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Technical Support Document (TSD): Preparation of
Emissions Inventories for the 2018v2 North American
Emissions Modeling Platform
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EPA-454/B-23-003
September 2023
Technical Support Document (TSD): Preparation of Emissions Inventories for the 2018v2 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 VII
LIST OF FIGURES X
ACRONYMS XI
1 INTRODUCTION 14
2 BASE YEAR EMISSIONS INVENTORIES AND APPROACHES 16
2.1 Point sources (ptegu, pt_oilgas, ptnonipm, airports) 20
2.1.1 EGU sector (ptegu) 23
2.1.2 Point source oil and gas sector (pt oilgas) 25
2.1.3 Non-IPMsector (ptnonipm) 27
2.1.4 Aircraft and ground support equipment (airports) 28
2.2 Nonpoint sources (afdust, fertilizer, livestock, np_oilgas, np_sol vents, rwc, nonpt) 29
2.2.1 Area fugitive dust (afdust) 29
2.2.2 Agricultural Livestock (livestock) 34
2.2.3 Agricultural Fertilizer (fertilizer) 35
2.2.4 Nonpoint Oil and Gas (np oilgas) 38
2.2.5 Residential Wood Combustion (rwc) 39
2.2.6 Solvents (np solvents) 40
2.2.7 Nonpoint (nonpt) 41
2.3 Onroad Mobile sources (onroad) 42
2.4 Nonroad Mobile sources (cmv, rail, nonroad) 51
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 51
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3) 55
2.4.3 Railway Locomotives (rail) 58
2.4.4 Nonroad Mobile Equipment (nonroad) 63
2.5 Fires (ptfire-wild, ptfire-rx, ptagfire) 67
2.5.1 Wild and Prescribed Fires (ptfire-rx, ptfire-wild) 67
2.5.2 Point source Agriculture Fires (ptagfire) 71
2.6 Biogenic Sources (beis) 73
2.7 Sources Outside of the United States 74
2.7.1 Point Sources in Canada and Mexico (othpt, canadaag, canada_og2D) 75
2.7.2 Fugitive Dust Sources in Canada (othafdust, othptdust) 75
2.7.3 Nonpoint and Nonroad Sources in Canada and Mexico (othar) 76
2.7.4 Onroad Sources in Canada and Mexico (onroadcan, onroadjnex) 76
2.7.5 Fires in Canada and Mexico (ptfire othna) 76
2.7.6 Fires in Canada and Mexico (ptfire othna) 76
2.7.7 Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury 77
3 EMISSIONS MODELING 78
3.1 Emissions modeling Overview 78
3.2 Chemical Speciation 82
3.2.1 VOC speciation 85
3.2.1.1 County specific profile combinations 88
3.2.1.2 Additional sector specific considerations for integrating HAP emissions from inventories into speciation 89
3.2.1.3 Oil and gas related speciation profiles 91
3.2.1.4 Mobile source related VOC speciation profiles 93
3.2.2 PM speciation 98
3.2.2.1 Mobile source related PM2.5 speciation profiles 98
3.2.3 NO x speciation 99
3.2.4 Creation of Sulfuric Acid Vapor (SULF) 100
3.3 Temporal Allocation 101
3.3.1 Use of FF10 format for finer than annual emissions 103
3.3.2 Electric Generating Utility temporal allocation (ptegu) 103
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3.3.2.1 Base year temporal allocation of EGUs 103
3.3.2.2 Analytic year temporal allocation of EGUs 108
3.3.3 Airport Temporal allocation (airports) 113
3.3.4 Residential Wood Combustion Temporal allocation (rwc) 114
3.3.5 Agricultural Ammonia Temporal Profiles (livestock) 118
3.3.6 Oil and gas temporal allocation (npoilgas) 119
3.3.7 Onroad mobile temporal allocation (onroad) 119
3.3.8 Nonroad mobile temporal allocation(nonroad) 125
3.3.9 Additional sector specific details (afidust, beis, cmv, rail, nonpt, ptnonipm, ptfire) 126
3.4 Spatial Allocation 129
3.4.1 Spatial Surrogates for U.S. emissions 129
3.4.2 Allocation method for airport-related sources in the U.S. 136
3.4.3 Surrogates for Canada and Mexico emission inventories 136
4 ANALYTIC YEAR EMISSIONS INVENTORIES AND APPROACHES 140
4.1 EGU Point Source Projections (ptegu) 144
4.2 Sectors with Projections Computed using CoST 147
4.2.1 Background on the Control Strategy Tool (CoST) 148
4.2.2 CoST CLOSURE Packet (ptnonipm, ptoilgas) 152
4.2.3 CoST PROJECTION Packets (afidust, airports, cmv, livestock, nonpt, np oilgas, np solvents, ptnonipm, pt oilgas,
rail, rwc) 152
4.2.3.1 Fugitive dust growth (afdust) 153
4.2.3.2 Airport sources (airports) 154
4.2.3.3 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 155
4.2.3.4 Category 3 Commercial Marine Vessels (cmv_c3) 156
4.2.3.5 Livestock population growth (livestock) 157
4.2.3.6 Nonpoint Sources (nonpt) 158
4.2.3.7 Solvents (np_solvents) 164
4.2.3.8 Oil and Gas Sources (np_oilgas, pt_oilgas) 166
4.2.3.1 Non-EGU point sources (ptnonipm) 169
4.2.3.2 Railroads (rail) 172
4.2.3.3 Residential Wood Combustion (rwc) 172
4.2.4 CoST CONTROL Packets (nonpt, np oilgas, ptnonipm, pt oilgas, np solvents) 175
4.2.4.1 Oil and Gas NSPS (np_oilgas, pt_oilgas) 176
4.2.4.2 RICE NSPS (nonpt, ptnonipm, np_oilgas, pt_oilgas) 181
4.2.4.3 Fuel Sulfur Rules (nonpt) 184
4.2.4.4 Natural Gas Turbines NOx NSPS (ptnonipm, pt_oilgas) 185
4.2.4.5 Process Heaters NOx NSPS (ptnonipm, pt_oilgas) 187
4.2.4.6 Ozone Transport Commission Rules (np_solvents) 189
4.2.4.7 Good Neighbor Plan 2015 Ozone NAAQS (ptnonipm, pt_oilgas) 190
4.3 Sectors with Projections Computed Outside of CoST 190
4.3.1 Nonroad Mobile Equipment Sources (nonroad) 190
4.3.2 Onroad Mobile Sources (onroad) 191
4.3.3 Sources Outside of the United States (onroadcan, onroadjnex, othpt, canadaag, canada_og2D, ptfire othna,
othar, othafdust, othptdust) 194
4.3.3.1 Canadian fugitive dust sources (othafdust, othptdust) 195
4.3.3.2 Point Sources in Canada and Mexico (othpt, canada_ag, canada_og2D) 195
4.3.3.3 Nonpoint sources in Canada and Mexico (othar) 196
4.3.3.4 Onroad sources in Canada and Mexico (onroad_can, onroad_mex) 197
5 EMISSION SUMMARIES 198
6 REFERENCES 202
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List of Tables
Table 2-1. Platform sectors for the 2018gg emissions modeling case 17
Table 2-2. Default stack parameter replacements 22
Table 2-3. Point source oil and gas sector NAICS Codes 25
Table 2-4. 2017-to-2018 projection factors for pt_oilgas sector 26
Table 2-5. 2017 NEI-based sources in pt oilgas (excluding offshore) before and after projections to 2018.. 27
Table 2-6. SCCs for the airports sector 28
Table 2-7. Afdust sector SCCs 29
Table 2-8. Total impact of fugitive dust adjustments to the unadjusted 2018 inventory 31
Table 2-9. SCCs for the livestock sector 34
Table 2-10. National projection factors for livestock: 2017 to 2018 35
Table 2-11. Source of input variables for EPIC 37
Table 2-12. SCCs for the residential wood combustion sector 39
Table 2-13. MOVES vehicle (source) types 43
Table 2-14. Fraction of IHS Vehicle Populations to Retain 49
Table 2-15. SCCs for cmv_clc2 sector 52
Table 2-16. Vessel groups in the cmv_clc2 sector 54
Table 2-17. SCCs for cmv_c3 sector 56
Table 2-18. Projection Factors for 2017 to 2018 for Category 3 Vessels 58
Table 2-19. SCCs for the rail sector 59
Table 2-20. 2017-to-2018 projection factors for the rail sector 59
Table 2-21. Alaska counties/census areas for which specific nonroad emissions were removed 66
Table 2-22. SCCs included in the ptfire sector 67
Table 2-23. SCCs included in the ptagfire sector 72
Table 2-24. Meteorological variables required by BEIS 3.7 73
Table 3-1. Key emissions modeling steps by sector 79
Table 3-2. Descriptions of the platform grids 81
Table 3-3. Emission model species produced for CB6R3AE7 for CMAQ 82
Table 3-4. Integration of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM) for
each sector 87
Table 3-5. Ethanol percentages by volume by Canadian province 88
Table 3-6. MOVES integrated species in M-profiles 89
Table 3-7. Basin/Region-specific profiles for oil and gas 92
Table 3-8. TOG MOVES-SMOKE Speciation Profiles for Nonroad Emissions 93
Table 3-9. Select mobile-related VOC profiles 94
Table 3-10. Onroad M-profiles 95
Table 3-11. MOVES process IDs 96
Table 3-12. MOVES Fuel subtype IDs 97
Table 3-13. MOVES regclass IDs 97
Table 3-14. Regional fire PM speciation profiles used in ptfire sectors 98
Table 3-15. Nonroad PM2.5 profiles 99
Table 3-16. NOx speciation profiles 100
Table 3-17. Sulfate split factor computation 100
Table 3-18. SO2 speciation profiles 101
Table 3-19. Temporal settings used for the platform sectors in SMOKE 102
Table 3-20. U.S. Surrogates available for this modeling platforms 130
Table 3-21. Off-Network Mobile Source Surrogates 132
Table 3-22. Spatial Surrogates for Oil and Gas Sources 132
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Table 3-23. Selected 2018 CAP emissions by sector for U.S. Surrogates (short tons in 12US1) 134
Table 3-24. Canadian Spatial Surrogates 137
Table 3-25. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3) 137
Table 4-1. Overview of projection methods for the analytic year cases 140
Table 4-2. EGU sector NOx emissions by State for the 2018v2 cases 146
Table 4-3. Subset of CoST Packet Matching Hierarchy 149
Table 4-4. Summary of non-EGU stationary projections subsections 150
Table 4-5. Reductions from all facility/unit/stack-level closures in 2032 from 2018 emissions levels 152
Table 4-6. Increase in PM2.5 emissions from projections in 2018v2 153
Table 4-7. TAF 2021 growth factors for major airports, 2016 to 2032 154
Table 4-8. Impact of 2016 to 2032 factors on airport emissions 155
Table 4-9. National projection factors for cmv_clc2 156
Table 4-10. California projection factors for cmv_clc2 156
Table 4-11. 2018-to-2030 CMV C3 projection factors outside of California 157
Table 4-12. 2018-to-2030 CMV C3 projection factors for California 157
Table 4-13. National projection factors for livestock: 2018 to 2032 158
Table 4-14. Impact of 2016-2026 factors on nonpt emissions in MARAMA states 159
Table 4-15. Impact of factors on nonpt PFC emissions in MARAMA states 159
Table 4-16. Impact of 2016-2026 factors on nonpt emissions in North Carolina 159
Table 4-17. Impact of 2016-2026 factors on nonpt emissions in New Jersey 160
Table 4-18. Impact of 2016-2026 industrial factors by SCC on nonpt emissions in non-MARAMA states. 160
Table 4-19. Impact of 2026-2032 industrial factors by SCC on nonpt emissions in non-MARAMA states. 161
Table 4-20. Impact of 2026-2032 factors other than by SCC on nonpt emissions in non-MARAMA states 161
Table 4-21. Impact of factors on nonpt finished fuel emissions 162
Table 4-22. SCCs in nonpt that use Human Population Growth for Projections 162
Table 4-23. Impact of 2016-2026 population-based factors on nonpt emissions in non-MARAMA states.. 163
Table 4-24. Impact of 2026-2030 population-based factors on nonpt emissions in non-MARAMA states.. 163
Table 4-25. SCCs in npsolvents that use Human Population Growth for Projections 164
Table 4-26. Impact of population-based factors on np solvents emissions in non-MARAMA states 165
Table 4-27. Impact of factors on np_solvents emissions in MARAMA states 166
Table 4-28. Impact of 2018-2032 projections on pt_oilgas emissions 168
Table 4-29. Year 2017-2019 high-level summary of national oil and gas exploration emissions 168
Table 4-30. Impact of 2018-2032 projections on np_oilgas emissions 168
Table 4-31. Impact of 2026-2032 MARAMA projections on ptnonipm emissions 169
Table 4-32. Annual Energy Outlook (AEO) 2022 tables used to project industrial sources 170
Table 4-33. Impact of 2026-2032 industrial projections by NAICS and SCC on ptnonipm emissions 170
Table 4-34. Impact of 2026-2032 industrial projections by SCC on ptnonipm emissions 171
Table 4-35. Impact of 2026-2028 factors on ptnonipm finished fuel emissions 171
Table 4-36. Impact of 2026-2028 factors on ptnonipm biorefinery emissions 171
Table 4-37. AEO2022 growth rates for rail sub-groups, 2026 to 2032 172
Table 4-38. Impact of projections on rail emissions 172
Table 4-39. Projection factors for Residential Wood Combustion 173
Table 4-40. Impact of projections on rwc emissions, 2017-2032 174
Table 4-41. Assumed new source emission factor ratios for NSPS rules 176
Table 4-42. Emissions reductions for the oil and gas sectors due to applying the Oil and Gas NSPS 177
Table 4-43. SCCs in np_oilgas for which the Oil and Gas NSPS controls were applied 177
Table 4-44. SCCs in pt_oilgas for which the Oil and Gas NSPS controls were applied 178
Table 4-45. Emissions reductions in nonpt due to RICE NSPS 182
Table 4-46. Emissions reductions in ptnonipm due to the RICE NSPS 182
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Table 4-47. Emissions reductions in np_oilgas due to the RICE NSPS 182
Table 4-48. Emissions reductions in pt_oilgas du to the RICE NSPS 182
Table 4-49. SCCs and Engine Types where RICE NSPS controls applied for nonpt and ptnonipm 182
Table 4-50. Non-point Oil and Gas SCCs where RICE NSPS controls are applied 183
Table 4-51. Point source SCCs in ptoilgas sector where RICE NSPS controls applied 184
Table 4-52. Summary of fuel sulfur rule impacts on nonpoint S02 emissions for 2032 184
Table 4-53. Stationary gas turbines NSPS analysis and resulting emission rates used to compute controls. 185
Table 4-54. Emissions reductions due to the Natural Gas Turbines NSPS 186
Table 4-55. SCCs in ptnonipm for which Natural Gas Turbines NSPS controls were applied 186
Table 4-56. SCCs in pt_oilgas for which Natural Gas Turbines NSPS controls were applied 187
Table 4-57. Process Heaters NSPS analysis and 2018v2 new emission rates used to estimate controls 187
Table 4-58. Emissions reductions due to the application of the Process Heaters NSPS 188
