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Technical Support Document (TSD): Preparation of
Emissions Inventories for the 2016v3 North American
Emissions Modeling Platform
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EPA-454/B-23-002
January 2023
Technical Support Document (TSD): Preparation of Emissions Inventories for the 2016v3 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)
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TABLE OF CONTENTS
LIST OF TABLES VIII
LIST OF FIGURES XI
LIST OF APPENDICES XII
ACRONYMS XIII
1 INTRODUCTION 1
2 EMISSIONS INVENTORIES AND APPROACHES 3
2.1 2016 POINT SOURCES (PTEGU, PT_OILGAS, PTNONIPM, AIRPORTS) 8
2.1.1 EGU sector (ptegu) 10
2.1.2 Point source oil and gas sector (pt oilgas) 12
2.1.3 Non-IPM sector (ptnonipm) 15
2.1.4 Aircraft and ground support equipment (airports) 16
2.2 2016 Nonpoint sources (afdust, fertilizer, livestock, np_oilgas, np_sol vents, rwc, nonpt) 18
2.2.1 Area fugitive dust (afdust) 18
2.2.2 Agricultural Livestock (livestock) 25
2.2.3 Agricultural Fertilizer (fertilizer) 27
2.2.4 Nonpoint Oil and Gas (np oilgas) 30
2.2.5 Residential Wood Combustion (rwc) 31
2.2.6 Solvents (np solvents) 32
2.2.7 Nonpoint (nonpt) 34
2.3 2016 Onroad Mobile sources (onroad) 34
2.3.1 Onroad Activity Data Development 35
2.3.2 MOVES Emission Factor Table Development 43
2.3.3 Onroad California Inventory Development (onroad ca) 46
2.4 2016 Nonroad Mobile sources (cmv, rail, nonroad) 46
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 46
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3) 50
2.4.3 Railway Locomotives (rail) 54
2.4.4 Nonroad Mobile Equipment (nonroad) 64
2.5 2016 Fires (ptfire-wild, ptfire-rx, ptagfire) 70
2.5.1 Wild and Prescribed Fires (ptfire) 70
2.5.2 Point Source Agricultural Fires (ptagfire) 78
2.6 2016 Biogenic Sources (beis) 82
2.7 Sources Outside of the United States 85
2.7.1 Point Sources in Canada and Mexico (othpt, canadaag, canada_og2D) 85
2.7.2 Fugitive Dust Sources in Canada (othafdust, othptdust) 86
2.7.3 Nonpoint and Nonroad Sources in Canada and Mexico (othar) 86
2.7.4 Onroad Sources in Canada and Mexico (onroadcan, onroadjnex) 86
2.7.5 Fires in Canada and Mexico (ptfire othna) 86
2.7.6 Ocean Chlorine, Sea Salt, and Lightning NOx 87
3 EMISSIONS MODELING 88
3.1 Emissions modeling Overview 88
3.2 Chemical Speciation 92
3.2.1 VOC speciation 95
3.2.1.1 County specific profile combinations 98
3.2.1.2 Additional sector specific considerations for integrating HAP emissions from inventories into speciation 99
3.2.1.3 Oil and gas related speciation profiles 101
3.2.1.4 Mobile source related VOC speciation profiles 104
3.2.2 PM speciation 109
3.2.2.1 Mobile source related PM2.5 speciation profiles Ill
3.2.3 NOx speciation 112
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3.2.4 Creation of Sulfuric Acid Vapor (SULF) 113
3.3 Temporal Allocation 114
3.3.1 Use of FF10 format for finer than annual emissions 116
3.3.2 Electric Generating Utility temporal allocation (ptegu) 116
3.3.2.1 Base year temporal allocation of EGUs 116
3.3.2.2 Analytic year temporal allocation of EGUs 121
3.3.3 Airport Temporal allocation (airports) 127
3.3.4 Residential Wood Combustion Temporal allocation (rwc) 129
3.3.5 Agricultural Ammonia Temporal Profiles (ag) 133
3.3.6 Oil and gas temporal allocation (npoilgas) 134
3.3.7 Onroad mobile temporal allocation (onroad) 135
3.3.8 Nonroad mobile temporal allocation(nonroad) 140
3.3.9 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire) 142
3.4 Spatial Allocation 144
3.4.1 Spatial Surrogates for U.S. emissions 144
3.4.2 Allocation method for airport-related sources in the U.S. 151
3.4.3 Surrogates for Canada and Mexico emission inventories 151
3.5 Preparation of Emissions for the CAMx model 154
3.5.1 Development of CAMx Emissions for Standard CAMx Runs 154
3.5.2 Development of CAMx Emissions for Source Apportionment CAMx Runs 156
4 DEVELOPMENT OF ANALYTIC YEAR EMISSIONS 159
4.1 EGU Point Source Projections (ptegu) 164
4.2 Non-EGU Point and Nonpoint Sector Projections 167
4.2.1 Background on the Control Strategy Tool (CoST) 168
4.2.2 CoST Plant CLOSURE Packet (ptnonipm, ptoilgas) 172
4.2.3 CoST PROJECTION Packets (afdust, airports, cmv, livestock, nonpt, np oilgas, np solvents, ptnonipm, pt oilgas,
rail, rwc) 172
4.2.3.1 Fugitive dust growth (afdust) 173
4.2.3.2 Livestock population growth (livestock) 174
4.2.3.3 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 175
4.2.3.4 Category 3 Commercial Marine Vessels (cmv_c3) 176
4.2.3.5 Oil and Gas Sources (pt oilgas, np oilgas) 177
4.2.3.6 Non-EGU point sources (ptnonipm) 183
4.2.3.7 A irport sources (airports) 184
4.2.3.8 Nonpoint Sources (nonpt) 185
4.2.3.9 Solvents (np solvents) 187
4.2.3.10 Residential Wood Combustion (rwc) 189
4.2.4 CoST CONTROL Packets (nonpt, np oilgas, ptnonipm, pt oilgas, np solvents) 192
4.2.4.1 Oil and Gas NSPS (np oilgas, pt oilgas) 193
4.2.4.2 LUCE NSPS (nonpt, ptnonipm, np oilgas, pt oilgas) 201
4.2.4.3 Fuel Sulfur Rules (nonpt, ptnonipm) 204
4.2.4.4 Natural Gas Turbines NOx NSPS (ptnonipm, pt oilgas) 205
4.2.4.5 Process Heaters NOx NSPS (ptnonipm, pt oilgas) 208
4.2.4.6 Ozone Transport Commission Rules (nonpt, solvents) 211
4.3 Projections Computed Outside of CoST 211
4.3.1 Nonroad Mobile Equipment Sources (nonroad) 211
4.3.2 Onroad Mobile Sources (onroad) 212
4.3.3 Locomotives (rail, ptnonipm) 216
4.3.4 Sources Outside of the United States (onroadcan, onroadjnex, othpt, canadaag, canada_og2D, ptfire othna,
othar, othafdust, othptdust) 218
4.3.4.1 Canadian fugitive dust sources (othafdust, othptdust) 218
4.3.4.2 Point Sources in Canada and Mexico (othpt, canadaag, canada_og2D) 219
4.3.4.3 Nonpoint sources in Canada and Mexico (othar) 219
4.3.4.4 Onroad sources in Canada and Mexico (onroad can, onroadjnex) 220
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5 EMISSION SUMMARIES 221
6 REFERENCES 228
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List of Tables
Table 2-1. Platform sectors for the 2016 emissions modeling case 5
Table 2-2. Default stack parameter replacements 10
Table 2-3. Point source oil and gas sector NAICS Codes 12
Table 2-4. Sources removed from ptoilgas due to Overlap with WRAP Oil and Gas Inventory 13
Table 2-5. 2014NEIv2-to-2016 projection factors for pt_oilgas sector for 2016vl inventory 14
Table 2-6. 2014NEI-based sources in 2016gf pt oilgas (excluding offshore) before and after the 2014-to-
2016 projections for (tons/year) 15
Table 2-7. 2016v2 platform SCCs for the airports sector 17
Table 2-8. Afdust sector SCCs 18
Table 2-9. Total impact of fugitive dust adjustments to the unadjusted 2016 inventory 22
Table 2-10. SCCs for the livestock sector 25
Table 2-11. National back-projection factors for livestock: 2017 to 2016 27
Table 2-12. Source of input variables for EPIC 29
Table 2-13. 2016 vl platform SCCs for the residential wood combustion sector 31
Table 2-14. MOVES vehicle (source) types 35
Table 2-15. Submitted data used to prepare 2016v2 onroad activity data 36
Table 2-16. State total differences between 2017 NEI and 2016 VMT data 37
Table 2-17. Fraction of IHS Vehicle Populations to Retain for 2016vl and 2017 NEI 45
Table 2-18. SCCs for cmv_clc2 sector 47
Table 2-19. Vessel groups in the cmv_clc2 sector 49
Table 2-20. SCCs for cmv_c3 sector 51
Table 2-21. 2017 to 2016 projection factors for C3 CMV 54
Table 2-22. 2016\ 1 SCCs for the Rail Sector 55
Table 2-23. Class I Railroad Reported Locomotive Fuel Use Statistics for 2016 55
Table 2-24. 2016 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal) 57
Table 2-25. Surface Transportation Board R-l Fuel Use Data - 2016 59
Table 2-26. 2016 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)4 59
Table 2-27. Expenditures and fuel use for commuter rail 61
Table 2-28. Submitted nonroad input tables by agency 68
Table 2-29. Alaska counties/census areas for which nonroad equipment sector-specific emissions are
removed in the 2016 platforms 69
Table 2-30. SCCs included in the 2016 ptfire sector 71
Table 2-31. National fire information databases used in 2016 ptfire inventory 71
Table 2-32. List of S/L/T agencies that submitted fire data for 2016vl with types and formats 73
Table 2-33. Brief description of fire information submitted for 2016vl inventory use 74
Table 2-34. SCCs included in the 2016 ptagfire sector 78
Table 2-35. Assumed field size of agricultural fires per state(acres) 80
Table 2-36. Hourly Meteorological variables required by BEIS4 83
Table 3-1. Key emissions modeling steps by sector 89
Table 3-2. Descriptions of the platform grids 91
Table 3-3. Emission model species produced for CB6R3AE7 for CMAQ 92
Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM)
for each platform sector 97
Table 3-5. Ethanol percentages by volume by Canadian province 99
Table 3-6. MOVES integrated species in M-profiles 100
Table 3-7. Basin/Region-specific profiles for oil and gas 103
Table 3-8. TOG MOVES-SMOKE Speciation for nonroad emissions used for the 2016 Platform 104
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Table 3-9. Select mobile-related VOC profiles 2016 105
Table 3-10. Onroad M-profiles 106
Table 3-11. MOVES process IDs 107
Table 3-12. MOVES Fuel subtype IDs 108
Table 3-13. MOVES regclass IDs 108
Table 3-14. Regional Fire Profiles 109
Table 3-15. Brake and tire PM2.5 profiles compared to those used in the 201 lv6.3 Platform Ill
Table 3-16. Nonroad PM2.5 profiles 112
Table 3-17. NOx speciation profiles 113
Table 3-18. Sulfate split factor computation 113
Table 3-19. SO2 speciation profiles 114
Table 3-20. Temporal settings used for the platform sectors in SMOKE 115
Table 3-21. U.S. Surrogates available for the 2016 modeling platforms 145
Table 3-22. Off-Network Mobile Source Surrogates 146
Table 3-23. Spatial Surrogates for Oil and Gas Sources 147
Table 3-24. Selected 2016 CAP emissions by sector for U.S. Surrogates (short tons in 12US1) 148
Table 3-25. Canadian Spatial Surrogates 151
Table 3-26. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3) 152
Table 3-27. Emission model species mappings for CMAQ and CAMx (for CB6R3AE7) 155
Table 3-28. State tags for USA modeling 157
Table 4-1. Overview of projection methods for the future year cases 159
Table 4-2. EGU sector NOx emissions by State for the 2016v3 cases 166
Table 4-3. Subset of CoST Packet Matching Hierarchy 169
Table 4-4. Summary of non-EGU stationary projections subsections 170
Table 4-5. Reductions from all facility/unit/stack-level closures in 2016v3 from 2019 emissions levels 172
Table 4-6. Increase in total afdust PM2.5 emissions from projections in 2016v3 174
Table 4-7. National projection factors for livestock: 2017 to 2023 and 2026 174
Table 4-8. National projection factors for cmv_clc2 175
Table 4-9. California projection factors for cmv_clc2 175
Table 4-10. 2016-to-2023 and 2016-to-2026 CMV C3 projection factors outside of California 177
Table 4-11. 2016-to-2023 and 2016-to-2026 CMV C3 projection factors for California 177
Table 4-12. Year 2017-2019 high-level summary of national oil and gas exploration emissions 180
Table 4-13. Point oil and gas sources held constant at 2018 or 2019 levels 180
Table 4-14. Annual Energy Outlook (AEO) 2022 tables used to project industrial sources 184
Table 4-15. SCCs in nonpt that use Human Population Growth for Projections 187
Table 4-16. SCCs in np solvents that use Human Population Growth for Projections 188
Table 4-17. Projection factors for RWC 190
Table 4-18. Assumed retirement rates and new source emission factor ratios for NSPS rules 193
Table 4-19. Non-point (npoilgas) SCCs in 2016v3 modeling platform where Oil and Gas NSPS controls
applied 194
Table 4-20. Emissions reductions for np oilgas sector due to application of Oil and Gas NSPS 196
Table 4-21. Point source SCCs in ptoilgas sector where Oil and Gas NSPS controls were applied 196
Table 4-22. VOC reductions (tons/year) for the pt oilgas sector after application of the Oil and Gas NSPS
CONTROL packet for both analytic years 2023 and 2026 201
Table 4-23. SCCs and Engine Types where RICE NSPS controls applied for nonpt and ptnonipm 202
Table 4-24. Non-point Oil and Gas SCCs in 2016v3 modeling platform where RICE NSPS controls applied
202
Table 4-25. Nonpoint Emissions reductions after the application of the RICE NSPS 203
Table 4-26. Ptnonipm Emissions reductions after the application of the RICE NSPS 203
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Table 4-27. Oil and Gas Emissions reductions for np oilgas sector due to application of RICE NSPS 204
Table 4-28. Point source SCCs in ptoilgas sector where RICE NSPS controls applied 204
Table 4-29. Emissions reductions (tons/year) in pt oilgas sector after the application of the RICE NSPS
CONTROL packet for analytic years 2023 and 2026 204
Table 4-30. Summary of fuel sulfur rule impacts on nonpoint S02 emissions for 2023 205
Table 4-31. Stationary gas turbines NSPS analysis and resulting emission rates used to compute controls. 206
Table 4-32. Ptnonipm SCCs in 2016vl modeling platform where Natural Gas Turbines NSPS controls
applied 207
Table 4-33. Ptnonipm emissions reductions after the application of the Natural Gas Turbines NSPS 207
Table 4-34. Point source SCCs in pt oilgas sector where Natural Gas Turbines NSPS control applied 207
Table 4-35. Emissions reductions (tons/year) for ptoilgas after the application of the Natural Gas Turbines
NSPS CONTROL packet for analytic years 207
Table 4-36. Process Heaters NSPS analysis and 2016vl new emission rates used to estimate controls 208
Table 4-37. Ptnonipm SCCs in 2016vl modeling platform where Process Heaters NSPS controls applied. 209
Table 4-38. Ptnonipm emissions reductions after the application of the Process Heaters NSPS 210
Table 4-39. Point source SCCs in pt oilgas sector where Process Heaters NSPS controls were applied 210
Table 4-40. NOx emissions reductions (tons/year) in pt oilgas sector after the application of the Process
Heaters NSPS CONTROL packet for analytics years 210
Table 4-41. Light duty greenhouse gas rule adjustments for 2023 and 2026 onroad emissions 213
Table 4-42. Factors used to Project VMT to analytic years 214
Table 4-43. Class I Line-haul Fuel Projections based on 2018 AEO Data 217
Table 4-44. Class I Line-haul Historic and Analytic Year Projected Emissions 217
Table 4-45. 2018 AEO growth rates for rail sub-groups 218
Table 5-1. National by-sector CAP emissions for the 2016gf case, 12US1 grid (tons/yr) 222
Table 5-2. National by-sector CAP emissions for the 2023gf case, 12US1 grid (tons/yr) 223
Table 5-3. National by-sector CAP emissions for the 2026gf case, 12US1 grid (tons/yr) 224
Table 5-4. National by-sector CAP emissions for the 2016gf case, 36US3 grid (tons/yr) 225
Table 5-5. National by-sector CAP emissions for the 2023gf case, 36US3 grid (tons/yr) 226
Table 5-6. National by-sector Ozone Season NOx emissions summaries 12US1 grid (tons/o.s.) 227
Table 5-7. National by-sector Ozone Season VOC emissions summaries 12US1 grid (tons/o.s.) 227
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List of Figures
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and
cumulative 24
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions 28
Figure 2-3. Representative Counties in 2016v2 and 2016v3 44
Figure 2-4. 2017NEI/2016 platform geographical extent (solid) and U.S. ECA (dashed) 48
Figure 2-5. 2016 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT) 56
Figure 2-6. Class I Railroads in the United States5 57
Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States 60
Figure 2-8. Class II and III Railroads in the United States5 61
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains 63
Figure 2-10. Processing flow for fire emission estimates in the 2016 inventory 76
Figure 2-11. Default fire type assignment by state and month where data are only from satellites 77
Figure 2-12. BlueSky Modeling Framework 78
Figure 2-13. Normbeis4 data flows for 2016v3 84
Figure 2-14. Tmpbeis4 data flow diagram for 2016v3 85
Figure 3-1. Air quality modeling domains 91
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation 97
Figure 3-3. Profiles composited for PM gas combustion related sources 110
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources 110
Figure 3-5. Eliminating unmeasured spikes in CEMS data 117
Figure 3-6. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification 119
Figure 3-7. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type 120
Figure 3-8. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type 120
Figure 3-9. Non-CEMS EGU Temporal Profile Application Counts 121
Figure 3-10. Analytic Year Emissions Follow the Pattern of Base Year Emissions 125
Figure 3-11. Excess Emissions Apportioned to Hours Less than the Historic Maximum 125
Figure 3-12. Regional Profile Applied due to not being able to Adjust below Historic Maximum 126
Figure 3-13. Regional Profile Applied, but Exceeds Historic Maximum in Some Hours 127
Figure 3-14. Diurnal Profile for all Airport SCCs 128
Figure 3-15. Weekly profile for all Airport SCCs 128
Figure 3-16. Monthly Profile for all Airport SCCs 129
Figure 3-17. Alaska Seaplane Profile 129
Figure 3-18. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold 131
Figure 3-19. RWC diurnal temporal profile 131
Figure 3-20. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr) 132
Figure 3-21. Day-of-week temporal profiles for OHH and Recreational RWC 133
Figure 3-22. Annual-to-month temporal profiles for OHH and recreational RWC 133
Figure 3-23. Example of animal NH3 emissions temporal allocation approach (daily total emissions) 134
Figure 3-24. Example of temporal variability of NOx emissions 136
Figure 3-25. Sample onroad diurnal profiles for Fulton County, GA 137
Figure 3-26. Methods to Populate Onroad Speeds and Temporal Profiles by Road Type 138
Figure 3-27. Regions for computing Region Average Speeds and Temporal Profiles 139
Figure 3-28. Example of Temporal Profiles for Combination Trucks 140
Figure 3-29. Example Nonroad Day-of-week Temporal Profiles 141
Figure 3-30. Example Nonroad Diurnal Temporal Profiles 141
Figure 3-31. Agricultural burning diurnal temporal profile 143
Figure 3-32. Prescribed and Wildfire diurnal temporal profiles 143
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Figure 4-1. EIA Oil and Gas Supply Regions as of AEO2022
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List of Appendices
Appendix A: CB6 Assignment for New Species
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE versions 4.5 and later that
were used in the 2016 alpha platforms
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
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Acronyms
AADT Annual average daily traffic
AE6 CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
AEO Annual Energy Outlook
AERMOD American Meteorological Society/Environmental Protection Agency Regulatory
Model
AIS Automated Identification System
APU Auxiliary power unit
BEIS Biogenic Emissions Inventory System
BELD Biogenic Emissions Land use Database
BenMAP Benefits Mapping and Analysis Program
BPS Bulk Plant Storage
BTP Bulk Terminal (Plant) to Pump
C1C2 Category 1 and 2 commercial marine vessels
C3 Category 3 (commercial marine vessels)
CAMD EPA's Clean Air Markets Division
CAMx Comprehensive Air Quality Model with Extensions
CAP Criteria Air Pollutant
CARB California Air Resources Board
CB05 Carbon Bond 2005 chemical mechanism
CB6 Version 6 of the Carbon Bond mechanism
CBM Coal-bed methane
CDB County database (input to MOVES model)
CEMS Continuous Emissions Monitoring System
CISWI Commercial and Industrial Solid Waste Incinerators
CMAQ Community Multiscale Air Quality
CMV Commercial Marine Vessel
CNG Compressed natural gas
CO Carbon monoxide
CONUS Continental United States
Co ST 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
FEST-C Fertilizer Emission Scenario Tool for CMAQ
FF10 Flat File 2010
FINN Fire Inventory from the National Center for Atmospheric Research
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FIPS
Federal Information Processing Standards
FHWA
Federal Highway Administration
HAP
Hazardous Air Pollutant
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
I/M
Inspection and Maintenance
IMO
International Marine Organization
IPM
Integrated Planning Model
LADCO
Lake Michigan Air Directors Consortium
LDV
Light-Duty Vehicle
LPG
Liquified Petroleum Gas
MACT
Maximum Achievable Control Technology
MARAMA
Mid-Atlantic Regional Air Management Association
MATS
Mercury and Air Toxics Standards
MCIP
Meteorology-Chemistry Interface Processor
MMS
Minerals Management Service (now known as the Bureau of Energy Management,
Regulation and Enforcement (BOEMRE)
MOVES
Motor Vehicle Emissions Simulator
MSA
Metropolitan Statistical Area
MTBE
Methyl tert-butyl ether
MWC
Municipal waste combustor
MY
Model year
NAAQS
National Ambient Air Quality Standards
NAICS
North American Industry Classification System
NBAFM
Naphthalene, Benzene, Acetaldehyde, Formaldehyde and Methanol
NCAR
National Center for Atmospheric Research
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NESCAUM
Northeast States for Coordinated Air Use Management
NH3
Ammonia
NLCD
National Land Cover Database
NO A A
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
PFC
Portable Fuel Container
PM2.5
Particulate matter less than or equal to 2.5 microns
PM10
Particulate matter less than or equal to 10 microns
PPm
Parts per million
ppmv
Parts per million by volume
PSAT
Particulate Matter Source Apportionment Technology
RACT
Reasonably Available Control Technology
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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
see
Source Classification Code
SMARTFIRE2
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
version 2
SMOKE
Sparse Matrix Operator Kernel Emissions
SOi
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
USD A
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), has created a 2016v3 platform as an update to the
2016v2 emissions modeling platform (EPA, 2021). The 2016v3 platform includes updates in response to
comments, some corrections, improved methods, and refinements to some projection factors due to newly
released data. The 2016v3 platform is designed to be used for studies focused on criteria air pollutants
and represents the base year of 2016 and analytic years of 2023 and 2026. The 2016v3 platform
primarily draws on data from the 2017 National Emissions Inventory (NEI) (EPA, 2021b), although the
emissions were updated to represent the year 2016 through the incorporation of 2016-specific state and
local data along with adjustment methods appropriate for each sector. The analytic year inventories were
developed starting with the base year 2016 inventory using sector-specific methods as described below.
The 2016v3 platform supports applications related to ozone transport and particulate matter.
The full air quality modeling platform consists of all the emissions inventories and ancillary data files
used for emissions modeling, as well as the meteorological, initial condition, and boundary condition files
needed to run the air quality model. This document focuses on the emissions modeling data and
techniques that comprise the emission modeling platform including the emission inventories, the ancillary
data files, and the approaches used to transform inventories for use in air quality modeling.
The 2016v3 platform retains some data from the National Inventory Collaborative effort to develop the
2016vl platform (EPA, 2021c). The National Emissions Inventory Collaborative is a partnership between
state emissions inventory staff, multi-jurisdictional organizations (MJOs), federal land managers (FLMs),
EPA, and others to develop a North American air pollution emissions modeling platform with a base year
of 2016 for use in air quality planning. The Collaborative planned for three versions of the 2016 platform:
alpha, beta, and Version 1. The numbering format for the 2016 platforms is different from previous EPA
platforms which had the first number based on the version of the NEI, and the second number as a
platform iteration for that NEI year (e.g., v7.3 where the 7 represents 2014-2016 NEI-based platforms,
and 3 means the third iteration of the platform). Using this older numbering method, the 2016v3 platform
is also known as the v7.5 platform.
This emissions modeling platform includes all criteria air pollutants (CAPs) and precursors, and a group
of hazardous air pollutants (HAPs). The group of HAPs are those explicitly used by the chemical
mechanism in the Community Multiscale Air Quality (CMAQ) model (Appel et al., 2018) for
ozone/particulate matter (PM): chlorine (CI), hydrogen chloride (HC1), benzene, acetaldehyde,
formaldehyde, methanol, naphthalene. The modeling domain includes the lower 48 states and parts of
Canada and Mexico. The modeling cases for this platform were developed for studies with both the
CMAQ model and with the Comprehensive Air Quality Model with Extensions (CAMx). The emissions
modeling process used first prepares outputs in the format used by CMAQ, after which those emissions
data are converted to the formats needed by CAMx.
The 2016v3 platform consists of cases that represent the years 2016, 2023, and 2026 with the
abbreviations 2016gf_16j, 2023gf_16j, and 2026gf_16j, respectively. Derivatives of these cases that
included source apportionment by state were also developed. This platform accounts for atmospheric
chemistry and transport within a state-of-the-art photochemical grid model. In the case abbreviation
2016gf_16j, 2016 is the year represented by the emissions; the "g" represents the base year emissions
modeling platform iteration, which here shows that g is the 2016 platform which started with the 2017
NEI; and the "f' stands for the sixth configuration of emissions modeled for that modeling platform. We
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note that the 2016v2 cases used fj for the abbreviation, while 2016v3 cases use gf due to the 2017 NEI
data being used for the bulk of the emissions instead of the 2014 NEI.
The gridded meteorological model used to provide input data for the emissions modeling was developed
using the Weather Research and Forecasting Model (WRF,
https://ral.ucar.edu/solutions/products/weather-research-and-forecasting-model-wrf) version 3.8,
Advanced Research WRF core (Skamarock, et al., 2008). The WRF Model is a mesoscale numerical
weather prediction system developed for both operational forecasting and atmospheric research
applications. The WRF was run for 2016 over a domain covering the continental U.S. at a 12km
resolution with 35 vertical layers. The run for this platform included high resolution sea surface
temperature data from the Group for High Resolution Sea Surface Temperature (GHRSST) (see
https://www.ghrsst.org/) and is given the EPA meteorological case label "16j." The full case abbreviation
includes this suffix following the emissions portion of the case name to fully specify the abbreviation of
the case as "2016gf_16j."
The emissions modeling platform includes point sources, nonpoint sources, commercial marine vessels
(CMV), onroad and nonroad mobile sources, and fires for the U.S., Canada, and Mexico. Some platform
categories use more disaggregated data than are made available in the NEI. For example, in the platform,
onroad mobile source emissions are represented as hourly emissions by vehicle type, fuel type process
and road type while the NEI emissions are aggregated to vehicle type/fuel type totals and annual temporal
resolution. A full NEI was not developed for the year 2016 because only point sources above a certain
potential to emit must be submitted for years between the full triennial NEI years (e.g., 2014, 2017,
2020). Emissions from Canada and Mexico are used for the modeling platform but are not part of the NEI.
The primary emissions modeling tool used to create the air quality model-ready emissions was the Sparse
Matrix Operator Kernel Emissions (SMOKE) modeling system (http://www.smoke-model.org/), version
4.9 (SMOKE 4.9). Emissions files were created for a 36-km national grid and for a 12-km national grid,
both of which include the contiguous states and parts of Canada and Mexico as shown in Figure 3-1.
Emissions at 36-km were only created for the inventory years 2016 and 2023.
This document contains six sections and several appendices. Section 2 describes the 2016 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. Methods to develop analytic year emissions are
described in Section 4. Data summaries are provided in Section 5. Section 6 provides references. The
Appendices provide additional details about specific technical methods or data.
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2 Emissions Inventories and Approaches
This section summarizes the emissions data that make up the 2016v3 platform. This section provides
details about the data contained in each of the platform sectors for the base year and the analytic year. The
original starting point for the emission inventories was the 2016v2 platform, which was released for
comment in September of 2021 and continued to be available for comments during the comment periods
following publication of EPA actions in response to state submissions of interstate transport state
implementation plans (SIPs) and the comment period for the Good Neighbor Plan for the 2015 Ozone
National Ambient Air Quality Standard (NAAQS). To create the 2016v3 platform, the 2016v2 inventories
were updated to incorporate additional data from the 2017 NEI, implement corrections for some source
categories, and to refine inventory methodologies and data in response to comments. Comments
submitted through these processes through July of 2022 were considered. Details of the updates for each
sector in the base and analytic years are described in this document.
Data and documentation for the 2017NEI, including a TSD, are available from https://www.epa.gov/air-
emissions-inventories/2017-national-emissions-inventory-nei-data (EPA, 2021b). In addition to the NEI-
based data for the broad categories of point, nonpoint, onroad, nonroad, and events (i.e., fires), emissions
from the Canadian and Mexican inventories and several other non-NEI data sources are included in the
2016 platform. The Canadian and Mexican inventories were updated in the 2016v2 platform but were not
changed in the 2016v3 platform.
The triennial year NEI data for CAPs are largely compiled from data submitted by state, local and tribal
(S/L/T) air agencies. A large proportion of HAP emissions data in the NEI are also from the S/L/T
agencies, but, are augmented by the EPA when not available from S/L/Ts. The EPA uses the Emissions
Inventory System (EIS) to compile the NEI. The EIS includes hundreds of automated quality assurance
checks to help improve data quality, and also supports tracking release point (e.g., stack) coordinates
separately from facility coordinates. The EPA collaborates extensively with S/L/T agencies to ensure a
high quality of data in the NEI. Because 2016 is not a triennial NEI year, all emissions modeling sectors
were modified in some way to represent the year 2016 to the extent possible.
For interim years other than triennial NEI years, point source data are typically pulled forward from the
most recent triennial NEI year for the sources that were not reported by S/L/Ts for the interim year. Thus,
the 2016 point source emission inventories for the platform include emissions primarily from S/L/T-
submitted data. In 2016v3, data that would have been pulled forward from 2014 were instead replaced
with data from 2017 NEI, where possible, as the 2017 emissions were more current and closer to the year
being modeling. Agricultural and wildland fire emissions represent the year 2016 and are consistent with
those in 2016v2. In 2016v3, emissions for nonpoint source sectors started with 2017 NEI emissions and
some were adjusted to better represent the year 2016. Fertilizer emissions represent the year 2016. Some
spatial reallocation was performed for CMV emissions to refine the assignment of the emissions to state
waters, but otherwise the 2016v3 CMV emissions are consistent with 2016v2. 2016v3 used new data
provided by California Air Resources Board (CARB) for mobile sources in California. Locomotive
emissions in the rail and ptnonipm sectors are consistent with those in 2016vl and v2, with the exception
of California. Nonpoint oil and gas emissions were developed using 2016-specific data for oil and gas
wells and their 2016 production levels in conjunction with data developed by the Western Regional Air
Partnership (WRAP).
Onroad and nonroad mobile source emissions were developed using the Motor Vehicle Emission
Simulator (MOVES). Onroad emissions were developed based on emissions factors output from
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M0VES3 for the year 2016, run with inputs derived from the 2017NEI along with activity data (e.g.,
vehicle miles traveled and vehicle populations) provided by state and local agencies for 2016vl or
otherwise backcast to the year 2016. Updates for 2016v3 include corrected emission factors for
combination trucks, corrections to road type distributions in some states, and activity data projections
based on the 2022 Annual Energy Outlook (AEO). Nonroad emissions were consistent with those in
2016v2 and were generated using MOVES3, including the spatial allocation factors that were updated for
the 2016vl platform.
For the purposes of preparing the air quality model-ready emissions, emissions from the five NEI data
categories are split into finer-grained sectors used for emissions modeling. The significance of an
emissions modeling or "platform sector" is that the data are run through the SMOKE programs
independently from the other sectors except for the final merge. The final merge program (Mrggrid)
combines the sector-specific gridded, speciated, hourly emissions together to create CMAQ-ready
emission inputs. For studies that use CAMx, the merged CMAQ-ready emissions inputs are converted
into the file formats needed by CAMx. Elevated point sources from those are merged into files containing
all sources for a given day and those files are converted into the CAMx point source format.
In addition to the NEI-based sectors, emissions for Canada and Mexico are included. In 2016v3, these
emissions are based on updated data that represent the base year of 2016 for Canada from ECCC and for
Mexico from SEMARNAT.
Table 2-1 presents an overview the sectors in the emissions modeling platform and how they generally
relate to the NEI as their starting point. The platform sector abbreviations are provided in italics. These
abbreviations are used in the SMOKE modeling scripts, inventory file names, and throughout the
remainder of this document. Additional details on the changes made in the 2016v3 platform for each
sector are available in the sector-specific subsections that follow.
Other natural emissions are also merged in with the sectors in Table 2-1: ocean chlorine and sea salt, and
new for 2016v3: lightning NOx. 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). In CMAQ,
the species name is "CL2". The sea salt emissions were developed with version 4.1 of the OCEANIC
pre-processor that comes with the CAMx model. The preprocessor estimates time/space-varying
emissions of aerosol sodium, chloride and sulfate; gas-phase chlorine and bromine associated with sea
salt; gaseous halo-methanes; and dimethyl sulfide (DMS). These additional oceanic emissions are
incorporated into the final model-ready emissions files for CAMx. For more information on the natural
emissions, including the lightning NOx, see Section 2.7.6.
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/2016v3-platform , The
platform informational text file describes the particular zipped files associated with each platform sector
and provides notes about how SMOKE should be run for each sector. A number of reports (i.e.,
summaries) are available in addition to the data files for the 2016 platform. The types of reports include
state summaries of inventory pollutants and model species by modeling platform sector and county annual
totals by modeling platform sector.
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Table 2-1. Platform sectors for the 2016 emissions modeling case
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
EGU units:
ptegu
Point
Point source electric generating units (EGUs) for 2016 from the Emissions
Inventory System (EIS), based on 2016vl with some updates. Includes some
adjustments to default stack parameters, additional closures, and a few units
that were previously in ptnonipm. The inventory emissions are replaced with
hourly 2016 Continuous Emissions Monitoring System (CEMS) values for
nitrogen oxides (NOx) and SO2 for any units that are matched to the NEI, and
other pollutants for matched units are scaled from the 2016 point inventory
using CEMS heat input. Emissions for all sources not matched to CEMS data
come from the annual inventory. For 2016v3 most 2014-projected emissions
were replaced with emissions from the 2017NEI. Annual resolution for
sources not matched to CEMS data, hourly for CEMS sources.
Point source oil and
gas:
ptoilgas
Point
Point sources for 2016 including S/L/T data for oil and gas production and
related processes and updated from 2016vl with the Western Regional Air
Partnership (WRAP) 2014 inventory. Includes sources from facilities with the
following NAICS: 2111,21111,211111,211112 (Oil and Gas Extraction);
213111 (Drilling Oil and Gas Wells); 213112 (Support Activities for Oil and
Gas Operations); 2212, 22121, 221210 (Natural Gas Distribution); 48611,
486110 (Pipeline Transportation of Crude Oil); 4862, 48621, 486210
(Pipeline Transportation of Natural Gas). Includes offshore oil and gas
platforms in the Gulf of Mexico (FIPS=85). For 2016v3 most emissions
projected from 2014 were replaced with 2017NEI emissions. Annual
resolution.
Aircraft and
ground support
equipment: airports
Point
Emissions from aircraft up to 3,000 ft elevation and emissions from ground
support equipment based on the January 2021 version of 2017 NEI and
backcast to 2016. For 2016v3 there were adjustments to Atlanta (ATL) and to
a few specific airports in Texas based on comments. Annual resolution.
Remaining non-
EGU point:
ptnonipm
Point
All 2016 point source inventory records not matched to the ptegu, airports, or
pt_oilgas sectors, including updates submitted by state and local agencies
including some sources that were not operating in 2016 but did operate in
later years. Year 2016 rail yard emissions were developed by the 2016vl rail
workgroup. For 2016v2 NOx control efficiencies were updated where new
information was available. A few sources were moved to ptegu in 2016v2 and
2016v3. For 2016v3 most 2014-projected emissions were replaced with
emissions from the 2017NEI, facilities found to overlap with the biorefinery
inventory were removed along with biofuel facilities that were double
counted in 2016v2, emissions were updated for five biofuel facilities, point
solvents not in 2016v2 were restored, California rail yard emissions were
updated to reflect new data from CARB, and some other minor changes.
Annual resolution.
Agricultural
fertilizer:
fertilizer
Nonpoint
Nonpoint agricultural fertilizer application emissions of ammonia estimated
for 2016 using the FEST-C model and captured from a run of CMAQ for
2016. For 2016v3 corrected to use NH3 molecular weight. County and
monthly resolution.
Agricultural
Livestock:
livestock
Nonpoint
Nonpoint agricultural livestock emissions including ammonia and other
pollutants (except PM2 5) backcast from 2017NEI based on animal population
data from the U.S. Department of Agriculture (USDA) National Agriculture
Statistics Service Quick Stats, where available. For 2016v3 Maryland and
Illinois emissions were corrected. County and annual resolution.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Agricultural fires
with point
resolution: ptagfire
Nonpoint
2016 agricultural fire sources based on EPA-developed data with state
updates, represented as point source day-specific emissions. They are in the
nonpoint NEI data category, but in the platform, they are treated as point
sources. For 2016v3 data are unchanged from 2016v2. Mostly at daily
resolution with some state-submitted data at monthly resolution.
Area fugitive dust:
afdust
Nonpoint
PMio and PM2 5 fugitive dust sources based on the 2017 NEI nonpoint
inventory, including building construction, road construction, agricultural
dust, and road dust. Agricultural dust, paved road dust, and unpaved road
dust were backcast to 2016 levels. The NEI emissions are reduced during
modeling according to a transport fraction (computed for the 2016 platform)
and a meteorology-based (precipitation and snow/ice cover) zero-out. Afdust
emissions from the portion of Southeast Alaska inside the 36US3 domain are
processed in a separate sector called 'afdust_ak'. For 2016v3 data are
unchanged from 2016v2. County and annual resolution.
Biogenic:
beis
Nonpoint
Year 2016, hour-specific, grid cell-specific emissions generated from the
BEIS4 model within SMOKE, including emissions in Canada and Mexico
using BELD6 land use data. For 2016v3 the versions of BEIS and BELD
were updated. Gridded and hourly resolution.
Category 1, 2 CMV:
cmv_clc2
Nonpoint
Category 1 and category 2 (C1C2) commercial marine vessel (CMV)
emissions sources backcast to 2016 from the 2017NEI using a multiplier of
0.98. Includes C1C2 emissions in U.S. state and Federal waters, and also all
non-U.S. C1C2 emissions including those in Canadian waters. For 2016v3,
spatial allocation to county boundaries was improved but otherwise emissions
were unchanged from 2016v2. Gridded and hourly resolution.
Category 3 CMV:
cmv_c3
Nonpoint
Category 3 (C3) CMV emissions converted to point sources based on the
center of the grid cells. Includes C3 emissions in U.S. state and Federal
waters, and also all non-U.S. C3 emissions including those in Canadian
waters. Emissions are backcast to 2016 from 2017NEI emissions based on
factors derived from U.S. Army Corps of Engineers Entrance and Clearance
data and information about the ships entering the ports. For 2016v3, spatial
allocation to county boundaries was improved but otherwise emissions were
unchanged from 2016v2. Gridded and hourly resolution.
Locomotives :
rail
Nonpoint
Line haul rail locomotives emissions developed by the 2016vl rail workgroup
based on 2016 activity and emission factors. Includes freight and commuter
rail emissions and incorporates state and local feedback. For 2016v3 data are
unchanged from 2016vl and 2016v2. County and annual resolution.
Solvents :
npsolvents
Nonpoint
(some
Point)
VOC emissions from solvents for 2016 derived using the VCPy framework
(Seltzer et al., 2021) along with some data from 2017 NEI. Includes cleaners,
personal care products, adhesives, architectural coatings, and aerosol
coatings, industrial coatings, allied paint products, printing inks, dry-cleaning
emissions, and agricultural pesticides. For 2016v3 updated methods were
used in VCPy. County and annual resolution.
Nonpoint source oil
and gas:
npoilgas
Nonpoint
2016 nonpoint oil and gas emissions. Based on output from the 2017NEI
version of the Oil and Gas tool for the year 2016 along with the 2014 WRAP
oil and gas inventory for production-related emissions in those states and
Pennsylvania's unconventional well inventory. Exploration-related emissions
are based on the 2017NEI version of the Oil and Gas Tool run for 2016. For
2016v3 updated Colorado, Oklahoma, and Texas emission. County and
annual resolution
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Residential Wood
Combustion:
rwc
Nonpoint
2017 NEI nonpoint sources from residential wood combustion (RWC)
processes backcastto the year 2016. For 2016v3 updated Idaho so use 2017
NEI emissions. County and annual resolution.
Remaining
nonpoint:
nonpt
Nonpoint
Nonpoint sources not included in other platform sectors. For 2016v3 updated
to use 2017NEI for all sources except biomass combustion and gas stations
which are backcast from 2017 to 2016. County and annual resolution.
Nonroad:
nonroad
Nonroad
2016 nonroad equipment emissions developed with MOVES3 using the
inputs that were updated for 2016vl. MOVES was used for all states except
California and Texas, which submitted emissions for 2016vl. For 2016v3
data are unchanged from 2016v2. County and monthly resolution.
Onroad:
onroad
Onroad
2016 onroad mobile source gasoline and diesel vehicles from moving and
non-moving vehicles that drive on roads, along with vehicle refueling.
Includes the following modes: exhaust, extended idle, auxiliary power units,
off network idling, starts, evaporative, permeation, refueling, and brake and
tire wear. For all states except California, developed using SMOKE-MOVES
with emission factor tables produced by MOVES3 coupled with activity data
backcast from 2017NEI to year 2016 or provided for 2016vl by S/L/T
agencies. Onroad emissions for Alaska, Hawaii, Puerto Rico and the Virgin
Islands were held constant from 2016vl (based on MOVES2014b) and are
part of the onroad_nonconus sector. For 2016v3 new starts were included for
20 Georgia counties, road type and hoteling changes in six states, inspection
and maintenance updates in North Carolina and Tennessee and corrected
emissions factors for combination trucks. County and hourly resolution.
Onroad California:
onroadcaadj
Onroad
2016 California-provided CAP onroad mobile source gasoline and diesel
vehicles based on the EMFAC model, gridded and temporalized using
MOVES3 outputs. Volatile organic compound (VOC) HAP emissions
derived from California-provided VOC emissions and MOVES-based
speciation. For 2016v3 minor updates to the NH3 and refueling emissions
that are based on MOVES due to changes in combination truck emission
factors. County and hourly resolution.
Point source fires-
ptfire-rx
ptfire-wild
Events
Point source day-specific wildfires and prescribed fires for 2016 computed
using Satellite Mapping Automated Reanalysis Tool for Fire Incident
Reconciliation version 2 (SMARTFIRE2) and BlueSky Framework (Sullivan,
2008 and Raffuse, 2007) for both flaming and smoldering processes (i.e.,
SCCs 281XXXX002). Smoldering is forced into layer 1 (by adjusting heat
flux). For 2016v3 data are unchanged from 2016v2. Daily resolution.
Non-US. Fires:
ptfireothna
N/A
Point source day-specific wildland fires for 2016 provided by Environment
Canada with data for missing months, and for Mexico and Central America,
filled in using fires from the Fire Inventory (FINN) from National Center for
Atmospheric Research (NCAR) fires (NCAR, 2016 and Wiedinmyer, C.,
2011). Includes any prescribed fires although they are not distinguished from
wildfires. For 2016v3 data are unchanged from 2016v2. Daily resolution.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
N/A
Fugitive dust sources of particulate matter emissions excluding land tilling
from agricultural activities, from Environment and Climate Change Canada
(ECCC) 2016 emission inventory updated for 2016vl. A transport fraction
adjustment is applied along with a meteorology-based (precipitation and
snow/ice cover) zero-out. For 2016v3 data are unchanged from 2016v2.
County and annual resolution.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Other Point
Fugitive dust
sources not from
the NEI:
othptdust
N/A
Fugitive dust sources of particulate matter emissions from land tilling from
agricultural activities, ECCC 2016 emission inventory updated for 2016vl,
but wind erosion emissions were removed. A transport fraction adjustment is
applied along with a meteorology-based (precipitation and snow/ice cover)
zero-out. Data were originally provided on a rotated 10-km grid for beta, but
were smoothed so as to avoid the artifact of grid lines in the processed
emissions. For 2016v3 data are unchanged from 2016v2. Monthly
resolution.
Other point sources
not from the NEI:
othpt
N/A
Point sources from the ECCC 2016 emission inventory updated for 2016vl.
Includes Canadian sources other than agricultural ammonia and low-level oil
and gas sources, along with emissions from Mexico's 2016 inventory. For
2016v3 data are unchanged from 2016v2. Monthly resolution for Canada
airport emissions, annual resolution for the remainder of Canada and all of
Mexico.
Canada ag not from
the NEI:
canadaag
N/A
Agricultural point sources from the ECCC 2016 emission inventory updated
from 2016vl, including agricultural ammonia. Agricultural data were
originally provided on a rotated 10-km grid, but were smoothed so as to avoid
the artifact of grid lines in the processed emissions. Data were forced into 2D
low-level emissions to reduce the size of othpt. For 2016v3 data are
unchanged from 2016v2. Monthly resolution.
Canada oil and gas
2D not from the
NEI:
Canada og2D
N/A
Low-level point oil and gas sources from the ECCC 2016 emission inventory
with emissions forced into 2D low-level to reduce the size of the othpt sector.
Point oil and gas sources which are subject to plume rise remain in the othpt
sector. For 2016v3 data are unchanged from 2016v2. Annual resolution.
Other non-NEI
nonpoint and
nonroad:
othar
N/A
Year 2016 Canada (province or sub-province resolution) emissions from the
ECCC inventory updated for 2016vl. Year 2016 Mexico (municipio
resolution) emissions from their 2016 inventory. For 2016v3 data are
unchanged from 2016v2. Resolution: Canada monthly for nonroad sources;
annual for rail and other nonpoint sectors, Mexico: annual nonpoint and
nonroad mobile inventories.
Other non-NEI
onroad sources:
onroad can
N/A
Year 2016 Canada (province resolution or sub-province resolution, depending
on the province) from the ECCC onroad mobile inventory updated for
2016vl. For 2016v3 data are unchanged from 2016v2. Monthly resolution.
Other non-NEI
onroad sources:
onroadmex
N/A
Year 2016 Mexico (municipio resolution) onroad mobile inventory based on
MOVES-Mexico runs for 2014 and 2018 then interpolated to 2016
(unchanged from 2016vl). For 2016v3 data are unchanged from 2016v2.
Monthly resolution.
2.1 2016 point sources (ptegu, pt_oilgas, ptnonipm, airports)
Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude/longitude)
are specified, as in the case of an individual facility. A facility may have multiple emission release points
that may be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have
multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas).
This section describes NEI point sources within the contiguous U.S. and the offshore oil platforms which
are processed by SMOKE as point source inventories. A full NEI is compiled every three years including
2011, 2014 and 2017. In the intervening years, emissions information about point sources that exceed
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certain potential to emit threshold as defined in the Air Emissions Reporting Requirements (AERR)1 are
required to be submitted to the EIS that is used to compile the NEI. A comprehensive description of how
EGU emissions were characterized and estimated in the NEI is located in Section 3.4 of the 2014 NEI
TSD (EPA, 2018). The methods for emissions estimation are similar for the interim year of 2016, but
there is no TSD available specific to the 2016 point source NEI. Information on state submissions for
point sources through the 2016vl collaborative process are available in the collaborative specification
sheets (http://views.cira.colostate.edu/wiki/wiki/10202).
The point source file used for the modeling platform was exported from EIS into the Flat File 2010
(FF10) format that is compatible with SMOKE (see
https://www.cmascenter.Org/smoke/documentation/4.9/html/ch06s02s08.htmn. The export of point source
emissions specific to 2016, including stack parameters and locations from EIS, was done on June 12,
2018, and some modifications were made since that time. For 2016v3, most sources with data not specific
to the year 2016 were replaced with data from the 2017 NEI that was exported on June 18, 2020. The flat
file was modified to remove sources without specific locations (i.e., their FIPS code ends in 777). Then
the point source FF10 was divided into point source sectors used in the platform: the EGU sector (ptegu),
point source oil and gas extraction-related emissions (pt oilgas), airport emissions were put into the
airports sector, and the remaining non-EGU sources into the non-IPM (ptnonipm) sector. The split was
done at the unit level for ptegu and facility level for pt oilgas such that a facility may have units and
processes in both ptnonipm and ptegu, but units cannot be in both pt oilgas and any other point sector.
The EGU emissions are split out from the other sources to facilitate the use of distinct SMOKE temporal
processing and analytic-year projection techniques where the Integrated Planning Model (IPM) is used to
project EGU emissions and other techniques are used to project non-EGU emissions. The oil and gas
sector emissions (pt oilgas) were processed separately for summary tracking purposes and distinct
analytic-year projection techniques from the remaining non-EGU emissions (ptnonipm).
The inventory pollutants processed through SMOKE for all point source sectors were carbon monoxide
(CO), NOx, VOC, SO2, ammonia (NH3), particles less than 10 microns in diameter (PM10), and particles
less than 2.5 microns in diameter (PM2.5), and all of the air toxics listed in Table 3-3. The pollutants
naphthalene, benzene, acetaldehyde, formaldehyde, and methanol (NBAFM) species are based on
speciation of VOCs. The resulting VOC in the modeling system may be higher or lower than the VOC
emissions in the NEI; they would only be the same if the HAP inventory and speciation profiles were
exactly consistent. For HAPs other than those in NBAFM, there is no concern for double-counting since
CMAQ handles these outside the CB6 mechanism.
The ptnonipm and pt oilgas sector emissions were provided to SMOKE as annual emissions. For those
ptegu sources with CEMS data that could be matched to the point inventory from EIS, hourly CEMS NOx
and SO2 emissions were used rather than the annual total NEI emissions. For all other pollutants at
matched units, the annual emissions were used as-is from the NEI, but were allocated to hourly values
using heat input from the CEMS data. For the sources in the ptegu sector not matched to CEMS data,
daily emissions were created using an approach described in Section 2.1.1. For non-CEMS units other
than municipal waste combustors and cogeneration units, region- and pollutant-specific diurnal profiles
were applied to create hourly emissions.
1 80 FR 8787 pubished 2/19/2015. See: https://www.federalregister.gov/documents/2015/02/19/2015-0347Q/revisions-to-the-
air-emissions-reporting-reauirements-revisions-to-lead-pb-reporting-threshold-and
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While reviewing recent point source inventories it was determined that data submitted by some agencies
used specific default values for certain stack parameters that are not necessarily appropriate to use for
those sources. This can impact modeling results, especially in fine scale modeling. When the stack
parameters were substantially different from average values for that source type, the defaulted stack
parameters were replaced with the value from the SMOKE PSTK file for that SCC. The agencies and
default values that were replaced are shown in Table 2-2. Comments for any impacted inventory records
were appended in the FF10 inventory files with comments of the form "stktemp replaced with ptsk
default" so the updated records could be identified.
Table 2-2. Default stack parameter replacements
Agency
abbreviation
Stkdiam
Stkhgt
Stktemp
Stkvel
CODPHE
0.1 ft
1 ft
70 degF or 72 degF
PADEP
0.1 ft
1 ft
70 degF
0.1 ft/s or 1000
ft/s
LADEQ
0.3 ft
70 degF or 77 degF
0.1 ft/s
ILEPA
0.33 ft
33 ft or 35 ft
70 degF
TXCEQ
1 ft or 3 ft
40 ft
72 degF
0.1 ft/s
NVBAQ
32.8 ft
72 degF
WIDNR
20 ft
3.281 ft/s
MIDEQ
70 degF or 72 degF
MNPCA
70 degF
IADNR
68 degF or 70 degF
ORDEQ
72 degF
MSDEQ
72 degF
SCDEQ
72 degF
1 ft/s
NCDAQ
72 degF
0.2 ft/s
INDEM
0 degF
0 ft/s
NEDEQ
350 degF
1.6666 ft/s
KYDAQ
0 ft/s
WYDEQ
11.46 ft/s
2.1.1 EGU sector (ptegu)
The ptegu sector contains emissions from EGUs in the 2016 NEI point inventory that could be
matched to units found in the National Electric Energy Data System (NEEDS) v6.20 database
(https://www.epa.gov/airmarkets/national-electric-energy-data-svstem-needs-v6 dated 8/3/2022). The
matching was prioritized according to the amount of the emissions produced by the source. In the
SMOKE point flat file, emission records for sources that have been matched to the NEEDS database have
a value filled into the IPMYN column based on the matches stored within EIS. The 2016 NEI point
inventory consists of data submitted by S/L/T agencies and EPA to the EIS for Type A (i.e., large) point
sources. Those EGU sources in the 2014 NEIv2 inventory that were not submitted or updated for 2016
and not identified as retired were retained in 2016, but for 2016v3 the emissions values were pulled from
the 2017 NEI where possible. For any 2014 EGU emissions that remain in the 2016v3 inventory, those
from the states CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV were projected
10
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from 2014 to 2016 values using factors provided by the Mid-Atlantic Regional Air Management
Association (M ARAM A).
When possible, units in the ptegu sector are matched to 2016 CEMS data from EPA's Clean Air Markets
Division (CAMD) via ORIS facility codes and boiler ID (see https://campd.epa.gov/). For the matched
units, SMOKE replaces the 2016 emissions of NOx and SO2 with the CEMS emissions, thereby ignoring
the annual values specified in the NEI annual FF10 flat file. For other pollutants at matched units, the
hourly CEMS heat input data are used to allocate the NEI annual emissions to hourly values. All stack
parameters, stack locations, and Source Classification Codes (SCC) for these sources come from the NEI
or updates provided by data submitters outside of EIS. Because these attributes are obtained from the
NEI, the chemical speciation of VOC and PM2.5 for the sources is selected based on the SCC or in some
cases, based on unit-specific data. If CEMS data exists for a unit, but the unit is not matched to the NEI,
the CEMS data for that unit are not used in the modeling platform. However, if the source exists in the
NEI and is not matched to a CEMS unit, the emissions from that source are still modeled using the annual
emission value in the NEI temporally allocated to hourly values. The EGU flat file inventory is split into
a flat file with CEMS matches and a flat file without CEMS matches to support analysis and temporal
allocation to hourly values.
In the SMOKE point FF10 file, emission records for point sources matched to CEMS data have values
filled into the ORIS FACILITY CODE and ORIS BOILER ID columns. The CEMS data in SMOKE-
ready format is available at https://gaftp.epa.gov/DMDnLoad/emissions/smoke/. Many smaller emitters
in the CEMS program are not identified with ORIS facility or boiler IDs that can be matched to the NEI
due to inconsistencies in the way a unit is defined between the NEI and CEMS datasets, or due to
uncertainties in source identification such as inconsistent plant names in the two data systems. Also, the
NEEDS database of units modeled by IPM includes many smaller emitting EGUs that do not have CEMS.
Therefore, there will be more units in the NEEDS database than have CEMS data. The temporal
allocation of EGU units matched to CEMS is based on the CEMS data, whereas regional profiles are used
for most of the remaining units. More details can be found in Section 3.3.2.
Some EIS units match to multiple CAMD units based on cross-reference information in the EIS alternate
identifier table. The multiple matches are used to take advantage of hourly CEMS data when a CAMD
unit specific entry is not available in the inventory. Where a multiple match is made, the EIS unit is split
and the ORIS facility and boiler IDs are replaced with the individual CAMD unit IDs. The split EIS unit
NOX and S02 emissions annual emissions are replaced with the sum of CEMS values for that respective
unit. All other pollutants are scaled from the EIS unit into the split CAMD unit using the fraction of
annual heat input from the CAMD unit as part of the entire EIS unit. The NEEDS ID in the "ipm_yn"
column of the flat file is updated with a "_M_" between the facility and boiler identifiers to signify that
the EIS unit had multiple CEMS matches. The inventory records with multiple matches had the EIS unit
identifiers appended with the ORIS boiler identifier to distinguish each CEMS record in SMOKE.
For sources not matched to CEMS data, except for municipal waste combustors (MWCs) waste-to-energy
and cogeneration units, daily emissions were computed from the NEI annual emissions using average
CEMS data profiles specific to fuel type, pollutant,2 and IPM region. To allocate emissions to each hour
of the day, diurnal profiles were created using average CEMS data for heat input specific to fuel type and
IPM region. See Section 3.3.2 for more details on the temporal allocation approach for ptegu sources.
2 The year to day profiles use NOx and SO2 CEMS for NOx and SO2, respectively. For all other pollutants, they use heat input
CEMS data.
11
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MWC and cogeneration units without CEMS data available were specified to use uniform temporal
allocation such that the emissions are allocated to constant levels for every hour of the year. These sources
do not use hourly CEMs, and instead use a PTDAY file with the same emissions for each day, combined
with a uniform hourly temporal profile applied by SMOKE.
After the completion of 2016vl, it was determined that SMOKE was having an issue properly processing
CEMS emissions when there are multiple CEMS units mapped to the same NEI unit. This caused NOx
and S02 emissions in 2016vl to be higher than they should have been at some units. This issue was
corrected in 2016v2 and 2016v3.
2.1.2 Point source oil and gas sector (pt_oilgas)
The ptoilgas sector consists of point source oil and gas emissions in United States, primarily pipeline-
transportation and some upstream exploration and production. Sources in the pt oilgas sector consist of
sources which are not electricity generating units (EGUs) and which have a North American Industry
Classification System (NAICS) code corresponding to oil and gas exploration, production, pipeline-
transportation or distribution. The pt oilgas sector was separated from the ptnonipm sector by selecting
sources with specific NAICS codes shown in Table 2-3. The use of NAICS to separate out the point oil
and gas emissions forces all sources within a facility to be in this sector, as opposed to ptegu where
sources within a facility can be split between ptnonipm and ptegu sectors. A major update in 2016v2 was
the incorporation of the WRAP oil and gas inventory for the states of Colorado, Montana, New Mexico,
North Dakota, South Dakota, Utah, and Wyoming. This inventory is described in more detail below and
in the WRAP Final report located here:
http://www.wrapair2.org/pdfAVRAP OGWG Report Baseline 17Sep2019.pdf (WRAP / Ramboll,
2019).
The 2016v3 pt_oilgas inventory includes 2017NEI emissions for many states, while others remain the
same as 2016v2. Additionally, Colorado emissions were retained from 2016vl and some New Mexico
sources' emissions were replaced with 2020NEI-based emissions backcast to 2016 in response to
comments. These changes are in addition those made in 2016v2, where several New Mexico sources were
removed from the ptnonipm sector because it was determined they duplicated sources in the WRAP oil
and gas inventory. The duplicate sources are listed in Table 2-4. Finally, following a review of the
incidence of default stack parameters in recent inventories, stack parameters in the states of Louisiana,
Illinois, Nebraska, Texas, Wisconsin, and Wyoming were updated in 2016v2 for sources with values
found to be defaults.
Table 2-3. Point source oil and gas sector NAICS Codes
NAICS
NAICS description
2111
Oil and Gas Extraction
211111
Crude Petroleum and Natural Gas Extraction
211112
Natural Gas Liquid Extraction
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
12
-------
NAICS
NAICS description
2212
Natural Gas Distribution
22121
Natural Gas Distribution
221210
Natural Gas Distribution
Oil and Gas Pipeline and Related Structures
237120
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
Table 2-4. Sources removed from ptoilgas due to Overlap with WRAP Oil and Gas Inventory
State+county
FIPS
Facility ID
Facility Name
35015
7411811
Artesia Gas Plant
35015
17128911
Chaparral Gas Plant
35015
7761811
DCP Midstream - Peco
35015
7584511
Empire Abo Gas Plant
35015
7905211
Oxy - Indian Basin G
35025
5228911
DCP Midstream - Euni
35025
8091311
Denton Gas Plant
35025
8092311
Eunice Gas Processing Plant
35025
5226911
Jal No3 Gas Plant
35025
8241211
Linam Ranch Gas Plant
35025
5226611
Maljamar Gas Plant
35025
8241411
Saunders Gas Plant
35025
8241311
Targa - Monument Gas Plant
35045
7230311
Kutz Canyon Processing Plant
35045
8091911
San Juan River Gas Plant
35045
7992811
Val Verde Treatment Plant
The starting point for most states in the 2016v3 emissions platform pt oilgas inventory was the 2016
point source NEI. The 2016 inventory includes data submitted by S/L/T agencies and EPA to the EIS for
Type A (i.e., large) point sources. For the federally-owned offshore point inventory of oil and gas
platforms, a 2017 inventory was developed by the U.S. Department of the Interior, Bureau of Ocean and
Energy Management, Regulation, and Enforcement (BOEM) and this was used in 2016v3, along with any
tribal submissions in the pt oilgas sector. Other states that used 2017NEI emissions for the pt oilgas
sector include Arkansas, California, Delaware, Georgia, Idaho, Indiana, Kentucky, Massachusetts,
13
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Mississippi, Nevada, Oklahoma3, Oregon, Pennsylvania, Rhode Island, South Carolina, Tennessee and
West Virginia. Although North Dakota provided some stack parameter updates for some ptoilgas
sources, they could not be matched to the WRAP oil and gas inventory used for North Dakota, so these
updates could not be implemented.
The year 2016 pt oilgas inventory in 2016v3 includes a limited number of sources with data carried
forward from the 2014NEIv2 point inventory and projected to 2016. The NEI year that the data was
submitted for is indicated by the calc_year field in the FF10 inventory files. The pt oilgas inventory was
split into two components: one for 2016 sources, and one for 2014 sources. The sources with calc_year
equal to 2016 were used in the platform without further modification.
For pt oilgas emissions that were carried forward from the 2014NEIv2, those emissions were projected to
represent the year 2016. Each state/SCC/NAICS combination in the inventory was classified as either an
oil source, a natural gas source, a combination of oil and gas, or designated as a "no growth" source.
Growth factors were based on historical state production data from the Energy Information
Administration (EIA) and are listed in Table 2-5. National 2016 pt oilgas emissions before and after
application of 2014-to-2016 projections are shown in Table 2-6. The historical production data for years
2014 and 2016 for oil and natural gas were taken from the following websites:
• https://www.eia.gov/dnav/pet/pet crd crpdn adc mbbl a.htm (Crude production)
• http://www.eia.gov/dnav/ng/ng sum lsum a epgO fgw mmcf a.htm (Natural gas production)
The "no growth" sources include all offshore and tribal land emissions, and all emissions with aNAICS
code associated with distribution, transportation, or support activities. As there were no 2015 production
data in the EIA for Idaho, no growth was assumed for this state; the only pt oilgas sources in Idaho were
pipeline transportation related. Maryland and Oregon had no oil production data on the EIA website. The
factors in Table 2-5 were applied to sources with NAICS = 2111,21111,211111, 211112, and 213111
and with production-related SCC processes. Table 2-5 provides a national summary of emissions before
and after this two-year projection for these sources in the pt_oilgas sector. States listed with N/A as values
do not have oil and gas activity data from which projection factors could be developed and therefore were
held flat with no change from 2014 to 2016. Table 2-6 shows the national emissions for pt oilgas
following the projection to 2016. These numbers are smaller than in 2016v2 because more 2017 data
were used in 2016v3 and the numbers only reflect the portion of the inventory projected from 2014 to
2016.
Table 2-5. 2014NEIv2-to-2016 projection factors for pt oilgas sector for 2016vl inventory
State
Natural Gas
growth
Oil growth
Combination gas/oil growth
Alabama
-9.0%
-17.5%
-13.2%
Alaska
1.9%
-1.1%
0.4%
Arizona
-55.7%
-85.7%
-70.7%
Kansas
-15.0%
-23.4%
-19.2%
Louisiana
-11.0%
-17.4%
-14.2%
Maryland
70.0%
N/A
N/A
Michigan
-12.6%
-23.4%
-18.0%
3 In Oklahoma, some facilities had significant differences between the 2016 and 2017 emissions. For those facilities, year 2016
data were used where emissions were submitted specifically for the year 2016.
14
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State
Natural Gas
growth
Oil growth
Combination gas/oil growth
Minnesota
N/A
N/A
N/A
North Carolina
N/A
N/A
N/A
Ohio
181.0%
44.4%
112.7%
Texas
-6.1%
1.0%
-2.6%
Virginia
-10.0%
-50.0%
-30.0%
Wisconsin
N/A
N/A
N/A
Table 2-6. 2014NEI-based sources in 2016gf ptoilgas (excluding offshore) before and after the
2014-to-2016 projections for (tons/year)
Pollutant
Before
projections
After projections
% change 2014 to 2016
CO
7,846
7,662
-2.3%
NH3
0.0525
0.0527
0.4%
NOX
12,927
12,719
-1.6%
PM10-PRI
529
528
-0.1%
PM25-PRI
498
497
-0.4%
S02
1,977
1,911
-3.3%
VOC
4,857
4,813
-0.9%
2.1.3 Non-IPM sector (ptnonipm)
With minor exceptions, the ptnonipm sector contains point sources that are not in the airport, ptegu or
pt oilgas sectors. For the most part, the ptnonipm sector reflects the non-EGU sources of the NEI point
inventory; however, it is likely that some small low-emitting EGUs not matched to the NEEDS database
or to CEMS data are present in the ptnonipm sector. The ptnonipm emissions in the 2016v3 platform have
been updated from the 2016 NEI point inventory and 2016v2 with the following changes.
Updates in 2016v3 platform as compared to 2016v2
• The point solvent emissions that had been removed in 2016v2 were added back (point source
solvents have been subtracted out of VCPy / nonpoint solvents.
• Three Iowa biofuel facilities that had been supplemented with EPA data that were double counted
with state-submitted data in the NEI were removed from the inventory (Facility IDs: OTAQ70212,
and two with the ID OTAQ70214).
• Biorefinery emissions for five Iowa biofuel facilities were adjusted based on state-submitted
emissions.
• Added three facilities in Kansas that were previously missing.
• Replaced emissions for all source-pollutants projected from 2014 to 2017 where a match could be
made to 2017.
• Some facilities moved from ptnonipm to pt oilgas due to NAICS changes between the platforms.
• Replaced release point IDs and stack parameters with 2019 release point IDs and stack parameters
for those North Dakota release points that were identified by ND.
15
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• Removed 17 biorefinery facilities that were found to overlap with the biorefinery inventory.
• Closed refineries that were not operating in 2016.
For 2016v2, A review of stack parameters (i.e., height, diameter, velocity, temperature) was performed to
look for default values submitted for many stacks for the same type of source in the inventory. When
these parameters were substantially different from average values for that source type, the defaulted stack
parameters were replaced with the value from the SMOKE PSTK file for that SCC as shown in Table 2-2.
