineral product
manufacturing
0
0
7,206
0
0
331
CAN Primary Metal Manufacturing
0
148
5,247
28
62
412
CAN Petroleum product wholesaler-
distributors
0
0
0
0
37,775
448
CAN clothing and clothing accessories
stores
0
0
0
0
178
562
CAN Waste management and
remediation services
2,707
1,230
2,300
2,159
16,100
601
CAN SCL12003 Petroleum Liquids
Transportation (PIRD)
0
0
12
154
6,042
602
CAN SCL12007 Oil Sands In-Situ
Extraction and Processing (PIRD)
0
0
0
0
110
603
CAN SCL12010 Light Medium Crude
Oil Production (PIRD)
0
0
0
0
2
604
CAN SCL12011 Well Drilling (PIRD)
0
0
0
607
658
605
CAN SCL12012 Well Servicing (PIRD)
0
0
0
68
73
606
CAN SCL12013 Well Testing (PIRD)
0
0
0
0
0
607
CAN SCL12014 Natural Gas
Production (PIRD)
0
28
1
0
191
608
CAN SCL12015 Natural Gas
Processing (PIRD)
0
0
0
0
0
611
CAN SCL:12019 Accidents and
Equipment Failures (PIRD)
0
0
0
0
90,229
612
CAN SCL12020 Natural Gas
Transmission and Storage (PIRD)
1
671
54
11
396
901
CAN Airport
0
98
9
0
0
156
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NOx
PM2.5
SO2
voc
921
CAN Commercial Fuel Combustion
190
21,587
2,373
435
940
923
CAN TOTAL INSTITUTIONAL AND
GOVERNEMNT
0
0
0
0
14,522
924
CAN Primary Industry
0
0
0
0
33,308
925
CAN Manufacturing and Assembly
0
0
0
0
70,606
926
CAN Distribution and Retail (no
petroleum)
0
0
0
0
6,666
927
CAN Commercial Services
0
0
0
0
30,828
933
CAN Rail-Passenger
1
3,089
63
1
115
934
CAN Rail-Freight
48
76,567
1,530
43
3,389
935
CAN Rail-Yard
1
4,536
95
1
276
940
CAN Paved Roads New
0
0
26,017
0
0
946
CAN Construction and Mining
44
2,842
163
281
41
951
CAN Wood Consumption Percentage
1,061
11,794
71,798
1,685
100,154
955
CAN U NPAVED_ROADS_AND_TRAILS
0
0
433,847
0
0
961
CAN 80110_Broilers
13,453
0
115
0
12,782
962
CAN 80111_Cattle_dairy_and_Heifer
61,989
0
276
0
40,501
963
CAN 80112_Cattle_non-Dairy
177,740
0
884
0
42,860
964
CAN 80113_Laying_hens_and_Pullets
10,085
0
40
0
10,592
965
CAN 80114_Horses
3,155
0
19
0
1,320
966
CAN 80115_Sheep_and_Lamb
2,278
0
6
0
170
967
CAN 80116_Swine
64,225
0
824
0
9,945
968
CAN 80117_Turkeys
5,215
0
41
0
4,507
969
CAN 80118_Goat
1,806
0
2
0
135
971
CAN 80119_Buffalo
2,258
0
6
0
517
972
CAN 80120_Llama_and_Alpacas
118
0
0
0
0
973
CAN 80121_Deer
20
0
0
0
0
974
CAN 80122_Elk
19
0
0
0
0
975
CAN 80123_Wild boars
37
0
0
0
0
976
CAN 80124_Rabbit
78
0
0
0
1
977
CAN 80125_Mink
287
0
0
0
951
978
CAN 80126_Fox
4
0
0
0
3
981
CAN Harvest_Annual
0
0
24,824
0
0
983
CAN Synthfert_Annual
164,425
3,513
2,111
5,807
127
985
CAN Tillage_Annual
0
0
106,806
0
0
996
CAN urban_area
0
0
3,716
0
0
1251
CAN OFFR_TOTFERT
84
59,946
4,056
57
113
1252
CAN OFFR_MINES
1
573
40
1
0
1253
CAN OFFR Other Construction not
Urban
68
37,617
4,378
46
231
1254
CAN OFFR Commercial Services
47
16,663
2,499
40
11,046
1255
CAN OFFR Oil Sands Mines
0
0
0
0
0
157
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NOx
PM2.5
SO2
voc
1256
CAN OFFR Wood industries CANVEC
9
3,245
257
7
86
1257
CAN OFFR Unpaved Roads Rural
24
10,275
642
21
934
158
-------
4 Analytic Year Emissions Inventories and Approaches
The emission inventories for the analytic year 2026 have been developed using projection methods that
are specific to the type of emissions source. Analytic year emissions are projected from the base year
either by running models to estimate analytic 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). The types of changes accounted for in the analytic year
inventories include changes in expected activity data for the sector (e.g, VMT for onroadway sources)
and changes in emission rates per unit of activity between the years. Emission rates can be predicted to
change due to the adoption of improved processes, changes in the fuels used, market-driven impacts, or
on-the-books regulations. In this platform, on-the-books federal and some state regulations that
impacted CAPs that were on-the-books as of April 2024 are reflected.
For some sectors, the same emissions are used in the base and analytic years, such as biogenic, all fire
sectors, and fertilizer. Emissions for these sectors are held constant in future years because the base
year meteorological data are also used for the future year air quality model runs, and emissions for
these sectors are highly correlated with meteorological conditions. 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. For sectors that were projected, the methods used to project
those sectors to the analytic year(s) for this platform are summarized in Table 4-1.
Table 4-1. Overview of projection methods by sector for the analytic years
Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
EGU units:
ptegu
For 2026, an engineering analysis approach was used to develop emissions based
on the most recently available measured emissions. More information on this
sector including a list of included rules is provided in Section 4.1.
Point source oil and
gas:
pt_oilgas
The production-related sources were grown from 2022 to 2026 based on growth
factors derived from the Annual Energy Outlook (AEO) 2023 data for oil, natural
gas, or a combination thereof. The grown emissions were then controlled to
account for the impacts of New Source Performance Standards (NSPS) for oil and
gas sources, process heaters, natural gas turbines, and reciprocating internal
combustion engines (RICE). Known closures were also applied to the 2022
pt_oilgas sources. See Section 4.2.3.8 and several subsections of Section 4.2.4 for
more details.
Airports:
airports
Point source airport emissions were grown from 2022 to 2026 using factors
derived from the 2023 Terminal Area Forecast (TAF) released in January 2024 (see
https://www.faa.gov/data research/aviation/taf/). Factors outside of a specific
range were set to state average factors. Analytic year emissions for the ATL
airport were provided by the state of Georgia. See Section 4.2.3.2 for more
details.
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Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
Remaining non-EGU
point:
ptnonipm
2022 emissions were projected to 2026 using factors derived from AEO2023.
Controls were applied to account for relevant NSPS for RICE, gas turbines, and
process heaters. Emissions were reduced to account for NESHAP rules related to
Hazardous Organic Compounds, Organic Liquids Distribution, and Taconite.
Known closures were applied to the 2022 ptnonipm sources. Railyards are grown
using the projection factors from the rail sector. Additional state-specific controls
were applied. See Section 4.2.3.9 and several subsections of Section 4.2.4 for
more details.
Category 1, 2 CMV:
cmv_clc2
Category 1 and category 2 (C1C2) CMV emissions sources outside of California
were projected to 2026 based on factors derived from the Freight Analysis
Framework version 5. See the Category 3 CMV documentation (EPA, 2024a) for
more details on the development of the projection factors for both C1C2 and C3
CMV vessels. See Section 4.2.3.3 for more details.
Category 3 CMV:
cmv_c3
Category 3 (C3) CMV emissions were projected to 2026 based on factors derived
from the Freight Analysis Framework version 5. An additional adjustment to NOx
was made to account for the penetration of cleaner engines over time based on
an extrapolation of trends from recent ship registry data sets. See the Category 3
CMV documentation (EPA, 2024a) for more details on the development of the
factors. See Section 4.2.3.4 for more details.
Locomotives:
rail
Rail emissions were projected based on factors derived for categories of
locomotives based on AEO (fuel use) growth rates including some adjustments.
See Section 4.2.3.10 for more details.
Area fugitive dust:
afdust
Paved road dust was grown to 2026 levels based on the growth in VMT from
2022. Emissions for the remainder of the sector were based on a combination of
employment projections and livestock projection data. See Section 4.2.3.1 for
more details.
Livestock: livestock
Livestock were projected from 2022 to 2026 using factors derived from
projections of animal counts from the Greenhouse Gas Inventory Tool versus the
base year animal counts. See Section 4.2.3.5 for more details.
Nonpoint source oil
and gas:
np_oilgas
Exploration-related sources were based on a multi-year average of 2017 through
2019 exploration data with NSPS controls applied, where applicable. Production-
related emissions were projected from 2022 to 2026 based on factors generated
from AEO2023 reference case. 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. See Section 4.2.3.9, Section 4.2.4.land Section 4.2.4.2 for more details.
Residential Wood
Combustion:
rwc
RWC emissions were held constant at 2022 levels for 2026. See Section 4.2.3.11
for more details.
Solvents:
np_solvents
Emissions were projected from 2022 to 2026 by multiplying base year emissions
by factors based on the ratio of the 'growth surrogate' for the analytic year
divided by the value for the base year. Growth surrogates were based on human
population, employment projections, and VMT projections. Controls were applied
to reflect various national rules. State-specific controls were applied. See Section
4.2.3.7 and Section 4.2.4.6 for more details.
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Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
Remaining nonpoint:
nonpt
Projected base year to 2026 by multiplying base year emissions by factors derived
from AEO 2023, human population projections, and employment projections.
Controls were applied to reflect NSPS rules for reciprocating internal combustion
engines (RICE). State-specific controls were also applied. See Section 4.2.3.6 and
Section 4.2.4.2 for more details.
Nonroad:
nonroad
Outside of California, MOVES4 was run for 2026. The fuels used are specific to the
analytic year, but the meteorological data represented the year 2022. Adjusted
growth factors were used for North Carolina nonroad industrial based on
information from NC. For CA 2016 platform inventories were used for 2026. See
Section 4.3.1 for more details.
Onroad:
on road
VMT was projected from 2022 to 2026 using projection factors based on
AEO2023 projections and applied nationally by fuel type and broad vehicle type
(light duty, medium duty for buses and single unit trucks, and heavy duty for
combination trucks). Diesel light duty cars were held flat in projections, but diesel
light duty trucks were projected using the AEO. Light duty VMT projections also
incorporated a county-level adjustment based on projected human population
trends, so that counties expected to grow more than the national average in
population receive a corresponding increase in VMT for those counties, and vice
versa. Four states (NJ, NY, NC, and Wl) provided VMT for each analytic year.
Additionally, projection factors were developed and applied to estimate the
impact of federal rules that are on the books but were not included in MOVES4
for all states. See Section 4.3.2 for more details.
Onroad California:
onroad_ca_adj
For California, emissions were provided by CARB for 2026. Additionally, projection
factors were developed and applied to estimate the impact of federal rules that
are on the books. See Section 4.3.2 for more details.
Canada Area Fugitive
dust:
canada_afdust
Area fugitive dust emissions were provided by ECCC for 2026. Mexico emissions
are not included in this sector. See Section 4.3.3.1 for more details.
Canada Point
Fugitive dust:
canadajptdust
Point source fugitive dust emissions were provided by ECCC for 2026. Mexico
emissions are not included in this sector. See Section 4.3.3.1 for more details.
Canada and Mexico
point sources:
canmex_point
Canada point source emissions were provided by ECCC for 2026. Mexico point
sources are held constant from the base year 2022 inventories. See Section
4.3.3.2 for more details.
Canada and Mexico
ag:
canmexjag
Canada agricultural emissions were provided by ECCC for 2026. Mexico
agricultural sources are held constant from the base year 2022 inventories. See
Section 4.3.3.2 for more details.
Canada oil and gas
2D:
canada_og2D
Low-level point oil and gas sources from the ECCC 2026 point source inventories.
See Section 4.3.3.2 for more details.
Canada and Mexico
nonpoint (except ag)
and nonroad:
canmex area
Canada nonpoint and nonroad emissions were provided by ECCC for 2026.
Mexico nonpoint and nonroad sources are held constant from the base year 2022
inventories. See Section 4.3.3.3 for more details.
Other non-NEI
onroad sources:
canada_onroad
For Canadian mobile onroad sources, analytic year inventories used Environment
and Climate Change Canada (ECCC) provided emissions for 2026. See Section
4.3.3.4 for more details.
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Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
Other non-NEI
onroad sources:
mexico_onroad
Monthly onroad mobile inventories were developed at municipio resolution
based on an interpolation of runs of MOVES-Mexico done for the 2016 platform
for 2026. See Section 4.3.3.4 for more details.
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4.1 EGU Point Source Projections (ptegu)
The analytic year EGU emissions inventories relied on Engineering Analysis for 2026.
Details on the development of the analytic year EGU emissions are as follows:
• EPA's 2026 Engineering Analysis emissions developed with the most recent data available as of
summer 2024:
• The starting point was 2023 NOx, S02, and Hg emissions reported to Clean Air and Power
Division (CAPD): https://campd.epa.gov/
• Known unit retirements, coal to gas conversions, control retrofits, unit specific rate
adjustments due to BART or state RACT rules, and new unit construction from the January
2024 NEEDS (which is equivalent to the data in the June 2024 NEEDS database).
• PM, VOC, NH3, and CO emissions were calculated using NEI 2022 and Energy Information
Administration (EIA) 860/923 emissions factors and CAPD generation data.
• No additional Good Neighbor Plan (GNP) related changes reflected in 2026 inventory. All but
two states were under their respective state budgets in 2023; these two states were under
their assurance levels in 2023.
• The 2026 engineering analysis data included emissions according to ORIS and CAPD IDs. The
Engineering Analysis units were matched to EIS facility and unit IDs using existing CAPD-EIS
matches from the 2022 base year point inventory and NEEDS database. For units with a CAPD
-EIS match, units from 2022 were retained, with emissions adjusted to match the engineering
analysis. All units from 2022 which were not matched in the engineering analysis were
carried forward to 2026 with the same emissions, except for units listed as retired in the 2026
analysis. For all units in the 2026 analysis which were not matched to a unit in the 2022 base
year inventory, new units were created with new point source IDs, SCCs for natural gas EGUs,
and default stack parameters.
Data files and summaries related to the analytic year EGU emissions are posted in the point reports
section of the FTP site.
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.
EPA's 2023 Reference case using IPM reflects current and existing state regulations, Renewable Portfolio
Standards and Clean Energy Standards as of end of 2023.
Some of the key parameters used in the IPM run are:
• Demand: AEO 2023 non-EV demand + on-the-books OTAQ GHG LMDV and HDV Rules
• Gas and Coal Market assumptions: Gas market assumptions as of end of 2021 (with LNG export
assumptions from AEO 2023) and coal market assumptions as of end of 2021 with adjustments
for historic consumption
• Cost and performance of fossil generation technologies: AEO 2023
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• Cost and performance of renewable energy generation technologies: NREL ATB 2023 (mid-case)
• Fleet: NEEDS rev 06-06-2024 (xlsx)
To maintain a temporal pattern consistent with the base year, the NOX and S02 values in the base year
hourly CEMS inventories are projected to match the total seasonal emissions values in analytic years as
described in Section 3.3.3.
The EGU sector NOx emissions by state are listed in Table 4-2 for the cases that comprise this platform.
The state total emissions in this table may not exactly match the sum of the emissions for each state in
the flat files for each season due to the process of apportioning seasonal total emissions to hours for
input to SMOKE followed by summing the daily emissions back up to annual. However, any difference
should be well within one percent of the state total emissions.
Table 4-2. EGU sector NOx emissions by State for the 2022vl cases
State
2022hc
2026hc
Alabama
16,510
13,773
Arizona
15,668
9,637
Arkansas
17,015
14,550
California
5,816
5,757
Colorado
17,778
12,496
Connecticut
3,076
2,654
Delaware
911
462
District of Columbia
NA
NA
Florida
38,816
33,010
Georgia
20,636
19,122
Idaho
1,420
1,680
Illinois
20,575
10,239
Indiana
41,679
25,883
Iowa
16,966
14,182
Kansas
13,554
9,477
Kentucky
31,989
28,366
Louisiana
31,107
21,147
Maine
3,594
3,406
Maryland
4,405
3,584
Massachusetts
5,584
5,309
Michigan
29,158
18,484
Minnesota
14,491
11,131
Mississippi
16,333
12,262
Missouri
48,204
34,976
Montana
10,459
10,382
Nebraska
20,178
18,453
Nevada
4,488
2,101
New Flampshire
1,504
1,167
New Jersey
4,835
4,332
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State
2022hc
2026hc
New Mexico
6,604
2,913
New York
13,762
11,768
North Carolina
26,865
24,036
North Dakota
28,897
28,549
Ohio
31,933
22,299
Oklahoma
18,700
18,150
Oregon
2,775
2,207
Pennsylvania
27,252
16,826
Rhode Island
302
619
South Carolina
14,016
13,900
South Dakota
1,144
1,085
Tennessee
8,262
6,834
Texas
93,611
86,224
Tribal Areas
8,412
7,616
Utah
23,396
9,442
Vermont
194
109
Virginia
12,598
10,898
Washington
7,659
3,201
West Virginia
30,156
22,916
Wisconsin
10,985
8,468
Wyoming
26,411
19,591
4.2 Sectors with Projections Computed using CoST
To project U.S. emissions for sectors other than EGUs, facility/unit closures information, growth
(projection) factors and/or controls were applied to certain categories within those sectors. Some facility
or sub-facility-level closure information was 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 NEI (e.g., biodiesel
and cellulosic plants, yet-to-be constructed cement kilns). This subsection provides details on the data
and projection methods used to develop analytic year emissions for sectors other than EGUs that were
developed using the Control Strategy Tool.
Because the projection and control data are developed mostly independently from how the emissions
modeling sectors are defined, 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 multiple 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
analytic year inventories: 1) for point sources, apply facility or sub-facility-level closure information via
CoST; 2) apply all PROJECTION packets via CoST (these contain multiplicative factors that could cause
increases or decreases); 3) apply all percent reduction-based CONTROL packets via CoST; and 4) append
any other analytic-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
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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 following the subsection titles.