Table 4-59. SCCs in ptnonipm for which Process Heaters NSPS controls were applied 188
Table 4-60. SCCs in pt oilgas for which Process Heaters NSPS controls were applied 189
Table 4-61. NOx emissions reductions after application of Good Neighbor Plan control packet 190
Table 4-62. Light duty greenhouse gas rule adjustments for 2032 onroad emissions 192
Table 4-63. Factors used to Project VMT to analytic years 193
Table 5-1. National by-sector CAP emissions for the 2018gg case, 12US1 grid (tons/yr) 199
Table 5-2. National by-sector CAP emissions for the 2032gg2 case, 12US1 grid (tons/yr) 200
Table 5-3. National by-sector CAP emissions for the 2018gg case, 36US3 grid (tons/yr) 201
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List of Figures
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction and precipitation 33
Figure 2-2. "Bidi" modeling system used to compute fertilizer application emissions 36
Figure 2-3. Map of Representative Counties 48
Figure 2-4. 2017NEI geographical extent of marine emissions (solid) and the U.S. ECA (dashed) 53
Figure 2-5. 2017 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT) 60
Figure 2-6. Class I Railroads in the United States 61
Figure 2-7. Class II and III Railroads in the United States 62
Figure 2-8. Amtrak Routes with Diesel-powered Passenger Trains 63
Figure 2-9. Processing flow for fire emission estimates 70
Figure 2-10. Default fire type assignment by state and month where data are only from satellites 70
Figure 2-11. Blue Sky Pipeline 71
Figure 3-1. Air quality modeling domains 81
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation 86
Figure 3-3. Eliminating unmeasured spikes in CEMS data 104
Figure 3-4. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification 105
Figure 3-5. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type 106
Figure 3-6. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type 107
Figure 3-7. Non-CEMS EGU Temporal Profile Aggregation Regions 108
Figure 3-8. Analytic Year Emissions Follow the Pattern of Base Year Emissions Ill
Figure 3-9. Excess Emissions Apportioned to Hours Less than the Historic Maximum Ill
Figure 3-10. Regional Profile Applied due to not being able to Adjust below Historic Maximum 112
Figure 3-11. Regional Profile Applied, but Exceeds Historic Maximum in Some Hours 112
Figure 3-12. Diurnal Profile for all Airport SCCs 113
Figure 3-13. Weekly profile for all Airport SCCs 113
Figure 3-14. Monthly Profile for all Airport SCCs 114
Figure 3-15. Alaska Seaplane Profile 114
Figure 3-16. Example of RWC temporal allocation using a 50 versus 60 °F threshold 115
Figure 3-17. RWC diurnal temporal profile 116
Figure 3-18. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr) 117
Figure 3-19. Day-of-week temporal profiles for OHH and Recreational RWC 117
Figure 3-20. Annual-to-month temporal profiles for OHH and recreational RWC 118
Figure 3-21. Example of animal NH3 emissions temporal allocation approach (daily total emissions) 119
Figure 3-22. Example of temporal variability of NOx emissions 120
Figure 3-23. Sample onroad diurnal profiles for Fulton County, GA 121
Figure 3-24. Methods to Populate Onroad Speeds and Temporal Profiles by Road Type 122
Figure 3-25. Regions for computing Region Average Speeds and Temporal Profiles 124
Figure 3-26. Example of Temporal Profiles for Combination Trucks 125
Figure 3-27. Example Nonroad Day-of-week Temporal Profiles 126
Figure 3-28. Example Nonroad Diurnal Temporal Profiles 126
Figure 3-29. Agricultural burning diurnal temporal profile 128
Figure 3-30. Prescribed and Wildfire diurnal temporal profiles 128
Figure 4-1. EIA Oil and Gas Supply Regions as of AEO2022 167
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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 2018 version 2 platform for use in air
quality modeling analyses. This platform primarily draws on data from the 2017 National Emissions
Inventory (NEI) (EPA, 2021b), although the emissions were updated to represent the year 2018 through
the incorporation of 2018-specific data along with adjustment methods appropriate for each sector. The
analytic year inventories were developed starting with the base year 2018 inventory using sector-specific
methods as described below. This 2018 platform supports applications related to particulate matter (PM).
An earlier version of a 2018 platform was developed in 2021 in support of ozone, PM and air toxics air
quality modeling analyses (EPA, 2022a).
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.
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 2018 platform consists of cases that represent the years 2018 and 2032 with the abbreviations
2018gg_18j and 2032gg2_18j, 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 2018gg_18j, 2018 is
the year represented by the emissions; the "g" represents the base year emissions modeling platform
iteration, which here shows that g is for the 2018 platform which started with the 2017 NEI; and the "g"
stands for the seventh configuration of emissions modeled for that modeling platform. In the script and
data directories this platform is known as "em_v8.1." Data and summary reports for this platform are
available from https://www.epa.gov/air-emissions-modeling/2018v2-emissions-modeling-platform. It is
distinguished from the original 2018 platform used for 2018 AirToxScreen that is called "em_v8." Note
that the original 2018 platform did not include analytic year emissions.
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 2018 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
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https://www.ghrsst.org/) and is given the EPA meteorological case label "18j." The full case abbreviation
includes this suffix following the emissions portion of the case name to fully specify the abbreviation of
the base year case as "2018gf_18j."
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 2018 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
4.8.1 (SMOKE 4.8.1). 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 year 2018.
This document contains six sections. Section 2 describes the base year 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. Note that all tables
of emissions totals in this document are in the units of short tons/year.
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2 Base Year Emissions Inventories and Approaches
This section summarizes the emissions data that make up the 2018 base year emissions and provides
details about the data contained in each of the platform sectors. The original starting point for the
emission inventories was the original 2018 platform, which incorporated data and methods from the 2017
NEI. The base year emissions for many of the sectors in this platform are consistent with original
platform, which had a case abbreviation of 2018gc.
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 U.S.
emissions from the NEI data categories of point, nonpoint, onroad, nonroad, and events (i.e., fires),
emissions from the Canadian and Mexican inventories are included in the 2018v2 platform. The Canadian
and Mexican inventories in the 2018v2 platform were not changed from those in the 2016v2 platform
(EPA, 2022b), although they were reprocessed for the year 2018. The Canadian inventories were provided
by Environment and Climate Change Canada (ECCC), and most of the inventories for Mexico are based
on data provided by SEMARNAT.
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. 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 2018 is not a triennial NEI year, the inventories for most emissions modeling
sectors were modified in some way to represent the year 2018 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 2018 point source emission inventories for the platform include emissions primarily from S/L/T-
submitted data. Agricultural and wildland fire emissions represent the year 2018 and are consistent with
those in 2018gc. In 2018gg, most anthropogenic emissions are consistent with those in 2018gc, although
some had minor adjustments as described in Table 2-1. 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 MOVES3 for the year 2018. Nonroad emissions were
consistent with those in 2018gc and were generated using MOVES3, including the spatial allocation
factors made for the 2016vl platform.
For the purposes of preparing the air quality model-ready emissions, emissions from the five NEI data
categories (i.e., point, nonpoint, onroad, nonroad, and events) 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.
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/2018v2-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. Summary reports are available in
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addition to the data files for the 2018v2 platform. The types of reports include state summaries of
inventory pollutants and model species by modeling platform sector and county totals by modeling
platform sector.
Table 2-1 presents an overview of how base year emission for the sectors in the emissions modeling
platform were developed and how they 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 2018v2 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
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." For more information on the natural emissions, see Section 2.7.7.
Table 2-1. Platform sectors for the 2018gg 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 2018 from the Emissions
Inventory System (EIS) based on the winter 2022 point flat file as was used
for 2018 AirToxScreen. The inventory emissions are replaced with hourly
2018 Continuous Emissions Monitoring System (CEMS) values for nitrogen
oxides (NOx) and sulfur dioxide (S02)for any units that are matched to the
NEI, and other pollutants for matched units are scaled from the 2018 point
inventory using CEMS heat input. Emissions for all sources not matched to
CEMS data come from the annual inventory. Annual resolution for sources
not matched to CEMS data, hourly for CEMS sources. EGUs closed in 2018
are not part of the inventory.
Point source oil and
gas:
ptoilgas
Point
Point sources for 2018 including S/L/T data for oil and gas production and
related processes for facilities with North American Industry Classification
System (NAICS) codes related to Oil and Gas Extraction, Natural Gas
Distribution, Drilling Oil and Gas Wells, Support Activities for Oil and Gas
Operations, Pipeline Transportation of Crude Oil, and Pipeline Transportation
of Natural Gas. Includes U.S. offshore oil production from the 2017NEI
Production-related sources without 2018 data were pulled forward from 2017
NEI and adjusted to 2018. In NM and UT, the WRAP inventory from the
2016v3 platform (EPA, 2023a) was used. Annual resolution.
Aircraft and
ground support
equipment: airports
Point
2017 NEI point source emissions from aircraft up to 3,000 ft elevation and
emissions from ground support equipment, adjusted to 2018 using Terminal
Area Forecast (TAF) data. Airport-specific factors were used where available,
state average factors were used for regional airports, and no change was made
to military aircraft from 2017. Annual resolution.
Remaining non-
EGU point:
ptnonipm
Point
All 2018 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 2018 but did operate in
later years use the winter 2022 inventory as was used for 2018 AirToxScreen.
Closures were reviewed and implemented based on the most recent
submissions to the Emissions Inventory System (EIS). Includes 2017 NEI rail
yard emissions, adjusted to 2018 using same projection factors as the rail
sector. Annual resolution.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Agricultural
fertilizer:
fertilizer
Nonpoint
Nonpoint agricultural fertilizer application emissions of ammonia computed
inline within CMAQ for 2018 through the bidirectional ammonia flux
process. Created an emissions inventory post-CMAQ to use for data
summaries but did not run SMOKE. Ran inline, but used county and monthly
resolution for the output inventory.
Agricultural
Livestock:
livestock
Nonpoint
2017 NEI nonpoint agricultural livestock emissions including ammonia and
other pollutants (except PM2 5). Same as 2018gc except included a correction
for Maryland. County and annual resolution.
Agricultural fires
with point
resolution: ptagfire
Nonpoint
2018 agricultural fire sources based on EPA-developed data, represented as
point source day-specific emissions. Same as 2018gc. They are in the NEI
nonpoint data category, but in the platform, they are treated as point sources.
Day-specific resolution.
Area fugitive dust:
afdust
Nonpoint
PM10 and PM2 5 fugitive dust sources based on the 2017 NEI nonpoint
inventory, including building construction, road construction, agricultural
dust, and paved and unpaved road dust; with paved road dust adjusted to 2018
based on vehicle miles traveled (VMT). Emissions are reduced during
modeling according to a transport fraction and a 2018 meteorology-based
(precipitation and snow/ice cover) zero-out. Afdust emissions from the
portion of southeast Alaska inside the 36US3 domain are processed in a
separate sector called 'afdust ak'. County and annual resolution.