These stack parameter changes were retained in 2016v3.
Changes that were made in the 2016v2 ptnonipm inventory and were retained in 2016v3 are:
• Select municipal waste combustion (MWC) sources were moved from ptnonipm to ptegu as a
result of better matching with NEEDS. These include EIS unit identifiers 85563113, 87378913,
119255113, 112010313.
• Sources that were identified to overlap with the WRAP oil and gas inventory including a number
of gas plants were removed from ptnonipm.
• Sources that were identified as not operating in 2016 but operating in other recent years were
added. These names (and EIS Facility IDs) of these sources were: COLOWYO COAL CO -
COLOWYO & COLLOM MINES (1839411), Northshore Mining Co - Silver Bay (6319411), US
Steel Corp - Keetac (13598411), United Taconite LLC - Fairlane Plant (6239611), MISSISSIPPI
SILICON LLC (17942211), TRIDENT (7766011), and WISCONSIN RAPIDS WWTF
(17658711).
• Year 2018 emissions were used for facilities 7766011, 17942211, and 1839411 because the 2018
inventory included CO and NOx, while year 2017 values were used for the others. Although two
of these sources were later found to have already been in the ptnonipm inventory but with lower
emissions, resulting in a double count in 2016 only.
• The Guardian Corp facility (#2989611) was removed because it closed in 2015.
• Emissions for specific rail yards in Georgia were updated at the request of the state. The specific
rail yards updated were: Austell, North Doraville, Krannert, Inman, Industry, Howells, and
Tilford.
• NOx control efficiencies were added to ptnonipm sources after a review of permitted limits was
conducted, but this does not impact base year emissions.
2.1.4 Aircraft and ground support equipment (airports)
The airport sector contains emissions of all pollutants from aircraft, categorized by their itinerant class
(i.e., commercial, air taxi, military, or general), as well as emissions from ground support equipment. The
starting point for the 2016v2 and 2016v3 platform year 2016 airport inventories is the airport emissions
from the January 2021 version of the 2017 NEI. The SCCs included in the airport sector are shown in
Table 2-7.
16
-------
Table 2-7. 2016v2 platform SCCs for the airports sector
S( (
Tier 1 description
Tier 2 (k'scriplion
Tier 3 (k'scriplion
Tier 4 (k'scriplion
2265008005
Mobile Sources
Off-highway Vehicle
Gasoline, 4-stroke
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2267008005
Mobile Sources
LPG
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2268008005
Mobile Sources
compressed natural gas
(CNG)
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2270008005
Mobile Sources
Off-highway Vehicle
Diesel
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2275001000
Mobile Sources
Aircraft
Military Aircraft
Total
2275020000
Mobile Sources
Aircraft
Commercial
Aircraft
Total: All Types
2275050011
Mobile Sources
Aircraft
General Aviation
Piston
2275050012
Mobile Sources
Aircraft
General Aviation
Turbine
2275060011
Mobile Sources
Aircraft
Air Taxi
Piston
2275060012
Mobile Sources
Aircraft
Air Taxi
Turbine
2275070000
Mobile Sources
Aircraft
Aircraft Auxiliary
Power Units
Total
40600307
Chemical
Evaporation
Transportation and
Marketing of Petroleum
Products
Gasoline Retail
Operations -
Stage I
Underground Tank
Breathing and
Emptying
Internal
Distillate Oil
(Diesel)
20200102
Combustion
Engines
Industrial
Reciprocating
The 2016vl airport emissions inventory was created from the 2017 NEI airport emissions that were
estimated using the Federal Aviation Administration's (FAA's) Aviation Environmental Design Tool
(AEDT). Additional information about the 2017NEI airport inventory and the AEDT can be found in the
2017 National Emissions Inventory Technical Support Document (EPA. 2021c). The 2017 NEI emissions
were adjusted from 2017 to represent year 2016 emissions using FAA data. Adjustment factors were
created using airport-specific numbers, where available, or the state default by itinerant class
(commercial, air taxi, and general) where there were not airport-specific values in the FAA data.
Emissions growth for facilities was capped at 500% and the state default growth was capped at 200%.
Military state default values were kept flat to reflect uncertainly in the data regarding these sources.
After the release of the April 2020 version of the 2017 NEI, an error in the computation of the NEI airport
emissions was identified and it was determined that they were overestimated. The error impacted
commercial aircraft emissions. The airport emissions in 2016v2 were recomputed based on corrected
2017 NEI emissions that were incorporated into the January 2021 release of 2017 NEI.
17
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For the 2016v3 airport inventory, updates were made to Hartsfield Jackson airport (ATL) to remove
minor double counting and to specific airports in Texas based on comments received from the Georgia
Department of Environmental Protection and the Texas Commissions on Environmental Quality (TCEQ).
Some airport runways cross the grid cell boundaries of the 12 km modeling domain. To provide more
realistic spatial apportionment for large airports emissions were allocated by area to the intersection of the
12 km grid cells and the corresponding runway polygons.
2.2 2016 N on point sources (afdust, fertilizer, livestock, npoilgas,
npsolvents, rwc, 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.
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.
2.2.1 Area fugitive dust (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 2-8 is a listing of the Source Classification Codes
(SCCs) in the afdust sector. For 2016v3 no changes were made from the year 2016 afdust inventory in
2016v2.
Table 2-8. Afdust sector SCCs
see
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2275085000
Mobile Sources
Aircraft
Unpaved Airstrips
Total
2294000000
Mobile Sources
Paved Roads
All Paved Roads
Total: Fugitives
2294000002
Mobile Sources
Paved Roads
All Paved Roads
Total: Sanding/Salting -
Fugitives
2296000000
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
2311000000
Industrial
Processes
Construction: SIC
15-17
All Processes
Total
2311010000
Industrial
Processes
Construction: SIC
15-17
Residential
Total
2311010070
Industrial
Processes
Construction: SIC
15-17
Residential
Vehicle Traffic
2311020000
Industrial
Processes
Construction: SIC
15-17
Industrial/Commercial/
Institutional
Total
18
-------
see
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2311030000
Industrial
Processes
Construction: SIC
15-17
Road Construction
Total
2325000000
Industrial
Processes
Mining and
Quarrying: SIC 14
All Processes
Total
2325060000
Industrial
Processes
Mining and
Quarrying: SIC 10
Lead Ore Mining and Milling
Total
2801000000
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Total
2801000003
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Tilling
2801000005
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Harvesting
2801000007
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Loading
2801000008
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Transport
2805001000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Dust Kicked-up by Hooves
(use 28-05-020, -001, -002,
or -003 for Waste
2805001100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Confinement
2805001200
Miscellaneous
Area Sources
Agriculture
Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Manure handling and storage
2805001300
Miscellaneous
Area Sources
Agriculture
Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Land application of manure
2805002000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle production composite
Not Elsewhere Classified
2805003100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on pasture/range
Confinement
2805007100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
dry manure management systems
Confinement
2805007300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
dry manure management systems
Land application of manure
2805008100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Confinement
2805008200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Manure handling and storage
2805008300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Land application of manure
2805009100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Confinement
2805009200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Manure handling and storage
2805009300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Land application of manure
2805010100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Confinement
2805010200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Manure handling and storage
2805010300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Land application of manure
19
-------
see
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2805018000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle composite
Not Elsewhere Classified
2805019100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Confinement
2805019200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Manure handling and storage
2805019300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Land application of manure
2805020002
Miscellaneous
Area Sources
Ag. Production -
Livestock
Cattle and Calves Waste
Emissions
Beef Cows
2805021100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Confinement
2805021200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Manure handling and storage
2805021300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Land application of manure
2805022100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Confinement
2805022200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Manure handling and storage
2805022300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Land application of manure
2805023100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Confinement
2805023200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Manure handling and storage
2805023300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Land application of manure
2805025000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production composite
Not Elsewhere Classified
(see also 28-05-039, -047, -
053)
Miscellaneous
Area Sources
Ag. Production -
Livestock
Not Elsewhere Classified
2805030000
Poultry Waste Emissions
(see also 28-05-007, -008, -
009)
2805030007
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Ducks
2805030008
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Geese
2805035000
Miscellaneous
Ag. Production -
Horses and Ponies Waste
Not Elsewhere Classified
Area Sources
Livestock
Emissions
2805039100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Confinement
2805039200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Manure handling and storage
2805039300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Land application of manure
2805040000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Sheep and Lambs Waste
Emissions
Total
2805045000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Goats Waste Emissions
Not Elsewhere Classified
20
-------
SCC
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2805047100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - deep-pit house
operations (unspecified animal
age)
Confinement
2805047300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - deep-pit house
operations (unspecified animal
age)
Land application of manure
2805053100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - outdoor
operations (unspecified animal
age)
Confinement
The starting point for the afdust emissions in 2016v3 is the 2017 NEI. The methodologies to estimate
emissions for each SCC in the preceding table are described in the 2017 NEI Technical Support
Document (EPA, 202Id). The 2017 afdust emissions were adjusted to better represent 2016 as described
below.
For paved roads (SCC 2294000000) in non-MARAMA states, the 2017 NEI paved road emissions in
afdust were projected to year 2016 based on differences in county total vehicle miles traveled (VMT)
between 2017 and 2016:
2016 afdust paved roads = 2017 afdust paved roads * (2016 county total VMT) (2017 county total VMT)
The development of the 2016 VMT is described in the onroad section. VMT data were updated for the
2016v3 to refine the road type distributions in the states of FL, IL, MN, MO, SC, and WV based on data
available from the 2020 NEI process. This was done because the 2016v2 and earlier road type
distributions had unrealistic spatial distributions of restricted roads. SCCs related to livestock production
were backcast using the same factors as were used for the livestock sector. All emissions other than those
for paved roads and livestock production are held constant with 2017 levels, including those from
unpaved roads.
Area Fugitive Dust Transport Fraction
The afdust sector is separated from other nonpoint sectors to allow for the application of a "transport
fraction," and meteorological/precipitation reductions. These adjustments are applied using a script that
applies land use-based gridded transport fractions based on landscape roughness, followed by another
script that zeroes out emissions for days on which at least 0.01 inches of precipitation occurs or there is
snow cover on the ground. The land use data used to reduce the NEI emissions determines the amount of
emissions that are subject to transport. This methodology is discussed in Pouliot, et al., 2010, and in
"Fugitive Dust Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). Both the
transport fraction and meteorological adjustments are based on the gridded resolution of the platform (i.e.,
12km grid cells); therefore, different emissions will result if the process were applied to different grid
resolutions. A limitation of the transport fraction approach is the lack of monthly variability that would
be expected with seasonal changes in vegetative cover. While wind speed and direction are not accounted
for in the emissions processing, the hourly variability due to soil moisture, snow cover and precipitation is
accounted for in the subsequent meteorological adjustment.
For the data compiled into the 2017 NEI, meteorological adjustments are applied to paved and unpaved
road SCCs but not transport adjustments. The meteorological adjustments that were applied (to paved
and unpaved road SCCs) in the 2017 NEI were backed out so that the entire sector could be processed
consistently in SMOKE and the same grid-specific transport fractions and meteorological adjustments
could be applied sector-wide. Thus, the FF10 that is run through SMOKE consists of 100% unadjusted
21
-------
emissions, and after SMOKE all afdust sources have both transport and meteorological adjustments
applied. The total impacts of the transport fraction and meteorological adjustments for 2016v2 an 2016v3
are shown in Table 2-9. Note that while totals from AK, HI, PR, and VI are included at the bottom of the
table, they are from non-continental U.S. (non-CONUS) modeling domains and are held constant from
2016vl.
Table 2-9. Total impact of fugitive dust adjustments to the unadjusted 2016 inventory
State
Unadjusted
PMio
Unadjusted
PM25
Change in
PM10
Change in
PM25
PM10
Reduction
PM25
Reduction
Alabama
301,220
40,516
-206,837
-27,820
69%
69%
Arizona
180,413
24,148
-65,952
-8,640
37%
36%
Arkansas
389,426
53,870
-261,601
-35,627
67%
66%
California
307,525
38,907
-133,858
-16,408
44%
42%
Colorado
276,798
40,283
-138,818
-19,548
50%
49%
Connecticut
24,307
4,007
-18,293
-3,032
75%
76%
Delaware
15,263
2,346
-9,201
-1,422
60%
61%
District of
Columbia
2,882
406
-1,804
-253
63%
62%
Florida
390,779
54,511
-208,568
-29,187
53%
54%
Georgia
290,522
41,465
-201,028
-28,482
69%
69%
Idaho
560,472
64,931
-295,880
-33,156
53%
51%
Illinois
1,107,780
159,636
-679,749
-97,634
61%
61%
Indiana
144,272
26,977
-95,341
-17,919
66%
66%
Iowa
385,014
56,805
-222,410
-32,650
58%
57%
Kansas
668,387
88,915
-300,638
-39,593
45%
45%
Kentucky
177,018
28,904
-128,875
-20,989
73%
73%
Louisiana
180,035
27,399
-115,251
-17,368
64%
63%
Maine
71,295
8,735
-59,096
-7,251
83%
83%
Maryland
74,347
11,904
-48,034
-7,748
65%
65%
Massachusetts
61,438
9,379
-47,183
-7,161
77%
76%
Michigan
292,345
38,470
-213,919
-27,925
73%
73%
Minnesota
423,012
59,575
-263,321
-36,486
62%
61%
Mississippi
448,193
54,854
-307,949
-37,331
69%
68%
Missouri
1,319,996
156,248
-858,902
-101,313
65%
65%
Montana
501,655
66,435
-277,120
-35,529
55%
53%
Nebraska
515,575
71,436
-246,621
-33,630
48%
47%
Nevada
138,466
18,305
-45,931
-6,047
33%
33%
New Hampshire
20,527
4,310
-16,979
-3,560
83%
83%
New Jersey
32,466
6,059
-21,778
-4,015
67%
66%
New Mexico
205,161
25,615
-80,428
-9,987
39%
39%
New York
238,564
33,653
-178,529
-25,035
75%
74%
22
-------
State
Unadjusted
PMio
Unadjusted
PM25
Change in
PM10
Change in
PM25
PM10
Reduction
PM25
Reduction
North Carolina
233,349
31,479
-160,106
-21,641
69%
69%
North Dakota
397,407
61,024
-211,752
-32,100
53%
53%
Ohio
273,211
42,880
-182,757
-28,709
67%
67%
Oklahoma
601,218
81,825
-313,021
-41,638
52%
51%
Oregon
605,831
68,330
-404,663
-44,666
67%
65%
Pennsylvania
135,564
24,365
-97,991
-17,891
72%
73%
Rhode Island
4,641
775
-3,308
-551
71%
71%
South Carolina
117,181
16,266
-77,402
-10,817
66%
66%
South Dakota
215,908
38,503
-106,792
-18,757
49%
49%
Tennessee
140,798
25,845
-95,578
-17,651
68%
68%
Texas
1,317,935
190,982
-632,794
-89,482
48%
47%
Utah
165,959
21,202
-84,561
-10,620
51%
50%
Vermont
76,398
8,509
-65,227
-7,237
85%
85%
Virginia
124,875
20,123
-90,751
-14,718
73%
73%
Washington
230,686
37,529
-128,255
-20,829
56%
56%
West Virginia
86,192
11,111
-72,997
-9,417
85%
85%
Wisconsin
182,302
30,984
-124,770
-21,188
68%
68%
Wyoming
542,620
60,863
-272,862
-30,182
50%
50%
Domain Total
(12km CONUS)
15,197,226
2,091,599
-8,875,481
-1,210,842
58%
58%
Alaska (vl)
112,025
110,562
-101,822
-10,508
91%
91%
Hawaii (vl)
109,120
11,438
-73,612
-7,673
67%
67%
Puerto Rico (vl)
5,889
1,313
-4,355
-984
74%
75%
Virgin Islands (vl)
3,493
467
-1,477
-195
42%
42%
Figure 2-1 illustrates the impact of each step of the adjustment. The reductions due to the transport
fraction adjustments alone are shown at the top of the figure. The reductions due to the precipitation
adjustments alone are shown in the middle of the figure. The cumulative emission reductions after both
transport fraction and meteorological adjustments are shown at the bottom of the figure. The top plot
shows how the transport fraction has a larger reduction effect in the east, where forested areas are more
effective at reducing PM transport than in many western areas. The middle plot shows how the
meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust
emissions.
23
-------
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction,
precipitation, and cumulative
2016fj (v2) afdust annual : PM2 5, xportfrac adjusted - unadjusted
2016fj (v2) afdust annual : PM2_5, precip adjusted - xportfrac adjusted
24
-------
2016fj (v2) afdust annual : PM2_5, xportfrac + precip adjusted - unadjusted
2.2.2 Agricultural Livestock (livestock)
The livestock sector includes NHS emissions from fertilizer and emissions of all pollutants other than
PM2.5 from livestock in the nonpoint (county-level) data category of the 2017NEI. PM2.5 from livestock
are in the Area Fugitive Dust (afdust) sector. Combustion emissions from agricultural equipment, such as
tractors, are in the nonroad sector. The livestock sector includes VOC and HAP VOC in addition to NH3.
The 2016v2 and v3 use a 2016 USDA-based county-level back-projection of 2017NEI livestock
emissions. The SCCs included in the ag sector are shown in Table 2-10. For 2016v3, corrections were
made to the 2016 livestock emissions in Maryland and Illinois. Otherwise, the 2016 livestock emissions in
2016v3 are unchanged from those in 2016v2.
Table 2-10. 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)
25
-------
SCC
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
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
The 2016v2 and v3 platform livestock emissions consist of a back-projection of 2017 NEI livestock
emissions to the year 2016 and include NH3 and VOC. The livestock waste emissions from 2017 NEI
contain emissions for beef cattle, dairy cattle, goats, horses, poultry, sheep, and swine. The data come
from both state-submitted emissions and EPA-calculated emission estimates. Further information about
the 2017 NEI emissions can be found in the 2017 National Emissions Inventory Technical Support
Document (EPA, 202Id). Back-projection factors for 2016 emission estimates are based on animal
population data from the USDA National Agriculture Statistics Service Quick Stats
(https://www.nass.usda.gov/Quick Stats). These estimates are developed by data collected from annual
agriculture surveys and the Census of Agriculture that is completed every five years. These data include
estimates for beef, layers, broilers, turkeys, dairy, swine, and sheep. Each SCC in the 2017 NEI livestock
inventory, except for 2805035000 (horses and ponies) and 2805045000 (goats), was mapped to one of
these USDA categories. Then, back-projection factors were calculated based on USDA animal
populations for 2016 and 2017. Emissions for animal categories for which population data were not
available (e.g., horses, goats) were held constant in the projection.
Maryland and Illinois year 2016 livestock emissions in 2016v3 are changed from 2016v2 but otherwise
the emissions are the same in both platforms. In Maryland, livestock omissions were discovered in the
2017 NEI. The latest version of the 2017 NEI (January 2021) also includes updated Illinois emissions
compared to the earlier version of 2017 NEI, resulting in slightly lower NH3 and significantly lower
VOC. The 2016v3 year 2016 inventory is based on a backcast of the improved 2017 Illinois and
Maryland emissions.
Back-projection factors were calculated at the county level, but only where county-level data were
available for a specific animal category. County-level factors were limited to a range of 0.833 to 1.2. Data
were not available for every animal category in every county. State-wide back-projection factors based on
state total animal populations were calculated and applied to counties where county-specific data was not
available for a given animal category. However, data were often not available for every animal category
in every state. For categories other than beef and dairy, data are not available for most states. In cases of
missing state-level data, a national back-projection factor was applied. Back-projection factors were not
pollutant-specific and were applied to all pollutants. The national back-projection factors, which were
only used when county or state data were not available, are shown in Table 2-11. The national factors
were created using a ratio between animal inventory counts for 2017 and 2016 from the USDA National
livestock inventory projections published in February 20184.
4 https://www.ers.usda. gov/webdocs/outlooks/87459/oce-2018-l.pdf?v=7587.1
26
-------
Table 2-11. National back-projection factors for livestock: 2017 to 2016
beef
-1.8%
swine
-3.6%
broilers
-2.0%
turkeys
-0.3%
layers
-2.3%
dairy
-0.4%
2.2.3 Agricultural Fertilizer (fertilizer)
Fertilizer emissions for 2016 are based on the Fertilizer Emission Scenario Tool for CMAQ (FEST-C)
model (https://www.cmascenter.org/fest-c/). These emissions are for SCC 2801700099 (Miscellaneous
Area Sources; Ag. Production - Crops; Fertilizer Application; Miscellaneous Fertilizers). The
bidirectional version of CMAQ (v5.3.2) and the Fertilizer Emissions Scenario Tool for CMAQ FEST-C
(vl.4) were used to estimate ammonia (NH3) emissions from agricultural soils. For 2016v3, a correction
to the 2016 livestock emissions was implemented by multiplying by 17/14 to reflect the correct molecular
weight for NH3. Otherwise, the fertilizer emissions in 2016v3 are consistent with those in 2016v2.
The approach to estimate year-specific fertilizer emissions consists of these steps:
• Run FEST-C to produce nitrate (N03), Ammonium (NH4+, including Urea), and organic
(manure) nitrogen (N) fertilizer usage estimates.
• Run the CMAQ model with bidirectional ("bidi") NH3 exchange to generate gaseous ammonia
NH3 emission estimates.
• Calculate county-level emission factors as the ratio of bidirectional CMAQ NH3 fertilizer
emissions to FEST-C total N fertilizer application.
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, 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://epicapex.tamu.edu/epic/) to simulate the agricultural practices and soil biogeochemistry and
provides information regarding fertilizer timing, composition, application method and amount.
An iterative calculation was applied to estimate fertilizer emissions for the 2016 platform. First, fertilizer
application by crop type was estimated using FEST-C modeled data. Then CMAQ v5.3 was run with the
Surface Tiled Aerosol and Gaseous Exchange (STAGE) deposition option with bidirectional exchange to
estimate fertilizer and biogenic NH3 emissions.
27
-------
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions
The Fertilizer Emission Scenario Tool for CMAQ
(FEST-C)
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 year 2016
using a national 12-km rectangular grid covering the continental U.S. The meteorological parameters in
Table 2-12 were used as EPIC model inputs.
28
-------
Table 2-12. 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 USD A Soil Conservation Service
(CSC) Soils-5 survey. The EPIC model then was run for 25 years using current fertilization and
agricultural cropping techniques to estimate soil nutrient content and pH for the 2016 EPIC/WRF/CMAQ
simulation.
The presence of crops in each model grid cell was determined using USD A Census of Agriculture data
(2012) and USGS National Land Cover data (2011). These two data sources were used to compute the
fraction of agricultural land in a model grid cell and the mix of crops grown on that land.
Fertilizer sales data and the 6-month period in which they were sold were extracted from the 2014
Association of American Plant Food Control Officials (AAPFCO,
http://www.aapfco.org/publications.htmn. AAPFCO data were used to identify the composition (e.g.,
urea, nitrate, organic) of the fertilizer used, and the amount applied is estimated using the modeled crop
demand. These data were useful in making a reasonable assignment of what kind of fertilizer is being
applied to which crops.
Management activity data refers to data used to estimate representative crop management schemes. The
USD A Agricultural Resource Management Survey (ARMS,
https://www.nass.usda.gov/Survevs/Guide to NASS Survevs/Ag Resource Management/) was used to
provide management activity data. These data cover 10 USD A 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.).
29
-------
2.2.4 Nonpoint Oil and Gas (np_oilgas)
While the major emissions sources associated with oil and gas collection, processing, and distribution
have traditionally been included in the National Emissions Inventory (NEI) as point sources (e.g., gas
processing plants, pipeline compressor stations, and refineries), the activities occurring "upstream" of
these types of facilities have not been as well characterized in the NEI. Here, upstream activities refer to
emission units and processes associated with the exploration and drilling of oil and gas wells, and the
equipment used at the well site to then extract the product from the well and deliver it to a central
collection point or processing facility. The types of unit processes found at upstream sites include
separators, dehydrators, storage tanks, and compressor engines.
The nonpoint oil and gas (npoilgas) sector, which consists of oil and gas exploration and production
sources, both onshore and offshore (state-owned only). For many states, these emissions are mostly based
on the EPA Oil and Gas Tool run with data specific to the year 2016, while some states submitted their
own inventory data. For 2016v3, updates were made to 2016 np oilgas emissions in Colorado, Oklahoma,
and Texas in response to comments. Because of the growing importance of these emissions, special
consideration is given to the speciation, spatial allocation, and monthly temporalization of nonpoint oil
and gas emissions, instead of relying on older, more generalized profiles.
EPA Oil and Gas Tool
EPA developed the 2016 non-point oil and gas inventory for the 2016v2 and v3 platform using the
2017NEI version of the Oil and Gas Emission Estimation Tool (the "Tool") with year 2016 oil and gas
production and exploration activity as input into the Tool. The Tool was previously used to estimate
emissions for the 2017 NEI. Year 2016 oil and gas activity data were supplied to EPA by some state air
agencies, and where state data were not supplied to EPA, EPA populated the 2016v2 inventory with the
best available data. The Tool is an Access database that utilizes county-level activity data (e.g., oil
production and well counts), operational characteristics (types and sizes of equipment), and emission
factors to estimate emissions. The Tool creates a CSV-formatted emissions dataset covering all national
nonpoint oil and gas emissions. This dataset is then converted to FF10 format for use in SMOKE
modeling. A separate report named "2017 Nonpoint Oil and Gas Emission Estimation Tool
RevisionsVl 41 l_2019.docx" (ERG, 2019a) was generated that provides technical details of how the
tool was applied for the 2017NEI. This 2017 NEI Tool document can be found at:
https://gaftp.epa.gov/air/nei/2017/doc/supporting data/nonpoint/.
Nonpoint Oil and Gas Alternative Datasets
Some states provided, or recommended use of, a separate emissions inventory for use in 2016v2 platform
instead of emissions derived from the EPA Oil and Gas Tool. For example, the California Air Resources
Board (CARB) developed their own np oilgas emissions inventory for 2016 for California that were used
for the 2016vl, v2, and v3 platforms.
In Pennsylvania for the 2016v2 and 2016v3 modeling platform, the emissions associated with
unconventional wells for year 2016 were supplied by the Pennsylvania Department of Environmental
Protection (PA DEP). The Oil and Gas Tool was used to produce the conventional well emissions for
2016. Together these unconventional and conventional well emissions represent the total non-point oil
and gas emissions for Pennsylvania.
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A major update in 2016v2 was the incorporation of the WRAP oil and gas inventory, which is described
in more detail below and in the WRAP Final report (WRAP / Ramboll, 2019). Specifically, production-
related emissions from the WRAP inventory were used, along with the exploration-related emissions from
the 2017NEI Oil and Gas Tool for the following states: CO, MT, ND, NM, SD, UT, and WY. The
exploration-related emissions were used from the Tool because they likely better align with exploration
activity in year 2016 vs the WRAP 2014 inventory which better represented exploration activity for year
2014.
The changes made to 2016v3 year 2016 np oilgas emissions as compared to 2016v2 are:
• Use the 2016vl emissions for Colorado production and exploration and continue using the WRAP
spatial surrogates per Colorado's request.
• Use the 2016vl emission for Texas production-related sources and use 2016v2 emissions for
exploration-relate sources.
• Use 2017 NEI data for Oklahoma production-related sources and use 2016v2 emissions for
exploration-related sources.
2.2.5 Residential Wood Combustion (rwc)
The RWC sector includes residential wood burning devices such as fireplaces, fireplaces with inserts, free
standing woodstoves, pellet stoves, outdoor hydronic heaters (also known as outdoor wood boilers),
indoor furnaces, and outdoor burning in firepits and chimneys. Free standing woodstoves and inserts are
further differentiated into three categories: 1) conventional (not EPA certified); 2) EPA certified,
catalytic; and 3) EPA certified, noncatalytic. Generally, the conventional units were constructed prior to
1988. Units constructed after 1988 had to meet EPA emission standards and they are either catalytic or
non-catalytic. The source classification codes (SCCs) in the RWC sector are listed in Table 2-13. For
2016v3, the 2016 rwc emissions for Idaho were replaced with those from the 2017 NEI, but otherwise the
emissions are unchanged from 2016v2.
Table 2-13. 2016 vl platform 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
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
31
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S( (
Tier 1 Description
Tier 2
Description
Tier 3
Description
Tier 4 Description
2104008400
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: pellet-fired,
general (freestanding or FP
insert)
2104008510
Stationary Source
Fuel Combustion
Residential
Wood
Furnace: Indoor, cordwood-
fired, non-EPA certified
2104008610
Stationary Source
Fuel Combustion
Residential
Wood
Hydronic heater: outdoor
2104008700
Stationary Source
Fuel Combustion
Residential
Wood
Outdoor wood burning
device, NEC (fire-pits,
chimneas, etc)
2104009000
Stationary Source
Fuel Combustion
Residential
Firelog
Total: All Combustor Types
For all states except Idaho, rwc emissions from the 2017NEI were backcast to 2016 using a single
projection factor (+3.254%) based on data from EIA/SEDS. Thus, rwc emissions are the same for 2016v2
and v3, with the exception of Idaho where 2017 NEI emissions were used.
2.2.6 Solvents (np_solvents)
The npsolvents 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 (i.e., VOCs) with 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); as such, these emissions are frequently
described as volatile chemical products (VCPs). In the 2016v2 and 2016v3 platforms, these products
comprise the np solvents sector. For 2016v3, updates to the methodology used to compute emissions in
the np solvents sector were implemented
The types of sources in the np solvents sector include, but are not limited to, solvent utilization for the
following:
• surface coatings such as architectural coatings, auto refinishing, traffic marking, textile
production, furniture finishing, and coating of paper, plastic, metal, appliances, and motor
vehicles;
• degreasing of furniture, metals, auto repair, electronics, and manufacturing;
• dry cleaning, graphic arts, plastics, industrial processes, personal care products, household
products, adhesives and sealants; and
• asphalt application, roofing asphalt, and pesticide application.
For the 2016v3 platform, emissions for the np solvents sector are derived using the VCPy framework
(Seltzer et al., 2021). The VCPy framework is based on the principle that the magnitude and speciation of
organic emissions from this sector are directly related to (1) the mass of chemical products used, (2) the
composition of these products, (3) the physiochemical properties of their constituents that govern
volatilization, and (4) the timescale available for these constituents to evaporate. National product usage is
preferentially estimated using economic statistics from the U.S. Census Bureau's Annual Survey of
Manufacturers (U.S. Census Bureau, 2021), commodity prices from the U.S. Department of
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Transportation's 2012 Commodity Flow Survey (U.S. Department of Transportation, 2015) and the U.S.
Census Bureau's Paint and Allied Products Survey (U.S. Census Bureau, 2011), and producer price
indices, which scale commodity prices to target years, are retrieved from the Federal Reserve Bank of St.
Louis (U.S. Bureau of Labor Statistics, 2020).
In circumstances in which data are unavailable, default usage estimates were derived using functional
solvent usage reported by a business research company (The Freedonia Group, 2016) or in sales reported
in a California Air Resources Board (CARB) California-specific survey (CARB, 2019). The composition
of products is estimated by generating composites from various CARB surveys (CARB, 2007; CARB,
2012; CARB 2014; CARB, 2018; CARB, 2019) and profiles reported in the U.S. EPA's SPECIATE
database (EPA, 2019). The physiochemical properties of all organic components are generated from the
quantitative structure-activity relationship model OPERA (Mansouri et al., 2018) and the characteristic
evaporation timescale of each component is estimated using previously published methods (Khare and
Gentner, 2018; Weschler and Nazaroff, 2008).
National-level emissions were allocated to the county-level using several proxies. Most emissions are
allocated using population as an allocation surrogate. This includes all cleaners, personal care products,
adhesives, architectural coatings, and aerosol coatings. Industrial coatings, allied paint products, printing
inks, and dry-cleaning emissions are allocated using county-level employment statistics from the U.S.
Census Bureau's County Business Patterns (U.S. Census Bureau, 2018) and follow the same mapping
scheme used in the EPA's 2017 National Emissions Inventory (EPA, 202Id). Agricultural pesticides are
allocated using county-level agricultural pesticide use, as taken from the 2017 NEI and traffic marking
coatings are allocated using estimates of vehicular lane miles traveled on paved roads from the Federal
Highway Administration and MOVES model. All activity data reflects the most recently available dataset.
Unlike the 2016v2 modeling platform, the 2016v3 reconciles point and nonpoint emissions for which
SCCs overlap using point source subtraction. Point source subtraction was performed at the county-level
using estimates of uncontrolled point source emissions. Uncontrolled point source emission calculations
were calculated, as necessary, using the submitted point source emissions, engineering judgement, and an
assumed control efficiency.
In addition, methodological updates to the underlying nonpoint solvents model made since the release of
the 2016v2 modeling platform were incorporated in 2016v3 to make the methods consistent with those
used to estimate emissions for the 2020 NEI. These updates include: (1) indoor usage assumptions at the
product-level to modulate evaporation characteristics, and (2) control assumptions for select states that
have implemented reduction strategies for select consumer and commercial products, as well as
architectural and industrial maintenance coatings. Details for all methodology can be found in the
Nonpoint Emissions Methodology and Operator Instructions (NEMO) document for the 2020 NEI.
Finally, remaining updates made in response to comments include: (1) reintroduction of all asphalt paving
emissions from the 2017 NEI, (2) reintroduction of non-VOC CAPs and HAPs that were not included in
the 2016v2 modeling platform but are included in the 2017 NEI, (3) reintroduction of CAP and HAP
emissions for the SCCs 2440020000, 2401010000, 2440000000, 2461023000, 2461800001, 2461800002,
2401050000, 2401045000, 2401035000, 2461000000, and 2461160000, all of which were not included in
the 2016v2 modeling platform but were included in the 2017 NEI, and (4) removal of emissions from
2420000000 and 2425000000 from Colorado, per Colorado's request to avoid double counting.
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2.2.7 Nonpoint (nonpt)
The starting points for the 2016v3 nonpt inventories is the 2017 NEI. The nonpt sector includes all
nonpoint sources that are not included in the sectors afdust, livestock, fertilizer, cmv_clc2, cmv_c3,
npoilgas, rail, rwc, or np solvents. The types of sources in the nonpt sector include, but are not limited
to:
• stationary source fuel combustion, including industrial, commercial, and residential and orchard
heaters;
• commercial sources such as commercial cooking;
• industrial processes such as chemical manufacturing, metal production, mineral processes,
petroleum refining, wood products, fabricated metals, and refrigeration;
• storage and transport of petroleum for uses such as gasoline service stations, aviation, and marine
vessels;
• storage and transport of chemicals;
• waste disposal (including composting);
• miscellaneous non-industrial sources such as cremation, hospitals, lamp breakage, and automotive
repair shops;
• bulk gasoline terminals;
• portable gas cans;
• cellulosic biorefining;
• biomass fuel combustion;
• stage 1 refueling emissions at gas stations;
• and any construction agricultural dust or waste that is not part of the afdust or livestock sectors.
For 2016v3, all emissions in nonpt were taken from 2017 NEI, although some for some SCCs adjustments
were applied to reflect 2016 levels. For biomass fuel combustion, 2017 NEI data were backcast to 2016
by applying a 4.27% reduction for industrial emissions and a 0.15% reduction for commercial emissions.
Refueling emissions at gas stations in the nonpt sector were interpolated to 2016 between 2002 NEI and
2017 NEI levels.
The use of 2017 NEI for nonpt replaced the overrides that were needed for the 2016v2 and 2016vl
platforms, including removal of emissions for SCCs for Industrial (2102004000) and
Commercial/Institutional (2103004000) Distillate Oil, Total: Boilers and Internal Combustion (IC)
Engines in New Jersey and removal of Industrial, Commercial, Institutional (ICI) Wood emissions
(2102008000) PM2.5 emissions in Alabama, in the beta version of this emissions modeling platform and
were significantly higher than other states' ICI Wood emissions, be removed from 2016vl platform
2.3 2016 Onroad Mobile sources (onroad)
Onroad mobile source include emissions from motorized vehicles operating on public roadways. These
include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks, heavy-duty trucks,
34
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and buses. The sources are further divided by the fuel they use, including diesel, gasoline, E-85, and
compressed natural gas (CNG) vehicles. The sector characterizes emissions from parked vehicle
processes (e.g., starts, hot soak, and extended idling) as well as from on-network processes (i.e., from
vehicles as they move along the roads). Except for California, all onroad emissions are generated using
the SMOKE-MOVES emissions modeling framework that leverages MOVES-generated emission factors,
county and SCC-specific activity data, and hourly meteorological data. The onroad source classification
codes (SCCs) in the modeling platform are more finely resolved than those in the National Emissions
Inventory (NEI). The NEI SCCs distinguish vehicles and fuels. The SCCs used in the modeling platform
also distinguish between emissions processes (i.e., off-network, on-network, and extended idle), and road
types. For 2016v3, updates from 2016v2 consisted of updated activity data for starts in 20 Georgia
counties; corrected road type distributions and hoteling for Florida, Illinois, Minnesota, Missouri, South
Carolina, and West Virginia; and corrected emission factors for combination long haul trucks nationwide.
Onroad emissions were computed with SMOKE-MOVES by multiplying appropriate vehicle activity data
by the corresponding emission factors for the process. This section includes discussions of the activity
data and the emission factor development. The vehicles (aka source types) for which MOVES3 computes
emissions are shown in Table 2-14. 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. For the
2016vl platform, missions for Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands were computed
by running SMOKE-MOVES for distinct grids covering each of those regions and are included in the
onroad nonconus sector. In some summary reports these non-CONUS emissions are aggregated with
emissions from the onroad sector. Onroad emissions computations outside of the contiguous U.S. were
not updated in the 2016v2 or 2016v3 platforms.
Table 2-14. 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
2.3.1 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 dataset started with the 2017 NEI activity
35
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data, which was then scaled back to 2016 using Federal Highway Administration (FHWA) VM-2 trends.
Second, data submitted by state and local agencies were incorporated where available, in place of the EPA
default data. EPA default activity was used for California, but the emissions were scaled to California-
supplied values during the emissions processing. The agencies for which 2016 submitted data, or 2017
submitted VMT and VPOP data backcast to 2016, were used for the 2016v2 and v3 platforms are shown
in Table 2-15. The 2017 NEI submissions are shown table to indicate states for which the 2016 data were
backcast from 2017 NEI activity data, in the event that no 2016-specific data were submitted.
Table 2-15. Submitted data used to prepare 2016v2 onroad activity data
Agency
2016 VMT
2016 VPOP
2017 NEI
Alaska
yes
Arizona - Maricopa
yes
Arizona - Pima
yes
yes
yes
Colorado
yes
yes
Connecticut
yes
yes
yes
Delaware
yes
District of Columbia
yes
Florida
yes
Georgia
yes
yes
yes
Idaho
yes
Illinois - Chicago area
yes
yes
Illinois - rest of state
yes
yes
yes
Indiana - Louisville area
yes
Kentucky - lefferson
yes
yes
yes
Kentucky - Louisville
exurbs
ves
Maine
yes
Maryland
yes
yes
yes
Massachusetts
yes
yes
yes
Michigan - Detroit area
yes
yes
Michigan - rest of state
yes
yes
yes
Minnesota
yes
yes
yes
Missouri
yes
Nevada - Clark
yes
yes
yes
Nevada - Washoe
yes
New Hampshire
ves
ves
yes
New lersev
ves
ves
yes
New York
yes
North Carolina
yes
yes
yes
Ohio
yes
Pennsylvania
yes
yes
yes
Rhode Island
yes
South Carolina
yes
yes
yes
Tennessee - Davidson
yes
Tennessee - Knox
yes
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Agency
2016 VMT
2016 VPOP
2017 NEI
Texas
yes
Vermont
yes
Virginia
yes
yes
yes
Washington
yes
West Virginia
yes
yes
yes
Wisconsin
yes
yes
yes
Vehicle Miles Traveled (VMT)
VMT data specific to 2016 were used where states provided it, and for the remaining states, 2016 VMT
data were backcast from 2017 NEI data. To compute default 2016 data for states that did not provide
2016-specific data, EPA backcast the 2017 NEI VMT (including state submitted 2017 data) to 2016. The
2017 NEI Technical Support Document has details on the development of the 2017 VMT (EPA, 2021d).
The factors to adjust VMT from 2017 to 2016 were based on VMT data from the FHWA county-level
VM-2 reports similar to the state-level reports at
https://www.fhwa.dot.gov/policvinformation/statistics/2016/vm2.cfm and
https://www.fhwa.dot.gov/policvinformation/statistics/2017/vm2.cfm. For most states, EPA calculated
county-road type adjustment factors based on FHWA VM-2 County data for 2017 and 2016. Separate
adjustment factors were calculated by vehicle type for each of the four MOVES road types. Some states
have a very different distribution of urban activity versus rural activity between 2017NEI and the FHWA
data, due to inconsistencies in the definition of urban versus rural between the data sets. For those
counties, a single county-wide projection factor based on total FHWA VMT across all road types was
applied to all VMT independent of road type. County-total-based (instead of county+road-type) factors
were used for all counties in IN, MS, MO, NM, TN, TX, and UT because many counties had large
increases in one particular road type and decreases in another road type. State-total-based factors were
used for all counties in Alaska and Puerto Rico because county level data were questionable. Note that
Alaska and Hawaii emissions have not yet been recomputed using MOVES3-based emission factors.
State total differences between the 2017 NEI and 2016 VMT data for all states are provided in Table 2-16.
Table 2-16. State total differences between 2017 NEI and 2016 VMT data
SI sill'
2017 M: 1-2016 %
Slsile
2017 M. 1-2016 %
Akihama
: i0..
Montana
0.4%
Alaska
4.9%
Nchaska
1.5%
Arizona
0.5%
Nc\ ada
-4
Arkansas
1.8%
New 1 lam pshi re
1 0%
California
1 1°,.
New .Iciscy
0.8%
Colorado
2.3%
New Mexico
6.4%
Connecticut
0.6%
New York
1.3%
Del aw arc
O Q0/
Z.o /0
North Carolina
-0.2%
District of Columbia
2.5%
North Dakota
-0.2%
I'lorida
-8.4%
Ohio
0.7%
(icoruia
4.2%
Oklahoma
0.8%
Hawaii
1.1%
Oregon
0.0%
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Idaho
0.5%
Pennsylvania
0.3%
Illinois
-0.8%
Rhode Island
-2.7%
Indiana
-1.5%
South Carolina
0.9%
Iowa
0.4%
South Dakota
2.0%
Kansas
0.5%
Tennessee
1.4%
Kentucky
0.2%
Texas
7.0%
Louisiana
0.1%
Utah
0.5%
Maine
-0.7%
Vermont
0.1%
Maryland
1.6%
Virgin Islands
0.5%
Massachusetts
5.5%
Virginia
0.0%
Michigan
1.0%
Washington
2.1%
Minnesota
1.9%
West Virginia
0.7%
Mississippi
0.3%
Wisconsin
2.3%
Missouri
2.5%
Wyoming
2.2%
For the 2016 platforms, VMT data submitted by state and local agencies were incorporated and used in
place of EPA defaults. Note that VMT data need to be provided to SMOKE for each county and SCC.
The onroad SCCs characterize vehicles by MOVES fuel type, vehicle (aka source) type, emissions
process, and road type. Any VMT provided at a different resolution than this were converted to a full
county-SCC resolution to prepare the data for processing by SMOKE. Details the on pre-processing of
submitted VMT and VPOP are provided in the TSD Preparation of Emissions Inventories for the 2016vl
North American Emissions Modeling Platform (EPA, 2021c). Some of the provided data were adjusted
following quality assurance, as described below in the VPOP section.
To ensure consistency in the 21/31/32 splits across the country, all state-submitted VMT for MOVES
vehicle types 21,31, and 32 (all of which are part of HPMS vehicle type 25) was summed, and then re-
split using the 21/31/32 splits from the EPA 2016v2 default VMT. VMT for each source type as a
percentage of total 21/31/32 VMT was calculated by county from the EPA default VMT. Then, state-
submitted VMT for 21/31/32 were summed and then re-split according to those percentages. This was
done for all states and counties listed above which submitted VMT for 2016. Most of the states listed
above did not provide VMT down to the source type, so splitting the light-duty vehicle VMT does not
create an inconsistency with state-provided data in those states. Exceptions are New Hampshire and
Pennsylvania: those two states provided SCC-level VMT, but these were reallocated to 21/31/32 so that
the splits are performed in a consistent way across the country. The 21/31/32 splits in the EPA default
VMT are based on the 2017 NEI VPOP data obtained from IHS-Polk through the Coordinating Research
Council (CRC) A-115 project (CRC, 2019).
For 2016v3, total 2016 VMT is unchanged from 2016v2. However, road type distributions were updated
to be consistent with those in 2020 NEI in Florida, Illinois, Minnesota, Missouri, South Carolina, and
West Virginia to correct anomalies found in the 2016vl and 2016v2 data.
Speed Activity (SPEED/SPDIST)
In SMOKE 4.7, SMOKE-MOVES was updated to use speed distributions similarly to how they are used
when running MOVES in inventory mode. This new speed distribution file, called SPDIST, specifies the
amount of time spent in each MOVES speed bin for each county, vehicle (aka source) type, road type,
38
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weekday/weekend, and hour of day. This file contains the same information at the same resolution as the
Speed Distribution table used by MOVES but is reformatted for SMOKE. Using the SPDIST file results
in a SMOKE emissions calculation that is more consistent with MOVES than the old hourly speed profile
(SPDPRO) approach, because emission factors from all speed bins can be used, rather than interpolating
between the two bins surrounding the single average speed value for each hour as is done with the
SPDPRO approach.
As was the case with the previous SPDPRO approach, the SPEED inventory that includes a single overall
average speed for each county, SCC, and month, must still be read in by the SMOKE program Smkinven.
SMOKE requires the SPEED dataset to exist even when speed distribution data are available, even though
only the speed distribution data affects the selection of emission factors. The SPEED and SPDIST
datasets are carried over from 2017NEI and are based on a combination of the CRC A-100 (CRC, 2017)
project data and 2017 NEI MOVES CDBs. There were no changes to the speed activity from 2016v2 to
2016v3.
Vehicle Population (VPOP)
The EPA default VPOP dataset was developed similarly to the default VMT dataset described above. In
the areas where we backcast 2017 NEI VMT:
2016v2 VPOP = 2016v2 VMT * (VPOP/VMT ratio by county-SCC6).
where the ratio by county-SCC is based on 2017NEI with MOVES3 fuel splits. In the areas where 2016vl
used VMT re-split to MOVES3 fuels, 2016v3 VPOP = 2016v2 = 2016vl VPOP with two re-splits: First,
source types 21/31/32 were re-split according to 2017 NEI EPA default county-specific 21/31/32 splits so
that the whole country has consistent 21/31/32 splits. Next, fuels were re-split to MOVES3 fuels. There
are some areas where 2016 VMT was submitted but 2016 VPOP was not; those areas use the 2016vl
VPOP (with re-splits). The same method was applied to the 2016 EPA default VMT to produce an EPA
default VPOP data set. There were no changes to the VPOP from 2016v2 to v3.
Hotelinq Hours (HOTELING)
Hoteling hours activity data are used to calculate emissions from extended idling and auxiliary power
units (APUs) for heavy duty diesel vehicles. Previously, states have commented that EPA estimates of
hoteling hours, and therefore emissions resulting from hoteling, are higher than they could be in reality
given the available parking spaces in some places. Therefore, recent hoteling activity datasets, including
the 2016vl, v2, and v3 platforms, incorporate reductions to hoteling activity data based on the availability
of truck stop parking spaces in each county, as described below. Starting with 2016vl, hoteling hours
were recomputed using a new factor identified by EPA's Office of Transportation and Air Quality as
more appropriate based on recent studies.
The method used is the following:
• Start with 2016 VMT for source type 62 on restricted roads, by county.
• Multiply that by 0.007248 hours/mile (EPA, 2020). (Note that this results in about 73.5% less
hoteling hours as compared to the 2014NEIv2 approach.)
39
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• Apply parking space reductions to keep hoteling within the estimated maximum hours by county,
except for states that requested we not do that (CO, ME, NJ, NY).
Hoteling hours were adjusted down in counties for which there were more hoteling hours assigned to the
county than could be supported by the known parking spaces. To compute the adjustment, the hoteling
hours for the county were computed using the above method, and then reductions were applied to the
2016 hoteling hours based on known parking space availability so that there were not more hours
assigned to the county than the available parking spaces could support if they were full every hour of
every day of the year.
A dataset of truck stop parking space availability with the total number of parking spaces per county was
used in the computation of the adjustment factors. This same dataset is used to develop the spatial
surrogate for hoteling emissions. For the 2016vl platform, the parking space dataset included several
updates compared to 2016beta platform, based on information provided by some states (e.g., MD). Since
there are 8,784 hours in the year 2016; the maximum number of possible hoteling hours in a particular
county is equal to 8,784 * the number of parking spaces in that county. Hoteling hours for each county
were capped at that theoretical maximum value for 2016 in that county, with some exceptions as outlined
below. The parking space dataset used in the hoteling hour computations was unchanged in 2016v2 and
2016v3.
Because the truck stop parking space dataset may be incomplete in some areas, and trucks may sometimes
idle in areas other than designated spaces, it was assumed that every county has at least 12 parking spaces,
even if fewer parking spaces are found in the parking space dataset. Therefore, hoteling hours are never
reduced below 105,408 hours for the year in any county. If the unreduced hoteling hours were already
below that maximum, the hours were left unchanged; in other words, hoteling activity are never increased
as a result of this analysis.
A handful of high activity counties that would otherwise be subject to a large reduction were analyzed
individually to see if their parking space count seemed unreasonably low. In the following counties, the
parking space count and/or the reduction factor was manually adjusted:
• 17043 / DuPage IL (instead of reducing hoteling by 84%, applied no adjustment)
• 39061 / Hamilton OH (parking spot count increased to 20 instead of the minimum 12)
• 47147 / Robertson TN (parking spot count increased to 52 instead of just 26)
• 51015 / Augusta VA (parking space count increased to 48 instead of the minimum 12)
• 51059 / Fairfax VA (parking spot count increased to 20 instead of the minimum 12)
Some state-specific hoteling hours data and methods were applied in the 2016 platforms:
• Georgia and New Jersey submitted hoteling activity for the 2016vl platform, which was carried
through into the 2016v2 and v3 platforms along with incorporating an updated APU factor for
2016 based on MOVES3. For these states, the EPA default hoteling hours were replaced with their
state data. New Jersey provided their hoteling activity in a series of HotellingHours MOVES-
formatted tables, which include separate activity for weekdays and weekends and for each month
40
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and which have units of hours-per-week. These data were converted to annual totals by county so
they could be used.
• Alaska Department of Natural Resources staff requested that hoteling activity be set to zero in
several counties due to the nature of driving patterns in their region.
• There are no hoteling hours or other emissions from long-haul combination trucks in Hawaii,
Puerto Rico, or the Virgin Islands.
• The states of Colorado, Maine, New Jersey, and New York requested that no reductions be applied
to the hoteling activity based on parking space availability. For these states, we no reductions were
applied based on parking space availability and in the case of New Jersey, their submitted activity
data were unchanged.
Finally, the county total hoteling was split into separate values for extended idling (SCC 2202620153)
and APUs (SCC 2202620191). Compared to earlier versions of MOVES, APU percentages have been
lowered for MOVES3. A 5.19% APU split was used for the year 2016, meaning that APUs are used for
5.19% of the hoteling hours. This APU percentage was applied nationwide, including in states where
hoteling activity was submitted.
For 2016v2, hoteling was calculated as: 2016v2 HOTELING = 2017NEI HOTELING * 2016v2
VMT/2017NEI VMT. This is effectively consistent with applying the 0.007248 factor directly to the
2016v2 VMT. Then, for counties that provided 2017 hoteling but did not have vehicle type 62 restricted
VMT in 2016 - that is, counties that should have hoteling, but do not have any VMT to calculate it from -
the 2017 hoteling was backcast to 2016 using the FHWA-based county total 2017 to 2016 trend. Finally,
the annual parking-space-based caps for hoteling hours were applied as described above. The same caps
were used as for 2017NEI, except recalculated for a leap year (multiplied by 366/365).
For the 2016v3, road type distributions and/or hoteling were adjusted in states where there was hoteling in
every county in the state: FL, IL, MN, MO, SC, and WV. 2016v2 VMT in those six states was
redistributed by road type based on 2020 NEI road type distributions (by county/vehicle, with
county/HPMS filling in where a county/vehicle isn't available in 2020 NEI), and then hoteling was
recalculated based on the new VMT in those six states using the standard VMT/HOTELING factor and
parking space adjustments. Notably, this resulted in an overall increase in hoteling in Missouri, although
hoteling is now in fewer counties). Hoteling hours in other states were unchanged between 2016v2 and
2016v3.
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.
MOVES3 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
41
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default data from instrumented vehicles (or user-provided values) to estimate the number of starts for
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, MOVES3 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, LD inspection and
maintenance programs, and ambient temperatures.
2016 STARTS = 2016 VMT * (2017 STARTS/ 2017 VMT by county&SCC6)
For 2016v3, Georgia Environmental Protection Division provided new weekday activity for starts per day
for 20 counties. These new starts were used for the weekdays for those 20 counties, while MOVES
default starts/day were used for weekend days. Since annual activity data are required by the FF10
activity file format, the number of starts/day was multiplied by the number of weekdays and weekends in
the year to calculate the annual total starts for the 20 counties by county and source type. The starts for
light duty vehicle source types 21,31, and 32 were summed and then re-split between the 21, 31, and 32
sources types based on splits from EPA default activity data, so that 21/31/32 splits are from a consistent
data source nationwide. Since George only provided their activity data by county and vehicle type, the
2016v2 splits were used as the basis for distribution of the starts to fuel type and month. Starts outside of
Georgia were unchanged in 2016v3 from 2016v2 levels.
Off-network Idling Hours
Off-network idling hours (ONI) activity data were needed to support the computation of ONI emissions
with MOVES3. ONI is defined in MOVES as time during which a vehicle engine is running idle and the
vehicle is somewhere other than on a roadway, 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 when ONI activity occurs 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 a roadway, 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 were then multiplied by each county's total VMT (aggregated by source type,
fuel type, and month) to obtain hours of ONI activity. There were no changes to the ONI activity data
between 2016v2 and 2016v3.
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2.3.2 MOVES Emission Factor Table Development
For 2016v2, MOVES3 was run in emission rate mode to create emission factor tables using CB6
speciation for the years 2016, 2023, and 2026, for all representative counties and fuel months. For
2016v3, MOVES3 was rerun for combination trucks to correct an issue with the age distribution for that
source type in 2016v2. For 2016vl, MOVES2014b was run for all counties in Alaska, Hawaii, and Virgin
Islands, and for a single representative county in Puerto Rico and those emissions were retained in 2016v2
and 2016v3.
The county databases CDBs used to run MOVES3 to develop the emission factor tables were derived
from those used for the 2017 NEI and therefore included any updated data provided and accepted for the
2017 NEI process. The 2017 NEI development included an extensive review of the various tables
including speed distributions were performed. Where state speed profiles, speed distributions, and
temporal profiles data were not accepted from S/L submissions, those data were obtained from the CRC
A-100 study. Once the data tables for 2017 NEI were incorporated into the CDBs, a new set of
representative counties was developed as part of the EQUATES project for the years 2002-2017 and was
slightly expanded for 2016v2.5 Each county in the continental U.S. was classified according to its state,
altitude (high or low), fuel region, the presence of inspection and maintenance programs, the mean light-
duty age, and the fraction of ramps. A binning algorithm was executed to identify "like counties", and
then specific requests for representative county groups by states for the 2017 NEI were honored. The
result was 332 representative counties (up from 315 in 2016vl) as shown in Figure 2-3.
For more information on the development of the 2016 age distributions and representative counties and
the review of the input data, see the memoranda "Onroad 2016-23-26-32_Documentation_20210824
_clean.docx" and "CMAQ_Representative_Counties_Analysis_20201009_addFY23-26-32
Parameters.xlsx" (ERG, 2021). There are no changes to representative counties between 2016v2 and
2016v3, although there are some changes for analytic year representative county assignments as noted
above and discussed in more detail in Section 4.3.2.
Age distributions are a key input to MOVES in determining emission rates. The base year CDB age
distributions were shifted back one year from 2017 to 2016 in all counties so that the recession of 2008-
2009 is reflected for the 2008-2009 model years instead of being shifted by one year. The 2016 age
distributions were then grown to the analytic years of 2023 and 2026 everywhere except Alaska. Alaska
age distributions were not changed in the analytic years because the 2016 distributions did not show a
recession dip around model year 2009 and the vehicle populations looked sparse compared to other areas.
The age distributions for 2016v2 were updated based on vehicle registration data obtained from the CRC
A-l 15 project, subject to reductions for older vehicles determined according to CRC A-l 15 methods but
using additional age distribution data that became available as part of the 2017 NEI submitted input data.
One of the findings of CRC project A-l 15 was that IHS data contain higher vehicle populations than state
agency analyses of the same Department of Motor Vehicles data, and the discrepancies tend to increase
with increasing vehicle age (i.e., there are more older vehicles in the IHS data). The CRC project dealt
with the discrepancy by releasing datasets based on raw (unadjusted) information and adjusted sets of age
distributions, where the adjustments reflected the differences in population by model year of 2014 IHS
data and 2014 submitted data from a single state.
5 One new representative county in Kentucky was added: Kenton County (FIPS code 21117) due to a change for year 2018.
Four new representative counties in North Carolina were added for the 2016, 2023, and 2026 runs: 37019, 37159, 37077, and
37135 due to inspection and maintenance programs changing in future years. In addition, one Nebraska county (FIPS code
31115) was moved into a similar group (representative county 31047) due to a small vehicle population and similar mean light-
duty vehicle age.
43
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Figure 2-3. Representative Counties in 2016v2 and 2016v3
Rifmfncm counties arv ouOmrnd in black.
Number of counts *ssjyntd to +»ch
r* ftrine 0 county »ri Jsbofhd.
For the 2017 NEI, and for the 2016v2 platform, EPA repeated the CRC's assessment of IHS vs. state
vehicles by age, but with updated information from the 2017 NEI and for more states. The 2017 light-
duty vehicle (LDV) populations from the CRC A-l 15 project were compared by model year to the
populations submitted by state/local (S/L) agencies for the 2017 NEI. The comparisons by model year
were used to develop adjustment factors that remove older LDVs from the IHS dataset. Out of 31 S/L
agencies that provided age distribution and vehicle population data for the 2017 NEI, sixteen agencies
provided LDV population and age distributions with snapshot dates of January 2017, July 2017, or 2018.
The other fifteen agencies had either unknown or older (e.g., 2013) data pull dates, so were not compared
to the 2017 IITS data. The vehicle populations by model year were compared with IHS data for each of
the sixteen agencies for source type 21 (passenger cars) and for source type 31 plus 32 (light trucks)
together. Prior to finalizing the activity data, the S/L agency populations of passenger cars (source type
21) and light trucks (source types 31 and 21) were matched to IHS car and light-duty truck splits by
county so that vehicles of the same model and year were consistently classified into MOVES source types
throughout the country. The IHS population of vehicles were found to be higher than the pooled state
data by 6.5 percent for cars and 5.9 percent for light trucks.
To adjust for the additional vehicles in the IHS data, vehicle age distribution adjustment factors of one (1)
minus the fraction of vehicles to remove from IHS to equal the state data were applied, with two
exceptions: (1) the model year range 2007 to 2017 received no adjustment and (2) the model years 1987
and earlier received a capped adjustment that equals the adjustment to 1988. Table 2-17 below shows the
fraction of vehicles to keep by model year based on this analysis. The adjustments were applied to the
2016 IHS-based age distributions from CRC project A-l 15 prior to their use in 2016vl. In addition, the
44
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age distributions to ensure the "tail" of the distribution corresponding to age 30 years and older vehicles
did not exceed 20% of the fleet. After limiting the age distribution tails, the age distributions were
renormalized to ensure they summed to one (1). In addition, antique license plate vehicles were removed
based on the registration summary from IHS. Nationally, the prevalence of antique plates is only 0.8
percent, but is as high as 6 percent in some states (e.g., Mississippi).
Table 2-17. Fraction of IHS Vehicle Populations to Retain for 2016vl and 2017 NEI
Model Year
Cars
Light
pre-1989
0.675
0.769
1989
0.730
0.801
1990
0.732
0.839
1991
0.740
0.868
1992
0.742
0.867
1993
0.763
0.867
1994
0.787
0.842
1995
0.776
0.865
1996
0.790
0.881
1997
0.808
0.871
1998
0.819
0.870
1999
0.840
0.874
2000
0.838
0.896
2001
0.839
0.925
2002
0.864
0.921
2003
0.887
0.942
2004
0.926
0.953
2005
0.941
0.966
2006
1
0.987
2007-2017
1
1
In addition to removing the older and antique plate vehicles from the IHS data, 25 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 Buses and Refuse Trucks 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
50 percent of the population in the entire county is 0 and 1 years old. These sorts of young fleets can
happen if the headquarters of a leasing or rental company is the owner/entity of a relatively large number
of vehicles relative to the county-wide population. While the business owner of thousands of new
vehicles may reside in a single county, the vehicles likely operate in broader areas without being
registered where they drive. To avoid creating artificial low spots of LDV emissions in these outlier
counties, data for all counties with more than 35% new vehicles were excluded from the final set of
grouped age distributions that went into the CDBs.
The final year 2016 age distributions were then grouped using a population-weighted average of the
source type populations of each county in the representative county group. The resulting end-product was
age distributions for each of the 13 source types in each of the 332 representative counties for 2016v2.
The long-haul truck source types 53 (Single Unit) and 62 (Combination Unit) are based on a nationwide
45
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average due to the long-haul nature of their operation. There were no changes to the age distributions
from 2016v2 to v3 except for the recomputation of combination long haul truck age distributions based on
data available from the 2020 NEI process.
To create the emission factors for 2016v2, MOVES3 was run separately for each representative county
and fuel month and for each temperature bin needed for calendar year 2016. For 2016v3, MOVES was
rerun for combination long haul trucks (source type 62) to reflect the updated age distributions and as a
result the emissions for source type 62 changed nationwide. The CDBs used to run MOVES include the
state-specific control measures such as the California low emission vehicle (LEV) program and the fuels
were updated to represent calendar year 2016. In addition, the range of temperatures run along with the
average humidities used were specific to the year 2016. The MOVES results were post-processed into
CSV-formatted emission factor tables that can be read by SMOKE-MOVES.
2.3.3 Onroad California Inventory Development (onroad_ca)
The California Air Resources Board (CARB) provided their own onroad emissions inventories based on
their EMFAC2017 model. EMFAC2017 was run by CARB for the years 2016, 2023, 2028, and 2035.
These inventories each include separate totals for on-network and off-network emissions, but they do not
include NH3 or refueling. California emissions were run through SMOKE-MOVES as a separate sector
from the rest of the country. The California onroad sector is called "onroadcaadj". Changes from
2016vl include:
• CARB refueling was backcast from 2017NEI to 2016 using MOVES trends, and then SMOKE-
MOVES was adjusted to match the backcast refueling.
• California NH3 was set to MOVES state total NH3, distributed to county-SCC following the
distribution of carbon monoxide (CO) as a surrogate for activity.
• For source types other than 62 where CARB provided "idling" emissions, those emissions were
mapped to ONI. For source type 62, the CARB-provided "idling" was split between hoteling and
ONI. For all other vehicle types (where CARB did not provide "idling" - generally LD vehicles),
CARB running exhaust was split between RPD and ONI. Using the updated ONI activity has
some effect on distributions of CARB emissions and the non-CARB portion of the emissions (e.g.,
NH3).
While most source type emission factors used in California remain unchanged from 2016v2, for 2016v3
the newly available emission factors for source type 62 (combination long haul trucks) were used which
impacted the emissions of NH3 and refueling slightly.
2.4 2016 N on road Mobile sources (cmv, rail, nonroad)
The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions (nonroad),
locomotive (rail), and CMV emissions.
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
The 2016v3 CMV emissions are based on the emissions developed for the 2017 NEI and are the same as
those used in the 2016v2 platform, except that for 2016v3 the spatial allocation to county boundaries was
improved in response to comments. More specifically, in 2016v3, the CMV emissions were allocated to
each county by 1-hour AIS location rather than using the centroid of the grid cell to assign the county in
which the emissions occurred. The improvement to county boundary allocation impacts the assignment of
the emissions to some counties, such as in the New York-New Jersey area, but the total emissions by
46
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model grid cell are unchanged. 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 (C1C2) marine diesel engines typically range in size
from about 700 to 11,000 hp. These engines are used to provide propulsion power on many kinds of
vessels including tugboats, towboats, supply vessels, fishing vessels, and other commercial vessels in and
around ports. They are also used as stand-alone generators for auxiliary electrical power on many types of
vessels. Category 1 represents engines up to 7 liters per cylinder displacement. Category 2 includes
engines from 7 to 30 liters per cylinder.
The cmv_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 in the 2016 inventory are categorized as operating
either in-port or underway and as main and auxiliary engines are encoded using the SCCs listed in Table
2-18.
Table 2-18. SCCs for cmv clc2 sector
see
Tier 1 Description
Tier 2 Description
Tier 3 Description
Tier 4 Description
2280002101
C1/C2
Diesel
Port
Main
2280002102
C1/C2
Diesel
Port
Auxiliary
2280002201
C1/C2
Diesel
Underway
Main
2280002202
C1/C2
Diesel
Underway
Auxiliary
Category 1 and 2 CMV emissions were developed for the 2017 NEI,6 The 2017 NEI emissions were
developed based signals from Automated Identification System (AIS) transmitters. AIS is a tracking
system used by vessels to enhance navigation and avoid collision with other AIS transmitting vessels.
The USEPA Office of Transportation and Air Quality received AIS data from the U.S. Coast Guard
(USCG) in order to quantify all ship activity which occurred between January 1 and December 31, 2017.
The provided AIS data extends beyond 200 nautical miles from the U.S. coast (Figure 2-4). This
boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the North
American ECA, although some non-EC A activity are captured as well.
6 Category 1 and 2 Commercial Marine Vessel 2017 Emissions Inventory (ERG, 2019b).
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Figure 2-4. 2017NEI/2016 platform geographical extent (solid) and U.S. ECA (dashed)
The AIS data were compiled into five-minute intervals by the USCG, providing a reasonably refined
assessment of a vessel's movement. For example, using a five-minute average, a vessel traveling at 25
knots would be captured every two nautical miles that the vessel travels. For slower moving vessels, the
distance between transmissions would be less. The ability to track vessel movements through AIS data
and link them to attribute data, has allowed for the development of an inventory of very accurate emission
estimates with excellent resolution in time and space. These AIS data were used to define the locations of
individual vessel movements, estimate hours of operation, and quantify propulsion engine loads. The
compiled AIS data also included the vessel's International Marine Organization (IMO) number and
Maritime Mobile Service Identifier (MMSI); which allowed each vessel to be matched to their
characteristics obtained from the Clarksons ship registry (Clarksons, 2018).
USEPA used the engine bore and stroke data to calculate cylinder volume. Any vessel that had a
calculated cylinder volume greater than 30 liters was incorporated into the USEPA's new Category 3
Commercial Marine Vessel (C3CMV) model. The remaining records were assumed to represent Category
1 and 2 (C1C2) or non-ship activity. The C1C2 AIS data were quality assured including the removal of
duplicate messages, signals from pleasure craft, and signals that were not from CMV vessels (e.g., buoys,
helicopters, and vessels that are not self-propelled). Following this, there were 422 million records
remaining.