The impacts of the projection and control factors on the emissions for each sector are shown in tables in
this section. In addition, the actual projection and control factors used to develop the analytic year
emissions are shown when they are general enough to fit into a table of reasonable length, although in
some cases, there are hundreds or thousands of factors used and the tables would be too large. To see
these factors, visit the spreadsheets under projection controls on the FTP site for this platform.
4.2.1 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 base year emissions modeling inventories to create analytic year inventories for the
following sectors: afdust, airports, cmv, livestock, nonpt, np_oilgas, np_solvents, pt_oilgas, ptnonipm,
rail, and rwc. Information about CoST and related data sets is available from
https://www.epa.gov/economic-and-cost-analvsis-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. Using these CoST datasets, also called "packets" or "programs,"
supports the process of developing and quality assuring control assessments as well as creating SMOKE-
ready analytic year (i.e., projected) inventories. Analytic year inventories are created for each emissions
modeling sector by applying a CoST control strategy type called "Project future year inventory" and each
strategy includes all base year inventories and applicable CoST packets. For reasons to be discussed
later, some emissions modeling sectors may 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 measure 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:
• CLOSURE: Closure packets are 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 release point. The
EPA uses these types of packets for known post-base year controls as well as information on
closures provided by states on specific facilities, units or release points. This packet type is only
used for the ptnonipm and pt_oilgas sectors.
• PROJECTION: Projection packets support the increase or decrease in emissions for virtually any
geographic and/or inventory source level. Projection factors are applied as multiplicative factors
to the base year emissions inventories prior to the application of any possible subsequent
CONTROLS. A PROJECTION packet is necessary whenever emissions increase from the base year
and is also desirable when information is based more on activity assumptions rather than on
known control measures. The EPA uses PROJECTION packet(s) for many modeling sectors.
• CONTROL: Control packets are applied after any/all CLOSURE and PROJECTION packet entries.
They support of similar level of specificity of geographic and/or inventory source level application
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as PROJECTION packets. Control factors are expressed as a percent reduction (0 - meaning no
reduction, to 100 - meaning full reduction) and can be applied in addition to any pre-existing
inventory control, or as a replacement control. For replacement controls, any controls specified
in the inventory are first backed out prior to the application of a more-stringent replacement
control).
These packets use comma-delimited formats and are stored as data sets within the Emissions Modeling
Framework. 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 base year inventory) or a
targeted analytic-year emissions value. Therefore, controls due to consent decrees and state comments
for specific cement kilns (expressed as CONTROL packet entries) need 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 (i.e., analytic) 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=l 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 base year
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, where
the fields in the table are similar to those used 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, FACIUTYJD, UNITJD, REL_POINT_ID, PROCESSJD, SCC, POLL
point
2
REGION_CD, FACIUTYJD, UNITJD, REL_POINT_ID, PROCESSJD, POLL
point
3
REGION_CD, FACIUTYJD, UNITJD, REL_POINT_ID, POLL
point
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Rank
Matching Hierarchy
Inventory Type
4
REGION_CD, FACIUTYJD, UNITJD, POLL
point
5
REGION_CD, FACIUTYJD, SCC, POLL
point
6
REGION_CD, FACIUTYJD, POLL
point
7
REGION_CD, FACIUTYJD, UNITJD, REL_POINT_ID, PROCESSJD, SCC
point
8
REGION_CD, FACIUTYJD, UNITJD, REL_POINT_ID, PROCESSJD
point
9
REGION_CD, FACIUTYJD, UNITJD, REL_POINT_ID
point
10
REGION_CD, FACIUTYJD, UNITJD
point
11
REGION_CD, FACIUTYJD, SCC
point
12
REGION_CD, FACIUTYJD
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 analytic year cases are described in
the following subsections. Year-specific projection factors (PROJECTION packets) for each analytic year
were used to create the analytic year cases, 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 subsections that are summarized in Table 4-4. Note that independent analytic year
inventories were used rather than projection or control packets for some sources.
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Table 4-4. Summary of non-EGU projections subsections
Subsection
Title
Sector(s)
Brief Description
4.2.2
CoST Plant CLOSURE
ptnonipm,
All facility/unit/stack closures information,
packet
pt_oilgas
primarily from Emissions Inventory System (EIS),
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 analytic year.
4.2.3.1
Fugitive dust growth
afdust
PROJECTION packet: county-level resolution,
based on VMT growth plus some other surrogates
such as livestock counts.
4.2.3.2
Airport sources
airports
PROJECTION packet: by-airport for all direct
matches to FAA Terminal Area Forecast data, with
state-level factors for non-matching airports.
4.2.3.3
Category 1 and 2
commercial marine
vessels
cmv_clc2
PROJECTION packet: Category 1 & 2 growth and
control by pollutant, vessel type, and region.
4.2.3.4
Category 3 commercial
marine vessels
cmv_c3
PROJECTION packet: Category 3 growth and
control impacts by pollutant, vessel type, and
region.
4.2.3.5
Livestock population
growth
livestock
PROJECTION packet: national, by-animal type
resolution, based on animal population
projections.
4.2.3.6
Nonpoint sources
nonpt
PROJECTION packet: States projected with AEO-
based factors for many sources. Human
population used as growth for applicable sources.
4.2.3.7
Solvents
np_solvents
PROJECTION packet: including population-based,
state factors and some other surrogtes.
4.2.3.8
Oil and gas and
nonpt,
Several PROJECTION packets: varying geographic
industrial source
np_oilgas,
resolutions from state, county, and by-
growth
ptnonipm,
pt_oilgas
process/fuel-type applications. Data derived from
AEO were used for nonpt, ptnonipm, np_oilgas,
and pt_oilgas sectors.
4.2.3.9
Non-EGU Point
Sources
ptnonipm
PROJECTION packet: AEO-based projection factors
for industrial sources.
4.2.3.10
Railroads
rail
PROJECTION packet: Based on AEO and
extrapolation from recent inventories.
4.2.3.11
Residential wood
combustion
rwc
Held Constant. No growth or control in this
platform
4.2.4
CoST CONTROL
ptnonipm,
Introduces and summarizes national impacts of all
packets
nonpt,
np_oilgas,
pt_oilgas,
np_solvents
CoST CONTROL packets to the analytic year.
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Subsection
Title
Sector(s)
Brief Description
4.2.4.1
Oil and Gas NSPS
np_oilgas,
pt_oilgas
CONTROL packets: reflect the impacts of the NSPS
for oil and gas sources.
4.2.4.2
RICE NSPS
ptnonipm,
nonpt,
np_oilgas,
pt_oilgas
CONTROL packets apply reductions for lean burn,
rich burn, and combined engines for identified
SCCs.
4.2.4.3
Organic Liquids
Distribution NESHAP
ptnonipm
CONTROL packet: applies VOC reductions based
on the NESHAP for organic liquids distribution.
4.2.4.4
Natural GasTurbines
NOx NSPS
ptnonipm
CONTROL packets apply NOx emission reductions
established by the NSPS for turbines.
4.2.4.5
Process Heaters NOx
NSPS
ptnonipm
CONTROL packet: applies NOx emission limits
established by the NSPS for process heaters.
4.2.4.6
State-specific controls
nonpt,
np_solvents,
ptnonipm
CONTROL packet: applies controls specific to
certain states
4.2.2 CoST CLOSURE Packet (ptnonipm, pt_oilgas)
Packets:
closures_2022vl_platform_fromEIS_16sep2024_vl
closures_2022vl_platform_fromSLT_21feb2025_vl
The CLOSURES packets contain facility, unit and stack-level closure information. The "fromEIS" closures
packet is derived from an Emissions Inventory System (EIS) unit-level report from July 2024, with closure
status equal to "PS" (permanent shutdown; i.e., post-2022 permanent facility/unit shutdowns known in
EIS as of the date of the report). The "fromSLT" closures packet consists of any data provided by
commenters for closures, updated to match the SMOKE FF10 inventory key fields, with all duplicates
removed. These changes impact sources in the ptnonipm and pt_oilgas sectors. The cumulative
reduction in emissions for ptnonipm and pt_oilgas from closures are shown in Table 4-5.
Table 4-5. Tons reduced from all facility/unit/stack-level closures in 2026 from 2022 emissions levels
Year
Pollutant
ptnonipm
pt_oilgas
2026
CO
10,059
961
2026
NH3
363
0
2026
NOX
11,082
1,984
2026
PM10
3,297
57
2026
PM2.5
2,691
57
2026
S02
8,755
3
2026
VOC
8,299
239
170
-------
4.2.3 CoST PROJECTION Packets (afdust, airports, cmv, livestock, nonpt,
np_oilgas, np_solvents, ptnonipm, pt_oilgas, rail)
For point inventories, after the application of any/all CLOSURE packet information, the next step CoST
performs when running a control strategy is to apply all of the PROJECTION packets. Regardless of
inventory type (point or nonpoint), the PROJECTION packets are applied prior to the CONTROL packets.
For several emissions modeling sectors (e.g., airports, np_oilgas, pt_oilgas), there is only one
PROJECTION packet applied for each analytic year. For other sectors, there may be several different
sources of projection data and as a result there are multiple PROJECTION packets that are concatenated
by CoST during a control strategy run. The outputs are then quality-assured regarding duplicates and
applicability to the inventories in the CoST strategy. Similarly, CONTROL packets are kept in distinct
datasets for different control programs. Having the PROJECTION (and CONTROL) packets separated into
"key" projection and control programs allows for quick summaries of the impacts of these distinct
control programs on emissions.
Quantitative impacts of the projections on the emissions by sector nationally and by state are available
in the reports folder on the FTP site. Some excerpts from this workbook are included in the subsections
that follow.
nonpoint_projection_packet_2022_platform_2022hc_to_2026_updates_24dec2024_csv_24dec2024_v0
For paved roads (SCC 2294000000), the afdust emissions were projected to based on differences in
county total VMT as follows:
Analytic year afdust paved roads = 2022 afdust paved roads * (Analytic year county total VMT) /
The VMT projections are described in the onroad section. Unpaved road dust emissions were held
constant.
Other SCCs were projected based on the average of AEO2023 U.S. Census region specific employment
and value of shipments (VOS) data to derive growth surrogates.
Where EMP is regional and industrial sector specific employment in millions of people and VOS is
regional and industrial sector specific value of shipments (or revenue) in billion 2012 dollars. The
average of analytic year over base year specific EMP and VOS factors were used as a growth factor, GF.
SCCs in the afdust sector used surrogates to derive projection factors in similar ways as shown above for
the paved roads. Table 4-6 shows the growth indicators used to grow SCCs in the afdust sector. Table
4-7 shows the impact of the projections on the afdust sector emissions.
4.2.3.1 Fugitive dust growth (afdust)
Packets:
(2022 county total VMT)
171
-------
Table 4-6. Growth Indicators used to grow SCCs in the afdust sector
see
Sector
Growth Indicator
Source
Geography
Dust - Paved Road
2294000000 ^ Total VMT
Dust
2022vl VMT
County
2296000000
Dust - Unpaved
Road Dust
No Growth
2311010000
Dust -
Construction Dust
EMPIND25-27 (Construction:
Building, Heavy/ Civil
Engineering, Specialty Trade);
REVIND48
AEO2023
Regional (Census
Division)
2311020000
Dust -
Construction Dust
EMPIND25-27 (Construction:
Building, Heavy/Civil
Engineering, Specialty Trade);
REVIND48
AEO2023
Regional (Census
Division)
2311030000
Dust -
Construction Dust
Total VMT
2022vl VMT
County
2325000000
Industrial
Processes -
Mining
EMPIND24 (Other Mining and
Quarrying); REVIND47
AEO2023
Regional (Census
Division)
2325020000
Industrial
Processes -
Mining
EMPIND24 (Other Mining and
Quarrying); REVIND47
AEO2023
Regional (Census
Division)
2325030000
Industrial
Processes -
Mining
EMPIND24 (Other Mining and
Quarrying); REVIND47
AEO2023
Regional (Census
Division)
2325060000
Industrial
Processes - Mining
EMPIND24 (Other Mining and
Quarrying); REVIND47
AEO2023
Regional (Census
Division)
2801000000
Agr
& L
culture - Crops
vestock Dust
EMPIND20 (Crop Production);
REVIND42
AEO2023
Regional (Census
Division)
2801000003
Agr
& L
culture - Crops
vestock Dust
EMPIND20 (Crop Production);
REVIND42
AEO2023
Regional (Census
Division)
2801000005
Agr
& L
culture - Crops
vestock Dust
EMPIND20 (Crop Production);
REVIND42
AEO2023
Regional (Census
Division)
2801000008
Agr
& L
culture - Crops
vestock Dust
EMPIND20 (Crop Production);
REVIND42
AEO2023
Regional (Census
Division)
2801530000
Agr
& L
culture - Crops
vestock Dust
EMPIND21 (Other
Agriculture); REVIND44
AEO2023
Regional (Census
Division)
2805100010
Agr
& L
culture - Crops
vestock Dust
Beef Cattle surrogate
EPA State GHG
Projections Tool
State
2805100020
Agr
& L
culture - Crops
vestock Dust
Dairy Cattle surrogate
EPA State GHG
Projections Tool
State
2805100030
Agr
& L
culture - Crops
vestock Dust
Young Chickens surrogate
EPA State GHG
Projections Tool
State
2805100040
Agr
& L
culture - Crops
vestock Dust
Young Chickens surrogate
EPA State GHG
Projections Tool
State
172
-------
see
Sector
Growth Indicator
Source
Geography
2805100050
Agriculture - Crops
& Livestock Dust
Hog surrogate
EPA State GHG
Projections Tool
State
2805100060
Agriculture - Crops
& Livestock Dust
Turkey surrogate
EPA State GHG
Projections Tool
State
Table 4-7. Increase in afdust PM2.5 emissions from projections
Sector
Year
PM2.5 Emissions
Percent Increase vs
2022
Paved Roads
2022
308,622
N/A
All afdust
2022
2,048,850
N/A
Paved Roads
2026
321,732
4.2%
All afdust
2026
2,073,013
1.2%
4.2.3.2 Airport sources (airports)
Packets:
airport_projections_itn_taf2023_2022_2026_for_2022vl_platform_09aug2024_v0
Airport emissions were projected based on factors derived from the 2023 Terminal Area Forecast (TAF)
data available from the Federal Aviation Administration (see
https://www.faa.gov/data research/aviation/taf/).
Projection factors were computed using the ratio of the itinerant (ITN) data from the Airport Operations
table between the base and projection year. Where possible, airport-specific projection factors were
used. For airports that could not be matched to a unit in the TAF data, state default growth factors by
itinerant class (i.e., commercial, air taxi, and general) were created from the set of unmatched airports.
Emission growth factors for facilities from 2022 to each analytic year were limited to a range of 0.2 (80%
reduction) to 5.0 (400% growth), and the state default projection factors were limited to a range of 0.5
(50% reduction) to 2.0 (100% growth). Military state default projection values were kept flat (i.e., equal
to 1.0) to reflect uncertainly in the data regarding these sources.
Table 4-8 shows the growth factors used for major airports from 2022 to 2026, respectively. Table 4-9
shows the impacts of the projections on the emissions at airports.
Table 4-8. TAF 2023 growth factors for major airports, 2022 to 2026
Facility ID
State
Airport
Commercial
Aviation
General
Aviation
Air Taxi
10583311
Arizona
Phoenix (PHX)
1.2128
0.9948
1.1883
2255111
California
Los Angeles (LAX)
1.2852
1.1855
0.5950
9997011
California
San Francisco (SFO)
1.5693
1.0780
0.4138
9816811
Colorado
Denver(DEN)
1.3866
1.1823
0.2922
9762111
Florida
Orlando (MCO)
1.3437
0.9609
1.3465
9791511
Florida
Fort Lauderdale (FLL)
1.2919
0.9084
1.0146
9806211
Florida
Miami (MIA)
1.1722
0.9639
0.9336
173
-------
Facility ID
State
Airport
Commercial
Aviation
General
Aviation
Air Taxi
9748811
Georgia
Atlanta (ATL)
1.3163
1.1589
n/a
2681611
Illinois
Chicago O'Hare (ORD)
1.3371
1.1628
0.2221
9562811
Massachusetts
Boston (BOS)
1.1995
1.1036
1.0157
9535411
Michigan
Detroit (DTW)
1.3157
1.2204
n/a
6151711
Minnesota
Minneapolis (MSP)
1.3491
1.2410
0.3224
9392311
Nevada
Las Vegas (LAS)
1.2692
0.9862
0.8545
9376211
New Jersey
Newark (EWR)
1.1204
1.3308
0.9782
9333211
New York
La Guardia (LGA)
1.0985
1.1385
1.0505
9333311
New York
John F Kennedy (JFK)
1.2094
1.5024
0.5801
9279611
North Carolina
Charlotte (CLT)
1.2281
0.9295
0.6935
9246511
Oregon
Portland (PDX)
1.3413
1.0948
1.0077
9185011
Pennsylvania
Philadelphia (PHL)
1.2649
0.9929
0.6014
9171111
Tennessee
Memphis (MEM)
1.1364
1.0512
0.7085
9076711
Texas
Dallas/Fort Worth (DFW)
1.3230
1.0249
n/a
9128911
Texas
Flouston Intercontinental (IAH)
1.2908
1.2748
0.2224
9076611
Utah
Salt Lake City (SLC)
1.1806
1.0166
0.7177
9063811
Virginia
Washington Dulles (IAD)
1.4615
1.0917
0.5722
9093911
Washington
Seattle (SEA)
1.166
1.7739
1.1613
Table 4-9. Impact of growth factors on 2022 airport emissions for 2026
Pollutant
2022
Emissions
2026
Emissions
2026
Emissions %
Change
CO
385,527
420,226
9%
NOX
121,944
140,528
15%
PM10-PRI
9,528
10,079
6%
PM25-PRI
8,475
8,979
6%
S02
12,502
14,357
15%
VOC
46,989
51,293
9%
4.2.3.3 Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
Packets:
projection_packet_CMV_ClC2_2022_2026_csv_19aug2024_v0
Category 1 and category 2 (C1C2) CMV emissions were projected based on factors derived from the
Freight Analysis Framework version 5. An additional adjustment was applied to NOx emissions. The
adjustment factors are intended to account for fleet turnover to newer vessels that meet stricter Tier-2
and Tier-3 emissions standards. See the Category 3 CMV documentation for more details on the
development of the projection factors for both C1C2 and C3 CMV vessels. Table 4-10 shows the CMV
C1C2 emissions by broad region in the base year and 2026.