Biogenic:
beis
Nonpoint
Year 2018, hour-specific, grid cell-specific emissions generated from a new
B3GRD files for 12US1 and 36US3 based on a corrected version of the
BEIS3.7 model within SMOKE, including emissions in Canada and Mexico
using BELD5 land use data. Gridded and hourly resolution.
Category 1, 2 CMV:
cmv_clc2
Nonpoint
2017 NEI Category 1 and category 2 (C1C2) commercial marine vessel
(CMV) emissions based on Automatic Identification System (AIS) data,
adjusted to 2018, including the county apportionment fix consistent with what
was done for 2016v3. Same as 2018gc. Includes C1C2 CMV emissions in
U.S. state and Federal waters along with non-U.S. C1C2 emissions within the
modeling domains. Gridded and hourly resolution.
Category 3 CMV:
cmv_c3
Nonpoint
2017 NEI Category 3 (C3) CMV emissions converted to point sources based
on the center of the grid cells and adjusted to 2018, including the county
apportionment fix consistent with what was done for 2016v3. Includes C3
emissions in U.S. state and Federal waters, and also all non-U.S. C3
emissions within the modeling domains. Same as 2018gc. Gridded and hourly
resolution.
Locomotives :
rail
Nonpoint
2017 NEI line haul rail locomotives emissions adjusted to 2018. Includes
freight and commuter rail emissions. Same as 2018gc. County and annual
resolution.
Solvents :
npsolvents
Nonpoint
(some
Point)
VOC emissions from solvents for the year 2018 derived using the January
2022 version of the VCPy framework (Seltzer et al., 2021). Includes
household cleaners, personal care products, adhesives, architectural coatings,
aerosol coatings, industrial coatings, allied paint products, printing inks, dry-
cleaning emissions, and agricultural pesticides. County and annual resolution.
Nonpoint source oil
and gas:
npoilgas
Nonpoint
2018 nonpoint oil and gas emissions output from the oil and gas tool using
2018 activity data. For exploration the 2018 oil and gas tool output were used
directly. For production used the WRAP inventory from the 2016v3 platform
in New Mexico and North Dakota; the 2017 NEI in California, Colorado,
Oklahoma, Texas, Utah, and Wyoming; and oil and gas tool outputs for 2018
in all other states. 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) with
no adjustments to 2018. Same as 2018gc. County and annual resolution.
Remaining
nonpoint:
nonpt
Nonpoint
2017 NEI nonpoint sources that are not included in other platform sectors
with no adjustments to 2018. Same as 2018gc. For 2018 used 2017 NEI for
all sources. County and annual resolution.
Nonroad:
nonroad
Nonroad
2018 nonroad equipment emissions developed with MOVES3, including the
updates made to spatial apportionment that were developed for the 2016vl
platform. MOVES3 was used for all states except California and Texas.
California submitted emissions for 2017 and 2023 which were interpolated to
2018; Texas submitted emissions for 2017 and 2020, which were interpolated
to 2018. Same as 2018gc. County and monthly resolution.
Onroad:
onroad
Onroad
2018 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. Activity data were
projected to 2018 using factors derived from data obtained from Federal
Highway Administration and state departments of transportation. Same as
2018gc. County and hourly resolution.
Onroad California:
onroadcaadj
Onroad
California-provided CAP onroad mobile source gasoline and diesel vehicles
based on the EMFAC2017 model interpolated to 2018 between 2017 and
2023. The 2018 data were gridded and temporalized using MOVES3 outputs.
Volatile organic compound (VOC) HAP emissions derived from California-
provided VOC emissions and MOVES-based speciation. Same as 2018gc.
County and hourly resolution.
Point source fires-
ptfire-rx
ptfire-wild
Events
Point source day-specific wildfires and prescribed fires for 2018 computed
using Satellite Mapping Automated Reanalysis Tool for Fire Incident
Reconciliation version 2 (SMARTFIRE2) and BlueSky Pipeline for both
flaming and smoldering processes (i.e., SCCs 281XXXX002). The ptfire-rx
sectors includes Flint Hills grasslands fires; wildfires were run in a separate
sector ptfire-wild. Smoldering emissions forced into layer 1 (by adjusting
heat flux). Same as 2018gc. Daily resolution.
Non-US. Fires:
ptfireothna
N/A
Point source day-specific wildfires and agricultural fires outside of the U.S.
for 2018 from v 1.5 of the Fire INventory (FINN) from National Center for
Atmospheric Research (NCAR, 2017 and Wiedinmyer, C., 2011) for Canada,
Mexico, Caribbean, Central American, and other international fires. Includes
any prescribed fires although they are not distinguished from wildfires. Same
as 2018gc. Daily resolution.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
N/A
Area fugitive dust sources of particulate matter emissions excluding dust
from livestock land tilling from agricultural activities, from Environment and
Climate Change Canada (ECCC) for 2016. Transport fraction adjustments
applied along with a 2018-specific meteorology-based (precipitation and
snow/ice cover) zero-out. Same as 2018gc. 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
2016 point source fugitive dust sources of particulate matter emissions
including dust from livestock and land tilling from agricultural activities,
provided by ECCC. Wind erosion emissions were not included. Transport
fraction adjustments applied along with a 2018-specific meteorology-based
(precipitation and snow/ice cover) zero-out. Same as 2018gc. Monthly
resolution.
Other point sources
not from the NEI:
othpt
N/A
2016 point source emissions from the ECCC including Canadian sources
other than agricultural ammonia and low-level oil and gas sources, along with
emissions from Mexico's 2016 inventory projected to 2018. Canada same as
2018gc, Mexico updated from 2018gc. Monthly resolution for Canada airport
emissions, annual resolution for the remainder of Canada and all of Mexico.
Canada ag not from
the NEI:
Canada ag
N/A
2016 agricultural point sources from the ECCC, including agricultural
ammonia. Same as 2018gc, except with these emissions split out from the
othpt sector. Monthly resolution.
Canada oil and gas
2D not from the
NEI:
Canada og2D
N/A
2016 low-level point oil and gas sources with emissions forced into 2D low-
level to reduce the size of the othpt sector. Point oil and gas sources subject to
plume rise remain in the othpt sector. Same as 2018gc, except with these
emissions split out from the othpt sector. Annual resolution.
Other non-NEI
nonpoint and
nonroad:
othar
N/A
2016 Canada emissions from the ECCC inventory, with nonroad emissions
projected from 2016 to 2018 using US nonroad trends. Mexico (municipio
resolution) emissions projected from 2016 to 2018. Canada same as 2018gc,
Mexico updated from 2018gc. Resolution: Canada: province or sub-province
resolution; monthly for nonroad sources and annual for rail and other
nonpoint sectors; Mexico: municipio resolution; annual nonpoint and nonroad
mobile inventories.
Other non-NEI
onroad sources:
onroadcan
N/A
Year 2016 Canada from the ECCC onroad mobile inventory projected to
2018 using US onroad trends. Separate trends applied to refueling and non-
refueling. Same as 2018gc. Province resolution or sub-province resolution,
depending on the province; Monthly resolution.
Other non-NEI
onroad sources:
onroad mex
N/A
Year 2018 Mexico onroad mobile inventory from MOVES-Mexico. Same as
from 2018gc. Municipio and monthly resolution.
2.1 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 2014, 2017 and 2020. In the intervening years, year-
specific emissions for point sources that exceed the potential to emit threshold as defined in the Air
Emissions Reporting Requirements (AERR)1 must be submitted by the responsible state, local, or tribal
1 80 FR 8787 published 2/19/2015. See: https://www.federalregister.gov/documents/2015/02/19/2015-03470/revisions-to-the-
air-emissions-reporting-reauirements-revisions-to-lead-pb-reporting-threshold-and
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agencies. These emissions, and any relevant closures, are submitted to the Emissions Inventory System
(EIS) used to compile the NEI. Sources not updated by the responsible agencies for the interim year are
either carried forward from the most recent triennial NEI if they have not been marked as closed. While
point source emissions are available in EIS for the year 2018, a full set of documentation on how the 2018
point source inventory was compiled is not available. The methods for point source emissions estimation
for the year of 2018 are similar to those used for the 2017 NEI. A comprehensive description of how point
source emissions were characterized and estimated in the 2017 NEI is available in the 2017 NEI TSD
(EPA, 2021).
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 2018, including stack parameters and locations from EIS, was done on March 22,
2022. 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).
In some cases, data about facility or unit closures are entered into EIS after the inventory modeling
inventory flat were reviewed and implemented based on the most recent submissions to EIS. Prior to
processing through SMOKE, submitted closures were reviewed and if closed sources were found in the
inventory, those were removed.
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. Defaulted values were noticed in data submissions for the states of Illinois, Louisiana,
Michigan, Pennsylvania, Texas, Wisconsin, and others. Using these default values 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 source classification code (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. These updates impacted the ptnonipm and pt oilgas inventories.
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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
The non-EGlJ stationary point source (ptnonipm) emissions were input to SMOKE as annual emissions.
The full description of how the NEI emissions were developed is provided in the NEI documentation - a
brief summary of their development follows:
a. CAP and HAP data were provided by States, locals and tribes under the Air Emissions Reporting
Rule (AERR) [the reporting size threshold is larger for inventory years between the triennial inventory
years of 2011, 2014, 2017, ...].
b. EPA corrected known issues and filled PM data gaps.
c. EPA added HAP data from the Toxic Release Inventory (TRI) where corresponding data was not
already provided by states/locals.
d. EPA stored and applied matches of the point source units to units with CEMS data and also for all
EGU units modeled by EPA's Integrated Planning Model (IPM).
e. Data for airports and rail yards were incorporated.
f. Off-shore platform data were added from the Bureau of Ocean Energy Management (BOEM).
The changes made to the NEI point sources prior to modeling with SMOKE are as follows:
• The tribal data, which do not use state/county Federal Information Processing Standards (FIPS)
codes in the NEI, but rather use the tribal code, were assigned a state/county FIPS code of 88XXX,
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where XXX is the 3-digit tribal code in the NEI This change was made because SMOKE requires
all sources to have a state/county FIPS code.
• Sources that did not have specific counties assigned (i.e., the county code ends in 777) were not
included in the modeling because it was only possible to know the state in which the sources
resided, but no more specific details related to the location of the sources were available.
Each of the point sectors is processed separately through SMOKE as described in the following
subsections.
The inventory pollutants processed through SMOKE for all point source sectors included carbon
monoxide (CO), oxides of nitrogen (NOx), volatile organic compounds (VOC), sulfur dioxide (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 hazardous air pollutants (HAPs) 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 of the CB6 chemical mechanism.
The ptnonipm and ptoilgas 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.
2.1.1 EGU sector (ptegu)
The ptegu sector contains emissions from EGUs in the 2018 NEI point inventory that could be matched to
units found in the National Electric Energy Data System (NEEDS) v6 database
(https://www.epa.gov/airmarkets/national-electric-energy-data-svstem-needs-v6 dated 2/14/2023).
NEEDS is used by the Integrated Planning Model (1PM) to develop future year EGU emissions. It was
necessary to put these EGlJs into a separate sector in the platform because EGlJs use different temporal
profiles than other sources in the point sector and it is useful to segregate these emissions from the rest of
the point sources to facilitate summaries of the data. Sources not matched to units found in NEEDS are
placed into the pt oilgas or ptnonipm sectors. For studies with future year cases, the sources in the ptegu
sector are fully replaced with the emissions output from IPM. It is therefore important that the matching
between the NEI and NEEDS database be as complete as possible because there can be double-counting
of emissions in future year modeling scenarios if emissions for units projected by IPM are not properly
matched to the units in the point source inventory
The matching of NEEDS to the NEI sources was prioritized according to the amount of the emissions
produced by the source. In the SMOKE point flat file, emission records for sources that have been
matched to the NEEDS database have a value filled into the IPM YN column based on the matches
stored within EIS. The 2018 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 2017 NEI inventory that were
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not submitted or updated for 2018 and not identified as retired were retained in 2018, but for 2018v2 the
emissions values were pulled from the 2017 NEI where possible.
When possible, units in the ptegu sector are matched to 2018 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 2018 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.
24
-------
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 temporal profile that allocates the same emissions for each
day, combined with a uniform hourly temporal profile applied by SMOKE.
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.
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
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
The starting point for most states in the 2018v2 emissions platform pt oilgas inventory was the 2018
point source NEI. The 2018 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 used that was developed by the U.S. Department of the Interior, Bureau
25
-------
of Ocean and Energy Management, Regulation, and Enforcement (BOEM). For 2018, New Mexico and
Utah used the WRAP oil and gas inventory from 2016v3 platform.
The NEI year that the data was submitted for is indicated by the calc_year field in the FF10 inventory
files. Sources in the 2018NEI in which the calc_year is 2017 were projected to 2018. 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
2-4. These factors were applied to sources withNAICS = 2111,21111,211111, 211112, and 213111 and
with production-related SCC processes 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 2017 to 2018.
For pipeline transportation, national projection factors of 17% for oil and 12% for gas were applied. The
"no growth" sources include all offshore and tribal land emissions, and all emissions with a NAICS code
associated with distribution, transportation, or support activities. The historical production data for years
2017 and 2018 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)
Table 2-5 shows the national emissions for ptoilgas following the projection to 2018; these numbers only
reflect the portion of the inventory projected from 2017 to 2018.