The emissions were calculated for each time interval between consecutive AIS messages for each vessel
and allocated to the location of the message following to the interval. Emissions were calculated
according to Equation 2-1.
Emissionsintervai= Time (hr)intervaix Power(kW) •'EF{g/k.Wh)'LLAF Equation 2-1
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Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and
emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF
represents the low load adjustment factor, a unitless factor which reflects increasing propulsive emissions
during low load operations. Time indicates the activity duration time between consecutive intervals.
Next, vessels were identified in order determine their vessel type, and thus their vessel group, power
rating, and engine tier information which are required for the emissions calculations. See the 2017 NEI
documentation for more details on this process. Following the identification, 108 different vessel types
were matched to the C1C2 vessels. Vessel attribute data was not available for all these vessel types, so the
vessel types were aggregated into 13 different vessel groups for which surrogate data were available as
shown in Table 2-19. 11,302 vessels were directly identified by their ship and cargo number. The
remaining group of miscellaneous ships represent 13 percent of the AIS vessels (excluding recreational
vessels) for which a specific vessel type could not be assigned.
Table 2-19. Vessel groups in the cmv_clc2 sector
Vessel Group
NEI Area Ship Count
Bulk Carrier
37
Commercial Fishing
1,147
Container Ship
7
Ferry Excursion
441
General Cargo
1,498
Government
1,338
Miscellaneous
1,475
Offshore support
1,149
Reefer
13
Ro
26
Tanker
100
Tug
3,994
Work Boat
77
Total in Inventory:
11.302
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 shown in Table 2-19. 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 2-18.
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
49
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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
Inventory.7 Hazardous air pollutants and ammonia were added to the inventory according to multiplicative
factors applied either to VOC or PM2.5.
For more information on the emission computations for 2017, see the supporting documentation for the
2017 NEI C1C2 CMV emissions. The emissions from the 2017 NEI were adjusted to represent 2016 in
the cmv_clc2 sector using factors derived from U.S. Army Corps of Engineers national vessel Entrance
and Clearance data8 by applying a factor of 0.98 to all pollutants (based on EIA fuel use data). For
consistency, the same methods were used for California, Canadian, and other non-U.S. emissions. The
2017 emissions were mapped to 2016 dates so that the activity occurred on the same day of the week in
the same sequential week of the year in both years. Emissions that occurred on a federal holiday in 2017
were mapped to the same holiday on the corresponding 2016 date. Individual vessels that released
emissions within the same grid cell for over 400 hours were flagged as hoteling. The emissions from the
hoteling vessels were scaled to the 400-hour cap.
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3)
The 2016v3 CMV emissions are based on the emissions developed for the 2017 NEI and are the same as
those used in the 2016v2 platform, except that for 2016v3 the spatial allocation to county boundaries was
improved in response to comments. The cmv_c3 inventory are the same as those in the 2016vl platform
and were developed in conjunction with the CMV inventory for the 2017 NEI. This sector contains large
engine CMV emissions. Category 3 (C3) marine diesel engines are those at or above 30 liters per
cylinder, typically these are the largest engines rated at 3,000 to 100,000 hp. C3 engines are typically used
for propulsion on ocean-going vessels including container ships, oil tankers, bulk carriers, and cruise
ships. Emissions control technologies for C3 CMV sources are limited due to the nature of the residual
7 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.
8 U.S. Army Corps of Engineers [USACE], Foreign Waterborne Transportation: Foreign Cargo Inbound and Outbound
Vessel Entrances and Clearances. US Army Corps of Engineers, 2018.
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fuel used by these vessels.9 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 2016vl inventory but are in separate files from the
emissions around the continental United States (CONUS). The cmv_c3 sources in the 2016v2 inventory
are categorized as operating either in-port or underway and are encoded using the SCCs listed in Table
2-20. and distinguish between diesel and residual fuel, in port areas versus underway, and main and
auxiliary engines. In addition to C3 sources in state and federal waters, the cmv_c3 sector includes
emissions in waters not covered by the NEI (FIPS = 98) and taken from the "ECA-IMO-based" C3 CMV
inventory.10 The ECA-IMO inventory is also used for allocating the FlPS-level emissions to geographic
locations for regions within the domain not covered by the AIS selection boxes as described in the next
section.
Table 2-20. SCCs for cmv c3 sector
sec
Tier 1 Description
Tier 2 Description
Tier 3 Description
Tier 4 Deseriplinn
228UUU21U3
C3
Diesel
Port
Main
2280002104
C3
Diesel
Port
Auxiliary
2280002203
C3
Diesel
Underway
Main
2280002204
C3
Diesel
Underway
Auxiliary
2280003103
C3
Residual
Port
Main
2280003104
C3
Residual
Port
Auxiliary
2280003203
C3
Residual
Underway
Main
2280003204
C3
Residual
Underway
Auxiliary
Prior to creation of the 2017 NEI, the EPA received Automated Identification System (AIS) data from
United States Coast Guard (USCG) to quantify all ship activity which occurred between January 1 and
December 31, 2017. The International Maritime Organization's (IMO's) International Convention for the
Safety of Life at Sea (SOLAS) requires AIS to be fitted aboard all international voyaging ships with gross
tonnage of 300 or more, and all passenger ships regardless of size.11 In addition, the USCG has mandated
that all commercial marine vessels continuously transmit AIS signals while transiting U.S. navigable
waters. As the vast majority of C3 vessels meet these requirements, any omitted from the inventory due to
lack of AIS adoption are deemed to have a negligible impact on national C3 emissions estimates. The
activity described by this inventory reflects ship operations within 200 nautical miles of the official U.S.
9 https://www.epa.gov/regulations-emissions-vehicles-and-engines/regulations-emissions-marine-vessels.
10 https://www.epa.gOv/sites/production/files/2017-08/documents/2014v7.0 2014 emismod tsdvl.pdf.
11 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.
51
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baseline. This boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the
North American ECA, although some non-ECA activity is captured as well (Figure 2-4).
The 2017 NEI data were computed based on the AIS data from the USCG for the year of 2017. The AIS
data were coupled with ship registry data that contained engine parameters, vessel power parameters, and
other factors such as tonnage and year of manufacture which helped to separate the C3 vessels from the
C1C2 vessels. Where specific ship parameters were not available, they were gap-filled. The types of
vessels that remain in the C3 data set include bulk carrier, chemical tanker, liquified gas tanker, oil tanker,
other tanker, container ship, cruise, ferry, general cargo, fishing, refrigerated vessel, roll-on/roll-off, tug,
and yacht.
Prior to use, the AIS data were reviewed - data deemed to be erroneous were removed, and data found to
be at intervals greater than 5 minutes were interpolated to ensure that each ship had data every five
minutes. The five-minute average data provide a reasonably refined assessment of a vessel's movement.
For example, using a five-minute average, a vessel traveling at 25 knots would be captured every two
nautical miles that the vessel travels. For slower moving vessels, the distance between transmissions
would be less.
The emissions were calculated for each C3 vessel in the dataset for each 5-minute time range and
allocated to the location of the message following to the interval. Emissions were calculated according to
Equation 2-2.
Emissionsintervai = Time (hr)intervaix Power(kW)xEF(g/kWh)xLLAF 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,12 but since the data were already in the form of point sources
at the center of each grid cell, and they were already hourly, no other processing was needed within
SMOKE. SMOKE requires an annual inventory file to go along with the hourly data, so those files were
also generated for each year.
12 Ammonia (NH3) was also added by SMOKE in the speciation step.
52
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On January 1st, 2015, the ECA initiated a fuel sulfur standard which regulated large marine vessels to use
fuel with 1,000 ppm sulfur or less. These standards are reflected in the cmv_c3 inventories.
There were some areas needed for modeling that the AIS request boxes did not cover (see Figure 2-4).
These include a portion of the St. Lawrence Seaway transit to the Great Lakes, a small portion of the
Pacific Ocean far offshore of Washington State, portions of the southern Pacific Ocean around off the
coast of Mexico, and the southern portion of the Gulf of Mexico that is within the 36-km domain used for
air quality modeling. In addition, a determination had to be made regarding whether to use the existing
Canadian CMV inventory or the more detailed AlS-based inventory. The AlS-based inventory was used
in the areas for which data were available, and the areas not covered were gap-filled with inventory data
from the 2016beta platform, which included data from ECCC and the 2011 ECA-IMO C3 inventory.
For the gap-filled areas not covered by AIS selected data areas or the ECCC inventory, the 2016 nonpoint
C3 inventory provided by ECCC was converted to a point inventory to support plume rise calculations for
C3 vessels. The nonpoint emissions were allocated to point sources using a multi-step allocation process
because not all of the inventory components had a complete set of county-SCC combinations. In the first
step, the county-SCC sources from the nonpoint file were matched to the county-SCC points in the 2011
ECA-IMO C3 inventory. The ECA-IMO inventory contains multiple point locations for each county-
SCC. The nonpoint emissions were allocated to those points using the PM2.5 emissions at each point as a
weighting factor.
For cmv_c3 underway emissions without a matching FIPS in the ECA-IMO inventory were allocated
using the 12 km 2014 offshore shipping activity spatial surrogate (surrogate code 806). Each county with
underway emissions in the area inventory was allocated to the centroids of the cells associated with the
respective county in the surrogate. The emissions were allocated using the weighting factors in the
surrogate.
The resulting point emissions centered on each grid cell were converted to an annual point 2010 flat file
format (FF10). A set of standard stack parameters were assigned to each release point in the cmv_c3
inventory. The assigned stack height was 65.62 ft, the stack diameter was 2.625 ft, the stack temperature
was 539.6 °F, and the velocity was 82.02 ft/s. Emissions were computed for each grid cell needed for
modeling.
Adjustment of the 2017 NEI CMV C3 to 2016
Because the NEI emissions data were for 2017, an analysis was performed of 2016 versus 2017 entrance
and clearance data (ERG, 2019c). Annual, monthly, and daily level data were reviewed. Annual ratios of
entrance and clearance activity were developed for each ship type as shown in Table 2-21. For vessel
types with low populations (C3 Yacht, tug, barge, and fishing vessels), an annual ratio of 0.98 was
applied. The 2017 emissions were mapped to 2016 dates so that the activity occurred on the same day of
the week in the same sequential week of the year in both years. Emissions that occurred on a federal
holiday in 2017 were mapped to the same holiday on the corresponding 2016 date. Individual vessels that
53
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released emissions within the same grid cell for over 400 hours were flagged as hoteling. The emissions
from the hoteling vessels were scaled to the 400-hour cap.
Table 2-21. 2017 to 2016 projection factors for C3 CMV
Ship Type
Annual Ratio"
Barge
1.551
Bulk Carrier
1.067
Chemical Tanker
1.031
Container Ship
1.0345
Cruise
1.008
Ferry Ro Pax
1.429
General Cargo
0.888
Liquified Gas Tanker
1.192
Miscellaneous Fishing
0.932
Miscellaneous Other
1.015
Offshore
0.860
Oil Tanker
1.101
Other Tanker
1.037
Reefer
0.868
Ro Ro
1.007
Service Tug
1.074
a The above ratios were applied to the 2017 emission values to estimate 2016 values;
thus ratios > 1 mean that emissions were larger in 2016
The cmv_c3 projection factors were pollutant-specific and region-specific. Most states are mapped to a
single region with a few exceptions. Pennsylvania and New York were split between the East Coast and
Great Lakes, Florida was split between the Gulf Coast and East Coast, and Alaska was split between
Alaska East and Alaska West. The non-federal factors listed in this table were applied to sources outside
of U.S. federal waters (FIPS 98). Volatile Organic Compound (VOC) Hazardous Air Pollutant (HAP)
emissions were projected using the VOC factors. NH3 emissions were computed by multiplying PM2.5
by 0.019247.
2.4.3 Railway Locomotives (rail)
There were no changes to the rail sector emissions inventories between 2016vl and 2016v2 aside from
updating emissions for seven rail yards in Georgia. There were no changes between the 2016v2 and
2016v3 rail emissions. The rail sector includes all locomotives in the NEI nonpoint data category. The rail
sector SCCs are shown in Table 2-22. This sector excludes railway maintenance activities. Railway
maintenance emissions are included in the nonroad sector. The point source yard locomotives are
included in the ptnonipm sector. In 2014NEIv2, rail yard locomotive emissions were present in both the
nonpoint (rail sector) and point (ptnonipm sector) inventories. For the 2016vl and 2016v2 platforms, rail
yard locomotive emissions are only in the ptnonipm sector of the point inventory. Therefore, SCC
2285002010 is not present in the rail sector, except in three California counties because the California Air
54
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Resources Board (CARB) submitted rail emissions, including rail yards, for 2016vl platform. In three
counties, CARB's rail yard emissions could not be mapped to point source rail yards, and so those
counties' emissions were included in the rail sector.
Table 2-22. 2016vl SCCs for the Rail Sector
see
Sector
Description: Mobile Sources prefix for all
2285002006
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
2285002007
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
2285002008
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains
(Amtrak)
2285002009
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
2285002010
rail
Railroad Equipment; Diesel; Yard Locomotives (nonpoint)
28500201
rail
Railroad Equipment; Diesel; Yard Locomotives (point)
Class I Line-haul Methodology
In 2008 air quality planners in the eastern US formed the Eastern Technical Advisory Committee
(ERTAC) for solving persistent emissions inventory issues. This work is the fourth inventory created by
the ERTAC rail group. For the 2016 inventory, the Class I railroads granted ERTAC Rail permission to
use the confidential link-level line-haul activity GIS data layer maintained by the Federal Railroad
Administration (FRA). In addition, the Association of American Railroads (AAR) provided national
emission tier fleet mix information. This allowed ERTAC Rail to calculate weighted emission factors for
each pollutant based on the percentage of the Class I line-haul locomotives in each USEPA Tier level
category. These two datasets, along with 2016 Class I line-haul fuel use data reported to the Surface
Transportation Board (Table 2-23), were used to create a link-level Class I emissions inventory, based on
a methodology recommended by Sierra Research. Rail Fuel Consumption Index (RFCI) is a measure of
fuel use per ton mile of freight. This link-level inventory is nationwide in extent, but it can be aggregated
at either the state or county level.
Table 2-23. Class I Railroad Reported Locomotive Fuel Use Statistics for 2016
Class I Railroads
2016 R-l Reported Locomotive
Fuel Use (gal/year)
RFCI
(ton-miles/gal)
Adjusted
RFCI
(ton-miles/gal)
Line-Haul*
Switcher
BNSF
1,243,366,255
40,279,454
972
904
Canadian National
102,019,995
6,570,898
1,164
1,081
Canadian Pacific
56,163,697
1,311,135
1,123
1,445
CSX Transportation
404,147,932
39,364,896
1,072
1,044
Kansas City
Southern
60,634,689
3,211,538
989
995
Norfolk Southern
437,110,632
28,595,955
920
906
Union Pacific
900,151,933
85,057,080
1,042
1,095
Totals:
3,203,595,133
204,390,956
1,006
993
* Includes work trains; Adjusted RFCI values calculated from FRA gross ton-mile data. RFCI total is ton-mile weighted mean.
55
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Annual default emission factors for locomotives based on operating patterns ("duty cycles") and the
estimated nationwide fleet mixes for both switcher and line-haul locomotives are available. However,
Tier level fleet mixes vary significantly between the Class I and Class II/III railroads. As can be seen in
Figure 2-5 and Figure 2-6, Class I railroad activity is highly regionalized in nature and is subject to
variations in terrain across the country which can have a significant impact on fuel efficiency and overall
fuel consumption.
Figure 2-5. 2016 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT)
Traffic Density
0.02 - 4.99 MGT
5.00 -9.99 MGT
10.00 -19.99 MGT
20.00 - 39.99 MGT
40.00 - 59.99 MGT
60.00 - 99.99 MGT
56
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Figure 2-6. Class I Railroads in the United States5
For the 2016 inventory, the AAR provided a national line-haul Tier fleet mix profile representing the
entire Class I locomotive fleet. A locomotive's Tier level determines its allowable emission rates based
on the year when it was built and/or re-manufactured. The national fleet mix data was then used to
calculate weighted average in-use emissions factors for the line-haul locomotives operated by the Class I
railroads as shown in Table 2-24.
Table 2-24. 2016 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal)
AAR
Tier Level
Fleet Mix
Ratio
PMio
HC
NOx
CO
Uncontrolled (pre-1973)
0.047494
6.656
9.984
270.4
26.624
Tier 0 (1973-2001)
0.188077
6.656
9.984
178.88
26.624
Tier 0+ (Tier 0 rebuilds)
0.141662
4.16
6.24
149.76
26.624
Tier 1 (2002-2004)
0.029376
6.656
9.776
139.36
26.624
Tier 1 + (Tier 1 rebuilds)
0.223147
4.16
6.032
139.36
26.624
Tier 2 (2005-2011)
0.124536
3.744
5.408
102.96
26.624
Tier 2+ (Tier 2 rebuilds)
0.093607
1.664
2.704
102.96
26.624
Tier 3 (2012-2014)
0.123113
1.664
2.704
102.96
26.624
Tier 4 (2015 and later)
0.028988
0.312
0.832
20.8
26.624
2016 Weighted EF's
1.000000
4.117
6.153
138.631
26.624
Based on values in EPA Technical Highlights: Emission Factors for Locomotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April
2009.
Weighted Emission Factors (EF) per pollutant for each gallon of fuel used (grams/gal or lbs/gal) were
calculated for the US Class I locomotive fleet based on the percentage of line-haul locomotives certified
at each regulated Tier level (Equation 2-3).
57
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10
EFt = * fT) Equation 2-3
7=1
where:
EFi = Weighted Emission Factor for pollutant i for Class I locomotive fleet (g/gal).
EFiT = Emission Factor for pollutant i for locomotives in Tier T (g/gal).
fr = Percentage of the Class I locomotive fleet in Tier T expressed as a ratio.
While actual engine emissions will vary within Tier level categories, the approach described above likely
provides reasonable emission estimates, as locomotive diesel engines are certified to meet the emission
standards for each Tier. It should be noted that actual emission rates may increase over time due to
engine wear and degradation of the emissions control systems. In addition, locomotives may be operated
in a manner that differs significantly from the conditions used to derive line-haul duty-cycle estimates.
Emission factors for other pollutants are not Tier-specific because these pollutants are not directly
regulated by USEPA's locomotive emission standards. PM2.5 was assumed to be 97% of PM10, the ratio
of volatile organic carbon (VOC) to (hydrocarbon) HC was assumed to be 1.053, and the emission factors
used for sulfur dioxide (SO2) and ammonia (NH3) were 0.0939 g/gal and 83.3 mg/gal, respectively. The
2016 SO2 emission factor is based on the nationwide adoption of 15 ppm ultra-low sulfur diesel (ULSD)
fuel by the rail industry.
The remaining steps to compute the Class I rail emissions involved calculating Class I railroad-specific
rail fuel consumption index values and calculating emissions per link. The final link-level emissions for
each pollutant were then aggregated by state/county FIPS code and then converted into an FF10 file
format for input to SMOKE. More detail on these steps is described in the specification sheet for the
2016vl rail sector emissions.
Rail yard Methodology
Rail yard emissions were computed based on fuel use and/or yard switcher locomotive counts for the class
I rail companies for all of the rail yards on their systems. Three railroads provided complete rail yard
datasets: BNSF, UP, and KCS. CSX provided switcher counts for its 14 largest rail yards. This reported
activity data was matched to existing yard locations and data stored in USEPA's Emissions Inventory
System (EIS) database. All existing EIS yards that had activity data assigned for prior years, but no
reported activity data for 2016 were zeroed out. New yard data records were generated for reported
locations that were not found in EIS. Special care was made to ensure that the new yards added to EIS
did not duplicate existing data records. Data for non-Class I yards was carried forward from the 2014
NEI. Georgia provided updates on seven rail yards that were incorporated into 2016v2.
Since the railroads only supplied switcher counts, average fuel use per switcher values was calculated for
each railroad. This was done by dividing each company's 2016 R-l yard fuel use total by the number of
switchers reported for each railroad. These values were then used to allocate fuel use to each yard based
on the number of switchers reported for that location. Table 2-25 summarizes the 2016 yard fuel use and
switcher data for each Class I railroad. The emission factors used for rail yard switcher engines are
shown in Table 2-26.
58
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Table 2-25. Surface Transportation Board R-l Fuel Use Data - 2016
Railroad
2016 R-l Yard
Fuel Use (gal)
ERTAC calculated
Fuel Use (gal)
Identified
Switchers
ERTAC per Switcher Fuel
Use (gal)
BNSF
40,279,454
40,740,317
442
92,173
CSXT
39,364,896
43,054,795
455
94,626
CN
6,570,898
6,570,898
103
63,795
KCS
3,211,538
3,211,538
176
18,247
NS
28,595,955
28,658,528
458
62,573
CPRS
1,311,135
1,311,135
70
18,731
UP
85,057,080
85,057,080
1286
66,141
All Class I's
204,390,956
208,604,291
2,990
69,767
Table 2-26. 2016 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)4
Tier Level
AAR Fleet
Mix Ratio
PMio
HC
NOx
CO
Uncontrolled (pre-1973)
0.2601
6.688
15.352
264.48
27.816
Tier 0(1973-2001)
0.2361
6.688
15.352
191.52
27.816
Tier 0+ (Tier 0 rebuilds)
0.2599
3.496
8.664
161.12
27.816
Tier 1 (2002-2004)
0.0000
6.536
15.352
150.48
27.816
Tier 1+ (Tier 1 rebuilds)
0.0476
3.496
8.664
150.48
27.816
Tier 2 (2005-2011)
0.0233
2.888
7.752
110.96
27.816
Tier 2+ (Tier 2 rebuilds)
0.0464
1.672
3.952
110.96
27.816
Tier 3 (2012-2014)
0.1018
1.216
3.952
68.4
27.816
Tier 4 (2015 and later)
0.0247
0.228
1.216
15.2
27.816
2016 Weighted EF's
0.9999
4.668
11.078
178.1195
27.813
Based on values in EPA Technical Highlights: Emission Factors for Locomotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April
2009. AAR fleet mix ratios did not add up to 1.0000, which caused a small error for the CO weighted emission factor as shown above.
In addition to the Class I rail yards, Emission estimates were calculated for four large Class III railroad
hump yards which are among the largest classification facilities in the United States. These four yards are
located in Chicago (Belt Railway of Chicago-Clearing and Indiana Harbor Belt-Blue Island) and Metro-
East St. Louis (Alton & Southern-Gateway and Terminal Railroad Association of St. Louis-Madison).
Figure 2-7 shows the spatial distribution of active yards in the 2016vl and 2017 NEI inventories.
59
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Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States
FwfcraJ RaA-curJ AArmn»Vatah
Class II and III Methodology
There are approximately 560 Class II and 111 Railroads operating in the United States, most of which are
members of the American Short Line and Regional Railroad Association (ASLRRA). While there is a lot
of information about individual Class II and III railroads available online, a significant amount of effort
would be required to convert this data into a usable format for the creation of emission inventories. In
addition, the Class II and III rail sector has been in a constant state of flux ever since the railroad industry
was deregulated under the Staggers Act in 1980. Some states have conducted independent surveys of
their Class II and III railroads and produced emission estimates, but no national level emissions inventory
existed for this sector of the railroad industry prior to ERTAC Rail's work for the 2008 NEI.
Class II and III railroad activities account for nearly 4 percent of the total locomotive fuel use in the
combined ERTAC Rail emission inventories and for approximately 35 percent of the industry's national
freight rail track mileage. These railroads are widely dispersed across the country and often utilize older,
higher emitting locomotives than their Class I counterparts Class II and III railroads provide
transportation services to a wide range of industries. Individual railroads in this sector range from small
switching operations serving a single industrial plant to large regional railroads that operate hundreds of
miles of track. Figure 2-8 shows the distribution of Class II and III railroads and commuter railroads
across the country. This inventory will be useful for regional and local modeling, helps identify where
Class II and III railroads may need to be better characterized, and provides a strong foundation for the
60
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eventual development of a more accurate nationwide short line and regional railroad emissions inventory.
The data sources, calculations, and assumptions used to develop the Class II and III inventory are
described in the 2016vl rail specification sheet.
Figure 2-8. Class II and III Railroads in the United States5
Commuter Rail Methodology
Commuter rail emissions were calculated in the same way as the Class II and III railroads. The primary
difference is that the fuel use estimates were based on data collected by the Federal Transit
Administration (FTA) for the National Transit Database. 2016 fuel use was then estimated for each of the
commuter railroads shown in Table 2-27 by multiplying the fuel and lube cost total by 0.95, then dividing
the result by Metra's average diesel fuel cost of $1.93/gallon. These fuel use estimates were replaced
with reported fuel use statistics for MARC (Maryland), MBTA (Massachusetts), Metra (Illinois), and NJT
(New Jersey). The commuter railroads were separated from the Class II and III railroads so that the
appropriate SCC codes could be entered into the emissions calculation sheet
Table 2-27. Expenditures and fuel use for commuter rail
IRA
Code
System
Cities Served
Propulsion
Type
DOT Fuel &
Lube Costs
Reported/Estimated
Fuel Use
ACEX
Altamont Corridor
Express
San Jose / Stockton
Diesel
$889,828
437,998.24
CMRX
Capital MetroRail
Austin
Diesel
No data
n/a
DART
A-Train
Denton
Diesel
$0
0.00
DRTD
Denver RTD: A&B
Lines
Denver
Electric
$0
0.00
61
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IRA
Code
System
Cities Served
Propulsion
Type
DOT Fuel &
Lube Costs
Reported/Estimated
Fuel Use
JPBX
Caltrain
San Francisco / San Jose
Diesel
$7,002,612
3,446,881.55
LI
MTA Long Island Rail
Road
New York
Electric and
Diesel
$13,072,158
6,434,481.92
MARC
MARC Train
Baltimore / Washington D.C.
Diesel and
Electric
$4,648,060
4.235.297.57
MBTA
MBTA Commuter Rail
Boston / Worcester / Providence
Diesel
$37,653,001
12.142.826.00
MNCW
MTA Metro-North
Railroad
New York / Yonkers / Stamford
Electric and
Diesel
$13,714,839
6,750,827.49
NICD
NICTD South Shore
Line
Chicago / South Bend
Electric
$181,264
0.00
NIRC
Metra
Chicago
Diesel and
Electric
$52,460,705
25.757.673.57
NJT
New Jersey Transit
New
York / Newark / Trenton / Philadelphia
Electric and
Diesel
$38,400,031
16.991.164.00
NMRX
New Mexico Rail
Runner
Albuquerque / Santa Fe
Diesel
$1,597,302
786,236.74
CFCR
SunRail
Orlando
Diesel
$856,202
421,446.58
MNRX
Northstar Line
Minneapolis
Diesel
$708,855
348,918.26
Not
Coded
SMART
San Rafael-Santa Rosa (Opened 2017)
Diesel
n/a
0.00
NRTX
Music City Star
Nashville
Diesel
$456,099
224,504.69
SCAX
Metrolink
Los Angeles / San Bernardino
Diesel
$19,245,255
9,473,052.98
SDNR
NCTD Coaster
San Diego / Oceanside
Diesel
$1,489,990
733,414.77
SDRX
Sounder Commuter
Rail
Seattle / Tacoma
Diesel
$1,868,019
919,491.22
SEPA
SEPTA Regional Rail
Philadelphia
Electric
$483,965
0.00
SLE
Shore Line East
New Haven
Diesel
No data
n/a
TCCX
Tri-Rail
Miami / Fort Lauderdale / West Palm
Beach
Diesel
$5,166,685
2,543,186.92
TREX
Trinity Railway
Express
Dallas / Fort Worth
Diesel
No data
n/a
UTF
UTA FrontRunner
Salt Lake City / Provo
Diesel
$4,044,265
1,990,700.39
VREX
Virginia Railway
Express
Washington, D.C.
Diesel
$3,125,912
1,538,661.35
WSTX
Westside Express
Service
Beaverton
Diesel
No data
n/a
*Reported fuel use values were used for MARC, MBTA, Metra, and New Jersey Transit.
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Intercity Passenger Methodology (Amtrak)
2016 marked the first time that a nationwide intercity passenger rail emissions inventory was created for
Amtrak. The calculation methodology mimics that used for the Class II and III and commuter railroads
with a few modifications. Since link-level activity data for Amtrak was unavailable, the default
assumption was made to evenly distribute Amtrak's 2016 reported fuel use across all of it diesel-powered
route-miles shown in Figure 2-9. Participating states were instructed that they could alter the fuel use
distribution within their jurisdictions by analyzing Amtrak's 2016 national timetable and calculating
passenger train-miles for each affected route. Illinois and Connecticut chose to do this and were able to
derive activity-based fuel use numbers for their states based on Amtrak's 2016 reported average fuel use
of 2.2 gallons per passenger train-mile. In addition, Connecticut provided supplemental data for selected
counties in Massachusetts, New Hampshire, and Vermont. Amtrak also submitted company-specific fleet
mix information and company-specific weighted emission factors were derived. Amtrak's emission rates
were 25% lower than the default Class II and III and commuter railroad emission rate. Details on the
computation of the Amtrak emissions are available in the rail specification sheet.
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains
Sort* FaMMndtamtoton < J«»l*
Other Data Sources
The California Air Resources Board (CARB) provided rail inventories for inclusion in the 2016vl
platform. CARB's rail inventories were used in California, in place of the national dataset described
above. For rail yards, the national point source rail yard dataset was used to allocate CARB-submitted rail
yard emissions to point sources where possible. That is, for each California county with at least one rail
yard in the national dataset, the emissions in the national rail yard dataset were adjusted so that county
total rail yard emissions matched the CARB dataset. In other words, 2016vl and 2016v2 platforms
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include county total rail yard emissions from CARB, but the locations of rail yards are based on the
national methodology. There are three counties with CARB-submitted rail yard emissions, but no rail
yard locations in the national dataset; for those counties, the rail yard emissions were included in the rail
sector using SCC 2285002010.
North Carolina separately provided passenger train (SCC 2285002008) emissions for use in the platform.
We used NC's passenger train emissions instead of the corresponding emissions from the Lake Michigan
Air Directors Consortium (LADCO) dataset.
None of these rail inventory sources included HAPs. For VOC speciation, the EPA preferred augmenting
the inventory with HAPs and using those HAPs for integration, rather than running the sector as a no-
integrate sector. So, Naphthalene, Benzene, Acetaldehyde, Formaldehyde, and Methanol (NBAFM)
emissions were added to all rail inventories, including the California inventory, using the same
augmentation factors as are used to augment HAPs in the NEI.
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 were computed by running the MOVES3,13 which incorporates
the NONROAD model. MOVES3 and its predecessor MOVES2014b incorporated updated nonroad
engine population growth rates, nonroad Tier 4 engine emission rates, and sulfur levels of nonroad diesel
fuels. MOVES3 provides a complete set of HAPs and incorporates updated nonroad emission factors for
HAPs. MOVES3 was used for all states other than California and Texas, which developed their own
emissions using their own tools. VOC and PM speciation profile assignments are determined by MOVES
and applied by SMOKE. The fuels data in MOVES3 for nonroad vehicles is slightly updated from the
MOVES2014b fuels for nonroad vehicles. 2016v2 and 2016v3 nonroad emissions are unchanged from
2016vl.
MOVES3 provides estimates of NONHAPTOG along with the speciation profile code for the
NONHAPTOG emission source. This was accomplished by using NHTOG#### as the pollutant code in
the Flat File 2010 (FF10) inventory file that can be read into SMOKE, where #### is a speciation profile
code. One of the speciation profile codes is '95335a' (lowercase 'a'); the corresponding inventory
pollutant is NONHAPTOG95335A (uppercase 'A') because SMOKE does not support inventory
pollutant names with lowercase letters. Since speciation profiles are applied by SCC and pollutant, no
changes to SMOKE were needed to use the inventory file with this profile information. This approach
was not used for California or Texas, because the datasets in those states included VOC.
MOVES3, also provides estimates of PM2.5 by speciation profile code for the PM2.5 emission source,
using PM25_#### as the pollutant code in the FF10 inventory file, where #### is a speciation profile
code. To facilitate calculation of coarse particulate matter (PMC) within SMOKE, and to help create
emissions summaries, an additional pollutant representing total PM2.5 called PM25TOTAL was added to
the inventory. As with VOC / TOG, this approach is not used for California or Texas.
13 https://www.epa.gov/moves.
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M0VES3 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
nonroad specification sheet.
• To reduce the size of the inventory, HAPs that are not needed for air quality modeling, such as
dioxins and furans, were removed from the inventory.
• To reduce the size of the inventory further, all emissions for sources (identified by county/SCC)
for which total CAP emissions are less than 1*10"10 were removed from the inventory. The
MOVES model attributes a very tiny amount of emissions to sources that are actually zero, for
example, snowmobile emissions in Florida. Removing these sources from the inventory reduces
the total size of the inventory by about 7%.
• Gas and particulate components of HAPs that come out of MOVES separately, such as
naphthalene, were combined.
• VOC was renamed VOC INV so that SMOKE does not speciate both VOC and NONHAPTOG,
which would result in a double count.
• PM25TOTAL, referenced above, was also created at this stage of the process.
• California and Texas emissions from MOVES were deleted and replaced with the CARB- and
TCEQ-supplied emissions, respectively.
Emissions for airport ground support vehicles (SCCs ending in -8005), and oil field equipment (SCCs
ending in -10010), were removed from the mobile nonroad inventory, to prevent a double count with the
ptnonipm and np oilgas sectors, respectively.
National Updates: Agricultural and Construction Equipment Allocation
The methodology for developing agricultural equipment allocation data for the 2016vl platform was
developed by the North Carolina Department of Environmental Quality (NCDEQ). EPA updated the
construction equipment allocation data used in MOVES for the 2016vl platform and the same updated
data were used in the 2016v2 and 2016v3 platforms.
NCDEQ compiled regional and state-level agricultural sector fuel expenditure data for 2016 from the US
Department of Agriculture, National Agricultural Statistics Service (NASS), August 2018 publication,
"Farm Production Expenditures 2017 Summary."14 This resource provides expenditures for each of 5
major regions that cover the Continental U.S., as well as state-level data for 15 major farm producing
states. Because of the limited coverage of the NASS source relative to that in MOVES, it was necessary to
identify a means for estimating the 2016 agricultural sector allocation data for the following States and
Territories from a different source: Alaska, Hawaii, Puerto Rico, and U.S. Virgin Islands. The approach
for these areas is described below.
14 Accessed from http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1066. November 2018.
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For the Continental U.S., NCDEQ first allocated the remainder of the regional fuel expenditures to states
in each region for which state-level data are not reported. For this allocation, NCDEQ relied on 2012 fuel
expenditure data from NASS' 2012 Census of Agriculture (note that 2017 data were not yet available at
the time of this effort).15 The next step to developing county-level allocation data for agricultural
equipment was to multiply the state-level fuel expenditure estimates by county-level allocation ratios.
These allocation ratios were computed from county-level fuel expenditure data from the NASS' 2012
Census of Agriculture. There were 17 counties for which fuel expenditure data were withheld in the
Census of Agriculture. For these counties, NCDEQ allocated the fuel expenditures that were not
accounted for in the applicable state via a surrogate indicator of fuel expenditures. For most states, the
2012 Census of Agriculture's total machinery asset value was the surrogate indicator used to perform the
allocation. This indicator was found to have the strongest correlation to agricultural sector fuel
expenditures based on analysis of 2012 state-level Census of Agriculture values for variables analyzed
(correlation coefficient of 0.87).16 Because the analyzed surrogate variables were not available for the two
counties in New York without fuel expenditure data, farm sales data from the 2012 Census of Agriculture
were used in the allocation procedure for these counties.
For Alaska and Hawaii, NCDEQ estimated 2016 state-level fuel production expenditures by first applying
the national change in fuel expenditures between 2012 and 2016 from NASS' "Farm Production
Expenditures" summary publications to 2012 state expenditure data from the 2012 Census of Agriculture.
Next, NCDEQ applied an adjustment factor to account for the relationship between national 2012 fuel
expenditures as reported by the Census of Agriculture and those reported in the Farm Production
Expenditures Summary. Hawaii's state-level fuel expenditures were allocated to counties using the same
approach as the states in the Continental U.S. (i.e., county-level fuel expenditure data from the NASS'
2012 Census of Agriculture). Alaska's fuel expenditures total was allocated to counties using a different
approach because the 2012 Census of Agriculture reports fuel expenditures data for a different list of
counties than the one included in MOVES. To ensure consistency with MOVES, NCDEQ allocated
Alaska's fuel expenditures based on the current allocation data in MOVES, which reflect 2002 harvested
acreage data from the Census of Agriculture.
Because NCDEQ did not identify any source of fuel expenditures data for Puerto Rico or the U.S. Virgin
Islands, the county allocation percentages that are represented by the 2002 MOVES allocation data were
used for these territories.17
For the construction sector, by default MOVES2014b used estimates of 2003 total dollar value of
construction by county to allocate national construction equipment populations to the state and local
levels.18 However, the 2016 Nonroad Collaborative Work Group sought to update the surrogate data used
to geographically allocate construction equipment with a more recent data source thought to be more
reflective of emissions-generating construction equipment activity at the county level: acres disturbed by
residential, non-residential, and road construction activity.
The nonpoint sector of the National Emissions Inventory (NEI) includes estimates of construction dust
(PM2.5), for which acreage disturbed by residential, non-residential, and road construction activity is a
15 Accessed from https://www.nass.usda.gov/Publications/AgCensus/2012/. November 2018.
16 Other variables analyzed were inventory of tractors and inventory of trucks.
17 For reference, these allocations were 0.0639 percent for Puerto Rico and 0.0002 percent for the U.S. Virgin Islands.
18 https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P1004LDX.pdf.
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function.19 The 2017 NEI Technical Support Document (EPA, 202 Id) includes a description of the
methods used to estimate acreage disturbed at the county level by residential, non-residential, and road
construction activity, for the 50 states.
Acreage disturbed by residential, non-residential, and road construction were summed together to arrive at
a single value of acreage disturbed by construction activities at the county level. County-level acreage
disturbed were then summed together to arrive at acreage disturbed at the state level. State totals were
then summed to arrive at a national total of acreage disturbed by construction activities.
Puerto Rico and the U.S. Virgin Islands are not included in the construction equipment geographic
allocation update, so their relative share of the national population of construction equipment remains the
same as MOVES2014b defaults.
For both the agricultural and construction equipment sectors, the surrogatequant and surrogateyearlD
fields in the model's nrstatesurrogate table, which allocates equipment from the state- to the county-level,
were populated with the county-level surrogates described above (fuel expenditures in 2016 for
agricultural equipment; acreage disturbed by construction activity in 2014 for construction equipment). In
addition, the nrbaseyearequippopulation table, which apportions the model's national equipment
populations to the state level, was adjusted so that each state's share of the MOVES base-year national
populations of agricultural and construction equipment is proportional to each state's share of national
acreage disturbed by construction activity (construction equipment) and agricultural fuel expenditures
(agricultural equipment). Additionally, the model"s nrsurrogate table, which defines the surrogate data
used in the nrstatesurrogate table, was updated to reflect the 2016vl changes to the agricultural and
construction equipment sectors.
Updated nrsurrogate, nrstatesurrogate, and nrbaseyearequippopulation tables, along with instructions for
utilizing these tables in MOVES runs, are available for download from EPA's ftp site at
https://gaftp.epa.gov/air/emismod/2016/vl/reports/nonroad/.
State-Supplied Nonroad Data
As shown Table 2-28., several state and local agencies provided nonroad inputs for use in the 2016vl
platform that were carried forward into the 2016v2 and 2016v3 platforms. Additionally, per the table
footnotes, EPA reviewed data submitted by state and local agencies for the 2014 and 2017 National
Emissions Inventories and utilized that information where appropriate (data specific to calendar years
2014 and 2017 were not used in 2016vl). The nrfuelsupply table from MOVES3 was used in 2016v2 and
2016v3 and is therefore not shown in this table.
19 https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventorv-nei-data.
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Table 2-28. Submitted nonroad input tables by agency
stateid
State or
County(ies) in
the Agency
nrbaseyearequippopulation
(source populations)
nrdayallocation
(allocation to day type)
nrgrowthindex
(population growth)
nrhourallocation
(allocation to diurnal pattern)
nrmonthallocation
(seasonal allocation)
nrsourceusetype
(yearly activity)
nrstatesurrogate
(allocations to counties)
countyyear
(Stage II information)
nrequipmenttype
(surrogate selection)
nrsurrogate
(surrogate identification)
4
ARIZONA -
Maricopa Co.
A
D
D
D
D
D
9
CONNECTIC
A
13
GEORGIA
D
16
IDAHO
C
17
ILLINOIS
E
18
INDIANA
C
E
19
IOWA
C
E
26
MICHIGAN
C
E
27
MINNESOTA
C
E
29
MISSOURI
E
36
NEW YORK
D
D
D
D
D
D
D
39
OHIO
C
E
49
UTAH
B
D
D
D
F
53
WASHINGT
D
D
D
55
WISCONSIN
E
A Submitted data with modification: updated the year ID to 2016.
"D
Submitted data with modification: deleted records that were not snowmobile source types 1002-1010.
c NEI 2014v2 data used for 2016vl platform.
D Submitted data.
P
Spreadsheet "ladco_nei2017_nrmonthallocation.xlsx."
P
Submitted data with modification: deleted records that were not the snowmobile surrogate ID 14.
Emissions Inside California and Texas
California nonroad emissions were provided by CARB for the years 2016, 2023, 2028, and 2035.
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 and PM2.5 emissions were allocated to
speciation profiles, and VOC HAPs were created, using MOVES data in California. For example, ratios
of VOC (PM2.5) by speciation profile to total VOC (PM2.5), and ratios of VOC HAPs to total VOC, 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.
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Texas nonroad emissions were provided by the Texas Commission on Environmental Quality for the
years 2016, 2023, and 2028, using TCEQ's TexN2 tool.20 This tool facilitates the use of detailed Texas-
specific nonroad equipment population, activity, fuels, and related data as inputs for MOVES2014b, and
accounts for Texas-specific emission adjustments such as the Texas Low Emission Diesel (TxLED)
program. Texas nonroad emissions were provided seasonally; that is, total emissions for winter, spring,
summer and fall; those emissions were evenly distributed between the months in each season. As in
California, VOC and PM2.5 emissions were allocated to speciation profiles, and VOC HAPs were created,
using MOVES data in Texas. For example, ratios of VOC (PM2.5) by speciation profile to total VOC
(PM2.5), and ratios of VOC HAPs to total VOC, were calculated by county and SCC from the MOVES
run in Texas, and then applied TCEQ-provided VOC (PM2.5) in the inventory so that Texas nonroad
emissions could be speciated consistently with the rest of the country.
Nonroad Updates from State Comments
The 2016 Nonroad Collaborative workgroup received a small number of comments on the 2016beta
inventory, all of which were addressed and implemented in the 2016vl nonroad inventory and carried into
2016v2:
• Georgia Department of Natural Resources: utilize updated geographic allocation factors
(,nrstatesurrogate table) for the Commercial, Lawn & Garden (commercial, public, and
residential), Logging, Manufacturing, Golf Carts, Recreational, Railroad Maintenance Equipment
and A/C/Refrigeration sectors, using data from the U.S. Census Bureau and U.S. Forest Service.
• Lake Michigan Air Directors Consortium (LADCO): update seasonal allocation of agricultural
equipment activity (nrmonthallocation table) for Illinois, Indiana, Iowa, Michigan, Minnesota,
Missouri, Ohio, and Wisconsin.
• Texas Commission on Environmental Quality: replace MOVES nonroad emissions for Texas
with emissions calculated with TCEQ's TexN2 model.
• Alaska Department of Environmental Conservation: remove emissions as calculated by
MOVES for several equipment sector-county/census areas combinations in Alaska, due to an
absence of nonroad activity (see Table 2-29).
Table 2-29. Alaska counties/census areas for which nonroad equipment sector-specific emissions are
removed in the 2016 platforms
Nonroiiil Kqiiipmenl Sector
Counties/Census Are.is (l-'ll'S) lor which equipment
sector emissions ;irc remo\eel in 2016
Agricultural
Aleutians East (02013), Aleutians West (02016), Bethel
Census Area (02050), Bristol Bay Borough (02060),
Dillingham Census Area (02070), Haines Borough (02100),
Hoonah-Angoon Census Area (02105), Ketchikan Gateway
(02130), Kodiak Island Borough (02150), Lake and
Peninsula (02164), Nome (02180), North Slope Borough
(02185), Northwest Arctic (02188), Petersburg Borough
20 For more information on the TexN2 tool please see: https://www.tcea.texas.gov/airqualitv/airmod/overview/am ei.html and
the FTP site amdafto.tcea .texas. gov/EI/nonroad/TexN2/.
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Nonroad Equipment Sector
Counties/Census Areas (FIPS) for which equipment
sector emissions are removed in 2016
(02195), Pr ofWales-Hyder Census Area (02198), Sitka
Borough (02220), Skagway Borough (02230), Valdez-
Cordova Census Area (02261), Wade Hampton Census Area
(02270), Wrangell City + Borough (02275), Yakutat City +
Borough (02282), Yukon-Koyukuk Census Area (02290)
Logging
Aleutians East (02013), Aleutians West (02016), Nome
(02180), North Slope Borough (02185), Northwest Arctic
(02188), Wade Hampton Census Area (02270)
Railway Maintenance
Aleutians East (02013), Aleutians West (02016), Bethel
Census Area (02050), Bristol Bay Borough (02060),
Dillingham Census Area (02070), Haines Borough (02100),
Hoonah-Angoon Census Area (02105), Juneau City +
Borough (02110), Ketchikan Gateway (02130), Kodiak
Island Borough (02150), Lake and Peninsula (02164), Nome
(02180),), North Slope Borough (02185), Northwest Arctic
(02188), Petersburg Borough (02195), Pr of Wales-Hyder
Census Area (02198), Sitka Borough (02220), Southeast
Fairbanks (02240), Wade Hampton Census Area (02270),
Wrangell City + Borough (02275), Yakutat City + Borough
(02282), Yukon-Koyukuk Census Area (02290)
2.5 2016 Fires (ptfire-wild, ptfire-rx, 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, 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.
2.5.1 Wild and Prescribed Fires (ptfire)
Wildfires and prescribed burns that occurred during the inventory year are included in the year 2016
version 1 (2016vl) inventory as event and point sources. Only minor adjustments were made to ptfire for
2016v2. These minor adjustments consisted of correcting emissions for the Soberanes fire in California
that occurred in summer of 2016 and a few improvements to the spatial allocation of large wildfires (no
emissions changed in the cases). For 2016v2, the wildfires and prescribed fires were broken up into two
different sectors, ptfire-wild and ptfire-rx, respectively. For 2016v3, the ptfire emissions were unchanged
from those used in 2016v2. The point agricultural fires inventory (ptagfire) is described in a separate
section. For purposes of emission inventory preparation, wildland fire (WLF) is defined as any non-
structure fire that occurs in the wildland. The wildland is defined an area in which human activity and
development are essentially non-existent, except for roads, railroads, power lines, and similar
transportation facilities. Wildland fire activity is categorized by the conditions under which the fire
occurs. These conditions influence important aspects of fire behavior, including smoke emissions.
In the 2016v2 inventory, data processing was conducted differently depending on the fire type, as defined
below:
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• Wildfire (WF): any fire started by an unplanned ignition caused by lightning; volcanoes; other acts
of nature; unauthorized activity; or accidental, human-caused actions, or a prescribed fire that has
developed into a wildfire.
• Prescribed (Rx) fire: any fire intentionally ignited by management actions in accordance with
applicable laws, policies, and regulations to meet specific land or resource management
objectives. Prescribed fire is one type of fire fuels treatment. Fire fuels treatments are vegetation
management activities intended to modify or reduce hazardous fuels. Fuels treatments include
prescribed fires, wildland fire use, and mechanical treatment.
The SCCs used for the ptfire sources are shown in Table 2-30. The ptfire inventory includes separate
SCCs for the flaming and smoldering combustion phases for wildfire and prescribed burns. Note that
prescribed grassland fires or Flint Hills, Kansas have their own SCC in the 2016v2 inventory. The year
2016 fire season also included some major wild grassland fires. These wild grassland fires were assigned
the standard wildfire SCCs shown in Table 2-30.
Table 2-30. SCCs included in the 2016 ptfire sector
SCC
Description
2801500170
Grassland fires; prescribed
2810001001
Forest Wildfires; Smoldering; Residual smoldering only (includes grassland
wildfires)
2810001002
Forest Wildfires; Flaming (includes grassland wildfires)
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming
National Fire Information Data
Numerous fire information databases are available from U.S. national government agencies. Some of the
databases are available via the internet while others must be obtained directly from agency staff. Table
2-31 provides the national fire information databases that were used for the 2016vl ptfire inventory,
including the website where the 2016 data were downloaded.
Table 2-31. National fire information databases used in 2016 ptfire inventory
Dataset Name
Fire
Types
Format
Agency
Coverage
Source
Hazard Mapping
System (HMS)
WF/RX
CSV
NO A A
North
America
https://www.ospo.noaa.gov/Products/land/
hms.html
Geospatial Multi-
Agency
Coordination(Geo
MAC)
WF
SHP
USGS
Entire US
httDs://wildfire.usgs.gov/geomac/GeoMA
CTransition.shtml. https://data-
nifc. ooendata. arcgi s. com/
Incident
Command System
Form 209:
Incident Status
Summary (ICS-
209)
WF/RX
CSV
Multi
Entire US
httDs://famit.nwcg.gov/aDDlications/FAM
Web
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Dataset Name
Fire
Types
Format
Agency
Coverage
Source
National
Association of
State Foresters
(NASF)
WF
CSV
Multi
Participati
ng US
states
httD s: //famit. nwcs. sov/aoolicati ons/F AM
Web (see Public Access Reports, Free Data
Extract, then NASF State Data Extract)
Monitoring
Trends in Burn
Severity (MTBS)
WF/RX
SHP
USGS,
USFS
Entire US
h tto s: //www. m tb s. yo v/di rect-do wn 1 oad
Forest Service
Activity Tracking
System (FACTS)
RX
SHP
USFS
Entire US
Hazardous Fuel Treatment Reduction:
Polygon at
httDs://data.fs.usda.eov/ecodata/cd\\/datascts.D
hn
US Fish and
Wildland Service
(USFWS) fire
database
WF/RX
CSV
USFWS
Entire US
Direct communication with USFWS
The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and
Atmospheric Administration's (NOAA) National Environmental Satellite and Data Information
Service (NESDIS) as a tool to identify fires over North America in an operational environment. The
system utilizes geostationary and polar orbiting environmental satellites. Automated fire detection
algorithms are employed for each of the sensors. When possible, HMS data analysts apply quality control
procedures for the automated fire detections by eliminating those that are deemed to be false and adding
hotspots that the algorithms have not detected via a thorough examination of the satellite imagery.
The HMS product used for the 2016vl inventory consisted of daily comma-delimited files containing fire
detect information including latitude-longitude, satellite used, time detected, and other information. The
Visible Infrared Imaging Radiometer Suite (VIIRS) satellite fire detects were introduced into the HMS in
late 2016. Since it was only available for a small portion of the year, the VIIRS fire detects were removed
for the entire year for consistency. In the 2016alpha inventory, the grassland fire detects were put in the
point agricultural fire sector (ptagfire). As there were a few significant grassland wildfires in Kansas and
Oklahoma in year 2016, all grassland fire detects were included in the ptfire sector for the 2016vl
inventory. These grassland fires were processed through Satellite Mapping Automated Reanalysis Tool
for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Framework.
GeoMAC (Geospatial Multi-Agency Coordination) is an online wildfire mapping application designed for
fire managers to access maps of current U.S. fire locations and perimeters. The wildfire perimeter data is
based upon input from incident intelligence sources from multiple agencies, GPS data, and infrared (IR)
imagery from fixed wing and satellite platforms.
The Incident Status Summary, also known as the "ICS-209" is used for reporting specific information on
significant fire incidents. The ICS-209 report is a critical interagency incident reporting tool giving daily
'snapshots' of the wildland fire management situation and individual incident information which include
fire behavior, size, location, cost, and other information. Data from two tables in the ICS-209 database
were merged and used for the 2016vl 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.
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The National Association of State Foresters (NASF) is a non-profit organization composed of the
directors of forestry agencies in the states, U.S. territories, and District of Columbia to manage and protect
state and private forests, which encompass nearly two-thirds of the nation's forests. The NASF compiles
fire incident reports from agencies in the organization and makes them publicly available. The NASF fire
information includes dates of fire activity, acres burned, and fire location information.
Monitoring Trends in Burn Severity (MTBS) is an interagency program whose goal is to consistently map
the burn severity and extent of large fires across the U.S. from 1984 to present. The MTBS data includes
all fires 1,000 acres or greater in the western United States and 500 acres or greater in the eastern United
States. The extent of coverage includes the continental U.S., Alaska, Hawaii, and Puerto Rico. Fire
occurrence and satellite data from various sources are compiled to create numerous MTBS fire products.
The MTBS Burned Areas Boundaries Dataset shapefiles include year 2016 fires and that are classified as
either wildfires, prescribed burns or unknown fire types. The unknown fire type shapes were omitted in
the 2016vl inventory development due to temporal and spatial problems found when trying to use these
data.
The US Forest Service (USFS) compiles a variety of fire information every year. Year 2016 data from the
USFS Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and
used for 2016vl emissions inventory development. This database includes information about activities
related to fire/fuels, silviculture, and invasive species. The FACTS database consists of shapefiles for
prescribed burns that provide acres burned, and start and ending time information.
The US Fish and Wildland Service (USFWS) also compiles wildfire and prescribed burn activity on their
federal lands every year. Year 2016 data were acquired from USFWS through direct communication with
USFWS staff and were used for 2016vl emissions inventory development. The USFWS fire information
provided fire type, acres burned, latitude-longitude, and start and ending times.
State/Local/Tribal Fire Information
During the 2016 emissions modeling platform development process, S/L/T agencies were invited by EPA
and 2016 Inventory Collaborative Fire Workgroup to submit all fire occurrence data for use in developing
the 2016vl fire inventory. A template form containing the desired format for data submittals was
provided to S/L/T air agencies. The list of S/L/T agencies that submitted fire data is provided in Table
2-32. Data from nine individual states and one Indian Tribe were used for the 2016vl ptfire inventory.
Table 2-32. List of S/L/T agencies that submitted fire data for 2016vl with types and formats.
S/L/T agency name
Fire
Types
Format
NCDEQ
WF/RX
CSV
KDHE
RX/AG
CSV
CO Smoke Mgmt Program
RX
CSV
Idaho DEQ
AG
CSV
Nez Perce Tribe
AG
CSV
GADNR
ALL
EIS
MN
RX/AG
CSV
WA ECY
AG
CSV
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S/L/T agency name
Fire
Types
Format
NJDEP
WF/RX
CSV
Alaska DEC
WF/RX
CSV
The data provided by S/L/T agencies were evaluated by EPA and further feedback on the data submitted
by the state was requested at times. Table 2-33 provides a summary of the type of data submitted by each
S/L/T agency and includes spatial, temporal, acres burned and other information provided by the
agencies.
Table 2-33. Brief description of fire information submitted for 2016vl inventory use.
S/L/T agency
name
Fire
Types
Description
NCDEQ
WF/RX
Fire type, period-specific, latitude-longitude and acres burned
information. Technical direction was to remove all fire detects
that were not reconciled with any other national or state
agency database.
Kansas DHE
RX/AG
Day-specific, county-centroid located, acres burned for Flint
Hills prescribed burns for Feb 27-May 4 time period.
Reclassified fuels for some agricultural burns. A grassland
gridding surrogate was used to spatially allocate the day-
specific grassland fire emissions.
Colorado Smoke
Mgmt Program
RX
Day-specific, latitude-longitude, and acres burned for
prescribed burns
Idaho DEQ
AG
Day-specific, latitude-longitude, acres burned for agricultural
burns. Total replacement of 2016 alpha fire inventory for
Idaho.
Nez Perce Tribe
AG
Day-specific, latitude-longitude, acres burned for agricultural
burns. Total replacement of 2016 alpha fire inventory within
the tribal area boundary.
Georgia DNR
ALL
Data submitted included all fires types via EIS. The wildfire
and prescribed burn data were provided as daily, point
emissions sources. The agricultural burns were provided as
day-specific point emissions sources.
Minnesota
RX/AG
Corrected latitude-longitude, day-specific and acres burned
for some prescribed and agricultural burns.
Washington ECY
AG
Month-specific, latitude-longitude, acres burned, fuel loading
and emissions for agricultural burns. Not day-specific so
allocation to daily implemented by EPA. WA state direction
included to continue to use the 2014NEIv2 pile burns that
were included in the non-point sector for 2016vl.
New Jersey DEP
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire
and prescribed burns.
Alaska DEC
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire
and prescribed burns.
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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 2016vl 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 2016vl inventory the
residual smoldering emissions were allocated to the smoldering SCCs listed in Table 2-30. The lofted
smoldering emissions were assigned to the flaming emissions SCCs in Table 2-30.
Figure 2-10 is a schematic of the data processing stream for the 2016vl inventory for wildfire and
prescribe burn sources. The ptfire inventory sources were estimated using Satellite Mapping Automated
Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Framework.
SMARTFIRE2 is an algorithm and database system that operate within a geographic information system
(GIS). SMARTFIRE2 combines multiple sources of fire information and reconciles them into a unified
GIS database. It reconciles fire data from space-borne sensors and ground-based reports, thus drawing on
the strengths of both data types while avoiding double-counting of fire events. At its core, SMARTFIRE2
is an association engine that links reports covering the same fire in any number of multiple databases. In
this process, all input information is preserved, and no attempt is made to reconcile conflicting or
potentially contradictory information (for example, the existence of a fire in one database but not
another).
For the 2016vl inventory, the national and S/L/T fire information was input into SMARTFIRE2 and then
merged and associated based on user-defined weights for each fire information dataset. The output from
SMARTFIRE2 was daily acres burned by fire type, and latitude-longitude coordinates for each fire. The
fire type assignments were made using the fire information datasets. If the only information for a fire was
a satellite detect for fire activity, then the flow described in Figure 2-11 was used to make fire type
assignment by state and by month.
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Figure 2-10. Processing flow for fire emission estimates in the 2016 inventory
Input Data Sets
(state/local/tribal and national data sets)
Fuel Moisture and
Fuel Loading Data
Smoke Modeling (BlueSky Framework)
Daily smoke emissions
for each fire
Emissions Post-Processing
Final Wildland Fire Emissions Inventory
Data Preparation
Data Aggregation and Reconciliation
(SmartFire2)
Daily fire locations
with fire size and type
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Figure 2-11. Default fire type assignment by state and month where data are only from satellites.
* _
Default Fire Type
Assignment
WF Months
¦ 4,5,6,7
~ 5,6,7,8
~ 5,6,7,8,9,10
~ 6,7,8
I None
The BlueSky Modeling Framework version 3.5 (revision #38169) was used to calculate fuel loading and
consumption, and emissions using various models depending on the available inputs as well as the desired
results. The contiguous United States and Alaska, where Fuel Characteristic Classification System
(FCCS) fuel loading data are available, were processed using the modeling chain described in Figure
2-12. The Fire Emissions Production Simulator (FEPS) in the BlueSky Framework generated the CAP
emission factors for wildland fires used in the 2016vl inventory. The FIAPs were derived from regional
emissions factors from Urbanski (2014).
For the 2016vl inventory, the FCCSv2 spatial vegetation cover was upgraded to the LANDFIRE vl.4
fuel vegetation cover (See: https://www.landfire.gov/fccs.php). The FCCSv3 fuel bed characteristics were
implemented along with LANDFIREvl.4 to provide better fuel classification for the BlueSky Framework.
The LANDFIREvl.4 raster data were aggregated from the native resolution and projection to 200 meter
resolution using a nearest-neighbor methodology. Aggregation and reprojection were required to facilitate
the use of these data in the BlueSky Framework.
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Figure 2-12. BlueSky Modeling Framework
Location
Dates
Type
¦jize
Consumption
(Consume v4j
Emission
Factors
(FEPS v2i
Emissions
Biuesky Framework vS.5.0
2.5.2 Point Source Agricultural Fires (ptagfire)
The point source agricultural fire (ptagfire) inventory sector contains daily agricultural burning emissions.
Daily fire activity was derived from the NOAA Hazard Mapping System (HMS) fire activity data. The
agricultural fires sector includes SCCs starting with '28015'. The first three levels of descriptions for
these SCCs are: 1) Fires - Agricultural Field Burning; Miscellaneous Area Sources; 2) Agriculture
Production - Crops - as nonpoint; and 3) Agricultural Field Burning - whole field set on fire. The SCC
2801500000 does not specify the crop type or burn method, while the more specific SCCs specify field or
orchard crops and, in some cases, the specific crop being grown. The SCCs for this sector listed are in
Table 2-34. For 2016v3, the ptagfire data are unchanged from 2016v2.
Table 2-34. SCCs included in the 2016 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
2801500100
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crops Unspecified
2801500112
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Alfalfa: Backfire Burning
2801500130
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Barley: Burning Techniques Not Significant
2801500141
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Bean (red): Headfire Burning
2801500150
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Corn: Burning Techniques Not Important
2801500151
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Double Crop Winter Wheat and Corn
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see
Description
2801500152
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;DoubleCrop Corn and Soybeans
2801500160
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Cotton: Burning Techniques Not Important
2801500170
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Grasses: Burning Techniques Not Important
2801500171
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Fallow
2801500182
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Hay (wild): Backfire Burning
2801500202
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Pea: Backfire Burning
2801500220
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Rice: Burning Techniques Not Significant
2801500250
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Sugar Cane: Burning Techniques Not Significant
2801500262
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Wheat: Backfire Burning
2801500263
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;DoubleCrop Winter Wheat and Cotton
2801500264
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;DoubleCrop Winter Wheat and Soybeans
2801500300
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop Unspecified
2801500320
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Apple
2801500350
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Cherry
2801500410
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Peach
2801500420
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Pear
2801500500
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire; Vine Crop Unspecified
2801500600
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Forest Residues Unspecified
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The EPA estimated biomass burning emissions using remote sensing data. These estimates were then
reviewed by the states and revised as resources allowed. As many states did not have the resources to
estimate emissions for this sector, remote sensing was necessary to fill in the gaps for regions where there
was no other source of data. Crop residue emissions result from either pre-harvest or post-harvest burning
of agricultural fields. The crop residue emission inventory for 2016 is day-specific and includes
geolocation information by crop type. The method employed and described here is based on the same
methods employed in the 2014 NEI with a few minor updates. It should be noted that grassland fires were
moved from the agricultural burning inventory sector to the prescribed and wildland fire sector for
2016beta and 2016vl inventories. This was done to prevent double-counting of fires and because the
largest fire (acres burned) in 2016 was a wild grassland fire in Kansas.
Daily, year-specific agricultural burning emissions were derived from HMS fire activity data, which
contains the date and location of remote-sensed anomalies. As point source inventories, the locations of
the fires are identified with latitude-longitude coordinates for specific fire events. The HMS activity data
were filtered using 2016 USD A cropland data layer (CDL). Satellite fire detects over agricultural lands
were assumed to be agricultural burns and assigned a crop type. Detects that were not over agricultural
lands were output to a separate file for use in the point source wildfire (ptfire) inventory sector. Each
detect was assigned an average size of between 40 and 80 acres based on crop type. The assumed field
sizes are found in Table 2-35.
Table 2-35. Assumed field size of agricultural fires per state(acres)
State
Field Size
Alabama
40
Arizona
80
Arkansas
40
California
120
Colorado
80
Connecticut
40
Delaware
40
Florida
60
Georgia
40
Idaho
120
Illinois
60
Indiana
60
Iowa
60
Kansas
80
Kentucky
40
Louisiana
40
Maine
40
Maryland
40
Massachusetts
40
Michigan
40
Minnesota
60
Mississippi
40
Missouri
60
Montana
120
Nebraska
60
Nevada
40
New Hampshire
40
80
-------
Sl;ite
Held Si/e
New Jersey
40
New Mexico
80
New York
40
North Carolina
40
North Dakota
60
Ohio
40
Oklahoma
80
Oregon
120
Pennsylvania
40
Rhode Island
40
South Carolina
40
South Dakota
60
Tennessee
40
Texas
80
Utah
40
Vermont
40
Virginia
40
Washington
120
West Virginia
40
Wisconsin
40
Wyoming
80
Another feature of the ptagfire database is that the satellite detections for 2016 were filtered out to
exclude areas covered by snow during the winter months. To do this, the daily snow cover fraction per
grid cell was extracted from a 2016 meteorological Weather Research Forecast (WRF) model simulation.
The locations of fire detections were then compared with this daily snow cover file. For any day in which
a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the
inventory. Due to the inconsistent reporting of fire detections for year 2016 from the Visible Infrared
Imaging Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as
VIIRS or Suomi National Polar-orbiting Partnership satellite were excluded. In addition, certain crop
types (corn and soybeans) were excluded from the following states: Iowa, Kansas, Indiana, Illinois,
Michigan, Missouri, Minnesota, Wisconsin, and Ohio. Kansas was not included in this list in the 2014NEI
but added for 2016. The reason for these crop types being excluded is because states have indicated that
these crop types are not burned.
Crop type-specific emissions factors were applied to each daily fire to calculate criteria and hazardous
pollutant emissions. In all prior NEIs for this sector, the HAP emission factors and the VOC emission
factors were known to be inconsistent. The HAP emission factors were copied from the HAP emission
factors for wildfires in the 2014 NEI and in the 2016 beta and version 1 modeling platforms. The VOC
emission factors were scaled from the CO emission factors in the 2014 NEI and the 2016 beta and version
1 modeling platforms. See Pouliot et al, 2017 for a complete table of emission factors and fuel loading by
crop type.
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Heat flux values for computing fire plume rise were calculated using the size and assumed fuel loading of
each daily fire. Emission factors and fuel loading by crop type are available in Table 1 of Pouliot et al.
(2017). This information is needed for a plume rise calculation within a chemical transport modeling
system. In prior year modeling platforms including 2014, all the emissions were placed into layer 1 (i.e.
ground level).
The daily agricultural and open burning emissions were converted from a tabular format into the
SMOKE-ready daily point Flat File 2010 (FF10) format. The daily emissions were also aggregated into
annual values by location and converted into the annual point flat file format.
2.6 2016 Biogenic Sources (beis)
Biogenic emissions for 2016v3 were developed using the Biogenic Emission Inventory System version 4
(BEIS4) within SMOKE. BEIS4 was released with SMOKE 4.9. BEIS4 is most compatible with MCIP
v5 meteorological data, although data output from MCIP v5 were not available for the year 2016. Minor
modifications were made to BEIS4 to accommodate the use of the available 2016 meteorological data that
was processed using MCIP v4.3. The landuse input into BEIS4 was the Biogenic Emissions Landuse
Dataset (BELD) version 6. The versions of BEIS and BELD were both updated for 2016v3 platform in
response to comments on air quality model performance.
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 where
taken from the NCAR Enclosure database ( Wiedinmyer 2001);
Agricultural land use from US Department of Agriculture (USDA) crop data layer
(https://www.nass.usda.gov/Research_and_Science/Cropland/SARSla.php)
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)
(https://www2.mmm.ucar.edu/wrf/users/download/get sources wps geog.html )
Canadian BELD land use (https://www.epa.gov/sites/default/files/2019-
08/documents/800am zhang 2 O.pdf).