174
-------
Table 4-10. Resulting C1C2 Emissions for 2026 Compared to 2022 (tons/yr)
Region
Pollutant
2022
2026
Alaska
CO
928
1,035
Alaska
CO 2
427,960
476,873
Alaska
NH3
3
3
Alaska
NOX
5,972
6,660
Alaska
PM10
154
172
Alaska
PM2 5
150
167
Alaska
S02
15
16
Alaska
VOC
196
219
Atlantic
CO
6,606
6,675
Atlantic
CO 2
3,064,688
3,105,114
Atlantic
NH3
21
22
Atlantic
NOX
43,265
43,718
Atlantic
PM10
1,146
1,159
Atlantic
PM2 5
1,111
1,123
Atlantic
S02
131
138
Atlantic
VOC
1,523
1,538
Gulf
CO
10,250
10,865
Gulf
CO 2
5,203,728
5,517,554
Gulf
NH3
35
37
Gulf
NOX
68,743
72,870
Gulf
PM10
1,884
1,998
Gulf
PM2 5
1,826
1,935
Gulf
S02
373
397
Gulf
VOC
2,697
2,859
Hawaii
CO
239
257
Hawaii
CO 2
106,808
114,778
Hawaii
NH3
1
1
Hawaii
NOX
1,564
1,680
Hawaii
PM10
41
44
Hawaii
PM2 5
39
42
Hawaii
S02
3
3
Hawaii
VOC
52
56
Inland
CO
4,211
4,177
Inland
CO 2
1,960,291
1,950,915
Inland
NH3
16
16
Inland
NOX
29,997
29,734
Inland
PM10
843
836
Inland
PM2 5
817
810
Inland
S02
133
136
Inland
VOC
1,289
1,275
Pacific
CO
3,490
3,706
Pacific
CO 2
1,652,962
1,758,788
Pacific
NH3
11
12
Pacific
NOX
22,571
23,974
Pacific
PM10
595
633
Pacific
PM2_5
577
613
175
-------
Region
Pollutant
2022
2026
Pacific
S02
78
85
Pacific
VOC
777
825
4.2.3.4 Category 3 Commercial Marine Vessels (cmv_c3)
Packets:
projection_packet_CMV_C3_2022_2026_csv_19aug2024_v0
Category 3 (C3) CMV emissions were projected based on factors derived from the Freight Analysis
Framework version 5. An additional adjustment was applied to NOx emissions. The adjustment factors
are intended to account for fleet turnover to newer vessels that meet stricter Tier-2 and Tier-3 emissions
standards. See the Category 3 CMV documentation for more details on the development of the factors.
Table 4-11 shows the CMV C3 emissions by broad region in the base year and 2026.
Table 4-11. Resulting C3 Emissions for 2026 Compared to 2022 (tons/yr)
Region
Pollutant
2022
2026
Alaska
CO
1,043
1,156
Alaska
CO 2
638,087
706,095
Alaska
NH3
4
4
Alaska
NOX
7,977
8,352
Alaska
PM10
212
231
Alaska
PM2 5
195
213
Alaska
S02
512
555
Alaska
VOC
485
538
Atlantic
CO
19,235
20,215
Atlantic
CO 2
9,427,314
9,864,041
Atlantic
NH3
71
74
Atlantic
NOX
156,984
155,296
Atlantic
PM10
3,992
4,163
Atlantic
PM2 5
3,673
3,830
Atlantic
S02
9,225
9,599
Atlantic
VOC
9,447
9,952
Gulf
CO
13,441
14,287
Gulf
CO 2
7,419,752
7,886,079
Gulf
NH3
45
47
Gulf
NOX
113,761
114,163
Gulf
PM10
2,513
2,671
Gulf
PM2 5
2,312
2,457
Gulf
S02
5,705
6,063
Gulf
VOC
6,268
6,666
Hawaii
CO
170
182
Hawaii
CO 2
118,423
127,172
Hawaii
NH3
1
1
Hawaii
NOX
1,617
1,643
Hawaii
PM10
32
34
Hawaii
PM2_5
29
31
176
-------
Region
Pollutant
2022
2026
Hawaii
S02
72
78
Hawaii
VOC
73
78
Inland
CO
820
801
Inland
CO 2
441,645
431,914
Inland
NH3
2
2
Inland
NOX
8,121
7,488
Inland
PM10
128
125
Inland
PM2 5
118
115
Inland
S02
269
263
Inland
VOC
387
378
Pacific
CO
13,797
15,039
Pacific
CO 2
6,586,834
7,165,481
Pacific
NH3
62
67
Pacific
NOX
114,530
117,870
Pacific
PM10
3,474
3,790
Pacific
PM2 5
3,196
3,486
Pacific
S02
8,282
9,035
Pacific
VOC
6,956
7,589
4.2.3.5 Livestock population growth (livestock)
Packets:
nonpoint_projection_packet_2022_platform_2022hc_to_2026_updates_24dec2024_csv_24dec2024_v0
The 2022vl livestock emissions were projected to year 2026 using projection factors created from the
Greenhouse Gas Inventory tool (EPA, 2024b) For each analytic year, projection factors were created
based on ratios between animal inventory counts between 2026 as compared to 2022. This process was
completed for the animal categories of beef, dairy, chickens, turkeys, and swine. National factors were
used to project emissions from beef and dairy cows, and state-specific factors were used to project
emissions from swine although North Carolina requested state swine emissions to be held constant.
Other livestock categories were held flat.
The projection factors were then applied to the base year emissions for the specific animal type to
estimate the NH3 and VOC emissions for 2026 are shown in Table 4-12.
Table 4-12. Impact of 2026 projection factors on livestock
Animal
Pollutant
Inventory
Final
Emissions
Emissions %
Emissions
Emissions
Change
Change
Beef
NH3
775,290
753,970
-21,320
-2.75%
Beef
VOC
62,023
60,249
-1,774
-2.86%
Chickens
NH3
473,844
473,844
0
0.00%
Chickens
VOC
37,908
37,908
0
0.00%
Dairy
NH3
350,829
349,496
-1,333
-0.38%
Dairy
VOC
28,066
28,201
134
0.48%
Swine
NH3
839,869
867,285
27,416
3.26%
Swine
VOC
67,190
69,383
2,193
3.26%
177
-------
Animal
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
Turkeys
NH3
82,538
82,538
0
0.00%
Turkeys
VOC
6,603
6,603
0
0.00%
4.2.3.6 Nonpoint Sources (nonpt)
Packets:
nonpoint_projection_packet_2022_platform_2022hc_to_2026_updates_24dec2024_csv_24dec2024_v0
In 2022vl, SCCs in the sector for nonpoint emissions not covered in other sectors were projected based
on factors derived from specific surrogates for each SCC as identified by the Collaborative Nonpoint task
force. One of the surrogates used was population. The county-specific population dataset used to
derive changes between the base and analytic years was the Woods and Poole dataset used by BenMAP.
The AEO energy consumption projections and economic projections used for many growth surrogates
was AEO 2023. VMT-based projections are based on the final county-level VMT data developed for each
of the years of the 2022vl platform. For a complete list of nonpoint growth surrogates by SCC, see the
NP_AnalyticYr_Crosswalk spreadsheet in the reports / nonpoint folder on the FTP site. Table 4-13 shows
the impacts of the projection factors on the nonpt sector for 2026. The task force recommended no
growth for the SCCs shown in Table 4-14.
Table 4-13. Impact of 2022-2026 projection factors on nonpt emissions
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
842,395
845,504
3,110
0.4%
NH3
69,594
68,102
-1,492
-2.1%
NOX
741,248
715,965
-25,283
-3.4%
PM10-PRI
489,860
500,617
10,757
2.2%
PM25-PRI
421,788
433,078
11,289
2.7%
S02
75,760
63,123
-12,637
-16.7%
VOC
949,760
973,450
23,690
2.5%
Table 4-14. SCCs in nonpt that were held constant
SCC
Description
2801600300
Miscellaneous Area Sources; Agriculture Production - Crops; Agricultural Field Burning - Pile
Burning; Orchard Crop Other Not Elsewhere Classified
2801600320
Miscellaneous Area Sources; Agriculture Production - Crops; Agricultural Field Burning - Pile
Burning; Orchard Crop is Apple
2801600330
Miscellaneous Area Sources; Agriculture Production - Crops; Agricultural Field Burning - Pile
Burning; Orchard Crop is Apricot
2801600350
Miscellaneous Area Sources; Agriculture Production - Crops; Agricultural Field Burning - Pile
Burning; Orchard Crop is Cherry
2801600410
Miscellaneous Area Sources; Agriculture Production - Crops; Agricultural Field Burning - Pile
Burning; Orchard Crop is Peach
2801600420
Miscellaneous Area Sources; Agriculture Production - Crops; Agricultural Field Burning - Pile
Burning; Orchard Crop is Pear
178
-------
see
Description
2801600430
Miscellaneous Area Sources; Agriculture Production - Crops; Agricultural Field Burning - Pile
Burning; Orchard Crop is Prune
2801600500
Miscellaneous Area Sources; Agriculture Production - Crops; Agricultural Field Burning - Pile
Burning; Vine Crop Other Not Elsewhere Classified
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
2104008300
Stationary Source Fuel Combustion; Residential; Wood; Woodstove: freestanding, general
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
2104008530
Stationary Source Fuel Combustion; Residential; Wood; Furnace: Indoor, pellet-fired, general
2104008610
Stationary Source Fuel Combustion; Residential; Wood; Hydronic heater: outdoor
2104008620
Stationary Source Fuel Combustion; Residential; Wood; Hydronic heater: indoor
2104008630
Stationary Source Fuel Combustion; Residential; Wood; Hydronic heater: pellet-fired
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
2296000000
Mobile Sources; Unpaved Roads; All Unpaved Roads; Total: Fugitives
2461021000
Solvent Utilization; Miscellaneous Non-industrial: Commercial; Cutback Asphalt; 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)
2501011014
Storage and Transport; Petroleum and Petroleum Product Storage; Residential Portable Gas
Cans; Refilling at the Pump - Vapor Displacement
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)
2501012014
Storage and Transport; Petroleum and Petroleum Product Storage; Commercial Portable Gas
Cans; Refilling at the Pump - Vapor Displacement
2501013010
Storage and Transport; Petroleum and Petroleum Product Storage; Residential/Commercial
Portable Gas Cans; Total: All Types
2535000000
Storage and Transport; Bulk Materials Transport; All Transport Types; Total: All Products
179
-------
see
Description
2601010000
Waste Disposal, Treatment, and Recovery; On-site Incineration; Industrial; Total
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Yard Waste - Leaf
2610000100
Species Unspecified
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Yard Waste - Weed
2610000300
Species Unspecified (incl Grass)
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Yard Waste - Brush
2610000400
Species Unspecified
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Land Clearing Debris
2610000500
(use 28-10-005-000 for Logging Debris Burning)
Waste Disposal, Treatment, and Recovery; Open Burning; Residential; Household Waste (use
2610030000
26-10-000-xxx for Yard Wastes)
Waste Disposal, Treatment, and Recovery; Soil and Groundwater Remediation; All Categories;
2635000000
Total
Waste Disposal, Treatment, and Recovery; Leaking Underground Storage Tanks; Leaking
2660000000
Underground Storage Tanks; Total: All Storage Types
2701200000
Natural Sources; Biogenic; Vegetation; Total
2701220000
Natural Sources; Biogenic; Vegetation/Agriculture; Total
Miscellaneous Area Sources; Agriculture Production - Livestock; Horses and Ponies Waste
2805035000
Emissions; Not Elsewhere Classified
Miscellaneous Area Sources; Agriculture Production - Livestock; Sheep and Lambs Waste
2805040000
Emissions; Total
Miscellaneous Area Sources; Agriculture Production - Livestock; Goats Waste Emissions; Not
2805045000
Elsewhere Classified
2806010000
Miscellaneous Area Sources; Domestic Animals Waste Emissions; Cats; Total
2806015000
Miscellaneous Area Sources; Domestic Animals Waste Emissions; Dogs; Total
Miscellaneous Area Sources; Other Combustion; Managed Burning, Slash (Logging Debris);
2810005000
Unspecified Burn Method (use 2610000500 for non-logging debris)
2810035000
Miscellaneous Area Sources; Other Combustion; Firefighting Training; Total
Miscellaneous Area Sources; Other Combustion; Aircraft/Rocket Engine Firing and Testing;
2810040000
Total
Human Population Growth
For SCCs that were 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 2022 and 2026. Population data for Populations for DE, NJ, and NC were state-provided.
These human population data were used to create modified county-specific projection factors. The
impacted SCCs are shown in Table 4-15. Growth factors were limited to 10% cumulative annual growth
(e.g., four times 10% growth compounded over four years), but none of the factors fell outside that
range. The state totals used for human population are shown in Table 4-16.
Table 4-15. SCCs in nonpt that use Human Population Growth for Projections
see
Description
2302002000
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking -
Charbroiling;Charbroiling Total
180
-------
see
Description
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking -
2302002100
Charbroiling;Conveyorized Charbroiling
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking -
2302002200
Charbroiling;Under-fired Charbroiling
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking - Frying;Deep Fat
2302003000
Frying
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking - Frying;Flat
2302003100
Griddle Frying
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking - Frying;Clamshell
2302003200
Griddle Frying
2601020000
Waste Disposal, Treatment, and Recovery;On-site lncineration;Commercial/lnstitutional;Total
2620000000
Waste Disposal, Treatment, and Recovery;Landfills;AII Categories;Total
2620010000
Waste Disposal, Treatment, and Recovery;Landfills;lndustrial;Total
2620020000
Waste Disposal, Treatment, and Recovery;Landfills;Commercial/lnstitutional;Total
2620030000
Waste Disposal, Treatment, and Recovery;Landfills;Municipal;Total
Waste Disposal, Treatment, and Recovery;Landfills;Municipal;Dumping/Crushing/Spreading of
2620030001
New Materials (working face)
2630010000
Waste Disposal, Treatment, and Recovery;Wastewater Treatment;lndustrial;Total Processed
Waste Disposal, Treatment, and Recovery;Wastewater Treatment;Public Owned;Total
2630020000
Processed
Waste Disposal, Treatment, and Recovery;Wastewater Treatment;Public Owned;Wastewater
2630020010
Treatment Processes Total
2640000000
Waste Disposal, Treatment, and Recovery;TSDFs;AII TSDF Types;Total: All Processes
Waste Disposal, Treatment, and Recovery;Scrap and Waste Materials;Scrap and Waste
2650000000
Materials;Total: All Processes
Waste Disposal, Treatment, and Recovery;Scrap and Waste Materials;Scrap and Waste
2650000002
Materials;Shredding
Waste Disposal, Treatment, and Recovery;Composting;100% Biosolids (e.g., sewage sludge,
2680001000
manure, mixtures of these matls);AII Processes
Waste Disposal, Treatment, and Recovery;Composting;Mixed Waste (e.g., a 50:50 mixture of
2680002000
biosolids and green wastes);AII Processes
Waste Disposal, Treatment, and Recovery;Composting;100% Green Waste (e.g., residential or
2680003000
municipal yard wastes);AII Processes
Miscellaneous Area Sources;Other Combustion;Residential Grilling (see 23-02-002-xxx for
2810025000
Commercial);Total
2810060100
Miscellaneous Area Sources;Other Combustion;Cremation;Humans
2810060200
Miscellaneous Area Sources;Other Combustion;Cremation;Animals
2850000000
Miscellaneous Area Sources;Health Services;Hospitals;Total: All Operations
2850001000
Miscellaneous Area Sources;Health Services;Dental Alloy Production;Overall Process
2851001000
Miscellaneous Area Sources;Laboratories;Bench Scale Reagents;Total
Miscellaneous Area Sources;Fluorescent Lamp Breakage;Fluorescent Lamp Breakage;Non-
2861000000
recycling Related Emissions: Total
Miscellaneous Area Sources;Fluorescent Lamp Breakage;Fluorescent Lamp Breakage;Recycling
2861000010
Related Emissions: Total
181
-------
Table 4-16. Human population projections by state
State
2022
2026
Alabama
5,092,444
5,224,148
Arizona
7,622,773
8,128,142
Arkansas
3,172,493
3,287,089
California
41,761,812
43,390,150
Colorado
5,912,984
6,230,097
Connecticut
3,721,597
3,790,209
Delaware
1,015,140
1,055,460
District of
Columbia
691,095
709,826
Florida
22,123,665
23,374,209
Georgia
11,100,196
11,665,279
Idaho
1,803,013
1,897,210
Illinois
13,312,931
13,546,464
Indiana
6,897,517
7,059,224
Iowa
3,193,365
3,242,193
Kansas
3,041,479
3,119,802
Kentucky
4,654,934
4,788,804
Louisiana
4,890,790
5,025,848
Maine
1,397,624
1,432,823
Maryland
6,434,564
6,685,708
Massachusetts
6,982,317
7,116,327
Michigan
10,119,130
10,228,231
Minnesota
5,834,058
6,040,297
Mississippi
3,149,156
3,235,500
Missouri
6,358,501
6,519,480
Montana
1,095,591
1,136,131
Nebraska
1,977,983
2,032,652
Nevada
3,201,664
3,403,943
New Hampshire
1,406,100
1,448,565
New Jersey
9,135,956
9,244,588
New Mexico
2,296,905
2,415,541
New York
20,274,542
20,567,411
North Carolina
10,705,403
11,241,251
North Dakota
802,775
839,955
Ohio
11,879,937
12,036,028
Oklahoma
4,134,219
4,274,562
Oregon
4,296,405
4,475,608
Pennsylvania
13,127,695
13,315,053
Rhode Island
1,082,808
1,097,899
South Carolina
5,278,020
5,525,359
South Dakota
904,319
934,348
Tennessee
7,091,037
7,387,966
182
-------
State
2022
2026
Texas
30,436,322
32,427,324
Utah
3,291,357
3,489,464
Vermont
663,235
682,819
Virginia
9,095,464
9,532,252
Washington
7,761,429
8,154,313
West Virginia
1,898,285
1,926,027
Wisconsin
6,043,228
6,195,531
Wyoming
636,149
665,384
ElA's Annual Energy Outlook (AEO) Reference Case Projections
Many of the nonpoint emissions were projected using the 2023 ElA's AEO (U.S. Energy Information
Administration, 2023). The AEO is an assessment of the outlook for energy markets through 2050. These
economic projections and energy consumption projections were mapped based on emissions processes.