Table 2-4. 2017-to-2018 projection factors for pt oilgas sector
State
Natural Gas
growth
Oil growth
Combination gas/oil growth
Alabama
-7.0%
-13.8%
-10.4%
Alaska
0.1%
-3.2%
-1.5%
Arizona
-25.0%
-15.4%
-20.2%
Arkansas
-15.1%
-5.4%
-10.2%
California
-4.6%
-2.4%
-3.5%
Colorado
8.3%
25.6%
17.0%
Florida
3.7%
-4.4%
-0.3%
Idaho
-50.8%
-3.3%
-27.0%
Illinois
15.6%
1.3%
8.5%
Indiana
-14.5%
-5.3%
-9.9%
Kansas
-8.3%
-3.1%
-5.7%
Kentucky
-5.3%
-8.6%
-6.9%
Louisiana
32.2%
-8.0%
12.1%
Maryland
-59.4%
#N/A
-59.4%
Michigan
-7.1%
-1.6%
-4.4%
Mississippi
-7.5%
-4.7%
-6.1%
Missouri
0.0%
-17.2%
-8.6%
Montana
-4.8%
4.0%
-0.4%
Nebraska
-4.8%
-3.2%
-4.0%
Nevada
0.0%
-10.8%
-5.4%
New Mexico
16.5%
44.6%
30.5%
26
-------
State
Natural Gas
growth
Oil growth
Combination gas/oil growth
New York
-6.5%
20.1%
6.8%
North Dakota
25.0%
17.9%
21.4%
Ohio
34.2%
13.8%
24.0%
Oklahoma
14.4%
20.2%
17.3%
Oregon
-24.3%
#N/A
-24.3%
Pennsylvania
14.9%
-1.3%
6.8%
South Dakota
-6.1%
-2.4%
-4.2%
Tennessee
10.1%
-22.5%
-6.2%
Texas District 1
4.1%
8.0%
6.1%
Texas District 10
-5.2%
-0.8%
-3.0%
Texas District 2
9.7%
10.4%
10.1%
Texas District 3
10.8%
21.2%
16.0%
Texas District 4
-5.8%
0.8%
-2.5%
Texas District 5
-6.9%
-5.8%
-6.3%
Texas District 6
19.1%
-2.2%
8.4%
Texas District 7B
-4.8%
-4.7%
-4.8%
Texas District 7C
15.4%
15.0%
15.2%
Texas District 8
45.9%
51.3%
48.6%
Texas District 8A
5.5%
2.9%
4.2%
Texas District 9
-7.5%
-5.7%
-6.6%
Utah
-6.1%
7.8%
0.8%
Virginia
-3.4%
-28.6%
-16.0%
West Virginia
17.0%
34.7%
25.8%
Wyoming
0.3%
16.2%
8.2%
Table 2-5. 2017 NEI-based sources in ptoilgas (excluding offshore) before and after projections to
2018
Pollutant
Before
projections
After projections
% change 2017 to 2018
CO
67,208
73,687
+9.6%
NH3
259.3
258.7
-0.3%
NOX
104,804
114,595
+9.3%
PM10-PRI
4,730
5,028
+6.3%
PM25-PRI
4,441
4,737
+6.7%
S02
2,725
2,847
+4.5%
VOC
64,152
71,193
+11.0%
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 2018v2 platform have
been updated from the 2018gc inventory by using a March 22, 2022 export from EIS.
27
-------
The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on
paved or unpaved roads at industrial facilities, coal handling at coal mines, and grain elevators. Sources
with state/county FIPS code ending with "777" are in the NEI but are not included in any modeling
sectors. These sources typically represent mobile (temporary) asphalt plants that are only reported for
some states and are generally in a fixed location for only a part of the year and are therefore difficult to
allocate to specific places and days as is needed for modeling. Therefore, these sources are dropped from
the point-based sectors in the modeling platform.
For 2018v2, 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.
Emissions from rail yards are included in the ptnonipm sector. Railyards were projected to 2018 from the
2017 NEI railyard inventory using factors derived from the Annual Energy Outlook 2018
(http s: //www, ei a. gov/outl ooks/archive/aeo 18/).
2.1.4 Aircraft and ground support equipment (airports)
Emissions at airports were separated from other sources in the point inventory based on sources that have
the facility source type of 100 (airports). The airports sector includes all aircraft types used for public,
private, and military purposes and aircraft ground support equipment. The Federal Aviation
Administration's (FAA) Aviation Environmental Design Tool (AEDT) is used to estimate emissions for
this sector. For 2017, Texas and California submitted aircraft emissions. Additional information about
aircraft emission estimates can be found in section 3.2.2 of the 2017 NEI TSD. Terminal Area Forecast
(TAF) data were used to project 2017 NEI emissions to 2018. EPA used airport-specific factors where
available. Regional airports were projected using state average factors. Military airports were unchanged
from 2017. An update for the 2018 platform was that airport emissions were spread out into multiple
12km grid cells when the airport runways were determined to overlap multiple grid cells. Otherwise,
airport emissions for a specific airport are confined to one air quality model grid cell. The SCCs included
in the airport sector are shown in Table 2-6.
Table 2-6. SCCs for the airports sector
SCC
Tier 1
description
Tier 2 description
Hit 3 description
Tier 4 description
2265008005
Mobile
Sources
Off-highway
Vehicle Gasoline,
4-stroke
Airport Ground
Support Equipment
Airport Ground Support
Equipment
2267008005
Mobile
Sources
LPG
Airport Ground
Support Equipment
Airport Ground Support
Equipment
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
28
-------
sec
Tier 1
description
Tier 2 description
Tier 3 description
Tier 4 description
2275060011
Mobile
Sources
Aircraft
Air Taxi
Piston
2275060012
Mobile
Sources
Aircraft
Air Taxi
Turbine
2275070000
Mobile
Sources
Aircraft
Aircraft Auxiliary
Power Units
Total
2.2 Nonpoint sources (afdust, fertilizer, livestock, np oilgas,
npsoivents, 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 2-7 is a listing of the Source Classification Codes
(SCCs) in the afdust sector. For 2018v2 no changes were made from the year 2018 afdust inventory in
2018gc.
Table 2-7. Afdust sector SCCs
sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2294000000
Mobile Sources
Paved Roads
All Paved Roads
Total: Fugitives
2296000000
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
2311010000
Industrial
Processes
Construction: SIC
15-17
Residential
Total
2311020000
Industrial
Processes
Construction: SIC
15-17
Industrial/Commercial/
Institutional
Total
2311030000
Industrial
Processes
Construction: SIC
15-17
Road Construction
Total
2325000000
Industrial
Processes
Mining and
Quarrying: SIC 14
All Processes
Total
29
-------
sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2325020000
Industrial
Processes
Mining and
Quarrying: SIC 14
Crushed and Broken Stone
Total
2325030000
Industrial
Processes
Mining and
Quarrying: SIC 14
Sand and Gravel
Total
2325060000
Industrial
Processes
Mining and
Quarrying: SIC 10
Lead Ore Mining and Milling
Total
2801000000
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Total
2801000003
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Tilling
2801000005
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Harvesting
2801000008
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Transport
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
2805001010
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy Cattle
Dust Kicked-up by Hooves
2805001020
Miscellaneous
Area Sources
Ag. Production -
Livestock
Broilers
Dust Kicked-up by Feet
2805001030
Miscellaneous
Area Sources
Ag. Production -
Livestock
Layers
Dust Kicked-up by Feet
2805001040
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine
Dust Kicked-up by Hooves
2805001050
Miscellaneous
Area Sources
Ag. Production -
Livestock
Turkeys
Dust Kicked-up by Feet
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.
Paved road dust emissions were projected from the 2017 NEI (January 2021 version) to 2018 based on
county-level VMT trends. 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) 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%
30
-------
unadjusted emissions, and after SMOKE all afdust sources have both transport and meteorological
adjustments applied. The total impacts of the transport fraction and meteorological adjustments are shown
in Table 2-8. 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 not included in this modeling.
Table 2-8. Total impact of fugitive dust adjustments to the unadjusted 2018 inventory
Sliilc
I iiiidjiislcd
PMio
I iiiidjuslcd
PM2.5
Ch.ingi' in
PM10
Ch.ingi' in
P\i: 5
PM10
Reduction
I'M:.?
Rcduclion
Alabama
305,367
41,144
-230,323
-31,003
75%
75%
Arizona
181,909
24,406
-66,546
-8,746
37%
36%
Arkansas
394,141
54,562
-291,744
-39,859
74%
73%
California
310,409
39,283
-129,849
-15,926
42%
41%
Colorado
282,333
41,177
-138,166
-19,575
49%
48%
Connecticut
24,373
4,018
-20,564
-3,402
84%
85%
Delaware
15,399
2,363
-10,975
-1,698
71%
72%
District of
Columbia
2,904
408
-2,045
-287
70%
70%
Florida
399,417
55,840
-232,626
-32,611
58%
58%
Georgia
296,293
42,313
-221,055
-31,383
75%
74%
Idaho
566,157
65,518
-293,324
-32,981
52%
50%
Illinois
1,113,448
160,670
-743,938
-106,939
67%
67%
Indiana
145,326
27,135
-104,222
-19,547
72%
72%
Iowa
388,521
57,174
-272,484
-40,050
70%
70%
Kansas
671,159
89,522
-326,621
-43,144
49%
48%
Kentucky
177,791
29,057
-143,563
-23,399
81%
81%
Louisiana
180,054
27,493
-124,363
-18,843
69%
69%
Maine
71,361
8,748
-62,096
-7,617
87%
87%
Maryland
75,016
12,001
-55,750
-8,968
74%
75%
Massachusetts
63,362
9,769
-53,378
-8,193
84%
84%
Michigan
295,317
38,890
-226,158
-29,569
77%
76%
Minnesota
426,574
60,081
-322,412
-45,022
76%
75%
Mississippi
450,394
55,051
-334,736
-40,639
74%
74%
Missouri
1,343,746
159,274
-923,739
-109,115
69%
69%
Montana
503,637
66,766
-315,146
-40,657
63%
61%
Nebraska
518,777
71,853
-287,865
-39,258
55%
55%
Nevada
137,960
18,342
-45,995
-6,095
33%
33%
New Hampshire
20,797
4,369
-18,572
-3,901
89%
89%
New Jersey
32,650
6,098
-25,454
-4,715
78%
77%
New Mexico
212,784
26,470
-81,954
-10,159
39%
38%
New York
235,609
33,253
-196,117
-27,572
83%
83%
North Carolina
237,482
32,163
-177,764
-24,084
75%
75%
31
-------
Stale
I nad.jiislcd
PMu.
I nad.jiislcd
I'M:..*
Chanel' in
PMu.
Chanel' in
I'M:?
PMio
Reduction
PM:;
Reduction
North Dakota
392,449
60,817
-249,067
-38,155
63%
63%
Ohio
273,606
42,727
-208,705
-32,606
76%
76%
Oklahoma
606,070
82,689
-324,863
-43,387
54%
52%
Oregon
611,834
69,018
-391,320
-43,250
64%
63%
Pennsylvania
136,244
24,437
-114,081
-20,670
84%
85%
Rhode Island
4,674
780
-3,735
-624
80%
80%
South Carolina
120,222
16,728
-85,592
-11,963
71%
72%
South Dakota
216,781
38,647
-127,869
-22,524
59%
58%
Tennessee
142,420
26,141
-109,301
-20,163
77%
77%
Texas
1,345,665
195,743
-683,391
-96,971
51%
50%
Utah
170,178
21,730
-84,218
-10,590
49%
49%
Vermont
76,848
8,552
-68,663
-7,617
89%
89%
Virginia
126,183
20,340
-101,285
-16,401
80%
81%
Washington
233,671
38,073
-127,588
-20,758
55%
55%
West Virginia
85,562
11,078
-77,773
-10,070
91%
91%
Wisconsin
184,558
31,386
-138,771
-23,555
75%
75%
Wyoming
545,710
61,315
-285,547
-31,827
52%
52%
Domain Total
(12km CONUS)
15,353,146
2,115,413
-9,661,314
-1,326,091
63%
63%
Alaska
107,706
11,726
-99,218
-10,749
92%
92%
Hawaii
18,243
2,381
-10,203
-1,359
56%
57%
Puerto Rico
1,138,725
152,073
-1,079,286
-144,873
95%
95%
Virgin Islands
1,777
245
-860
-120
48%
49%
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.
32
-------
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction and
precipitation
2018gc afdust annual : PM2 5, xportfrac adjusted - unadjusted
Max: 0.0 Min: -1835.166'
2018gc afdust annual : PM2 5, precip adjusted - xportfrac adjusted
Max: 0.0 Min: -1782
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2018gc 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 SCCs included in the livestock sector are shown in Table 2-9. The livestock SCCs are related to beef
and dairy cattle, poultry production and waste, swine production, waste from horses and ponies, and
production and waste for sheep, lambs, and goats. The sector does not include quite all of the livestock
NH3 emissions, as there is a very small amount of NH3 emissions from livestock in the ptnonipm
inventory (as point sources). In addition to NEb.the sector includes livestock emissions from all pollutants
other than PM2.5. PM2.5 from livestock are in the afdust sector. For 2018v2, corrections were made to the
livestock emissions in Maryland and Illinois. Otherwise, the livestock emissions are unchanged from
those in 2018gc.