BEIS4 has some important updates from earlier versions of BEIS. These include the incorporation of
Version 6 of the Biogenic Emissions Landuse Database (BELD6), the option to include 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
(WRFv3.8) representation of LAI rather than the estimated LAI values just from BELD data.
See https://github.com/USEPA/CMAO/wiki/CMAQ-Release-Notes:-Emissions-Updates:-BEIS-Biogenic-
Emissions for more technical information on BEIS4.
BEIS4 includes a two-layer canopy model. Layer structure varies with light intensity and solar zenith
angle. Both layers of the canopy model include estimates of sunlit and shaded leaf area based on solar
zenith angle and light intensity, direct and diffuse solar radiation, and leaf temperature (Bash et al., 2016).
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The new algorithm requires additional meteorological variables over previous versions of BEIS. The
variables output from the Meteorology-Chemistry Interface Processor (MCIP) that are used for BEIS4
processing are shown in Table 2-36. The WSATPX variable was not available for the version of WRF
and MCIP used in the 2016 modeling platform, as this variable became available with WRFv4 and future
versions. For 2016 modeling, minor code modifications were made to BEIS4 to calculate WSAT based
on soil type (SLTYP) and soil moisture (SOIM1) in a very similar manner that is done in BEIS3. The
WSAT PX variable only impacts the nitric oxide emissions from soils in BEIS models. The 2016 BEIS4
modeling for year 2016 included processing for both a 36km (36US3) and 12km domain (12US1) (see
Figure 3-1). The 12US2 modeling domain can also be supported by taking a subset or window of the
12US1 BEIS4 emissions dataset.
Table 2-36. Hourly Meteorological variables required by BEIS4
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2m
RADYNI
inverse of aerodynamic resistance
RC
convective precipitation
RGRND
solar radiation reaching surface
RN
nonconvective precipitation
RSTOMI
inverse of bulk stomatal resistance
SLTYP
soil texture type by USD A category
SOIM1
volumetric soil moisture in top cm
SOIT1
soil temperature in top cm
SOIT2
soil temperature in top m
TEMPG
skin temperature at ground
TEMP2
Temperature at 2m
USTAR
cell averaged friction velocity
WSAT PX
soil saturation from (Pleim-Xiu Land Surface
Model) PX-LSM
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.
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• The quantum yield for isoprene emissions (ALPHA) was updated to the mean value in Niinemets
et al. 2010a ( https://doi.org/10.1029/2010JG0Q1436) and the integration coefficient (CL) was
updated to yield 1 when PAR = 1000 following Niinemets et al 2010b
(https://doi.org/10.5194/bg-7-1809-201Q).
o This updated resulted in a slight reduction in isoprene, methanol, and MBO emissions.
The SMOKE-BEIS4 modeling system consists of two programs named: 1) Normbeis4 and 2) Tmpbeis4.
Normbeis4 uses emissions factors and BELD6 landuse and gridded biomass data to compute gridded
normalized emissions for chosen model domain (see Figure 2-13). The BEIS4 emissions factor file
(BEISFAC) contains leaf-area-indices (LAI), dry leaf biomass, winter biomass factor, indicator of
specific leaf weight, Agricultural land type Yes/No (AG_YN), and normalized emission fluxes for 35
different species/compounds. The BELD6 file is the gridded landuse for 200+ different landuse types.
The output gridded domain is the same as the input domain for the land use data. Output emission fluxes
(BEISNORMEMIS) are normalized to 30°C, and isoprene and methyl-butenol fluxes are also
normalized to a photosynthetic active radiation of 1000 |imol/m2s.
The normalized emissions output from Normbeis4 (BEIS NORM EMIS) are input into Tmpbeis4 along
with the MCIP meteorological data, chemical speciation profile to use for desired chemical mechanism,
and soil moisture data file. Figure 2-14 illustrates the data flows for the Tmpbeis4 program. The output
from Tmpbeis includes gridded, speciated, hourly emissions both in moles/second (B4GTS L) and
tons/hour (B4GTS S). Biogenic emissions do not use an emissions inventory and do not have SCCs. .
Please see the SMOKEv4.9 User's Manual for more information on BEIS4
(https://www.cmascenter.Org/smoke/documentation/4.9/html/ch04sl9.html)
Figure 2-13. Normbeis4 data flows for 2016v3
PROGRAM
FILE
Shows input or
output
*
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Figure 2-14. Tmpbeis4 data flow diagram for 2016v3
2.7 Sources Outside of the United States
The emissions from Canada and Mexico and other areas outside of the U.S. are included in these
emissions modeling sectors: othpt, othar, othafdust, othptdust, onroadcan, onroadmex, and
ptfire othna. The "oth" refers to the fact that these emissions are usually "other" than those in the NEI,
and the remaining characters provide the SMOKE source types: "pt" for point, "ar" for "area and nonroad
mobile," "afdust" for area fugitive dust (Canada only), and "ptdust" for point fugitive dust. Because
Canada and Mexico onroad mobile emissions are modeled differently from each other, they are separated
into two sectors: onroad can and onroad mex. Additional details for these sectors can be found in the
2016vl platform specification sheets.
Canadian emissions were taken from the Environment and Climate Change Canada (ECCC) 2016
emission inventory, which was new for the 2016v2 platform. New 2016 emissions were also provided for
Mexico by SEMARNAT for 2016v2. The 2016v3 emissions for Canada and Mexico are unchanged from
those in the 2016v2 platform.
2.7.1 Point Sources in Canada and Mexico (othpt, canada_ag, canada_og2D)
Canadian point sources were taken from the ECCC 2016 emission inventory, which was new for the
2016v2 platform. The provided point source inventories include upstream oil and gas emissions,
agricultural ammonia and VOC. The Mexico point sources were taken from the SEMARNAT 2016
inventory. These inventories were unchanged in the 2016v3 platform.
Due to the large number of points in the Canada inventories, for 2016v2 the agricultural sources were split
into a separate sector called canada ag so that the sources could be placed into layer 1 as plume rise
calculations were not needed. Similarly, there were a very large number of Canadian oil and gas point
sources, most of which would be appropriate modeled in layer 1. These sources were placed into the
canada_og2D sector for layer 1 modeling. Reducing the size of the othpt sector sped up the air quality
model run. The Canadian point source inventory is pre-speciated for the CB6 chemical mechanism. Also
for Canada, agricultural data were originally provided on a rotated 10-km grid for the 2016beta platform.
These were smoothed out to avoid the artifact of grid lines in the processed emissions. The data were
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monthly resolution for Canadian agricultural and airport emissions, along with some Canadian point
sources, and annual resolution for the remainder of Canada and all of Mexico.
2.7.2 Fugitive Dust Sources in Canada (othafdust, othptdust)
Fugitive dust sources of particulate matter emissions excluding land tilling from agricultural activities,
were provided by Environment and Climate Change Canada (ECCC) as part of their 2016 emission
inventory. Different source categories were provided as gridded point sources and area (nonpoint) source
inventories.
Gridded point source emissions resulting from land tilling due to agricultural activities were provided as
part of the ECCC 2016 emission inventory. The provided wind erosion emissions were removed. The
data were originally provided on a rotated 10-km grid for the 2016 beta platform, but these were
smoothed to avoid the artifact of grid lines appearing in the emissions output from SMOKE. The
othptdust emissions have a monthly resolution.
A transport fraction adjustment that reduces dust emissions based on land cover types was applied to both
point and nonpoint dust emissions, along with a meteorology-based (precipitation and snow/ice cover)
zero-out of emissions when the ground is snow covered or wet. There were no updates made to the
Canadian dust sources in the 2016v3 platform.
2.7.3 Nonpoint and Nonroad Sources in Canada and Mexico (othar)
ECCC provided year 2016 Canada province, and in some cases sub-province, resolution emissions from
for nonpoint and nonroad sources. The nonroad sources were monthly while the nonpoint and rail
emissions were annual. For Mexico, the 2016 Mexico nonpoint and nonroad inventories from
SEMARNAT were used. All Mexico inventories were annual resolution. Canadian CMV inventories
that had been included in the othar sector in past modeling platforms are now included in the cmv_clc2
and cmv_c3 sectors as point sources. There were no updates made to the other sector emissions in the
2016v3 platform.
2.7.4 Onroad Sources in Canada and Mexico (onroad_can, onroad_mex)
ECCC provided monthly year 2016 onroad emissions for Canada at the province resolution or sub-
province resolution depending on the province. For Mexico, monthly year 2016 onroad inventories at the
municipio resolution unchanged from 2016vl were used. The Mexico onroad emissions are based on
MOVES-Mexico runs for 2014 and 2017 that were interpolated to 2016.
2.7.5 Fires in Canada and Mexico (ptfire_othna)
Annual point source 2016 day-specific wildland emissions for Mexico, Canada, Central America, and
Caribbean nations were developed from a combination of the Fire Inventory from NCAR (FINN) daily
fire emissions and fire data provided by ECCC when available. ECCC emissions were used for Canada
wildland fire emissions for April through November and FINN fire emissions were used to fill in the
annual gaps from January through March and December. Only CAP emissions are provided in the
ptfireothna sector inventories. In 2016v2 and 2016v3, the ptfireothna emissions are unchanged from
those used in 2016vl.
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
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to be wildfires rather than prescribed. FINN fire detects less than 50 square meters (0.012 acres) are
removed from the inventory. The locations of FINN fires are geocoded from latitude and longitude to
FIPS code.
2.7.6 Ocean Chlorine, Sea Salt, and Lightning NOx
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. The CL2 emissions are constant in all ocean grid cells. These data
are unchanged from the data in 2016vl and are passed to both CMAQ and CAMx. Separately from the
ocean chlorine, CMAQ computes sea salt particulate emissions inline during the model run.
For CAMx modeling, the OCEANIC preprocessor is used to compute emissions for the following
pollutants over ocean water: sodium (NA), chlorine (PCL), sulfate (PS04), dimethy sulfide (DMS), and
gas phase bromine (SSBR) and chlorine (SSCL). Additional information is provided in Section 3.5.
The 2016 lightning NOx emissions were created using lightning flashes observed from the World Wide
Lightning Location Network (WWLLN, operated by the University of Washington:
http://www.wwlln.net). The observed lightning flashes were first gridded into the modeling grid cells as
lightning flash density (flashes/km2*hr), then the flash density was adjusted by applying the deMpas
(http://wwlln.net/deMaps) factors to achieve a uniform global detection efficiency (DE) (Hutchins, 2012).
The DE-adjusted WWLLN flash density was further scaled using factors derived based on climatological
flash density ratios between lightning flashes observed from the National Lightning Detection Network
(NLDN), which provides Cloud-to-Ground (CG) lightning observations with a DE of >95% and a
location accuracy of about 150 m over the contiguous United States, and the lightning flashes observed
from WWLLN. The scale factors vary over the month of the year and grid cell classifications (land versus
ocean) (Kang, 2022). The scaled WWLLN (WWLLNs) flash densities were then used as lightning data
input to a Fortran program (LNOx generator), that is part of the inline lightning NOx emissions
production module in the CMAQ model since CMAQv5.2 but separated from the CMAQ code as a
standalone program to generate lightning NOx emissions diagnostic files. To generate the 2D and 3D
lightning NOx emissions files, the LNOx generator needs three other input files: METCR02D to provide
the surface pressure and horizontal domain configurations, METCR03D to provide the vertical structure
to distribute LNOx vertically, and a lightning parameter file that contains the geographically distributed
climatological CGto cloud-to-cloud lightning flash ratios and the ocean masks (to identify grid cells over
land vs over ocean).
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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 discussed in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary
across sectors and may be hourly, daily, monthly, or annual total emissions. The spatial resolution may
be individual point sources; totals by county (U.S.), province (Canada), or municipio (Mexico); or
gridded emissions. This section provides some basic information about the tools and data files used for
emissions modeling as part of the modeling platform. For additional details that may not be covered in
this section, see the specification sheets provided with the 2016vl platform as many contain additional
sector-specific information in spatial allocation, temporal allocation, and speciation that is still relevant
for 2016v2 and 2016v3.
3.1 Emissions modeling Overview
SMOKE version 4.9 was used to process the raw emissions inventories into emissions inputs for each
modeling sector into a format compatible with CMAQ, which were then converted to CAMx. For sectors
that have plume rise, the in-line plume rise capability allows for the use of emissions files that are much
smaller than full three-dimensional gridded emissions files. For quality assurance of the emissions
modeling steps, emissions totals by specie for the entire model domain are output as reports that are then
compared to reports generated by SMOKE on the input inventories to ensure that mass is not lost or
gained during the emissions modeling process.
When preparing emissions for the air quality model, emissions for each sector are processed separately
through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sector-
specific 2-D gridded emissions across sectors. The SMOKE settings in the run scripts and the data in the
SMOKE ancillary files control the approaches used by the individual SMOKE programs for each sector.
Table 3-1 summarizes the major processing steps of each platform sector with the columns as follows.
The "Spatial" column shows the spatial approach used: "point" indicates that SMOKE maps the source
from a point location (i.e., latitude and longitude) to a grid cell; "surrogates" indicates that some or all of
the sources use spatial surrogates to allocate county emissions to grid cells; and "area-to-point" indicates
that some of the sources use the SMOKE area-to-point feature to grid the emissions (further described in
Section 3.4.2).
The "Speciation" column indicates that all sectors use the SMOKE speciation step, though biogenic
speciation is done within the Tmpbeis4 program and not as a separate SMOKE step.
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The "Inventory resolution" column shows the inventory temporal resolution from which SMOKE needs
to calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no input inventory;
instead, activity data and emission factors are used in combination with meteorological data to compute
hourly emissions.
Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used. These
sectors are the only ones with emissions in aloft layers based on plume rise. The term "in-line" means
that the plume rise calculations are done inside of the air quality model instead of being computed by
SMOKE. 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.
Table 3-1. Key emissions modeling steps by sector.
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust ad]
Surrogates
Yes
Annual
afdust ak adj
(36US3 only)
Surrogates
Yes
Annual
airports
Point
Yes
Annual
None
beis
Pre-gridded
land use and
biomass data
in BEIS4
computed hourly
Canada ag
Point
Yes
monthly
None
Canada og2D
Point
Yes
Annual
None
cmv clc2
Point
Yes
hourly
in-line
cmv c3
Point
Yes
hourly
in-line
fertilizer
Surrogates
No
monthly
livestock
Surrogates
Yes
Annual
nonpt
Surrogates &
area-to-point
Yes
Annual
nonroad
Surrogates
Yes
monthly
np oilgas
Surrogates
Yes
Annual
np solvents
Surrogates
Yes
annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
onroadcaadj
Surrogates
Yes
monthly activity,
computed hourly
onroad nonconus
(36US3 only)
Surrogates
Yes
monthly activity,
computed hourly
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Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
onroad can
Surrogates
Yes
monthly
onroad mex
Surrogates
Yes
monthly
othafdust adj
Surrogates
Yes
annual
othar
Surrogates
Yes
annual &
monthly
othpt
Point
Yes
annual &
monthly
in-line
othptdust ad]
Point
Yes
monthly
None
ptagfire
Point
Yes
daily
in-line
pt oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire-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
Biogenic emissions can be modeled two different ways in the CMAQ model. The BEIS model in SMOKE
can produce gridded biogenic emissions that are then included in the gridded CMAQ-ready emissions
inputs, or alternatively, CMAQ can be configured to create "in-line" biogenic emissions within CMAQ
itself. For this platform, biogenic emissions were processed in SMOKE and included in the gridded
CMAQ-ready emissions. When CAMx is the targeted air quality model, BEIS is run within SMOKE and
the resulting emissions are included with the ground-level emissions input to CAMx.
In 2016v3 platform for the 2016gf case, SMOKE was run in such a way that it produced both diesel and
non-diesel outputs for onroad and nonroad emissions that later get merged into the low-level emissions
fed into the air quality model. This facilitates advanced speciation treatments that are sometimes used in
CMAQ. The onroad emissions were processed in a single sector and were not split between gas a diesel
for the 2023gf and 2026gf cases.
SMOKE has the option of grouping sources so that they are treated as a single stack when computing
plume rise. For this platform, no grouping was performed because grouping combined with "in-line"
processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-
dimensional files). This occurs when stacks with different stack parameters or latitudes/longitudes are
grouped, thereby changing the parameters of one or more sources. The most straightforward way to get
the same results between in-line and offline is to avoid the use of grouping.
SMOKE was run for two modeling domains: a 36-km resolution CONtinental United States "CONUS"
modeling domain (36US3), and a 12-km resolution domain. Specifically, SMOKE was run on the 12US1
domain and emissions were extracted from 12US1 data files to create 12US2 emissions for 2016, 2023,
and 2026. Emissions were developed for 36US3 for 2016 and 2023 only. The outputs of CAMx on the
36US3 grid are used to create boundary conditions for the 12US2 domains. For 2026 , the 2023 boundary
conditions were used. The domains are shown in Figure 3-1. All grids use a Lambert-Conformal
projection, with Alpha = 33°, Beta = 45° and Gamma = -97°, with a center of X = -97° and Y = 40°. Table
3-2 describes the grids for the three domains.
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Table 3-2. Descriptions of the platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig, xcell,
ycell, ncols, nrows, nthik
Continental
36km grid
36 km
Entire conterminous
US, almost all of
Mexico, most of
Canada (south of
60°N)
36US3
'LAM 40N97W, -2952000, -2772000,
36.D3, 36.D3, 172, 148, 1
Continental
12km grid
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_459X299
'LAM 40N97W',-2556000,-1728000,
12.D3, 12.D3, 459,299, 1
US 12 km or
"smaller"'
CONUS-12
12 km
Smaller 12km
CONUS plus some of
Mexico/Canada
12US2
'LAM 40N97W',-2412000,
-1620000, 12.D3, 12.D3, 396, 246, 1
Figure 3-1. Air quality modeling domains
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3.2 Chemical Speciation
The emissions modeling step for chemical speciation creates the "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual chemical
compounds (i.e., "explicit species") or groups of species (i.e., "lumped species"). The chemical
mechanism used for the 2016 platform is the CB6R3AE7 mechanism (Yarwood, 2010, Luecken, 2019).
In CB6R3AE7, additional species that are not included in the CB6 chemical mechanism include acetic
acid (ACET), alpha pinene (APIN), formic acid (FACD), and intermediate volatility organic compounds
(IVOC). This mapping uses a new systematic methodology for mapping low volatility compounds.
Compounds with very low vapor pressure are mapped to model species NVOL and intermediate volatility
compounds are mapped to a species called IVOC. In previous mappings, some of these low vapor
pressure compounds were mapped to CB6 species. The mechanism and mapping are described in more
detail in a memorandum (Ramboll, 2020) describing the mechanism files supplied with the Speciation
Tool, the software used to create the CB6 profiles used in SMOKE. It should be noted that the onroad
mobile sector does not use this newer mapping because the speciation is done within MOVES and the
mapping change was made after MOVES had been run. This platform generates the PM2.5 model species
associated with the CMAQ Aerosol Module version 7 (AE7).
For 2016v3, the key changes to speciation involved updating some speciation cross references and using
newly available speciation profiles for solvents, oil and gas, and some point source SCCs. In addition, the
mapping for SOAALK species were updated to exclusively include linear and branched alkanes with
more than 8 carbons or cyclic alkanes with more than 6 carbons (Pye, 2012).
Table 3-3 lists the model species produced by SMOKE in the platform used for this study. Updates to
species assignments for CB05 and CB6 were made for the 2014v7.1 platform. These continue to be used
in the 2016v3 platform and are described in Appendix A.
Table 3-3. Emission model species produced for CB6R3AE7 for CMAQ
Inventory Pollutant
Model
Species
Model species description
Cl2
CL2
Atomic gas-phase chlorine
HC1
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 (not part of CB05)
VOC
CH4
Methane
VOC
ETH
Ethene
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Inventory Pollutant
Model
Species
Model species description
VOC
ETHA
Ethane
VOC
ETHY
Ethyne
VOC
ETOH
Ethanol
VOC
FACD
Formic acid
VOC
FORM
Formaldehyde
VOC
IOLE
Internal olefin carbon bond (R-C=C-R)
VOC
ISOP
Isoprene
VOC
IVOC
Intermediate volatility organic compounds
VOC
KET
Ketone Groups
VOC
MEOH
Methanol
VOC
NAPH
Naphthalene
VOC
NVOL
Non-volatile compounds
VOC
OLE
Terminal olefin carbon bond (R-C=C)
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 <10 microns
PM2.5
PEC
Particulate elemental carbon <2.5 microns
PM2.5
PN03
Particulate nitrate <2.5 microns
PM2.5
POC
Particulate organic carbon (carbon only) < 2.5 microns
PM2.5
PS04
Particulate Sulfate <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
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One additional species in the emissions files but not in the above table is non-methane organic gases
(NMOG). This facilitates ongoing advanced work in speciation and is created using an additional GSPRO
component that creates NMOG for all TOG and NONHAPTOG profiles plus all integrate HAPs. This
species is not used for traditional ozone and particulate matter-focused modeling applications.
The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach for 2016v3
were developed from the SPECIATE 5.2 database (https://www.epa.gov/air-emissions-modeling/speciate-
2), the EPA's repository of TOG and PM speciation profiles of air pollution sources. Noting that the
2016v2 platform used profiles from a draft of SPECIATE 5.2. The SPECIATE database development and
maintenance is a collaboration involving the EPA's Office of Research and Development (ORD), Office
of Transportation and Air Quality (OTAQ), and the Office of Air Quality Planning and Standards
(OAQPS), in cooperation with ECCC (EPA, 2016). The SPECIATE database contains speciation
profiles for TOG, speciated into individual chemical compounds, VOC-to-TOG conversion factors
associated with the TOG profiles, and speciation profiles for PM2.5.
As with previous platforms, some Canadian point source inventories are provided from ECCC as pre-
speciated emissions; although not all CB6 species are provided, the inventories were not supplemented
with missing species due to the minimal impact of supplementation.
Speciation updates made for 2016v3 platform included:
• Updated assignments to VOC profiles for 6 SCCs (all pulp and paper) and PM2.5 profiles for 3
SCCs (2 pulp and paper, 1 natural gas).
• Updated profile assignments for solvents.
• Re-mapped the profile for SCC 2310010200 from 2487 to 95247.
• Remapped all point and nonpoint SCCs that were mapped to profile 1011 to 95404. The major
SCCs mapped to this profile are associated with oil production processes related fugitive
leaks/venting. Profile 95404 is a composite profile from untreated oil wells.
• Remapped all point and nonpoint SCCs that were mapped to profile 1207 to profile 95782 (a
profile for produced water for non-coal bed methane). These are for non-CBM produced water.
We note that CBM produced water is using a Wyoming profile and 95782 is a non-CBM produced
water profile also sampled in Wyoming.
Some updates to speciation profiles from previous platforms include the following:
• Additional oil and gas profiles were added (e.g., UTUBOGC, UTUBOGE, UTUBOGF);
• WRAP oil and gas profiles were used for the WRAP oil and gas inventory, although many WRAP
profiles were also used in the 2016vl platform.
Updates to the VOC speciation cross reference implemented in 2016v2 and carried into 2016v3 included:
• changed all 8746 to G8746 (Profile name: Rice Straw and Wheat Straw Burning Composite of
G4420 and G4421);
• changed 2104008230/330 from 1084 to 4642 to match all other RWC SCCs (corrections_changes
.docx said 4462 but this was an obvious typo and should be 4642);
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• changed 2680001000 from 0000 to G95241 TOG;
• updated cross reference to use Uinta Basin oil/gas profiles
• substituted profile 95417 with either UTUBOGC (2310010300, 2310011500, 2310111401,
2310010700, 2310010400, 31000107) or UTUBOGD (other SCCs);
• substituted profile 95418 with UTUBOGF;
• substituted profile 95419 with UTUBOGE;
• for Pennsylvania oil and gas profiles, substituted all 8949 with PAGAS01 (FIPS 42059 only),
PAGAS02 (FIPS 42019 only), PAGAS03 (FIPS 42125 only);
• for Colorado SCC 2310030300:,Set Archuleta/La Plata to SUIROGWT (counties are in Southern
Ute reservation), rest of Colorado to DJTFLR95;
• for Colorado SCC 2310030220: Set to DJTFLR95 (formerly FLR99);
• for Colorado 2310021010: Set Archul eta/La Plata to SUIROGCT (counties are in Southern Ute
reservation), rest of Colorado to 95398;
• for SCC 2310000551 (CBM produced water) use the new profile CBMPWWY.
Updates to PM speciation cross references implemented in 2016v2 and carried into 2016v3 included:
• where the comment says the "Heat Treating" profile should be used, changed the profile code to
91123 which is the actual Heat Treating profile;
• for SCC 2801500250, changed to profile SUGP02 (a new sugar cane burning profile);
• for SCC 30400740, changed to profile 95475;
• used new fire profiles for fire PM. Note that all US states (not DC/HI/PR/VI) now use one of the
new profiles for all fire SCCs, including grassland fires. The profiles themselves aren't entirely
state-specific; there are four representative states for forest fires and two representative states for
grass fires, and all states are mapped to one of the four representative forest states and one of the
two representative grass states. The GSREFs still have a non-FIPS-specific assignment to the
previous profile 3766AE6 for fires outside of the United States.
Speciation profiles and cross-references for this study platform are available in the SMOKE input files for
the 2016 platform. Emissions of VOC and PM2.5 emissions by county, sector and profile for all sectors
other than onroad mobile can be found in the sector summaries for the case. Totals of each model species
by state and sector can be found in the state-sector totals workbook for this case.
3.2.1 VOC speciation
The speciation of VOC includes HAP emissions from the NEI in the speciation process. Instead of
speciating VOC to generate all species listed in Table 3-3, emissions of five specific HAPs from the NEI
were "integrated" with the NEI VOC. These HAPs include naphthalene, benzene, acetaldehyde,
formaldehyde and methanol (collectively known as "NBAFM"). The integration combines these HAPs
with the VOC in a way that does not double count emissions and uses the HAP inventory directly in the
speciation process. The basic process is to subtract the specified HAPs emissions mass from the VOC
emissions mass, and to use a special "integrated" profile to speciate the remainder of VOC to the model
species excluding the specific HAPs. The EPA believes that the HAP emissions in the NEI are often
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more representative of emissions than HAP emissions generated via VOC speciation, although this varies
by sector.
The NBAFM HAPs were chosen for integration because they are the only explicit VOC HAPs in the
CMAQ version 5.2. Explicit means that they are not lumped chemical groups like PAR, IOLE and
several other CB6 model species. These "explicit VOC HAPs" are model species that participate in the
modeled chemistry using the CB6 chemical mechanism. The use of inventory HAP emissions along with
VOC is called "HAP-CAP integration."
The integration of HAPs with VOC is a feature available in SMOKE for all inventory formats, including
PTDAY (the format used for the ptfire and ptagfire sectors). The ability to use integration with the
PTDAY format is used for the ptfire-rx and ptfire-wild sectors in the 2016 platform, but not for the
ptagfire sector which does not include HAPs. SMOKE allows the user to specify the particular HAPs to
integrate via the INVTABLE. This is done by setting the "VOC or TOG component" field to "V" for all
HAP pollutants chosen for integration. SMOKE allows the user to also choose the particular sources to
integrate via the NHAPEXCLUDE file (which actually provides the sources to be excluded from
integration21). For the "integrated" sources, SMOKE subtracts the "integrated" HAPs from the VOC (at
the source level) to compute emissions for the new pollutant "NONHAPVOC." The user provides
NONHAPVOC-to-NONHAPTOG factors and NONHAPTOG speciation profiles.22 SMOKE computes
NONHAPTOG and then applies the speciation profiles to allocate the NONHAPTOG to the other air
quality model VOC species not including the integrated HAPs. After determining if a sector is to be
integrated, if all sources have the appropriate HAP emissions, then the sector is considered fully
integrated and does not need a NHAPEXCLUDE file. If, on the other hand, certain sources do not have
the necessary HAPs, then an NHAPEXCLUDE file must be provided based on the evaluation of each
source's pollutant mix. The EPA considered CAP-HAP integration for all sectors in determining whether
sectors would have full, no or partial integration (see Figure 3-2). For sectors with partial integration, all
sources are integrated other than those that have either the sum of NBAFM > VOC or the sum of
NBAFM = 0.
In this platform, NBAFM species are created from the no-integrate source VOC emissions using
speciation profiles and do not use HAPs from the inventory. Figure 3-2 illustrates the integrate and no-
integrate processes for U.S. Sources. Since Canada and Mexico inventories do not contain HAPs, we use
the approach of generating the HAPs via speciation, except for Mexico onroad mobile sources where
emissions for integrate HAPs were available.
It should be noted that even though NBAFM were removed from the SPECIATE profiles used to create
the GSPRO for both the NONHAPTOG TOG profiles, there still may be small fractions for "BENZ",
"FORM", "ALD2", and "MEOH" present. This is because these model species may have come from
species in SPECIATE that are mixtures. The quantity of these model species is expected to be very small
compared to the BAFM in the NEI. There are no NONHAPTOG profiles that produce "NAPH."
21 Since SMOKE version 3.7, the options to specify sources for integration are expanded so that a user can specify the
particular sources to include or exclude from integration, and there are settings to include or exclude all sources within a sector.
In addition, the error checking is significantly stricter for integrated sources. If a source is supposed to be integrated, but it is
missing NBAFM or VOC, SMOKE will now raise an error.
22 These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a specified list of
pollutants, for example NBAFM.
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In SMOKE, the INVTABLE allows the user to specify the HAPs to integrate. Two different INVTABLE
files were used for different sectors of the platform. For sectors that had no integration across the entire
sector (see Table 3-4), a "no HAP use" INVTABLE in which the "KEEP" flag was set to "N" for
NBAFM pollutants was used. Thus, any NB AFM pollutants in the inventory input into SMOKE are
automatically dropped. This approach both avoids double-counting of these species and assumes that the
VOC speciation is the best available approach for these species for sectors using this approach. The
second INVTABLE, used for sectors in which one or more sources are integrated, causes SMOKE to keep
the inventory NBAFM pollutants and indicates that they are to be integrated with VOC. This is done by
setting the "VOC or TOG component" field to "V" for all five HAP pollutants. For the onroad and
nonroad sectors, "full integration" includes the integration of benzene, 1,3 butadiene, formaldehyde,
acetaldehyde, naphthalene, acrolein, ethyl benzene, 2,2,4-Trimethylpentane, hexane, propionaldehyde,
styrene, toluene, xylene, and methyl tert-butyl ether (MTBE).
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation
; Emissions Ready for SMOKE
CMAQ-CB6 species
Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol
(NBAFM) for each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
ptegu
No integration, create NBAFM from VOC speciation
ptnonipm
No integration, create NBAFM from VOC speciation
ptfire-rx
Partial integration (NBAFM)
ptfire-wild
Partial 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)
ptfire othna
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptagfire
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
airports
No integration, create NBAFM from VOC speciation
afdust
N/A - sector contains no VOC
beis
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
cmv clc2
Full integration (NBAFM)
cmv c3
Full integration (NBAFM)
fertilizer
N/A - sector contains no VOC
livestock
Partial integration (NBAFM)
rail
Full integration (NBAFM)
nonpt
Partial integration (NBAFM)
np solvents
Partial integration (NBAFM)
nonroad
Full integration (internal to MOVES)
np oilgas
Partial integration (NBAFM)
othpt
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
pt oilgas
No integration, create NBAFM from VOC speciation
rwc
Partial integration (NBAFM)
onroad
Full integration (internal to MOVES); however, MOVES2014a speciation was CB6-CAMx,
not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-CMAQ
onroad can
No integration, no NBAFM in inventory, create NBAFM from speciation
onroadmex
Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation was
CB6-CAMx, not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-CMAQ
othafdust
N/A - sector contains no VOC
othptdust
N/A - sector contains no VOC
othar
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
Canada ag
No integration, no NBAFM in inventory, create NBAFM from speciation
Canada og2D
No integration, no NBAFM in inventory, create NBAFM from speciation
Integration for the mobile sources estimated from MOVES (onroad and nonroad sectors, other than for
California) is done differently. Briefly, there are three major differences: 1) for these sources integration
is done using more than just NBAFM, 2) all sources from the MOVES model are integrated, and 3)
integration is done fully or partially within MOVES. For onroad mobile, speciation is done fully within
MOVES3 such that the MOVES model outputs emission factors for individual VOC model species along
with the HAPs. This requires MOVES to be run for a specific chemical mechanism. For this platform
MOVES was run for the CB6R3AE7 mechanism. Following the run of SMOKE-MOVES, NMOG
emissions were added to the data files through a post-SMOKE processor.
For nonroad mobile, speciation is partially done within MOVES such that it does not need to be run for a
specific chemical mechanism. For nonroad, MOVES outputs emissions of HAPs and NONHAPTOG are
split by speciation profile. Taking into account that integrated species were subtracted out by MOVES
already, the appropriate speciation profiles are then applied in SMOKE to get the VOC model species.
HAP integration for nonroad uses the same additional HAPs and ethanol as for onroad.
3.2.1.1 County specific profile combinations
SMOKE can compute speciation profiles from mixtures of other profiles in user-specified proportions via
two different methods. The first method, which uses a GSPRO COMBO file, has been in use since the
2005 platform; the second method (GSPRO with fraction) was used for the first time in the 2014v7.0
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platform. The GSPROCOMBO method uses profile combinations specified in the GSPROCOMBO
ancillary file by pollutant (which can include emissions mode, e.g., EXH VOC), state and county (i.e.,
state/county FIPS code) and time period (i.e., month). Different GSPRO COMBO files can be used by
sector, allowing for different combinations to be used for different sectors; but within a sector, different
profiles cannot be applied based on SCC. The GSREF file indicates that a specific source uses a
combination file with the profile code "COMBO." SMOKE computes the resultant profile using the
fraction of each specific profile assigned by county, month and pollutant.
Starting with the 2016v7.2 beta and regional haze platforms, a GSPRO COMBO is used to specify a mix
of EO and E10 fuels in Canada. ECCC provided percentages of ethanol use by province, and these were
converted into EO and E10 splits. For example, Alberta has 4.91% ethanol in its fuel, so we applied a mix
of 49.1% E10 profiles (4.91% times 10, since 10% ethanol would mean 100% E10), and 50.9% EO fuel.
Ethanol splits for all provinces in Canada are listed in Table 3-5. The Canadian onroad inventory includes
four distinct FIPS codes in Ontario, allowing for application of different E0/E10 splits in Southern
Ontario versus Northern Ontario. In Mexico, only EO profiles are used.
Table 3-5. Ethanol percentages by volume by Canadian province
Province
Ethanol % by volume (E10 = 10%)
Alberta
4.91%
British Columbia
5.57%
Manitoba
9.12%
New Brunswick
4.75%
Newfoundland & Labrador
0.00%
Nova Scotia
0.00%
NW Territories
0.00%
Nunavut
0.00%
Ontario (Northern)
0.00%
Ontari o ( S outhern)
7.93%
Prince Edward Island
0.00%
Quebec
3.36%
Saskatchewan
7.73%
Yukon
0.00%
A new method to combine multiple profiles became available in SMOKE4.5. It allows multiple profiles
to be combined by pollutant, state and county (i.e., state/county FIPS code) and SCC. This was used
specifically for the oil and gas sectors (pt oilgas and np oilgas) because SCCs include both controlled
and uncontrolled oil and gas operations which use different profiles.
3.2.1.2 Additional sector specific considerations for integrating HAP
emissions from inventories into speciation
The decision to integrate HAP emissions into the speciation was made on a sector-by-sector basis. For
some sectors, there is no integration and VOC is speciated directly; for some sectors, there is full
integration meaning all sources are integrated; and for other sectors, there is partial integration, meaning
some sources are not integrated and other sources are integrated. The integrated HAPs are either NBAFM
or, in the case of MOVES (onroad, nonroad, and MOVES-Mexico), a larger set of HAPs plus ethanol are
integrated. Table 3-4 above summarizes the integration method for each platform sector.
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Speciation for the onroad sector is unique. First, SMOKE-MOVES is used to create emissions for these
sectors and both the MEPROC and INVTABLE files are involved in controlling which pollutants are
processed. Second, the speciation occurs within MOVES itself, not within SMOKE. The advantage of
using MOVES to speciate VOC is that during the internal calculation of MOVES, the model has complete
information on the characteristics of the fleet and fuels (e.g., model year, ethanol content, process, etc.),
thereby allowing it to more accurately make use of specific speciation profiles. This means that MOVES
produces emission factor tables that include inventory pollutants (e.g., TOG) and model-ready species
(e.g., PAR, OLE, etc).23 SMOKE essentially calculates the model-ready species by using the appropriate
emission factor without further speciation.24 Third, MOVES' internal speciation uses full integration of
an extended list of HAPs beyond NBAFM (called "M-profiles"). The M-profiles integration is very
similar to NBAFM integration explained above except that the integration calculation (see Figure 3-2) is
performed on emissions factors instead of on emissions, and a much larger set of pollutants are integrated
besides NBAFM. The list of integrated pollutants is described in Table 3-6. An additional run of the
Speciation Tool was necessary to create the M-profiles that were then loaded into the MOVES default
database. Fourth, for California, the EPA applied adjustment factors to SMOKE-MOVES to produce
California adjusted model-ready files. By applying the ratios through SMOKE-MOVES, the CARB
inventories are essentially speciated to match EPA estimated speciation. This resulted in changes to the
VOC HAPs from what CARB submitted to the EPA.
Table 3-6. MOVES integrated species in M-profiles
MOVES ID
Pollutant Name
5
Methane (CH4)
20
Benzene
21
Ethanol
22
MTBE
24
1,3-Butadiene
25
Formaldehyde
26
Acetaldehyde
27
Acrolein
40
2,2,4-Trimethylpentane
41
Ethyl Benzene
42
Hexane
43
Propionaldehyde
44
Styrene
45
Toluene
46
Xylene
185
Naphthalene gas
23 Because the EF table has the speciation "baked" into the factors, all counties that are in the county group (i.e., are mapped to
that representative county) will have the same speciation.
24 For more details on the use of model-ready EF, see the SMOKE 3.7 documentation:
https ://www. cmascenter. org/smoke/documentation/3.7/html/.
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For the nonroad sector, all sources are integrated using the same list of integrated pollutants as shown in
Table 3-6. The integration calculations are performed within MOVES. For California and Texas, all
VOC HAPs were recalculated using MOVES HAP/VOC ratios based on the MOVES run so that VOC
speciation methodology would be consistent across the country. NONHAPTOG emissions by speciation
profile were also calculated based on MOVES data in California in Texas.
For nonroad emissions in California and Texas, where state-provided emissions were used, MOVES-style
speciation has been implemented in 2016v2 and carried into 2016v3, with NONHAPTOG and PM2.5 pre-
split by profiles and with all the HAPs needed for VOC speciation augmented based on MOVES data in
CA and TX. This means in 2016v2 and 2016v3, onroad emissions in California and Texas are speciated
consistently with the rest of the country, while in 2016vl they were speciated using older speciation
profiles.
MOVES-MEXICO for onroad used the same speciation approach as for the U.S. in that the larger list of
species shown in Table 3-6 was used. However, MOVES-MEXICO used an older version of the CB6
mechanism sometimes referred to as "CB6-CAMx". That mechanism is missing the model species
XYLMN and SOAALK and were added post-SMOKE as follows:
• XYLMN = XYL[1]-0.966*NAPHTHALENE[1]
• PAR = PAR[l]-0.00001 *NAPHTHALENE[1]
• SOAALK = 0.108*PAR[1]
The CB6R3AE7 mechanism includes other new species which are not part of CB6-CAMx, such as IVOC.
CB6R3AE7-specific species were not added to the MOVES-MEXICO emissions because those extra
species would be expected to have only a minor impact.
For the beis sector, the speciation profiles used by BEIS are not included in SPECIATE. BEIS4 includes
the species (SESQ) that is mapped to the BEIS model species SESQT (Sesquiterpenes). The profile code
associated with BEIS4 for use with CB05 is "B10C5," while the profile for use with CB6 is "B10C6."
The main difference between the profiles is the explicit treatment of acetone emissions in B10C6. The
biogenic speciation files are managed in the CMAQ Github repository 25.
3.2.1.3 Oil and gas related speciation profiles
Several oil and gas profiles were developed or assigned to sources in np oilgas and pt oilgas to better
reflect region-specific differences in VOC composition and whether the process SCC would include
controlled emissions, considering the controls are not part of the SCC. For example, SCC 2310030300
(Gas Well Water Tank Losses) in Colorado are controlled by a 95% efficient flare, so a profile
(DJTFLR95) was developed to represent the composition of the VOC exiting the flare. Region-specific
profiles were also available for several areas, some of which were included in SPECIATE5.1 and others
added to SPECIATE v5.2. These profiles are used in the 2016v3 platform and are listed in Appendix B.
Additional documentation is available in the SPECIATE database.
For the profiles in SPECIATE v5.2:
• The Southern Ute profiles (SUIROGCT and SUIROGWT) applied to Archuleta and La Plata
counties in southwestern Colorado were developed from data provided in Tables 19 and 20 of the
report by Oakley Hayes, Matt Wampler, Danny Powers (December 2019), "Final Report for 2017
25 https://github.eom/USEPA/CMAO/blob/main/CCTM/src/bio g/beis4/gspro bio genics .txt.
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Southern Ute Indian Tribe Comprehensive Emissions Inventory for Criteria Pollutants, Hazardous
Air Pollutants, and Greenhouse Gases."26
• A composite coal bed methane produced water profile, CBMPWWY, was developed by
compositing a subset of the SPECIATE 5.0 pond profiles associated with coal bed methane wells.
The SPECIATE 5.0 pond profiles were developed based on the publication: "Lyman, Seth N,
Marc L Mansfield, Huy NQ Tran, Jordan D Evans, Colleen Jones, Trevor O'Neil, Ric Bowers,
Ann Smith, and Cara Keslar. 2018. 'Emissions of Organic Compounds from Produced Water
Ponds I: Characteristics and Speciation', Science of the Total Environment, 619: 896-90527." Note
that the pond profiles from this publication are included in SPECIATE 5.0; but a composite to
represent coal bed methane wells had not been developed for SPECIATE 5.0 and this new profile
is in SPECIATE 5.2.
• The DJTFLR95 profile, DJ Condensate Flare Profile with DRE 95%, filled a need for the flared
condensate and produced water tanks for Colorado's oil and gas operations. This profile was
developed using the same approach as was used for the FLR99 (and other FLR**) SPECIATE 4.5
profiles, but instead of using profile 8949 for the uncombusted gas, it uses the Denver-Julesburg
Basin Condensate composite (95398) and it quantifies the combustion by-products based on a
95% DRE. The approach for combining profile 95398 with combustion by-products based on the
TCEQ's flare study (Allen, David T, and Vincent M Torres, University of Texas, Austin. 2011.
'TCEQ 2010 Flare Study Final Report', Texas Commission on Environmental Quality28) is the
same as used in the workbook for the FLR** SPECIATE4.5 profiles and can be found in the flr99
zip file referenced in the SPECIATE database. The approach uses the analysis developed by
Ramboll (Ramboll and EPA, 2017).
In addition to region-specific assignments, multiple profiles were assigned to select county/SCC
combinations using the SMOKE feature discussed in 3.2.1.1. Oil and gas SCCs for associated gas,
condensate tanks, crude oil tanks, dehydrators, liquids unloading and well completions represent the total
VOC from the process, including the portions of process that may be flared or directed to a reboiler. For
example, SCC 2310021400 (gas well dehydrators) consists of process, reboiler, and/or flaring
emissions. There are not separate SCCs for the flared portion of the process or the reboiler. However, the
VOC associated with these three portions can have very different speciation profiles. Therefore, it is
necessary to have an estimate of the amount of VOC from each of the portions (process, flare, reboiler) so
that the appropriate speciation profiles can be applied to each portion. The Nonpoint Oil and Gas
Emission Estimation Tool generates an intermediate file which provides flare, non-flare (process), and
reboiler (for dehydrators) emissions for six source categories that have flare emissions: by county FIPS
and SCC code for the U.S. From these emissions the fraction of the emissions to assign to each profile
was computed and incorporated into the 2016v2 and v3 platforms. These fractions can vary by county
FIPS, because they depend on the level of controls, which is an input to the Speciation Tool.
26 https://www.southernute-nsn.gov/wp-content/uploads/sites/15/2019/12/191203-SUIT-CY2017-Emissions-Inventorv-Report-
FINAL.pdf.
27 http://doi.Org/10.1016/i.scitotenv.2017.ll.161.
28 https://downloads.regulations.gov/EPA-HQ-OAR-2012-0133-0Q47/attachment 32.pdf
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Table 3-7. Basin/Region-specific profiles for oil and gas
Profile Code
Description
Region
(if not in
profile
name)
DJVNT R
Denver-Julesburg Basin Produced Gas Composition from Non-CBM Gas Wells
PNC01 R
Piceance Basin Produced Gas Composition from Non-CBM Gas Wells
PNC02 R
Piceance Basin Produced Gas Composition from Oil Wells
PNC03 R
Piceance Basin Flash Gas Composition for Condensate Tank
PNCDH
Piceance Basin, Glycol Dehydrator
PRBCB R
Powder River Basin Produced Gas Composition from CBM Wells
PRBCO R
Powder River Basin Produced Gas Composition from Non-CBM Wells
PRM01 R
Permian Basin Produced Gas Composition for Non-CBM Wells
SSJCB R
South San Juan Basin Produced Gas Composition from CBM Wells
SSJCO R
South San Juan Basin Produced Gas Composition from Non-CBM Gas Wells
SWFLA R
SW Wyoming Basin Flash Gas Composition for Condensate Tanks
SWVNT R
SW Wyoming Basin Produced Gas Composition from Non-CBM Wells
UNT01 R
Uinta Basin Produced Gas Composition from CBM Wells
WRBCO R
Wind River Basin Produced Gagres Composition from Non-CBM Gas Wells
95087a
Oil and Gas - Composite - Oil Field - Oil Tank Battery Vent Gas
East
Texas
95109a
Oil and Gas - Composite - Oil Field - Condensate Tank Battery Vent Gas
East
Texas
95417
Uinta Basin, Untreated Natural Gas
95418
Uinta Basin, Condensate Tank Natural Gas
95419
Uinta Basin, Oil Tank Natural Gas
95420
Uinta Basin, Glycol Dehydrator
95398
Composite Profile - Oil and Natural Gas Production - Condensate Tanks
Denver-
Julesburg
95399
Composite Profile - Oil Field - Wells
California
95400
Composite Profile - Oil Field - Tanks
California
95403
Composite Profile - Gas Wells
San
Joaquin
UTUBOGC
Raw Gas from Oil Wells - Composite Uinta basin
UTUBOGD
Raw Gas from Gas Wells - Composite Uinta basin
UTUBOGE
Flash Gas from Oil Tanks - including Carbonyls - Composite Uinta basin
UTUBOGF
Flash Gas from Condensate Tanks - including Carbonyls - Composite Uinta basin
PAGAS01
Oil and Gas-Produced Gas Composition from Gas Wells-Greene Co, PA
PAGAS02
Oil and Gas-Produced Gas Composition from Gas Wells-Butler Co, PA
PAGAS03
Oil and Gas-Produced Gas Composition from Gas Wells-Washington Co, PA
SUIROGCT
Flash Gas from Condensate Tanks - Composite Southern Ute Indian Reservation
CMU01
Oil and Gas - Produced Gas Composition from Gas Wells - Central Montana Uplift
- Montana
WIL01
Oil and Gas - Flash Gas Composition from Tanks at Oil Wells - Williston Basin
North Dakota
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Profile Code
Description
Region
(if not in
profile
name)
WIL02
Oil and Gas - Flash Gas Composition from Tanks at Oil Wells - Williston Basin
Montana
WIL03
Oil and Gas - Produced Gas Composition from Oil Wells - Williston Basin North
Dakota
WIL04
Oil and Gas - Produced Gas Composition from Oil Wells - Williston Basin Montana
3.2.1.4 Mobile source related VOC speciation profiles
The VOC speciation approach for mobile source and mobile source-related source categories is
customized to account for the impact of fuels and engine type and technologies. The impact of fuels also
affects the parts of the nonpt and ptnonipm sectors that are related to mobile sources such as portable fuel
containers and gasoline distribution.
The VOC speciation profiles for the nonroad sector other than for California are listed in Table 3-8. They
include new profiles (i.e., those that begin with "953") for 2-stroke and 4-stroke gasoline engines running
on EO and E10 and compression ignition engines with different technologies developed from recent EPA
test programs, which also supported the updated toxics emission factor in MOVES2014a (Reichle, 2015
and EPA, 2015b).
Table 3-8. TOG MOVES-SMOKE Speciation for nonroad emissions used for the 2016 Platform
Profile
Profile Description
Engine
Type
Engine
Technology
Engine
Size
Horse-
power
category
Fuel
Fuel
Sub-
type
Emission
Process
95327
SI 2-stroke E0
SI 2-stroke
all
All
All
Gasoline
E0
exhaust
95328
SI 2-stroke E10
SI 2-stroke
all
All
All
Gasoline
E10
exhaust
95329
SI 4-stroke E0
SI 4-stroke
all
All
All
Gasoline
E0
exhaust
95330
SI 4-stroke E10
SI 4-stroke
all
All
All
Gasoline
E10
exhaust
95331
CI Pre-Tier 1
CI
Pre-Tier 1
All
All
Diesel
All
exhaust
95332
CI Tier 1
CI
Tier 1
All
All
Diesel
All
exhaust
95333
CI Tier 2
CI
Tier 2 and 3
all
All
Diesel
All
exhaust
95333a
29
CI Tier 2
CI
Tier 4
<56 kW
(75 hp)
S
Diesel
All
exhaust
8775
ACES Phase 1 Diesel
Onroad
CI Tier 4
Tier 4
>=56 kW
(75 hp)
L
Diesel
All
exhaust
8753
E0 Evap
SI
all
all
All
Gasoline
E0
evaporative
8754
E10 Evap
SI
all
all
All
Gasoline
E10
evaporative
8766
E0 evap permeation
SI
all
all
All
Gasoline
E0
permeation
8769
E10 evap permeation
SI
all
all
All
Gasoline
E10
permeation
8869
E0 Headspace
SI
all
all
All
Gasoline
E0
headspace
8870
E10 Headspace
SI
all
all
All
Gasoline
E10
headspace
29 95333a replaced 95333. This correction was made to remove alcohols due to suspected contamination. Additional
information is available in SPECIATE.
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Horse-
Fuel
Engine
Engine
Engine
power
Sub-
Emission
Profile
Profile Description
Type
Technology
Size
category
Fuel
type
Process
1001
CNG Exhaust
All
all
all
All
CNG
All
exhaust
8860
LPG exhaust
All
all
all
All
LPG
All
exhaust
Speciation profiles for VOC in the nonroad sector account for the ethanol content of fuels across years. A
description of the actual fuel formulations can be found in the NEI TSD. For previous platforms, the EPA
used "COMBO" profiles to model combinations of profiles for EO and E10 fuel use, but beginning with
2014v7.0 platform, the appropriate allocation of EO and E10 fuels is done by MOVES.
Combination profiles reflecting a combination of E10 and EO fuel use ideally would be used for sources
upstream of mobile sources such as portable fuel containers (PFCs) and other fuel distribution operations
associated with the transfer of fuel from bulk terminals to pumps (BTP), which are in the nonpt sector.
For these sources, ethanol may be mixed into the fuels, in which case speciation would change across
years. The speciation changes from fuels in the ptnonipm sector include BTP distribution operations
inventoried as point sources. Refinery-to-bulk terminal (RBT) fuel distribution and bulk plant storage
(BPS) speciation does not change across the modeling cases because this is considered upstream from the
introduction of ethanol into the fuel. The mapping of fuel distribution SCCs to PFC, BTP, BPS, and RBT
emissions categories can be found in Appendix C. In 2016v3 platform, all of these sources get E10
speciation.
Table 3-9 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for
2016. The term "COMBO" indicates that a combination of the profiles listed was used to speciate that
subcategory using the GSPROCOMBO file.
Table 3-9. Select mobile-related VOC profiles 2016
Sector
Sub-category
Profile
Nonroad non-US
gasoline exhaust
COMBO
8750a
Pre-Tier 2 E0 exhaust
8751a
Pre-Tier 2 E10 exhaust
nonpt/
ptnonipm
PFC and BTP
COMBO
8869
E0 Headspace
8870
E10 Headspace
nonpt/
ptnonipm
Bulk plant storage (BPS)
and refine-to-bulk terminal
(RBT) sources
8870
E10 Headspace
The speciation of onroad VOC occurs completely within MOVES. MOVES accounts for fuel type and
properties, emission standards as they affect different vehicle types and model years, and specific
emission processes. Table 3-10 describes the M-profiles available to MOVES depending on the model
year range, MOVES process (processID), fuel sub-type (fuelSubTypelD), and regulatory class
(regClassID). m While MOVES maps the liquid diesel profile to several processes, MOVES only
estimates emissions from refueling spillage loss (processID 19). The other evaporative and refueling
processes from diesel vehicles have zero emissions.
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Table 3-11 through Table 3-13 describe the meaning of these MOVES codes. For a specific
representative county and analytic year, there will be a different mix of these profiles. For example, for
HD diesel exhaust, the emissions will use a combination of profiles 8774M and 8775M depending on the
proportion of HD vehicles that are pre-2007 model years (MY) in that particular county. As that county is
projected farther into the future, the proportion of pre-2007 MY vehicles will decrease. A second
example, for gasoline exhaust (not including E-85), the emissions will use a combination of profiles
8756M, 8757M, 8758M, 8750aM, and 875 laM. Each representative county has a different mix of these
key properties and, therefore, has a unique combination of the specific M-profiles. More detailed
information on how MOVES speciates VOC and the profiles used is provided in the technical document,
"Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles in
MOVES2014" (EPA, 2015c).
Table 3-10. Onroad M-profiles
Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
1001M
CNG Exhaust
1940-2050
1,2,15,16
30
48
4547M
Diesel Headspace
1940-2050
11
20,21,22
0
4547M
Diesel Headspace
1940-2050
12,13,18,19
20,21,22
10,20,30,40,41,
42,46,47,48
8753M
E0 Evap
1940-2050
12,13,19
10
10,20,30,40,41,42,
46,47,48
8754M
E10 Evap
1940-2050
12,13,19
12,13,14
10,20,30,40,41,
42,46,47,48
8756M
Tier 2 E0 Exhaust
2001-2050
1,2,15,16
10
20,30
8757M
Tier 2 E10 Exhaust
2001-2050
1,2,15,16
12,13,14
20,30
8758M
Tier 2 El5 Exhaust
1940-2050
1,2,15,16
15,18
10,20,30,40,41,
42,46,47,48
8766M
E0 evap permeation
1940-2050
11
10
0
8769M
E10 evap permeation
1940-2050
11
12,13,14
0
8770M
El5 evap permeation
1940-2050
11
15,18
0
8774M
Pre-2007 MY HDD
exhaust
1940-2006
1,2,15,16,17,90
20, 21, 22
40,41,42,46,47, 48
8774M
Pre-2007 MY HDD
exhaust
1940-2050
9130
20,21,22
46,47
8774M
Pre-2007 MY HDD
exhaust
1940-2006
1,2,15,16
20,21,22
20,30
8775M
2007+ MY HDD
exhaust
2007-2050
1,2,15,16
20, 21, 22
20,30
8775M
2007+ MY HDD
exhaust
2007-2050
1,2,15,16,17,90
20, 21, 22
40,41,42,46,47,48
8855M
Tier 2 E85 Exhaust
1940-2050
1,2,15,16
50, 51, 52
10,20,30,40,41,
42,46,47,48
8869M
E0 Headspace
1940-2050
18
10
10,20,30,40,41,
42,46,47,48
8870M
E10 Headspace
1940-2050
18
12,13,14
10,20,30,40,41,
42,46,47,48
30 91 is the processed for APUs which are diesel engines not covered by the 2007 Heavy-Duty Rule, so the older technology
applies to all years.
106
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Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
8871M
El5 Headspace
1940-2050
18
15,18
10,20,30,40,41,
42,46,47,48
8872M
El5 Evap
1940-2050
12,13,19
15,18
10,20,30,40,41,
42,46,47,48
8934M
E85 Evap
1940-2050
11
50,51,52
0
8934M
E85 Evap
1940-2050
12,13,18,19
50,51,52
10,20,30,40,41,
42,46,47,48
8750aM
Pre-Tier 2 E0 exhaust
1940-2000
1,2,15,16
10
20,30
8750aM
Pre-Tier 2 E0 exhaust
1940-2050
1,2,15,16
10
10,40,41,42,46,47,48
875 laM
Pre-Tier 2 E10 exhaust
1940-2000
1,2,15,16
11,12,13,14
20,30
875 laM
Pre-Tier 2 E10 exhaust
1940-2050
1,2,15,16
11,12,13,14,15,
1831
10,40,41,42,46,47,48
95120m
Liquid Diesel
19602060
11
20,21,22
0
95120m
Liquid Diesel
19602060
12,13,18,19
20,21,22
10,20,30,40,41,42,46,47,4
8
95335a
2010+MY HDD
exhaust
20102060
1,2,15,16,17,90
20,21,22
40,41,42,46,47,48
m While MOVES maps the liquid diesel profile to several processes, MOVES only estimates emissions from
refueling spillage loss (processID 19). The other evaporative and refueling processes from diesel vehicles have zero
emissions.
Table 3-11. MOVES process IDs
Process ID
Process Name
1
Running Exhaust*
2
Start Exhaust
9
Brakewear
10
Tirewear
11
Evap Permeation
12
Evap Fuel Vapor Venting
13
Evap Fuel Leaks
15
Crankcase Running Exhaust*
16
Crankcase Start Exhaust
17
Crankcase Extended Idle Exhaust
18
Refueling Displacement Vapor Loss
19
Refueling Spillage Loss
20
Evap Tank Permeation
21
Evap Hose Permeation
22
Evap RecMar Neck Hose Permeation
23
Evap RecMar Supply/Ret Hose Permeation
24
Evap RecMar Vent Hose Permeation
30
Diurnal Fuel Vapor Venting
31 The profile assignments for pre-2001 gasoline vehicles fueled on E15/E20 fuels (subtypes 15 and 18) were corrected for
MOVES2014a. This model year range, process, fuelsubtype regclass combination is already assigned to profile 8758.
107
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31
HotSoak Fuel Vapor Venting
32
RunningLoss Fuel Vapor Venting
40
Nonroad
90
Extended Idle Exhaust
91
Auxiliary Power Exhaust
* Off-network idling is a process in MOVE S3 that is part ofprocesses 1 and 15
but assigned to road type 1 (off-network) instead of types 2-5
Table 3-12. MOVES Fuel subtype IDs
Fuel Subtype ID
Fuel Subtype Descriptions
10
Conventional Gasoline
11
Reformulated Gasoline (RFG)
12
Gasohol (E10)
13
Gasohol (E8)
14
Gasohol (E5)
15
Gasohol (E15)
18
Ethanol (E20)
20
Conventional Diesel Fuel
21
Biodiesel (BD20)
22
Fischer-Tropsch Diesel (FTD100)
30
Compressed Natural Gas (CNG)
50
Ethanol
51
Ethanol (E85)
52
Ethanol (E70)
Table 3-13. MOVES regclass IDs
Reg. Class ID
Regulatory Class Description
0
Doesn't Matter
10
Motorcycles
20
Light Duty Vehicles
30
Light Duty Trucks
40
Class 2b Trucks with 2 Axles and 4 Tires (8,500 lbs < GVWR <= 10,000 lbs)
41
Class 2b Trucks with 2 Axles and at least 6 Tires or Class 3 Trucks (8,500 lbs < GVWR <= 14,000
lbs)
42
Class 4 and 5 Trucks (14,000 lbs < GVWR <= 19,500 lbs)
46
Class 6 and 7 Trucks (19,500 lbs < GVWR <= 33,000 lbs)
47
Class 8a and 8b Trucks (GVWR > 33,000 lbs)
48
Urban Bus (see CFR Sec 86.091 2)
For portable fuel containers (PFCs) and fuel distribution operations associated with the bulk-plant-to-
pump (BTP) distribution, a 10% ethanol mix (E10) was assumed for speciation purposes. Refinery to
bulk terminal (RBT) fuel distribution and bulk plant storage (BPS) speciation are considered upstream
from the introduction of ethanol into the fuel; therefore, a single profile is sufficient for these sources. No
108
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refined information on potential VOC speciation differences between cellulosic diesel and cellulosic
ethanol sources was available; therefore, cellulosic diesel and cellulosic ethanol sources used the same
SCC (30125010: Industrial Chemical Manufacturing, Ethanol by Fermentation production) for VOC
speciation as was used for corn ethanol plants.
3.2.2 PM speciation
In addition to VOC profiles, the SPECIATE database also contains profiles for speciating PM2.5. PM2.5
was speciated into the AE6 species associated with CMAQ 5.0.1 and later versions. Most of the PM
profiles come from the 911XX series (Reff et. al, 2009), which include updated AE6 speciation.32
Starting with the 2014v7.1 platform, profile 91112 (Natural Gas Combustion - Composite) was replaced
with 95475 (Composite -Refinery Fuel Gas and Natural Gas Combustion). This updated profile is an
AE6-ready profile based on the median of 3 SPECIATE4.5 profiles from which AE6 versions were made
and the resulting profile added to SPECIATE5.0: boilers (95125a), process heaters (95126a) and internal
combustion combined cycle/cogen plant exhaust (95127a). As with profile 91112, these profiles are
based on tests using natural gas and refinery fuel gas (England et al., 2007). Profile 91112 which is also
based on refinery gas and natural gas is thought to overestimate EC.
Profile 95475 (Composite -Refinery Fuel Gas and Natural Gas Combustion) is shown along with the
underlying profiles composited in Figure 3-3. Figure 3-4 shows a comparison of the new profile as of the
2014v7.1 platform with the one that we had been using in the 2014v7.0 and earlier platforms.
The newest PM profile for the 2016v2 platform that is also included in the 2016v3 platform is the Sugar
Cane Pre-Harvest Burning Mexico profile (SUGP02). This profile falls under the sector ptagfire and are
included in SPECIATE 5.2.
Additionally, a series of regional fire profiles were added to SPECIATE 5.1 and used in 2016v2 and
2016v3. These fall under the sector ptfire and are as shown in Table 3-14.
Table 3-14. Regional Fire Profiles
Sector
Pollutan
t
Profile
Code
Profile Description
Ptfire
PM
95793
Forest Fire-Flaming-Oregon AE6
Ptfire
PM
95794
Forest Fire-Smoldering-Oregon AE6
Ptfire
PM
95798
Forest Fire-Flaming-North Carolina AE6
Ptfire
PM
95799
Forest Fire-Smoldering-North Carolina AE6
Ptfire
PM
95804
Forest Fire-Flaming-Montana AE6
Ptfire
PM
95805
Forest Fire-Smoldering-Montana AE6
Ptfire
PM
95807
Forest Fire Understory-Flaming-Minnesota AE6
Ptfire
PM
95808
Forest Fire Understory-Smoldering-Minnesota AE6
Ptfire
PM
95809
Grass Fire-Field-Kansas AE6
32 The exceptions are 5675AE6 (Marine Vessel - Marine Engine - Heavy Fuel Oil) used for cmv_c3 and 92018 (Draft
Cigarette Smoke - Simplified) used in nonpt. 5675AE6 is an update of profile 5675 to support AE6 PM speciation.
109
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Figure 3-3. Profiles composited for PIY1 gas combustion related sources
Zinc
Sulfate
Silicon
Potassium
Particulate Non-Carbon Organic Matter
Other Unspeciated PM2.5
Organic carbon
Nitrate
Nickel
Metal-bound Oxygen
Iron
Elemental Carbon
Copper
Chloride ion
Calcium
Bromine Atom
Ammonium
Aluminum
0 10 20 30 40 50
Weight Percent
I Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
Gas-fired process heater exhaust 95126a
s Gas-fired internal combustion combined cycle/cogeneration plant exhaust 95127a
Gas-fired boiler exhaust 95125a
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources
Zinc
Sulfate
Silicon
Potassium
Particulate Non-Carbon Organic Matter
Other Unspeciated PM2.5
Organic carbon
Nitrate
Nickel
Metal-bound Oxygen
Iron
Elemental Carbon
Copper
Chloride ion
Calcium
Bromine Atom
Ammonium
Aluminum
20 30 40
Weight Percent
I Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
Natural Gas Combustion - Composite 91112
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3.2.2.1 Mobile source related PM2.5 speciation profiles
For the onroad sector, for all processes except brake and tire wear, PM speciation occurs within MOVES
itself, not within SMOKE (similar to the VOC speciation described above). The advantage of using
MOVES to speciate PM is that during the internal calculation of MOVES, the model has complete
information on the characteristics of the fleet and fuels (e.g., model year, sulfur content, process, etc.) to
accurately match to specific profiles. This means that MOVES produces EF tables that include total PM
(e.g., PMio and PM2.5) and speciated PM (e.g., PEC, PFE). SMOKE essentially calculates the PM
components by using the appropriate EF without further speciation.33 The specific profiles used within
MOVES include two CNG profiles, 45219 and 45220, which were added to SPECIATE4.5. A list of
profiles is provided in the technical document, "Speciation of Total Organic Gas and Particulate Matter
Emissions from On-road Vehicles in MOVES2014" (EPA, 2015c). No changes to the mobile source PM
speciation profiles were made in the 2016v3 platform.
For onroad brake and tire wear, the PM is speciated in the moves2smk postprocessor that prepares the
emission factors for processing in SMOKE. The formulas for this are based on the standard speciation
factors from brake and tire wear profiles, which were updated from the v6.3 platform based on data from
a Health Effects Institute report (Schauer, 2006). Table 3-15 shows the differences in the v7.1 (alpha) and
201 lv6.3 profiles.
Table 3-15. Brake and tire PM2.5 profiles compared to those used in the 2011v6.3 Platform
Inventory
Model
V6.3 platform
SPECIATE4.5
V6.3
SPECIATE4.5
Pollutant
Species
brakewear
brakewear profile:
platform
tirewear profile:
profile: 91134
95462 from
tirewear
95460 from
Schauer(2006)
profile:
Schauer(2006)
91150
PM2 5
PAL
0.00124
0.000793208
6.05E-04
3.32401E-05
PM2 5
PCA
0.01
0.001692177
0.00112
PM2 5
PCL
0.001475
0.0078
PM2 5
PEC
0.0261
0.012797085
0.22
0.003585907
PM2 5
PFE
0.115
0.213901692
0.0046
0.00024779
PM2 5
PH20
0.0080232
0.007506
PM2 5
PK
1.90E-04
0.000687447
3.80E-04
4.33129E-05
PM2 5
PMG
0.1105
0.002961309
3.75E-04
0.000018131
PM2 5
PMN
0.001065
0.001373836
1.00E-04
1.41E-06
PM2 5
PMOTHR
0.4498
0.691704999
0.0625
0.100663209
PM2 5
PNA
1.60E-04
0.002749787
6.10E-04
7.35312E-05
PM2 5
PNCOM
0.0428
0.020115749
0.1886
0.255808124
PM2 5
PNH4
3.00E-05
1.90E-04
PM2 5
PN03
0.0016
0.0015
PM2 5
POC
0.107
0.050289372
0.4715
0.639520309
PM2 5
PSI
0.088
0.00115
PM2 5
PS04
0.0334
0.0311
PM2 5
PTI
0.0036
0.000933341
3.60E-04
5.04E-06
33 Unlike previous platforms, the PM components (e.g., POC) are now consistently defined between MOVES2014 and CMAQ.
For more details on the use of model-ready EF, see the SMOKE 3.7 documentation:
https ://www. cmascenter. org/smoke/documentation/3.7/html/.