For economic based projections, an average of the projected change in employment and the project
change in revenue was used for the growth indicator. These SCCs are shown in Table 4-17. For more in-
depth details on the indicators see the NP_AnalyticYr_Crosswalk spreadsheet in the reports / nonpoint
folder on the FTP site
Table 4-17. Cs in nonpt that use ElA's AE for Projections
see
SCC description
Growth Indicator
2102001000
Stationary Source Fuel Combustion; Industrial; Anthracite Coal;
Total: All Boiler Types
Industrial/Other Industrial
Coal
2102002000
Stationary Source Fuel Combustion; Industrial;
Bituminous/Subbituminous Coal; Total: All Boiler Types
Industrial/Other Industrial
Coal
2102004000
Stationary Source Fuel Combustion; Industrial; Distillate Oil; Total:
Boilers and IC Engines
Industrial/Distillate Fuel Oil
2102004001
Stationary Source Fuel Combustion; Industrial; Distillate Oil; All
Boiler Types
Industrial/Distillate Fuel Oil
2102004002
Stationary Source Fuel Combustion; Industrial; Distillate Oil; All IC
Engine Types
Industrial/Distillate Fuel Oil
2102005000
Stationary Source Fuel Combustion; Industrial; Residual Oil; Total:
All Boiler Types
Industrial/Residual Fuel Oil
2102006000
Stationary Source Fuel Combustion; Industrial; Natural Gas; Total:
Boilers and IC Engines
Industrial/Natural Gas
2102007000
Stationary Source Fuel Combustion; Industrial; Liquified
Petroleum Gas (LPG); Total: All Boiler Types
Industrial/Flydrocarbon Gas
Liquids
2102008000
Stationary Source Fuel Combustion; Industrial; Wood; Total: All
Boiler Types
Industrial/Renewable Energy
2102010000
Stationary Source Fuel Combustion; Industrial; Process Gas; Total:
All Boiler Types
Industrial/Total Energy
2102011000
Stationary Source Fuel Combustion; Industrial; Kerosene; Total:
All Boiler Types
Industrial/Other Petroleum
183
-------
see
SCC description
Growth Indicator
2103001000
Stationary Source Fuel Combustion; Commercial/Institutional;
Anthracite Coal; Total: All Boiler Types
Commercial/Coal
2103002000
Stationary Source Fuel Combustion; Commercial/Institutional;
Bituminous/Subbituminous Coal; Total: All Boiler Types
Commercial/Coal
2103004000
Stationary Source Fuel Combustion; Commercial/Institutional;
Distillate Oil; Total: Boilers and IC Engines
Commercial/Distillate Fuel Oil
2103004001
Stationary Source Fuel Combustion; Commercial/Institutional;
Distillate Oil; Boilers
Commercial/Distillate Fuel Oil
2103004002
Stationary Source Fuel Combustion; Commercial/Institutional;
Distillate Oil; IC Engines
Commercial/Distillate Fuel Oil
2103005000
Stationary Source Fuel Combustion; Commercial/Institutional;
Residual Oil; Total: All Boiler Types
Commercial/Residual Fuel Oil
2103006000
Stationary Source Fuel Combustion; Commercial/Institutional;
Natural Gas; Total: Boilers and IC Engines
Commercial/Natural Gas
2103007000
Stationary Source Fuel Combustion; Commercial/Institutional;
Liquified Petroleum Gas (LPG); Total: All Combustor Types
Commercial/Propane
2103008000
Stationary Source Fuel Combustion; Commercial/Institutional;
Wood; Total: All Boiler Types
Commercial/Renewable
Energy
2103010000
Stationary Source Fuel Combustion; Commercial/Institutional;
Process Gas; POTW Digester Gas-fired Boilers
Commercial/Total Energy
2103011000
Stationary Source Fuel Combustion; Commercial/Institutional;
Kerosene; Total: All Combustor Types
Commercial/Kerosene
2104001000
Stationary Source Fuel Combustion; Residential; Anthracite Coal;
Total: All Combustor Types
Residential/Coal
2104002000
Stationary Source Fuel Combustion; Residential;
Bituminous/Subbituminous Coal; Total: All Combustor Types
Residential/Coal
2104004000
Stationary Source Fuel Combustion; Residential; Distillate Oil;
Total: All Combustor Types
Residential/Distillate Fuel Oil
2104006000
Stationary Source Fuel Combustion; Residential; Natural Gas;
Total: All Combustor Types
Residential/Natural Gas
2104007000
Stationary Source Fuel Combustion; Residential; Liquified
Petroleum Gas (LPG); Total: All Combustor Types
Residential/Propane
2104011000
Stationary Source Fuel Combustion; Residential; Kerosene; Total:
All Heater Types
Residential/Distillate Fuel Oil
2301000000
Industrial Processes; Chemical Manufacturing: SIC 28; All
Processes; Total
EMPIND8-9 (Bulk Chemicals;
Other Chemical Products);
REVIND 15-24
2301010000
Industrial Processes; Chemical Manufacturing: SIC 28; Industrial
Inorganic Chemical Manufacturing; Total
EMPIND8 (Bulk Chemicals);
REVIND15
2301020000
Industrial Processes; Chemical Manufacturing: SIC 28; Process
Emissions from Synthetic Fibers Manuf (NAPAP cat. 107); Total
EMPIND8 (Bulk Chemicals);
REVIND18
184
-------
see
SCC description
Growth Indicator
2302000000
Industrial Processes; Food and Kindred Products: SIC 20; All
Processes; Total
EMPIND1-2 (Food Products;
Beverage & Tobacco
Products); REVIND2-6
2302010000
Industrial Processes; Food and Kindred Products: SIC 20; Meat
Products; Total
EMPIND1 (Food Products);
REVIND4
2302040000
Industrial Processes; Food and Kindred Products: SIC 20; Grain
Mill Products; Total
EMPIND1 (Food Products);
REVIND2
2302050000
Industrial Processes; Food and Kindred Products: SIC 20; Bakery
Products; Total
EMPIND1 (Food Products);
REVIND5
2302070000
Industrial Processes; Food and Kindred Products: SIC 20;
Fermentation/Beverages; Total
EMPIND2 (Beverage &
Tobacco Products); REVIND6
2302070001
Industrial Processes; Food and Kindred Products: SIC 20;
Fermentation/Beverages; Breweries
EMPIND2 (Beverage &
Tobacco Products); REVIND6
2302070005
Industrial Processes; Food and Kindred Products: SIC 20;
Fermentation/Beverages; Wineries
EMPIND2 (Beverage &
Tobacco Products); REVIND6
2302070010
Industrial Processes; Food and Kindred Products: SIC 20;
Fermentation/Beverages; Distilleries
EMPIND2 (Beverage &
Tobacco Products); REVIND6
2302080000
Industrial Processes; Food and Kindred Products: SIC 20;
Miscellaneous Food and Kindred Products; Total
EMPIND1 (Food Products);
REVIND5
2302080002
Industrial Processes; Food and Kindred Products: SIC 20;
Miscellaneous Food and Kindred Products; Refrigeration
EMPIND1 (Food Products);
REVIND5
2304000000
Industrial Processes; Secondary Metal Production: SIC 33; All
Processes; Total
EMPIND13 (Primary Metals);
REVIND33-35
2305000000
Industrial Processes; Mineral Processes: SIC 32; All Processes;
Total
EMPIND12 (Nonmetallic
Minerals); REVIND28-32
2306000000
Industrial Processes; Petroleum Refining: SIC 29; All Processes;
Total
EMPIND10 (Petroleum and
Coal Products); REVIND25-26
2306010000
Industrial Processes; Petroleum Refining: SIC 29; Asphalt Mixing
Plants and Paving/Roofing Materials; Asphalt Paving/Roofing
Materials: Total
EMPIND10 (Petroleum and
Coal Products); REVIND25-26
2306010100
Industrial Processes; Petroleum Refining: SIC 29; Asphalt Mixing
Plants and Paving/Roofing Materials; Asphalt Mixing Plants: Total
EMPIND10 (Petroleum and
Coal Products); REVIND25-26
2307000000
Industrial Processes; Wood Products: SIC 24; All Processes; Total
EMPIND4 (Wood Products);
REVIND8
2307020000
Industrial Processes; Wood Products: SIC 24; Sawmills/Planing
Mills; Total
EMPIND4 (Wood Products);
REVIND8
2308000000
Industrial Processes; Rubber/Plastics: SIC 30; All Processes; Total
EMPIND11 (Plastics and
Rubber Products); REVIND27
2309000000
Industrial Processes; Fabricated Metals: SIC 34; All Processes;
Total
EMPIND14 (Fabricated Metal
Products); REVIND36
185
-------
see
SCC description
Growth Indicator
2311010000
Industrial Processes; Construction: SIC 15 -17; Residential; Total
EMPIND25-27 (Construction:
Building, Heavy/Civil
Engineering, Speciality Trade);
REVIND48
2311020000
Industrial Processes; Construction: SIC 15 -17;
Industrial/Commercial/Institutional; Total
EMPIND25-27 (Construction:
Building, Heavy/Civil
Engineering, Speciality Trade);
REVIND48
2312000000
Industrial Processes; Machinery: SIC 35; All Processes; Total
EMPIND15 (Machinery);
REVIND37
2325000000
Industrial Processes; Mining and Quarrying: SIC 10 and SIC 14; All
Processes; Total
EMPIND24 (Other Mining and
Quarrying); REVIND47
2325020000
Industrial Processes; Mining and Quarrying: SIC 10 and SIC 14;
Crushed and Broken Stone; Total
EMPIND24 (Other Mining and
Quarrying); REVIND47
2325030000
Industrial Processes; Mining and Quarrying: SIC 10 and SIC 14;
Sand and Gravel; Total
EMPIND24 (Other Mining and
Quarrying); REVIND47
2325060000
Industrial Processes; Mining and Quarrying: SIC 10 and SIC 14;
Lead Ore Mining and Milling; Total
EMPIND24 (Other Mining and
Quarrying); REVIND47
2399000000
Industrial Processes; Industrial Processes: NEC; Industrial
Processes: NEC; Total
EMPIND19 (Miscellaneous
Manufacturing); REVIND41
2401010000
Solvent Utilization; Surface Coating; Textile Products: SIC 22;
Total: All Solvent Types
EMPIND3 (Textiles, Apparel,
and Leather); REVIND7
2401015000
Solvent Utilization; Surface Coating; Factory Finished Wood: SIC
2426 thru 242; Total: All Solvent Types
EMPIND4 (Wood Products);
REVIND8
2401020000
Solvent Utilization; Surface Coating; Wood Furniture: SIC 25;
Total: All Solvent Types
EMPIND5 (Furniture and
Related Products); REVIND9
2401025000
Solvent Utilization; Surface Coating; Metal Furniture: SIC 25;
Total: All Solvent Types
EMPIND5 (Furniture and
Related Products); REVIND9
2401030000
Solvent Utilization; Surface Coating; Paper: SIC 26; Total: All
Solvent Types
EMPIND6 (Paper Products);
REVIND10
2401035000
Solvent Utilization; Surface Coating; Plastic Products: SIC 308;
Total: All Solvent Types
EMPIND11 (Plastics and
Rubber Products); REVIND27
2401040000
Solvent Utilization; Surface Coating; Metal Cans: SIC 341; Total:
All Solvent Types
EMPIND14 (Fabricated Metal
Products); REVIND36
2401045000
Solvent Utilization; Surface Coating; Metal Coils: SIC 3498; Total:
All Solvent Types
EMPIND14 (Fabricated Metal
Products); REVIND36
2401050000
Solvent Utilization; Surface Coating; Miscellaneous Finished
Metals: SIC 34 - (341 + 3498); Total: All Solvent Types
EMPIND14 (Fabricated Metal
Products); REVIND36
2401055000
Solvent Utilization; Surface Coating; Machinery and Equipment:
SIC 35; Total: All Solvent Types
EMPIND15 (Machinery);
REVIND37
2401060000
Solvent Utilization; Surface Coating; Large Appliances: SIC 363;
Total: All Solvent Types
EMPIND18 (Appliance and
Electrical Equipment);
REVIND40
186
-------
see
SCC description
Growth Indicator
2401065000
Solvent Utilization; Surface Coating; Electronic and Other
Electrical: SIC 36 - 363; Total: All Solvent Types
EMPIND18 (Appliance and
Electrical Equipment);
REVIND40
2401070000
Solvent Utilization; Surface Coating; Motor Vehicles: SIC 371;
Total: All Solvent Types
EMPIND17 (Transportation
Equipment); REVIND39
2401075000
Solvent Utilization; Surface Coating; Aircraft: SIC 372; Total: All
Solvent Types
EMPIND17 (Transportation
Equipment); REVIND39
2401080000
Solvent Utilization; Surface Coating; Marine: SIC 373; Total: All
Solvent Types
EMPIND17 (Transportation
Equipment); REVIND39
2401085000
Solvent Utilization; Surface Coating; Railroad: SIC 374; Total: All
Solvent Types
EMPIND17 (Transportation
Equipment); REVIND39
2401090000
Solvent Utilization; Surface Coating; Miscellaneous
Manufacturing; Total: All Solvent Types
EMPIND19 (Miscellaneous
Manufacturing); REVIND41
2415000000
Solvent Utilization; Degreasing; All Processes/All Industries; Total:
All Solvent Types
EMPIND19 (Miscellaneous
Manufacturing); REVIND41
2440000000
Solvent Utilization; Miscellaneous Industrial; All Processes; Total:
All Solvent Types
EMPIND19 (Miscellaneous
Manufacturing); REVIND41
2461850000
Solvent Utilization; Miscellaneous Non-industrial: Commercial;
Pesticide Application: Agricultural; All Processes
EMPIND20 (Crop Production);
REVIND42
2501000000
Storage and Transport; Petroleum and Petroleum Product
Storage; All Storage Types: Breathing Loss; Total: All Products
Total Energy/Petroleum and
Other Liquids Subtotal
2501000120
Storage and Transport; Petroleum and Petroleum Product
Storage; All Storage Types: Breathing Loss; Gasoline
Total Energy/Motor Gasoline
2501050000
Storage and Transport; Petroleum and Petroleum Product
Storage; Bulk Terminals: All Evaporative Losses; Total: All
Products
Total Energy/Petroleum and
Other Liquids Subtotal
2501050120
Storage and Transport; Petroleum and Petroleum Product
Storage; Bulk Terminals: All Evaporative Losses; Gasoline
Total Energy/Motor Gasoline
2501055120
Storage and Transport; Petroleum and Petroleum Product
Storage; Bulk Plants: All Evaporative Losses; Gasoline
Total Energy/Motor Gasoline
2501060051
Storage and Transport; Petroleum and Petroleum Product
Storage; Gasoline Service Stations; Stage 1: Submerged Filling
Total Energy/Motor Gasoline
2501060052
Storage and Transport; Petroleum and Petroleum Product
Storage; Gasoline Service Stations; Stage 1: Splash Filling
Total Energy/Motor Gasoline
2501060053
Storage and Transport; Petroleum and Petroleum Product
Storage; Gasoline Service Stations; Stage 1: Balanced Submerged
Filling
Total Energy/Motor Gasoline
2501060201
Storage and Transport; Petroleum and Petroleum Product
Storage; Gasoline Service Stations; Underground Tank: Breathing
and Emptying
Total Energy/Motor Gasoline
2501070053
Storage and Transport; Petroleum and Petroleum Product
Storage; Diesel Service Stations; Stage 1: Balanced Submerged
Filling
Transportation/Distillate Fuel
Oil
187
-------
see
SCC description
Growth Indicator
2501080050
Storage and Transport; Petroleum and Petroleum Product
Storage; Airports : Aviation Gasoline; Stage 1: Total
Transportation/Other
Petroleum
2501080100
Storage and Transport; Petroleum and Petroleum Product
Storage; Airports : Aviation Gasoline; Stage 2: Total
Transportation/Other
Petroleum
2501080201
Storage and Transport; Petroleum and Petroleum Product
Storage; Airports : Aviation Gasoline; Underground Tank:
Breathing and Emptying
Transportation/Other
Petroleum
2501995120
Storage and Transport; Petroleum and Petroleum Product
Storage; All Storage Types: Working Loss; Gasoline
Total Energy/Motor Gasoline
2505000030
Storage and Transport; Petroleum and Petroleum Product
Transport; All Transport Types; Crude Oil
Total Energy/Petroleum and
Other Liquids Subtotal
2505010000
Storage and Transport; Petroleum and Petroleum Product
Transport; Rail Tank Car; Total: All Products
Total Energy/Petroleum and
Other Liquids Subtotal
2505020000
Storage and Transport; Petroleum and Petroleum Product
Transport; Marine Vessel; Total: All Products
Total Energy/Petroleum and
Other Liquids Subtotal
2505020030
Storage and Transport; Petroleum and Petroleum Product
Transport; Marine Vessel; Crude Oil
Total Energy/Petroleum and
Other Liquids Subtotal
2505020060
Storage and Transport; Petroleum and Petroleum Product
Transport; Marine Vessel; Residual Oil
Total Energy/Residual Fuel Oil
2505020090
Storage and Transport; Petroleum and Petroleum Product
Transport; Marine Vessel; Distillate Oil
Total Energy/Distillate Fuel Oil
2505020120
Storage and Transport; Petroleum and Petroleum Product
Transport; Marine Vessel; Gasoline
Total Energy/Motor Gasoline
2505020150
Storage and Transport; Petroleum and Petroleum Product
Transport; Marine Vessel; Jet Naphtha
Total Energy/Jet Fuel
2505020180
Storage and Transport; Petroleum and Petroleum Product
Transport; Marine Vessel; Kerosene
Total Energy/Kerosene
2505030120
Storage and Transport; Petroleum and Petroleum Product
Transport; Truck; Gasoline
Transportation/Motor
Gasoline
2505040000
Storage and Transport; Petroleum and Petroleum Product
Transport; Pipeline; Total: All Products
Total Energy/Petroleum and
Other Liquids Subtotal
2505040120
Storage and Transport; Petroleum and Petroleum Product
Transport; Pipeline; Gasoline
Total Energy/Motor Gasoline
2510000000
Storage and Transport; Organic Chemical Storage; All Storage
Types: Breathing Loss; Total: All Products
EMPIND8 (Bulk Chemicals);
REVIND16
2510050000
Storage and Transport; Organic Chemical Storage; Bulk
Stations/Terminals: Breathing Loss; Total: All Products
EMPIND8 (Bulk Chemicals);
REVIND16
2515040000
Storage and Transport; Organic Chemical Transport; Pipeline;
Total: All Products
EMPIND8 (Bulk Chemicals);
REVIND16
2520010000
Storage and Transport; Inorganic Chemical Storage;
Commercial/Industrial: Breathing Loss; Total: All Products
EMPIND8 (Bulk Chemicals);
REVIND15
2801000000
Miscellaneous Area Sources; Agriculture Production - Crops;
Agriculture - Crops; Total
EMPIND20 (Crop Production);
REVIND42
188
-------
SCC
SCC description
Growth Indicator
2801000003
Miscellaneous Area Sources; Agriculture Production - Crops;
Agriculture - Crops; Tilling
EMPIND20 (Crop Production);
REVIND42
2801000005
Miscellaneous Area Sources; Agriculture Production - Crops;
Agriculture - Crops; Harvesting
EMPIND20 (Crop Production);
REVIND42
2801000008
Miscellaneous Area Sources; Agriculture Production - Crops;
Agriculture - Crops; Transport
EMPIND20 (Crop Production);
REVIND42
2801520000
Miscellaneous Area Sources; Agriculture Production - Crops;
Orchard Heaters; Total, all fuels
EMPIND21 (Other
Agriculture); REVIND44
2801530000
Miscellaneous Area Sources; Agriculture Production - Crops;
Country Grain Elevators; Total
EMPIND21 (Other
Agriculture); REVIND44
2802004001
Miscellaneous Area Sources; Agricultural Crop Usage; Agriculture
Silage; Storage
EMPIND21 (Other
Agriculture); REVIND44
2802004002
Miscellaneous Area Sources; Agricultural Crop Usage; Agriculture
Silage; Mixing
EMPIND21 (Other
Agriculture); REVIND44
2802004003
Miscellaneous Area Sources; Agricultural Crop Usage; Agriculture
Silage; Feeding
EMPIND21 (Other
Agriculture); REVIND44
4.2.3.7 Solvents (np_solvents)
Packets:
nonpoint_projection_packet_2022_platform_2022hc_to_2026_updates_24dec2024_csv_24dec2024_v0
Solvent emissions were projected in a way similar to how the nonpt sector was projected. Many SCCs in
np_solvents that are projected using human population growth are shown in Table 4-18. For a complete
list of solvent growth surrogates by SCC, see the NP_AnalyticYr_Crosswalk spreadsheet in the reports /
nonpoint folder on the FTP site.