Table 2-9. 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
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S( (
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2805010100
Miscellaneous Area
Sources
Ag. Production -
Livestock
Poultry production - turkeys
Confinement
2805018000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Dairy cattle composite
Not Elsewhere Classified
2805025000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Swine production composite
Not Elsewhere Classified
(see also 28-05-039, -047, -
053)
2805035000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Horses and Ponies Waste
Emissions
Not Elsewhere Classified
2805040000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Sheep and Lambs Waste
Emissions
Total
2805045000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Goats Waste Emissions
Not Elsewhere Classified
Agricultural livestock emissions in the 2018 platform were projected from the 2017 NEI (January 2021
version), which is a mix of state-submitted data and EPA estimates. USDA Survey data for 2017 and
2018 was used to create projection factors (https://quickstats.nass.usda.gov/). The resulting projections
factors are shown in Table 2-10. Livestock emissions utilized improved animal population data. VOC
livestock emissions, new for this sector, were estimated by multiplying a national VOC/NH3 emissions
ratio by the county NH3 emissions. The 2017 NEI approach for livestock utilizes daily emission factors by
animal and county from a model developed by Carnegie Mellon University (CMU) (Pinder, 2004,
McQuilling, 2015) and 2017 U.S. Department of Agriculture (USDA) National Agricultural Statistics
Service (NASS) survey. Details on the approach are provided in Section 4.5 of the 2017 NEI TSD. The
livestock sector includes VOC and HAP VOC in addition to NH3.
Table 2-10. National projection factors for livestock: 2017 to 2018
beef
+0.74%
swine
+2.66%
broilers
+2.18%
turkeys
-1.37%
layers
+2.19%
dairy
+0.55%
2.2.3 Agricultural Fertilizer (fertilizer)
Using the same method described in the 2017 NEI TSD, fertilizer emissions for 2018 are based on the
FEST-C model (https://www.cmascenter.org/fest-c/). Unlike most of the other emissions that are input to
the CMAQ model, fertilizer emissions are actually output from a run of CMAQ in bi-directional mode
and summarized for inclusion with the rest of the emissions. The bidirectional version of CMAQ (v5.3)
and the Fertilizer Emissions Scenario Tool for CMAQ FEST-C (vl.3) were used to estimate ammonia
(NH3) emissions from agricultural soils. The computed emissions were saved during the CMAQ run for
the purposes of summaries and other model runs that did not use the bidirectional method.
Fertilizer emissions are associated with the SCC 2801700099 (Miscellaneous Area Sources; Ag.
Production - Crops; Fertilizer Application; Miscellaneous Fertilizers).
The approach to estimate year-specific fertilizer emissions consists of these steps:
35
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• 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
NHS emission estimates.
• Calculate county-level emission factors as the ratio of bidirectional CMAQ NH3 fertili zer
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.
As illustrated in Figure 2-2, an iterative calculation was applied to estimate fertilizer emissions for the
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 NHS emissions.
Figure 2-2. "Bidi" modeling system used to compute fertilizer application emissions
The Fertilizer Emission Scenario Tool for CMAQ
(FEST-C)
36
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Fertilizer Activity Data
The following activity parameters were input into the EPIC model:
• Grid cell meteorological variables from WRF
• Initial soil profiles/soil selection
• Presence of 21 major crops: irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn,
silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans,
spring wheat, winter wheat, canola, and other crops (e.g., lettuce, tomatoes, etc.)
• Fertilizer sales to establish the type/composition of nutrients applied
• Management scenarios for the 10 USDA production regions. These include irrigation, tile
drainage, intervals between forage harvest, fertilizer application method (injected versus surface
applied), and equipment commonly used in these production regions.
The WRF meteorological model was used to provide grid cell meteorological parameters for the base year
using a national 12-km rectangular grid covering the continental U.S. The meteorological parameters in
Table 2-11 were used as EPIC model inputs.
Table 2-11. Source of input variables for EPIC
EPIC input variable
Variable Source
Daily Total Radiation (MJ/m2 )
WRF
Daily Maximum 2-m Temperature (C)
WRF
Daily minimum 2-m temperature (C)
WRF
Daily Total Precipitation (mm)
WRF
Daily Average Relative Humidity (unitless)
WRF
Daily Average 10-m Wind Speed (m s"1 )
WRF
Daily Total Wet Deposition Oxidized N (g/ha)
CMAQ
Daily Total Wet Deposition Reduced N (g/ha)
CMAQ
Daily Total Dry Deposition Oxidized N (g/ha)
CMAQ
Daily Total Dry Deposition Reduced N (g/ha)
CMAQ
Daily Total Wet Deposition Organic N (g/ha)
CMAQ
Initial soil nutrient and pH conditions in EPIC were based on the 1992 USDA Soil Conservation Service
(CSC) Soils-5 survey. The EPIC model was then run for 25 years using current fertilization and
agricultural cropping techniques to estimate soil nutrient content and pH.
The presence of crops in each model grid cell was determined using USDA Census of Agriculture data
(2012) and USGS National Land Cover data (2011). These two data sources were used to compute the
fraction of agricultural land in a model grid cell and the mix of crops grown on that land.
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
37
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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.).
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 (np oilgas) 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 2018. 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 2018 non-point oil and gas inventory for the 2018v2 platform using the 2017NEI
version of the Oil and Gas Emission Estimation Tool (the "Tool") with year 2018 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 2018 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 2018v2 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 Revisions Vl 41 l_2019.docx" (ERG,
2019a) was generated that provides technical details of how the tool was applied for the 2017NEI. The
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 2018v2 platform
instead of emissions derived from the EPA Oil and Gas Tool. The 2017NEI oil and gas emissions for
38
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production-related sources were used for the states of California, Colorado, Oklahoma, Texas, Utah and
Wyoming. New Mexico and North Dakota used the WRAP inventory used in the 2016v3 modeling for
production-related sources. Emissions from exploration-related sources can vary year to year more so than
production-related sources, so the 2018 Oil and Gas Tool emissions for exploration-related sources were
used for every state for the 2018v2 modeling platform.
In Pennsylvania for the 2018v2 modeling platform, the emissions associated with unconventional wells
for year 2018 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 2018. Together these
unconventional and conventional well emissions represent the total non-point oil and gas emissions for
Pennsylvania.
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 2018 platform RWC emissions are unchanged from the data in the 2017 NEI. Some improvements to
RWC emissions estimates were made for the 2017 NEI and were included in this study. The EPA, along
with the Commission on Environmental Cooperation (CEC), the Northeast States for Coordinated Air Use
Management (NESCAUM), and Abt Associates, conducted a national survey of wood-burning activity in
2018. The results of this survey were used to estimate county-level burning activity data. The activity data
for RWC processes is the amount of wood burned in each county, which is based on data from the CEC
survey on the fraction of homes in each county that use each wood-burning appliance and the average
amount of wood burned in each appliance. These assumptions are used with the number of occupied
homes in each county to estimate the total amount of wood burned in each county, in cords for cordwood
appliances and tons for pellet appliances. Cords of wood are converted to tons using county-level density
factors from the U.S. Forest Service. RWC emissions were calculated by multiplying the tons of wood
burned by emissions factors. For more information on the development of the residential wood
combustion emissions, see Section 4.15 of the 2017 NEI TSD
The source classification codes (SCCs) in the RWC sector are listed in Table 2-12. For both 2018gc and
2018v2, the emissions use the 2017 NEI.
Table 2-12. SCCs for the residential wood combustion sector
SCC
Tier 1 Description
Tier 2
Description
Tier 3
Description
Tier 4 Description
2104008100
Stationary Source
Fuel Combustion
Residential
Wood
Fireplace: general
2104008210
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts;
non-EPA certified
2104008220
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts;
EPA certified; non-catalytic
39
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S( (
Tier 1 Description
Tier 2
Description
Tier 3
Description
Tier 4 Description
2104008230
Slaliuiiary Suui'ce
Fuel Combustion
Residential
Wood
Wuudsluve. fireplace inserts,
EPA certified; catalytic
2104008310
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding, non-
EPA certified
2104008320
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding, EPA
certified, non-catalytic
2104008330
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding, EPA
certified, catalytic
2104008400
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: pellet-fired, general
(freestanding or FP insert)
2104008510
Stationary Source
Fuel Combustion
Residential
Wood
Furnace: Indoor, cordwood-fired,
non-EPA certified
2104008610
Stationary Source
Fuel Combustion
Residential
Wood
Hydronic heater: outdoor
2104008700
Stationary Source
Fuel Combustion
Residential
Wood
Outdoor wood burning device,
NEC (fire-pits, chimineas, etc)
2104009000
Stationary Source
Fuel Combustion
Residential
Firelog
Total: All Combustor Types
2.2.6 Solvents (np_solvents)
The npsolvents sector includes a diverse collection of 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).
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 2018v2 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
40
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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 and are retrieved from the Federal Reserve Bank of
St. Louis (U.S. Bureau of Labor Statistics, 2020).
When national product usage 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). All methods are thoroughly documented in Section 32 of
the 2020 NEI Technical Source Document.
National-level emissions estimates 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, 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 2020 National Emissions Inventory (EPA, 2023b). 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.
In addition, point and nonpoint emissions for which SCCs overlap are reconciled 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.
2.2.7 Nonpoint (nonpt)
The 2018 platform nonpt sector inventory is mostly unchanged from the January 2021 version of the 2017
NEI, aside from the removal of emissions from accidental releases in a few states. The nonpt sector
includes all nonpoint sources that are not included in the sectors afdust, livestock, fertilizer, cmv_clc2,
cmv_c3, np oilgas, 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;
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• waste disposal (including composting);
• miscellaneous non-industrial sources such as cremation, hospitals, lamp breakage, and automotive
repair shops;
• bulk gasoline terminals;
• portable fuel containers (i.e., gas cans);
• cellulosic biorefining;
• biomass fuel combustion;
• stage 1 refueling emissions at gas stations;
• and any construction agricultural dust that is not part of the area fugitive dust or livestock sectors.
2.3 Onroad Mobile sources (onroad)
Onroad mobile source include emissions from motorized vehicles operating on public roadways. These
include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks, heavy-duty trucks,
and buses. The sources are further divided by the fuel they use, including diesel, gasoline, E-85, and
compressed natural gas (CNG) vehicles. The sector characterizes emissions from parked vehicle
processes (e.g., starts, hot soak, and extended idle) as well as from on-network processes (i.e., from
vehicles as they move along the roads). For more details on the approach and for a summary of the
MOVES inputs submitted by states, see section 6.5.1 of the 2017 NEI TSD.
For the 2018 modeling platform, VMT were projected from 2017 to 2018 based mostly on Federal
Highways administration (FHWA) annual VMT changes at the county level. In a few cases, state
Department of Transportation (DOT) data were used instead of FHWA data. Other activity data (i.e.,
starts, on-network idling, VPOP, and hoteling) are projected by applying a ratio of 2017-based
VMT/activity ratios to the 2018 VMT. In addition, a number of states submitted 2017-specific activity
data for incorporation into this platform. Finally, a new MOVES run for 2018 was done using MOVES3.
Except for California, all onroad emissions are generated using the SMOKE-MOVES emissions modeling
framework that leverages MOVES-generated emission factors https://www.epa.gov/moves). county and
SCC-specific activity data, and hourly 2018 meteorological data. Specifically, EPA used MOVES3 inputs
for representative counties, vehicle miles traveled (VMT), vehicle population (VPOP), and hoteling hours
data for all counties, along with tools that integrated the MOVES model with SMOKE. In this way, it was
possible to take advantage of the gridded hourly temperature data available from meteorological modeling
that are also used for air quality modeling. The onroad source classification codes (SCCs) in the modeling
platform are more finely resolved than those in the National Emissions Inventory (NEI). The NEI SCCs
distinguish vehicles and fuels. The SCCs used in the model platform also distinguish between emissions
processes (i.e., off-network, on-network, and extended idle), and road types.
MOVES3 includes the following updates from MOVES2014b:
• Updated emission rates:
o Updated heavy-duty (HD) diesel running emission rates based on manufacturer in-use
testing data from hundreds of HD trucks
o Updated HD gasoline and compressed natural gas (CNG) trucks
o Updated light-duty (LD) emission rates for hydrocarbons (HC), CO, NOx, and PM
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• Includes updated fuel information
• Incorporates HD Phase 2 Greenhouse Gas (GHG) rule, allowing for finer distinctions among HD
vehicles
• Accounts for glider vehicles that incorporate older engines into new vehicle chassis
• Accounts for off-network idling - emissions beyond the idling that is already considered in the
MOVES drive cycle
• Includes revisions to inputs for hoteling
• Adds starts as a separate type of rate and activity data
2.3.1 Inventory Development using SMOKE-MOVES
Except for California, onroad emissions were computed with SMOKE-MOVES by multiplying specific
types of vehicle activity data by the appropriate emission factors. This section includes discussions of the
activity data and the emission factor development. The vehicles (aka source types) for which MOVES
computes emissions are shown in Table 2-13. 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 portion of Southeast Alaska which lies inside the 36US3 modeling domain, SMOKE-
MOVES was run using meteorology for the 36US3 domain; this extra run is included in the
onroad nonconus sector. In some summary reports these non-CONUS emissions are aggregated with
emissions from the onroad sector.