Ill
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The formulas used based on brake wear profile 95462 and tire wear profile 95460 are as follows:
POC = 0.6395 * PM25TIRE + 0.0503 * PM25BRAKE
PEC = 0.0036 * PM25TIRE + 0.0128 * PM25BRAKE
PN03 = 0.000 * PM25TIRE + 0.000 * PM25BRAKE
PS04 = 0.0 * PM25TIRE + 0.0 * PM25BRAKE
PNH4 = 0.000 * PM25TIRE + 0.0000 * PM25BRAKE
PNCOM = 0.2558 * PM25TIRE + 0.0201 * PM25BRAKE
For California onroad emissions, adjustment factors were applied to SMOKE-MOVES to produce
California adjusted model-ready files. California did not supply speciated PM, therefore, the adjustment
factors applied to PM2.5 were also applied to the speciated PM components. By applying the ratios
through SMOKE-MOVES, the CARB inventories are essentially speciated to match EPA estimated
speciation.
For nonroad PM2.5, speciation is partially done within MOVES such that it does not need to be run for a
specific chemical mechanism. For nonroad, MOVES outputs emissions of PM2.5 split by speciation
profile. Similar to how VOC and NONHAPTOG are speciated, PM2.5 is now also speciated this way
starting with MOVES2014b. For California and Texas, PM2.5 emissions split by speciation profile are
estimated from total PM2.5 based on MOVES data in California and Texas, so that PM is speciated
consistently across the country. The PM2.5 profiles assigned to nonroad sources are listed in Table 3-16.
Table 3-16. Nonroad PM2.5 profiles
SPECIATE4.5
Profile Code
SPECIATE4.5 Profile Name
Assigned to Nonroad
sources based on Fuel
Type
8996
Diesel Exhaust - Heavy-heavy duty truck - 2007
model year with NCOM
Diesel
91106
HDDV Exhaust - Composite
Diesel
91113
Nonroad Gasoline Exhaust - Composite
Gasoline
95219
CNG Transit Bus Exhaust
CNG and LPG
3.2.3 NOx speciation
NOx emission factors and therefore NOx inventories are developed on a NO2 weight basis. For air quality
modeling, NOx is speciated into NO, NO2, and/or HONO. For the non-mobile sources, the EPA used a
single profile "NHONO" to split NOx into NO and NO2.
The importance of HONO chemistry, identification of its presence in ambient air and the measurements of
HONO from mobile sources have prompted the inclusion of HONO in NOx speciation for mobile
sources. Based on tunnel studies, a HONO to NOx ratio of 0.008 was chosen (Sarwar, 2008). For the
mobile sources except for onroad (e.g., nonroad, cmv, rail, othon sectors), and for specific SCCs in othar
and ptnonipm, the profile "HONO" is used. Table 3-17 gives the split factor for these two profiles. The
onroad sector does not use the "HONO" profile to speciate NOx. MOVES2014 produces speciated NO,
NO2, and HONO by source, including emission factors for these species in the emission factor tables used
by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOx. The NO fraction
varies by heavy duty versus light duty, fuel type, and model year.
112
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The NO2 fraction = 1 - NO - HONO. For more details on the NOx fractions within MOVES, see EPA
report "Use of data from 'Development of Emission Rates for the MOVES Model,'
Sierra Research, March 3, 2010" available at
https://nepis.epa. gov/Exe/ZyPDF.cgi?Dockev=Pl 00FlA5.pdf.
Table 3-17. NOx speciation profiles
Profile
pollutant
species
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 Creation of Sulfuric Acid Vapor (SULF)
Since at least the 2002 Platform, sulfuric acid vapor (SULF) has been estimated through the SMOKE
speciation process for coal combustion and residual and distillate oil fuel combustion sources. Profiles
that compute SULF from SO2 are assigned to coal and oil combustion SCCs in the GSREF ancillary file.
The profiles were derived from information from AP-42 (EPA, 1998), which identifies the fractions of
sulfur emitted as sulfate and SO2 and relates the sulfate as a function of S02.
Sulfate is computed from SO2 assuming that gaseous sulfate, which is comprised of many components, is
primarily H2SO4. The equation for calculating FhSO/ds given below.
Emissions of SULF (as H2S04) Equation 3-1
fraction of S emitted as sulfate MW H2S04
= S07 emissions x — - x
fraction of S emitted as S02 MW S02
In the above, MW is the molecular weight of the compound. The molecular weights of H2SO4 and SO2
are 98 g/mol and 64 g/mol, respectively.
This method does not reduce SO2 emissions; it solely adds gaseous sulfate emissions as a function of S02
emissions. The derivation of the profiles is provided in Table 3-18; a summary of the profiles is provided
in Table 3-19.
Table 3-18. Sulfate split factor computation
fuel
SCCs
Profile
Code
Fraction
as S02
Fraction as
sulfate
Split factor (mass
fraction)
Bituminous
1-0X-002-YY, where X is 1,
2 or 3 and YY is 01 thru 19
and 21-ZZ-002-000 where
ZZ is 02,03 or 04
95014
0.95
0.014
.014/.95 * 98/64 =
0.0226
Subbituminous
1-0X-002-YY, where X is 1,
2 or 3 and YY is 21 thru 38
87514
.875
0.014
.014/.875 * 98/64 =
0.0245
113
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fuel
SCCs
Profile
Code
Fraction
as S02
Fraction as
sulfate
Split factor (mass
fraction)
Lignite
1-0X-003-YY, where X is 1,
2 or 3 and YY is 01 thru 18
and 21-ZZ-002-000 where
ZZ is 02,03 or 04
75014
0.75
0.014
.014/.75 * 98/64 =
0.0286
Residual oil
1-0X-004-YY, where X is 1,
2 or 3 and YY is 01 thru 06
and 21-ZZ-005-000 where
ZZ is 02,03 or 04
99010
0.99
0.01
.01/ 99 * 98/64 =
0.0155
Distillate oil
1-0X-005-YY, where X is 1,
2 or 3 and YY is 01 thru 06
and 21-ZZ-004-000 where
ZZ is 02,03 or 04
99010
0.99
0.01
Same as residual oil
Table 3-19. 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
3.3 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.
For 2016v3, temporal profile assignments to SCCs were updated for solvents and for some point and
nonpoint SCCs. The new profiles for solvents only impacted the diurnal profiles and are based on
Gkatzelis et al. (2021).
The temporal factors applied to the inventory are selected using some combination of country, state,
county, SCC, and pollutant. Table 3-20 summarizes the temporal aspects of emissions modeling by
comparing the key approaches used for temporal processing across the sectors. In the table, "Daily
temporal approach" refers to the temporal approach for getting daily emissions from the inventory using
the SMOKE Temporal program. The values given are the values of the SMOKE L TYPE setting. The
"Merge processing approach" refers to the days used to represent other days in the month for the merge
step. If this is not "all," then the SMOKE merge step runs only for representative days, which could
114
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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 3-20. Temporal settings used for the platform sectors in SMOKE
Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process holidays
as separate days
afdust adj
Annual
Yes
week
All
Yes
afdust ak adj
Annual
Yes
week
All
Yes
airports
Annual
Yes
week
week
Yes
beis
Hourly
No
n/a
All
No
Canada ag
Monthly
No
mwdss
mwdss
No
Canada og2D
Annual
Yes
mwdss
mwdss
No
cmv clc2
Annual
Yes
aveday
aveday
No
cmv c3
Annual
Yes
aveday
aveday
No
fertilizer
Monthly
No
All
all
No
livestock
Annual
Yes
All
all
No
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
No
mwdss
mwdss
Yes
np oilgas
Annual
Yes
aveday
aveday
No
np solvents
Annual
Yes
aveday
aveday
No
onroad
Annual & monthly1
No
All
all
Yes
onroad ca adj
Annual & monthly1
No
All
all
Yes
onroad nonconus
Annual & monthly1
No
All
all
Yes
othafdust adj
Annual
Yes
week
all
No
othar
Annual & monthly
Yes
week
week
No
onroad can
Monthly
No
week
week
No
onroad mex
Monthly
No
week
week
No
othpt
Annual & monthly
Yes
mwdss
mwdss
No
othptdust adj
Monthly
No
week
all
No
pt oilgas
Annual
Yes
mwdss
mwdss
Yes
ptegu
Annual & hourly
Yes2
All
all
No
ptnonipm
Annual
Yes
mwdss
mwdss
Yes
ptagfire
Daily
No
All
all
No
ptfire-rx
Daily
No
All
all
No
ptfire-wild
Daily
No
All
all
No
ptfire othna
Daily
No
All
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No3
met-based3
All
No3
'Note the annual and monthly "inventory" actually refers to the activity data (VMT, hoteling, and VPOP) for onroad.
VMT and hoteling is monthly and VPOP is annual. The actual emissions are computed on an hourly basis.
2Only units that do not have matching hourly CEMS data use monthly temporal profiles.
3Except for 2 SCCs that do not use met-based speciation
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The following values are used in the table. The value "all" means that hourly emissions are computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the
month. The value "mwdss" means hourly emissions for one representative Monday, representative
weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each month.
This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within
the month, but not week-to-week variation within the month. The value "aveday" means hourly
emissions computed for one representative day of each month, meaning emissions for all days within a
month are the same. Special situations with respect to temporal allocation are described in the following
subsections.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2016, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2015). For most sectors, emissions from December 2016
(representative days) were used to fill in emissions for the end of December 2015. For biogenic
emissions, December 2015 emissions were processed using 2015 meteorology.
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
livestock, nonroad, onroad, onroad can, onroadmex, othar, and othpt.
3.3.2 Electric Generating Utility temporal allocation (ptegu)
3.3.2.1 Base year temporal allocation of EGUs
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 annual values in the 2016 annual inventory because the CEMS data replaces 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
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were found to be more than three times the annual mean for that unit, the data for those hours are replaced
with annual mean values (Adelman et aL 2012). These adjusted CEMS data were then used for the
remainder of the temporal allocation process described below (see Figure 3-5 for an example).
Figure 3-5. Eliminating unmeasured spikes in CEMS data
2016 January CEMs for 6068 3
6000 -
2000 ¦
2016 Original CEMs
2016 Corrected CEMs
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In modeling platforms prior to 2016 beta, unmatched EGUs were temporally allocated using daily and
diurnal profiles weighted by CEMS values within an EPM region, season, and by fuel type (coal, gas, and
other). All unit types (peaking and non-peaking) were given the same profile within a region, season and
fuel bin. Units identified as municipal waste combustors (MWCs) or cogeneration units (cogens) were
given flat daily and diurnal profiles. Beginning with the 2016 beta platform and continuing for the
2016vl, 2016v2,v2, and 2016v3 platforms, the small EGU temporalization process considers peaking
units.
The region, fuel, and type (peaking or non-peaking) were identified for each input EGU with CEMS data
that are used for generating profiles. The identification of peaking units was based on hourly heat input
data from the 2016 base year and the two previous years (2014 and 2015). The heat input was summed for
each year. Equation 3-2 shows how the annual heat input value is converted from heat units (BTU/year) to
power units (MW) using the unit-level heat rate (BTU/kWh) derived from the NEEDS v6 database. In
Equation 3-3 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
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a maximum capacity factor of less than 0.2 for every year (2014, 2015, and 2016) and a 3-year average
capacity factor of less than 0.1.
Annual Unit Power Output
nnual Unit Output (MW) =
Equation 3-2
Unit Capacity Factor
nnual Unit Output (MW) =
Equation 3-3
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 are 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. The number of units used to calculate the daily and diurnal EGU temporal profiles
are shown in Figure 3-6 by region, fuel, and for peaking/non-peaking. 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 3-6. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification
Small EGU 2016 Temporal Profile Input Unit Counts
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Figure 3-7. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type
Daily Small EGU Profile for LADCO gas
2016
Havi
Figure 3-8. 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
2016 platforms, 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. As with the input unit the
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regions are assigned using the state from the unit FIPS. The fuel was assigned by SCC to 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.
MWC and cogen units were identified using the NEEDS primary fuel type and cogeneration flag,
respectively, from the NEEDS v6 database. The number of EGU units assigned each profile group are
shown by region in Figure 3-9.
Figure 3-9. Non-CEMS EGU Temporal Profile Application Counts
Small EGU 2016 Temporal Profile Application Counts
LADCO
3.3.2.2 Analytic year temporal allocation of EGUs
For analytic year temporal allocation of unit-level EGU emissions, estimates of average winter
(representing December through February), average winter shoulder (October through November and
March through April), and average summer (May through September) values were provided by the IPM
for all units. The winter shoulder was newly separated from the winter months starting with the 2Q16v2
platform and continuing for the 2016v3 platform. The seasonal emissions for the analytic year cases were
produced by post processing of the IPM outputs. The unit-level data were converted into hourly values
through the temporal allocation process using a 3-step methodology: annualized summer/winter value to
month, month to day, and day to hour. CEMS data from the air quality analysis year (e.g., 2016) is used
as much as possible to temporally allocate the EGU emissions.
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The goal of the temporal allocation process is to reflect the variability in the unit-level emissions that can
impact air quality over seasonal, daily, or hourly time scales, in a manner compatible with incorporating
analytic-year emission projections into analytic-year air quality modeling. The temporal allocation
process is applied to the seasonal emission projections for the three IPM seasons: summer (May through
September), winter shoulder (October through November and March through April), and winter
(December through February). The Flat File used as the input to the temporal allocation process contains
unit-level emissions and stack parameters (i.e., stack location and other characteristics consistent with
information found in the NEI). When the Flat File is produced from post-processed IPM outputs, a cross
reference is used to map the units in version 6 of the NEEDS database to the stack parameter and facility,
unit, release point, and process identifiers used in the NEI. This cross reference also maps sources to the
hourly CEMS data used to temporally allocate the emissions in the base year air quality modeling.
All units have seasonal information provided in the analytic year Flat File, the monthly values in the Flat
File input to the temporal allocation process are computed by multiplying the average summer day,
average winter shield day, and average winter day emissions by the number of days in the respective
month. When generating seasonal emissions totals from the Flat File winter shield emissions are summed
with the winter emissions to create a total winter season. In summary, the monthly emission values shown
in the Flat File are not intended to represent an actual month-to-month emission pattern. Instead, they are
interim values that have translated IPM's seasonal projections into month-level data that serve as a
starting point for the temporal allocation process.
The monthly emissions within the Flat File undergo a multi-step temporal allocation process to yield the
hourly emission values at each unit, as is needed for air quality modeling: summer or winter value to
month, month to day, and day to hour. For sources not matched to unit-specific CEMS data, the first two
steps are done outside of SMOKE and the third step to get to hourly values is done by SMOKE using the
daily emissions files created from the first two steps. For each of these three temporal allocation steps,
NOx and SO2 CEMS data are used to allocate NOxand SO2 emissions, while CEMS heat input data are
used to allocate all other pollutants. The approach defined here gives priority to temporalization based on
the base year CEMS data to the maximum extent possible for both base and analytic year modeling.
Prior to using the 2016 CEMS data to develop monthly, daily, and hourly profiles, the CEMS data were
processed through the CEMCorrect tool to make adjustments for hours for which data quality flags
indicated the data were not measured and that the reported values were much larger than the annual mean
emissions for the unit. These adjusted CEMS data were used to compute the monthly, daily, and hourly
profiles described below.
For units that have CEMS data available and that have CEMS units matched to the NEI sources, the
emissions are temporalized according to the base year (i.e., 2016) CEMS data for that unit and pollutant.
For units that are not matched to the NEI or for which CEMS data are not available, the allocation of the
seasonal emissions to months is done using average fuel-specific season-to-month factors for both
peaking and non-peaking units generated for each of the eight regions shown in Figure 5. These factors
are based on a single year of CEMS data for the modeling base year associated with the air quality
modeling analysis being performed, such as 2016. The fuels used for creating the profiles for a region
were coal, natural gas, oil, and "other". The "other "fuels category is a broad catchall that includes fuels
such as wood and waste. Separate profiles are computed for NOx, SO2, and heat input, where heat input is
used to temporally allocate emissions for pollutants other than NOx and SO2. An overall composite
profile across all fuels is also computed and can be used in the event that a region has too few units of a
fuel type to make a reasonable average profile, or in the case when a unit changes fuels between the base
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and analytic year and there were previously no units with that fuel in the region containing the unit. A
complete description of the generation and application of these regional fuel profiles is available in the
base year temporalization section.
The monthly emission values in the Flat File were first reallocated across the months in that season to
align the month-to-month emission pattern at each stack with historic seasonal emission patterns. While
this reallocation affects the monthly pattern of each unit's analytic-year seasonal emissions, the seasonal
totals are held equal to the IPM projection for that unit and season. Second, the reallocated monthly
emission values at each stack are disaggregated down to the daily level consistent with historic daily
emission patterns in the given month at the given stack using separate profiles for NOx, SO2, and heat
input. This process helps to capture the influence of meteorological episodes that cause electricity
demand to vary from day-to-day, as well as weekday-weekend effects that change demand during the
course of a given week. Third, this data set of emission values for each day of the year at each unit is
input into SMOKE, which uses temporal profiles to disaggregate the daily values into specific values for
each hour of the year.
For units without or not matched to CEMS data, or for which the CEMS data are found to be unsuitable
for use in the analytic year, emissions were allocated from month to day using IPM-region and fuel-
specific average month-to-day factors based on CEMS data from the base year of the air quality modeling
analysis. These instances include units that did not operate in the base year or for which it may not have
been possible to match the unit to a specific unit in the NEI. Regional average profiles may be used for
some units with CEMS data in the base year when one of the following cases is true: (1) units are
projected to have substantially increased emissions in the analytic year compared to its emissions in the
base (historic) year; (2) CEMS data were only available for a limited number of hours in that base year;
(3) the unit is new in the analytic year; (4) when there were no CEMS data for one season in the base year
but IPM runs the unit during both seasons; or (5) units experienced atypical conditions during the base
year, such as lengthy downtimes for maintenance or installation of controls.
The temporal profiles that map emissions from days to hours were computed based on the region and
fuel-specific seasonal (i.e., winter and summer) average day-to-hour factors derived from the CEMS data
for heat input for those fuels and regions and for that season. Heat input was used because it is the
variable that is the most complete in the CEMS data and should be present for all of the hours in which
the unit was operating. SMOKE uses these diurnal temporal profiles to allocate the daily emissions data
to hours of each day. Note that this approach results in each unit having the same hourly temporal
allocation for all the days of a season.
The emissions from units for which unit-specific profiles were not used were temporally allocated to
hours reflecting patterns typical of the region in which the unit is located. Analysis of CEMS data for
units in each of the 8 regions shown in Figure 3-6 revealed that there were differences in the temporal
patterns of historic emission data that correlate with fuel type (e.g., coal, gas, oil, and other), time of year,
pollutant, season (i.e., winter versus summer) and region of the country. The correlation of the temporal
pattern with fuel type is explained by the relationship of units' operating practices with the fuel burned.
For example, coal units take longer to ramp up and ramp down than natural gas units, and some oil units
are used only when electricity demand cannot otherwise be met. Geographically, the patterns were less
dependent on state location than they were on regional location. Figure 3-7 provides an example of daily
profiles for gas fuel in the LADCO region. The EPA developed seasonal average emission profiles, each
derived from base year CEMS data for each season across all units sharing both IPM region and fuel type.
Figure 3-8 provides an example of seasonal profiles that allocate daily emissions to hours in the MANE-
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VU region. These average day-to-hour temporal profiles were also used for sources during seasons of the
year for which there were no CEMS data available, but for which IPM predicted emissions in that season.
This situation can occur for multiple reasons, including how the CEMS was run at each source in the base
year.
For units that do have CEMS data in the base year and were matched to units in the IPM output, the base
year CEMS data were scaled so that their seasonal emissions match the IPM-projected totals. The scaling
process used the fraction of the unit's seasonal emissions in the base year as computed for each hour of
the season, and then applied those fractions to the seasonal emissions from the analytic year Flat File. Any
pollutants other than NOx and SO2 were temporally allocated using heat input. Through the temporal
allocation process, the analytic year emissions will have the same temporal pattern as the base year CEMS
data, where available, while the analytic-year seasonal total emissions for each unit match the analytic-
year unit-specific projection for each season (see example in Figure 3-10). The year IPM output for 2025
maps to the year 2026 and was therefore used for the 2026 modeling case.
In cases when the emissions for a particular unit are projected to be substantially higher in the analytic
year than in the base year, the proportional scaling method to match the emission patterns in the base year
described above can yield emissions for a unit that are much higher than the historic maximum emissions
for that unit. To help address this issue in the analytic case, the maximum measured emissions of NOx and
SO2 in the period of 2014-2017 were computed. The temporally allocated emissions were then evaluated
at each hour to determine whether they were above this maximum. The amount of "excess emissions"
over the maximum were then computed. For units for which the "excess emissions" could be reallocated
to other hours, those emissions were distributed evenly to hours that were below the maximum. Those
hourly emissions were then reevaluated against the maximum, and the procedure of reallocating the
excess emissions to other hours was repeated until all of the hours had emissions below the maximum,
whenever possible (see example in Figure 3-11).
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Figure 3-10. Analytic Year Emissions Follow the Pattern of Base Year Emissions
2030 and 2016 Summer CEMs for 2277 1
2016 CEMs
2030 CEMs
2030 Adjusted CEMs
Annual unit max
May
2016
Jun
Jul
Aug
Figure 3-11. Excess Emissions Apportioned to Hours Less than the Historic Maximum
2030 and 2016 Summer CEMs for 3943 2
2016 CEMs
2030 CEMs
2030 Adjusted CEMS
Annual unit max
Using the above approach, it was not always possible to reallocate excess emissions to hours below the
historic maximum, such as when the total seasonal emissions of NOx or SO2 for a unit divided by the
number of hours of operation are greater than the 2014-2017 maximum emissions level. For these units,
the regional fuel-specific average profiles were applied to all pollutants, including heat input, for the
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respective season (see example in Figure 3-12). It was not possible for SMOKE to use regional profiles
for some pollutants and adjusted CEMS data for other pollutants for the same unit and season, therefore,
all pollutants in the unit and season are assigned to regional profiles when regional profiles are needed.
For some units, hourly emissions values still exceed the 2014-2017 annual maximum for the unit even
after regional profiles were applied (see example in Figure 3-13).
Figure 3-12. Regional Profile Applied due to not being able to Adjust below Historic Maximum
2000 -
1750 -
1500 -
1250 -
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750 -
500 -
250 -
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May Jun Jul Aug Sep
2016
Date
2030 and 2016 Summer CEMs for 6095 2
2016 CEMs
2030 CEMs
2030 Adjusted CEMs
2030 Season Fuel
Annual unit max
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Figure 3-13. Regional Profile Applied, but Exceeds Historic Maximum in Some Hours
2030 and 2016 Summer CEMs for 2103J.
•Q
2016 CEMS
2030 CEMS
2030 Adjusted CEMs
2030 Season Fuel
Annual unit max
May
2016
3000 -
2000 -
1000 -
3.3.3 Airport Temporal allocation (airports)
Airport temporal profiles were updated in 2014v7.0 and were kept the same for the 2016v3 platform. All
airport SCCs (i.e., 2275*, 2265008005, 2267008005, 2268008005 and 2270008005) were given the same
hourly, weekly and monthly profile for all airports other than Alaska seaplanes (which are not in the
CMAQ modeling domain). Hourly airport operations data were obtained from the Aviation System
Performance Metrics (ASPM) Airport Analysis website (https://aspm.faa.gov/apm/svs/AnalvsisAP.asp).
A report of 2014 hourly Departures and Arrivals for Metric Computation was generated. An overview of
the ASPM metrics is at
http://aspmhelp.faa.gov/index.php/Aviation Performance Metrics %28APM%29. Figure 3-14 shows
the diurnal airport profile.
Weekly and monthly temporal profiles are based on 2014 data from the FAA Operations Network Air
Traffic Activity System (http://aspm.faa.gov/opsnet/sys/Tenninal.asp). A report of all airport operations
(takeoffs and landings) by day for 2014 was generated. These data were then summed to month and day-
of-week to derive the monthly and weekly temporal profiles shown in Figure 3-14, Figure 3-15, and
Figure 3-16. An overview of the Operations Network data system is at
http://aspmhelp.faa.gov/index.php/Operations Network %28QPSNET%29. The weekly and monthly
profiles from 2014 are still used in the 2016 platforms.
Alaska seaplanes, which are outside the CONUS domain use the same monthly profile as in the 2011
platform shown in Figure 3-17. These were assigned based on the facility ID.
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Figure 3-14. Diurnal Profile for all Airport SCCs
Diurnal Airport Profile
Hour
Figure 3-15. Weekly profile for all Airport SCCs
Weekly Airport Profile
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0.16
0.14
0.12
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0.06
0.04
0.02
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Figure 3-16. Monthly Profile for all Airport SCCs
Monthly Airport Profile
0.04
0.03
0.02
0.01
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 3-17. Alaska Seaplane Profile
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3.3.4 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
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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 Technical Summary Aug2012 Final.pd
f and https://www.cmascenter.Org/smoke/documentation/4.5/html/ch05s03s05.html respectively.
For the RWC algorithm, Gentpro uses the daily minimum temperature to determine the temporal
allocation of emissions to days of the year. Gentpro was used to create an annual-to-day temporal profile
for the RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of
the year. On days where the minimum temperature does not drop below a user-defined threshold, RWC
emissions for most sources in the sector are zero. Conversely, the program temporally allocates the
largest percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total
annual emissions do not change, only the distribution of the emissions within the year is affected. The
temperature threshold for RWC emissions was 50 °F for most of the country, and 60 °F for the following
states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas. 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 are normalized to sum to 1 to ensure that the total annual emissions are
unchanged (or minimally changed) during the temporal allocation process.
Figure 3-18 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.
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Figure 3-18. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold
The diurnal profile used for most RWC sources (see Figure 3-19) places more of the RWC emissions in
the morning and the evening when people are typically using these sources. This profile is based on a
2004 MANE-VU survey based temporal profiles34. 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 3-19. RWC diurnal temporal profile
Comparison of RWC diurnal profile
The temporal allocation for "Outdoor Hydronic Heaters" (i.e., "OHH," SCC=2104008610) and "Outdoor
wood burning device, NEC (fire-pits, chimineas, etc.)" (i.e., "recreational RWC," SCC=21040087000) is
not based on temperature data, because the meteorologically-based temporal allocation used for the rest of
the rwc sector did not agree with observations for how these appliances are used.
34 https://s3 .amazonaws.com/marama.org/wp-
content/uploads/2019/11/13093804/Qpen Burning Residential Areas Emissions Report-2004.pdf
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For OHH, the annual-to-month, day-of-week and diurnal profiles were modified based on information in
the New York State Energy Research and Development Authority's (NYSERDA) "Environmental,
Energy Market, and Health Characterization of Wood-Fired Hydronic Heater Technologies, Final Report"
(NYSERDA, 2012), as well as a Northeast States for Coordinated Air Use Management (NESCAUM)
report "Assessment of Outdoor Wood-fired Boilers" (NESCAUM, 2006). A Minnesota 2008 Residential
Fuelwood Assessment Survey of individual household responses (MDNR, 2008) provided additional
annual-to-month, day-of-week, and diurnal activity information for OHH as well as recreational RWC
usage.
Data used to create the diurnal profile for OHH, shown in Figure 3-20, are based on a conventional single-
stage heat load unit burning red oak in Syracuse, New York. As shown in Figure 3-21, the NESCAUM
report describes how for individual units, OHH are highly variable day-to-day but that in the aggregate,
these emissions have no day-of-week variation. In contrast, the day-of-week profile for recreational RWC
follows a typical "recreational" profile with emissions peaked on weekends.
Annual-to-month temporal allocation for OHH as well as recreational RWC were computed from the
MDNR 2008 survey and are illustrated in Figure 3-22. The OHH emissions still exhibit strong seasonal
variability, but do not drop to zero because many units operate year-round for water and pool heating. In
contrast to all other RWC appliances, recreational RWC emissions are used far more frequently during the
warm season.
Figure 3-20. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr)
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Figure 3-21. Day-of-week temporal profiles for OHH and Recreational RWC
Figure 3-22. Annual-to-month temporal profiles for OHH and recreational RWC
3.3.5 Agricultural Ammonia Temporal Profiles (ag)
For the agricultural livestock NH3 algorithm, the GenTPRO algorithm is based on an equation derived by
Jesse Bash of the EPA's ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation is
based on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to
estimate diurnal NH3 emission variations from livestock as a function of ambient temperature,
aerodynamic resistance, and wind speed. The equations are:
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Ea = [161500/Ti./, x e("1380/T,,/;)] x AR,-./,
PE;,/; = Ea / Sum(E;,/;)
Equation 3-4
Equation 3-5
where
• PE;,/; = Percentage of emissions in county i on hour h
• Eij, = Emission rate in county i on hour h
• Tin = Ambient temperature (Kelvin) in county i on hour h
• AR;,/; = Aerodynamic resistance in county i
GenTPRO was run using the "BASH NH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized
to the month. Figure 3-23 compares the daily emissions for Minnesota from the "old" approach (uniform
monthly profile) with the "new" approach (GenTPRO generated month-to-hour profiles) for 2014.
Although the GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the
same between the two approaches.
Figure 3-23. Example of animal NFb emissions temporal allocation approach (daily total emissions)
2014fd Minnesota ag NH3 livestock daily temporal profiles
1600
1/1/2014 2/1/2014 3/4/2014 4/4/2014 5/5/2014 6/5/2014 7/6/2014 B/6/2014 9/6/2014 10/7/2D1411/7/201412/8/2014
month^ hourly
approach approach
For the 2016 platform, the GenTPRO approach is applied to all sources in the livestock and fertilizer
sectors, NH3 and non- NH3. Monthly profiles are based on the daily-based EPA livestock emissions and
are the same as those used in 2014v7.0. Profiles are by state/SCC_category, where SCC_category is one
of the following: beef, broilers, layers, dairy, swine.
3.3.6 Oil and gas temporal allocation (np_oilgas)
Monthly oil and gas temporal profiles by county and SCC were updated to use 2016 activity information
for the 2016vl platform. Weekly and diurnal profiles are flat and are based on comments received on a
version of the 2011 platform.
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3.3.7 Onroad mobile temporal allocation (onroad)
For the onroad sector, the temporal distribution of emissions is a combination of traditional temporal
profiles and the influence of meteorology. This section will discuss both the meteorological influences
and the development of the temporal profiles for this platform.
The "inventories" referred to in Table 3-20 consist of activity data for the onroad sector, not emissions.
For the off-network emissions from the rate-per-profile (RPP) and rate-per-vehicle (RPV) processes, the
VPOP activity data is annual and does not need temporal allocation. For rate-per-hour (RPH) processes
that result from hoteling of combination trucks, the HOTELING inventory is annual and was
temporalized to month, day of the week, and hour of the day through temporal profiles. 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 rate-per-distance (RPD) processes, the VMT activity data is annual for some sources and
monthly for other sources, depending on the source of the data. Sources without monthly VMT were
temporalized from annual to month through temporal profiles. VMT was also temporalized from month
to day of the week, and then to hourly through temporal profiles. The RPD processes require a speed
profile (SPDPRO) that consists of vehicle speed by hour for a typical weekday and weekend day. For
onroad, the temporal profiles and SPDPRO will impact not only the distribution of emissions through
time but also the total emissions. Because SMOKE-MOVES (for RPD) calculates emissions based on the
VMT, speed and meteorology, if one shifted the VMT or speed to different hours, it would align with
different temperatures and hence different emission factors. In other words, two SMOKE-MOVES runs
with identical annual VMT, meteorology, and MOVES emission factors, will have different total
emissions if the temporal allocation of VMT changes. Figure 3-24 illustrates the temporal allocation of
the onroad activity data (i.e., VMT) and the pattern of the emissions that result after running SMOKE-
MOVES. In this figure, it can be seen that the meteorologically varying emission factors add variation on
top of the temporal allocation of the activity data.
Meteorology is not used in the development of the temporal profiles, but rather it impacts the calculation
of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the
hourly level by grid cell. More specifically, the on-network (RPD) and the off-network parked vehicle
(RPV, RPH, RPHO, RPS, and RPP) processes use the gridded meteorology (MCIP) either directly or
indirectly. For RPD, RPV, RPS, RPH, and RPHO, Movesmrg determines the temperature for each hour
and grid cell and uses that information to select the appropriate emission factor for the specified
SCC/pollutant/mode combination. For RPP, instead of reading gridded hourly meteorology, Movesmrg
reads gridded daily minimum and maximum temperatures. The total of the emissions from the
combination of these four processes (RPD, RPV, RPH, RPHO, RPS, and RPP) comprise the onroad
sector emissions. The temporal patterns of emissions in the onroad sector are influenced by meteorology.
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Figure 3-24. Example of temporal variability of NOx emissions
A _
2014v2 onroad RPD hourly NOX and VMT: Wake County, NC
a
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7/9/140:00
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Date and time (GMT)
New VMT day-of-week and hour-of-day temporal profiles were developed for use in the 2014NEIv2 and
later platforms as part of the effort to update the inputs to MOVES and SMOKE-MOVES under CRC A-
100 (Coordinating Research Council, 2017). CRC A-100 data includes profiles by region or county, road
type, and broad vehicle category. There are three vehicle categories: passenger vehicles (11/21/31),
commercial trucks (32/52), and combination trucks (53/61/62). CRC A-100 does not cover buses, refuse
trucks, or motor homes, so those vehicle types were mapped to other vehicle types for which CRC A-100
did provide profiles as follows: 1) Intercity/transit buses were mapped to commercial trucks; 2) Motor
homes were mapped to passenger vehicles for day-of-week and commercial trucks for hour-of-day; 3)
School buses and refuse trucks were mapped to commercial trucks for hour-of-day and use a new custom
day-of-week profile called LOWSATSUN that has a very low weekend allocation, since school buses and
refuse trucks operate primarily on business days. In addition to temporal profiles, CRC A-100 data were
also used to develop the average hourly speed data (SPDPRO) used by SMOKE-MOVES. In areas where
CRC A-100 data does not exist, hourly speed data is based on MOVES county databases.
The CRC A-100 dataset includes temporal profiles for individual counties, Metropolitan Statistical Areas
(MSAs), and entire regions (e.g., West, South). For counties without county or MSA temporal profiles
specific to itself, regional temporal profiles are used. Temporal profiles also vary by each of the MOVES
road types, and there are distinct hour-of-day profiles for each day of the week. Plots of hour-of-day
profiles for passenger vehicles in Fulton County, GA, are shown in Figure 3-25. Separate plots are shown
for Monday, Friday, Saturday, and Sunday, and each line corresponds to a particular MOVES road type
(i.e., road type 2 = rural restricted, 3 = rural unrestricted, 4 = urban restricted, and 5 = urban unrestricted).
Figure 3-26 shows which counties have temporal profiles specific to that county, and which counties use
MSA or regional average profiles. Figure 3-27 shows the regions used to compute regional average
profiles.
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Figure 3-25. Sample on road diurnal profiles for Fulton County, GA
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Figure 3-26. Methods to Populate Onroad Speeds and Temporal Profiles by Road Type
138
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Figure 3-27. Regions for computing Region Average Speeds and Temporal Profiles
For hoteling, day-of-week profiles are the same as non-hoteling for combination trucks, while hour-of-day
non-hoteling profiles for combination trucks were inverted to create new hoteling profiles that peak
overnight instead of during the day. The combination truck profiles for Fulton County are shown in
Figure 3-28.
The CRC A-100 temporal profiles were used in the entire contiguous United States, except in California.
All California temporal profiles were carried over from 2014v7.0, although California hoteling uses CRC
A-100-based profiles just like the rest of the country, since CARB didn't have a hoteling-specific profile.
Monthly profiles in all states (national profiles by broad vehicle type) were also carried over from
2014v7.0 and applied directly to the VMT. For California, CARB supplied diurnal profiles that varied by
vehicle type, day of the week,35 and air basin. These CARB-specific profiles were used in developing
EPA estimates for California. Although the EPA adjusted the total emissions to match California-
submitted emissions for 2016, the temporal allocation of these emissions took into account both the state-
specific VMT profiles and the SMOKE-MOVES process of incorporating meteorology.
35 California's diurnal profiles varied within the week.
Tuesday, Wednesday, Thursday had the same profile.
Monday, Friday, Saturday, and Sunday had unique profiles and
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Monday
Figure 3-28. Example of Temporal Profiles for Combination Trucks
Fulton Co combo Friday Fulton Co combo
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
Saturday
Fulton Co
combo
Sunday
Fulton Co
combo
5 6 7 8 9 10 11 12 13 14 15 16 17 13 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
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 a separate set of temporal profiles
specifically for starts activity. For starts, there is one day-of-week temporal profile for each source type
(e.g., motorcycles, passenger cars, combination long haul trucks), and two hour-of-day temporal profiles
for each source type, one for weekdays and one for weekends. The temporal profiles for starts are applied
nationally and are based on the default starts-per-day-per-vehicle and starts-hour-fraction tables from
MOVES.
3.3.8 Nonroad mobile temporal allocation(nonroad)
For nonroad mobile sources, temporal allocation is performed differently for different SCCs. Beginning
with the final 2011 platform and continued into the 2016 platforms, some improvements to temporal
allocation of nonroad mobile sources were made to make the temporal profiles more realistically reflect
real-world practices. Some specific updates were made for agricultural sources (e.g., tractors),
construction, and commercial residential lawn and garden sources.
Figure 3-29 shows two previously existing temporal profiles (9 and 18) and a new temporal profile (19)
which has lower emissions on weekends. In the 2016 platform, construction and commercial lawn and
garden sources were updated from profile 18 to the new profile 19 which has lower emissions on
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weekends. Residental lawn and garden sources continue to use profile 9 and agricultural sources continue
to use profile 19.
Figure 3-29. 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
0.06
0.04
0.02
0
mooday tuesday Wednesday thursday friday Saturday Sunday
Figure 3-30 shows the previously existing temporal profiles 26 and 27 along with new temporal profiles
(25a and 26a) which have lower emissions overnight. In the 2016 platform, construction sources
previously used profile 26 and were updated to use profile 26a. Commercial lawn and garden and
agriculture sources also previously used profile 26 but were updated to use the new profiles 26a and 25a,
respectively. Residental lawn and garden sources were updated from profile 26 to use profile 27.
Figure 3-30. Example Nonroad Diurnal Temporal Profiles
Hour of Day Profiles
0.11
0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
hi h2 h3 b4 h5 h6 h7 hS hS hlO hll hl2 hl3hl4 hl5hl6 hl7hlS hi9 h20 h21 h22 h23h24
26a-New 27 25a-New 26
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3.3.9 Additional sector specific details (afdust, beis, cmv, rail, nonpt,
ptnonipm, ptfire)
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to
reduce the total emissions based on meteorological conditions. These adjustments are applied through
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and
then subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the
ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are
subject to transport. This methodology is discussed in (Pouliot et al., 2010), and in "Fugitive Dust
Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). The precipitation adjustment is
applied to remove all emissions for hours where measurable rain occurs, or where there is snow cover.
Therefore, the afdust emissions vary day-to-day based on the precipitation and/or snow cover for each
grid cell and hour. Both the transport fraction and meteorological adjustments are based on the gridded
resolution of the platform; therefore, somewhat different emissions will result from different grid
resolutions. For this reason, to ensure consistency between grid resolutions, afdust emissions for the
36US3 grid are aggregated from the 12US1 emissions. Application of the transport fraction and
meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid modeling as
compared to ambient samples.
Biogenic emissions in the beis sector vary by every day of the year because they are developed using
meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions are
computed using appropriate emission factors according to the vegetation in each model grid cell, while
taking the meteorological data into account.
For the cmv sectors, most areas use hourly emission inventories derived from the 5-minute AIS data. In
some areas where AIS data are not available, such as in Canada between the St. Lawrence Seaway and the
Great Lakes and in the southern Caribbean, the flat temporal profiles are used for hourly and day-of-week
values. Most regions without AIS data also use a flat monthly profile, with some offshore areas using an
average monthly profile derived from the 2008 ECA inventory monthly values. These areas without AIS
data also use flat day of week and hour of day profiles.
For the rail sector, new monthly profiles were developed for the 2016 platform. Monthly temporal
allocation for rail freight emissions is based on AAR Rail Traffic Data, Total Carloads and Intermodal, for
2016. For passenger trains, monthly temporal allocation is flat for all months. Rail passenger miles data
is available by month for 2016 but it is not known how closely rail emissions track with passenger activity
since passenger trains run on a fixed schedule regardless of how many passengers are aboard, and so a flat
profile is chosen for passenger trains. Rail emissions are allocated with flat day of week profiles, and
most emissions are allocated with flat hourly profiles.
For the ptagfire sector, the inventories are in the daily point fire format FF10 PTDAY. The diurnal
temporal profile for ag fires reflects the fact that burning occurs during the daylight hours - see Figure
3-31 (McCarty et al., 2009). This puts most of the emissions during the workday and suppresses the
emissions during the middle of the night.
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Figure 3-31. 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. Separate hourly
profiles for prescribed and wildfires were used. Figure 3-32 below shows the profiles used for each state
for the 2016v2 and 2016v3 modeling platforms. The wildfire diurnal profiles are similar but vary
according to the average meteorological conditions in each state. The 2016v2 and v3 platforms used
diurnal profiles for prescribed profile that better reflect flaming and residual smoldering phases and
average burn practices. These flaming and residual smoldering diurnal profiles vary slightly by region.
Figure 3-32. Prescribed and Wildfire diurnal temporal profiles
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For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from MOVES. For California, CARB's annual inventory was temporalized to
monthly using monthly temporal profiles applied in SMOKE by SCC. This is an improvement over the
2011 platforms, which applied monthly temporal allocation in California at the broader SCC7 level.
3.4 Spatial Allocation
The methods used to perform spatial allocation are summarized in this section. The spatial factors are
typically applied by county and SCC. As described in Section 3.1, spatial allocation was performed for
national 36-km and 12-km domains. To accomplish this, SMOKE used national 36-km and 12-km spatial
surrogates and a SMOKE area-to-point data file. For the U.S., the spatial surrogates are based on circa
2016 to 2017 data wherever possible. For Mexico, the spatial surrogates used as described below. For
Canada, surrogates were provided by ECCC for the 2016v7.2 (beta) platform and those continue to be
used in the 2016v3 platform. The U.S., Mexican, and Canadian 36-km and 12-km surrogates cover the
entire CONUS domain 12US1 shown in Figure 3-1. The 36US3 domain includes a portion of Alaska, and
since Alaska emissions are typically not included in air quality modeling for the contiguous U.S., special
considerations are taken to include Alaska emissions in 36-km modeling.
2016v3 platform uses the same surrogates and surrogate assignments as 2016v2 platform, except for new
SCCs introduced in the np solvents sector which did not have an existing assignment. Documentation of
the origin of the spatial surrogates for the platform is provided in the 2016v2 surrogate specifications
workbook. The remainder of this subsection summarizes the data used for the spatial surrogates and the
area-to-point data which is used for airport refueling.
3.4.1 Spatial Surrogates for U.S. emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 36-km and 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-
to-point approach overrides the use of surrogates for an airport refueling sources. Table 3-21 lists the
codes and descriptions of the surrogates. Surrogate names and codes listed in italics are not directly
assigned to any sources for the 2016 platforms, but they are sometimes used to gapfill other surrogates, or
as an input for merging two surrogates to create a new surrogate that is used. The WRAP oil and gas
surrogates used in 2016v2 and 2016v3 are not listed in Table 3-21 but are listed in Table 3-23.
Many surrogates were updated or newly developed for use in the 2014v7.0 platform (Adelman, 2016).
They include the use of the 2011 National Land Cover Database (the previous platform used 2006) and
development of various development density levels such as open, low, medium high and various
combinations of these. These NLCD-based surrogates largely replaced the FEMA category (500 series)
surrogates that were used in the 2011 platform. Additionally, onroad surrogates were developed using
average annual daily traffic counts from the highway monitoring performance system (HPMS).
Previously, the "activity" for the onroad surrogates was length of road miles. These and other surrogates
are described in a reference (Adelman, 2016).
Several surrogates were updated or developed as new surrogates for the 2016 platforms:
oil and gas surrogates represent activity during the year 2016;
onroad spatial allocation uses surrogates that do not distinguish between urban and rural road
types, correcting the issue arising in some counties due to the inconsistent urban and rural
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definitions between MOVES, the activity data, and the surrogate data, and were further updated
for the 2016 platform;
spatial surrogates for onroadway sources use annual average daily traffic (AADT) for 2017;
- the surrogate used for truck stops was updated in 2019;
a public schools surrogate (#508) was added in the 2016v2 platform;
- the use of 500 series surrogates (except for the new #508) were phased out; and
rail surrogates were updated to fix some misallocated emissions in 2016v2.
The surrogates for the U.S. were mostly generated using the Surrogate Tools DB tool. The tool and
documentation for the Surrogate Tools DB is available at
https://www.cmascenter.org/surrogate tools db/.
Table 3-21. U.S. Surrogates available for the 2016 modeling platforms
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2)
318
NLCD Pasture Land
100
Population
319
NLCD Crop Land
110
Housing
320
NLCD Forest Land
131
urban Housing
321
NLCD Recreational Land
132
Suburban Housing
340
NLCD Land
134
Rural Housing
350
NLCD Water
137
Housing Change
508
Public Schools
140
Housing Change and Population
650
Refineries and Tank Farms
150
Residential Heating - Natural Gas
670
Spud Count - CBM Wells
160
Residential Heating - Wood ;
671
Spud Count - Gas Wells
170
Residential Heating - Distillate Oil
672
Gas Production at Oil Wells
180
Residential Heating - Coal
673
Oil Production at CBM Wells
190
Residential Heating - LP Gas
674
Unconventional Well Completion Counts
201
Urban Restricted Road Miles
676
Well Count - All Producing
202
Urban Restricted AADT
677
Well Count-All Exploratory
205
Extended Idle Locations
678
Completions at Gas Wells
211
Rural Restricted Road Miles
679
Completions at CBM Wells
212
Rural Restricted AADT i
681
Spud Count - Oil Wells
221
Urban Unrestricted Road Miles I
683
Produced Water at All Wells
222
Urban Unrestricted AADT
6831
Produced water at CBM wells
231
Rural Unrestricted Road Miles ;
6832
Produced water at gas wells
232
Rural Unrestricted AADT
6833
Produced water at oil wells
239
Total Road AADT
685
Completions at Oil Wells
240
Total Road Miles
686
Completions at All Wells
241
Total Restricted Road Miles s
687
Feet Drilled at All Wells
242
All Restricted AADT
689
Gas Produced - Total
243
Total Unrestricted Road Miles i
691
Well Counts - CBM Wells
244
All Unrestricted AADT
692
Spud Count-All Wells
258
Intercity Bus Terminals
693
Well Count - All Wells
259
Transit Bus Terminals
694
Oil Production at Oil Wells
260
Total Railroad Miles
695
Well Count - Oil Wells
261
NT AD Total Railroad Density
696
Gas Production at Gas Wells
145
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Code
Surrogate Description
Code
Surrogate Description
271
NT AD Class 12 3 Railroad Density
697
Oil Production at Gas Wells
272
NTAD Amtrak Railroad Density
698
Well Count - Gas Wells
273
NTAD Commuter Railroad Density
699
Gas Production at CBM Wells
275
ERTACRail Yards
710
Airport Points
280
Class 2 and 3 Railroad Miles
711
Airport Areas
300
NLCD Low Intensity Development
801
Port Areas
301
NLCD Med Intensity Development ]
802
Shipping Lanes
302
I
NLCD High Intensity Development
805
Offshore Shipping Area
303
NLCD Open Space
806
Offshore Shipping NEI2014 Activity
304
NLCD Open + Low
807
Navigable Waterway Miles
305
NLCD Low + Med
808
2013 Shipping Density
306
NLCD Med + High
820
Ports NEI2014 Activity
307
NLCD All Development
850
Golf Courses
308
NLCD Low + Med + High
860
Mines
309
NLCD Open + Low + Med
890
Commercial Timber
310
NLCD Total Agriculture
For the onroad sector, the on-network (RPD) emissions were spatially allocated differently from other off-
network processes (e.g., RPV, RPP, RPHO). 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 3-22. 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. The underlying
data for this surrogate were updated during the development of the 2016 platforms to include additional
data sources and corrections based on comments received and these updates were carried into this
platform.
Table 3-22. 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
NLCD Low + Med +
32
Light Commercial Truck
308
High
41
Intercity 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 3-23 using 2016 data consistent with what was used to develop the 2016v2 nonpoint oil and gas
emissions. The primary activity data source used for the development of the oil and gas spatial
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surrogates was data from Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2017). This
database contains well-level location, production, and exploration statistics at the monthly level.
Due to a proprietary agreement with DI Desktop, individual well locations and ancillary
production cannot be made publicly available, but aggregated statistics are allowed. These data were
supplemented with data from state Oil and Gas Commission (OGC) websites (Alaska, Arizona, Idaho,
Illinois, Indiana, Kentucky, Louisiana, Michigan, Mississippi, Missouri, Nevada, Oregon and
Pennsylvania, 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) was downloaded and
used. Under that methodology, both completion date and date of first production from HPDI were used to
identify wells completed during 2016. In total, over 1 million unique wells were compiled from the above
data sources. The wells cover 34 states and over 1,100 counties. (ERG, 2018).
Table 3-23. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
670
Spud Count - CBM Wells
671
Spud Count - Gas Wells
672
Gas Production at Oil Wells
673
Oil Production at CBM Wells
674
Unconventional Well Completion Counts
676
Well Count - All Producing
677
Well Count - All Exploratory
678
Completions at Gas Wells
679
Completions at CBM Wells
681
Spud Count - Oil Wells
683
Produced Water at All Wells
685
Completions at Oil Wells
686
Completions at All Wells
687
Feet Drilled at All Wells
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
2688
WRAP Gas production at oil wells
2689
WRAP Gas production at all wells
2691
WRAP Well count - CBM wells
2693
WRAP Well count - all wells
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Surrogate Code
Surrogate Description
2694
WRAP Oil production at oil wells
2695
WRAP Well count - oil wells
2696
WRAP Gas production at gas wells
2697
WRAP Oil production at gas wells
2698
WRAP Well count - gas wells
2699
WRAP Gas production at CBM wells
6831
Produced water at CBM wells
6832
Produced water at gas wells
6833
Produced water at oil wells
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-21 were not assigned to any SCCs, although many of the "unused"
surrogates are actually used to "gap fill" other surrogates that are used. When the source data for a
surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in
those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the
emission inventories. Table 3-24 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by
sector assigned to each spatial surrogate.
Table 3-24. Selected 2016 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
303,187
0
0
afdust
304
NLCD Open + Low
0
0
826,942
0
0
afdust
306
NLCD Med + High
0
0
52,278
0
0
afdust
308
NLCD Low + Med + High
0
0
117,313
0
0
afdust
310
NLCD Total Agriculture
0
0
788,107
0
0
fertilizer
310
NLCD Total Agriculture
1,436,969
0
0
0
0
livestock
310
NLCD Total Agriculture
2,502,587
0
0
0
219,703
nonpt
100
Population
34,304
0
0
0
208
nonpt
150
Residential Heating - Natural Gas
33,550
204,371
4,041
1,365
12,055
nonpt
170
Residential Heating - Distillate Oil
1,531
30,031
3,284
11,510
1,039
nonpt
180
Residential Heating - Coal
1
3
1
3
3
nonpt
190
Residential Heating - LP Gas
98
31,061
163
712
1,181
nonpt
239
Total Road AADT
0
22
541
0
306,341
nonpt
244
All Unrestricted AADT
0
0
0
0
101,255
nonpt
271
NTAD Class 12 3 Railroad Density
0
0
0
0
2,203
nonpt
300
NLCD Low Intensity Development
4,823
19,093
94,548
2,882
72,599
nonpt
304
NLCD Open + Low
0
0
0
0
0
nonpt
306
NLCD Med + High
23,609
272,532
241,511
131,494
112,071
nonpt
307
NLCD All Development
85
25,798
110,610
8,256
69,262
nonpt
308
NLCD Low + Med + High
885
156,231
15,679
10,080
10,047
nonpt
310
NLCD Total Agriculture
0
0
38
0
0
nonpt
319
NLCD Crop Land
0
0
97
72
299
148
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nonpt
320
NLCD Forest Land
3,953
68
273
0
279
nonpt
650
Refineries and Tank Farms
0
16
0
0
106,401
nonpt
711
Airport Areas
0
0
0
0
621
nonpt
801
Port Areas
0
0
0
0
6,730
nonroad
261
NT AD Total Railroad Density
3
2,154
227
1
426
nonroad
304
NLCD Open + Low
4
1,824
159
4
2,761
nonroad
305
NLCD Low + Med
94
15,985
3,832
119
115,955
nonroad
306
NLCD Med + High
305
183,591
11,839
328
94,299
nonroad
307
NLCD All Development
99
31,526
15,338
108
170,212
nonroad
308
NLCD Low + Med + High
498
338,083
28,486
241
51,957
nonroad
309
NLCD Open + Low + Med
119
21,334
1,256
151
45,828
nonroad
310
NLCD Total Agriculture
422
378,356
28,344
214
40,771
nonroad
320
NLCD Forest Land
15
5,910
699
9
3,944
nonroad
321
NLCD Recreational Land
83
11,616
6,517
89
246,560
nonroad
350
NLCD Water
188
115,168
5,952
232
355,808
nonroad
850
Golf Courses
13
2,001
117
16
5,647
nonroad
860
Mines
2
2,691
281
1
521
np oilgas
670
Spud Count - CBM Wells
0
0
0
0
97
np oilgas
671
Spud Count - Gas Wells
0
0
0
0
5,925
np oilgas
674
Unconventional Well Completion
Counts
20
25,363
819
20
1,307
np oilgas
678
Completions at Gas Wells
0
5,348
136
2,976
18,333
np oilgas
679
Completions at CBM Wells
0
2
0
80
415
np oilgas
681
Spud Count - Oil Wells
0
0
0
0
14,747
np oilgas
683
Produced Water at All Wells
0
0
0
0
13,876
np oilgas
685
Completions at Oil Wells
0
259
0
888
29,548
np oilgas
687
Feet Drilled at All Wells
0
46,704
1,478
44
2,661
np oilgas
689
Gas Produced - Total
0
1,311
167
13
27,266
np oilgas
691
Well Counts - CBM Wells
0
14,390
264
6
16,907
np oilgas
694
Oil Production at Oil Wells
0
603
0
11,354
500,150
np oilgas
695
Well Count - Oil Wells
0
113,164
2,562
74
456,274
np oilgas
696
Gas Production at Gas Wells
0
1,539
0
0
299,205
np oilgas
698
Well Count - Gas Wells
0
265,108
4,831
242
434,613
np oilgas
699
Gas Production at CBM Wells
0
44
5
0
3,373
np oilgas
2688
WRAP Gas production at oil wells
0
7,747
0
5,487
221,022
np oilgas
2689
WRAP Gas production at all wells
0
26,598
780
1,133
28,306
np oilgas
2691
WRAP Well count - CBM wells
0
225
19
0
1,524
np oilgas
2693
WRAP Well count - all wells
0
17,239
460
17
1,768
np oilgas
2694
WRAP Oil production at oil wells
0
35,144
543
18,367
110,330
np oilgas
2695
WRAP Well count - oil wells
0
2,726
244
12
75,349
np oilgas
2696
WRAP Gas production at gas wells
0
4,294
42
2
37,580
np oilgas
2697
WRAP Oil production at gas wells
0
551
0
10
75,738
np oilgas
2698
WRAP Well count - gas wells
0
8,160
513
14
120,726
149
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
np oilgas
2699
WRAP Gas production at CBM wells
0
9,157
282
9
7,593
np oilgas
6831
Produced water at CBM wells
0
0
0
0
966
np oilgas
6832
Produced water at gas wells
0
0
0
0
5,742
np oilgas
6833
Produced water at oil wells
0
0
0
0
21,502
np solvents
100
Population
0
0
0
0
1,456,107
np solvents
240
Total Road Miles
0
0
0
0
51,483
np solvents
306
NLCD Med + High
33
27
300
1
493,575
np solvents
307
NLCD All Development
24
6
19
5
403,847
np solvents
308
NLCD Low + Med + High
0
0
129
0
29,372
np solvents
310
NLCD Total Agriculture
0
0
0
0
172,111
onroad
205
Extended Idle Locations
318
41,411
1,094
17
5,733
onroad
242
All Restricted AADT
35,490
1,252,856
34,860
7,513
166,585
onroad
244
All Unrestricted AADT
67,069
1,885,571
65,860
16,707
459,731
onroad
259
Transit Bus Terminals
12
2,634
65
2
485
onroad
304
NLCD Open + Low
0
863
27
0
6,329
onroad
306
NLCD Med + High
860
96,718
4,861
85
22,594
onroad
307
NLCD All Development
3,768
237,388
6,263
1,544
620,009
onroad
308
NLCD Low + Med + High
230
25,814
534
94
35,326
onroad
508
Public Schools
15
2,396
126
2
687
rail
261
NT AD Total Railroad Density
13
33,389
996
15
1,647
rail
271
NTAD Class 12 3 Railroad Density
313
525,992
14,823
442
24,435
rwc
300
NLCD Low Intensity Development
16,940
35,198
308,965
8,247
334,158
For 36US3 modeling in the 2016 platforms, most U.S. emissions sectors were processed using 36-km
spatial surrogates, and if applicable, 36-km meteorology. Exceptions include:
- For the onroad and onroad ca adj sectors, instead of running SMOKE-MOVES with 36km
meteorological data, 36US3 emissions were aggregated from 12US1 by summing emissions from
a 3x3 group of 12-km cells into a single 36-km cell. Differences in the 12-km and 36-km
meteorology can introduce differences in onroad emissions, so this approach ensures that the 36-
km and 12-km onroad emissions are consistent. However, this approach means that 36US3 onroad
does not include emissions in Southeast Alaska; therefore, Alaska onroad emissions are included
in a separate sector called onroadnonconus that is processed for only the 36US3 domain. The
36US3 onroad nonconus emissions are spatially allocated using 36-km surrogates and processed
with 36-km meteorology.
Similarly to onroad, because afdust emissions incorporate meteorologically-based adjustments,
afdust adj emissions for 36US3 were aggregated from 12US1 to ensure consistency in emissions
between modeling domains. Again, similarly to onroad, this means 36US3 afdust does not include
emissions in Southeast Alaska; therefore, Alaska afdust emissions are processed in a separate
sector called afdustakadj. The 36US3 afdustakadj emissions are spatially allocated using 36-
km surrogates and adjusted with 36-km meteorology.
The ag and rwc sectors are processed using 36-km spatial surrogates, but using temporal profiles
based on 12-km meteorology.
150
-------
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 approach is described in detail in the 2002 platform documentation:
https://www.epa.gov/sites/default/files/2020-10/documents/emissions tsd voll 02-28-08.pdf. The
ARTOPNT file that lists the nonpoint sources to locate using point data were unchanged from the 2005-
based platform.
3.4.3 Surrogates for Canada and Mexico emission inventories
Spatial surrogates for allocating Mexico municipio level emissions were updated in the 2014v7.1 platform
and carried forward into the 2016 platforms. For the 2016 beta (v7.2) platform, a set of Canada shapefiles
were provided by ECCC along with cross references to spatially allocate the year 2015 Canadian
emissions. Gridded surrogates were generated using the Surrogate Tool (previously referenced); Table
3-25 provides a list. For computational reasons, total roads (1263) were used instead of the unpaved rural
road surrogate provided. The population surrogate for Mexico; surrogate code 11, uses 2015 population
data at 1 km resolution and replaced the previous population surrogate code 10. The other surrogates for
Mexico are circa 1999 and 2000 and were based on data obtained from the Sistema Municipal de Bases de
Datos (SIMBAD) de INEGI and the Bases de datos del Censo Economico 1999. Most of the CAPs
allocated to the Mexico and Canada surrogates are shown in Table 3-26.
Table 3-25. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
TOTAL INSTITUTIONAL AND
100
Population
923
GOVERNEMNT
101
total dwelling
924
Primary Industry
104
capped total dwelling
925
Manufacturing and Assembly
106
ALL INDUST
926
Distribution and Retail (no petroleum)
113
Forestry and logging
927
Commercial Services
200
Urban Primary Road Miles
932
CANRAIL
210
Rural Primary Road Miles
940
PAVED ROADS NEW
211
Oil and Gas Extraction
945
Commercial Marine Vessels
212
Mining except oil and gas
946
Construction and mining
220
Urban Secondary Road Miles
948
Forest
221
Total Mining
951
Wood Consumption Percentage
222
Utilities
955
UNPAVED ROADS AND TRAILS
230
Rural Secondary Road Miles
960
TOTBEEF
233
Total Land Development
970
TOTPOUL
240
capped population
980
TOTS WIN
308
Food manufacturing
990
TOTFERT
321
Wood product manufacturing
996
urban area
323
Printing and related support activities
1251
OFFR TOTFERT
324
Petroleum and coal products manufacturing
1252
OFFR MINES
151
-------
Code
Canadian Surrogate Description
Code
Description
326
Plastics and rubber products manufacturing
1253
OFFR Other Construction not Urban
327
Non-metallic mineral product manufacturing
1254
OFFR Commercial Services
331
Primary Metal Manufacturing
1255
OFFR Oil Sands Mines
350
Water
1256
OFFR Wood industries CANVEC
412
Petroleum product wholesaler-distributors
1257
OFFR UNPAVED ROADS RURAL
448
clothing and clothing accessories stores
1258
OFFR Utilities
482
Rail transportation
1259
OFFR total dwelling
562
Waste management and remediation services
1260
OFFR water
901
AIRPORT
1261
OFFR ALL INDUST
902
Military LTO
1262
OFFR Oil and Gas Extraction
903
Commercial LTO
1263
OFFR ALLROADS
904
General Aviation LTO
1265
OFFR CANRAIL
921
Commercial Fuel Combustion
9450
Commercial Marine Vessel Ports
Table 3-26. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3)
Sector
Code
Mexican / Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
othafdust
106
CAN ALL INDUST
0
0
609
0
0
othafdust
212
CAN Mining except oil and gas
0
0
3,142
0
0
othafdust
221
CAN Total Mining
0
0
17,315
0
0
othafdust
222
CAN Utilities
0
0
2,792
0
0
othafdust
940
CAN Paved Roads New
0
0
29,862
0
0
othafdust
955
CAN UNPAVEDROADSANDTRAILS
0
0
426,511
0
0
othar
26
MEX Total Agriculture
560,091
82,958
48,439
1,987
18,052
othar
32
MEX Commercial Land
0
391
8,511
0
102,447
othar
34
MEX Industrial Land
164
4,244
4,135
11
102,903
othar
36
MEX Commercial plus Industrial Land
7
23,149
1,551
12
234,277
othar
40
MEX Residential (RES1-
4)+Comercial+Industrial+Institutional+Governmen
t
4
90
424
12
105,233
othar
42
MEX Personal Repair (COM3)
0
0
0
0
25,999
othar
44
MEX Airports Area
0
16,295
216
1,183
6,834
othar
48
MEX Brick Kilns
0
2,778
55,550
5,031
1,352
othar
50
MEX Mobile sources - Border Crossing
3
71
2
0
57
othar
100
CAN Population
795
52
622
15
225
othar
101
CAN total dwelling
0
0
0
0
151,094
othar
104
CAN Capped Total Dwelling
361
31,746
2,335
2,671
1,650
othar
113
CAN Forestry and logging
152
1,818
9,778
37
5,140
othar
211
CAN Oil and Gas Extraction
1
43
433
74
2,122
othar
212
CAN Mining except oil and gas
0
0
11
0
0
othar
221
CAN Total Mining
0
0
293
0
0
othar
222
CAN Utilities
57
3,439
166
464
65
othar
308
CAN Food manufacturing
0
0
19,253
0
17,468
othar
321
CAN Wood product manufacturing
873
4,822
1,646
383
16,605
152
-------
Sector
Code
Mexican / Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
othar
323
CAN Printing and related support activities
0
0
0
0
11,778
othar
324
CAN Petroleum and coal products manufacturing
0
1,201
1,632
467
9,368
othar
326
CAN Plastics and rubber products manufacturing
0
0
0
0
24,270
othar
327
CAN Non-metallic mineral product manufacturing
0
0
6,541
0
0
othar
331
CAN Primary Metal Manufacturing
0
158
5,598
30
72
othar
412
CAN Petroleum product wholesaler-distributors
0
0
0
0
45,634
othar
448
CAN clothing and clothing accessories stores
0
0
0
0
143
othar
482
CAN Rail Transportation
1
4,106
89
1
258
othar
562
CAN Waste management and remediation services
247
1,981
2,747
2,508
9,654
othar
901
CAN Airport
0
108
10
0
11
othar
921
CAN Commercial Fuel Combustion
206
24,819
2,435
1,669
1,254
othar
923
CAN TOTAL INSTITUTIONAL AND
GOVERNEMNT
0
0
0
0
14,847
othar
924
CAN Primary Industry
0
0
0
0
40,409
othar
925
CAN Manufacturing and Assembly
0
0
0
0
70,468
othar
926
CAN Distribution and Retail (no petroleum)
0
0
0
0
7,475
othar
927
CAN Commercial Services
0
0
0
0
32,096
othar
932
CAN CANRAIL
52
91,908
1,822
48
3,901
othar
946
CAN Construction and Mining
0
0
0
0
10,211
othar
951
CAN Wood Consumption Percentage
1,010
11,223
113,852
1,603
161,174
othar
990
CAN TOTFERT
49
4,185
276
6,834
160
othar
996
CAN urbanarea
0
0
3,182
0
0
othar
1251
CAN OFFR TOTFERT
79
65,830
4,646
54
6,266
othar
1252
CAN OFFR MINES
1
905
67
1
134
othar
1253
CAN OFFR Other Construction not Urban
63
40,640
4,880
43
11,607
othar
1254
CAN OFFR Commercial Services
42
16,193
2,443
36
37,663
othar
1255
CAN OFFR Oil Sands Mines
23
12,478
410
12
1,330
othar
1256
CAN OFFR Wood industries CANVEC
8
3,180
288
6
1,102
othar
1257
CAN OFFR Unpaved Roads Rural
26
11,244
734
23
32,322
othar
1258
CAN OFFRUtilities
8
4,471
229
6
930
othar
1259
CAN OFFR total dwelling
17
6,485
649
15
13,317
othar
1260
CAN OFFRwater
23
6,495
493
33
34,204
othar
1261
CAN OFFR ALL INDUST
4
5,654
185
2
1,105
othar
1262
CAN OFFR Oil and Gas Extraction
1
1,291
77
1
212
othar
1263
CAN OFFRALLROADS
3
1,826
185
2
494
othar
1265
CAN OFFRCANRAIL
0
550
18
0
44
onroad_can
200
CAN Urban Primary Road Miles
1,742
84,596
2,810
367
8,888
onroad_can
210
CAN Rural Primary Road Miles
714
49,909
1,626
153
3,945
onroad_can
220
CAN Urban Secondary Road Miles
3,279
134,909
5,613
776
23,625
onroad_can
230
CAN Rural Secondary Road Miles
1,898
95,447
3,152
418
10,899
onroad_can
240
CAN Total Road Miles
346
63,465
1,500
88
117,123
onroad_mex
11
MEX 2015 Population
0
281,135
1,872
533
291,816
onroad_mex
22
MEX Total Road Miles
10,316
1,207,878
54,789
25,837
251,800
onroad_mex
36
MEX Commercial plus Industrial Land
0
7,971
142
29
9,187
153
-------
3.5
Preparation of Emissions for the CAMx model
3.5.1 Development of CAMx Emissions for Standard CAMx Runs
To perform air quality modeling with the Comprehensive Air Quality Model with Extensions (CAMx
model), the gridded hourly emissions output by the SMOKE model are output in the format needed by the
CMAQ model, but must be converted to the format required by CAMx. For "regular" CAMx modeling
(i.e., without two-way nesting), the CAMx conversion process consists of the following:
• Convert all emissions file formats from the I/O API NetCDF format used by CMAQ to the UAM
format used by CAMx, including the merged, gridded low-level emissions files that include
biogenics
• Shift hourly emissions files from the 25 hour format used by CMAQ to the averaged 24 hour
format used by CAMx
• Rename and aggregate model species for CAMx
• Convert 3D wildland and agricultural fire emissions into CAMx point format
• Merge all inline point source emissions files together for each day, including layered fire
emissions originally from SMOKE
• Add sea salt aerosol emissions to the converted, gridded low-level emissions files
Conversion of file formats from I/O API to CAMx (i.e., UAM) format is performed using a program
called "cmaq2uam". In the CAMx conversion process, all SMOKE outputs are passed through this step
first. Unlike CMAQ, the CAMx model does not have an inline biogenics option, and so for the purposes
of CAMx modeling, emissions from SMOKE must include biogenic emissions.