Table 4-18. SCCs in np_solvents that use Human Population Growth for Projections
SCC
SCC Descriptions
2401001000
Solvent Utilization;Surface Coating;Architectural Coatings;Total: All Solvent Types
2401005000
Solvent Utilization;Surface Coating;Auto Refinishing: SIC 7532;Total: All Solvent Types
2401005700
Solvent Utilization;Surface Coating;Auto Refinishing: SIC 7532;Top Coats
2401005800
Solvent Utilization;Surface Coating;Auto Refinishing: SIC 7532;Clean-up Solvents
2401100000
Solvent Utilization;Surface Coating;lndustrial Maintenance Coatings;Total: All Solvent
Types
2401200000
Solvent Utilization;Surface Coating;Other Special Purpose Coatings;Total: All Solvent
Types
2420000000
Solvent Utilization;Dry Cleaning;AII Processes;Total: All Solvent Types
2420000055
Solvent Utilization;Dry Cleaning;AII Processes;Perchloroethylene
2420000999
Solvent Utilization;Dry Cleaning;AII Processes;Solvents: NEC
2425000000
Solvent Utilization;Graphic Arts;AII Processes;Total: All Solvent Types
2440000000
Solvent Utilization;Miscellaneous Industrial;All Processes;Total: All Solvent Types
2460030999
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;Lighter
Fluid, Fire Starter, Other Fuels;Total: All Volatile Chemical Product Types
189
-------
see
SCC Descriptions
2460100000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;AII
Personal Care Products;Total: All Solvent Types
2460200000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;AII
Household Products;Total: All Solvent Types
2460400000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;AII
Automotive Aftermarket Products;Total: All Solvent Types
2460500000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;AII
Coatings and Related Products;Total: All Solvent Types
2460600000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;AII
Adhesives and Sealants;Total: All Solvent Types
2460800000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;AII 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
2461023000
Solvent Utilization;Miscellaneous Non-industrial: Commercial;Asphalt Roofing;Total: All
Solvent Types
2461100000
Solvent Utilization;Miscellaneous Non-industrial: Commercial;Solvent Reclamation: All
Processes;Total: All Solvent Types
2461800001
Solvent Utilization;Miscellaneous Non-industrial: Commercial;Pesticide Application: All
Processes;Surface Application
Table 4-19. Impact of projection factors on np_solvents emissions
Year
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
2026
VOC
2,634,832
2,712,204
77,371
2.9%
4.2.3.8 Oil and Gas Sources (np_oilgas, pt_oilgas)
Packets:
np_oilgas_projection_packet_2026hc_KSappend_csv_llsep2024_v0
pt_oilgas_projection_packet_2026hc_KSappend_csv_21feb2025_vl
Analytic year projections for the 2022vl platform were generated for point and nonpoint oil and gas
sources for 2026. This projection consisted of three components: (1) applying facility closures to the
pt_oilgas sector using the CoST CLOSURE packet (see Section 4.2.4); (2) using historical and/or forecast
activity data to generate analytic-year emissions before applicable control technologies are applied
using the CoST PROJECTION packet; and (3) estimating impacts of applicable control technologies on
analytic-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 in Table 4-5.
For pt_oilgas growth, the oil and gas sources were separated into production-related and pipeline-
related sources by NAICS and SCC. These sources were further subdivided by fuel-type and by NAICS and
SCC into either OIL, natural gas (NGAS), or BOTH (where oil or natural gas fuels are possible). The next
two subsections describe the growth component of the process.
190
-------
For np_oilgas growth oil and gas sources were separated into production-related and exploration-
related sources. These sources were further separated into oil, natural gas or coal bed methane
production related.
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 2022 to year 2023. These historical data were acquired from EIA from the
following links:
• Historical Natural Gas: http://www.eia.gov/dnav/ng/ng sum Isum a epgO few 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 si a.htm
The second step involved using the Annual Energy Outlook (AEO) 2023 reference case for the Lower 48
forecast production tables to project from the year 2023 to the desired analytic year. Specifically, AEO
2023 Table 58 "Lower 48 Crude Oil Production and Wellhead Prices by Supply Region" and AEO 2023
Table 59 "Lower 48 Natural Gas Production and Supply Prices by Supply Region" were used in this
projection process. The AEO2023 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 AEO2023
Pacific
The result of this second step is a growth factor for each Supply Region from 2023 to 2026. 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
191
-------
three different Supply Regions and portions of New Mexico are in two different supply regions. The
state-level historical factor (from 2022 to 2023) was then multiplied by the Supply Region factor to
produce a state-level or FlPS-level factor to grow from 2023 to 2026. 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 SCC and 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.
Texas provided updated basin specific production for 2022 and 2023 to allow for a better calculation of
the estimated growth for this three-year period
(http://webapps.rrc.texas.gov/PDQ/generalReportAction.do). The AEO2023 was used as described
above for the three AEO Oil and Gas Supply Regions that include Texas counties to grow from 2023 to
analytic year.
The state of New Mexico is broken up into two AEO Oil and Gas Supply Regions. County production data
for New Mexico was obtained from their state website
(https://wwwapps.emnrd.nm.gov/ocd/ocdpermitting/Reporting/Production/CountyProductionlniection
Summary.aspx ) so that a better estimate of growth from 2022 to 2023 for the AEO Supply Regions in
New Mexico could be calculated.
The state of Kansas provided county specific growth factors for production-related sources. Kansas
used historical well information to derive their growth factors for oil and gas SCCs.
Transmission-related Sources (pt_oilgas)
Projection factors for transmissions-related sources were generated using the same AEO2023 tables
used for production sources. These growth factors sources were developed solely using AEO 2023 data
for the entire lower 48 states. For each analytic year, one national factor was used for oil transmission
and another national factor was used for natural gas transmission. The 2022 to 2026 growth for oil
transmissions is 9.3% and for natural gas transmission is -0.5%. The impact of the projection factors on
the pt_oilgas emissions is shown in Table 4-20.
Table 4-20. Impact of projections on pt_oilgas emissions
Inventory
Final
Emissions
Emissions
Year
Pollutant
Emissions
Emissions
Change
% Change
2026
CO
164,139
169,414
5,275
3.2%
2026
NH3
322
305
-17
-5.3%
2026
NOX
330,214
338,671
8,457
2.6%
2026
PM10-PRI
12,959
13,456
497
3.8%
2026
PM25-PRI
11,477
11,841
364
3.2%
2026
S02
29,576
31,308
1,732
5.9%
2026
VOC
194,885
207,653
12,767
6.6%
Exploration-related Sources (np_oilgas)
192
-------
Years 2018, 2019 and 2022 exploration activity were averaged and the resulting 3-year average activity
used in the 2020NEI version of the Oil and Gas Tool to generate exploration emissions for 2026. Table
4-21 provides a high-level national summary of the emissions data for the three year-average. This
three-year averaged-activity derived emissions data were used in 2022vl because they reflected the
most recent average of exploration activity and emissions. Note that CoST was not used to perform this
projection step for exploration sources, but is used to apply controls to exploration sources for each
analytic year. The change in emissions from 2022 to 2026 due to the impact of the projections is shown
in Table 4-22.
Table 4-21. Three year average of national oil and gas exploration emissions
Pollutant
Emissions
(tons)
CO
14,809
NH3
15
NOX
52,611
PM10-PRI
1,075
PM25-PRI
1,039
SO 2
6,383
VOC
106,427
Table 4-22. Impact of projections on np_oilgas emissions
Year
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
2026
CO
680,698
717,896
37,198
5.5%
2026
NH3
3,771
4,287
516
13.7%
2026
NOX
543,771
579,378
35,607
6.5%
2026
PM10-PRI
9,481
9,856
375
4.0%
2026
PM25-PRI
9,465
9,841
376
4.0%
2026
SO 2
278,368
305,846
27,478
9.9%
2026
VOC
2,501,145
2,710,839
209,694
8.4%
4.2.3.9 Non-EGU point sources (ptnonipm)
Packets:
ptnonipm_projection_packet_2022vl_revision_2022hc_to_2026_02jan2025_21feb2025_vl
Projection_2022_2026_for_2022vl_ptnonipm_NJ_overrides_20dec2024_v0
Projection factors for ptnonipm were developed by industrial sector from AEO 2023 to project emissions
from 2022 to 2026. Emissions were mapped to AEO categories by NAICS and SCC (combination of NAICS
and SCC first, SCC only after that) and projection factors were created using a ratio between the base
year and projection year estimates from each specific AEO category. SCC/NAICS combinations with
emissions >100tons/year for any CAP were mapped to AEO sector and fuel. Table 4-23 details the
AEO2023 tables used to map SCCs to AEO categories for the projections of industrial sources. The impact
of the projection packets other than the refinery adjustments from 2022 to 2026 is shown in Table 4-24.
193
-------
The NJ override packets act to hold facilities flat which would have otherwise been decreased (state
comment) so a table for that really isn't applicable. The computation of the refinery adjustments is
described in the latter part of this subsection.
Table 4-23. Annual Energy Outlook (AEO) 2023 tables used to project industrial sources
AEO 2023Table#
AEO Table name
2
Energy Consumption by Sector and Source
24
Refining Industry Energy Consumption
25
Food Industry Energy Consumption
26
Paper Industry Energy Consumption
27
Bulk Chemical Industry Energy Consumption
28
Glass Industry Energy Consumption
29
Cement Industry Energy Consumption
30
Iron and Steel Industries Energy Consumption
31
Aluminum Industry Energy Consumption
32
Metal Based Durables Energy Consumption
33
Other Manufacturing Sector Energy Consumption
34
Nonmanufacturing Sector Energy Consumption
Table 4-24. Impact of projections other than refinery adjustments on ptnonipm emissions
Year
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
2026
CO
1,191,726
1,161,131
-30,595
-2.6%
2026
NH3
60,624
60,081
-544
-0.9%
2026
NOX
733,122
715,388
-17,734
-2.4%
2026
PM10-PRI
341,507
337,484
-4,023
-1.2%
2026
PM25-PRI
220,869
217,184
-3,684
-1.7%
2026
SO 2
429,542
418,997
-10,545
-2.5%
2026
VOC
720,157
700,431
-19,726
-2.7%
4.2.3.10 Railroads (rail)
Packets:
Projection_rail_2022hc_to_2026_future_year_16aug2024_v0
Rail projection factors are relatively flat. Rail emissions were projected based on factors derived for
categories of locomotives based on AEO (fuel use) growth rates including some adjustments. Table 4-25
shows the projection factors used for the various locomotive categories.
194
-------
Table 4-25. Projection factors for Rail SCCs from the 2022 Base Year
STB R-l Fuel Use Data
Trends 2005-2023
Passenger Rail
SCCs:
2285002008,
2285002009
2022 Switcher &
class 2/3 SCCs:
28500201,
2285002007
Line Haul 2022
Projection Factors
SCCs: 2285002006
2022
1.000
1.000
1.000
2023
1.038
0.986
0.986
2024
1.060
1.026
1.045
2025
1.075
1.002
1.027
2026
1.091
0.959
0.991
4.2.3.11 Residential Wood Combustion (rwc)
For residential wood combustion emissions in the 2022vl platform, it was determined to hold the
emissions flat at the base year levels for 2026.
4.2.4 CoST CONTROL Packets (nonpt, np_oilgas, ptnonipm, pt_oilgas,
np_sol vents)
The final step in the projection of emissions to an analytic year is the application of any control
technologies or programs. For analytic-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-l]Fn + (l-Ri)tFe + [l-(l-Ri)t]Fn]} Equation 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 analytic 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 Equation 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) for nonpt and ptnonipm sectors, the
simplified Equation 4-2 was used for analytic year projections:
„ , . . .... ir.n /„ [(p/aoa*—i)xFrt+(i-Hi)12+fi-(i-R012)xFn]\ Equation 4-2
Control Efficiency2o2ar(%) = 100 x (1 - 1 — —1 —L M
V "/2D2Jf f
195
-------
For example, to compute the control efficiency for 2026 from a base year of 2022 the existing source
emissions factor (Fe) is set to 1.0; 2026 (the analytic year) minus 2022 (the base year) is 4, and the new
source emission factor (Fn) is the ratio of the NSPS emission factor to the existing emission factor. Note
for the np_oilgas and pt_oilgas sectors the Fe is not assumed to be 1.0 for the Oil and Gas NSPS.
The NSPS are applied to sectors and with the specified retirement rates (R) as follows:
• The Oil and Gas NSPS from 2024 is applied to the np_oilgas and pt_oilgas sectors with no
assumed retirement rate.
• The RICE NSPS for Compression Ignition (CI) engines that originated in 2006 but was
amended as recently as 2024 is applied to the np_oilgas, pt_oilgas, nonpt, and ptnonipm
sectors with an assumed retirement rate of 40 years (2.5%). The same retirement rate was
used for the RICE NSPS for spark ignition engines that originated in 2008 and was amended as
recently as 2024.
• The Gas Turbines NSPS that originated as subpart GG I 1979 but for subpart KKKK originated
in 2006 is applied to the pt_oilgas and ptnonipm sectors with an assumed retirement rate of
45 years (2.2%).
• The Process Heaters NSPS with origination date around 2006-2010 is applied to the pt_oilgas
and ptnonipm sectors with an assumed retirement rate of 30 years (3.3%).
Table 4-26 shows the values for the emission factors for new sources (Fn) with respect to each NSPS
regulation and other conditions within. 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 (EPA, 2017).
Table 4-26. Assumed new source emission factor ratios for NSPS rules
NSPS
Pollutants
Applied where?
New Source Emission
Factor(Fn)
Oil and Gas
voc
Storage Tanks
Varies by state-SCC
Oil and Gas
voc
Gas Well Completions: 95% control (regardless)
0.05
Oil and Gas
voc
Pneumatic controllers, not high-bleed >6scfm or
low-bleed
Varies by state-SCC
Oil and Gas
voc
Pneumatic controllers, high-bleed >6scfm or low-
bleed
Varies by state-SCC
Oil and Gas
voc
Compressor Seals
Varies by state-SCC
Oil and Gas
voc
Fugitive Emissions: 60% Valves, flanges,
connections, pumps, open-ended lines, and other
Varies by state-SCC
RICE
NOx
Lean burn: PA, all other states
0.25, 0.606
RICE
NOx
Rich Burn: PA, all other states
0.1, 0.069
RICE
NOx
Combined (average) LB/RB: PA, other states
0.175, 0.338
RICE
CO
Lean burn: PA, all other states
1.0 (n/a), 0.889
RICE
CO
Rich Burn: PA, all other states
0.15, 0.25
RICE
CO
Combined (average) LB/RB: PA, other states
0.575, 0.569
RICE
VOC
Lean burn: PA, all other states
0.125, n/a
RICE
VOC
Rich Burn: PA, all other states
0.1, n/a
RICE
voc
Combined (average) LB/RB: PA, other states
0.1125, n/a
196
-------
NSPS
Pollutants
Applied where?