Table 2-13. MOVES vehicle (source) types
MOYKS vehicle Ivpe
Description
II P.MS vehicle Ivpe
11
Motorcycle
10
21
Passenger Car
25
31
Passenger Truck
25
32
Light Commercial Truck
25
41
Intercity Bus
40
42
Transit Bus
40
43
School Bus
40
51
Refuse Truck
50
52
Single Unit Short-haul Truck
50
53
Single Unit Long-haul Truck
50
54
Motor Home
50
61
Combination Short-haul Truck
60
62
Combination Long-haul Truck
60
SMOKE-MOVES makes use of emission rate "lookup" tables generated by MOVES that differentiate
emissions by process (i.e., running, start, vapor venting, etc.), vehicle type, road type, temperature, speed,
hour of day, etc. To generate the MOVES emission rates that could be applied across the U.S., EPA used
an automated process to run MOVES to produce year 2018-specific emission factors by temperature and
speed for a series of "representative counties," to which every other county was mapped. The
representative counties for which emission factors are generated are selected according to their state,
elevation, fuels, age distribution, ramp fraction, and inspection and maintenance programs. Each county is
then mapped to a representative county based on its similarity to the representative county with respect to
those attributes. For this study, there are 291 representative counties in the continental U.S. and a total of
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329 including the non-CONUS areas. The representative counties that were used for the 2018 platform are
very close to what was used in EPA's Air QUAlity TimE Series (EQUATES) project for the years 2016
and 2017 (EPA 2023c). The EPA added some additional representative counties to the set used for
EQUATES to account for altitude and variations in I&M programs and fuels.
Once representative counties have been identified, emission factors are generated with MOVES for each
representative county and for two "fuel months" - January to represent winter months, and July to
represent summer months - due to the different types of fuels used. SMOKE selects the appropriate
MOVES emissions rates for each county, hourly temperature, SCC, and speed bin and then multiplies the
emission rate by appropriate activity data. For on-roadway emissions, vehicle miles travelled (VMT) is
the activity data, vehicle population (VPOP) is used for many off-network processes, and hoteling hours
are used to develop emissions for extended idling of combination long-haul trucks. These calculations are
done for every county and grid cell in the continental U.S. for each hour of the year.
The SMOKE-MOVES process for creating the model-ready emissions consists of the following steps:
1) Determine which counties will be used to represent other counties in the MOVES runs.
2) Determine which months will be used to represent other month's fuel characteristics.
3) Create inputs needed only by MOVES. MOVES requires county-specific information on vehicle
populations, age distributions, and inspection-maintenance programs for each of the
representative counties.
4) Create inputs needed both by MOVES and by SMOKE, including temperatures and activity
data.
5) Run MOVES to create emission factor tables for the temperatures found in each county.
6) Run SMOKE to apply the emission factors to activity data (VMT, VPOP, STARTS, off-network
idling, and HOTELING) to calculate emissions based on the gridded hourly temperatures in the
meteorological data.
7) Aggregate the results to the county-SCC level for summaries and quality assurance.
The onroad emissions are processed in six processing streams that are merged together into the onroad
sector emissions after each of the six streams have been processed:
• rate-per-distance (RPD) uses VMT as the activity data plus speed and speed profile information to
compute on-network emissions from exhaust, evaporative, permeation, refueling, and brake and
tire wear processes;
• rate-per-vehicle (RPV) uses VPOP activity data to compute off-network emissions from exhaust,
evaporative, permeation, and refueling processes;
• rate-per-profile (RPS) uses STARTS activity data to compute off-network emissions from vehicles
starts;
• rate-per-profile (RPP) uses VPOP activity data to compute off-network emissions from
evaporative fuel vapor venting, including hot soak (immediately after a trip) and diurnal (vehicle
parked for a long period) emissions;
• rate-per-hour (RPH) uses hoteling hours activity data to compute off-network emissions for idling
of long-haul trucks from extended idling and auxiliary power unit process; and
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• rate-per-hour off-network idling (RPHO) uses off network idling hours activity data to compute
off-network idling emissions for all types of vehicles.
The onroad emissions inputs to MOVES for the 2018 platform are based on the 2017 NEI, development
of which is described in more detail in Section 6 of the 2017 NEI TSD. These inputs include:
• Key parameters in the MOVES County databases (CDBs) including Low Emission Vehicle (LEV)
table
• Fuel months
• Activity data (e.g., VMT, VPOP, speed, HOTELING)
Fuel months and other inputs were consistent with those in the 2017 NEI. Age distributions in the
MOVES databases were adjusted to represent the year 2018. States that submitted activity data and
development of the EPA default activity data sets for VMT, VPOP, and hoteling hours are described in
detail in the 2017 NEI TSD and supporting documents. Hoteling hours activity are used to calculate
emissions from extended idling and auxiliary power units (APUs) by combination long-haul trucks.
2.3.2 Onroad Activity Data Development
SMOKE-MOVES uses vehicle miles traveled (VMT), vehicle population (VPOP), vehicle starts, hours of
off-network idling (ONI), and hours of hoteling, to calculate emissions. These datasets are collectively
known as "activity data." For each of these activity datasets, first a national dataset was developed; this
national dataset is called the "EPA default" dataset. The default dataset started with the 2017 NEI activity
data, which was supplemented with data submitted by state and local agencies. EPA default activity was
used for California, but the emissions were scaled to California-supplied values during the emissions
processing.
Vehicle Miles Traveled (VMT) and Vehicle Population (VPOP)
States that submitted activity data and development of the EPA default activity data sets for VMT, VPOP,
and hoteling hours are described in detail in the 2017 NEI TSD (EPA, 2021) and supporting documents.
For the 2018 modeling platform, VMT were projected from 2017 to 2018 based mostly on Federal
Highways administration (FHWA) annual VMT changes at the county level. In Georgia, state Department
of Transportation (DOT) data were used instead of FHWA data. In Oklahoma, human population trends
were used.
Speed Activity (SPEED/SPDIST)
In SMOKE 4.7, SMOKE-MOVES was updated to use speed distributions similarly to how they are used
when running MOVES in inventory mode. This new speed distribution file, called SPDIST, specifies the
amount of time spent in each MOVES speed bin for each county, vehicle (aka source) type, road type,
weekday/weekend, and hour of day. This file contains the same information at the same resolution as the
Speed Distribution table used by MOVES but is reformatted for SMOKE. Using the SPDIST file results
in a SMOKE emissions calculation that is more consistent with MOVES than the old hourly speed profile
(SPDPRO) approach, because emission factors from all speed bins can be used, rather than interpolating
between the two bins surrounding the single average speed value for each hour as is done with the
SPDPRO approach.
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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 for
2017NEI are based on a combination of the CRC A-100 (CRC, 2017) project data and 2017 NEI MOVES
CDBs.
Hoteline Hours (HOTELING)
Hoteling hours were capped by county at a theoretical maximum and any excess hours of the maximum
were reduced. For calculating reductions, a dataset of truck stop parking space availability was used,
which includes a total number of parking spaces per county. This same dataset is used to develop the
spatial surrogate for allocating county-total hoteling emissions to model grid cells. The parking space
dataset includes several recent updates based on new truck stops opening and other new information.
There are 8,760 hours in the year 2018; therefore, the maximum number of possible hoteling hours in a
particular county is equal to 8,760 * the number of parking spaces in that county. Hoteling hours were
capped at that theoretical maximum value for 2017 in all counties, with some exceptions.
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. Four states requested that no reductions be applied to the hoteling activity
based on parking space availability: CO, ME, NJ, and NY. For these states, reductions based on parking
space availability were not applied.
The final step related to hoteling activity is to split county totals into separate values for extended idling
(SCC 2202620153) and Auxiliary Power Units (APUs) (SCC 2202620191). New Jersey's submittal of
hoteling activity specified a 30% APU split, and this was used throughout NJ. For the rest of the country,
a 12.4% APU split was used, meaning that during 12.4% of the hoteling hours auxiliary power units are
assumed to be running.
Starts
Onroad "start" emissions are the instantaneous exhaust emissions that occur at the engine start (e.g., due
to the fuel rich conditions in the cylinder to initiate combustion) as well as the additional running exhaust
emissions that occur because the engine and emission control systems have not yet stabilized at the
running operating temperature. Operationally, start emissions are defined as the difference in emissions
between an exhaust emissions test with an ambient temperature start and the same test with the
engine and emission control systems already at operating temperature. As such, the units for start
emission rates are instantaneous grams/start.
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
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
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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.
2018 STARTS = 2018 VMT * (2017 STARTS/ 2017 VMT by county&SCC6)
Off-network Idlins Hours
After creating VMT inputs for SMOKE-MOVES, Off-network idle (ONI) activity data were also needed.
ONI is defined in MOVES as time during which a vehicle engine is running idle and the vehicle is
somewhere other than on the road, such as in a parking lot, a driveway, or at the side of the road. This
engine activity contributes to total mobile source emissions but does not take place on the road network.
Examples of ONI activity include:
light duty passenger vehicles idling while waiting to pick up children at school or to pick up
passengers at the airport or train station,
single unit and combination trucks idling while loading or unloading cargo or making
deliveries, and
vehicles idling at drive-through restaurants.
Note that ONI does not include idling that occurs on the road, such as idling at traffic signals, stop signs,
and in traffic—these emissions are included as part of the running and crankcase running exhaust
processes on the other road types. ONI also does not include long-duration idling by long-haul
combination trucks (hoteling/extended idle), as that type of long duration idling is accounted for in other
MOVES processes.
ONI activity hours were calculated based on VMT. For each representative county, the ratio of ONI hours
to onroad VMT (on all road types) was calculated using the MOVES ONI Tool by source type, fuel type,
and month. These ratios are then multiplied by each county's total VMT (aggregated by source type, fuel
type, and month) to get hours of ONI activity.
2.3.3 MOVES Emission Factor Table Development
MOVES3 was run in emission rate mode to create emission factor tables using CB6 speciation for 2018,
for all representative counties and fuel months. The county databases used to run MOVES to develop the
emission factor tables included the state-specific control measures such as the California LEV program,
and fuels represented the year 2018. The range of temperatures run along with the average humidities
used were specific to the year 2018. The remaining settings for the CDBs are documented in the 2017
NEITSD. To create the emission factors, MOVES was run separately for each representative county and
fuel month for each temperature bin needed for the calendar year 2018. The MOVES results were post-
processed into CSV-formatted emission factor tables that can be read by SMOKE-MOVES. Additionally,
MOVES was run for all counties in Alaska, Hawaii, and Virgin Islands, and for a single representative
county in Puerto Rico, although no air quality modeling was done for these areas outside the contiguous
US.
The county databases CDBs used to run MOVES to develop the emission factor tables were 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
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were not accepted from S/L submissions, those data were obtained from the CRC A-100 study. 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, and the mean light-duty age. A binning algorithm was
executed to identify "like counties." The result was 291 representative counties for CONUS and 38 for
Alaska, Hawaii, Puerto Rico, and the US Virgin Islands, similar to the one shown in Figure 2-3 except for
a few changes in North Carolina and Nebraska. In North Carolina there are 12 representative counties for
2018, while the figure shows 16. In Nebraska, Loop County (31115) is a separate representative county
but that is not shown in the figure.
Figure 2-3. Map of Representative Counties
Age distributions are a key input to MOVES in determining emission rates. The age distributions for 2017
were updated based on vehicle registration data obtained from the CRC A-l 15 project (CRC, 2019),
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 is that IHS data contain higher vehicle populations than state agency
analyses of the same Department of Motor Vehicles data, and the discrepancies tend to increase with
increasing vehicle age (i.e., there are more older vehicles in the IHS data).
For the 2017 NEI, 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)
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agencies for the 2017 NEI. The comparisons by model year were used to develop adjustment factors that
remove older age LDVs from the IHS dataset. Out of 31 S/L agencies that provided 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 (back to 2013) data pull dates, so were compared to the 2017 IHS 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 source type 21 and light trucks to match 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 as one
minus the fraction of vehicles to remove from IHS to equal the state data, with two exceptions: (1) the
model year range 2006/2007 to 2017 receives no adjustment and (2) the model year 1987 receives a
capped adjustment that equals the adjustment to 1988. Table 2-14 below shows the fraction of vehicles to
keep by model year based on this analysis. The adjustments were applied to the 2017 IHS-based age
distributions from CRC project A-l 15 prior to use in this 2017 platform. In addition, the 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 30 and up bins, 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 as high as 6 percent in some states (e.g., Mississippi).
Table 2-14. Fraction of IHS Vehicle Populations to Retain
Model
Cars
Light
ore-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
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limited to LDV source types 21,31, and 32. Many rural counties have outliers for low-population source
types such as Transit Bus and Refuse Truck due to small sample sizes, but these do not have much of an
impact on the inventory overall and reflect sparse data in low-population areas and therefore do not
require correction.