One difference between CMAQ-ready emissions files and CAMx-ready emissions files involves hourly
temporalization. A daily emissions file for CMAQ includes data for 25 hours, where the first hour is 0:00
GMT of a given day, and the last hour is 0:00 GMT of the following day. For the CAMx model, a daily
emissions file must only include data for 24 hours, not 25. Furthermore, to match the hourly configuration
expected by CAMx, each set of consecutive hourly timesteps from CMAQ-ready emissions files must be
averaged. For example, the first hour of a CAMx-ready emissions file will equal the average of the first
two hours from the corresponding CMAQ-ready emissions file, and the last (24th) hour of a CAMx-ready
emissions file will equal the average of the last two hours (24th and 25th) from the corresponding CMAQ-
ready emissions file. This time conversion is incorporated into each step of the CAMx-ready emissions
conversion process.
The CAMx model uses a slightly different version of the CB6 speciation mechanism than does the
CMAQ model. SMOKE prepares emissions files for the CB6 mechanism used by the CMAQ model
("CB6-CMAQ"), and therefore, the emissions must be converted to the CB6 mechanism used by the
CAMx model ("CB6-CAMx") during the CAMx conversion process. In addition to the mechanism
differences, CMAQ and CAMx also occasionally use different species naming conventions. For CAMx
modeling, we also create additional tracer species. A summary of the differences between CMAQ input
species and CAMx input species for CB6 (VOC), AE6 (PM2.5), and other model species, is provided in
Table 3-27. Each step of the CAMx-ready emissions conversion process includes conversion of CMAQ
species to CAMx species using a species mapping table which includes the mappings in Table 3-27.
154
-------
Table 3-27. Emission model species mappings for CMAQ and CAMx (for CB6R3AE7)
Inventory Pollutant
CMAQ Model Species
CAMx Model Species
Cl2
CL2
CL2
HC1
HCL
HCL
CO
CO
CO
NOx
NO
NO
N02
N02
HONO
HONO
S02
S02
S02
SULF
SULF
nh3
NH3
NH3
NH3 FERT
n/a (not used in CAMx)
voc
AACD
AACD
ACET
ACET
ALD2
ALD2
ALDX
ALDX
BENZ
BENZ and BNZA (duplicate species)
CH4
CH4
ETH
ETH
ETHA
ETHA
ETHY
ETHY
ETOH
ETOH
FACD
FACD
FORM
FORM
IOLE
IOLE
ISOP
ISOP and ISP (duplicate species)
IVOC
IVOA
KET
KET
MEOH
MEOH
NAPH + XYLMN (sum)
XYL and XYLA (duplicate species)
NVOL
n/a (not used in CAMx)
OLE
OLE
PAR
PAR
PRPA
PRPA
SESQ
SOT
SOAALK
n/a (not used in CAMx)
TERP + APIN (sum)
TERP and TRP (duplicate species)
TOL
TOL and TOLA (duplicate species)
UNR + NR (sum)
NR
PM10
PMC
CPRM
PM2.5
PEC
PEC
PN03
PN03
POC
POC
PS04
PS04
PAL
PAL
PCA
PCA
PCL
PCL
PFE
PFE
PK
PK
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Inventory Pollutant
CMAQ Model Species
CAMx Model Species
PH20
PH20
PMG
PMG
PMN
PMN
PMOTHR
FPRM
PNA
NA
PNCOM
PNCOM
PNH4
PNH4
PSI
PSI
PTI
PTI
POC + PNCOM (sum)
POA1
1 The POA species, which is the sum of POC and PNCOM, is passed to the CAMx model in addition to individual species POC
and PNCOM.
One feature which is part of CMAQ and is not part of CAMx involves plume rise for fires. For CMAQ
modeling, we process fire emissions through SMOKE as inline point sources, and plume rise for fires is
calculated within CMAQ using parameters from the inline emissions files (heat flux, etc). This is similar
to how non-fire point sources are handled, except that the fire parameters are used to calculate plume rise
instead of traditional stack parameters. The CAMx model supports inline plume rise calculations using
traditional stack parameters, but it does not support inline plume rise for fire sources. Therefore, for the
purposes of CAMx modeling, we must have SMOKE calculate plume rise for fires using the Laypoint
program. In this modeling platform, this must be done for the ptfire, ptfire othna, and ptagfire sectors. To
distinguish these layered fire emissions from inline fire emissions, layered fire emissions are processed
with the sector names "ptfire-wild3D", "ptfire-rx3D", "ptfire_othna3D", and "ptagfire3D". When
converting layered fire emissions files to CAMx format, stack parameters are added to the CAMx-ready
fire emissions files to force the correct amount of fire emissions into each layer for each fire location.
CMAQ modeling uses one gridded low-level emissions file, plus multiple inline point source emissions
files, per day. CAMx modeling also uses one gridded low-level emissions file per day - but instead of
reading multiple inline point source emissions files at once, CAMx can only read a single point source file
per day. Therefore, as part of the CAMx conversion process, all inline point source files are merged into a
single "mrgpt" file per day using the program ptsmrg. The mrgpt file includes the layered fire emissions
described in the previous paragraph, in addition to all non-fire elevated point sources from the cmv_clc2,
cmv_c3, othpt, ptegu, ptnonipm, and pt oilgas sectors.
The remaining step in the CAMx emissions process is to generate sea salt aerosol emissions, which are
distinct from ocean chlorine emissions. Sea salt emissions do not need to be included in CMAQ-ready
emissions because they are calculated by the model, but they do need to be included in CAMx-ready
emissions. After the merged low-level emissions are converted to CAMx format, sea salt emissions are
generated using a program called "seasalt" and added to the low-level emissions. Sea salt emissions
depend on meteorology, vary on a daily and hourly basis, and exist for model species sodium (NA),
chlorine (PCL), sulfate (PS04), dimethy sulfide (DMS), and gas phase bromine (SSBR) and chlorine
(SSCL).
3.5.2 Development of CAMx Emissions for Source Apportionment CAMx
Runs
The CAMx model supports source apportionment modeling for ozone and PM sources using techniques
called Ozone Source Apportionment Technology (OSAT) and Particulate Matter Source Apportionment
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Technology (PSAT). These source apportionment techniques allow emissions from different types of
sources to be tracked through the CAMx model. Source apportionment model runs are most commonly
performed using one-way nesting (i.e., the inner grid takes boundary information from the outer grid but
the inner grid does not feed any concentration information back to the outer grid).
Source Apportionment modeling involves assigning tags to different categories of emissions. These tags
can be applied by region (e.g., state), by emissions type (e.g., SCC or sector), or a combination of the two.
For the Revised CSAPR Update study, emissions tagging was applied by state. All emissions from US
states, except for biogenics, fires, and fugitive dust (afdust), were assigned a state-specific tag. Emissions
from tribal lands are assigned a separate tag, as well as offshore emissions. Other tags include a tag for
biogenics and afdust; a tag for all fires, both inside and outside the US; and a tag for all anthropogenic
emissions from Canada and Mexico. A list of tags used in recent studies for state source apportionment
modeling is provided in Table 3-28. State-level tags 2 through 51 exclude emissions from biogenics,
fugitive dust, and fires, which are included in other tags.
For OSAT and PSAT modeling, all emissions must be input to CAMx in the form of a point source
(mrgpt) file, including low level sources that are found in gridded files for regular CAMx runs. In
addition, for any two-way nested modeling, all emissions must be input in a single mrgpt file, rather than
separate mrgpt files for each of the domains. Note that fire emissions require special consideration in two-
way nested model runs and for PSAT and OSAT modeling. That same consideration must be given to
any sector in which emissions are being gridded by SMOKE.
There are two main approaches for tagging emissions for CAMx modeling. One approach is to tag
emissions within SMOKE. Here, SMOKE will output tagged point source files (SGINLN files), which
can then be converted to CAMx point source format with the tags applied by SMOKE carried forward
into the CAMx inputs. The second approach is to, if necessary, depending on the nature of the tags, split
sectors into multiple components by tag so that each sector corresponds to a single tag. Then, the gridded
and/or point source format SMOKE outputs from those split sectors are converted to CAMx point source
format, and then merged into the full mrgpt file, with the tags applied at that last step. In some situations,
a mix of the two approaches is appropriate.
For ozone transport modeling runs, the first approach is used for most sectors, meaning tags are applied in
SMOKE. The exceptions are when the entire sector receives only one tag, e.g.: afdust, beis,
onroad ca adj, ptfire-rx, ptfire-wild, ptagfire, ptfire othna, and all Canada and Mexico sectors. Afdust
emissions are not tagged by state because the current tagging methodology does not support applying
transportable fraction and meteorological adjustments to tagged emissions.
Once the individual sector tagging is complete, the point source files for all of the sectors are merged
together to create the mrgpt file which includes all emissions, with the desired tags and appropriate
resolution throughout the domain for OSAT or PSAT modeling.
Table 3-28. State tags for USA modeling
Tag
Emissions applied to tag
1
All biogenics (beis sector) and US
fugitive dust (afdust sector)
2
Alabama
3
Arizona
Tag
Emissions applied to tag
4
Arkansas
5
California
6
Colorado
7
Connecticut
157
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Tag
Emissions applied to tag
8
Delaware
9
District of Columbia
10
Florida
11
Georgia
12
Idaho
13
Illinois
14
Indiana
15
Iowa
16
Kansas
17
Kentucky
18
Louisiana
19
Maine
20
Maryland
21
Massachusetts
22
Michigan
23
Minnesota
24
Mississippi
25
Missouri
26
Montana
27
Nebraska
28
Nevada
29
New Hampshire
30
New Jersey
31
New Mexico
Tag
Emissions applied to tag
32
New York
33
North Carolina
34
North Dakota
35
Ohio
36
Oklahoma
37
Oregon
38
Pennsylvania
39
Rhode Island
40
South Carolina
41
South Dakota
42
Tennessee
43
Texas
44
Utah
45
Vermont
46
Virginia
47
Washington
48
West Virginia
49
Wisconsin
50
Wyoming
51
Tribal Data
52
Canada and Mexico (except fires)
53
Offshore
54
All fires from US, Canada, and
Mexico, including ag fires
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4 Development of Analytic Year Emissions
The emission inventories for analytic years of 2023 and 2026 have been developed using projection
methods that are specific to the type of emissions source. Analytic year emissions are projected from the
2016 base case 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). 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 2016 meteorological data is 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 2023 and 2026 are summarized in Table
4-1. Detailed information about the changes in the 2016v3 platform is provided in the subsections that
follow.
Table 4-1. Overview of projection methods for the future year cases
Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
EGU units:
ptegu
The Integrated Planning Model (IPM) outputs from the Updated Summer 2021
version of the IPM platform were used. For 2023. the 2023 IPM output vear was
used and for 2026 the 2025 output year was used. Emission inventory Flat Files for
input to SMOKE were generated using post-processed IPM output data. A list of
included rules is provided in Section 4.1.
Point source oil and
gas:
ptoilgas
First, known closures were applied to the 2016 pt_oilgas sources. Production-
related sources were then grown from 2016 to 2021 using historic production data.
The production-related sources were then grown to 2023 and 2026 based on
growth factors derived from the Annual Energy Outlook (AEO) 2022 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). Some sources were held at 2018 or 2019 levels.
WRAP future year inventories are used in all of the WRAP states except for New
Mexico (CO, MT, ND, SD, UT and WY). The future year WRAP inventories are
the same for all analytic years. New Mexico emissions are projected from 2016
along with the non-WRAP states.
Airports:
airports
Point source airport emissions were grown from 2016 to each analytic year using
factors derived from the 2021 Terminal Area Forecast (TAF) released in June 2022
(see https://www.faa.aov/data research/aviation/taf/). Corrections to emissions for
ATL from the state of Georgia are included, as well as some corrections for
specific airports in the state of Texas.
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Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
Remaining non-
EGU point:
ptnonipm
2019 NEI data (EPA, 2022) were used for 2023 for most sources. Known closures
were applied to ptnonipm sources. Closures were obtained from the Emission
Inventory System (EIS) and also submitted by the states of Alabama, North
Carolina, Ohio, Pennsylvania, and Virginia. Industrial emissions were grown
according to factors derived from AEO2022 to reflect growth from 2023 onward.
Rail yard emissions were grown using the same factors as line haul locomotives in
the rail sector. Controls were applied to account for relevant NSPS for RICE, gas
turbines, refineries (subpart Ja), and process heaters. The Boiler MACT is assumed
to be fully implemented in 2016 except for North Carolina. Controls are reflected
for the regional haze program in Arizona. Changes to ethanol plants and
biorefineries are included. In 2016v3, additional closures were implemented, new
sources were added based on 2019 NEI, and growth in MARAMA states was
updated using MARAMA spreadsheets after incorporating AEO 2022 data.
Railyards in California were updated with CARB data for 2023 and 2026. Point
source solvents are based on 2019 NEI and projected to 2023 and 2026.
Category 1, 2 CMV:
cmv_clc2
Category 1 and category 2 (C1C2) CMV emissions sources outside of California
were projected to 2023 and 2026 based on factors from the Regulatory Impact
Analysis (RIA) Control of Emissions of Air Pollution from Locomotive Engines
and Marine Compression Ignition Engines Less than 30 Liters per Cylinder.
California emissions were projected based on factors provided by the state.
Projection factors for Canada for 2026 were based on ECCC-provided 2023 and
2028 data interpolated to 2026. The 2023 and 2026 emissions are unchanged from
2016v2 except for the improved spatial allocation to counties.
Category 3 CMV:
cmv_c3
Category 3 (C3) CMV emissions were projected to 2023 and 2026 using an EPA
report on projected bunker fuel demand that projects fuel consumption by region
out to the year 2026. Bunker fuel usage was used as a surrogate for marine vessel
activity. Factors based on the report were used for all pollutants except NOx. The
NOx growth rates from the EPA C3 Regulatory Impact Assessment (RIA) were
refactored to use the new bunker fuel usage growth rates. Assumptions of changes
in fleet composition and emissions rates from the C3 RIA were preserved and
applied to bunker fuel demand growth rates for 2023 and 2026 to arrive at the final
growth rates. Projection factors for Canada for 2026 were based on ECCC-
provided 2023 and 2028 data interpolated to 2026. The 2023 and 2026 emissions
are unchanged from 2016v2 except for the improved spatial allocation to counties.
Locomotives:
rail
Passenger and freight locomotives were projected using separate factors. Freight
emissions were computed for analytic years based on fuel use values for 2023 and
2026. Specifically, they were based on AEO2019 and 2020 freight rail energy use
growth rate projections along with emission factors based on historic emissions
trends that reflect the rate of market penetration of new locomotive engines.
Area fugitive dust:
afdust, afdust ak
Paved road dust was grown to 2023 and 2026 levels based on the growth in VMT
from 2016. The remainder of the sector including building construction, road
construction, agricultural dust, and unpaved road dust was held constant at 2016
levels, except in the MARAMA region and NC where some factors were provided
for categories other than paved roads. The projected emissions were reduced
during modeling (as they are for the base year) according to a transport fraction
computed using a new method for the 2016 beta platform and a meteorology-based
zero-out that accounts for precipitation and snow/ice cover.
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Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
Livestock: livestock
Livestock were projected to 2023 and 2026 based on factors created from
USDA National livestock inventory projections published in 2022
(https://www.ers.usda.eov/publications/pub-details/?pubid=92599). NC and NJ
projections were state provided.
Nonpoint source oil
and gas:
npoilgas
Exploration-related sources were baed on an average of 2017 through 2019
exploration data with NSPS controls applied, where applicable. Production-related
emissions were initially projected to 2021 using historical data and then grown to
2023 and 2026 based on factors generated from AEO2022 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. WRAP future year inventories are
used in seven WRAP states for 2023 and 2026 (except for NM, which is projected
based on AEO).
Residential Wood
Combustion:
rwc
The 2016v3 emissions are the same as 2016v2, with the exception of Idaho, which
uses the 2017 NEI for the base year emissions. RWC emissions were projected
from 2016 to 2023 and 2026 based on growth and control assumptions compatible
with EPA's 201 lv6.3 platform, which accounts for growth, retirements, and
NSPS, although implemented in the Mid-Atlantic Regional Air Management
Association (MARAMA)'s growth tool. Factors provided by North Carolina were
used for that state. RWC emissions in California, Oregon, and Washington were
held constant at 2017 levels.
Solvents:
solvents
Solvents are based on an updated method for 2016v3. The same projection and
control factors were applied to solvent emissions as if these SCCs were in nonpt.
Additional SCCs in the new inventory that correlate with human population were
also projected. Solvent emissions associated with oil and gas activity were
projected using the same projection factors as the oil and gas sectors. The 2016vl
NC and NJ nonpoint packets were used for 2023 and interpolated to 2026, and
updated to apply to more SCCs. OTC controls for solvents were applied - both DE
and NY provided new controls.
Remaining
nonpoint:
nonpt
Industrial emissions were grown according to factors derived from AEO2022 to
reflect growth from 2021 onward. Data from earlier AEOs were used to derive
factors for 2016 through 2021. Portions of the nonpt sector were grown using
factors based on expected growth in human population. The MARAMA projection
tool was used to project emissions to 2023 and 2026 after the AEO-based factors
were updated to AEO2022. Factors provided by North Carolina and New Jersey
were preserved. Controls were applied to reflect relevant NSPS rules (i.e.,
reciprocating internal combustion engines (RICE), natural gas turbines, and
process heaters). Emissions were also reduced in 2016v2 and v3 to account for fuel
sulfur rules in the mid-Atlantic and northeast not fully implemented by 2017. OTC
controls for PFCs are included.
Nonroad:
nonroad
Outside California and Texas and Texas, the MOVES3 model was run to create
nonroad emissions for 2023 and 2026. The fuels used are specific to the analytic
year, but the meteorological data represented the year 2016. EPA received new
CARB data for analytic years for 2016v3. Texas nonroad emissions were
provided by TCEQ for 2023 and 2028, and interpolated to 2026.
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Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
Onroad:
onroad,
onroadnonconus
Activity data for 2016 were backcast from the 2017 NEI then projected from 2016
to 2019 based on trends in FHWA VM-2 trends. Activity data were held flat from
2019 to 2021, and then projected from 2021 to 2023 and 2026 using factors
derived from AEO2022. Where S/Ls provided activity data for 2023, those data
were used. To create the emission factors, MOVES3 was run for the years 2023
and 2026 using 2016 meteorological data and fuels, but with age distributions
projected to represent the analytic years and the remaining inputs consistent with
those used in 2017. The analytic year activity data and emission factors were then
combined using SMOKE-MOVES to produce the 2023 and 2026 emissions.
Inspection and maintenance updates were included for NC and TN (this changed
the representative county groupings for analytic years). Section 4.3.2 describes
the applicable rules that were considered when projecting onroad emissions.
Onroad California:
onroadcaadj
CARB-provided emissions were used for California, but temporally allocated
using MOVES3-based data. The 2016v3 platform uses new onroad emissions data
provided by CARB for 2023 and 2026.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
Othafdust emissions for the analytic years were provided by ECCC in 2016vl.
Projection factors were derived from those 2023 and 2028 inventories and applied
to the 2016v2 inventory. 2026 projection factors were interpolated from 2023 and
2028. No changes were made to 2023 or 2026 othafdust emissions in 2016v3.
Mexico emissions are not included in this sector.
Other Point Fugitive
dust sources not
from the NEI:
othptdust
Wind erosion emissions were removed from the point fugitive dust inventories.
Base year 2016 inventories with the rotated grid pattern removed were held flat for
the analytic years, including the same transport fraction as the base year and the
meteorology-based (precipitation and snow/ice cover) zero-out. No changes were
made to 2023 or 2026 othptdust emissions between 2016v2 and 2016v3.
Other point sources
not from the NEI:
othpt
Canada emissions for analytic years were provided by ECCC for use in 2016vl.
Projection factors were derived from those 2023 and 2028 inventories and applied
to the 2016v2 inventory. 2026 projection factors were interpolated from 2023 and
2028. No changes were made to othpt emissions between 2016v2 and 2016v3.
Canada projections were applied by province-subclass where possible (i.e., where
subclasses did not change from between platforms). For inventories where that was
not possible, including airports and most stationary point sources except for oil and
gas, projections were applied by province. For Mexico sources, Mexico's 2016
inventory was grown using to the analytic years 2023 and 2026, using
state+pollutant factors based on the 2016vl platform inventories.
Canada ag not from
the NEI:
canadaag
Reallocated base year emissions low-level agricultural sources that were originally
developed on the rotated 10-km grid were projected to 2023 and 2026 using
projection factors based on data provided by ECCC and applied by province,
pollutant, and ECCC sub-class code. No changes were made to canada_ag
emissions between 2016v2 and 2016v3.
Canada oil and gas
2D not from the
NEI:
canada_og2D
Low-level point oil and gas sources from the ECCC 2016 emission inventory were
projected to the analytic years based on province-subclass changes in the ECCC-
provided data used for 2016vl. 2026 projection factors were interpolated from
2023 and 2028. No changes were made to Canada og2D emissions between
2016v2 and 2016v3.
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Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
Other non-NEI
nonpoint and
nonroad:
othar
Analytic year Canada nonpoint inventories were provided by ECCC for 2016vl.
For Canadian nonroad sources, factors were provided from which the analytic year
inventories could be derived. Projection factors were derived from those 2023 and
2028 inventories and applied to the 2016v2 inventory. 2026 projection factors
were interpolated from 2023 and 2028. No changes were made to othar emissions
between 2016v2 and 2016v3 in either Canada or Mexico.
For Mexico nonpoint and nonroad sources, state-pollutant projection factors for
2023 and 2028 were calculated from the 2016vl inventories, and then applied to
the 2016v2 base year inventories. 2026 projection factors were interpolated from
2023 and 2028 in Mexico.
Other non-NEI
onroad sources:
onroadcan
For Canadian mobile onroad sources, analytic year inventories were projected
from 2016 to 2023 and 2026 using ECCC-provided projection data from vl
platform at the province and subclass (which is similar to SCC but not exactly)
level, with 2026 interpolated from 2023 and 2028. No changes were made to
onroad can emissions between 2016v2 and 2016v3.
Other non-NEI
onroad sources:
onroadmex
Monthly onroad mobile inventories were developed at municipio resolution based
runs of MOVES-Mexico for 2023, 2028, and 2035. 2023 was reused from the
2016vl platform; 2026 was interpolated between 2023 and 2028 for 2016v2. No
changes were made to onroad mex emissions between 2016v2 and 2016v3.
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4.1 EGU Point Source Projections (ptegu)
The 2023 and 2026 EGU emissions inventories used the outputs of the EPA's Updated Summer 2021
Reference Case of the Integrated Planning Model (IPM). IPM is a linear programming model that
accounts for variables and information such as energy demand, planned unit retirements, and planned
rules to forecast unit-level energy production and configurations. The following specific rules and
regulations are included in the IPM v6 platform run (see the EPA's Updated Summer 2021 Reference
Case web page for more details):
The Revised Cross-State Air Pollution Rule (CSAPR) Update, a federal regulatory measure
affecting EGU emissions from 12 states to address transport under the 2008 National Ambient Air
Quality Standards (NAAQS) for ozone.
The Standards of Performance for Greenhouse Gas Emissions from New, Modified, and
Reconstructed Stationary Sources: Electric Utility Generating Units through rate limits.
• The Mercury and Air Toxics Rule (MATS) finalized in 2011. MATS establishes National
Emissions Standards for Hazardous Air Pollutants (NESHAP) for the "electric utility steam
generating unit" source category.
Current and existing state regulations, including current and existing Renewable Portfolio
Standards and Clean Energy Standards as of the summer of 2021.
The latest actions EPA has taken to implement the Regional Haze Regulations and Guidelines for
Best Available Retrofit Technology (BART) Determinations Final Rule. The BART limits
approved in these plans (as of summer 2020) that will be in place for EGUs are represented in the
Updated Summer 2021 Reference Case.
California AB 32 C02 allowance price projections and the Regional Greenhouse Gas Initiative
(RGGI) rule.
Three non-air federal rules affecting EGUs: National Pollutant Discharge Elimination System-
Final Regulations to Establish Requirements for Cooling Water Intake Structures at Existing
Facilities and Amend Requirements at Phase I Facilities, Hazardous, and Solid Waste
Management System; Disposal of Coal Combustion Residuals from Electric Utilities; and the
Effluent Limitation Guidelines and Standards for the Steam Electric Power Generating Point
Source Category.
IPM is run for a set of years, including 2023 and 2025 (the latter was used for the 2026 case). All inputs,
outputs and full documentation of EPA's IPM v6 Updated Summer 2021 Reference Case and the
associated NEEDS version from 08-03-2022 is available on the power sector modeling website
(https://www.epa.gov/power-sector-modeling/supporting-documentation-2015-ozone-naaqs-actions).
Some of the key parameters used in the IPM run are:
• Demand: AEO 2020
• Gas and Coal Market assumptions: updated as of Summer 2022
• Cost and performance of fossil generation technologies: AEO 2020
• Cost and performance of renewable energy generation technologies: NREL ATG 2020 (mid-case)
• Nuclear unit operational costs: AEO 2020 with some adjustments
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• Environmental rules and regulations (on-the-books): Revised CSAPR, MATS, BART, CA AB 32,
RGGI, various RPS and CES, non-air rules (Cooling Water Intake, ELC, CCR), State Rules and
mandates as of Summer 2022 (see supplemental documentation on Updated Summer 2021
Reference Case page)
• Financial assumptions: 2016-2020 data, reflects tax credit extensions from Consolidated
Appropriations Act of 2021
• Transmission: updated data with build options
• Retrofits: carbon capture and sequestration option for CCs
• Operating reserves (in select runs): Greater detail in representing interaction of load, wind, and
solar, ensuring availability of quick response of resources at higher levels of RE penetration
• Fleet: Summer 2022 reference case NEEDS (NEEDS rev: 08-03-2022)
The EGU emissions are calculated for the inventory using the output of the IPM model for the forecast
year. Units that are identified to have a primary fuel of landfill gas, fossil waste, non-fossil waste, residual
fuel oil, or distillate fuel oil may be missing emissions values for certain pollutants in the generated
inventory flat file. Units with missing emissions values are gapfilled using projected base year values. The
projections are calculated using the ratio of the analytic year seasonal generation in the IPM parsed file
and the base year seasonal generation at each unit for each fuel type in the unit as derived from the 2018
EIA-923 tables and the 2018 NEI. New controls identified at a unit in the IPM parsed file are accounted
for with appropriate emissions reductions in the gapfill projection values. When base year unit-level
generation data cannot be obtained no gapfill value is calculated for that unit.
Once IPM has been run, a process is performed to first parse the results to unit level and then to generate a
flat file in a format that SMOKE can read. To accomplish this, a cross reference file is needed to map the
NEEDS IDs to NEI IDs for facility and unit and for stack parameters. The cross reference file used for the
2016v3 IPM outputs was "NEEDS NEI xref 2016 2019stk 13apr22.xlsx" and incorporates information
about unit and stack configurations from the 2019 NEI Point source inventory. The flat file that results
from this process includes emissions for five summer months (May to September), four "shoulder"
months (March, April, October, November) and three winter months (January, February, and December).
The emissions from each of these "seasons" were placed into separate flat files so that SMOKE can
preserve the total emissions within each season to the extent possible within rounding errors. Large EGUs
in the IPM-derived flat file inventory are associated with hourly CEMS data for NOX and S02 emissions
values in the base year. To maintain a temporal pattern consistent with the 2016 base year, the NOX and
S02 values in the hourly CEMS inventories are projected to match the total seasonal emissions values in
the analytic years as described in Section 3.3.2.2.
Combined cycle units produce some of their energy from process steam that turns a steam turbine. The
IPM model assigns a fraction of the total combined cycle production to the steam turbine. When the
emissions are calculated these steam units are assigned emissions values that come from the combustion
portion of the process. In the base year NEI steam turbines are usually implicit to the total combined cycle
unit. To achieve the proper plume rise for the total combined cycle emissions, the stack parameters for the
steam turbine units were updated with the parameters from the combustion release point. Additionally,
some units, such as landfill gas, may not be assigned a valid SCC in the initial flat file. The SCCs for
these units were updated based on the base year SCC for the unit-fuel type.
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The EGU sector N0X emissions by state are listed in Table 4-2 for each of the 2016v3 cases. 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 2016v3 cases
State
2016gf
2023gf
2026gf
Alabama
28,835
13,389
11,987
Arizona
21,996
16,955
5,806
Arkansas
27,261
23,832
23,117
California
6,836
12,820
14,239
Colorado
30,243
15,324
13,584
Connecticut
4,062
2,772
2,411
Delaware
1,492
300
335
District of
Columbia
NA
35
36
Florida
64,582
26,442
22,648
Georgia
29,359
29,119
9,594
Idaho
1,307
989
495
Illinois
32,180
13,598
8,753
Indiana
83,763
53,932
40,810
Iowa
22,950
23,989
22,944
Kansas
14,940
12,599
9,747
Kentucky
57,627
31,294
28,442
Louisiana
47,877
22,555
17,769
Maine
4,897
4,897
3,055
Maryland
10,449
2,895
2,510
Massachusetts
7,619
5,659
5,394
Michigan
43,330
24,830
22,606
Minnesota
21,646
19,609
11,961
Mississippi
16,407
9,913
3,811
Missouri
57,365
51,442
46,476
Montana
15,104
9,440
9,051
Nebraska
20,608
26,547
22,542
Nevada
3,898
8,367
2,500
New Hampshire
2,092
1,466
708
New Jersey
6,499
3,564
3,761
New Mexico
20,119
1,608
1,274
New York
18,972
11,108
10,199
North Carolina
35,329
38,958
26,228
North Dakota
38,220
33,180
30,907
Ohio
57,645
37,352
37,140
166
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State
2016gf
2023gf
2026gf
Oklahoma
25,151
17,221
12,762
Oregon
4,005
1,262
2,042
Pennsylvania
83,540
31,181
11,850
Rhode Island
548
565
512
South Carolina
14,721
17,262
15,936
South Dakota
1,060
1,281
1,216
Tennessee
19,237
13,202
4,077
Texas
110,761
88,633
54,322
Tribal Areas
35,076
6,276
5,847
Utah
26,917
33,823
18,668
Vermont
256
267
27
Virginia
27,996
10,012
7,724
Washington
8,811
5,222
1,884
West Virginia
52,332
34,935
33,712
Wisconsin
16,209
14,232
6,337
Wyoming
36,098
22,729
13,987
4.2 Non-EGU Point and Nonpoint Sector Projections
To project all U.S. non-EGU stationary sources, facility/unit closures information and growth
(PROJECTION) factors and/or controls were applied to certain categories within the afdust, ag, cmv, rail,
nonpt, np oilgas, ptnonipm, pt oilgas and rwc platform sectors. Some facility or sub-facility-level
closure information was also applied to the point sources. There are also a handful of situations where
new inventories were generated for sources that did not exist in the NEI (e.g., biodiesel and cellulosic
plants, yet-to-be constructed cement kilns). This subsection provides details on the data and projection
methods used for the non-EGU point and nonpoint sectors.
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 four emissions modeling sectors). The rest of this section is organized in the order that the
EPA uses the Control Strategy Tool (CoST) in combination with other methods to produce 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
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 projection and
control factors applied by CoST to prepare the analytic year emissions are provided with other 2016v3
input data and reports on the 2016v3 FTP site.
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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 2016-based 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-analvsis-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 2016 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-2016 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 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
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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
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, "REGIONCD" is the
county-state-county FIPS code (e.g., Harris county Texas is 48201) and "STATE" would be the 2-digit
state FIPS code with three trailing zeroes (e.g., Texas is 48000).
Table 4-3. Subset of CoST Packet Matching Hierarchy
Rank
Matching Hierarchy
Inventory Type
1
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, SCC, POLL
point
2
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, POLL
point
3
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, POLL
point
4
REGION CD, FACILITY ID, UNIT ID, POLL
point
5
REGION CD, FACILITY ID, SCC, POLL
point
6
REGION CD, FACILITY ID, POLL
point
7
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, SCC
point
8
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID
point
9
REGION CD, FACILITY ID, UNIT ID, REL POINT ID
point
10
REGION CD, FACILITY ID, UNIT ID
point
11
REGION CD, FACILITY ID, SCC
point
12
REGION CD, FACILITY ID
point
13
REGION CD, NAICS, SCC, POLL
point, nonpoint
14
REGION CD, NAICS, POLL
point, nonpoint
15
STATE, NAICS, SCC, POLL
point, nonpoint
16
STATE, NAICS, POLL
point, nonpoint
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Rank
Matching Hierarchy
Inventory Type
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 future year
were used to create the future 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.
Table 4-4. Summary of non-EGU stationary projections subsections
Subsection
Title
Sector(s)
Brief Description
4.2.2
CoST Plant CLOSURE
packet
ptnonipm,
ptoilgas
All facility/unit/stack closures information,
primarily from Emissions Inventory System (EIS),
but also includes information from states and other
organizations.
4.2.3
CoST PROJECTION
packets
All
Introduces and summarizes national impacts of all
CoST PROJECTION packets to the analytic year.
4.2.3.1
Fugitive dust growth
Afdust
PROJECTION packet: county-level resolution,
primarily based on VMT growth.
4.2.3.2
Livestock population
growth
Livestock
PROJECTION packet: national, by-animal type
resolution, based on animal population projections.
4.2.3.3
Category 1 and 2
commercial marine
vessels
cmv clc2
PROJECTION packet: Category 1 & 2: CMV uses
SCC/poll for all states except Calif.
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Subsection
Title
Sector(s)
Brief Description
4.2.3.4
Category 3 commercial
marine vessels
cmv c3
PROJECTION packet: Category 3: region-level by-
pollutant, based on cumulative growth and control
impacts from rulemaking.
4.2.3.5
Oil and gas and
industrial source
growth
nonpt,
npoilgas,
ptnonipm,
ptoilgas
Several PROJECTION packets: varying geographic
resolutions from state, county, and by-process/fuel-
type applications. Data derived from AEO2022
were used for nonpt, ptnonipm, np oilgas, and
pt oilgas sectors.
4.2.3.6
Non-IPM Point
Sources
Ptnonipm
Several PROJECTION packets: specific projections
from MARAMA region and states, AEO-based
projection factors for industrial sources for non-
MARAMA states.
4.2.3.7
Airport Sources
Ptnonipm
PROJECTION packet: by-airport for all direct
matches to FAA Terminal Area Forecast data, with
state-level factors for non-matching NEI airports.
4.2.3.8
Nonpoint sources
nonpt
Several PROJECTION packets: MARAMA states
projection for Portable Fuel Containers and for all
other nonpt sources. Non-MARAMA states
projected with AEO-based factors for industrial
sources. Evaporative Emissions from Finished
Fuels projected using AEO-based factors. Human
population used as growth for applicable sources.
4.2.3.9
Solvents
npsolvents
Several PROJECTION packets including
population-based, and MARAMA state factors.
4.2.3.10
Residential wood
combustion
rwc
PROJECTION packet: national with exceptions,
based on appliance type sales growth estimates and
retirement assumptions and impacts of recent
NSPS.
4.2.4
CoST CONTROL
ptnonipm,
Introduces and summarizes national impacts of all
packets
nonpt,
npoilgas,
pt oilgas
CoST CONTROL packets to the analytic year.
4.2.4.1
Oil and Gas NSPS
npoilgas,
pt oilgas
CONTROL packets: reflect the impacts of the
NSPS for oil and gas sources.
4.2.4.2
RICE NSPS
ptnonipm,
nonpt,
npoilgas,
pt oilgas
CONTROL packets apply reductions for lean burn,
rich burn, and combined engines for identified
SCCs.
4.2.4.3
Fuel Sulfur Rules
ptnonipm,
nonpt
CONTROL packet: updated by MARAMA, applies
reductions to specific units in ten states.
4.2.4.4
Natural Gas Turbines
NOx NSPS
ptnonipm
CONTROL 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
Ozone Transport
Commission Rules
nonpt,
np solvents
CONTROL packets reflecting rules for solvents and
portable fuel containers.
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4.2.2 CoST Plant CLOSURE Packet (ptnonipm, pt_oilgas)
Packets:
CLOSURES_2016v3_platform_ptnonipm_26aug2022_nf_vl
The CLOSURES packet contains facility, unit and stack-level closure information derived from an
Emissions Inventory System (EIS) unit-level report from June 9, 2021, with closure status equal to "PS"
(permanent shutdown; i.e., post-2016 permanent facility/unit shutdowns known in EIS as of the date of
the report). For 2016v3, additional closures were added and those are cumulative with the closures in
2016v2. Any data provided by commenters for closures were updated to match the SMOKE FF10
inventory key fields, with all duplicates removed, and a single CoST packet was generated. These
changes impact sources in the ptnonipm and ptoilgas sectors. Additional closures provided in comments
on the 2016v2 inventories were incorporated in the 2016v3 platform for multiple states including Ohio,
Wisconsin, North Carolina, and North Dakota. The spreadsheet in the reports folder on the 2016v3 FTP
site called point controlsjpacket 2016v3.xlsx lists all closures, while the spreadsheet called
ptnonipm 19 2023gf new closures.xlsx available lists the closures there were new in 2016v3 and their
impacts. The cumulative reduction in emissions for ptnonipm and pt oilgas are shown in Table 4-5. The
amount of emission reductions are from 2019 emissions levels, not 2016 emissions, because the closures
were applied to the 2019 inventory that was used as the starting point for the projection to 2023.
Table 4-5. Reductions from all facility/unit/stack-level closures in 2016v3 from 2019 emissions levels
Pollutant
Ptnonipm
ptoilgas
CO
5,428
1,343
NH3
631
0
NOX
6,652
2,846
PM10
3,185
49
PM2.5
2,240
49
S02
6,461
178
VOC
5,040
388
4.2.3 CoST PROJECTION Packets (afdust, airports, cmv, livestock, nonpt,
np_oilgas, np_solvents, ptnonipm, pt_oilgas, rail, rwc)
For point inventories, after the application of any/all CLOSURE packet information, the next step CoST
performs when running a control strategy is the application of all PROJECTION packets. Regardless of
inventory type (point or nonpoint), the PROJECTION packets are applied prior to the CONTROL
packets. For several emissions modeling sectors (i.e., airports, np oilgas, pt oilgas), there is only one
PROJECTION packet applied for each analytic year. For all other sectors, there are 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 and 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 these distinct control programs.
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For the 2016vl platform MARAMA provided PROJECTION and CONTROL packets for years 2023 and
2028 for states including: Connecticut, Delaware, Maryland, Massachusetts, New Hampshire, New York,
New Jersey, North Carolina, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia, Maine, and
the District of Columbia. MARAMA provided pt oilgas and np oilgas packets only for Rhode Island,
Maryland and Massachusetts. For 2016v2, new spreadsheets of projection factors were provided that
facilitated the incorporation of data from the AEO 2022 and other surrogate data for projection factors.
The new spreadsheets also to reflect sources affected by the Pennsylvania Reasonably Available Control
Technology (RACT) II including 2023 emissions for one Pennsylvania facility (Anchor Hocking LLC,
Monaca Plant) affected by the rule. For that facility, emissions values were swapped in after applying all
other projections and controls. For states not covered by the MARAMA packets, projection factors were
developed using nationally available data and methods.
4.2.3.1 Fugitive dust growth (afdust)
Packets:
Proj ection_2016_2023_afdust_versionl_platform_MARAMA_l 5jul2 l_v2
Proj ection_2016_2023_afdust_versionl_platform_NJ_20aug202 l_vl
Proj ection_2016_2023_afdust_version3_platform_national_03 aug2022_v0
Proj ection_2016_2023_all_nonpoint_versionl_platform_NC_24jun202 l_nf_v5
Proj ection_2016_2026_afdust_version l_platform_MARAMA_nopavedroads_noNCNJ_l 5jul202 l_v0
Proj ection_2016_2026_afdust_versionl_platform_NJ_nopavedroads_20jul202 l_v0
Proj ection_2016_2026_afdust_version3_platform_national_3 0aug2022_v0
Proj ection_2016_2026_all_nonpoint_version2_platform_NC_3 0aug2022_nf_v2
MARAMA States
MARAMA provided a spreadsheet tool that could be used to compute projection factors for their states to
project 2016 afdust emissions to analytic years 2023 and 2026. These county-specific projection factors
impacted paved roads (SCC 2294000000), residential construction dust (SCC 2311010000),
industrial/commercial/institutional construction dust (SCC 2311020000), road construction dust (SCC
2311030000), dust from mining and quarrying (SCC 2325000000), agricultural crop tilling dust (SCC
2801000003), and agricultural dust kick-up from beef cattle hooves (SCC 2805001000). Other afdust
emissions, including unpaved road dust emissions, were held constant in analytic year projections. North
Carolina and New Jersey provided their own packets for this sector for 2023 and 2028, which were
interpolated to 2026. For paved roads, new VMT-based projection factors based on 2016v3 VMT were
used in place of projection factors provided by MARAMA, NC, and NJ for all years, since their factors
were based on older VMT.
Non-MARAMA States
For paved roads (SCC 2294000000), the 2016 afdust emissions were projected to analytic years 2023 and
2026 based on differences in county total VMT:
Analytic year afdust paved roads = 2016 afdust paved roads * (Analytic year county total VMT) /
(2016 county total VMT)
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The VMT projections are described in the onroad section. Paved road dust emissions were projected this
way in all states, including MARAMA states.
In non-MARAMA states, all emissions other than paved roads are held constant in the analytic year
projections. The impacts of the projections are shown in Table 4-6.
Table 4-6. Increase in total afdust PM2.5 emissions from projections in 2016v3
2016
Emissions
2023
Emissions
percent
Increase
2023
2026
Emissions
percent
Increase
2026
2,254,168
2,296,234
1.87%
2,314,652
2.68%
4.2.3.2 Livestock population growth (livestock)
Packets:
Proj ection_2016_2023_all_nonpoint_versionl_platform_NC_24jun202 l_nf_v5
Proj ection_2016_2026_all_nonpoint_version2_platform_NC_l 9jul202 l_nf_vl
Proj ection_2017_2023_ag_livestock_version3_platform_05aug2022_v0
Proj ecti on_2017_2023_ag_version 1 _platform_NJ_20aug2021_v 1
Proj ection_2017_2026_ag_livestock_version3_platform_3 0aug2022_v0
Proj ection_2017_2026_livestock_version2_platform_NJ_l 6jul202 l_vO
The 2017NEI livestock emissions were projected to year 2023 and 2026 using projection factors created
from USDA National livestock inventory projections published in February 2022
(https://www.ers.usda.gov/publications/pub-details/?pubid=103309) and are shown in Table 4-7. For
emission projections to 2023, a ratio was created between animal inventory counts for 2023 and 2017 to
create a projection factor. This process was completed for the animal categories of beef, dairy, broilers,
layers, turkeys, and swine. The projection factor was then applied to the 2017NEI base emissions for the
specific animal type to estimate 2023 NH3 and VOC emissions. For emission projections to 2026, the
equivalent method was used to develop and apply the factors. New Jersey (NJ) provided NJ-specific
projection factors that were used to grow livestock waste emissions from 2017 to 2023 and 2028. The
factors were interpolated to obtain factors for 2026. North Carolina (NC) provided NC-specific projection
factors that used a 2016-based projection; therefore, NC's livestock waste emissions are projected from
the 2016 base year emissions to the years 2023 and 2026. As in New Jersey, North Carolina provided
projection factors for 2023 and 2028, which were interpolated to 2026.
Table 4-7. National projection factors for livestock: 2017 to 2023 and 2026
Animal
2017-to-2023
2017-to-2026
Beef
-1.79%
-0.32%
Swine
+5.73%
+6.93%
Broilers
+9.06%
+12.97%
Turkeys
-0.85%
+2.10%
Layers
+4.45%
+9.67%
Dairy
-1.06%
-0.85%
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4.2.3.3 Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
Packets:
Proj ecti on_2016_2023_cmv_c 1 c2_version 1 _platform_04oct2019_v 1
Proj ection_2016_2023_cmv_Canada_versionl_platform_24sep2019_v0
Proj ection_2016_2026_cmv_c 1 c2_version2_platform_14jul202 l_vO
Proj ection_2016_2026_cmv_Canada_version2_platform_l 5jul202 l_vO
Category 1 and category 2 (C1C2) CMV emissions sources outside of California were projected to 2023
and 2026 based on factors derived from the Regulatory Impact Analysis (RIA) Control of Emissions of
Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters
per Cylinder (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-
emissions-air-pollution-locomotive). The 2023 cmv_clc2 emissions for 2016v3 are based on the same
raw data as the 2016vl and 2016v2 emissions, but an improved method to spatially allocate the emissions
to counties was used. The projection factors to obtain the 2026 emissions are equivalent to interpolating
2016vl emissions projection factors between 2023 and 2028. California emissions were projected based
on factors provided by the state. Table 4-8 lists the pollutant-specific projection factors to 2023 and 2028
that were used for cmv_clc2 sources outside of California. California sources were projected to 2023 and
2028 using the factors in Table 4-9, which are based on data provided by CARB.
Projection factors for Canada for 2026 were based on ECCC-provided 2023 and 2028 data interpolated to
2026.
Table 4-8. National projection factors for cmv_clc2
Pollutant
2016-to-2023 (%)
2016-to-2026 (%)
CO
-1.3%
-0.4%
NOX
-29.3%
-39.0%
PM10
-28.3%
-37.8%
PM2.5
-28.3%
-37.8%
S02
-65.3%
-65.7%
VOC
-31.5%
-42.0%
Table 4-9. California projection factors for cmv_clc2
Pollutant
2016-to-2023 (%)
2016-to-2026 (%)
CO
+20.1%
+23.2%
NOX
-15.0%
-16.6%
PM10
-29.9%
-32.1%
PM2.5
-29.9%
-32.1%
S02
+24.1%
+38.9%
VOC
+1.5%
+1.7%
175
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4.2.3.4 Category 3 Commercial Marine Vessels (cmv_c3)
Packets:
Proj ection_2016_2023_cmv_c3_versionl_platform_04oct2019_v2_Mexico36
Proj ection_2016_2023_cmv_c3_version l_platform_24sep2019_v 1
Proj ection_2016_2023_cmv_Canada_versionl_platform_24sep2019_v0
Proj ection_2016_2026_cmv_c3_version2_platform_l 5jul202 l_vO
Proj ection_2016_2026_cmv_Canada_version2_platform_l 5jul202 l_vO
Growth rates for cmv_c3 emissions from 2016 to 2023, and 2026 were projected using an EPA report on
projected bunker fuel demand. Bunker fuel usage was used as a surrogate for marine vessel activity.
Bunker fuel usage was used as a surrogate for marine vessel activity. Factors based on the report were
used for all pollutants except NOx.
Growth factors for NOx emissions were handled separately to account for the phase in of Tier 3 vessel
engines. To estimate these emissions, the NOx growth rates from the EPA C3 Regulatory Impact
Assessment (RIA)37 were refactored to use the new bunker fuel usage growth rates. The assumptions of
changes in fleet composition and emissions rates from the C3 RIA were preserved and applied to the new
bunker fuel demand growth rates for 2023 and 2026 to arrive at the final growth rates. The Category 3
marine diesel engines Clean Air Act and International Maritime Organization standards from April, 2010
(https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-emissions-new-
marine-compression-O) were also considered when computing the emissions.
The 2023 cmv_c3 emissions for 2016v3 are based on the same raw data as the 2016vl and 2016v2
emissions, but an improved method to spatially allocate the emissions to counties using 1-hour AIS
locations rather than grid cell centroids was used. The 2026 projection factors are equivalent to
interpolating the 2016vl emissions between 2023 and 2028. Projection factors for Canada for 2026 were
based on ECCC-provided 2023 and 2028 data interpolated to 2026.
The 2023 and 2026 projection factors are shown in Table 4-10. Some regions for which 2016 projection
factors were available did not have 2023 or 2026 projection factors specific to that region, so factors from
another region were used as follows:
• Alaska was projected using North Pacific factors.
• Hawaii was projected using South Pacific factors.
• Puerto Rico and Virgin Islands were projected using Gulf Coast factors.
• Emissions outside Federal Waters (FIPS 98) were projected using the factors given in
Table 4-10 for the region "Other".
• California was projected using a separate set of state-wide projection factors based on
CMV emissions data provided by the California Air Resources Board (CARB). These
factors are shown in Table 4-11
36 2023 has a Mexico packet is because the Mexico CMV inventory covers some ports, but no offshore underway. This
inventory has emissions in the 36US3 domain only, not 12US1 and was not projected to 2026 or 2032.
37 https://nepis.epa.gov/Exe/ZvPURL.cgi?Dockev=P 1005ZGH.TXT.
176
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Table 4-10. 2016-to-2023 and 2016-to-2026 CMV C3 projection factors outside of California
Region
2016-1 o-2023
2016-lo-2023
2016-lo-2026
2016-lo-2026
\()\
other pollutants
\()\
oilier polliiliinls
US East Coast
-6.1%
+27.7%
-6.9%
+41.4%
US South Pacific
(ex. California)
-24.8%
+20.9%
-30.3%
+36.6%
US North Pacific
-3.4%
+22.6%
-3.8%
+34.6%
US Gulf
-6.9%
+20.8%
-10.2%
+29.8%
US Great Lakes
+8.7%
+14.6%
+15.4%
+22.7%
Other
+23.1%
+23.1%
+35.0%
+35.0%
Non-I'ederal Waters
2016-lo-2023
20I6-1O-2026
S02
-77.2%
-75.0%
PM (main engines)
-36.1%
-29.9%
PM (aux. engines)
-39.7%
-33.9%
Other pollutants
+23.1%
+35.0%
Table 4-11. 2016-to-2023 and 2016-to-2026 CMV C3 projection factors for California
Pollutant
20l6-lo-2023
20I6-IO-2026
CO
1.180
1.276
Nox
1.156
1.259
PMio / PM2.5
1.205
1.311
S02
1.183
1.272
voc
1.242
1.373
4.2.3.5 Oil and Gas Sources (pt_oilgas, np_oilgas)
Packets:
Proj ection_2016_2023_np_oilgas_version3_platform_24aug2022_v0
Proj ection_2016_2023_pt_oilgas_version3_platform_24aug2022_v0
Proj ection_2016_2026_np_oilgas_version3_platform_24aug2022_v0
Proj ection_2016_2026_pt_oilgas_version3_platform_24aug2022_v0
Year 2028 inventories for seven of the WRAP states were provided by The details about these non-point
and point source oil and gas data can be found here:
http://www.wrapair2.org/pdfAVRAP OGWG 2028 OTB RevFinalReport 05March2020.pdf (WRAP /
Ramboll, 2020). As provided, this WRAP data for npoilgas and ptoilgas were intended to be the same
for all analytic years. Therefore emissions in 2023 are the same as in 2026 except that New Mexico
np oilgas emissions were projected and controlled using the EPA methodology of using historical
production data from the state of New Mexico38 along with AEO2022-based forecast information.
For areas outside of the WRAP states, analytic year projections for the 2016v3 platform were generated
for point oil and gas sources for years 2023 and 2026. These projections consisted of three components:
(1) applying facility closures to the pt oilgas sector using the CoST CLOSURE packet; (2) using
historical and/or forecast activity data to generate analytic-year emissions before applicable control
38 https://wwwapps.emnrd.nm.gov/ocd/ocdpermitting/Reporting/Production/CountvProductionIniectionSummarv.aspx
177
-------
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 ptoilgas sector resulted in small emissions changes to the national summary
shown in Table 4-5. Note that the closures applied for the years 2023 and 2026 are the same.
For pt oilgas growth to 2023 and 2026, the oil and gas sources were separated into production-related and
exploration-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), BOTH (where oil or natural gas fuels are
possible), or coal-bed methane (CBM). The next two subsections describe the growth component of the
process.
For npoilgas growth to 2023 and 2026, 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 2016 to year 2021. These historical data were acquired from EIA from the following links:
• Historical Natural Gas: http://www.eia.gov/dnav/ng/ng sum lsum a epgO fgw mmcf a.htm
• Historical Crude Oil: http://www.eia.gov/dnav/pet/pet crd crpdn adc mbbl a.htm
• Historical CBM: https://www.eia.gov/dnav/ng/ng prod coalbed si a.htm
The second step involved using the Annual Energy Outlook (AEO) 2022 reference case for the Lower 48
forecast production tables to project from the year 2021 to the years of 2023 and 2026. Specifically,
AEO 2022 Table 58 "Lower 48 Crude Oil Production and Wellhead Prices by Supply Region " and AEO
2022 Table 59 "Lower 48 Natural Gas Production and Supply Prices by Supply Region " were used in
this projection process. The AEO2022 forecast production is supplied for each EIA Oil and Gas Supply
region shown in Figure 4-1.
178
-------
Figure 4-1. EIA Oil and Gas Supply Regions as of AEO2022
Pacific
The result of this second step is a growth factor for each Supply Region from 2021 to 2023 and from 2021
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 three different Supply Regions and portions of New Mexico are in two different
supply regions. The state-level historical factor (from 2016 to 2021) was then multiplied by the Supply
Region factor (from 2021 to the analytic years) to produce a state-level or FlPS-level factor to grow from
2016 to 2023 and from 2016 to 2026. This process was done using cmde production forecast information
to generate a factor to apply to oil-production related SCCs or NAICS-SCC combinations and it was also
done using natural gas production forecast information to generate a factor to apply to natural gas-
production related NAICS-SCC combinations. For the NAICS-SCC combinations that are designated
"BOTH" the average of the oil-production and natural-gas production factors was calculated and applied
to these specific combinations.
The state of Texas provided specific comments on the growth of production-related point sources. Texas
provided updated basin specific production for 2016 and 2021 to allow for a better calculation of the
estimated growth for this three-year period (http://webapps.rrc.texas.gov/PDQ/generalReportAction.do).
The AEO2022 was used as described above for the three AEO Oil and Gas Supply Regions that include
Texas counties to grow from 2021 to 2023 and 2026. However, Texas only wanted these growth factors
applied to sources in the Permian and Eagle Ford basins and the oil and gas production point sources in
the other basins in Texas were not grown.
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/CountvProductionIniectionSu
mmary.aspx ) so that a better estimate of growth from 2016 to 2021 for the AEO Supply Regions in New
Mexico could be calculated.
179
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Transmission-related Sources (ptoilgas)
Projection factors for transmissions-related sources were generated using the same AEO2022 tables used
for production sources. These growth factors sources were developed solely using AEO 2022 data for the
entire lower 48 states (one national factor for oil transmission and one national factor for natural gas
transmission). The WRAP future year inventory was used for 6 of the 7 states in that inventory. The
exception was New Mexico where the projection method described in this section was used.
Exploration-related Sources (npoilgas)
Due to the year 2016 being a low exploration activity year when compared to exploration activity in other
recent years, years 2017 through 2019 exploration emissions were generated using the 2017NEI version
of the Oil and Gas Tool. Table 4-12 provides a high-level national summary of the emissions data for the
three years. This three-year average (2017-2019) emissions data were used in 2016v3 because they
reflected the most recent average of exploration activity and emissions. These averaged emissions were
used for both the 2023 and 2026 analytic years. Note that CoST was not used to perform this projection
step for exploration sources.
Table 4-12. Year 2017-2019 high-level summary of national oil and gas exploration emissions
2017
emissions
(tons)
2018
emissions
(tons)
2019
emissions
(tons)
Three Year
avg (2017-
2019) (tons)
NOX
73,992
123,908
108,957
102,285
VOC
118,004
136,916
106,505
120,474
Projection overrides (pt oilgas)
A draft set of projected point oil and gas emissions were reviewed and compared to recent emissions data
from 2018 and 2019. In cases where the recent and projected emissions were substantially different,
projected emissions were instead taken from a recent year of emissions and held constant through the
analytic years. The affected sources are shown in Table 4-13.
Table 4-13. Point oil and gas sources held constant at 2018 or 2019 levels
Invento
ry year
Count
yFIPS
State
County
Facility
ID
Facility Name
2018
17041
Illinois
Douglas Co
2749511
Trunkline Gas Co
2018
18003
Indiana
Allen Co
4544011
PANHANDLE EASTERN PIPE LINE CO
EDGERT
2018
21197
Kentucky
Powell Co
5787411
TN Gas Pipeline Co LLC - Station 106
2018
39039
Ohio
Defiance Co
7938111
ANR Pipeline Company (0320010169)
2019
01129
Alabama
Washington
Co
1028711
American Midstream Chatom, LLC
2019
04005
Arizona
Coconino Co
1115011
EPNG - WILLIAMS COMPRESSOR STATION
2019
05083
Arkansas
Logan Co
973211
DUNN COMPRESSOR STATION
2019
05091
Arkansas
Miller Co
7737711
NATURAL GAS PIPELINE CO OF AMERICA-
305
180
-------
Invento
ry year
Count
yFIPS
State
County
Facility
ID
Facility Name
2019
12007
Florida
Bradford Co
2574711
FLORIDA GAS TRANSMISSION COMPANY
2019
12095
Florida
Orange Co
845511
FLORIDA GAS TRANSMISSION COMPANY
2019
13195
Georgia
Madison Co
2803411
Transcontinental Gas Pipe Line Company, LLC -
Compressor Station 130
2019
17183
Illinois
Vermilion Co
5401911
Midwestern Gas Transmission
2019
18037
Indiana
Dubois Co
4887211
ANR PIPELINE CO CELESTINE
COMPRESSOR ST
2019
18075
Indiana
Jay Co
7957111
ANR PIPELINE COMPANY PORTLAND
COMPRES
2019
19181
Iowa
Warren Co
2962011
NATURAL GAS PIPELINE CO OF AMERICA
- STATION 108
2019
20057
Kansas
Ford Co
3839911
Natural Gas Pipeline of America - Minneola
Station 103
2019
20067
Kansas
Grant Co
3508811
Scout Energy - Ulysses West Main Station
2019
20097
Kansas
Kiowa Co
5027511
Northern Natural Gas - Mullinville Station
2019
21089
Kentucky
Greenup Co
6096911
TN Gas Pipeline Co LLC - Station 200
2019
21107
Kentucky
Hopkins Co
5830611
ANR Pipeline Co (Madisonville Compressor Sta)
2019
22001
Louisiana
Acadia Par
6082411
ANR Pipeline Co - Eunice Compressor Station
2019
22001
Louisiana
Acadia Par
7364911
Florida Gas Transmission Co C/S 7
2019
22009
Louisiana
Avoyelles Par
5987211
Gulf South Pipeline Co LLC - Marksville
Compressor Station
2019
22011
Louisiana
Beauregard
Par
5998611
Transcontinental Gas Pipe Line Co LLC
(TRANSCO) - Transco Compressor Station 45
2019
22013
Louisiana
Bienville Par
6000211
Southern Natural Gas Co - Bear Creek Storage
Facility
2019
22021
Louisiana
Caldwell Par
6426511
Texas Gas Transmission LLC - Columbia
Compressor Station
2019
22023
Louisiana
Cameron Par
1361051
1
Sabine Pass LNG LP - Sabine Pass Liquefaction
LLC
2019
22053
Louisiana
Jefferson
Davis Par
5283311
Tennessee Gas Pipeline Company LLC - Kinder
Compressor Station 823
2019
22073
Louisiana
Ouachita Par
5735011
Enable Mississippi River Transmission LLC -
Perryville Compressor Station
2019
22075
Louisiana
Plaquemines
Par
7449511
East Bay Central Facility
2019
22079
Louisiana
Rapides Par
5740711
Columbia Gulf Transmission Co - Alexandria
Compressor Station
2019
22079
Louisiana
Rapides Par
5740911
Texas Gas Transmission LLC - Pineville
Compressor Station
2019
22083
Louisiana
Richland Par
5607811
ANR Pipeline Co - Delhi Compressor Station
2019
22087
Louisiana
St Bernard Par
5608211
Southern Natural Gas Co - Toca Compressor
Station
2019
22113
Louisiana
Vermilion Par
5064311
Sea Robin Pipeline Co LLC - Erath Compressor
Station
2019
22119
Louisiana
Webster Par
5357411
ETC Texas Pipeline Ltd - Minden Gas Plant
2019
22119
Louisiana
Webster Par
8019911
XTO Energy Inc - Cotton Valley Gas Plant
181
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Invento
ry year
Count
yFIPS
State
County
Facility
ID
Facility Name
2019
26021
Michigan
Berrien Co
8195311
ANR Pipeline Company - Bridgman Compressor
Station
2019
26035
Michigan
Clare Co
4007011
Great Lakes Gas - Farwell Compressor Station 12
2019
28063
Mississip
pi
Jefferson Co
7035611
TEXAS EASTERN TRANSMISSION LP,
UNION CHU
2019
31131
Nebraska
Otoe Co
7767611
Northern Natural Gas Company
2019
37157
North
Carolina
Rockingham
Co
8492911
Transcontinental Gas Pipe Line Company, LLC -
Station 160
2019
39045
Ohio
Fairfield Co
8259811
CRAWFORD COMPRESSOR STATION
(0123000137)
2019
39059
Ohio
Guernsey Co
8008011
Kinder MorganTennessee Gas Pipeline Station
209 (0630000001)
2019
39157
Ohio
Tuscarawas
Co
7996211
Dominion Energy Transmission, Inc. - Gilmore
Station (0679000075)
2019
40007
Oklahoma
Beaver Co
8131911
BEAVER COMPRESSOR STATION
2019
40139
Oklahoma
Texas Co
8402511
TYRONE CMPSR STA
2019
47069
Tennessee
Hardeman Co
3787511
TENNESSEE GAS PIPELINE COMPANY
L L C., STATION 71
2019
47079
Tennessee
Henry Co
2896511
ANR PIPELINE COMPANY, COTTAGE
GROVE
2019
47181
Tennessee
Wayne Co
4188011
Tennessee Gas Pipeline Company, LLC -
Compressor Station 555
2019
48003
Texas
Andrews Co
4171311
ANDREWS BOOSTER
2019
48003
Texas
Andrews Co
4898411
FULLERTON GAS PLANT
2019
48019
Texas
Bandera Co
4898811
BANDERA COMPRESSOR STATION
2019
48103
Texas
Crane Co
4163111
BLOCK 31 GAS PLANT
2019
48103
Texas
Crane Co
6492411
SAND HILLS PLANT
2019
48103
Texas
Crane Co
6507911
CRANE BOOSTER STATION
2019
48135
Texas
Ector Co
3968211
ANDECTOR BOOSTER STATION
2019
48135
Texas
Ector Co
6507511
GOLDSMITH GAS PLANT
2019
48195
Texas
Hansford Co
2904911
SHERHAN GAS PLANT
2019
48195
Texas
Hansford Co
6534211
EG HILL COMPRESSOR
2019
48227
Texas
Howard Co
5652011
EAST VEALMOOR GAS PLANT
2019
48241
Texas
Jasper Co
4862311
COMPRESSOR STATION 32
2019
48263
Texas
Kent Co
6379311
SALT CREEK FIELD GAS PLANT
2019
48329
Texas
Midland Co
4832311
PEGASUS GAS PLANT
2019
48371
Texas
Pecos Co
5765911
COYANOSA GAS PLANT
2019
48371
Texas
Pecos Co
6498211
YATES GAS PLANT
2019
48501
Texas
Yoakum Co
6648711
PLAINS COMPRESSOR STATION
2019
54021
West
Virginia
Gilmer Co
6256711
Columbia Gas - GLENVILLE 4C1170
2019
54099
West
Virginia
Wayne Co
6341411
Columbia Gas - CEREDO 4C3360
182
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4.2.3.6 Non-EGU point sources (ptnonipm)
Packets:
Proj ection_2023_2026_finished_fuels_volpe_l 6jul202 l_vO
Projection_2023_2026_industrial_byNAICS_SCC_version3_platform_07sep2022_v0
Projection_2023_2026_industrial_bySCC_version3_platform_09nov2022_vl
Projection_2023_2026_ptnonipm_version2_platform_MARAMA_23jul2021_nf_vl
projection_2023_2026interp_corn_ethanol_E0B0_Volpe_23jul2021_v0
Projection_2023_2026interp_ptnonipm_version2_platform_NC_23jul2021_v0
Projection_2023_2026interp_ptnonipm_version2_platform_NJ_23jul2021_v0
Projection_2023_2026interp_ptnonipm_version2_platform_VA_23jul2021_v0
To account for many changes to point sources between 2016 and 2023, following the removal of sources
known to have closed between 2019 and 2023 emissions for the 2023 ptnonipm sector were set equal to
emissions from the 2019 NEI point source emissions file dated March 25, 2022. The 2019 point source
inventory was the most recent complete point source inventory available at the time the projections were
performed. The 2019 emissions automatically included fuel changes and emissions controls applied
during the intervening years. Due to this change in methodology, the factors provided by Wisconsin for
use in the 2016vl and 2016v2 platforms were no longer used.
The 2026 ptnonipm projections were projected from the 2023 point source emissions and involved several
growth and projection methods described here. The projection of oil and gas sources is explained in the
oil and gas section.
2023 and 2026 Point Inventory - inside MARAMA region
2023-to-2026 projection packets for point sources were based on the projection factors provided by
MARAMA for the following states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and
WV. The factors were developed using the MARAMA projection tool and by selecting 2023 for the base
year and 2026 for the projection year.
The MARAMA projection packets were used throughout the MARAMA region, except in North
Carolina, New Jersey, and Virginia. Those three states provided their own projection packets for the
ptnonipm sector in 2016vl, and those projection packets were used instead of the MARAMA packets in
those states in 2016v2 and v3 as well. The Virginia growth factors for one facility were edited to
incorporate emissions limits provided by MARAMA for that facility. A separate adjustment was made to
emissions of a Pennsylvania source (process ID 13629614) based on updated information provided by
MARAMA.