New Source Emission
Factor(Fn)
Gas Turbines
NOx
California and NOx SIP Call states
0.595
Gas Turbines
NOx
All other states
0.238
Process
Heaters
NOx
Nationally to Process Heater SCCs
0.41
4.2.4.1 Oil and Gas NSPS (np_oilgas, pt_oilgas)
Packets:
Control_2022_2026_OilGas_NSPS_np_oilgas_2022vlplatform_03dec2024_v0
Control_2022_2026_OilGas_NSPS_np_oilgas_2022vlplatform_KSupdate_03dec2024_v0
Control_2022_2026_OilGas_NSPS_pt_oilgas_2022vlplatform_03dec2024_v0
Control_2022_2026_OilGas_NSPS_pt_oilgas_2022vlplatform_KSupdate_03dec2024_v0
New packets to reflect the Oil and Gas NSPS were developed for the 2022 platform. For oil and gas NSPS
controls, except for gas well completions (a 95 percent control), the assumption of no equipment
retirements through year 2032 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 4-26, 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. Note that Fe and Fn emissions factor ratios for oil and gas
emissions vary by state and by SCC in this emissions modeling platform. In some cases, pneumatic
devices/pumps emissions are estimated to reach 100% control based on information available at the
time, thus emissions are zero.
The packets with KSupdate in their names cover only the state of Kansas, while the other packets cover
all other states. For details on growth and control factors used in CoST see
https://gaftp.epa.gov/Air/emismod/2022/vl/reports/proiection controls/final analytic/ for report
summaries.
Table 4-27 shows the emission reductions for the oil and gas sectors as a result of applying the oil and
gas NSPS. Table 4-28 and Table 4-29 list the SCCs in the np_oilgas and pt_oilgas sectors for which the Oil
and Gas NSPS controls were. Note that controls are applied to both production and exploration-related
SCCs.) For np_oilgas, the exploration-related pre-CoST emissions for 2022 are computed using an
average across multiple years and are different than the 2022hc emissions. Thus, the two sets of
emissions are shown in different columns.
197
-------
Table 4-27. Emissions reductions for the oil and gas sectors due to applying the Oil and Gas NSPS
Sector
Year
Pollutant
2022hc
2022 pre-CoST
emissions
Emissions change
from 2022
%
change
np_°ilgas
2026
voc
2,767,230
2,788,266
-892,682
-32.0%
pt_°ilgas
2026
voc
211,419
211,419
-14,701
-7.0%
Table 4-28. SCCs in np_oilgas for which the Oil and Gas NSPS controls were applied
see
PRODUCT
OG_NSPS_SCC
TOOL OR
STATE
Source category
SCC Description*
Crude Petroleum;Oil Well Tanks -
Flashing &
2310010200
OIL
1. Storage Tanks
TOOL
PRODUCTION
Standing/Working/Breathing
3. Pneumatic
Crude Petroleum;Oil Well
2310010300
OIL
Devices
TOOL
PRODUCTION
Pneumatic Devices
5a. Associated Gas
On-Shore Oil Production;Associated
2310011001
OIL
Venting
TOOL
PRODUCTION
Gas Venting
On-Shore Oil Production;Storage
2310011020
OIL
1. Storage Tanks
STATE
PRODUCTION
Tanks: Crude Oil
2310011450
OIL
4. Fugitives
STATE
PRODUCTION
On-Shore Oil Production;Wellhead
On-Shore Oil Production;Fugitives:
2310011500
OIL
4. Fugitives
STATE
PRODUCTION
All Processes
On-Shore Oil Production;Fugitives:
2310011501
OIL
4. Fugitives
TOOL
PRODUCTION
Connectors
On-Shore Oil Production;Fugitives:
2310011502
OIL
4. Fugitives
TOOL
PRODUCTION
Flanges
On-Shore Oil Production;Fugitives:
2310011503
OIL
4. Fugitives
TOOL
PRODUCTION
Open Ended Lines
On-Shore Oil Production;Fugitives:
2310011505
OIL
4. Fugitives
TOOL
PRODUCTION
Valves
On-Shore Gas Production;Storage
2310021010
NGAS
1. Storage Tanks
TOOL
PRODUCTION
Tanks: Condensate
3. Pneumatic
On-Shore Gas Production;Gas Well
2310021300
NGAS
Devices
TOOL
PRODUCTION
Pneumatic Devices
3. Pneumatic
On-Shore Gas Production;Gas Well
2310021310
NGAS
Devices
STATE
PRODUCTION
Pneumatic Pumps
On-Shore Gas Production;Fugitives:
2310021501
NGAS
4. Fugitives
TOOL
PRODUCTION
Connectors
On-Shore Gas Production;Fugitives:
2310021502
NGAS
4. Fugitives
TOOL
PRODUCTION
Flanges
On-Shore Gas Production;Fugitives:
2310021503
NGAS
4. Fugitives
TOOL
PRODUCTION
Open Ended Lines
On-Shore Gas Production;Fugitives:
2310021505
NGAS
4. Fugitives
TOOL
PRODUCTION
Valves
On-Shore Gas Production;Fugitives:
2310021506
NGAS
4. Fugitives
TOOL
PRODUCTION
Other
On-Shore Gas Production;Fugitives:
2310021509
NGAS
4. Fugitives
STATE
PRODUCTION
All Processes
On-Shore Gas Production;Gas Well
2310021602
NGAS
2. Well Completions
STATE
EXPLORATION
Venting - Recompletions
Coal Bed Methane Natural
2310023010
CBM
1. Storage Tanks
TOOL
PRODUCTION
Gas;Storage Tanks: Condensate
198
-------
SCC
PRODUCT
OG_NSPS_SCC
TOOL OR
STATE
Source category
SCC Description*
3. Pneumatic
Coal Bed Methane Natural
2310023300
CBM
Devices
TOOL
PRODUCTION
Gas;Pneumatic Devices
3. Pneumatic
Coal Bed Methane Natural
2310023310
CBM
Devices
TOOL
PRODUCTION
Gas;Pneumatic Pumps
Coal Bed Methane Natural
2310023509
CBM
4. Fugitives
STATE
PRODUCTION
Gas;Fugitives
Coal Bed Methane Natural
2310023511
CBM
4. Fugitives
TOOL
PRODUCTION
Gas;Fugitives: Connectors
Coal Bed Methane Natural
2310023512
CBM
4. Fugitives
TOOL
PRODUCTION
Gas;Fugitives: Flanges
Coal Bed Methane Natural
2310023513
CBM
4. Fugitives
TOOL
PRODUCTION
Gas;Fugitives: Open Ended Lines
Coal Bed Methane Natural
2310023515
CBM
4. Fugitives
TOOL
PRODUCTION
Gas;Fugitives: Valves
Coal Bed Methane Natural
2310023516
CBM
4. Fugitives
TOOL
PRODUCTION
Gas;Fugitives: Other
Coal Bed Methane Natural Gas;CBM
2310023600
CBM
2. Well Completions
TOOL
EXPLORATION
Well Completion: All Processes
3. Pneumatic
On-Shore Oil Exploration;Oil Well
2310111401
OIL
Devices
TOOL
PRODUCTION
Pneumatic Pumps
On-Shore Oil Exploration;Oil Well
2310111700
OIL
2. Well Completions
TOOL
EXPLORATION
Completion: All Processes
3. Pneumatic
On-Shore Gas Exploration;Gas Well
2310121401
NGAS
Devices
TOOL
PRODUCTION
Pneumatic Pumps
On-Shore Gas Exploration;Gas Well
2310121700
NGAS
2. Well Completions
TOOL
EXPLORATION
Completion: All Processes
On-Shore Gas Exploration;Gas Well
2310121702
NGAS
2. Well Completions
STATE
EXPLORATION
Completion: Venting
On-Shore Gas Production -
Conventional;Storage Tanks:
2310321010
NGAS
1. Storage Tanks
STATE
PRODUCTION
Condensate
On-Shore Gas Production -
Unconventional;Storage Tanks:
2310421010
NGAS
1. Storage Tanks
STATE
PRODUCTION
Condensate
* All SCC descriptions in this table start with "Industrial Processes;Oil and Gas Exploration and Production;"
Table 4-29. SCCs in pt_oilgas for which the Oil and Gas NSPS controls were applied
SCC
Fuel
OG_NSPS_
SCC
SCC Description*
2. Well
Industrial Processes;Oil and Gas Production;Crude Oil Production;Well
31000101
OIL
Completions
Completion;;
Industrial Processes;Oil and Gas Production;Crude Oil Production;Valves:
31000124
OIL
4. Fugitives
General;;
Industrial Processes;Oil and Gas Production;Crude Oil Production;Relief
31000125
OIL
4. Fugitives
Valves;;
Industrial Processes;Oil and Gas Production;Crude Oil Production;Pump
31000126
OIL
4. Fugitives
Seals;;
Industrial Processes;Oil and Gas Production;Crude Oil Production;Flanges
31000127
OIL
4. Fugitives
and Connections;;
1. Storage
Industrial Processes;Oil and Gas Production;Crude Oil Production;Storage
31000133
OIL
Tanks
Tank;;
199
-------
see
Fuel
OG_NSPS_
sec
SCC Description*
31000151
OIL
3. Pneumatic
Devices
Industrial Processes;Oil and Gas Production;Crude Oil
Production;Pneumatic Controllers, Low Bleed;;
31000152
OIL
3. Pneumatic
Devices
Industrial Processes;Oil and Gas Production;Crude Oil
Production;Pneumatic Controllers High Bleed >6 scfh;;
31000153
OIL
3. Pneumatic
Devices
Industrial Processes;Oil and Gas Production;Crude Oil
Production;Pneumatic Controllers Intermittent Bleed;;
31000207
NGAS
4. Fugitives
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Valves: Fugitive Emissions;;
31000212
NGAS
1. Storage
Tanks
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Condensate Storage Tank;;
31000213
NGAS
1. Storage
Tanks
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Produced Water Storage Tank;;
31000214
NGAS
1. Storage
Tanks
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Natural Gas Liquids Storage Tank;;
31000220
NGAS
4. Fugitives
Industrial Processes;Oil and Gas Production;Natural Gas Production;AII
Equipt Leak Fugitives (Valves, Flanges, Connections, Seals, Drains;;
31000222
NGAS
2. Well
Completions
Industrial Processes;Oil and Gas Production;Natural Gas Production;Well
Completions;;
31000223
NGAS
4. Fugitives
Industrial Processes;Oil and Gas Production;Natural Gas Production;Relief
Valves;;
31000224
NGAS
3. Pneumatic
Devices
Industrial Processes;Oil and Gas Production;Natural Gas Production;Pump
Seals;;
31000225
NGAS
6.
Compressors
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Compressor Seals;;
31000226
NGAS
4. Fugitives
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Flanges and Connections;;
31000231
NGAS
4. Fugitives
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Fugitives: Drains;;
31000233
NGAS
3. Pneumatic
Devices
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Pneumatic Controllers, Low Bleed;;
31000234
NGAS
3. Pneumatic
Devices
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Pneumatic Controllers, High Bleed >6 scfh;;
31000235
NGAS
3. Pneumatic
Devices
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Pneumatic Controllers Intermittent Bleed;;
31000306
NGAS
4. Fugitives
Industrial Processes;Oil and Gas Production;Natural Gas
Processing;Process Valves;;
31000307
NGAS
4. Fugitives
Industrial Processes;Oil and Gas Production;Natural Gas Processing;Relief
Valves;;
31000308
NGAS
4. Fugitives
Industrial Processes;Oil and Gas Production;Natural Gas Processing;Open-
ended Lines;;
31000309
NGAS
6.
Compressors
Industrial Processes;Oil and Gas Production;Natural Gas
Processing;Compressor Seals;;
31000311
NGAS
4. Fugitives
Industrial Processes;Oil and Gas Production;Natural Gas
Processing;Flanges and Connections;;
31000312
NGAS
6a.
Centrifugal
Compressors
Industrial Processes;Oil and Gas Production;Natural Gas
Processing;Centrifugal Compressor;;
31000313
NGAS
6b.
Reciprocating
Compressors
Industrial Processes;Oil and Gas Production;Natural Gas
Processing;Reciprocating Compressor;;
31000506
OIL
1. Storage
Tanks
Industrial Processes;Oil and Gas Production;Liquid Waste Treatment;Oil-
Water Separation Wastewater Holding Tanks;;
200
-------
see
Fuel
OG_NSPS_
SCC
SCC Description*
31088801
BOTH
4. Fugitives
Industrial Processes;Oil and Gas Production;Fugitive Emissions;Specify in
Comments Field;;
31088811
BOTH
4. Fugitives
Industrial Processes;Oil and Gas Production;Fugitive Emissions;Fugitive
Emissions;;
31700101
NGAS
3. Pneumatic
Devices
Industrial Processes;NGTS;Natural Gas Transmission and Storage
Facilities;Pneumatic Controllers Low Bleed;;
31700103
NGAS
3. Pneumatic
Devices
Industrial Processes;NGTS;Natural Gas Transmission and Storage
Facilities;Pneumatic Controllers Intermittent Bleed;;
* For all entries in this table, TOOL OR STATE = STATE and SRC_CAT = PRODUCTION; In the SCC description, IP is an
abbreviation for Industrial Processes and CE is an abbreviation for Chemical Evaporation
4.2.4.2 RICE NSPS (nonpt, ptnonipm, np_oilgas, pt_oilgas)
Packets:
Control_2022_2026_RICE_NSPS_np_oilgas_2022vlplatform_09sep2024_v0
Control_2022_2026_RICE_NSPS_np_oilgas_2022vlplatform_KSupdate_16oct2024_v0
Control_2022_2026_RICE_NSPS_pt_oilgas_2022vlplatform_09sep2024_v0
Control_2022_2026_RICE_NSPS_pt_oilgas_2022vlplatform_KSupdate_16oct2024_v0
Control_2022_2026_RICE_NSPS_nonpt_ptnonipm_2022vlplatform_07oct2024_vl
Multiple sectors are affected by the RICE NSPS controls (https://www.epa.gov/stationary-
engines/compliance-requirements-stationary-engines). For the pt_oilgas and np_oilgas sectors, year-
specific RICE NSPS factors were generated for 2026. New growth factors based on AEO2023 and state-
specific production data were calculated for the oil and gas sectors which were included in the
calculation of the new RICE NSPS control factors. Note that Kansas specific county growth factors were
used and therefore, the packets with KSupdate in their names are used in addition to the other packets
that contain data used for the rest of the states. 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. The NSPS reduction was applied to lean burn,
rich burn and "combined" engines using Equation 4-2 and information listed in Table 4-26.
Table 4-30, Table 4-31, Table 4-32 and Table 4-33 show the reductions in emissions in the nonpt,
ptnonipm, and np_oilgas and pt_oilgas sectors after the application of the RICE NSPS CONTROL packet.
Note that for nonpoint oil and gas, VOC reductions were only appropriate in the state of Pennsylvania.
For np_oilgas, the exploration-related pre-CoST emissions for 2022 are computed using an average
across multiple years and are different than the 2022hc emissions. Thus, the two sets of emissions are
shown in different columns in Table 4-32. Table 4-34, Table 4-35, and Table 4-36 show the SCCs to
which the NSPS controls are applied in the nonpt, ptnonipm, np_oilgas, and pt_oilgas sectors.
201
-------
Table 4-30. Emissions reductions in nonpt due to RICE NSPS
year
Poll
2022hc (tons)
Emissions changes
(tons)
% change
2026
CO
842,395
-5,684
-0.7%
2026
NOX
741,248
-10,601
-1.4%
2026
VOC
949,760
-55
0.0%
Table 4-31. Emissions reductions in ptnonipm due to the RICE NSPS
year
poll
2022hc (tons)
Emissions changes
(tons)
% change
2026
CO
1,207,678
-107
-0.01%
2026
NOX
780,504
-226
-0.03%
2026
VOC
732,606
-1
0.00%
Table 4-32. Emissions reductions in np_oilgas due to the RICE NSPS
Year
Poll
2022hc (tons)
2022 pre-CoST
emissions
Emissions
change
% change
2026
CO
705,089
695,507
-26,585
-3.8%
2026
NOX
679,016
596,382
-45,346
-7.6%
2026
VOC
2,767,230
2,788,266
-31
0.00%
Table 4-33. Emissions reductions in pt_oilgas due to the RICE NSPS
Year
Pollutant
2022hc (tons)
Emissions
change (tons)
% change
2026
CO
188,876
-4,942
-2.6%
2026
NOX
365,805
-11,614
-3.2%
2026
VOC
211,419
-45
-0.02%
Table 4-34. SCCs and Engine Types where RICE NSPS controls applied for nonpt and ptnonipm
see
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
31000203
Combined
Industrial Processes;Oil and Gas Production;Natural Gas
Production;Compressors (See also 310003-12 and -13)
2102006000
Combined
Stationary Source Fuel Combustion; Industrial; Natural Gas; Total: Boilers
and IC Engines
2103006000
Combined
Stationary Source Fuel Combustion; Commercial/Institutional; Natural Gas;
Total: Boilers and IC Engines
202
-------
Table 4-35. Non-point Oil and Gas SCCs where RICE NSPS controls are applied
Lean /
see
Rich/
Combined
Product
Source Category
SCC Description
Industrial Processes;Oil and Gas Exploration and
2310020600
Combined
NGAS
PRODUCTION
Production;Natural Gas;Compressor Engines
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Natural Gas
Fired 4Cycle Lean Burn Compressor Engines 50 To
2310021202
Lean
NGAS
PRODUCTION
499 HP
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Total: All
Natural Gas Fired 4Cycle Lean Burn Compressor
2310021209
Lean
NGAS
PRODUCTION
Engines
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Lateral
2310021251
Lean
NGAS
PRODUCTION
Compressors 4 Cycle Lean Burn
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Natural Gas
Fired 4Cycle Rich Burn Compressor Engines 50 To
2310021302
Rich
NGAS
PRODUCTION
499 HP
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Total: All
Natural Gas Fired 4Cycle Rich Burn Compressor
2310021309
Rich
NGAS
PRODUCTION
Engines
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Lateral
2310021351
Rich
NGAS
PRODUCTION
Compressors 4 Cycle Rich Burn
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;CBM
Fired 4Cycle Lean Burn Compressor Engines 50 To
2310023202
Lean
CBM
PRODUCTION
499 HP
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Lateral
2310023251
Lean
CBM
PRODUCTION
Compressors 4 Cycle Lean Burn
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;CBM
Fired 4Cycle Rich Burn Compressor Engines 50 To
2310023302
Rich
CBM
PRODUCTION
499 HP
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Lateral
2310023351
Rich
CBM
PRODUCTION
Compressors 4 Cycle Rich Burn
Table 4-36. Point source SCCs in pt_oilgas sector where RICE NSPS controls applied
SCC
Lean, Rich, or
Combined
SCCDESC
20100202
Combined
Internal Combustion Engines;Electric Generation;Natural Gas;Reciprocating
20200202
Combined
Internal Combustion Engines;lndustrial;Natural Gas;Reciprocating
20200204
Combined
Internal Combustion Engines;lndustrial;Natural Gas;Reciprocating: Cogeneration
20200253
Rich
Internal Combustion Engines;lndustrial;Natural Gas;4-cycle Rich Burn
20200254
Lean
Internal Combustion Engines;lndustrial;Natural Gas;4-cycle Lean Burn
20200256
Combined
Internal Combustion Engines;lndustrial;Natural Gas;4-cycle Clean Burn
203
-------
see
Lean, Rich, or
Combined
SCCDESC
20201702
Combined
Internal Combustion Engines;lndustrial;Gasoline;Reciprocating Engine
20300201
Combined
Internal Combustion Engines;Commercial/lnstitutional;Natural Gas;Reciprocating
26500320
Combined
Internal Combustion Engines;Off-highway 4-stroke Gasoline Engines;lndustrial
Equipment;lndustrial Fork Lift: Gasoline Engine (4-stroke)
31000203
Combined
Industrial Processes;Oil and Gas Production;Natural Gas Production;Compressors
(See also 310003-12 and -13)
31000313
Combined
Industrial Processes;Oil and Gas Production;Natural Gas Processing;Reciprocating
Compressor
4.2.4.3 Organic Liquids Distribution NESHAP (ptnonipm)
Packets:
Control_2022_2026_Organic_Liquids_Distribution_NESHAP_2022vlplatform_02oct2024_vl
Control_2022_203X_Organic_Liquids_Distribution_NESHAP_2022vlplatform_02oct2024_nf_v3
The Organic Liquids Distribution National Emissions Standards for Hazardous Air Pollutants (NESHAP) is
an EPA rule to reduce emissions of toxic air pollutants from facilities that distribute organic liquids other
than gasoline. Affected facilities were listed in Appendix A of the Review of the RACT/BACT/LAER
Clearinghouse Database for the Organic Liquids Distribution Source Category memo found in the
regulatory docket. Facility information was pulled from EIS to check control information. If no VOC
controls existed at a facility, an 8% VOC emissions reduction was applied. Table 4-37 summarizes the
impact of the organic liquids distribution NESHAP on VOC emissions in the ptnonipm sector.