The most extreme examples of LDV outliers were Light Commercial Truck age distributions where over
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 2017 NEI age distributions were then grouped using a population-weighted average of the source
type populations of each county in the representative county group, and were updated to represent the
year 2018. The resulting end-product was age distributions for each of the 13 source types in each of the
representative counties. The long-haul truck source types 53 (Single Unit) and 62 (Combination Unit) are
based on a nationwide average due to the long-haul nature of their operation.
To create the emission factors, MOVES was run separately for each representative county and fuel month
and for each temperature bin needed for calendar year 2018. The CDBs used to run MOVES include the
state-specific control measures such as the California low emission vehicle (LEV) program, except that
fuels were updated to represent calendar year 2018. In addition, the range of temperatures run along with
the average humidities used were specific to the year 2018. The MOVES results were post-processed into
CSV-formatted emission factor tables that can be read by SMOKE-MOVES.
2.3.4 Onroad California Inventory Development (onroad ca)
California uses their own emission model, EMFAC, to develop onroad emissions inventories and provides
those inventories to EPA. EMFAC uses emission inventory codes (EICs) to characterize the emission
processes instead of SCCs. The EPA and California worked together to develop a code mapping to better
match EMFAC's EICs to EPA MOVES' detailed set of SCCs that distinguish between off-network and
on-network and brake and tire wear emissions. This detail is needed for modeling but not for the NEI.
California provided emissions for 2023 as part of the 2016vl platform development. EPA interpolated
between the 2017 and 2023 emissions to calculate the 2018 onroad emissions for California. The
California inventory had CAPs only and did not have NH3 or refueling emissions. The EPA added NH3 to
the CARB inventory by using the state total NH3 from MOVES and allocating it at the county level based
on CO. Refueling emissions were projected from the 2017 NEI using county total refueling VOC from
EQUATES 2017 and the 2018 MOVES3 onroad run for California. CARB VOCs were speciated to VOC
HAPs using MOVES VOC speciation. All other HAPs (e.g., metals and PAHs) are from MOVES.
The California onroad mobile source emissions were created through a hybrid approach of combining
state-supplied annual emissions with EPA-developed SMOKE-MOVES runs. Through this approach, the
platform was able to reflect the California-developed emissions, while leveraging the more detailed SCCs
and the highly resolved spatial patterns, temporal patterns, and speciation from SMOKE-MOVES. The
basic steps involved in temporally allocating onroad emissions from California based on SMOKE-
MOVES results were:
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1) Run CA using EPA inputs through SMOKE-MOVES to produce hourly emissions hereafter
known as "EPA estimates." These EPA estimates for CA are run in a separate sector called
"onroadca."
2) Calculate ratios between state-supplied emissions and EPA estimates. The ratios were calculated
for each county/SCC/pollutant combination based on the California onroad emissions inventory.
Unlike in previous platforms, the California data separated off and on-network emissions and
extended idling. However, the on-network did not provide specific road types, and California's
emissions did not include information for vehicles fueled by E-85, so these differentiations were
obtained using MOVES.
3) Create an adjustment factor file (CFPRO) that includes EPA-to-state estimate ratios.
4) Rerun CA through SMOKE-MOVES using EPA inputs and the new adjustment factor file.
Through this process, adjusted model-ready files were created that sum to annual totals from California,
but have the temporal and spatial patterns reflecting the highly resolved meteorology and SMOKE-
MOVES. After adjusting the emissions, this sector is called "onroadcaadj " Note that in emission
summaries, the emissions from the "onroad" and "onroad ca adj" sectors are summed and designated as
the emissions for the onroad sector.
2.4 Nonroad Mobile sources (cmv, rail, nonroad)
The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions (nonroad),
locomotive (rail), and CMV emissions.
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
The cmv_clc2 sector contains Category 1 and 2 CMV emissions. Category 1 and 2 vessels use diesel
fuel. All emissions in this sector are annual and at county-SCC resolution; however, in the NEI they are
provided at the sub-county level (i.e., port shape ids, where applicable) and by SCC. For more
information on CMV sources in the 2017 NEI, see Section 4.21 of the 2017 NEI TSD and the
supplemental documentation for 2017 NEI CMV.3 CI and C2 emissions that occur outside of state waters
are not assigned to states. For this modeling platform, all CMV emissions in the cmv_clc2 sector were
treated as hourly gridded point sources with stack parameters that should result in them being placed in
layer 1. The C1C2 CMV emissions were projected from 2017 to 2018 by applying an adjustment factor of
1.012 to the 2017 NEI emissions values.
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
3 https://www.epa.gov/sites/default/files/2019-l 1/cmv methodology documentation.zip.
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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 base year inventory are categorized as operating
either in-port or underway and as main and auxiliary engines are encoded using the SCCs listed in Table
2-13.
Table 2-15. SCCs for cmv clc2 sector
sec
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,4 The 2017 NEI emissions were
developed based signals from Automated Identification System (AIS) transmitters. AIS is a tracking
system used by vessels to enhance navigation and avoid collision with other AIS transmitting vessels. The
USEPA Office of Transportation and Air Quality received AIS data from the U.S. Coast Guard (USCG)
in order to quantify all ship activity which occurred between January 1 and December 31, 2017. The
provided AIS data extends beyond 200 nautical miles from the U.S. coast (Figure 2-4). This boundary is
roughly equivalent to the border of the U.S Exclusive Economic Zone and the North American ECA,
although some non-ECA activity are captured as well.
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).
4 Category 1 and 2 Commercial Marine Vessel 2017 Emissions Inventory (ERG, 2019b).
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Figure 2-4. 2017NEI geographical extent of marine emissions (solid) and the U.S. ECA (dashed)
The engine bore and stroke data were used to calculate cylinder volume. Any vessel with a calculated
cylinder volume greater than 30 liters was incorporated into the USEPA's 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.
Emissions interval = Time (hr interval* Power(kW) EF(g/kWh) LLAF Equation 2-1
Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and
emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF
represents the low load adjustment factor, a unit I ess 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 to determine their vessel type, and thus their vessel group, power
rating, and engine tier information which are required for the emissions calculations. See the 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 2-16. In total, 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.
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Table 2-16. 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. 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 2-15.
The final components of the emissions computation equation are the emission factors and the low load
adjustment factor. The emission factors used in this inventory take into consideration the EPA's marine
vessel fuel regulations as well as exhaust standards that are based on the year that the vessel was
manufactured to determine the appropriate regulatory tier. Emission factors in g/kWhr by tier for NOx,
PMio, PM2.5, CO, CO2, SO2 and VOC were developed using Tables 3-7 through 3-10 in USEPA's (2008)
Regulatory Impact Analysis on engines less than 30 liters per cylinder. To compile these emissions
factors, population-weighted average emission factors were calculated per tier based on C1C2 population
distributions grouped by engine displacement. Boiler emission factors were obtained from an earlier
Swedish Environmental Protection Agency study (Swedish EPA, 2004). If the year of manufacture was
unknown then it was assumed that the vessel was Tier 0, such that actual emissions may be less than those
estimated in this inventory. Without more specific data, the magnitude of this emissions difference cannot
be estimated.
Propulsive emissions from low-load operations were adjusted to account for elevated emission rates
associated with activities outside the engines' optimal operating range. The emission factor adjustments
were applied by load and pollutant, based on the data compiled for the Port Everglades 2015 Emission
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Inventory.5 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 2018 in
the cmv_clc2 sector by applying a factor of 1.012 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 2018 dates so that the activity occurred on the same day of the week in
the same sequential week of the year in both years. Holidays and days of the week were mapped from the
dates in 2017 to the corresponding dates in 2018 to preserve weekday-weekend and holiday-centered
fluctuations in emissions in each of the years. 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. Both the annual and hourly inventory files were projected to 2018 using the same
projection factor of 1.012.
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3)
This sector contains large engine CMV emissions. Category 3 (C3) marine diesel engines are those at or
above 30 liters per cylinder, typically these are the largest engines rated at 3,000 to 100,000 hp. C3
engines are typically used for propulsion on ocean-going vessels including container ships, oil tankers,
bulk carriers, and cruise ships. Emissions control technologies for C3 CMV sources are limited due to the
nature of the residual fuel used by these vessels.6 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. For more information on CMV sources in the 2017 NEI, see Section
4.21 of the 2017 NEI TSD and the supplemental documentation for 2017 NEI CMV.7
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 2018 inventory but are in separate files from the emissions
around the continental United States (CONUS). The cmv_c3 sources in the 2018 inventory are
categorized as operating either in-port or underway and are encoded using the SCCs listed in Table 2-17.
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.8
5 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.
6 https://www.epa.gov/regulations-emissions-vehicles-and-engines/regulations-emissions-marine-vessels.
7 https://www.epa.gov/sites/default/files/2019-l 1/cmv methodology documentation.zip.
8 https://www.epa.gOv/sites/production/files/2017-08/documents/2014v7.0 2014 emismod tsdvl.pdf.
55
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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 2-17. SCCs for cmv c3 sector
see
Tier 1 Description
Tier 2 Description
Tier 3 Description
Tier 4 Deseriplinn
2280002103
C3
Diesel
Port
Main
2280002104
C3
Diesel
Port
Auxiliary
2280002203
C3
Diesel
Underway
Main
2280002204
C3
Diesel
Underway
Auxiliary
2280003103
C3
Residual
Port
Main
2280003104
C3
Residual
Port
Auxiliary
2280003203
C3
Residual
Underway
Main
2280003204
C3
Residual
Underway
Auxiliary
Prior to creation of the 2017 NEI, the EPA received Automated Identification System (AIS) data from
United States Coast Guard (USCG) 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.9 In addition, the USCG has mandated
that all commercial marine vessels continuously transmit AIS signals while transiting U.S. navigable
waters. As the vast majority of C3 vessels meet these requirements, any omitted from the inventory due to
lack of AIS adoption are deemed to have a negligible impact on national C3 emissions estimates. The
activity described by this inventory reflects ship operations within 200 nautical miles of the official U.S.
baseline. This boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the
North American ECA, although some non-ECA activity is captured as well (Figure 2-4).
The 2017 NEI CMV emissions 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 their 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.
9 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.
56
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The emissions were calculated for each C3 vessel in the dataset for each 5-minute time range and
allocated to the location of the message following to the interval. Emissions were calculated according to
Equation 2-2.
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,10 but since the data were already in the form of point sources
at the center of each grid cell, and they were already hourly, no other processing was needed within
SMOKE. SMOKE requires an annual inventory file to go along with the hourly data, so those files were
also generated for each year.
On January 1st, 2015, the ECA initiated a fuel sulfur standard which regulated large marine vessels to use
fuel with 1,000 ppm sulfur or less. These standards are reflected in the cmv_c3 inventories.
There were some areas needed for modeling that the AIS request boxes did not cover (see Figure 2-4).
These include a portion of the St. Lawrence Seaway transit to the Great Lakes, a small portion of the
Pacific Ocean far offshore of Washington State, portions of the southern Pacific Ocean around off the
coast of Mexico, and the southern portion of the Gulf of Mexico that is within the 36-km domain used for
air quality modeling. In addition, a determination had to be made regarding whether to use the existing
Canadian CMV inventory or the more detailed 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
10 Ammonia (NH3) was also added by SMOKE in the speciation step.
57
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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 2018
Both the annual and hourly Category 3 (C3) CMV emissions were projected from 2017 to 2018 using
factors derived from an EPA report on projected bunker fuel demand (See Table 2-18). The report
projects bunker fuel consumption by region out to the year 2030. Bunker fuel usage was used as a
surrogate for marine vessel activity. Factors based on the report were used for all pollutants except NOx.
Growth factors for NOx emissions were handled separately to account for the phase in of Tier 3 vessel
engines. The projection factors are shown in Table 2-18.
Table 2-18. Projection Factors for 2017 to 2018 for Category 3 Vessels
Region
NOx
All other pollutants
US East Coast
0.9869
1.0346
US South Pacific
0.9494
1.0153
US North Pacific
0.9926
1.0246
US Gulf of Mexico
0.9910
1.0253
US Great Lakes
1.0051
1.0173
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)
The rail sector includes all locomotives in the NEI nonpoint data category including line haul locomotives
on Class 1, 2, and 3 railroads along with emissions from commuter rail lines and Amtrak. The rail sector
58
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excludes railway maintenance locomotives and point source yard locomotives. Railway maintenance
emissions are included in the nonroad sector. The point source yard locomotives are included in the
ptnonipm sector. Typically in the NEI, yard locomotive emissions are split between the nonpoint and
point categories, but for this study, all yard locomotive emissions are represented as point sources and
included in the ptnonipm sector.
This study uses the 2017 rail inventory developed for the 2017 NEI by the Lake Michigan Air Directors
Consortium (LADCO) and the State of Illinois with support from various other states. Class I railroad
emissions are based on confidential link-level line-haul activity GIS data layer maintained by the Federal
Railroad Administration (FRA). In addition, the Association of American Railroads (AAR) provided
national emission tier fleet mix information. Class II and III railroad emissions are based on a
comprehensive nationwide GIS database of locations where short line and regional railroads operate.