2026 Point Inventories - outside MARAMA region
Projection factors were developed by industrial sector from AEO 2022 in order to project emissions from
2023 to 2026. The SCCs were mapped to AEO categories and projection factors were created using a
ratio between the base year and projection year estimates from each specific AEO category. Table 4-14
below details the AEO2022tables used to map SCCs to AEO categories for the projections of industrial
sources. Depending on the category, a projection factor may be national or regional. The maximum
projection factor for projecting from 2023 to 2026 was capped at 1.3. MARAMA states were not
projected using this method. Also in 2016v2 and 2016v3, more SCCs were mapped to the AEO categories
183
-------
for SCCs that had not been projected in 2016vl. For example, SCCs for the cement kilns that did not
specify a fuel are now mapped to the "Value of Shipments" as a generic indicator for projected growth.
An SCC-NAICS projection was also developed using AEO2022. SCC/NAICS combinations with
emissions >100tons/year for any CAP39 were mapped to AEO sector and fuel. Projection factors for this
method were capped at 1.3 for projecting emissions from 2023 to 2026.
New units were added for 2016v2 and carried into 2016v3 based on 2018NEI analysis, although these are
also added in 2016 as described in Section 2.1.3. Emissions for taconite-related facilities in Minnesota
were replaced with preliminary 2021 emissions obtained from the state. These emissions should reflect
controls that are installed at those facilities between 2016 and 2021. In addition, reductions for the Fernley
Plant in Nevada are implemented in the 2026 inventory due to the timing of the planned controls in
response to the consent decree reached for EPA case number 09-2011-050640 expected to come online
between 2023 and 2026.
Any control efficiencies that were set to 100 in the 2016 base year inventory were identified and adjusted
prior to projecting the inventories. Note that a control efficiency equal to 100 means that there would be
no emissions, so control efficiencies equal to 100 are assumed to be in error.
Table 4-14. Annual Energy Outlook (AEO) 2022 tables used to project industrial sources
AEO 2022 Table #
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
4.2.3.7 Airport sources (airports)
Packets:
airport_proj ections_itn_taf2021_2016_2023_25apr2022_v0
airport_proj ections_itn_taf2021_2016_2026_25apr2022_v0
Airport emissions for 2016v3 were projected from the 2016 airport emissions to 2023 and 2026 using the
same projection approach as for 2016vl and 2016v2, but the factors were based on TAF 2021 based on
the corrected 2017 NEI airport emissions (released in June 2022), and starting from the base year 2016
39 The "100 tpy" criterion for this purpose was based on emissions in the emissions values in the 2016 beta platform.
40 https://echo.epa.gov/enforcement-case-report?activitv id=2600059825
184
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instead of 2017. The Terminal Area Forecast (TAF) data available from the Federal Aviation
Administration (see https://www.faa.gov/data research/aviation/taf/Y
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 for facilities from 2016 to 2023 and 2026 was capped at 500% and the state default growth was
capped at 200%. Military state default projection values were kept flat (i.e., equal to 1.0) to reflect
uncertainly in the data regarding these sources.
4.2.3.8 Nonpoint Sources (nonpt)
Packets:
Proj ection_2016_2023_all_nonpoint_versionl_platform_NC_04oct2019_v2
Proj ection_2016_2023_finished_fuels_volpe_04oct2019_v2
Proj ection_2016_2023_industrial_by SCC_version3_platform_09nov2022_vl
Proj ection_2016_2023_nonpt_other_version3_platform_MARAMA_22aug2022_v0
Proj ection_2016_2023_nonpt_PF C_version l_platform_MARAMA_20sep2019_v 1
Proj ection_2016_2023_nonpt_population_beta_platform_ext_20sep2019_v 1
Proj ection_2016_2023_nonpt_version l_platform_NJ_04oct2019_v 1
Proj ection_2016_2026_all_nonpoint_version2_platform_NC_3 0aug2022_nf_v2
Proj ection_2016_2026_finished_fuels_volpe_l 6jul202 l_v0
Proj ection_2016_2026_industrial_by SCC_version3_platform_09nov2022_vl
Proj ection_2016_2026_nonpt_other_version3_platform_MARAMA_22aug2022_v0
Proj ection_2016_2026_nonpt_PFC_version2_platform_MARAMA_noNC_l 6jul202 l_vl
Proj ection_2016_2026_nonpt_population_version2_platform_noMARAMA_l 6jul202 l_v0
Proj ection_2016_2026_nonpt_version2_platform_NJ_l 6jul202 l_v0
In 2016v3, emissions sources in the nonpt sectors are based on 2017 NEI, which was determined to better
represent 2017 emissions than the 2014 NEI emissions projected to 2016 using factors based on
surrogates such as changes in population and employment. Therefore, controls fully implemented by
2017, such as sulfur rules in some northeast states and boiler rules, do not need to be reflected in these
projection factors. The projected 2023 and 2026 emissions were developed by applying the factors in the
projection packets to the base year emissions.
Inside MARAMA region
2016-to-2023 and 2016-to-2026 projection packets for all nonpoint sources were provided by MARAMA
for the following states and updated with data from AEO2022: CT, DE, DC, ME, MD, MA, NH, NJ, NY,
NC, PA, RI, VT, VA, and WV. MARAMA provided one projection packet per year for portable fuel
containers (PFCs), and a second projection packet per year for all other nonpt sources.
The MARAMA projection packets were used throughout the MARAMA region, except in North Carolina
and New Jersey. Both NC and NJ provided separate projection packets for the nonpt sector for 2016vl
and those projection packets were used instead of the MARAMA packets in those two states. New Jersey
did not provide projection factors for PFCs, and so NJ PFCs were projected using the MARAMA PFC
growth packet.
185
-------
Industrial Sources outside MARAMA region
Because each AEO only includes data for one or two years prior to its publication year, projection factors
were developed from 2016 to 2023 and 2016 to 2026 by industrial sector using a series of AEOs to cover
the period from 2016 through 2023: AEO2018 was used to go from 2016 to 2017; AEO2019 to go from
2017 to 2020; AEO2021 to go from 2020 to 2021; and AEO2022 to go from 2021 to 2023 and 2026.
SCCs were mapped to AEO categories and projection factors were created using a ratio between the base
year and projection year estimates from each specific AEO category. For the nonpt sector, only AEO
Table 2 was used to map SCCs to AEO categories for the projections of industrial sources. Depending on
the category, a projection factor may be national or regional. The maximum projection factor was capped
at a factor of 1.75 for 2016 to 2023, and a factor of 2.25 for 2016 to 2026. Sources within the MARAMA
region were not projected with these factors, but with the MARAMA-provided growth factors.
In response to comments, a change in the 2016v3 platform was to hold distillate emissions for SCCs
2103004000, 2103004001, and 2103004002 flat with a 1.0 projection factor instead of showing increasing
emissions in 2023 and 2026.
Evaporative Emissions from Transport of Finished Fuels outside MARAMA region
Estimates on growth of evaporative emissions from transporting finished fuels are partially covered in the
nonpoint and point oil and gas projection packets. However, there are some processes with evaporative
emissions from storing and transporting finished fuels which are not included in the nonpoint and point
oil and gas projection packets, e.g., withdrawing fuel from tanks at bulk plants, filling tanks at service
stations, etc., and those processes are included in nonpoint other. AEO2018 was used as a starting point
for projecting volumes of finished fuel that would be transported in analytic years. Then these volumes
were used to calculate inventories associated with evaporative emissions in 2016, 2023, and 2028 using
upstream modules in the Emissions Modeling Framework. Those emission inventories were mapped to
the appropriate SCCs and projection packets were generated from 2016 to 2023 and 2016 to 2028 using
the upstream modules. Inventories for 2026 were developed by interpolating between the inventories for
2023 and 2028. Sources within the MARAMA region were not projected with these factors, but with the
MARAMA-provided growth factors.
Human Population Growth outside MARAMA region
For SCCs that 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 2017, 2023, and 2026. These human population data were used to create modified
county-specific projection factors. The impacted SCCs are shown in Table 4-15. Note that 2017 is being
used as the base year since 2016 human population is not available in this dataset. A newer human
population dataset was assessed but it did not have realistic near-term (e.g., 2023/2026) projections, and
was not used; for example, rural areas of NC were projected to have more growth than urban areas, which
is the opposite of what one would expect. Growth factors were limited to 5% cumulative annual growth
(e.g. 35% annual growth over 7 years), but none of the factors fell outside that range. Sources within the
MARAMA region were not projected with these factors, but with the MARAMA-provided growth
factors.
186
-------
Table 4-15. SCCs in nonpt that use Human Population Growth for Projections
see
Description
2302002100
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking -
Ch arb ro i 1 i ng:Convcvorized Charbroiling
2302002200
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking -
Charbroiling;Under-fired Charbroiling
2302003000
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking - Frying;Deep
Fat Frying
2302003100
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking - Frying;Flat
Griddle Frying
2302003200
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking -
Frying;Clamshell Griddle Frying
2501011011
Storage and Transport;Petroleum and Petroleum Product Storage;Residential Portable Gas
Cans;Permeation
2501011012
Storage and Transport;Petroleum and Petroleum Product Storage;Residential Portable Gas
Cans;Evaporation (includes Diurnal losses)
2501011013
Storage and Transport;Petroleum and Petroleum Product Storage;Residential Portable Gas
Cans;Spillage During Transport
2501011014
Storage and Transport;Petroleum and Petroleum Product Storage;Residential Portable Gas
Cans;Refilling at the Pump - Vapor Displacement
2501011015
Storage and Transport;Petroleum and Petroleum Product Storage;Residential Portable Gas
Cans;Refilling at the Pump - Spillage
2501012011
Storage and Transport;Petroleum and Petroleum Product Storage;Commercial Portable Gas
Cans;Permeation
2501012012
Storage and Transport;Petroleum and Petroleum Product Storage;Commercial Portable Gas
Cans;Evaporation (includes Diurnal losses)
2501012013
Storage and Transport;Petroleum and Petroleum Product Storage;Commercial Portable Gas
Cans;Spillage During Transport
2501012014
Storage and Transport;Petroleum and Petroleum Product Storage;Commercial Portable Gas
Cans;Refilling at the Pump - Vapor Displacement
2501012015
Storage and Transport;Petroleum and Petroleum Product Storage;Commercial Portable Gas
Cans;Refilling at the Pump - Spillage
2630020000
Waste Disposal, Treatment, and Recovery;Wastewater Treatment;Public Owned;Total
Processed
2640000000
Waste Disposal, Treatment, and Recovery;TSDFs;All TSDF Types;Total: All Processes
2810025000
Miscellaneous Area Sources;Other Combustion;Residential Grilling (see 23-02-002-xxx for
Commercial) ;Total
2810060100
Miscellaneous Area Sources;Other Combustion;Cremation;Humans
4.2.3.9 Solvents (np_solvents)
Packets:
Proj ection_2016_2023_solvents_v2platform_MARAMA_noNCNJ_09nov2022_v2
Proj ection_2016_2023_solvents_v2platform_NC_09nov2022_v2
Proj ection_2016_2023_solvents_v2platform_NJ_09nov2022_v 1
Proj ection_2016_2023_solvents_v2platform_population_noMARAMA_09nov2022_v 1
Proj ection_2016_202X_solvents_v3platform_Idaho_asphalt_09aug2022_v0
Proj ection_2016_2026_solvents_v2platform_MARAMA_noNCNJ_09nov2022_v 1
Proj ection_2016_2026_solvents_v2platform_NC_09nov2022_v 1
187
-------
Proj ection_2016_2026_solvents_v2platform_NJ_09nov2022_vl
Proj ection_2016_2026_solvents_v2platform_population_noMARAMA_09nov2022_v 1
The projection methodology for npsolvents is similar to the method used in the 2016v2 platform. The
MARAMA, NC, and NJ nonpt projection packets all affect solvents. Elsewhere, solvents are projected
using human population trends for most solvent categories. All of these packets were checked to confirm
they cover all SCCs in the solvents sector, and packets were supplemented with additional SCCs as
needed, copied from factors for existing SCCs. The SCCs in np solvents that are projected using human
population growth are shown in Table 4-16.
The following updates were made in 2016v3 to supplement the SCCs included in the projection packets:
all 2460- SCCs and 2402000000 use human population (copied from an existing 2460- SCC);
most surface coating and graphic arts SCCs use either human population (MARAMA and non-
MARAMA regions) or employment data (some SCCs in MARAMA region only);
added new SCC 2460030999 (lighter fluid) to project based on human population in all regions.
The 2026 projection packets were interpolated from 2023 and 2028 for NC and NJ. Two SCCs which
were projected based on VMT in North Carolina used 2016v3 VMT as the basis for those projections.
For 2016v3, Idaho asphalt emissions (SCCs = 2461021000, 2461022000) were reduced by 14.2% based
on a comment from the state.
Table 4-16. 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
2401008000
Solvent Utilization;Surface Coating;Traffic Markings;Total: All Solvent Types
2401010000
Solvent Utilization;Surface Coating;Textile Products: SIC 22;Total: All Solvent Types
2401015000
Solvent Utilization;Surface Coating;Factory Finished Wood: SIC 2426 thru 242;Total: All Solvent Types
2401020000
Solvent Utilization;Surface Coating;Wood Furniture: SIC 25;Total: All Solvent Types
2401025000
Solvent Utilization;Surface Coating;Metal Furniture: SIC 25;Total: All Solvent Types
2401030000
Solvent Utilization;Surface Coating;Paper: SIC 26;Total: All Solvent Types
2401035000
Solvent Utilization;Surface Coating;Plastic Products: SIC 308;Total: All Solvent Types
2401040000
Solvent Utilization;Surface Coating;Metal Cans: SIC 341;Total: All Solvent Types
2401045000
Solvent Utilization;Surface Coating;Metal Coils: SIC 3498;Total: All Solvent Types
2401050000
Solvent Utilization;Surface Coating;Miscellaneous Finished Metals: SIC 34 - (341 + 3498);Total: All
Solvent Types
2401055000
Solvent Utilization;Surface Coating;Machinery and Equipment: SIC 35;Total: All Solvent Types
2401060000
Solvent Utilization;Surface Coating;Large Appliances: SIC 363;Total: All Solvent Types
2401065000
Solvent Utilization;Surface Coating;Electronic and Other Electrical: SIC 36 - 363;Total: All Solvent Types
2401070000
Solvent Utilization;Surface Coating;Motor Vehicles: SIC 371;Total: All Solvent Types
2401075000
Solvent Utilization;Surface Coating;Aircraft: SIC 372;Total: All Solvent Types
2401080000
Solvent Utilization;Surface Coating;Marine: SIC 373;Total: All Solvent Types
2401085000
Solvent Utilization;Surface Coating;Railroad: SIC 374;Total: All Solvent Types
188
-------
see
SCC Descriptions
2401090000
Solvent Utilization;Surface Coating:Miscellaneous Manufacturing;Total: All Solvent Types
2401100000
Solvent Utilization;Surface Coating:Industrial Maintenance Coatings;Total: All Solvent Types
2401200000
Solvent Utilization;Surface Coating;Other Special Purpose Coatings;Total: All Solvent Types
2425000000
Solvent Utilization;Graphic Arts;All Processes;Total: All Solvent Types
2425020000
Solvent Utilization;Graphic Arts;Letterpress;Total: All Solvent Types
2425030000
Solvent Utilization;Graphic Arts;Rotogravure;Total: All Solvent Types
2440000000
Solvent Utilization;Miscellaneous Industrial;All Processes;Total: All Solvent Types
2440020000
Solvent Utilization;Miscellaneous Industrial;Adhesive (Industrial) Application;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
2460100000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;All Personal Care
Products;Total: All Solvent Types
2460200000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;All Household Products;Total:
All Solvent Types
2460400000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;All Automotive Aftermarket
Products;Total: All Solvent Types
2460500000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;All Coatings and Related
Products;Total: All Solvent Types
2460600000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;All Adhesives and
Sealants;Total: All Solvent Types
2460800000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;All FIFRA Related
Products;Total: All Solvent Types
2460900000
Solvent Utilization;Miscellaneous Non-industrial: Consumer and Commercial;Miscellaneous Products (Not
Otherwise Covered);Total: All Solvent Types
2461800001
Solvent Utilization;Miscellaneous Non-industrial: Commercial;Pesticide Application: All Processes;Surface
Application
4.2.3.10 Residential Wood Combustion (rwc)
Packets:
Proj ection_2016_2023_all_nonpoint_versionl_platform_NC_24jun202 l_nf_v5
Proj ection_2016_2023_rwc_version2_platform_fromMARAMA_22jun202 l_v0
Proj ection_2016_2026_all_nonpoint_version2_platform_NC_l 9jul202 l_nf_vl
Proj ection_2016_2026_rwc_version2_platform_fromMARAMA_l 9jul202 l_v0
For residential wood combustion, the growth and control factors are computed together into merged
factors in the same packets. For states other than California, Oregon, and Washington, RWC emissions
from 2016 were projected to 2023 and 2026 using projection factors derived using the MARAMA tool
that is based on the projection methodology from EPA's 201 lv6.3 platform. The development of
projected growth in RWC emissions to year 2023 starts with the projected growth in RWC appliances
derived from year 2012 appliance shipments reported in the Regulatory Impact Analysis (RIA) for
Proposed Residential Wood Heaters NSPS Revision Final Report available at:
http://www2.epa.gov/sites/production/files/2013-12/documents/ria-20140103.pdf. The 2012 shipments
are based on 2008 shipment data and revenue forecasts from a Frost & Sullivan Market Report (Frost &
189
-------
Sullivan, 2010). Next, to be consistent with the RIA, growth rates for new appliances for certified wood
stoves, pellet stoves, indoor furnaces and OHH were based on forecasted revenue (real GDP) growth rate
of 2.0% per year from 2013 through 2023 and 2026 as predicted by the U.S. Bureau of Economic
Analysis (BEA, 2012). While this approach is not perfectly correlated, in the absence of specific
shipment projections, the RIA assumes the overall trend in the projection is reasonable. The growth rates
for appliances not listed in the RIA (fireplaces, outdoor wood burning devices (not elsewhere classified)
and residential fire logs) are estimated based on the average growth in the number of houses between
2002 and 2012, about 1% (U.S. Census, 2012).
In addition to new appliance sales and forecasts extrapolating beyond 2012, assumptions on the
replacement of older, existing appliances are needed. Based on long lifetimes, no replacement of
fireplaces, outdoor wood burning devices (not elsewhere classified) or residential fire logs is assumed. It
is assumed that 95% of new woodstoves will replace older non-EPA certified freestanding stoves (pre-
1988 NSPS) and 5% will replace existing EPA-certified catalytic and non-catalytic stoves that currently
meet the 1988 NSPS (Houck, 2011).
Equation 4-1 was applied with RWC-specific factors from the rule. The EPA RWC NSPS experts assume
that 10% of new pellet stoves and OHH replace older units and that because of their short lifespan, that
10% of indoor furnaces are replaced each year; these are the same assumptions used since the 2007
emissions modeling platform (EPA, 2012d). The resulting growth factors for these appliance types varies
by appliance type and also by pollutant because the emission rates, from EPA RWC tool (EPA, 2013rwc),
vary by appliance type and pollutant. For EPA certified units, the projection factors for PM are lower
than those for all other pollutants. The projection factors also vary because the total number of existing
units in 2016 varies greatly between appliance types. For 2016v3, the projection factors are the same as
those used in 2016v2, although the rwc emissions for Idaho differ due to the 2016 emissions being
updated to use data from 2017 NEI.
Table 4-17 contains the factors to adjust the emissions from 2016 to 2023 and 2026. California, Oregon,
and Washington RWC were held constant at 2017 NEI levels for all of the years 2016, 2023, and 2026
due to the unique control programs that those states have in place.
Table 4-17. Projection factors for RWC
see
SCC description
Pollutant*
2016-to-
2023
2016-to-
2026
2104008100
Fireplace: general
7.19%
10.29%
2104008210
Woodstove: fireplace inserts; non-EPA
certified
-13.92%
-17.97%
2104008220
Woodstove: fireplace inserts; EPA
certified; non-catalytic
PM10-PRI
4.09%
5.08%
2104008220
Woodstove: fireplace inserts; EPA
certified; non-catalytic
PM25-PRI
4.09%
5.08%
2104008220
Woodstove: fireplace inserts; EPA
certified; non-catalytic
8.34%
10.28%
2104008230
Woodstove: fireplace inserts; EPA
certified; catalytic
PM10-PRI
6.06%
7.68%
2104008230
Woodstove: fireplace inserts; EPA
certified; catalytic
PM25-PRI
6.06%
7.68%
2104008230
Woodstove: fireplace inserts; EPA
certified; catalytic
12.08%
15.27%
190
-------
see
SCC description
Pollutant*
2016-to-
2023
2016-to-
2026
Woodstove: freestanding, non-EPA
2104008310
certified
CO
-12.09%
-15.72%
2104008310
Woodstove: freestanding, non-EPA
certified
PM10-PRI
-12.67%
-16.52%
Woodstove: freestanding, non-EPA
2104008310
certified
PM25-PRI
-12.67%
-16.52%
2104008310
Woodstove: freestanding, non-EPA
certified
VOC
-11.40%
-14.84%
2104008310
Woodstove: freestanding, non-EPA
certified
-12.09%
-15.72%
Woodstove: freestanding, EPA certified,
2104008320
non-catalytic
PM10-PRI
4.09%
5.08%
Woodstove: freestanding, EPA certified,
2104008320
non-catalytic
PM25-PRI
4.09%
5.08%
Woodstove: freestanding, EPA certified,
2104008320
non-catalytic
8.34%
10.28%
Woodstove: freestanding, EPA certified,
2104008330
catalytic
PM10-PRI
6.07%
7.69%
Woodstove: freestanding, EPA certified,
2104008330
catalytic
PM25-PRI
6.07%
7.69%
Woodstove: freestanding, EPA certified,
2104008330
catalytic
12.08%
15.27%
2104008400
Woodstove: pellet-fired, general
(freestanding or FP insert)
PM10-PRI
30.09%
38.02%
2104008400
Woodstove: pellet-fired, general
(freestanding or FP insert)
PM25-PRI
30.09%
38.02%
2104008400
Woodstove: pellet-fired, general
(freestanding or FP insert)
26.96%
33.85%
Furnace: Indoor, cordwood-fired, non-EPA
2104008510
certified
CO
-64.93%
-84.78%
Furnace: Indoor, cordwood-fired, non-EPA
2104008510
certified
PM10-PRI
-62.99%
-82.89%
Furnace: Indoor, cordwood-fired, non-EPA
2104008510
certified
PM25-PRI
-62.99%
-82.89%
Furnace: Indoor, cordwood-fired, non-EPA
2104008510
certified
VOC
-65.02%
-84.89%
Furnace: Indoor, cordwood-fired, non-EPA
2104008510
certified
-64.93%
-84.78%
2104008530
Furnace: Indoor, pellet-fired, general
PM10-PRI
30.09%
38.02%
2104008530
Furnace: Indoor, pellet-fired, general
PM25-PRI
30.09%
38.02%
2104008530
Furnace: Indoor, pellet-fired, general
26.96%
33.85%
2104008610
Hydronic heater: outdoor
PM10-PRI
0.06%
-0.40%
2104008610
Hydronic heater: outdoor
PM25-PRI
0.06%
-0.40%
2104008610
Hydronic heater: outdoor
-0.73%
-1.30%
2104008620
Hydronic heater: indoor
PM10-PRI
0.06%
-0.40%
2104008620
Hydronic heater: indoor
PM25-PRI
0.06%
-0.40%
2104008620
Hydronic heater: indoor
-0.73%
-1.30%
2104008630
Hydronic heater: pellet-fired
PM10-PRI
0.06%
-0.40%
2104008630
Hydronic heater: pellet-fired
PM25-PRI
0.06%
-0.40%
191
-------
SCC
SC'C description
I'oNlllillU"
2016-1 o-
2023
2016-I0-
2026
21U4
-------
to Emissions Inventories for the Version 6.3, 2011 Emissions Modeling Platform for the Year 2023
technical support document (EPA, 2017).
Table 4-18. Assumed retirement rates and new source emission factor ratios for NSPS rules
NSPS
Rule
Sector(s)
Retirement
Rate years
(%/year)
Pollutant
Impacted
Applied where?
New Source
Emission
Factor(Fn)
Oil and
Gas
npoilgas,
ptoilgas
No
assumption
VOC
Storage Tanks: 70.3% reduction in
growth-only (>1.0)
0.297
Gas Well Completions: 95% control
(regardless)
0.05
Pneumatic controllers, not high-bleed
>6scfm or low-bleed: 77% reduction in
growth-only (>1.0)
0.23
Pneumatic controllers, high-bleed
>6scfm or low-bleed: 100% reduction in
growth-only (>1.0)
0.00
Compressor Seals: 79.9% reduction in
growth-only (>1.0)
0.201
Fugitive Emissions: 60% Valves,
flanges, connections, pumps, open-ended
lines, and other
0.40
Pneumatic Pumps: 71.3%; Oil and Gas
0.287
RICE
npoilgas,
pt_oilgas,
nonpt,
ptnonipm
40, (2.5%)
NOx
Lean burn: PA, all other states
0.25, 0.606
Rich Burn: PA, all other states
0.1, 0.069
Combined (average) LB/RB: PA, other
states
0.175, 0.338
CO
Lean burn: PA, all other states
1.0 (n/a), 0.889
Rich Burn: PA, all other states
0.15, 0.25
Combined (average) LB/RB: PA, other
states
0.575, 0.569
VOC
Lean burn: PA, all other states
0.125, n/a
Rich Burn: PA, all other states
0.1, n/a
Combined (average) LB/RB: PA, other
states
0.1125, n/a
Gas
Turbines
pt_oilgas,
ptnonipm
45 (2.2%)
NOx
California and NOx SIP Call states
0.595
All other states
0.238
Process
Heaters
pt_oilgas,
ptnonipm
30(3.3%)
NOx
Nationally to Process Heater SCCs
0.41
4.2.4.1 Oil and Gas NSPS (np_oilgas, pt_oilgas)
Packets:
Control_2016_2023_Oilgas_NSPS_np_oilgas_v3_platform_07sep2022_v 1
Control_2016_2023_Oilgas_NSPS_pt_oilgas_v3_platform_07sep2022_v 1
Control_2016_2026_Oilgas_N SPS_np_oilgas_v3_platform_09nov2022_v 1
Control_2016_2026_Oilgas_N SPS_pt_oilgas_v3_platform_09nov2022_v 1
New packets to reflect the oil and gas NSPS were developed for the 2016v3 platform. For oil and gas
NSPS controls, except for gas well completions (a 95 percent control), the assumption of no equipment
193
-------
retirements through year 2026 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 4-18, the 70.3 percent VOC NSPS control to this new growth
will result in a 23.4 percent control: 100 *(70.3 * (1.5 -1) / 1.5); this yields an "effective" growth rate
(combined PROJECTION and CONTROL) of 1.1485, or a 70.3 percent reduction from 1.5 to 1.0. The
impacts of all non-drilling completion VOC NSPS controls are therefore greater where growth in oil and
gas production is assumed highest. Conversely, for oil and gas basins with assumed negative growth in
activity/production, VOC NSPS controls will be limited to well completions only. These reductions are
year-specific because projection factors for these sources are year-specific. Table 4-19 (npoilgas) and
Table 4-21 (ptoilgas) list the SCCs where Oil and Gas NSPS controls were applied; note controls are
applied to production and exploration-related SCCs. Table 4-20 (np oilgas) and Table 4-22 (pt oilgas)
shows the reduction in VOC emissions after the application of the Oil and Gas NSPS CONTROL packet
for analytic years. The emissions totals in these tables include emissions in WRAP states, although
WRAP states other than New Mexico use a separate analytic year inventory. Additional effort was
implemented to reflect New Mexico's new Oil and Gas rule as a 60% reduction to VOC for three SCCs
(2310010700, 2310021509, and 2310023509). These additional controls in New Mexico Administrative code
20.2.5041 were reflected in the overall "Oil and Gas NSPS" control packet so that they could be included
in time to develop the new inventories for use in the 2016v3 emissions modeling platform.
Table 4-19. Non-point (np oilgas) SCCs in 2016v3 modeling platform where Oil and Gas NSPS
controls applied
see
PRODUCT
OG_NSPS_SCC
TOOL
OR
STATE
SRC_CAT
SCC_Description
2310010300
OIL
3. Pnuematic
controllers: not
high or low
bleed
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Crude Petroleum;Oil Well Pneumatic
Devices;;
2310010700
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Crude Petroleum;Oil Well Fugitives;;
2310011020
OIL
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Oil Production;Storage Tanks:
Crude Oil;;
2310011500
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Oil Production;Fugitives: All
Processes;;
2310011501
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Oil Production;Fugitives:
Connectors;;
2310011502
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Oil Production;Fugitives:
Flanges;;
2310011503
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Oil Production;Fugitives: Open
Ended Lines;;
2310011505
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Oil Production;Fugitives:
Valves;;
2310011506
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Oil Production;Fugitives:
Other;;
41 See https://www.srca.nm.gov/parts/title20/20.002.005Q.html and https://www.env.nm.gov/air-aualitv/compliance-and-
enforcement/
194
-------
see
PRODUCT
OG_NSPS_SCC
TOOL
OR
STATE
SRC_CAT
SCC_Description
2310020700
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Natural Gas;Gas Well Fugitives;;
2310021010
NGAS
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Storage Tanks:
Condensate;;
2310021011
NGAS
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Condensate Tank
Flaring;;
2310021300
NGAS
3. Pnuematic
controllers: not
high or low
bleed
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Gas Well
Pneumatic Devices;;
2310021310
NGAS
6. Pneumatic
Pumps
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Gas Well
Pneumatic Pumps;;
2310021500
NGAS
2. Well
Completions
TOOL
EXPLORATION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Gas Well
Completion - Flaring;;
2310021501
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Fugitives:
Connectors;;
2310021502
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Fugitives:
Flanges;;
2310021503
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Fugitives: Open
Ended Lines;;
2310021505
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Fugitives:
Valves;;
2310021506
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Fugitives:
Other;;
2310021509
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Fugitives: All
Processes;;
2310021601
NGAS
2. Well
Completions
TOOL
EXPLORATION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Gas Well
Venting - Initial Completions;;
2310023000
CBM
6. Pneumatic
Pumps
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Dewatering
Pump Engines;;
2310023010
CBM
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Storage
Tanks: Condensate;;
2310023300
CBM
3. Pnuematic
controllers: not
high or low
bleed
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Pneumatic
Devices;;
2310023310
CBM
6. Pneumatic
Pumps
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Pneumatic
Pumps;;
2310023509
CBM
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Fugitives;;
2310023511
CBM
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Fugitives:
Connectors;;
2310023512
CBM
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Fugitives:
Flanges;;
195
-------
see
PRODUCT
OG_NSPS_SCC
TOOL
OR
STATE
SRC_CAT
SCC_Description
2310023513
CBM
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Fugitives:
Open Ended Lines;;
2310023515
CBM
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Fugitives:
Valves;;
2310023516
CBM
5. Fugitives
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Fugitives:
Other;;
2310023600
CBM
2. Well
Completions
TOOL
EXPLORATION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;CBM Well
Completion: All Processes;;
2310030220
NGAS
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Natural Gas Liquids;Gas Well Tanks -
Flashing & Standing/Working/Breathing, Controlled;;
2310030300
NGAS
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Natural Gas Liquids;Gas Well Water Tank
Losses;;
2310111401
OIL
6. Pneumatic
Pumps
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Oil Exploration;Oil Well
Pneumatic Pumps;;
2310111700
OIL
2. Well
Completions
TOOL
EXPLORATION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Oil Exploration;Oil Well
Completion: All Processes;;
2310121401
NGAS
6. Pneumatic
Pumps
TOOL
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Exploration;Gas Well
Pneumatic Pumps;;
2310121700
NGAS
2. Well
Completions
TOOL
EXPLORATION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Exploration;Gas Well
Completion: All Processes;;
2310321010
NGAS
1. Storage Tanks
STATE
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production -
Conventional;Storage Tanks: Condensate;;
2310421010
NGAS
1. Storage Tanks
STATE
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production -
Unconventional;Storage Tanks: Condensate;;
Table 4-20. Emissions reductions for npoilgas sector due to application of Oil and Gas NSPS
year
poll
2016v3
2016
pre-CoST
emissions
emissions
change from
2016
%
change
2023
voc
2543889
2591022
-666598
-25.7%
2026
voc
2543889
2591022
-721370
-27.8%
Table 4-21. Point source SCCs in pt oilgas sector where Oil and Gas NSPS controls were applied
see
GAS
or OIL
OG NSPS
sec
TOOL
OR
STATE
SRC_CAT
NP PT
NPPT
SCC_Description
30180010
NGAS
4.
Compressor
Seals
STATE
PRODUCTION
PT
Industrial Processes;Chemical
Manufacturing;Equipment Leaks;Compressor Seals:
Gas Stream;;
30600801
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Pipeline Valves and Flanges;;
196
-------
see
GAS
or OIL
OG NSPS
sec
TOOL
OR
STATE
SRC_CAT
NP PT
NPPT
SCC_Description
30600802
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions; Vessel Relief Valves;;
30600803
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Pump Seals w/o Controls;;
30600804
OIL
4.
Compressor
Seals
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Compressor Seals;;
30600805
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Miscellaneous: Sampling/Non-Asphalt
Bio wing/Purging/etc.;;
30600806
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Pump Seals with Controls;;
30600811
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Pipeline Valves: Gas Streams;;
30600812
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Pipeline Valves: Light Liquid/Gas
Streams;;
30600813
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Pipeline Valves: Heavy Liquid Streams;;
30600815
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Open-ended Valves: All Streams;;
30600816
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Flanges: All Streams;;
30600817
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Pump Seals: Light Liquid/Gas Streams;;
30600818
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Pump Seals: Heavy Liquid Streams;;
30600819
OIL
4.
Compressor
Seals
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Compressor Seals: Gas Streams;;
30600820
OIL
4.
Compressor
Seals
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Compressor Seals: Heavy Liquid
Streams;;
30600822
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions; Vessel Relief Valves: All Streams;;
30688801
OIL
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Petroleum Industry;Fugitive
Emissions;Specify in Comments Field;;
31000101
OIL
2. Well
Completion
s
STATE
EXPLORATIO
N
PT
Industrial Processes;Oil and Gas Production;Crude
Oil Production;Well Completion;;
31000130
OIL
4.
Compressor
Seals
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Crude
Oil Production;Fugitives: Compressor Seals;;
31000151
OIL
3.
Pnuematic
controllers:
high or low
bleed
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Crude
Oil Production;Pneumatic Controllers, Low Bleed;;
31000152
OIL
3.
Pnuematic
controllers:
high or low
bleed
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Crude
Oil Production;Pneumatic Controllers High Bleed
>6 scfh;;
31000153
OIL
3.
Pnuematic
controllers:
not high or
low bleed
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Crude
Oil Production;Pneumatic Controllers Intermittent
Bleed;;
31000207
NGAS
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Natural
Gas Production; Valves: Fugitive Emissions;;
197
-------
see
GAS
or OIL
OG NSPS
sec
TOOL
OR
STATE
SRC_CAT
NP PT
NPPT
SCC_Description
31000220
NGAS
5. Fugitives
STATE
PRODUCTION
NP AND
_ PT
Industrial Processes;Oil and Gas Production;Natural
Gas Production;All Equipt Leak Fugitives (Valves,
Flanges, Connections, Seals, Drains;;
31000225
NGAS
4.
Compressor
Seals
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Natural
Gas Production;Compressor Seals;;
31000231
NGAS
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Natural
Gas Production;Fugitives: Drains;;
31000233
NGAS
3.
Pnuematic
controllers:
high or low
bleed
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Natural
Gas Production;Pneumatic Controllers, Low Bleed;;
31000235
NGAS
3.
Pnuematic
controllers:
not high or
low bleed
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Natural
Gas Production;Pneumatic Controllers Intermittent
Bleed;;
31000309
NGAS
4.
Compressor
Seals
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Natural
Gas Processing;Compressor Seals;;
31000324
NGAS
3.
Pnuematic
controllers:
high or low
bleed
STATE
PRODUCTION
NP AND
_ PT
Industrial Processes;Oil and Gas Production;Natural
Gas Processing;Pneumatic Controllers Low Bleed;;
31000325
NGAS
3.
Pnuematic
controllers:
high or low
bleed
STATE
PRODUCTION
NP AND
_ PT
Industrial Processes;Oil and Gas Production;Natural
Gas Processing;Pneumatic Controllers, High Bleed
>6 scfh;;
31000326
NGAS
3.
Pnuematic
controllers:
not high or
low bleed
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Natural
Gas Processing;Pneumatic Controllers Intermittent
Bleed;;
31000506
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas Production;Liquid
Waste Treatment;Oil-Water Separation Wastewater
Holding Tanks;;
31088801
BOTH
5. Fugitives
STATE
PRODUCTION
PT
Industrial Processes;Oil and Gas
Production;Fugitive Emissions;Specify in
Comments Field;;
31088811
BOTH
5. Fugitives
STATE
PRODUCTION
NP AND
PT
Industrial Processes;Oil and Gas
Production;Fugitive Emissions;Fugitive Emissions;;
31700101
NGAS
3.
Pnuematic
controllers:
high or low
bleed
STATE
PRODUCTION
PT
Industrial Processes;NGTS;Natural Gas
Transmission and Storage Facilities;Pneumatic
Controllers Low Bleed;;
39090001
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Residual Oil: Breathing
Loss;;
39090002
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Residual Oil: Working
Loss;;
39090003
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Distillate Oil (No. 2):
Breathing Loss;;
198
-------
see
GAS
or OIL
OG NSPS
sec
TOOL
OR
STATE
SRC_CAT
NP PT
NPPT
SCC_Description
39090004
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Distillate Oil (No. 2):
Working Loss;;
39090005
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Oil No. 6: Breathing
Loss;;
39090006
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Oil No. 6: Working
Loss;;
39090007
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Methanol: Breathing
Loss;;
39090008
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Methanol: Working
Loss;;
39090009
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Residual Oil/Crude Oil:
Breathing Loss;;
39090010
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Residual Oil/Crude Oil:
Working Loss;;
39090012
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Industrial Processes;In-process Fuel Use;Fuel
Storage - Fixed Roof Tanks;Dual Fuel (Gas/Oil):
Working Loss;;
40301001
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 13: Breathing Loss (67000
Bbl. Tank Size);;
40301002
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 10: Breathing Loss (67000
Bbl. Tank Size);;
40301003
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 7: Breathing Loss (67000 Bbl.
Tank Size);;
40301004
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 13: Breathing Loss (250000
Bbl. Tank Size);;
40301005
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 10: Breathing Loss (250000
Bbl. Tank Size);;
40301007
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 13: Working Loss (Tank
Diameter Independent);;
40301008
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 10: Working Loss (Tank
Diameter Independent);;
40301009
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 7: Working Loss (Tank
Diameter Independent);;
40301010
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Crude
Oil RVP 5: Breathing Loss (67000 Bbl. Tank Size);;
199
-------
see
GAS
or OIL
OG NSPS
sec
TOOL
OR
STATE
SRC_CAT
NP PT
NPPT
SCC_Description
40301011
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Crude
Oil RVP 5: Breathing Loss (250000 Bbl. Tank
Size);;
40301012
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Crude
Oil RVP 5: Working Loss (Tank Diameter
Independent);;
40301013
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Jet
Naphtha (JP-4): Breathing Loss (67000 Bbl. Tank
Size);;
40301015
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Jet
Naphtha (JP-4): Working Loss (Tank Diameter
Independent);;
40301019
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying
Sizes);Distillate Fuel #2: Breathing Loss (67000
Bbl. Tank Size);;
40301021
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying
Sizes);Distillate Fuel #2: Working Loss (Tank
Diameter Independent);;
40301065
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Grade
6 Fuel Oil: Breathing Loss (250000 Bbl. Tank
Size);;
40301075
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Grade
6 Fuel Oil: Working Loss (Independent Tank
Diameter);;
40301079
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Grade
1 Fuel Oil: Working Loss (Independent Tank
Diameter);;
40301097
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Other
Liquids: Breathing Loss (67000 Bbl. Tank Size);;
40301098
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Other
Liquids: Breathing Loss (250000 Bbl. Tank Size);;
40301099
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fixed Roof Tanks (Varying Sizes);Other
Liquids: Working Loss (Tank Diameter
Independent);;
40388801
OIL
5. Fugitives
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Product Storage at
Refineries;Fugitive Emissions; General;;
40400300
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Liquids Storage
(non-Refinery);Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank: Flashing Loss;;
40400301
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Liquids Storage
(non-Refinery);Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank: Breathing Loss;;
40400302
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Liquids Storage
(non-Refinery);Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank: Working Loss;;
40400311
OIL
1. Storage
Tanks
STATE
PRODUCTION
NP AND
PT
Chemical Evaporation;Petroleum Liquids Storage
(non-Refmery);Oil and Gas Field Storage and
200
-------
see
GAS
or OIL
OG NSPS
sec
TOOL
OR
STATE
SRC_CAT
NP PT
NPPT
SCC_Description
Working Tanks;Fixed Roof Tank, Condensate,
working+breathing+flashing losses;;
40400312
OIL
1. Storage
Tanks
STATE
PRODUCTION
NP AND
_ PT
Chemical Evaporation;Petroleum Liquids Storage
(non-Refmery);Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Crude Oil,
working+breathing+flashing losses;;
40400313
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Liquids Storage
(non-Refmery);Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Lube Oil,
working+breathing+flashing losses;;
40400314
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Liquids Storage
(non-Refmery);Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Specialty Chem-
working+breathing+flashing;;
40400315
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Liquids Storage
(non-Refmery);Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Produced Water,
working+breathing+flashing;;
40400316
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Petroleum Liquids Storage
(non-Refmery);Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Diesel,
working+breathing+flashing losses;;
40701613
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Organic Chemical
Storage;Fixed Roof Tanks - Alkanes
(Paraffins);Petroleum Distillate: Breathing Loss;;
40701614
OIL
1. Storage
Tanks
STATE
PRODUCTION
PT
Chemical Evaporation;Organic Chemical
Storage;Fixed Roof Tanks - Alkanes
(Paraffins);Petroleum Distillate: Working Loss;;
Table 4-22. VOC reductions (tons/year) for the ptoilgas sector after application of the Oil and Gas
NSPS CONTROL packet for both analytic years 2023 and 2026
Year
Pollutant
2016v3
Emissions Reductions
% change
2023
VOC
240,361
-19,141
-8.0%
2026
VOC
240,361
-22,628
-9.4%
4.2.4.2 RICE NSPS (nonpt, ptnonipm, np_oilgas, pt_oilgas)
Packets:
CONTROL_2016_2023_RICE_NSPS_nonpt_ptnonipm_beta_platform_extended_l 0sep2019_v0
Control_2016_2023_RICE_NSPS_pt_oilgas_v3_platform_24aug2022_v0
Control_2016_2023_RICE_NSPS_np_oilgas_v3_platform_23aug2022_v0
Control_2016_2026_RICE_NSPS_nonpt_v2_platform_l 6jul202 l_v0
Control_2016_2026_RICE_NSPS_pt_oilgas_v3_platform_24aug2022_v0
Control_2016_2026_RICE_NSPS_np_oilgas_v3_platform_23aug2022_v0
Control_2023_2026interp_RICE_NSPS_ptnonipm_v2_platform_MARAMA_22jul2021_v0
Control_2023_2026interp_RICE_NSPS_ptnonipm_v2_platform_noMARAMA_22jul2021_v0
Multiple sectors are affected by the RICE NSPS controls. The packet names include the sectors to which
the specific packet applies. For the ptnonipm sector, 2023 packets for RICE NSPS were not needed for
ptnonipm due to the approach in 2016v3 that used year 2019 non-EGU point source emissions for 2023.
201
-------
The 2026 packets for ptnonipm and nonpt were developed by interpolating between the 2016vl packets
for 2023 and 2028.
For the pt oilgas and np oilgas sectors, year-specific RICE NSPS factors were generated for 2023 and
2026. New growth factors based on AEO2022 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,
although the actual control efficiency calculation methodology did not change from 2016v2 to 2016v3.
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 for lean burn, rich burn and "combined" engines using Equation
4-2 and information listed in Table 4-18. Table 4-23, Table 4-24, and Table 4-28 list the SCCs for which
RICE NSPS controls were applied for the 2016v3 platform. Table 4-25, Table 4-26, Table 4-27 and Table
4-29 show the reductions in emissions in the nonpoint, ptnonipm, and point and nonpoint oil and gas
sectors after the application of the RICE NSPS CONTROL packet for the analytic years. Note that for
nonpoint oil and gas, VOC reductions were only appropriate in the state of Pennsylvania.
Based on a state comment, the NOx emissions for facility 7667111 (Transcontinental Gas Pipeline -
Station) in Virginia were further reduced to 70 tons per year following the applications of other controls.
Table 4-23. 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
2102006000
Combined
Stationary Source Fuel Combustion; Industrial; Natural Gas; Total: Boilers
and IC Engines
2102006002
Combined
Stationary Source Fuel Combustion; Industrial; Natural Gas; All IC Engine
Types
2103006000
Combined
Stationary Source Fuel Combustion; Commercial/Institutional; Natural Gas;
Total: Boilers and IC Engines
Table 4-24. Non-point Oil and Gas SCCs in 2016v3 modeling platform where RICE NSPS controls
applied
see
Lean/ Rich/
Combined
Product
Source
Category
SCCDescription
2310000220
Combined
BOTH
EXPLORATION
Industrial Processes;Oil and Gas Exploration and
Production;All Processes;Drill Rigs;;
2310000660
Combined
BOTH
EXPLORATION
Industrial Processes;Oil and Gas Exploration and
Production;All Processes;Hydraulic Fracturing Engines;;
2310020600
Combined
NGAS
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Natural Gas;Compressor Engines;;
202
-------
see
Lean/ Rich/
Combined
Product
Source
Category
SCCDescription
2310021202
Lean
NGAS
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Natural Gas Fired
4Cycle Lean Burn Compressor Engines 50 To 499 HP;;
2310021251
Lean
NGAS
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Lateral Compressors 4
Cycle Lean Burn;;
2310021302
Rich
NGAS
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Natural Gas Fired
4Cycle Rich Burn Compressor Engines 50 To 499 HP;;
2310021351
Rich
NGAS
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;On-Shore Gas Production;Lateral Compressors 4
Cycle Rich Burn;;
2310023202
Lean
CBM
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;CBM Fired 4Cycle
Lean Burn Compressor Engines 50 To 499 HP;;
2310023251
Lean
CBM
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Lateral
Compressors 4 Cycle Lean Burn;;
2310023302
Rich
CBM
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;CBM Fired 4Cycle
Rich Burn Compressor Engines 50 To 499 HP;;
2310023351
Rich
CBM
PRODUCTION
Industrial Processes;Oil and Gas Exploration and
Production;Coal Bed Methane Natural Gas;Lateral
Compressors 4 Cycle Rich Burn;;
2310300220
Combined
NGAS
EXPLORATION
Industrial Processes;Oil and Gas Exploration and
Production;All Processes - Conventional;Drill Rigs;;
2310400220
Combined
BOTH
EXPLORATION
Industrial Processes;Oil and Gas Exploration and
Production;All Processes - Unconventional;Drill Rigs;;
Table 4-25. Nonpoint Emissions reductions after the application of the RICE NSPS
year
Poll
2016v3 (tons)
Emissions reductions
(tons)
% change
2023
CO
1,939,947
-20,440
-1.1%
2023
NOX
750,215
-30,573
-4.1%
2026
CO
1,939,947
-25,283
-1.3%
2026
NOX
750,215
-38,855
-5.2%
Table 4-26. Ptnonipm Emissions reductions after the application of the RICE NSPS
year
poll
2023gf (tons)
Emissions
reductions (tons)
% change
2026
CO
1,359,690
-85
-0.01%
2026
NOX
848,409
-142
-0.02%
2026
VOC
759,289
-0.5
0.00%
203
-------
Table 4-27. Oil and Gas Emissions reductions for npoilgas sector due to application of RICE NSPS
year
Poll
2016v3
2016pre-CoST
emissions
Emissions
reduction
% change
2023
CO
765,734
767,969
-93,051
-12.1%
2023
NOX
587,919
610,402
-86,508
-14.2%
2023
VOC
2,543,889
2,591,022
-463
0.0%
2026
CO
765,734
767,969
-105,028
-13.7%
2026
NOX
587,919
610,402
-100,126
-16.4%
2026
VOC
2,543,889
2,591,022
-513
0.0%
Table 4-28. Point source SCCs in ptoilgas sector where RICE NSPS controls applied.
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
Combined
Internal Combustion Engines; Industrial; Natural Gas;4-cycle Clean Burn
20300201
Combined
Internal Combustion Engines; Commercial/Institutional; Natural Gas;
Reciprocating
31000203
Combined
Industrial Processes; Oil and Gas Production; Natural Gas Production;
Compressors (See also 310003-12 and -13)
Table 4-29. Emissions reductions (tons/year) in pt oilgas sector after the application of the RICE
NSPS CONTROL packet for analytic years 2023 and 2026.
Year
Pollutant
2016v3
Emissions Reductions
% change
2023
CO
205,468
-23,724
-11.5%
2023
NOX
414,623
-58,080
-14.0%
2023
VOC
240,361
-279
-0.1%
2026
CO
205,468
-29,200
-14.2%
2026
NOX
414,623
-69,120
-16.7%
2026
VOC
240,361
-313
-0.1%
4.2.4.3 Fuel Sulfur Rules (nonpt, ptnonipm)
Packets:
Control_2016_202X_MANEVU_Sulfur_fromMARAMA_v l_platform_22aug2022_nf_v 1
The control packet for fuel sulfur rules is the same for all analytic years. Fuel sulfur rules controls are
reflected for the following states: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey,
Rhode Island, and Vermont. The fuel limits for these states are incremental starting after year 2012, but
are fully implemented by July 1, 2018, in these states. The control packet representing these controls was
updated by MARAMA for the 2016vl platform. For 2016v3, states that had fully implemented their
controls by 2017 were removed (namely Delaware, New York, and Pennsylvania) because 2017 NEI was
used for nonpoint emissions.
204
-------
Summaries of the sulfur rules by state, with emissions reductions relative to the entire sector emissions
and relative to the analytic year emissions for the affected SCCs are provided in Table 4-30, which
reflects the impacts of the MARAMA packet only, as these reductions are not estimated in non-
MARAMA states. A negligible amount of reductions occur in the ptoilgas sector. Note that ptnonipm
sources are not impacted in 2016v3 platform since the starting point for the 2023 emissions was the 2019
NEI.
Table 4-30. Summary of fuel sulfur rule impacts on nonpoint S02 emissions for 2023
Pollutant
State
2023 pre-control
Emissions (tons)
2023 post-
control
Emissions (tons)
Change in
emissions
(tons)
Percent
change
NOX
Connecticut
3,691
3,424
-268
-7.2%
NOX
Maine
6,502
6,150
-353
-5.4%
NOX
Massachusetts
9,391
8,887
-504
-5.4%
NOX
New Hampshire
6,480
6,223
-257
-4.0%
NOX
Rhode Island
879
814
-65
-7.4%
NOX
Vermont
878
798
-80
-9.1%
NOX
Six state total
27,822
26,296
-1,526
-5.5%
S02
Connecticut
1,412
82
-1,330
-94.2%
S02
Maine
1,220
35
-1,185
-97.1%
S02
Massachusetts
2,251
88
-2,163
-96.1%
S02
New Hampshire
4,142
21
-4,121
-99.5%
S02
Rhode Island
359
38
-320
-89.3%
S02
Vermont
399
26
-374
-93.6%
S02
Six state total
9,783
290
-9,493
-97.0%
S02
ALL state total
167,817
158,324
-9,493
-5.6%
4.2.4.4 Natural Gas Turbines NOx NSPS (ptnonipm, pt_oilgas)
Packets:
Control_2016_2023_NG_Turbines_NSPS_pt_oilgas_v3_platform_24aug2022_v0
Control_2023_2026interp_NG_Turbines_NSPS_ptnonipm_v2_platform_MARAMA_22jul2021_v0
Control_2023_2026interp_NG_Turbines_NSPS_ptnonipm_v2_platform_nonMARAMA_22jul2021_v0
Control_2016_2026_NG_Turbines_NSPS_pt_oilgas_v3_platform_24aug2022_v0
For ptnonipm, the packets for 2023 were reused from the 2016vl platform; the packets for 2026 were
interpolated between the 2023 and 2028 packets for the 2016vl platform. For pt oilgas, the packets for
2016v3 are based on updated growth information for that sector from state-historical production data and
the AEO2022 production forecast database. The new growth factors were to calculate the new control
efficiencies for all analytic years (2023 and 2026). The control efficiency calculation methodology did
not change from 2016v2 to 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
205
-------
of performance for new stationary combustion turbines in 40 CFR part 60, subpart KKKK. The standards
reflect changes in NOx emission control technologies and turbine design since standards for these units
were originally promulgated in 40 CFR part 60, subpart GG. The 2006 NSPSs affecting NOx and SO2
were established at levels that bring the emission limits up-to-date with the performance of current
combustion turbines. Stationary combustion turbines were also regulated by the NOx State
Implementation Plan (SIP) Call, which required affected gas turbines to reduce their NOx emissions by
60 percent. Table 4-3 lcompares the 2006 NSPS emission limits 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/csapr/final-update-nox-sip-call-
regulations-emissions-monitoring-provisions-state-implementation. 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 4-31. Stationary gas turbines NSPS analysis and resulting emission rates used to compute
controls
NOx Emission Limits for New Stationary Combustion Turbines
<50
50-850
>850
Firing Natural Gas
MMBTU/hr
MMBTU/hr
MMBTU/hr
Federal NSPS
100
25
15
Ppm
5-100
100-250
>250
State RACT Regulations
MMBTU/hr
MMBTU/hr
MMBTU/hr
Connecticut
225
75
75
Ppm
Delaware
42
42
42
Ppm
Massachusetts
65*
65
65
Ppm
New Jersey
50*
50
50
Ppm
New York
50
50
50
Ppm
New Hampshire
55
55
55
Ppm
* Only applies to 25-100 MMBTU/hr
Notes: The above state RACT table is from a 2001 analysis. The current NY State regulations have the
same emission limits.
New source emission rate (Fn)
NOx ratio (Fn)
Control (%)
NOx SIP Call states plus CA
= 25 / 42 =
0.595
40.5%
Other states
= 25 / 105 =
0.238
76.2%
For control factor development, the existing source emission ratio was set to 1.0 for combustion turbines.
The new source emission ratio for the NOx SIP Call states and California is the ratio of state NOx
emission limit to the Federal NSPS. A complicating factor in the above is the lack of size information in
the stationary source SCCs. Plus, the size classifications in the NSPS do not match the size differentiation
used in state air emission regulations. We accepted a simplifying assumption that most industrial
applications of combustion turbines are in the 100-250 MMBtu/hr size range and computed the new
source emission rates as the NSPS emission limit for 50-850 MMBtu/hr units divided by the state
emission limits. We used a conservative new source emission ratio by using the lowest state emission
limit of 42 ppmv (Delaware). This yields a new source emission ratio of 25/42, or 0.595 (40.5 percent
reduction) for states with existing combustion turbine emission limits. States without existing turbine
NOx limits would have a lower new source emission ratio: the uncontrolled emission rate (105 ppmv via
206
-------
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. Also, Natural Gas Turbines control
factors supplied by MARAMA were used within the MARAMA region for 2023 and 2026.
Table 4-32 and Table 4-34 list the point source SCCs where Natural Gas Turbines NSPS controls were
applied for the 2016vl platform. Table 4-33 and Table 4-35 show the reduction in NOx emissions after
the application of the Natural Gas Turbines NSPS CONTROL packet to the analytic years. The values in
Table 4-33 and Table 4-35 include emissions both inside and outside the MARAMA region.
Table 4-32. Ptnonipm SCCs in 2016vl modeling platform where Natural Gas Turbines NSPS
controls 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 4-33. Ptnonipm emissions reductions after the application of the Natural Gas Turbines NSPS
year
Poll
2023gf (tons)
emissions
reduction (tons)
0/
/O
change
2026
NOX
848,409
-334
-0.04%
Table 4-34. Point source SCCs in ptoilgas sector where Natural Gas Turbines NSPS control
applied.
SCC
SCC description
20200201
Internal Combustion Engines; Industrial; Natural Gas; Turbine
20200209
Internal Combustion Engines; Industrial; Natural Gas; Turbine: Exhaust
20300202
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine
20300209
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine: Exhaust
20200203
Internal Combustion Engines; Industrial; Natural Gas; Turbine: Cogeneration
20200714
Internal Combustion Engines; Industrial; Process Gas; Turbine: Exhaust
20300203
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine:
Cogeneration
Table 4-35. Emissions reductions (tons/year) for pt oilgas after the application of the Natural Gas
Turbines NSPS CONTROL packet for analytic years.
Year
Pollutant
2016v3
Emissions
Reduction
%
change
2023
NOX
414,623
-12,132
-2.9%
2026
NOX
414,623
-13,648
-3.3%
207
-------
4.2.4.5 Process Heaters NOx NSPS (ptnonipm, pt_oilgas)
Packets:
Control_2023_2026interp_Process_Heaters_NSPS_ptnonipm_v2_platform_22jul2021_v0
Control_2016_2023_Process_Heaters_NSPS_pt_oilgas_v3_platform_24aug2022_v0
Control_2016_2026_Process_Heaters_NSPS_pt_oilgas_v3_platform_24aug2022_v0
For ptnonipm, no additional controls for process heaters were applied to the 2023 emissions; the packet
for 2023 to 2026 was developed based on an interpolation between the 2023 and 2028 factors for the
2016vl platform. For pt oilgas, the packets were newly developed for 2016v3 based on updated
information.
Process heaters are used throughout refineries and chemical plants to raise the temperature of feed
materials to meet reaction or distillation requirements. Fuels are typically residual oil, distillate oil,
refinery gas, or natural gas. In some sense, process heaters can be considered as emission control devices
because they can be used to control process streams by recovering the fuel value while destroying the
VOC. The criteria pollutants of most concern for process heaters are NOx and SO2. In 2016, it is assumed
that process heaters have not been subject to regional control programs like the NOx SIP Call, so most of
the emission controls put in-place at refineries and chemical plants have resulted from RACT regulations
that were implemented as part of SIPs to achieve ozone NAAQS in specific areas, and refinery consent
decrees. The boiler/process heater NSPS established NOx emission limits for new and modified process
heaters. These emission limits are displayed in Table 4-36.
Table 4-36. Process Heaters NSPS analysis and 2016vl new 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 for new sources (Fn) is 0.41 (58.6 percent control). The retirement rate is the inverse
of the expected unit lifetime. There is limited information in the literature about process heater lifetimes.
This information was reviewed at the time that the Western Regional Air Partnership (WRAP) developed
its initial regional haze program emission projections, and energy technology models used a 20-year
lifetime for most refinery equipment. However, it was noted that in practice, heaters would probably have
a lifetime that was on the order of 50 percent above that estimate. Therefore, a 30-year lifetime was used
to estimate the effects of process heater growth and retirement. This yields a 3.3 percent retirement rate.
This control was then plugged into Equation 4-2 as a function of the year-specific projection factor. Table
208
-------
4-37 and Table 4-39 list the point source SCCs where Process Heaters NSPS controls were applied for the
2016vl platform. Table 4-38 and Table 4-40 show the reduction in NOx emissions after the application
of the Process Heaters NSPS CONTROL packet for the analytic years.
Table 4-37. Ptnonipm SCCs in 2016vl modeling platform where Process Heaters NSPS controls
applied.
see
SCC Description
30190003
Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Process Heater:
Natural Gas
30190004
Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Process Heater:
Process Gas
30590002
Industrial Processes; Mineral Products; Fuel Fired Equipment; Residual Oil: Process
Heaters
30590003
Industrial Processes; Mineral Products; Fuel Fired Equipment; Natural Gas: Process
Heaters
30600101
Industrial Processes; Petroleum Industry; Process Heaters; Oil-fired
30600102
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600103
Industrial Processes; Petroleum Industry; Process Heaters; Oil
30600104
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600105
Industrial Processes; Petroleum Industry; Process Heaters; Natural Gas-fired
30600106
Industrial Processes; Petroleum Industry; Process Heaters; Process Gas-fired
30600107
Industrial Processes; Petroleum Industry; Process Heaters; Liquified Petroleum Gas
(LPG)
30600199
Industrial Processes; Petroleum Industry; Process Heaters; Other Not Classified
30990003
Industrial Processes; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas:
Process Heaters
31000401
Industrial Processes; Oil and Gas Production; Process Heaters; Distillate Oil (No. 2)
31000402
Industrial Processes; Oil and Gas Production; Process Heaters; Residual Oil
31000403
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil
31000404
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas
31000405
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas
31000406
Industrial Processes; Oil and Gas Production; Process Heaters; Propane/Butane
31000413
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil: Steam
Generators
31000414
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas: Steam
Generators
31000415
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas: Steam
Generators
39900501
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Distillate Oil
39900601
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Natural Gas
39990003
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous
Manufacturing Industries; Natural Gas: Process Heaters
209
-------
Table 4-38. Ptnonipm emissions reductions after the application of the Process Heaters NSPS
year
pollutant
2023gf
(tons)
emissions
reduction (tons)
0/
/o
change
2026
NOX
848,409
-2,202
-0.3%
Table 4-39. Point source SCCs in ptoilgas sector where Process Heaters NSPS controls were
applied
see
SCC Description
30190003
Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Process Heater:
Natural Gas
30600102
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600104
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600105
Industrial Processes; Petroleum Industry; Process Heaters; Natural Gas-fired
30600106
Industrial Processes; Petroleum Industry; Process Heaters; Process Gas-fired
30600199
Industrial Processes; Petroleum Industry; Process Heaters; Other Not Classified
30990003
Industrial Processes; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas:
Process Heaters
31000401
Industrial Processes; Oil and Gas Production; Process Heaters; Distillate Oil (No. 2)
31000402
Industrial Processes; Oil and Gas Production; Process Heaters; Residual Oil
31000403
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil
31000404
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas
31000405
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas
31000413
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil: Steam
Generators
31000414
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas: Steam
Generators
31000415
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas: Steam
Generators
39900501
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Distillate Oil
39900601
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Natural Gas
Table 4-40. NOx emissions reductions (tons/year) in pt oilgas sector after the application of the
Process Heaters NSPS CONTROL packet for analytics years.
Year
Pollutant
2016v3
Emissions Reduction
%
change
2023
NOX
414,623
-2,234
-0.5%
2026
NOX
414,623
-2,520
-0.6%
210
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4.2.4.6 Ozone Transport Commission Rules (nonpt, solvents)
Packets:
Control_2016_202X_solvents_OTC_v3_platform_MARAMA_30jun2022_v0
Control_2016_202X_nonpt_PF C_v l_platform_MARAMA_04oct2019_v 1
Several MARAMA states have adopted rules reflecting the recommendations of the Ozone Transport
Commission (OTC) for reducing VOC emissions from consumer products, architectural and industrial
maintenance coatings, and various other solvents. The rules affected 27 different SCCs in the surface
coatings (2401xxxxxx), degreasing (2415000000), graphic arts (2425010000), miscellaneous industrial
(2440020000), and miscellaneous non-industrial consumer and commercial (246xxxxxxx) categories.
The packet applies only to MARAMA states and not all states adopted all rules. This packet applies to
emissions in the new solvents sector. The new SCCs in the solvents sector were added to the packet.
The OTC also developed a model rule to address VOC emissions from portable fuel containers (PFCs) via
performance standards and phased-in PFC replacement that was implemented in two phases. Some states
adopted one or both phases of the OTC rule, while others relied on the Federal rule. MARAMA
calculated control factors to reflect each state's compliance dates and, where states implemented one or
both phases of the OTC requirements prior to the Federal mandate, accounted for the early reductions in
the control factors. The rules affected permeation, evaporation, spillage, and vapor displacement for
residential (250101 lxxx) and commercial (2501012xxx) portable gas can SCCs. This packet applies to
the nonpt sector.
MARAMA provided control packets to apply the solvent and PFC rule controls. The 2016vl PFC packet
is reused and the same for all years. For 2016v3, the OTC solvents packet was updated to include new
controls in Delaware and New York based on comments from those states.
4.3 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 and Texas, the MOVES3 model was run separately for each analytic year, including
2023 and 2026, resulting in a separate inventory for each year. The fuels used are specific to each analytic
year, but the meteorological data represented the year 2016. The 2023 and 2026 nonroad emissions
include all nonroad control programs finalized as of the date of the MOVES3.0.0 release, including most
recently:
• Emissions Standards for New Nonroad Spark-Ignition Engines, Equipment, and Vessels: October
2008 (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-
emissions-nonroad-spark-ignition);
• Growth and control from Locomotives and Marine Compression-Ignition Engines Less than 30
Liters per Cylinder: March 2008 (https://www.epa.gov/regulations-emissions-vehicles-and-
engines/final-rule-control-emissions-air-pollution-locomotive); and
• Clean Air Nonroad Diesel Final Rule - Tier 4: May 2004 (https://www.epa.gov/regulations-
emissions-vehicles-and-engines/final-rule-control-emissions-air-pollution-nonroad-dieseP.
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The resulting analytic year inventories were processed into the format needed by SMOKE in the same
way as the base year emissions.
Inside California and Texas, CARB and TCEQ provided separate datasets for various analytic years. For
2016v3, CARB provided new nonroad inventories for 2023 and 2026. The TCEQ inventories from
2016vl and 2016v2 were reused in 2016v3, including a 2023 dataset, and a 2026 dataset interpolated
from TCEQ-provided 2023 and 2028 inventories. VOC and PM2.5 by speciation profile, and VOC HAPs,
were added to all analytic year California and Texas nonroad inventories using the same procedure as for
the 2016 inventory, but based on the analytic year MOVES runs instead of the 2016 MOVES run.
4.3.2 Onroad Mobile Sources (onroad)
For 2016v2, MOVES3 was run separately for 2023 and 2026, resulting in separate emission factors for
each year. The 2023 and 2026 onroad emission factors account for changes in activity data and the impact
of on-the-books rules that are implemented into MOVES3. These include regulations such as:
• 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).
Local inspection and maintenance (I/M) and other onroad mobile programs are included such as:
California LEVIII, the National Low Emissions Vehicle (LEV) and Ozone Transport Commission (OTC);
LEV regulations, local fuel programs, and Stage II refueling control programs. Note that MOVES3
emission rates for model years 2017 and beyond are equivalent to CA LEVIII rates for NOx and VOC.
Therefore, it was not necessary to update the rates used for states that have adopted the rules in the 2020s.
The emission factors used for 2016v2 and 2016v3 were the same except for combination long haul trucks.
For 2016v3, MOVES3 was run for combination long haul trucks only for 2023 and 2026 using an updated
age distribution and the resulting emission factors were used. For 2016v3, representative county
assignments were adjusted in three North Carolina counties (Lee, Onslow, and Rockingham) to reflect
changes in inspection and maintenance programs in those counties. Also, to reflect changes in inspection
and maintenance programs in Tennessee, MOVES was rerun for three representative counties in that state
(Davidson, Hamilton, and Rutherford).
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The fuels used are specific to each analytic year, the age distributions were projected to the analytic year,
and the meteorological data represented the year 2016. The resulting emission factors were combined
with analytic year activity data using SMOKE-MOVES run in a similar way as the base year. The
development of the analytic year activity data is described later in this section. CARB provided separate
emissions datasets for each analytic year. The CARB-provided emissions for 2023 and 2026 were
adjusted to match the temporal and spatial patterns of the SMOKE-MOVES based emissions.