Table 4-37. Summary of Organic Liquids Distribution NESHAP controls on ptnonipm emissions
Year
Pollutant
2022hc (tons)
Emissions Change
(tons)
% change
2026
VOC
732,606
-2,244
-0.3%
4.2.4.4 Natural Gas Turbines NOx NSPS (ptnonipm, pt_oilgas)
Packets:
Control_2022_2026_NG_Turbines_NSPS_ptnonipm_2022vlplatform_30sep2024_v0
Control_2022_2026_NG_Turbines_pt_oilgas_2022vlplatform_05sep2024_v0
Control_2022_2026_NG_Turbines_pt_oilgas_2022vlplatform_KSupdate_16oct2024_v0
For ptnonipm, the Natural Gas Turbines NSPS packet was reused from the 2016v2 platform because the
last finalized regulation was dated March 20, 2009. For pt_oilgas, packets are based on updated growth
information for that sector from state-historical production data and the AEO2023 production forecast
database. The new growth factors were to calculate the new control efficiencies for 2026. The control
efficiency calculation methodology did not change from the 2016v3 modeling platform.
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
204
-------
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 4-38 compares the federal 2006 NSPS NOx emission limits for new stationary combustion
turbines with the NOx Reasonably Available Control Technology (RACT) regulations in selected states
within the NOx SIP Call region. More information on the NOx SIP call is available at
https://www.epa.gov/power-sector/final-update-nox-sip-call-regulations (84 FR 8422). 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 4-38. Stationary gas turbines NSPS analysis and RACT regulations in selected states
Firing Natural Gas limits:
<50 MMBTU/hr
50-850
MMBTU/hr
>850
MMBTU/hr
Federal NSPS
100
25
15
Ppm
State
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
The above state RACT table is from a 2001 analysis. Note that the current New York State regulations
use the same emission limits. The resulting new source NOx ratio (Fn) for NOx SIP call state and
California = 0.595 or 40.5% control (as computed by dividing 25 by 42). For other states Fn = 0.238 or
76.2% control (as computed by dividing 25 by 105).
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 Equation 4-2 as a function of the year-specific projection factor. The Natural Gas Turbines control
packet for pt_oilgas also includes additional controls for the EPNG Williams facility in Arizona, in order to
reduce the post-control facility total of 584.77 tons/yr NOx.
205
-------
Table 4-39 shows the reduction in NOx emissions after the application of the Natural Gas Turbines NSPS
CONTROL. Table 4-40 and Table 4-41 list the point source SCCs for which Natural Gas Turbines NSPS
controls were applied in ptnonipm and pt_oilgas, respectively.
Table 4-39. Emissions reductions due to the Natural Gas Turbines NSPS
Sector
Year
Pollutant
2022hc (tons)
Emissions
change (tons)
%
change
pt_°ilgas
2026
NOX
365,805
-7,531
-2.1%
ptnonipm
2026
NOX
780,504
-846
-0.1%
Table 4-40. SCCs in ptnonipm for which Natural Gas Turbines NSPS controls were applied
see
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
20300203
Internal Combustion Engines
Commercial/Institutional; Natural Gas; Turbine: Cogeneration
Table 4-41. SCCs in pt_oilgas for which Natural Gas Turbines NSPS controls were applied
see
SCC description
20100201
Internal Combustion Engines;Electric Generation;Natural Gas;Turbine
20100209
Internal Combustion Engines;Electric Generation;Natural Gas;Turbine: Exhaust
20200201
Internal Combustion Engines;lndustrial;Natural Gas;Turbine
20200203
Internal Combustion Engines;lndustrial;Natural Gas;Turbine: Cogeneration
20200209
Internal Combustion Engines;lndustrial;Natural Gas;Turbine: Exhaust
20300202
Internal Combustion Engines;Commercial/lnstitutional;Natural Gas;Turbine
20300203
Internal Combustion Engines;Commercial/lnstitutional;Natural Gas;Turbine: Cogeneration
20300209
Internal Combustion Engines;Commercial/lnstitutional;Natural Gas;Turbine: Exhaust
4.2.4.5 Process Heaters NOx NSPS (ptnonipm, pt_oilgas)
Packets:
Control_2022_2026_Process_Heaters_NSPS_ptnonipm_2022vlplatform_30sep2024_v0
Control_2022_2026_Process_Heaters_pt_oilgas_2022vlplatform_06sep2024_v0
Control_2022_2026_Process_Heaters_pt_oilgas_2022vlplatform_KSupdate_16oct2024_v0
For ptnonipm, the packet was reused from the 2016v2 platform. For pt_oilgas, the packets were newly
developed for 2022v2 based on updated information including the AEO2023 forecast oil and gas
production.
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
206
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destroying the VOC. The criteria pollutants of most concern for process heaters are NOx and SO2. In
2022, 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 4-42.
Table 4-42. Process Heaters NSPS analysis emission rates used to estimate controls
NOx emission rate Existing PPMV (=Fe)
Natural Draft
(fraction)
Forced Draft
(fraction)
Average
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 used for new sources (Fn) is 0.41 (59 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 Equation 4-2 as a function of the year-
specific projection factor.
The impact on emissions from applying the process heaters NSPS is shown in Table 4-43. Table 4-44 and
Table 4-45 list the point source SCCs for which the Process Heaters NSPS controls were applied in the
ptnonipm and pt_oilgas sectors, respectively.
Table 4-43. Emissions reductions due to the application of the Process Heaters NSPS
Sector
Year
Pollutant
2022hc
(tons)
Emissions
change (tons)
%
change
pt_°ilgas
2026
NOX
365,805
-1,398
-0.4%
ptnonipm
2026
NOX
780,504
-3,888
-0.5%
207
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Table 4-44. SCCs in ptnonipm for which Process Heaters NSPS controls were applied
see
SCC Description*
30190003
IP;Chemical Manufacturing;Fuel Fired Equipment;Process Heater: Natural Gas
30190004
IP;Chemical Manufacturing;Fuel Fired Equipment;Process Heater: Process Gas
30590002
IP;Mineral Products;Fuel Fired Equipment;Residual Oil: Process Heaters
30590003
IP;Mineral Products;Fuel Fired Equipment;Natural Gas: Process Heaters
30600103
IP;Petroleum lndustry;Process Heaters;Oil
30600104
IP;Petroleum lndustry;Process Heaters;Gas
30600105
IP;Petroleum lndustry;Process Heaters;Natural Gas
30600106
IP;Petroleum Industry;Process Heaters;Process Gas
30600107
IP;Petroleum lndustry;Process Heaters;Liquified Petroleum Gas (LPG)
30600199
IP;Petroleum lndustry;Process Heaters;Other Not Elsewhere Classified
30990003
IP;Fabricated Metal Products;Fuel Fired Equipment;Natural Gas: Process Heaters
31000401
IP;Oil and Gas Production;Process Heaters;Distillate Oil (No. 2)
31000403
IP;Oil and Gas Production;Process Heaters;Crude Oil
31000404
IP;Oil and Gas Production;Process Heaters;Natural Gas
31000405
IP;Oil and Gas Production;Process Heaters;Process Gas
31000406
IP;Oil and Gas Production;Process Heaters;Propane/Butane
31000414
IP;Oil and Gas Production;Process Heaters;Natural Gas: Steam Generators
31000415
IP;Oil and Gas Production;Process Heaters;Process Gas: Steam Generators
39900501
IP;Miscellaneous Manufacturing lndustries;Process Heater/Furnace;Distillate Oil
39900601
IP;Miscellaneous Manufacturing lndustries;Process Heater/Furnace;Natural Gas
IP;Miscellaneous Manufacturing lndustries;Miscellaneous Manufacturing
39990003
lndustries;Natural Gas: Process Heaters
* IP = Industrial Processes
Table 4-45. SCCs in pt_oilgas for which Process Heaters NSPS controls were applied
SCC
SCC Description
30190001
Industrial Processes;Chemical Manufacturing;Fuel Fired Equipment;Process Heater: Distillate Oil (No.
2)
30190003
Industrial Processes;Chemical Manufacturing;Fuel Fired Equipment;Process Heater: Natural Gas
30190004
Industrial Processes;Chemical Manufacturing;Fuel Fired Equipment;Process Heater: Process Gas
30290003
Industrial Processes;Food and Agriculture;Fuel Fired Equipment;Natural Gas: Process Heaters
30390003
Industrial Processes;Primary Metal Production;Fuel Fired Equipment;Natural Gas: Process Heaters
30590003
Industrial Processes;Mineral Products;Fuel Fired Equipment;Natural Gas: Process Heaters
30600103
Industrial Processes;Petroleum lndustry;Process Heaters;Oil
30600104
Industrial Processes;Petroleum lndustry;Process Heaters;Gas
30600105
Industrial Processes;Petroleum lndustry;Process Heaters;Natural Gas
30600106
Industrial Processes;Petroleum lndustry;Process Heaters;Process Gas
30600107
Industrial Processes;Petroleum lndustry;Process Heaters;Liquified Petroleum Gas (LPG)
30600199
Industrial Processes;Petroleum lndustry;Process Heaters;Other Not Elsewhere 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)
208
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see
SCC Description
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
31000411
Industrial Processes;Oil and Gas Production;Process Heaters;Distillate Oil (No. 2): 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
31390001
Industrial Processes;Electrical Equipment;Process Heaters;Distillate Oil (No. 2)
31390003
Industrial Processes;Electrical Equipment;Process Heaters;Natural Gas
39900601
Industrial Processes;Miscellaneous Manufacturing lndustries;Process Heater/Furnace;Natural Gas
39900701
Industrial Processes;Miscellaneous Manufacturing lndustries;Process Heater/Furnace;Process Gas
39990003
Industrial Processes;Miscellaneous Manufacturing lndustries;Miscellaneous Manufacturing
lndustries;Natural Gas: Process Heaters
4.2.4.6 State-specific controls (nonpt, np_solvents, ptnonipm)
Packets:
Control_2022_20XX_2022vl_point_nonpoint_SLT_controls_07oct2024_vl
A few states submitted state-specific controls for the nonpt, np_solvents, and ptnonipm sectors. For
nonpt sectors, Utah and Delaware both submitted controls that were applied to their state emissions for
the years 2026. Table 4-46 shows which SCCs and pollutants in nonpt these controls were applied. Table
4-47 shows the impacts of the controls on the three sectors.
Table 4-46. SCCs in nonpt, np_solvents, and ptnonipm for which state-specific controls were applied
State
Pollutant
SCC
SCC Description
Utah
NOX
2102006000
Stationary Source Fuel Combustion;lndustrial;Natural Gas;Total:
Boilers and IC Engines
Utah
NOX
2103006000
Stationary Source Fuel Combustion;Commercial/lnstitutional;Natural
Gas;Total: Boilers and IC Engines
Delaware
VOC
2501060053
Storage and Transport;Petroleum and Petroleum Product
Storage;Gasoline Service Stations;Stage 1: Bal
Delaware
VOC
2501060201
Storage and Transport;Petroleum and Petroleum Product
Storage;Gasoline Service Stations;Underground
Delaware
Many
Many
Delaware submitted population projections for all counties within
their State and requested all relevant SCC to use this provided data in
making emissions projections.
New
Jersey
Many
Many
New Jersey submitted population projections for all counties within
their State and requested all relevant SCC to use this provided data in
making emissions projections.
For ptnonipm, several states submitted emission reductions and controls due to fuel switching, consent
decrees, or state or local rules that would reduce emissions in the future. Iowa submitted NOx and SO2
209
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reductions for Iowa State University (EIS Facility ID: 18936211) due to a fuel switch from coal to natural
gas that occurred in 2023. Iowa reduced SO2 at ADM - Des Moines Soybean facility (EIS Facility ID:
3163611) due to an Iowa State consent order and Polk County local program that required a coal fired
boiler to be decommissioned in 2023 and replaced with a natural gas boiler. In Washington State,
Cardinal FG Company Winlock (EIS Facility ID: 1262611) installed a silicon controlled rectifier (SCR) on its
glass furnace in 2024, reducing NOx emissions. In Texas, the Streetman Lightweight Agregate Plant (EIS
Facility ID: 4946511) installed SO2 controls in late 2022.
Table 4-47. Summary of SLT-provided controls on 2022 emissions
Sector
Year
Pollutant
2022hc (tons)
Emissions change
(tons)
% change
nonpt
2026
NOX
741,248
-240
-0.03%
nonpt
2026
VOC
949,760
-84
-0.01%
np_solvents
2026
VOC
2,634,832
-34
-0.001%
ptnonipm
2026
NOX
780,504
-771
-0.10%
ptnonipm
2026
SO 2
438,306
-2,864
-0.65%
4.3 Sectors with Projections Computed Outside of CoST
Projections for sectors not calculated using CoST are discussed in this section.
4.3.1 Nonroad Mobile Equipment Sources (nonroad)
Outside of California, the MOVES4 model was run for 2026. The fuels used are specific to the analytic
year, but the meteorological data represented the year 2022. The analytic year nonroad emissions
include all nonroad control programs finalized as of the date of the MOVES4 release.
The resulting analytic year inventories were processed into the format needed by SMOKE in the same
way as the base year emissions.
From the data review: North Carolina commented that growth relative to 2022 of industrial SCCs was
too aggressive, and suggested we cap growth of nonroad industrial emissions in NC as follows:
2026 = 1.096
We adjusted the MOVES outputs so that growth of NC industrial never exceeds the above limits,
resulting in a notable decrease in emissions. For example, if 2026/2022 = 1.2, then we reduced the 2026
emissions by a factor of 0.9133 (1.096 / 1.2) so that 2026/2022 = 1.096. If the existing growth was under
the cap, then no change was made. When doing this, we preserved VOC and PM speciation, such that
growth of a particular species, VOC HAP, or NONHAPTOG or PM2.5 component may exceed the cap, but
growth of overall VOC or PM2.5 will not.
In California, California Air Resources Board (CARB) provided inventories were used for 2026.
210
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4.3.2 Onroad Mobile Sources (onroad)
For 2022vl, MOVES4 was run to obtain onroad emission factors that account for the impact of on-the-
books rules that are implemented into MOVES4. These include regulations such as:
• Revised 2023 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions Standards, 86
FR 74434 (December 30, 2021);
• Safer Affordable Fuel Efficient (SAFE) Vehicles Final Rule for Model Years 2021-2026 (March
2020);
• Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty
Engines and Vehicles - Phase 2 (October 2016);
• Tier 3 Vehicle Emission and Fuel Standards Program (March 2014)
(https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-air-
pollution-motor-vehicles-tier-3);
• 2017 and Later Model Year Light-Duty Vehicle GHG Emissions and Corporate Average Fuel
Economy Standards (October 2012);
• Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty
Engines and Vehicles (September 2011);
• Regulation of Fuels and Fuel Additives: Modifications to Renewable Fuel Standard Program
(RFS2) (December 2010); and
• Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy
Standards Final Rule for Model-Year 2012-2016 (May 2010).
State-level adjustment factors were developed to account for California and other Section 177 states
that have adopted California's Advanced Clean Trucks regulation. This regulation requires greater sales
of zero-emission heavy-duty trucks than EPA's Greenhouse Gas Emissions Standards for Heavy-Duty
Vehicles—Phase 3 rule, especially prior to 2030. Adjustment factors by calendar year, state, pollutant,
and SCC are calculated as the ratio of MOVES5 output to MOVES4 output.
Adjustment factors for the year 2022 were developed for states that have decommissioned Stage II
refueling programs, where their decommissioning is accounted for in MOVES5 but not in MOVES4.