An update in 2016v3 was to apply adjustment factors to reflect the impacts of the light duty greenhouse
gas rule finalized in the Revised 2023 and Later Model Year Light-Duty Vehicle Greenhouse Gas
Emissions Standards, 86 FR 74434 (December 30, 2021)42. The adjustment factors that reflect the impacts
of the rule on CAPs are shown in Table 4-41. These adjustment factors are intended to represent not only
the effects of the rule on onroad emissions in 2023 and 2026, but also ancillary effects on stationary
emissions such as increased electricity production for electric vehicles.
Table 4-41. Light duty greenhouse gas rule adjustments for 2023 and 2026 onroad emissions
Year
Source Type
Fuel Type
CO
VOC
NOx
S02
PM
2023
Light Truck
Diesel
-0.04%
-0.01%
0.83%
12.42%
1.29%
2023
Light Truck
E85
0.12%
0.10%
0.52%
35.07%
1.10%
2023
Light Truck
Gasoline
0.06%
-0.40%
0.24%
10.77%
-0.04%
2023
Passenger Car
Diesel
0.44%
0.59%
1.16%
48.83%
1.39%
2023
Passenger Car
E85
0.49%
0.78%
1.55%
92.53%
2.57%
2023
Passenger Car
Gasoline
0.30%
0.00%
0.43%
7.17%
0.10%
2026
Light Truck
Diesel
-4.46%
-14.23%
-10.62%
-15.77%
-19.70%
2026
Light Truck
E85
0.63%
0.94%
3.12%
225.12%
6.88%
2026
Light Truck
Gasoline
0.14%
-2.37%
1.28%
65.92%
1.02%
2026
Passenger Car
Diesel
2.05%
2.47%
4.81%
206.91%
5.12%
2026
Passenger Car
E85
1.83%
2.90%
5.92%
373.97%
9.77%
2026
Passenger Car
Gasoline
0.72%
-1.86%
0.67%
25.86%
-1.20%
Analytic year 2023 and 2026 VMT were developed as follows:
• VMT were projected from 2016 to 2019 using VMT data from the FHWA county-level VM-2
reports. At the time of this study, these reports were available for each year up through 2019. As
with the original 2016 backcast, EPA calculated county-road type factors based on FHWA VM-2
county-level data for each of the three years, and county total factors were applied instead of
county-road factors in states with significant changes in road type classifications from year to
year.
• Total VMT were held flat from 2019 to 2021 to reflect impacts from the COVID-19 pandemic.
For 2021, VMT was re-split by fuel type according to fuel splits from the 2020NEI VMT. During
this step, VMT totals by county, source type, and road type were preserved, but fuel splits from
2020NEI were applied and the percentage of electric vehicles increased as a result.
• VMT were then projected from 2021 to 2023 using AEO2022.
42 https ://www. govinfo. gov/content/pkg/FR-2021-12-30/pdf/2021-27854.pdf
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• VMT data submitted by state and local agencies for the year 2023 for the 2016vl platform were
incorporated where available, in place of the EPA default 2023 projection. The following states or
agencies submitted 2023 VMT: Connecticut, Georgia, Massachusetts, New Jersey, North
Carolina, Ohio, Wisconsin, Louisville metro (KY/IN), Pima County AZ, and Clark County NV.
• The resulting 2023 VMT data, including VMT submitted by local agencies, were projected to
2026 using AEO2022. Thus the 2026 projected VMT used 2023 as the baseline and incorporated
submitted 2023 VMT.
Annual VMT data from the AEO2022 reference case by fuel and vehicle type were used to project VMT
from 2021 to the projection years. Specifically, the following two AEO2021 tables were used:
• Light Duty (LD): Light-Duty VMT by Technology Type (table #41):
https://www.eia. gov/outlooks/aeo/data/browser/#/?id=51-AEQ2021&sourcekev=0
• Heavy Duty (HD): Freight Transportation Energy Use (table #49):
https://www.eia. gov/outlooks/aeo/data/browser/#/?id=58-
AEQ2021 &cases=ref2021 ~aeo2020ref& sourcekev=0
To develop the VMT projection factors, total VMT for each MOVES fuel and vehicle grouping was
calculated for the years 2021, 2023, and 2026 based on the AEO-to-MOVES mappings above. From these
totals, 2021-2023 and 2023-2026 VMT trends were calculated for each fuel and vehicle grouping. Those
trends became the national VMT projection factors. The AEO2022 tables include data starting from the
year 2021. MOVES fuel and vehicle types were mapped to AEO fuel and vehicle classes. The resulting
2021-to-analytic year national VMT projection factors used for the 2016v3 platform are provided in Table
4-42. These factors were adjusted to prepare county-specific projection factors for light duty vehicles
based on human population data available from the BenMAP model by county for the years 2023 and
202643 (https://www.woodsandpoole.comA circa 2015). The purpose of this adjustment based on
population changes helps account for areas of the country that are growing more than others.
Table 4-42. Factors used to Project VMT to analytic years
2021 to 2023
2023 to 2026
S( ( 6
description
lactor
factor
220111
LD gas
1.08
1.05
220121
LD gas
1.08
1.05
220131
LD gas
1.08
1.05
220132
LD gas
1.08
1.05
220141
Buses gas
1.03
1.05
220142
Buses gas
1.03
1.05
220143
Buses gas
1.03
1.05
220151
MHD gas
1.03
1.05
220152
MHD gas
1.03
1.05
220153
MHD gas
1.03
1.05
220154
MHD gas
1.03
1.05
43 The final year of the population dataset used is 2030
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2021 to 2023
2023 to 2026
SCC6
description
factor
factor
220161
HHD gas
0.80
0.77
220221
LD diesel
1.09
1.05
220231
LD diesel
1.09
1.05
220232
LD diesel
1.09
1.05
220241
Buses diesel
1.04
1.04
220242
Buses diesel
1.04
1.04
220243
Buses diesel
1.04
1.04
220251
MHD diesel
1.04
1.04
220252
MHD diesel
1.04
1.04
220253
MHD diesel
1.04
1.04
220254
MHD diesel
1.04
1.04
220261
HHD diesel
1.04
1.03
220262
HHD diesel
1.04
1.03
220341
Buses CNG
1.06
1.02
220342
Buses CNG
1.06
1.02
220343
Buses CNG
1.06
1.02
220351
MHD CNG
1.06
1.02
220352
MHD CNG
1.06
1.02
220353
MHD CNG
1.06
1.02
220354
MHD CNG
1.06
1.02
220361
HHD CNG
1.04
1.00
220521
LD E-85
1.02
0.93
220531
LD E-85
1.02
0.93
220532
LD E-85
1.02
0.93
220921
LD Electric
1.83
1.83
220931
LD Electric
1.83
1.83
220932
LD Electric
1.83
1.83
In areas where the EPA default analytic year VMT projection were used, analytic year VPOP data were
projected using calculations of VMT/VPOP ratios for each county, based on 2017 NEI with MOVES3
fuels splits. Those ratios were then applied to the analytic year projected VMT to estimate analytic year
VPOP. Analytic year VPOP data submitted by state and local agencies were incorporated into the VPOP
projections for 2023. Analytic year VPOP data for 2023 were provided by state and local agencies in NH,
NJ, NC, WI, Pima County, AZ, and Clark County, NV. In addition, 2023 VPOP was carried forward from
the 2016vl platform in CT, GA, MA, and the Louisville metro areas; as those areas only submitted VMT
for 2023 and not VPOP, but keeping the 2016vl VPOP in those areas ensures consistency between the
VMT and VPOP. Additionally, North Carolina bus VMT and VPOP (based on EPA defaults) was carried
forward from the 2016vl platform so that all VMT and VPOP in North Carolina would be the same as
2016vl. Both VMT and VPOP were redistributed between the light duty car and truck vehicle types
(21/31/32) based on light duty vehicle splits from the EPA computed default projection.
Hoteling hours were projected to the analytic years by calculating 2016 inventory HOTELING/VMT
ratios for each county for combination long-haul trucks on restricted roads only. Those ratios were then
applied to the analytic year projected VMT for combination long-haul trucks on restricted roads to
calculate analytic year hoteling. Some counties had hoteling activity but did not have combination long-
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haul truck restricted road VMT in 2016; in those counties, the national AEO-based projection factor for
diesel combination trucks was used to project 2016 hoteling to the analytic years. This procedure gives
county-total hoteling for the analytic years. Each analytic year also has a distinct APU percentage based
on MOVES input data that was used to split county total hoteling to each SCC: 12.91% APU for 2023,
and 20.46% for 2026 . New Jersey provided 2023 hoteling data for 2016vl and those data were used for
the 2016v3 platform although the new APU fraction for MOVES3 2023 (12.91%) was incorporated. As in
the 2016 backcast, for counties that had 2017 hoteling hours, but do not have vehicle type 62 VMT on
restricted road type (i.e., counties that should have hoteling, but do not have any VMT to calculate it
from) we projected 2016 to 2019 using the FHWA-based county total 2016 to 2019 trend, and then used
the AEO-based factors for heavy duty diesel to project beyond 2019.
Analytic year starts were calculated using 2017NEI-based VMT ratios, similar to how 2016 starts were
calculated:
Analytic year STARTS = Analytic year VMT * (2017 STARTS / 2017 VMT by county+SCC6)
Analytic year ONI activity was calculated using a similar formula, but with 2016-based ratios rather than
2017-based ratios, in order to reflect the new method used to calculate ONI activity for 2016:
Analytic year ONI = Analytic year VMT * (2016 ONI / 2016 VMT by county+SCC6)
In California, onroad emissions in SMOKE-MOVES are adjusted to match CARB-provided data using
the same procedure described in Section 2.3.3. For 2016v3 platform, CARB provided new EMFAC2017-
based emissions for 2023 and 2026.
4.3.3 Locomotives (rail, ptnonipm)
Outside of California, for 2023, rail emissions are unchanged from 2016vl, including rail yards (which
already included the Georgia-provided update for 2023 in 2016vl). Rail emissions for 2026 were
interpolated from the 2023 and 2028 emissions in 2016vl. Factors to compute emissions for analytic year
of 2030 were based on analytic year fuel use values from the Energy Information Administration's 2018
Annual Energy Outlook (AEO) freight rail energy use growth rate projections for 2016 thru 2030 (see
Table 4-43) and emission factors based on historic emissions trends that reflect the rate of market
penetration of new locomotive engines.
For 2016v3, CARB provided new locomotive emissions for 2023 and 2026. In addition to updating the
nonpoint rail inventory in California, the point rail yard emissions in ptnonipm were also updated to better
reflect the new rail yard emissions in the California rail inventory.
A correction factor was added to adjust the AEO projected fuel use for 2017 to match the actual 2017 R-l
fuel use data. The additive effect of this correction factor was carried forward for each subsequent year
from 2018 thru 2030. The modified AEO growth rates were used to calculate analytic year Class I line-
haul fuel use totals for 2020, 2023, 2026, and 2030. As shown in Table 4-43 the analytic year fuel use
values ranged between 3.2 and 3.4 billion gallons, which matched up well with the long-term line-haul
fuel use trend between 2005 and 2018. The emission factors for NOx, PM10 and VOC were derived from
trend lines based on historic line-haul emission factors from the period of 2007 through 2017.
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Table 4-43. Class I Line-haul Fuel Projections based on 2018 AEO Data
Year
AEO Freight
Factor
Projection
Factor
Corrected AEO Fuel
Raw AEO Fuel
2016
1
1
3,203,595,133
3,203,595,133
2017
1.0212
1.0346
3,314,384,605
3,271,393,249
2018
1.0177
1.0311
3,303,215,591
3,260,224,235
2019
1.0092
1.0226
3,275,939,538
3,232,948,182
2020
1.0128
1.0262
3,287,479,935
3,244,488,580
2021
1.0100
1.0235
3,278,759,301
3,235,767,945
2022
0.9955
1.0090
3,232,267,591
3,189,276,235
2023
0.9969
1.0103
3,236,531,624
3,193,540,268
2024
1.0221
1.0355
3,317,383,183
3,274,391,827
2025
1.0355
1.0489
3,360,367,382
3,317,376,026
2026
1.0410
1.0544
3,377,946,201
3,334,954,845
2027
1.0419
1.0553
3,380,697,189
3,337,705,833
2028
1.0356
1.0490
3,360,491,175
3,317,499,820
2029
1.0347
1.0529
3,373,114,601
3,314,913,891
2030
1.0319
1.0561
3,383,235,850
3,305,890,648
The projected fuel use data was combined with the emission factor estimates to create analytic year link-
level emission inventories based on the MGT traffic density values contained in the FRA's 2016
shapefile. The link-level data created for 2020, 2023, 2026 and 2030 were aggregated to create county,
state, and national emissions estimates (see Table 4-44) which were then converted into FF10 format for
use in the 2016 emissions platforms.
Table 4-44. Class I Line-haul Historic and Analytic Year Projected Emissions
Inventory
CO
HC
NH3
NOx
PM10
PM2.5
S02
2007 (2008 NEI)
110,969
37,941
347
754,433
25,477
23,439
7,836
2014 NEI
107,995
29,264
338
609,295
19,675
18,101
381
2016 v2
96,068
22,991
301
492,999
14,351
13,889
427
2017 NEI
97,272
21,560
304
492,385
14,411
13,979
343
2023 Projected
97,514
17,265
305
403,207
10,816
10,477
431
2026 Projected
99,840
15,524
312
375,121
9,714
9,412
438
2030 Projected
99,338
12,512
311
349,868
8,014
7,766
436
Other rail emissions were projected based on AEO growth rates as shown in Table 4-45. See the 2016vl
rail specification sheet for additional information on rail projections.
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Table 4-45. 2018 AEO growth rates for rail sub-groups
Sector
2016
2023
2026
2030
Rail Yards
1.0
0.9969
1.0410
1.0284
Class II/III Railroads
1.0
0.9969
1.0410
1.0284
Commuter/Passenger
1.0
1.0879
1.1310
1.2220
4.3.4 Sources Outside of the United States (onroad_can, onroad_mex, othpt,
canada_ag, canada_og2D, ptfire_othna, othar, othafdust, othptdust)
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. Most of
the Canada and Mexico projections are based on inventories and other data from the 2016vl platform
applied to the 2016v2 platform base year inventories.
For the 2016vl platform, ECCC provided data from which Canadian analytic year projections could be
derived in a file called "Projected_CAN2015_2023_2028.xlsx", which includes emissions data for 2015,
2023, and 2028 by pollutant, province, ECCC sub-class code, and other source categories. ECCC sub-
class codes are present in most Canadian inventories and are similar to SCC, but more detailed for some
types of sources and less detailed for other types of sources. For most Canadian inventories, 2023 and
2026 inventories were projected from the new 2016 base year inventory using projection factors based on
the ECCC sub-class level data from the 2016vl platform, except with the 2015-to-2023 trend reduced to a
2016-to-2023 trend (i.e., reduce the total change by 1/8), and with 2026 interpolated between 2023 and
2028. Exceptions to this general procedure are noted below. For example, ECCC sub-class level data
could not be used to project inventories where the sub-class codes changed from 2016vl to 2016v2. Fire
emissions in Canada and Mexico in the ptfire othna sector were not projected.
4.3.4.1 Canadian fugitive dust sources (othafdust, othptdust)
Canadian area source dust (othafdust)
For Canadian area source dust sources, ECCC sub-class level data from 2016vl platform was used to
project the 2016v2 base year inventory to 2023 and 2028. Emissions for 2026 were interpolated between
the 2023 and 2028 emissions. As with the base year, the analytic year dust emissions are pre-adjusted, so
analytic year othafdust follows the same emissions processing methodology as the base year with respect
to the transportable fraction and meteorological adjustments.
Canadian point source dust (othptdust)
In 2016vl platform, ECCC provided sub-class level emissions data for the othptdust sector for the base
and analytic years. Since the othptdust projections in 2016vl were nearly flat, we decided to not project
othptdust for the 2016v2 or 2016v3 platforms (i.e., the 2016f] othptdust emissions were reused for all
analytic year cases).
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4.3.4.2 Point Sources in Canada and Mexico (othpt, canada_ag,
canada_og2D)
Canada point agriculture and oil and gas emissions
For Canadian agriculture and upstream oil and gas sources, ECCC sub-class level data from 2016vl
platform was used to project the 2016v2 base year inventory to 2023 and 2028. Emissions for 2026 were
interpolated between 2023 and 2028. This procedure was applied to the entire canada ag and
canada_og2D sectors, and to the oil and gas elevated point source inventory in the othpt sector. For the ag
inventories, the sub-class codes are similar in detail to SCCs: fertilizer has a single sub-class code, and
animal emissions categories (broilers, dairy, horses, sheep, etc) each have a separate sub-class code.
Airports and other Canada point sources
For the Canada airports inventory in the othpt sector, the ECCC sub-class codes changed from 2016vl to
2016v2 platform. Therefore, the ECCC sub-class level data from 2016vl platform could not be used to
project the 2016v2 base year inventory. Instead, projection factors were based on total airport emissions
from the 2016vl Canada inventory by province and pollutant. As with other sectors, 2026 emissions were
interpolated between 2023 and 2028.
In the 2016vl platform, analytic year projections for stationary point sources (excluding ag) were
provided by ECCC for 2023 and 2028 rather than calculated by way of ECCC sub-class code data.
Additionally, projection information for many sub-class codes in the 2016v2 base year stationary point
inventories was not available in the 2016vl sub-class code data. Therefore, sub-class code data was not
used to project stationary point sources, and instead, those sources were projected using factors based on
total stationary (excluding ag and upstream oil and gas) point source emissions from the 2016vl platform
for 2015, 2023, and 2028, by province and pollutant. This is the same procedure that was used for
airports, except using different projection factors based on only the stationary sources.
Mexico
The othpt sector includes a general point source inventory in Mexico which was updated for the 2016v2
platform. Similar to the procedure for projecting Canadian stationary point sources, factors for projecting
from 2016 to 2023 and 2026 were calculated from the 2016vl platform Mexico point source inventories
by state and pollutant. Mexico point source emissions for 2026 were interpolated between 2023 and 2028.
4.3.4.3 Nonpoint sources in Canada and Mexico (othar)
Canadian stationary sources
In the 2016vl platform, analytic year projections for stationary area sources in Canada were provided by
ECCC for 2023 and 2028 rather than calculated by way of ECCC sub-class code data. Additionally,
projection information for many sub-class codes in the 2016v2 base year stationary area source inventory
was not available in the 2016vl sub-class code data. Therefore, sub-class code data was not used to
project stationary area sources, and instead, those sources were projected using factors based on total
stationary area source emissions from the 2016vl platform for 2015, 2023, and 2028, by province and
pollutant. This is the same procedure that was used for airports and stationary point sources, except using
different projection factors based on only the stationary area sources. Projection factors for 2026 were
interpolated from the factors for 2023 and 2028.
For the 2016vl platform, ECCC provided an additional stationary area source inventory for 2023 and
2028 representing electric power generation (EPG). According to ECCC, this inventory's emissions do
219
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not double count the 2023 and 2028 point source inventories, and it is appropriate to include this area
source EPG inventory in the othar sector as an additional standalone inventory in the analytic years.
Therefore, the 2016vl area source EPG inventory was included in the 2016v2 platform analytic year
cases. Emissions for 2026 were interpolated between 2023 and 2028.
Canadian mobile sources
Projection information for mobile nonroad sources, including rail and CMV, is covered by the ECCC sub-
class level data for 2015, 2023, and 2028. ECCC sub-class level data from 2016vl platform was used to
project the 2016v2 base year inventory to 2023 and 2028. Emissions for 2026 were interpolated from
2023 and 2028. For the nonroad inventory, the sub-class code is analogous to the SCC7 level in U.S.
inventories. For example, there are separate sub-class codes for fuels (e.g., 2-stroke gasoline, diesel, LPG)
and nonroad equipment sector (e.g., construction, lawn and garden, logging, recreational marine) but not
for individual vehicle types within each category (e.g., snowmobiles, tractors). For rail, the sub-class code
is closer to full SCCs in the NEI.
Mexico
The othar sector includes two Mexico inventories, a stationary area source inventory and a nonroad
inventory. Similar to point, factors for projecting the 2016v2 base year inventories to 2023 and 2028 were
calculated from the 2016vl platform Mexico area and nonroad inventories by state and pollutant. Separate
projections were calculated for the area and nonroad inventories. Emissions for 2026 were interpolated
between 2023 and 2028, including for nonroad (unlike in Canada).
4.3.4.4 Onroad sources in Canada and Mexico (onroad_can,
onroad_mex)
For Canadian mobile onroad sources, projection information is covered by the ECCC sub-class level data
for 2015, 2023, and 2028. ECCC sub-class level data from 2016vl platform was used to project the
2016v2 base year inventory to 2023 and 2028. Emissions for 2026 were interpolated from 2023 and 2028.
For the onroad inventory, the sub-class code is analogous to the SCC6+process level in U.S. inventories,
in that it specifies fuel type, vehicle type, and process (e.g., brake, tire, exhaust, refueling), but not road
type.
For Mexican mobile onroad sources, MOVES-Mexico was run to create emissions inventories for years
2023, 2028, and 2035. The emissions for 2023 were reused from the 2016vl platform, and 2026
emissions were interpolated between 2023-2028.
220
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5 Emission Summaries
Tables 5-1 through Table 5-3 summarize annual emissions by sector for the 2016gf, 2023gf, and 2026gf
cases 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. Table 5-4 and Table 5-5 provide similar
summaries for the 36-km domain (36US3) for 2016 and 2023. 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 how far north and south the grids extend. Note that the
afdust sector emissions here represent the emissions after application of both the land use (transport
fraction) and meteorological adjustments; therefore, this sector is called "afdust adj" in these summaries.
The afdust emissions in the 36km domain are smaller than those in the 12km domain due to how the
adjustment factors are computed and the size of the grid cells. The onroad sector totals are post-SMOKE-
MOVES totals, representing air quality model-ready emission totals, and include CARB emissions for
California. The cmv sectors include U.S. emissions within state waters only; these extend to roughly 3-5
miles offshore and include CMV emissions at U.S. ports. "Offshore" represents CMV emissions that are
outside of U.S. state waters. The total of all US sectors is listed as "Con U.S. Total."
Table 5-6 and Table 5-7 summarize ozone season NOx and VOC emissions, respectively, for the 2016gf,
2023gf, and 2026gf cases.
State totals and other summaries are available in the reports area on the FTP site for the 2016v3 platform (
https://gaftp.epa.gov/Air/emismod/2016/v3/reports).
221
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Table 5-1. National by-sector CAP emissions for the 2016gf case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,314,612
880,002
airports
479,736
0
123,838
9,952
8,675
14,827
53,420
cmv clc2
23,710
84
163,598
4,486
4,348
636
6,477
cmv c3
14,267
40
112,701
2,246
2,066
4,609
8,822
fertilizer
1,436,969
livestock
2,502,587
219,703
nonpt
1,921,889
102,840
739,465
567,555
470,789
166,391
827,897
nonroad
10,593,504
1,845
1,110,243
109,008
103,047
1,513
1,134,711
np oilgas
762,177
20
585,683
13,236
13,145
40,748
2,532,881
np solvents
36
58
34
469
448
5
2,606,495
onroad
18,313,321
107,791
3,546,597
233,680
113,716
25,969
1,317,694
pt oilgas
195,308
375
374,037
13,132
12,736
42,815
238,673
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
655,873
23,850
1,318,074
164,132
133,606
1,565,196
33,755
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
ptnonipm
1,381,321
64,168
913,795
390,628
250,076
636,685
765,281
rail
104,551
326
559,381
16,344
15,819
457
26,082
rwc
2,230,478
16,940
35,198
309,854
308,965
8,247
334,158
beis
3,390,977
1,001,873
31,014,251
Con. U.S. Total + beis
54,067,638
4,549,105
10,822,258
9,651,686
3,580,122
2,623,203
44,251,723
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
491,788
104,968
Canada oil and gas 2D
667
7
3,241
186
186
3,944
510,623
Canada othafdust
696,793
108,328
Canada othar
2,191,451
3,819
323,152
225,620
177,134
16,294
740,566
Canada onroad can
1,849,517
7,685
407,423
26,017
14,012
1,739
158,429
Canada othpt
1,116,192
19,482
651,451
90,042
43,051
990,049
148,216
Canada othptdust
152,566
53,684
Canada ptfire othna
761,402
13,032
16,359
84,481
71,749
6,731
185,476
Canada CMV
10,587
36
92,110
1,641
1,525
2,807
5,122
Mexico othar
115,887
112,005
60,196
105,146
34,788
1,733
362,643
Mexico onroad mex
1,828,101
2,789
442,410
15,151
10,836
6,247
158,812
Mexico othpt
109,015
1,096
190,997
54,044
37,491
355,883
35,768
Mexico ptfire othna
383,162
7,436
16,604
44,994
38,178
2,785
131,499
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
32,745
126
289,633
7,051
6,530
27,482
15,956
Offshore cmv outside
Federal waters
23,579
444
259,993
25,074
23,074
183,595
11,207
Offshore pt oilgas
51,872
8
49,962
636
635
462
38,833
Non-U.S. Total
8,474,180
659,754
2,803,533
1,529,440
621,202
1,599,752
2,608,117
222
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Table 5-2. National by-sector CAP emissions for the 2023gf case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,378,445
893,940
airports
497,816
0
135,028
9,990
8,731
16,261
55,592
cmv clc2
23,741
60
117,171
3,212
3,113
243
4,559
cmv c3
17,448
49
109,834
2,753
2,533
5,634
10,876
fertilizer
1,436,969
livestock
2,593,384
226,860
nonpt
1,920,941
103,603
725,280
560,108
474,271
121,178
789,771
nonroad
10,890,827
2,114
742,436
71,039
66,532
1,057
891,025
np oilgas
832,845
22
605,993
14,702
14,603
87,319
2,675,843
np solvents
37
61
36
499
476
6
2,702,053
onroad
12,803,175
97,304
1,646,377
188,180
62,338
11,172
806,689
pt oilgas
214,732
357
393,667
17,484
16,766
72,802
217,443
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
542,096
25,741
888,700
121,657
102,620
1,195,002
39,915
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
ptnonipm
1,355,805
67,871
836,547
371,162
235,390
495,093
755,872
rail
105,631
331
476,559
13,104
12,677
375
20,807
rwc
2,207,014
16,738
36,856
302,922
302,016
7,704
330,504
beis
3,390,977
1,001,873
31,014,251
Con. U.S. Total + beis
48,803,574
4,635,816
7,954,098
9,557,608
3,458,688
2,128,948
43,673,481
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
583,282
104,584
Canada oil and gas 2D
477
7
1,920
128
128
3,305
412,111
Canada othafdust
782,334
121,430
Canada othar
2,196,835
3,729
267,788
219,440
164,701
16,198
740,364
Canada onroad can
1,590,905
6,850
254,786
26,537
11,305
937
102,118
Canada othpt
1,129,621
22,315
553,839
72,613
42,672
877,388
154,137
Canada othptdust
152,566
53,684
Canada ptfire othna
761,402
13,032
16,359
84,481
71,749
6,731
185,476
Canada CMV
11,436
39
66,994
1,776
1,650
2,979
5,461
Mexico other
126,192
109,995
69,552
107,496
36,249
1,953
404,664
Mexico onroad mex
1,772,026
3,266
427,900
17,023
11,764
7,556
161,115
Mexico othpt
123,814
1,321
187,731
59,146
40,987
292,546
44,668
Mexico ptfire othna
383,162
7,436
16,604
44,994
38,178
2,785
131,499
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
39,301
148
254,448
8,306
7,673
34,226
19,059
Offshore cmv outside
Federal waters
28,839
280
317,415
15,797
14,538
41,868
13,690
Offshore ptoilgas
51,872
8
49,962
636
635
462
38,833
Non-U.S. Total
8,215,884
751,708
2,485,299
1,593,275
617,343
1,288,934
2,517,779
223
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Table 5-3. National by-sector CAP emissions for the 2026gf case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,403,935
899,647
airports
535,239
0
151,407
10,302
9,024
18,330
59,611
cmv clc2
23,990
52
102,154
2,807
2,720
244
3,919
cmv c3
19,005
53
110,076
3,000
2,760
6,128
11,890
fertilizer
1,436,969
livestock
2,629,312
230,160
nonpt
1,930,169
103,868
723,871
568,094
481,072
122,437
762,403
nonroad
11,081,612
2,159
657,502
62,218
58,045
1,075
850,020
np oilgas
830,088
22
597,874
14,822
14,723
89,777
2,610,993
np solvents
38
64
38
519
496
6
2,781,475
onroad
11,298,677
97,669
1,303,964
187,332
56,017
13,689
680,034
pt oilgas
211,373
334
385,071
17,371
16,650
73,983
213,838
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
410,878
25,514
663,681
96,276
83,619
855,909
35,019
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
ptnonipm
1,376,876
68,146
848,119
374,437
237,646
496,819
757,055
rail
108,234
339
453,446
12,052
11,657
384
19,167
rwc
2,196,869
16,667
37,258
300,475
299,564
7,522
329,113
beis
3,390,977
1,001,873
31,014,251
Con. U.S. Total + beis
47,414,514
4,672,382
7,274,075
9,555,990
3,436,322
1,801,407
43,490,369
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
632,182
104,570
Canada oil and gas 2D
497
7
1,493
133
133
3,550
447,884
Canada othafdust
825,908
128,106
Canada other
2,206,851
3,717
254,419
218,350
161,430
16,174
755,871
Canada onroad can
1,504,701
6,461
210,090
26,684
10,386
893
82,677
Canada othpt
1,154,185
23,274
495,903
75,829
44,714
872,534
159,956
Canada othptdust
152,566
53,684
Canada ptfire othna
761,402
13,032
16,359
84,481
71,749
6,731
185,476
Canada CMV
11,823
40
70,103
1,837
1,706
3,094
5,644
Mexico other
130,146
110,429
73,150
108,612
36,855
2,038
423,290
Mexico onroad mex
1,677,896
3,546
407,181
18,048
12,307
8,141
163,311
Mexico othpt
131,373
1,445
200,959
63,917
44,176
301,303
48,989
Mexico ptfire othna
383,162
7,436
16,604
44,994
38,178
2,785
131,499
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
42,791
160
244,576
9,007
8,313
37,795
20,739
Offshore cmv outside
Federal waters
31,565
306
347,299
17,303
15,923
45,919
14,981
Offshore ptoilgas
51,872
8
49,962
636
635
462
38,833
Non-U.S. Total
8,088,265
802,045
2,388,098
1,648,303
628,296
1,301,417
2,583,718
224
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Table 5-4. National by-sector CAP emissions for the 2016gf case, 36US3 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,318,693
880,413
airports
480,474
0
123,989
9,977
8,699
14,850
53,513
cmv clc2
23,713
84
163,617
4,487
4,349
636
6,478
cmv c3
14,477
40
114,661
2,275
2,093
4,678
8,932
fertilizer
1,436,969
livestock
2,502,588
219,703
nonpt
1,922,341
102,861
740,522
567,613
470,839
166,402
828,180
nonroad
10,598,518
1,845
1,110,424
109,045
103,082
1,514
1,135,706
np oilgas
762,177
20
585,683
13,236
13,145
40,748
2,532,881
np solvents
36
58
34
469
448
5
2,606,991
onroad
18,313,321
107,791
3,546,597
233,680
113,716
25,969
1,317,694
pt oilgas
195,308
375
374,037
13,132
12,736
42,815
238,673
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
655,909
23,850
1,318,272
164,137
133,610
1,565,196
33,760
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
ptnonipm
1,381,324
64,168
913,821
390,669
250,087
636,685
765,282
rail
104,551
326
559,381
16,344
15,819
457
26,082
rwc
2,255,551
16,969
35,687
314,299
313,410
8,324
334,761
beis
3,545,278
1,011,401
32,014,201
36US3 U.S. Total + beis
54,253,467
4,549,158
10,835,865
9,660,408
3,585,127
2,623,383
45,254,258
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
507,030
107,661
Canada oil and gas 2D
732
7
3,548
203
203
4,432
606,218
Canada othafdust
722,629
112,358
Canada othar
2,352,757
4,115
358,976
239,649
188,729
17,031
779,607
Canada onroad can
1,926,698
7,980
428,161
27,152
14,692
1,802
164,479
Canada othpt
1,379,994
21,394
832,840
102,218
50,224
1,124,153
203,402
Canada othptdust
152,834
52,953
Canada ptfire othna
6,282,821
104,683
134,301
685,169
580,963
60,914
1,501,988
Canada CMV
13,086
45
115,294
2,098
1,946
4,279
6,439
Mexico othar
1,699,433
562,057
235,176
465,425
252,429
12,630
1,588,164
Mexico onroad mex
6,273,194
10,319
1,497,028
74,169
56,782
26,400
552,952
Mexico othpt
319,500
3,314
485,613
213,413
141,638
1,453,380
111,716
Mexico ptfire othna
7,133,496
120,584
346,990
1,155,563
745,860
45,208
2,259,747
Mexico CMV
64,730
0
204,997
16,286
15,087
109,778
8,817
Offshore cmv in Federal
waters
34,594
153
309,815
8,614
7,969
38,843
16,798
Offshore cmv outside
Federal waters
89,532
1,199
1,019,219
93,844
86,363
693,479
40,839
Offshore ptoilgas
51,872
8
49,962
636
635
462
38,833
Annual Total
27,622,440
1,342,889
6,021,918
3,959,903
2,308,831
3,592,792
7,987,661
225
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Table 5-5. National by-sector CAP emissions for the 2023gf case, 36US3 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,382,529
894,351
airports
498,620
0
135,173
10,015
8,754
16,284
55,689
cmv clc2
23,744
60
117,185
3,213
3,114
243
4,560
cmv c3
17,704
49
111,755
2,789
2,566
5,718
11,011
fertilizer
1,436,969
livestock
2,593,386
226,860
nonpt
1,921,427
103,626
726,404
560,167
474,321
121,189
790,046
nonroad
10,895,359
2,114
742,572
71,065
66,556
1,057
891,713
np oilgas
832,845
22
605,993
14,702
14,603
87,319
2,675,843
np solvents
37
61
36
499
476
6
2,702,549
onroad
12,807,931
97,317
1,646,798
188,227
62,355
11,173
807,048
pt oilgas
214,732
357
393,667
17,484
16,766
72,802
217,443
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
542,096
25,741
888,700
121,657
102,620
1,195,002
39,915
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
ptnonipm
1,355,811
67,871
836,598
371,204
235,401
495,093
755,874
rail
105,631
331
476,559
13,104
12,677
375
20,807
rwc
2,229,572
16,766
37,295
306,858
305,951
7,773
331,081
beis
3,545,278
1,011,401
32,014,201
36US3 U.S. Total + beis
48,991,277
4,635,884
7,967,876
9,565,863
3,463,193
2,129,137
44,676,061
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
600,883
107,266
Canada oil and gas 2D
527
7
2,115
142
142
3,714
489,811
Canada othafdust
810,859
125,871
Canada othar
2,356,241
4,019
308,601
232,951
175,488
17,180
780,201
Canada onroad can
1,655,613
7,109
268,025
27,680
11,859
971
106,159
Canada othpt
1,364,416
24,576
686,691
80,094
48,582
993,177
214,520
Canada othptdust
152,834
52,953
Canada ptfire othna
6,282,821
104,683
134,301
685,169
580,963
60,914
1,501,988
Canada CMV
14,048
49
83,837
2,264
2,099
4,599
6,821
Mexico other
1,821,647
552,207
263,072
483,534
266,265
13,459
1,731,394
Mexico onroad mex
6,053,503
12,083
1,447,199
94,407
72,468
31,838
560,284
Mexico othpt
381,638
4,088
537,165
251,989
167,147
1,416,350
141,037
Mexico ptfire othna
7,133,496
120,584
346,990
1,155,563
745,860
45,208
2,259,747
Mexico CMV
79,677
0
252,331
20,046
18,571
19,304
10,853
Offshore cmv in Federal
waters
41,533
181
271,245
10,194
9,410
47,974
20,074
Offshore cmv outside
Federal waters
109,733
756
1,249,272
59,007
54,301
157,866
50,170
Offshore ptoilgas
51,872
8
49,962
636
635
462
38,833
Non-U.S. Total
27,346,764
1,431,234
5,900,805
4,067,368
2,332,614
2,813,016
8,019,159
226
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Table 5-6. National by-sector Ozone Season NOx emissions summaries 12US1 grid (tons/o.s.)
Sector
2016gf
2023gf
2026gf
airports
55,023
59,995
67,273
cmv clc2 12
90,952
64,979
56,539
cmv c3 12
265,891
279,095
289,473
nonpt
222,645
216,582
216,691
nonroad
566,188
380,010
335,761
npoilgas
243,554
251,456
248,158
npsolvents
14
15
16
onroad
1,425,690
658,513
512,550
onroadcaadj
102,061
45,954
41,631
ptoilgas
177,844
186,161
182,555
ptagfire
3,193
3,193
3,193
ptegu
604,426
372,414
289,044
ptnonipm
383,072
350,480
355,312
rail
236,771
201,707
191,917
rwc
4,279
4,527
4,595
Total U.S. Anthro
4,381,606
3,075,082
2,794,707
beis
599,643
599,643
599,643
ptfire-rx
20,531
20,531
20,531
ptfire-wild
55,500
55,500
55,500
Grand Total
5,057,280
3,750,756
3,470,382
Table 5-7. National by-sector Ozone Season VOC emissions summaries 12US1 grid (tons/o.s.)
Sector
2016gf
2023gf
2026gf
airports
23,735
24,700
26,486
cmv_clc2_12
3,550
2,487
2,132
cmv_c3_12
14,613
18,040
19,796
livestock
152,495
157,531
159,757
nonpt
346,753
330,090
318,876
nonroad
573,637
435,998
411,793
npoilgas
1,039,662
1,092,687
1,065,914
npsolvents
1,095,342
1,135,471
1,168,836
onroad
556,510
339,025
281,483
onroadcaadj
44,562
25,378
22,281
ptoilgas
116,259
107,378
105,868
ptagfire
6,314
6,314
6,314
ptegu
16,220
17,993
16,199
ptnonipm
320,043
316,084
316,575
rail
11,039
8,806
8,112
rwc
36,547
37,975
38,354
Total U.S. Anthro
4,357,282
4,055,957
3,968,775
beis
24,776,664
24,776,664
24,776,664
ptfire-rx
277,019
277,019
277,019
ptfire-wild
1,005,261
1,005,261
1,005,261
Grand Total
30,416,226
30,114,902
30,027,720
227
-------
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Production Rate, Washington, DC. https://www.eia.gov/petroleum/wells/
233
-------
Wang, Y., P. Hopke, O. V. Rattigan, X. Xia, D. C. Chalupa, M. J. Utell. (2011) "Characterization of
Residential Wood Combustion Particles Using the Two-Wavelength Aethalometer", Environ. Sci.
Technol., 45 (17), pp 7387-7393. Available at https://doi.org/10.1021/es2Q13984.
Weschler, C. J., andNazaroff, W. W., 2008. Semivolatile organic compounds in indoor environments,
Atmos Environ, 42, 9018-9040.
Wiedinmyer, C., 2001. NCAR BVOC Enclosure Database. National Center for Atmospheric Research,
Boulder, CO
Wiedinmyer, C., S.K. Akagi, R.J. Yokelson, L.K. Emmons, J.A. Al-Saadi3, J. J. Orlando1, and A. J. Soja.
(2011) "The Fire INventory from NCAR (FINN): a high resolution global model to estimate the
emissions from open burning", Geosci. Model Dev., 4, 625-641. http://www.geosci-model-
dev.net/4/625/2011/ doi: 10.5194/gmd-4-625-2011.
Wilson, Barry Tyler; Lister, Andrew J.; Riemann, Rachel I.; Griffith, Douglas M. 2013a. Live tree species
basal area of the contiguous United States (2000-2009). Newtown Square, PA: USD A Forest
Service, Rocky Mountain Research Station. https://doi.org/10.2737/RDS-2013-0013
Wilson, Barry Tyler; Woodall, Christopher W.; Griffith, Douglas M. 2013b. Forest carbon stocks of the
contiguous United States (2000-2009). Newtown Square, PA: U.S. Department of Agriculture,
Forest Service, Northern Research Station. https://doi.org/10.2737/RDS-2013-00Q4
WRAP / Ramboll, 2019. Revised Final Report: Circa-2014 Baseline Oil and Gas Emission Inventory for
the WESTAR-WRAP Region, September 2019. Available at:
http://www.wrapair2.org/pdf/WRAP OGWG Report Baseline 17Sep2019.pdf.
WRAP / Ramboll, 2020. Revised Final Report: 2028 Future Year Oil and Gas Emission Inventory for
WESTAR-WRAP States - Scenario #1: Continuation of Historical Trends
http://www.wrapair2.org/pdf/WRAP OGWG 2028 OTB RevFinalReport 05March2020.pdf.
Yarwood, G., J. Jung,, G. Whitten, G. Heo, J. Mellberg, and M. Estes,2010: Updates to the Carbon Bond
Chemical Mechanism for Version 6 (CB6). Presented at the 9th Annual CMAS Conference,
Chapel Hill, NC. Available at
https://www.cmascenter.org/conference/2010/abstracts/emery updates carbon 2010.pdf.
Zhu, Henze, et al, 2013. "Constraining U.S. Ammonia Emissions using TES Remote Sensing
Observations and the GEOS-Chem adjoint model", Journal of Geophysical Research:
Atmospheres, 118: 1-14. Available at https://doi.org/10.1002/igrd.5Q166.
234
-------
Appendix A: CB6 Assignment for New Species
235
-------
September 27,2016
MEMORANDUM
To: Alison Eftii and Madeleine Strum., OAQP5, EPA
Frarn. Rcss Bears = ey and Greg Yarwcsd, Rsmhc 1 Envirsn
Subject: Species Mappingsfor CSC and CBC5 for use vv th SPECIATE4.5
Summary
Raruball Environ jftEj reviewed version 4.5 of the SPECIATE database, anil created CB05 and CBS
mechanism = cedes mappings for newly aciied compounds. in addition, the mapping guidelines for
Carton lend ICS) mechanisms were expanded to promote consistency in current and future wort.
Background
The En»#: rz'-mrr.a Pritec^o* --a". • 5=ECIA I -sDoarrrv cir-ts'-i gs£ s-r :5ti:.-2te matter
spedatibn profiles of air pollution sources, which- are used in the generation of emissions data for air
susl'ty '"-de'i iuc- =: C'.'-D..-t:::;.v.v.-,P.c'n3sce*7sr 3-|.c-iaq-i =*: CA"*
{http://umw.camx.c0m). However, the condensed chemical mechanisms used with' - t- ==s
photochemical models utiles fewer species than SPECIATE to represent ps stase chemistry, a*d
thus the SPECIATE compounds must be assigned to the AQM model species cf the :on£er&ed
mechanisms. A chemical mapping is used to show the representation of ocp - ic :hex': a spec =s by
the model compounds of the condensed mechanisms.
T"!s "siemc-rf.d." iazf bes -:*• Csl "ias pings '.we developed from SPECIATE 4.5
compounds to model species of the CB methanol. specifics' v CEOS
{http:#www.camxxom/pubf/piifs/CBCS_Fi''e '_Reporr_l20SD5.3df| and CBS
fhttp://aqrp.ceer.uteras:.ecJu/iif ojectinfoPi' 12_i E/i 2-012/11-012%2QFii«f!42QR5 sorts af>.
Methods
CB Mode' Species
Organic gases ace mapped to the CI mechanism either as explicitly represented individual
:rr-s^nci .= Ate: *c ¦ i:e:a : = , :r as « ssnrt 'at':*" c-" "¦ Dde : Dec as the: "ep-iii^t
common structural group (E.g. aiDX *cr ether aldehydes, PAR foralkyl groups). Table t lists all of
the axpl ± = t s:'u::u a rrode spec == h CSC5 *¦-=:'sris r-j. = = :h -:f :h ¦=p~EEi~:i r
defined nurw cf car:>:m atsms al,?>."i"f eor carbon to be conserved in aK cases. CM contains four
Tre =;:p. :i: r:o= spec a: thai CECr =-i a- a:;r :^a it--. |rsus to -ep -=:a't
-------
ENVIRON
in addition to the explicit and structural species, there are two model species that are used to
represent organic gases that are not treated by the CB mechanism:
NV'OL— Very low volatility 5PEC1ATE compounds that reside predominantly in the particle phase and
should be excluded from the gas phase mechanism. These compounds are mapped by setting
NVOL equal to the molecular weight (e.g. decabramodiphenyl oxide is mapped as 959.2
NVOL}, which a Hows for the total mass of all NVOL to be determined.
UN K- Compounds that are unable to be mapped to CB using the available model species. This
approach should be avoided unless absolutely necessary, and will tead to a warning messa|e
in the speciation tool.
Table 1. Model spedes in the CBQ5 and CB6 chemical mechanisms.
Included in
Model
Number
CBOS
Species
Hi
(structural
Included
Maine
Oesra'jjtiwn
Cars o*i 5
mapping]
in CUE
Expliiit model spec es
ACET
Acetar.e i^proporione)
1
No 13PAR|
res
ald2
Acet: dehyde [etharialp
2
res
res
3ENZ
3er,iene
6
Mo(l PAR. 3
UNR|
res
CH4
Methane
1
yes
res
ETH
Sihene (ethylene]
2
res
res
ET HA
Ethane
2
Yes
res
ET HT
Ethyne [acetylene)
2
Mo (1 PAR,. 1
JNR|
res
ETOH
Ethanol
2
res
res
=ORM
:ormaldehvde (methanol}
1
'r'ts
res
SOP
saprene [2-mBthyl-1..3-butBdiene)
5
res
res
y EOH
Methanol
1
>es
res
PRPA
=rnparis
3
Mo. (1.3 PA3,
1.5 UNR'i
res
Coinmofl Structural groups
ALDX
-gher: dehyde graup |-C-CHO|
2
res
res
OLE
ntsmal cielin group R.,>C=CC=0|
1
Mo (1 PAR|
res
OLE
Termini' c stir group (H R >C=C|
2
res
res
°AR
^arsfnic group (R -Zirto, CA9499S 2
V*i413.B39jCCTH F+l 4U.E99jQr7D(7
237
-------
238
-------
ENVIRON
Mapping guidelines for nan-explicit organic gases using C6 model species
SPECIATE compound; that = re not treated explicitly are mapped to CB model species that represent
common structural groups. Table 2 lists the carbon number and general mapping guide lines for each
of the structure model species.
Table 2. General Guidelines for mapping using CB6 structural model species.
CB4
Species
Name
Number of
Cart] his
RepiesErts
ALOX
2
Aldehyde group. ALIJX represents 2 carbons and additional cartons are rEp^esEnted a;
alkyl groups [mostly PAR), E.g. propionaldehyds is ALOX + PAR
'OLE
4
-itamal clefin grc jp. IOLE represents 4 carbons and adcatic-iBl carbons are represented as
alkyl groups [mostly PAR}, E.g. 2-pentene isomers are IOLE + PAR.
S-tcfprbni:
* OLE with 2 carton brands or, both sides of the double Send are downgraded to
OLE
-------
T^fTl
ENVIRON
Table 3. Mapping guidelines for same difficult to map compound classes and structural groups
Compound
Class/S'ructufal
group
CB model species representation
Chlorotemeres and
other h alogenated
benienes-
Guideline:
* 3 or less halogens - i PAS, 3 JNR
* 4 or more halogen: -6 U^JR
Example!:
* 1,3,3-CMorctsmene -1 PAR, 3 UN 3
* Tetrachlorotenier.es - 6 UMR
QtifHKWfe
Guideline:
* 1 OLE with additional carton: represented » alkyl greens (generally
PAH)
Examples:
* MelfiylCYdopentadiene-1 OLE, 2 PAS
* ¦¦fetiffl&pQQwjli/LrsC,- 1 IC .E. 3 PAR
:uranSi,:!-pTrjiEj
Guideline:
* 2 OLE with additional carbcrts represented as alKyl gruLps (generally
PAR)
Examples:
* i-ButylfLrsn — 2 C .E, 4 PAR
* 2-Pentylftiran - 2 OLE, 3 PAR
* ^pTTOle -iO.E
* i-Meth>1p»mMc - 2 OLE, 1 PAR
-eterocyclit aromatic
compounds
containing 2 non-
carton atams
Guideline:
* 1 OLE with remaining cartons represented is alfeyl groups (generally
PAR)
Examples:
* Ettvjlpyraiine — 1 OLE, 4 3AS
* l-fietiylpyraiole — 1 OLE. 2 PAR
* 4.3-Oirnethyloiazole -1 C .E, 3 PAR
Tnple borid(s]>
Guideline:
* Tnple bond: are treated a: =AS unless they are the only reactive
functional group, ra compound contains mors than one triple tond
and ra other readme functional groups, then one ctthe trole tonds
s treated as OLE with additional cartons treated as alleyl groups.
Examples:
* l-Penter-a-me — 1 C.E 3 PAR
* IJ5-HBKadien-3-jrie- 2 C.E. 2 PAR
* 1,6— eotadivne - 1 OLE. 3 PAR
These guide! ines we re usee tc nr a p tfie new species from SPEICATE45, s nd alsc tc revise some
previously mapped compounds. Overall, a total of 175 new species from 5PECIATEV4.5 were mapped
and 7 previously mapped species were revised based on the new guidelines.
3apfccl1 Environ US Cnporation, 773 San Marin Drwe, Sute 2113, Hmrrtor&3®M 4
V+1413.099J57CH F-U413.SSSJ37B7
240
-------
ENVIRON
Recommendation
1. Comp lete a systematic review of the ma ppi rig of al I specie; tc ensura conformity with cu rrerit
mapping guidelines. The assignments of existing compounds that are similar to new species were
reviewed and revised to promote consistency in mapping approaches, but the majority of
existing species mappings were not reviewed as it was outside the scope of this work.
2. Develop a methodology for classifying and tracking larger organic compounds based on their
volatility [semi, intermediate, or low volatility | tc improve support for secondary organic aerosol
|5QA| modeling using the volatility basis set |VBS| SOA model, which is available in both CMAQ
and CAM*. A preliminary investigate of the pcssioilhy of doing so has been performed, and is
discussed in a separate memorandum.
3anEnll Emiran US Cnpcratic4\ 773 an \1orin HrrvE. SutE 2113. Nnvnto, CA 3-SSE
VM413.BSS.D71H F41 J13.399.CT7D7
241
3
-------
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE versions 4.5 and
later that were used in the 2016 platforms
Table B-l Profiles first used in 2016beta, 2016vl, and 2016v2 platforms
Sector
Pollutant
Profile code
Profile description
SPECIATE
version
ptfire,
ptagfire
VOC
G8746
Rice Straw and Wheat Straw Burning Composite of G4420
and G4421
5.0
livestock
VOC
G95241TOG
Swine Farm and Animal Waste with gapfilled methane and
ethane
5.0
npoilgas.
pt oilgas
VOC
UTUBOGC
Raw Gas from Oil Wells - Composite Uinta basin
5.1
npoilgas.
pt oilgas
VOC
UTUBOGD
Raw Gas from Gas Wells - Composite Uinta basin
5.1
npoilgas.
pt oilgas
VOC
UTUBOGE
Flash Gas from Oil Tanks - including Carbonyls -
Composite Uinta basin
5.1
npoilgas.
pt oilgas
VOC
UTUBOGF
Flash Gas from Condensate Tanks - including Carbonyls -
Composite Uinta basin
5.1
npoilgas.
pt oilgas
VOC
PAGAS01
Oil and Gas-Produced Gas Composition from Gas Wells-
Greene Co, PA
5.1
npoilgas.
pt oilgas
VOC
PAGAS02
Oil and Gas-Produced Gas Composition from Gas Wells-
Butler Co, PA
5.1
npoilgas.
pt oilgas
VOC
PAGAS03
Oil and Gas-Produced Gas Composition from Gas Wells-
Washington Co, PA
5.1
npoilgas.
pt oilgas
VOC
SUIROGCT
Flash Gas from Condensate Tanks - Composite Southern Ute
Indian Reservation
5.2
npoilgas.
pt oilgas
VOC
CBMPWWY
Coal Bed Methane Produced Water Profile - WY ponds
5.2
npoilgas.
pt oilgas
VOC
DJTFLR95
DJ Condensate Flare Profile with DRE 95%
5.2
npoilgas.
pt oilgas
VOC
CMU01
Oil and Gas - Produced Gas Composition from Gas Wells -
Central Montana Uplift - Montana
5.1
npoilgas.
pt oilgas
VOC
WIL01
Oil and Gas - Flash Gas Composition from Tanks at Oil
Wells - Williston Basin North Dakota
5.1
npoilgas.
pt oilgas
VOC
WIL02
Oil and Gas - Flash Gas Composition from Tanks at Oil
Wells - Williston Basin Montana
5.1
npoilgas.
pt oilgas
VOC
WIL03
Oil and Gas - Produced Gas Composition from Oil Wells -
Williston Basin North Dakota
5.1
npoilgas.
pt oilgas
VOC
WIL04
Oil and Gas - Produced Gas Composition from Oil Wells -
Williston Basin Montana
5.1
cmv_clc2,
cmv c3
VOC
95331NEIHP
Marine Vessel - 95331 blend with CMV HAP
5.1
ptagfire
PM
SUGP02
Sugar Cane Pre-Harvest Burning Mexico
5.1
ptfire
PM
95793
Forest Fire-Flaming-Oregon AE6
5.1
ptfire
PM
95794
Forest Fire-Smoldering-Oregon AE6
5.1
ptfire
PM
95798
Forest Fire-Flaming-North Carolina AE6
5.1
242
-------
Sector
Pollutant
Profile code
Profile description
SPECIATE
version
ptfire
PM
95799
Forest Fire-Smoldering-North Carolina AE6
5.1
ptfire
PM
95804
Forest Fire-Flaming-Montana AE6
5.1
ptfire
PM
95805
Forest Fire-Smoldering-Montana AE6
5.1
ptfire
PM
95807
Forest Fire Understory-Flaming-Minnesota AE6
5.1
ptfire
PM
95808
Forest Fire Understory-Smoldering-Minnesota AE6
5.1
ptfire
PM
95809
Grass Fire-Field-Kansas AE6
5.1
Table B-2 Profiles first used in 2016 alpha platform
Profile
SPECIATE
Comment
Sector
Pollutant
code
Profile description
version
5.0
Replacement for v4.5
profile 95223; Used 70%
methane, 20% ethane,
and the 10% remaining
Poultry Production - Average of Production
VOC is from profile
nonpt
voc
G95223TOG
Cycle with gapfilled methane and ethane
95223
5.0
Replacement for v4.5
profile 95240. Used 70%
methane, 20% ethane;
Nonpt,
Beef Cattle Farm and Animal Waste with
the 10% remaining VOC
ptnonipm
voc
G95240TOG
gapfilled methane and ethane
is from profile 95240.
5.0
Replacement for v4.5
profile 95241. Used 70%
methane, 20% ethane;
the 10% remaining VOC
nonpt
voc
G95241TOG
Swine Farm and Animal Waste
is from profile 95241
nonpt,
5.0
Composite of AE6-ready
ptnonipm,
versions of SPECIATE4.5
pt_oilgas,
Composite -Refinery Fuel Gas and Natural
profiles 95125, 95126,
ptegu
PM2.5
95475
Gas Combustion
and 95127
Spark-Ignition Exhaust Emissions from 2-
4.5
stroke off-road engines - E10 ethanol
nonroad
VOC
95328
gasoline
Spark-Ignition Exhaust Emissions from 4-
4.5
stroke off-road engines - E10 ethanol
nonroad
VOC
95330
gasoline
Diesel Exhaust Emissions from Pre-Tier 1
4.5
nonroad
VOC
95331
Off-road Engines
Diesel Exhaust Emissions from Tier 1 Off-
4.5
nonroad
VOC
95332
road Engines
Diesel Exhaust Emissions from Tier 2 Off-
4.5
nonroad
VOC
95333
road Engines
Oil and Gas - Composite - Oil Field - Oil
4.5
nP_oilgas
VOC
95087a
Tank Battery Vent Gas
Oil and Gas - Composite - Oil Field -
4.5
nP_oilgas
voc
95109a
Condensate Tank Battery Vent Gas
Composite Profile - Oil and Natural Gas
4.5
nP_oilgas
voc
95398
Production - Condensate Tanks
243
-------
Profile
SPECIATE
Comment
Sector
Pollutant
code
Profile description
version
nP_oilgas
voc
95403
Composite Profile - Gas Wells
4.5
Oil and Gas Production - Composite Profile
4.5
nP_oilgas
voc
95417
- Untreated Natural Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95418
- Condensate Tank Vent Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
nP_oilgas
voc
95419
- Oil Tank Vent Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95420
- Glycol Dehydrator, Uinta Basin
Oil and Gas -Denver-Julesburg Basin
4.5
Produced Gas Composition from Non-CBM
np_oilgas
voc
DJVNT R
Gas Wells
np_oilgas
voc
FLR99
Natural Gas Flare Profile with DRE >98%
4.5
Oil and Gas -Piceance Basin Produced Gas
4.5
np_oilgas
voc
PNC01 R
Composition from Non-CBM Gas Wells
Oil and Gas -Piceance Basin Produced Gas
4.5
nP_oilgas
voc
PNC02 R
Composition from Oil Wells
Oil and Gas -Piceance Basin Flash Gas
4.5
np_oilgas
voc
PNC03 R
Composition for Condensate Tank
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
PNCDH
- Glycol Dehydrator, Piceance Basin
Oil and Gas -Powder River Basin Produced
4.5
np_oilgas
voc
PRBCB R
Gas Composition from CBM Wells
Oil and Gas -Powder River Basin Produced
4.5
np_oilgas
voc
PRBCO R
Gas Composition from Non-CBM Wells
Oil and Gas -Permian Basin Produced Gas
4.5
np_oilgas
voc
PRM01 R
Composition for Non-CBM Wells
Oil and Gas -South San Juan Basin
4.5
Produced Gas Composition from CBM
np_oilgas
voc
SSJCB R
Wells
Oil and Gas -South San Juan Basin
4.5
Produced Gas Composition from Non-CBM
np_oilgas
voc
SSJCO R
Gas Wells
Oil and Gas -SW Wyoming Basin Flash Gas
4.5
np_oilgas
voc
SWFLA R
Composition for Condensate Tanks
Oil and Gas -SW Wyoming Basin Produced
4.5
np_oilgas
voc
SWVNT R
Gas Composition from Non-CBM Wells
Oil and Gas -Uinta Basin Produced Gas
4.5
np_oilgas
voc
UNT01 R
Composition from CBM Wells
Oil and Gas -Wind River Basin Produced
4.5
np_oilgas
voc
WRBCO R
Gas Composition from Non-CBM Gas Wells
Chemical Manufacturing Industrywide
4.5
pt_oilgas
voc
95325
Composite
pt_oilgas
voc
95326
Pulp and Paper Industry Wide Composite
4.5
pt_oilgas,
4.5
ptnonipm
voc
95399
Composite Profile - Oil Field - Wells
pt_oilgas
voc
95403
Composite Profile - Gas Wells
4.5
Oil and Gas Production - Composite Profile
4.5
pt_oilgas
voc
95417
- Untreated Natural Gas, Uinta Basin
244
-------
Profile
SPECIATE
Comment
Sector
Pollutant
code
Profile description
version
Oil and Gas -Denver-Julesburg Basin
4.5
Produced Gas Composition from Non-CBM
pt_oilgas
voc
DJVNT R
Gas Wells
pt_oilgas,
4.5
ptnonipm
voc
FLR99
Natural Gas Flare Profile with DRE >98%
Oil and Gas -Piceance Basin Produced Gas
4.5
pt_oilgas
voc
PNC01 R
Composition from Non-CBM Gas Wells
Oil and Gas -Piceance Basin Produced Gas
4.5
pt_oilgas
voc
PNC02 R
Composition from Oil Wells
Oil and Gas Production - Composite Profile
4.5
pt_oilgas
voc
PNCDH
- Glycol Dehydrator, Piceance Basin
pt_oilgas,
Oil and Gas -Powder River Basin Produced
4.5
ptnonipm
voc
PRBCO R
Gas Composition from Non-CBM Wells
pt_oilgas,
Oil and Gas -Permian Basin Produced Gas
4.5
ptnonipm
voc
PRM01 R
Composition for Non-CBM Wells
Oil and Gas -South San Juan Basin
4.5
pt_oilgas,
Produced Gas Composition from Non-CBM
ptnonipm
voc
SSJCO R
Gas Wells
pt_oilgas,
Oil and Gas -SW Wyoming Basin Produced
4.5
ptnonipm
voc
SWVNT R
Gas Composition from Non-CBM Wells
Composite Profile - Prescribed fire
4.5
ptfire
voc
95421
southeast conifer forest
Composite Profile - Prescribed fire
4.5
ptfire
voc
95422
southwest conifer forest
Composite Profile - Prescribed fire
4.5
ptfire
voc
95423
northwest conifer forest
Composite Profile - Wildfire northwest
4.5
ptfire
voc
95424
conifer forest
ptfire
voc
95425
Composite Profile - Wildfire boreal forest
4.5
Chemical Manufacturing Industrywide
4.5
ptnonipm
voc
95325
Composite
ptnonipm
voc
95326
Pulp and Paper Industry Wide Composite
4.5
onroad
PM2.5
95462
Composite - Brake Wear
4.5
Used in SMOKE-MOVES
onroad
PM2.5
95460
Composite - Tire Dust
4.5
Used in SMOKE-MOVES
245
-------
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
The table below provides a crosswalk between fuel distribution SCCs and classification type for portable
fuel containers (PFC), fuel distribution operations associated with the bulk-plant-to-pump (BTP), refinery
to bulk terminal (RBT) and bulk plant storage (BPS).
see
Typ
e
Description
40301001
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Breathing Loss (67000 Bbl. Tank Size)
40301002
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 10: Breathing Loss (67000 Bbl. Tank Size)
40301003
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 7: Breathing Loss (67000 Bbl. Tank Size)
40301004
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Breathing Loss (250000 Bbl. Tank Size)
40301006
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 7: Breathing Loss (250000 Bbl. Tank Size)
40301007
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Working Loss (Tank Diameter Independent)
40301101
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 13: Standing Loss (67000 Bbl. Tank Size)
40301102
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 10: Standing Loss (67000 Bbl. Tank Size)
40301103
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 7: Standing Loss (67000 Bbl. Tank Size)
40301105
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 10: Standing Loss (250000 Bbl. Tank Size)
40301151
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline: Standing Loss - Internal
40301202
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor
Space; Gasoline RVP 10: Filling Loss
40301203
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor
Space; Gasoline RVP 7: Filling Loss
40400101
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400102
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400103
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400104
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank
40400105
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank
40400106
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Breathing Loss (250000 Bbl Capacity) - Fixed Roof Tank
40400107
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Working Loss (Diam. Independent) - Fixed Roof Tank
40400108
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Working Loss (Diameter Independent) - Fixed Roof Tank
40400109
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Working Loss (Diameter Independent) - Fixed Roof Tank
40400110
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
246
-------
see
Typ
e
Description
40400111
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
40400112
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss (67000 Bbl Capacity)- Floating Roof Tank
40400113
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400114
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400115
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400116
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float Rf Tnk
40400117
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (250000 Bbl Cap.) - Float Rf Tnk
40400118
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400119
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400120
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400130
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
40400131
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400132
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400133
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
40400140
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Ext. Float Roof Tank w/ Secondy Seal
40400141
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400142
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400143
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400148
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
40400149
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
40400150
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Miscellaneous Losses/Leaks: Loading Racks
40400151
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Valves, Flanges, and Pumps
40400152
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Collection Losses
40400153
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Control Unit Losses
40400160
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
40400161
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
40400162
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
247
-------
see
Typ
e
Description
40400163
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
40400170
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400171
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400172
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400173
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400178
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal)
40400179
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)
40400199
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
40400201
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400202
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400203
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400204
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400205
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400206
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400207
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank
40400208
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank
40400210
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float Rf Tnk
40400211
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400212
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400213
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
248
-------
see
Typ
e
Description
40400230
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
40400231
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400232
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400233
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
40400240
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400241
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400248
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10/13/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
40400249
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
40400250
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Loading Racks
40400251
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Valves, Flanges, and Pumps
40400252
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Collection Losses
40400253
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Control Unit Losses
40400260
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
40400261
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
40400262
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
40400263
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
40400270
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400271
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400272
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
249
-------
see
Typ
e
Description
40400273
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400278
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10/13/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal)
40400279
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)
40400401
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 13: Breathing Loss
40400402
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 13: Working Loss
40400403
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 10: Breathing Loss
40400404
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 10: Working Loss
40400405
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 7: Breathing Loss
40400406
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 7: Working Loss
40600101
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading
40600126
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading
40600131
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Normal Service)
40600136
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading (Normal Service)
40600141
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Balanced Service)
40600144
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading (Balanced Service)
40600147
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Clean Tanks)
40600162
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Loaded with Fuel (Transit Losses)
40600163
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Return with Vapor (Transit Losses)
250
-------
see
Typ
e
Description
40600199
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Not Classified
40600231
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Cleaned and Vapor Free Tanks
40600232
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers
40600233
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Cleaned and Vapor Free Tanks
40600234
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Ballasted Tank
40600235
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Ocean Barges Loading - Ballasted Tank
40600236
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Uncleaned Tanks
40600237
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Ocean Barges Loading - Uncleaned Tanks
40600238
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Uncleaned Tanks
40600239
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tankers: Ballasted Tank
40600240
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Average Tank Condition
40600241
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tanker Ballasting
40600299
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Not Classified
40600301
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Splash Filling
40600302
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Submerged Filling w/o Controls
40600305
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Unloading
40600306
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Balanced Submerged Filling
40600307
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Underground Tank Breathing and Emptying
40600399
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Not Classified **
40600401
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling
Vehicle Gas Tanks - Stage II; Vapor Loss w/o Controls
40600501
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Leaks
251
-------
see
Typ
e
Description
40600502
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Venting
40600503
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station
40600504
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station Leaks
40600602
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage II; Liquid Spill Loss w/o Controls
40600701
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Splash Filling
40600702
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Submerged Filling w/o Controls
40600706
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Balanced Submerged Filling
40600707
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Underground Tank Breathing and Emptying
40688801
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Fugitive
Emissions; Specify in Comments Field
2501050120
RBT
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Terminals: All Evaporative
Losses; Gasoline
2501055120
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Plants: All Evaporative
Losses; Gasoline
2501060050
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Total
2501060051
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Submerged Filling
2501060052
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Splash Filling
2501060053
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Balanced Submerged Filling
2501060200
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Total
2501060201
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Breathing and Emptying
2501995000
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; All Storage Types: Working
Loss; Total: All Products
2505000120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; All Transport Types; Gasoline
2505020120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline
252
-------
see
Typ
e
Description
2505020121
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline -
Barge
2505030120
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Transport; Truck; Gasoline
2505040120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Pipeline; Gasoline
2660000000
BTP
/BPS
Waste Disposal, Treatment, and Recovery; Leaking Underground Storage Tanks; Leaking
Underground Storage Tanks; Total: All Storage Types
253
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-23-002
Environmental Protection Air Quality Assessment Division January 2023
Agency Research Triangle Park, NC
254
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