Local inspection and maintenance (l/M) and other onroad mobile programs are included such as:
California LEVIN, the National Low Emissions Vehicle (LEV) and Ozone Transport Commission (OTC); LEV
regulations, local fuel programs, and Stage II refueling control programs. Note that MOVES3 emission
rates for model years 2017 and beyond are equivalent to CA LEVIN rates for NOx and VOC. Therefore, it
was not necessary to update the rates used for states that have adopted the rules in 2020 or later years.
The most recent California rules passed after 2020 are not reflected in this platform. Onroad emissions
in California were based on emissions provided by CARB for 2026. In a similar fashion to the adjustments
applied in other states to reflect rules not included in MOVES4, adjustment factors were also developed
and applied to California emissions to estimate the impact of the federal rules not reflected in CARB's
EMFAC2017 model. No attempt was made to account for recent California regulations other than
Advanced Clean Trucks.
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VMT data were projected from 2022 to 2026 using projection factors based on AEO2023 projections and
applied nationally by fuel type and broad vehicle type (light duty, medium duty for buses and single unit
trucks, and heavy duty for combination trucks). Diesel light duty cars were held flat in projections, but
diesel light duty trucks were projected using the AEO. Light duty VMT projections also incorporated a
county-level adjustment based on projected human population trends, so that counties expected to
grow more than the national average in population receive a corresponding increase in VMT for those
counties, and vice versa. The AEO2023-based VMT projection factors are shown in Table 4-48. Four
states (NJ, NY, NC, and Wl) provided VMT for each analytic year. Massachusetts gave us VMT for 2026 as
well, but it was not in time to be incorporated into the draft version of the emissions. Thus since the
VMT they gave us was higher than that used in the draft version, there are increases in onroad emissions
in the final version of the 2026 emissions.
Table 4-48. Projection factors for VMT by Fuel and Vehicle Class
2022-to-2026
Gas light duty
1.036
Gas medium duty
1.108
Gas heavy duty
1.103
Diesel light duty cars
1.000
Diesel light duty trucks
1.252
Diesel medium duty
1.008
Diesel heavy duty
1.018
CNG medium duty
1.073
CNG heavy duty
1.080
E-85 light duty
0.900
Electric light duty
3.339
In addition, a small, negligible amount of VMT was created for CNG combination long haul trucks, and
for all electric heavy duty vehicle types, for the analytic years. These fuel and source type combinations
are newly supported in MOVES4, and activity for these SCCs was created to support future
considerations. For the moment, activity for these new MOVES4 SCCs is very small and does not impact
the results.
Vehicle population is computed as: analytic year VPOP = base year VPOP * (analytic year VMT / base
year VMT) by county and SCC6 (fuel + vehicle type). Wisconsin provided VPOP for each analytic year.
Vehicle starts for analytic years are computed as:
analytic year starts = base year starts * (analytic year VPOP / base year VPOP).
Long haul hoteling hours are computed at the county level using the formula:
analytic year hoteling = base year hoteling * (analytic year VMT / base year VMT)
212
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where only the VMT from combination long haul trucks on restricted roads are considered. Where
hoteling exists in counties with zero combo-long-haul-restricted-road VMT, hoteling from the base year
was projected using the national diesel heavy duty projection factors for VMT from AEO2023. Year-
specific APU fractions were used to split county-level hoteling to individual SCCs as follows: 17.0% for
2026.
On-network idling hours (ONI) activity data were calculated based on VMT. For each representative
county, the ratio of ONI hours to onroad VMT (on all road types) was calculated using the MOVES ONI
Tool by source type, fuel type, and month. These ratios were then multiplied by each county's total VMT
(aggregated by source type, fuel type, and month) to get hours of ONI activity.
In comments received DE, DC, LA, NJ, and WA were noted to have unrealistic increases in refueling
emissions between the base and analytic years. OTAQ-provided adjustment factors for refueling were
originally applied to 2026. Future years then reflected the elimination of Stage II controls in those five
states. This resulted in an artificial increase in refueling emissions in the five states in analytic years
versus 2022hc (in which the Stage II updates were not accounted for). In response to the state
comments, we took away adjustment factors to refueling in those five states.
4.3.3 Sources Outside of the United States (canada_onroad,
mexico_onroad, canmex_point, canmex_ag, canada_og2D, ptfire_othna,
canmex_area, canada_afdust, canada_ptdust)
This section discusses the projection of emissions from Canada and Mexico. Information about the base
year inventory used for these projections or the naming conventions can be found in Section 2.7. The
Canada projections for 2026 are based on ECCC-provided inventories, while most Mexico emissions are
not projected to 2026 from the base year. Fire emissions in Canada and Mexico in the ptfire_othna
sector were not projected.
4.3.3.1 Canadian fugitive dust sources (canada_afdust, canada_ptdust)
For Canadian area and point source dust sectors, emissions were provided by ECCC for 2026 and follow a
methodology consistent with their 2022 inventory. As with the base year, the analytic year dust
emissions are pre-adjusted, so analytic year dust follows the same emissions processing methodology as
the base year with respect to the transportable fraction and meteorological adjustments.
4.3.3.2 Point Sources in Canada and Mexico (canmex_point,
canada_og2D)
Canadian point source inventories were provided by ECCC for 2026 and follow a methodology consistent
with their 2022 inventory.
Mexico point source inventories from 2022 were held flat through 2026.
4.3.3.3 Nonpoint sources in Canada and Mexico (canmex_area,
canmex_ag)
Canadian area source inventories, including nonpoint, nonroad, and agricultural sources, were provided
by ECCC for 2026 and follow a methodology consistent with their 2022 inventory.
213
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Mexico area source inventories from 2022 were held flat to 2026.
4.3.3.4 Onroad sources in Canada and Mexico (canada_onroad,
canada_onroad)
For Canadian mobile onroad sources, ECCC provided the year 2026 emissions.
For Mexican mobile onroad sources, monthly onroad mobile inventories for 2026 were developed at
municipio resolution based on an interpolation of 2023 and 2027 runs of MOVES-Mexico done for the
2016 platform.
214
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5 Emission Summaries
Table 5-1 and Table 5-2 summarize base year emissions by sector for CAPs and key HAPs for the year
2022 in this platform. Similarly, Table 5-3 and Table 5-4 show emissions for the year 2026. 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. Note that totals for
the 12US2 domain are not available here, but the sum of the U.S. sectors would be essentially the same
and only the Canadian and Mexican emissions would change according to the extent of the grids to the
north and south of the continental United States. 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 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 include 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 FTP site for the 2022 platform
(https://gaftp.epa.gov/Air/emismod/2022/vl).
215
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Table 5-1. National by-sector CAP emissions for the 2022 platform, year 2022, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2_5
S02
voc
afdust_adj
6,146,958
855,377
Airports
369,923
0
116,884
9,098
8,077
11,956
43,850
cmv_clc2
20,296
70
137,145
3,748
3,632
626
5,265
cmv_c3
10,207
32
84,352
1,833
1,686
4,141
4,676
Fertilizer
1,671,402
Livestock
2,590,376
207,230
Nonpt
802,522
69,110
721,734
478,209
411,444
74,590
933,778
Nonroad
11,095,444
2,013
773,485
75,879
71,154
921
923,704
nP_oilgas
700,012
3,823
676,201
12,366
12,248
280,038
2,757,565
np_solvents
0
0
0
0
0
0
2,590,493
Onroad
13,332,341
185,022
2,066,044
189,078
70,302
8,749
982,106
Openburn
1,394,786
79,167
45,645
231,930
212,185
13,867
104,784
Ptegu
466,676
17,913
851,055
106,981
91,801
879,719
26,332
Ptagfire
873,964
9,946
37,243
119,877
75,028
12,029
128,359
ptfire-rx
7,653,954
67,401
129,063
1,260,341
1,117,863
77,351
1,567,213
ptfire-wild
6,856,611
70,503
70,961
1,440,745
938,357
68,088
1,850,700
Ptnonipm
1,204,525
60,957
768,611
345,152
224,138
434,482
730,824
pt_oilgas
180,921
9,324
327,107
13,442
12,774
32,115
209,689
Rail
96,147
303
456,604
11,803
11,448
341
18,789
Rwc
2,944,487
22,597
44,594
450,210
448,814
11,893
450,881
Beis
3,376,155
964,950
30,694,065
CONUS w/ beis
51,378,972
4,859,958
8,271,679
10,897,651
4,566,328
1,910,908
44,230,301
Canada ag
506,067
6,564
1,875
124,234
Canada oil and gas 2D
8
293,600
Canada afdust
975,005
183,021
Canada ptdust
3,980
510
Canada area
2,061,247
5,978
312,938
184,538
133,031
14,092
712,989
Canada onroad
1,715,237
7,135
357,211
25,404
13,469
955
120,229
Canada point
1,034,599
19,020
521,418
113,269
43,293
440,207
150,300
Canada fires
2,650,916
24,845
30,977
590,473
332,539
13,904
633,450
Canada cmv_clc2
3,193
10
20,631
545
529
66
726
Canada cmv_c3
8,394
22
66,152
1,255
1,155
2,625
4,082
Mexico ag
137,454
54,305
11,689
Mexico area
97,707
26,199
57,912
41,688
21,073
21,910
412,170
Mexico onroad
1,594,936
2,887
389,027
15,190
10,549
6,665
144,126
Mexico point
158,096
979
199,363
90,722
53,873
341,028
32,822
Mexico fires
297,069
4,862
13,226
43,610
34,575
2,574
62,461
Mexico cmv_clc2
199
1
1,296
35
34
7
50
Mexico cmv_c3
9,626
95
95,412
5,362
4,933
14,099
4,777
Offshore cmv_clc2
4,864
15
31,122
822
797
123
1,148
Offshore cmv_c3
52,623
313
470,598
17,673
16,259
44,675
25,782
Offshore pt_oilgas
28,551
5
34,660
422
416
321
31,406
216
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Table 5-2. National by-sector VOC HAP emissions for the 2022 platform, year 2022, 12US1 grid
(tons/yr)
Sector
Acetaldehyde
Benzene
Formaldehyde
Methanol
Naphthalene
Acrolein
1,3-
Butadiene
airports
1,559
614
4,448
651
470
928
660
cmv_clc2
22
11
97
0
10
10
5
cmv_c3
20
10
86
0
9
9
5
livestock
1,478
473
0
13,661
0
nonpt
9,460
3,357
5,357
14,606
451
9,460
336
nonroad
8,056
25,536
19,848
1,157
1,411
1,180
4,333
nP_oilgas
2,912
35,872
50,849
3,820
112
1,941
458
np_solvents
73
336
10
13,780
8,117
onroad
8,324
17,172
10,319
1,407
1,324
750
2,263
openburn
2,143
4,626
2,218
0
57
134
701
ptegu
14
883
8,253
189
4
202
2
ptagfire
10,074
9,493
7,502
0
0
988
ptfire-rx
65,156
21,017
126,604
89,682
18,170
25,708
16,273
ptfire-wild
54,041
15,732
97,891
99,573
18,453
16,382
8,331
ptnonipm
3,902
21,465
11,487
23,589
730
853
653
pt_oilgas
1,261
2,153
9,418
298
56
1,857
262
rail
1,471
423
4,190
0
51
301
35
rwc
51,243
13,303
35,899
0
6,945
1,949
3,615
beis
374,228
513,183
2,110,685
CONUS w/ beis
595,436
172,476
907,658
2,373,098
56,368
61,661
38,922
Can. ag
1,398
159
0
32,657
0
Can. oil & gas 2D
0
877
0
0
0
Can. Area
15,252
12,725
12,871
4,082
2,589
Can. Onroad
2,170
5,247
2,997
0
40
Can. Point
1,543
1,986
5,262
10,627
26
Can. Fires
22,127
5,988
44,383
49,869
7,330
6,739
3,566
Can. cmv_clc2
3
1
13
0
1
1
1
Can. cmv_c3
17
8
75
0
8
8
4
Mex. Area
3,085
1,742
2,539
2,666
469
Mex. Onroad
591
3,376
1,438
665
200
102
494
Mex. Point
65
1,208
2,587
519
11
Mex. Fires
3,406
892
3,772
1,386
168
0
0
Mex. cmv_clc2
0
0
1
0
0
0
0
Mex. cmv_c3
15
7
67
0
23
9
5
Off. cmv_clc2
5
2
21
0
2
2
1
Off. cmv_c3
97
47
423
0
80
48
26
Off. pt_oilgas
631
121
1,070
41
0
0
0
217
-------
Table 5-3. National by-sector CAP emissions for the 2022 platform, year 2026,12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2_5
S02
voc
afdust_adj
6,203,842
866,327
Airports
403,009
0
135,130
9,619
8,554
13,768
47,811
cmv_clc2
20,967
72
141,595
3,872
3,752
662
5,428
cmv_c3
10,743
34
83,629
1,929
1,775
4,360
4,928
Fertilizer
1,671,402
Livestock
2,595,138
207,785
Nonpt
800,002
67,631
685,799
488,859
422,715
61,902
956,983
Nonroad
11,377,186
2,074
651,041
63,282
59,036
942
862,143
nP_oilgas
701,014
15
583,910
10,892
10,841
308,766
2,095,720
np_solvents
0
0
0
0
0
0
2,667,432
onroad
10,973,776
169,669
1,398,945
184,377
62,516
11,452
786,425
openburn
1,394,786
79,167
45,645
231,930
212,185
13,867
104,784
ptegu
411,310
32,477
665,673
102,988
89,934
603,941
27,277
ptagfire
873,964
9,946
37,243
119,877
75,028
12,029
128,359
ptfire-rx
7,653,954
67,401
129,063
1,260,341
1,117,863
77,351
1,567,213
ptfire-wild
6,856,611
70,503
70,961
1,440,745
938,357
68,088
1,850,700
ptnonipm
1,166,259
60,185
733,559
338,082
217,959
412,759
702,269
pt_oilgas
179,652
9,307
314,246
13,773
13,052
33,717
207,331
rail
95,644
302
454,085
11,744
11,391
339
18,694
rwc
2,944,487
22,597
44,594
450,210
448,814
11,893
450,881
beis
3,376,155
964,950
30,694,065
CONUS w/ beis
49,239,521
4,857,919
7,140,070
10,936,362
4,560,100
1,635,837
43,386,227
Canada ag
537,399
6,579
1,880
124,415
Canada oil and gas 2D
7
244,808
Canada afdust
1,065,831
197,478
Canada ptdust
4,392
561
Canada area
2,057,231
5,918
293,945
177,603
123,038
13,389
721,003
Canada onroad
1,775,762
7,169
344,288
26,593
13,354
1,203
119,545
Canada point
1,036,019
20,345
403,929
117,450
46,201
438,599
158,061
Canada fires
2,650,916
24,845
30,977
590,473
332,539
13,904
633,450
Canada cmv_clc2
3,193
10
20,631
545
529
66
726
Canada cmv_c3
8,394
22
66,152
1,255
1,155
2,625
4,082
Mexico ag
137,454
54,305
11,689
Mexico area
97,707
26,199
57,912
41,688
21,073
21,910
412,170
Mexico onroad
1,677,654
3,544
407,007
18,039
12,301
8,138
163,294
Mexico point
158,096
979
199,363
90,722
53,873
341,028
32,822
Mexico fires
297,069
4,862
13,226
43,610
34,575
2,574
62,461
Mexico cmv_clc2
199
1
1,296
35
34
7
50
Mexico cmv_c3
9,626
95
95,412
5,362
4,933
14,099
4,777
Offshore cmv_clc2
5,043
16
32,268
853
826
128
1,191
Offshore cmv_c3
54,783
320
471,623
18,061
16,616
45,527
26,892
Offshore pt_oilgas
28,543
5
34,654
422
416
321
31,396
218
-------
Table 5-4. National by-sector VOC HAP emissions for the 2022 platform, year 2026,12US1 grid
(tons/yr)
Sector
Acetaldehyde
Benzene
Formaldehyde
Methanol
Naphthalene
Acrolein
1,3-
Butadiene
airports
1,700
670
4,841
708
511
1,000
711
cmv_clc2
23
11
100
0
10
10
5
cmv_c3
21
10
90
0
9
0
0
livestock
1,514
481
0
13,711
0
nonpt
10,053
3,373
5,437
15,673
461
10,053
348
nonroad
6,753
24,622
16,374
1,064
1,281
888
4,261
nP_oilgas
2,747
29,342
47,162
3,993
100
2,057
487
np_solvents
75
334
11
14,074
8,428
onroad
5,971
12,726
6,532
1,162
857
473
1,570
openburn
2,143
4,626
2,218
0
57
134
701
ptegu
13
967
10,516
183
3
ptagfire
10,074
9,493
7,502
0
0
988
ptfire-rx
65,156
21,017
126,604
89,682
18,170
25,708
16,273
ptfire-wild
54,041
15,732
97,891
99,573
18,453
16,382
8,331
ptnonipm
3,681
21,168
11,201
21,828
694
821
615
pt_oilgas
1,388
2,197
9,779
300
58
1,922
269
rail
1,464
421
4,169
0
51
299
35
rwc
51,243
13,303
35,899
0
6,945
1,949
3,615
beis
374,228
513,183
2,110,685
CONUS w/ beis
592,287
160,490
899,508
2,372,636
56,089
61,695
38,209
Can. ag
1,400
160
0
32,704
0
Can. oil & gas 2D
0
727
0
0
0
Can. Area
13,737
12,419
11,796
4,302
2,426
Can. Onroad
2,124
5,207
2,931
0
40
Can. Point
1,555
1,971
5,336
11,488
33
Can. Fires
22,127
5,988
44,383
49,869
7,330
6,739
3,566
Can. cmv_clc2
3
1
13
0
1
1
1
Can. cmv_c3
17
8
75
0
8
8
4
Mex. Area
3,085
1,742
2,539
2,666
469
Mex. Onroad
646
3,502
1,612
720
213
111
493
Mex. Point
65
1,208
2,587
519
11
Mex. Fires
3,406
892
3,772
1,386
168
0
0
Mex. cmv_clc2
0
0
1
0
0
0
0
Mex. cmv_c3
15
7
67
0
23
9
5
Off. cmv_clc2
5
2
22
0
2
2
1
Off. cmv_c3
102
49
443
0
83
50
27
Off. pt_oilgas
631
121
1,070
41
0
0
0
219
-------
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