Passenger rail (Amtrak) emissions follow a similar procedure as Class II and III, except using a database
of Amtrak rail lines. Yard locomotive emissions are based on a combination of yard data provided by
individual rail companies, and by using Google Earth and other tools to identify rail yard locations for rail
companies which did not provide yard data. Information on specific yards were combined with fuel use
data and emission factors to create an emissions inventory for rail yards. Pollutant-specific factors were
applied on top of the activity-based changes for the Class I rail. The inventory SCCs are shown in Table
2-19. More detailed information on the development of the 2017 NEI rail inventory for this study is
available in the 2017 NEI TSD. The 2017 NEI rail inventory was projected to 2018 using activity-based
factors shown in Table 2-20. This activity-based factor was based on AEO fuel data.
Table 2-19. SCCs for the rail sector
sec
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)
Table 2-20. 2017-to-2018 projection factors for the rail sector
NOx
PM
VO(
Other pollutants
+2.44%
-3.29%
-2.95%
+6.63%
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 2017 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) for 2016. In addition, the Association of American Railroads (AAR) provided
national emission tier fleet mix information. This allowed ERTAC Rail to calculate weighted emission
59
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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 2017 Class I line-haul fuel use data reported to the
Surface Transportation Board 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.
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. 2017 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT)
Source: Federal Railroad Administration - June 2018
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
>= 100.00 MGT
60
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Figure 2-6. Class I Railroads in the United States
NS
UP
Source: Federal Railroad Administration - December 2016
Class II and III Methodology
There are approximately 560 Class II and III Railroads operating in the United States, most of which are
members of the American Short Line and Regional Railroad Associ ation (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 2-7 shows the distribution of Class II and III railroads and commuter railroads
across the country.
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Figure 2-7. Class II and III Railroads in the United States
P Haircufci Ad»un vr»i uyn -J une 2018
Commuter Railroads
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. For the 2017 NEI, 2016 fuel use was estimated
for each of the commuter railroads 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.
Intercity Passenger Methodology (Amtrak)
2016 and 2017 marked the first times 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 2-8. 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
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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.
Figure 2-8. Amtrak Routes with Diesel-powered Passenger Trains
Scurc* FdkijJ R**wd - 1m »I*
Other Data Sources
The California Air Resources Board (CARB) provided rail inventories for inclusion in the 2017 NEI.
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, county total rail yard emissions from CARB are
used, 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.
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 off-road vehicles, pleasure craft, and construction, agricultural, mining, and lawn and garden
equipment. Nonroad equipment emissions were computed by running MOVES3 which incorporates the
NONROAD model. MOVES3 incorporated updated nonroad engine population growth rates, nonroad
Tier 4 engine emission rates, and sulfur levels of nonroad diesel fuels. MOVES provides a complete set of
HAPs and incorporates updated nonroad emission factors for HAPs. MOVES3 was used for all states
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other than California, which uses their own model, and the Texas Commission on Environmental Quality
(TCEQ), which provided their own emissions. California nonroad emissions were provided by the
California Air Resources Board (CARB) for the 2017 NEI. The 2018 California nonroad emissions were
interpolated from the 2017 NEI and a 2023 projection from the 2016vl modeling platform, with HAP
augmentation. For Texas, the EPA interpolated to 2018 between data provided for 2017 and 2020 and
applied HAP augmentation.
MOVES creates a monthly emissions inventory for criteria air pollutants (CAPs) and a full set of HAPs,
plus additional pollutants such as NONHAPTOG and ETHANOL, which are not part of the NEI but are
used for speciation. MOVES provides estimates of NONHAPTOG along with the speciation profile code
for the NONHAPTOG emission source. This was accomplished by using NHTOG#### as the pollutant
code in the Flat File 2010 (FF10) inventory file that can be read into SMOKE, where #### is a speciation
profile code. For California and Texas, NHTOG####-VOC and HAP-VOC ratios from MOVES-based
emissions were applied to VOC emissions so that VOC emissions can be speciated consistently with other
states.
MOVES also provides estimates of PM2.5 by speciation profile code for the PM2.5 emission source, using
PM25_#### as the pollutant code in the FF10 inventory file, where #### is a speciation profile code. To
facilitate calculation of PMC within SMOKE, and to help create emissions summaries, an additional
pollutant representing total PM2.5 called PM25TOTAL was added to the inventory. As with VOC,
PM25_####-PM25TOTAL ratios were calculated and applied to PM2.5 emissions in California and Texas
so that PM2.5 emissions in California and Texas can be speciated consistently with other states.
MOVES3 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
available as part of the 2017 NEI.
• 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 created at this stage of the process to facilitate the
calculation of PMC within SMOKE and for the development of emissions summaries.
• Emissions for airport ground support vehicles (SCCs ending in -8005), and oil field equipment
(SCCs ending in -10010), were removed from the inventory at this stage, to prevent a double
count with the airports and np oilgas sectors, respectively.
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• California and Texas emissions from MOVES were deleted and replaced with CARB- and TCEQ-
supplied emissions, 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 ad those updates are
retained for use in this platform. Updated nrsurrogate, nrstate surrogate, and nrbaseyearequippopulation
tables that implement these updates, along with instructions for utilizing these tables in MOVES runs, are
available for download from EPA's ftp site: https://gaftp.epa.gov/air/emismod/2016/vl/reports/nonroad/).
Note that these updates are not included in M0VES3.
More information on the development of the updates to agricultural and construction equipment
allocations is available in Section 2.4.4 of the 2016v3 platform TSD (EPA, 2023a).
Emissions Inside California and Texas
California nonroad emissions were provided by CARB for 2017NEI, and for several years including 2016
and 2023 as part of the 2016 version 1 modeling platform. The 2017 and 2023 datasets provided by
CARB were used to estimate California nonroad emissions for 2018. Specifically, county-level trends by
pollutant were calculated for the six year period from 2017 to 2023, converted (interpolated) to a one year
trend, and then applied to the 2017 emissions to estimate 2018. Trends based on county totals were used
instead of more specific trends (e.g. by SCC) because of possible differences in SCC delineations between
the different CARB datasets.
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.
Texas nonroad emissions were provided by TCEQ for years 2017 and 2020, and then interpolated to 2018
for each county, SCC, and pollutant. The Texas nonroad inventories are seasonal (summer, fall, winter,
spring), split to monthly by dividing the seasonal total by three for each month. As in California, VOC
and PM2.5 emissions were allocated to speciation profiles, and VOC HAPs were created, using MOVES
data in Texas.
Nonroad Updates from State Comments
The 2016 Nonroad Collaborative workgroup received a small number of comments on the 2016beta
inventory (EPA and NEIC, 2019), all of which were addressed and implemented in the 2017 NEI nonroad
inventory and the 2018 inventory used for this study:
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• Georgia Department of Natural Resources: utilize updated geographic allocation factors
(nr state surrogate 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 2-21). Note that this is only relevant for the 36km grid
used in this study because Alaska does not overlap the 12km grid.
Table 2-21. Alaska counties/census areas for which specific nonroad emissions were removed
N oil road
Kquipmenl
Sector
Coiinlies/Censiis Areas (KIPS) for which equipment sector emissions are
removed
Agricultural
Aleutians East (02013), Aleutians West (02016), Bethel Census Area (02050),
Bristol Bay Borough (02060), Dillingham Census Area (02070), Haines
Borough (02100), Hoonah-Angoon Census Area (02105), Ketchikan Gateway
(02130), Kodiak Island Borough (02150), Lake and Peninsula (02164), Nome
(02180), North Slope Borough (02185), Northwest Arctic (02188), Petersburg
Borough (02195), Pr of Wales-Hyder Census Area (02198), Sitka Borough
(02220), Skagway Borough (02230), Valdez-Cordova Census Area (02261),
Wade Hampton Census Area (02270), Wrangell City + Borough (02275),
Yakutat City + Borough (02282), Yukon-Koyukuk Census Area (02290)
Logging
Aleutians East (02013), Aleutians West (02016), Nome (02180), North Slope
Borough (02185), Northwest Arctic (02188), Wade Hampton Census Area
(02270)
Railway
Maintenance
Aleutians East (02013), Aleutians West (02016), Bethel Census Area (02050),
Bristol Bay Borough (02060), Dillingham Census Area (02070), Haines
Borough (02100), Hoonah-Angoon Census Area (02105), Juneau City +
Borough (02110), Ketchikan Gateway (02130), Kodiak Island Borough (02150),
Lake and Peninsula (02164), Nome (02180), ), North Slope Borough (02185),
Northwest Arctic (02188), Petersburg Borough (02195), Pr of Wales-Hyder
Census Area (02198), Sitka Borough (02220), Southeast Fairbanks (02240),
Wade Hampton Census Area (02270), Wrangell City + Borough (02275),
Yakutat City + Borough (02282), Yukon-Koyukuk Census Area (02290)
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2.5 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, respectively, and agricultural fires that comprise
the ptagfire sector. All ptfire and ptagfire fires are in the United States. Fires outside of the United States
are described in the ptfire othna sector later in this document.
2.5.1 Wild and Prescribed Fires (ptfire-rx, ptfire-wild)
Wildfire and prescribed burning emissions are contained in the ptfire-wild and ptfire-rx sectors, respectively. The
ptfire sector has emissions provided at geographic coordinates (point locations) and has daily emissions values. The
ptfire sector excludes agricultural burning and other open burning sources that are included in the ptagfire sector.
Emissions are day-specific and include satellite-derived latitude/longitude of the fire's origin and other parameters
associated with the emissions such as acres burned and fuel load, which allow estimation of plume rise.
The SCCs used for the ptfire-rx and ptfire-wild sources are shown in Table 2-22. The ptfire-rx and ptfire-
wild inventories include separate SCCs for the flaming and smoldering combustion phases for wildfire
and prescribed burns. Note that prescribed grassland fires for the Flint Hills in Kansas have their own
SCC (2811021000) in the inventory. These wild grassland fires were assigned the standard wildfire SCCs
shown in Table 2-22.
Table 2-22. SCCs included in the ptfire sector
SCC
Description
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
2811020002
Prescribed Rangeland Burning
2811021000
Prescribed Rangeland Burning - Tallgrass Prairie
Fire Information Data
Inputs to SMARTFIRE2 for 2018 include:
• The National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping System
(HMS) fire location information
• GeoMAC (Geospatial Multi-Agency Coordination), an online wildfire mapping application
designed for fire managers to access maps of current fire locations and perimeters in the
United States
• The Incident Status Summary, also known as the "ICS-209", used for reporting specific
information on fire incidents of significance
• Incident reports including dates of fire activity, acres burned, and fire locations from the
National Association of State Foresters (NASF)
• Hazardous fuel treatment reduction polygons for prescribed burns from the Forest Service
Activity Tracking System (FACTS)
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• Fire activity on federal lands from the United States Fish and Wildlife Service (USFWS)
• Wildfire and prescribed date, location, and locations from a few S/L/T activity submitters
(includes Georgia, Florida and Kanas(Flint Hills only))
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 2018 inventory consisted of daily comma-delimited files containing fire
detect information including latitude-longitude, satellite used, time detected, and other information. These
detects were processed through Satellite Mapping Automated Reanalysis Tool for Fire Incident
Reconciliation version 2 (SMARTFIRE2) and BlueSky Pipeline.
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 ptfire inventory: the SIT209_HISTORY_INCIDENT_209_REPORTS table
contained daily 209 data records for large fires, and the SIT209 HISTORY INCIDENTS table contained
summary data for additional smaller fires.
The National Association of State Foresters (NASF) is a non-profit organization composed of the
directors of forestry agencies in the states, U.S. territories, and District of Columbia to manage and protect
state and private forests, which encompass nearly two-thirds of the nation's forests. The NASF compiles
fire incident reports from agencies in the organization and makes them publicly available. The NASF fire
information includes dates of fire activity, acres burned, and fire location information.
Monitoring Trends in Burn Severity (MTBS) is an interagency program whose goal is to consistently map
the burn severity and extent of large fires across the U.S. from 1984 to present. The MTBS data includes
all fires 1,000 acres or greater in the western United States and 500 acres or greater in the eastern United
States. The extent of coverage includes the continental U.S., Alaska, Hawaii, and Puerto Rico. Fire
occurrence and satellite data from various sources are compiled to create numerous MTBS fire products.
The MTBS Burned Areas Boundaries Dataset shapefiles include year 2018 fires and that are classified as
either wildfires, prescribed burns, or unknown fire types. The unknown fire type shapes were omitted in
the 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 2018 data from the
USFS Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and
used for emissions inventory development. This database includes information about activities related to
fire/fuels, silviculture, and invasive species. The FACTS database consists of shapefiles for prescribed
burns that provide acres burned and start and ending time information.
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The US Fish and Wildland Service (USFWS) also compiles wildfire and prescribed burn activity on their
federal lands every year. Year 2018 data were acquired from USFWS through direct communication with
USFWS staff and were used for 2018 platform development. The USFWS fire information provided fire
type, acres burned, latitude-longitude, and start and ending times.
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 2018 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). For the purposes of the inventory the residual smoldering emissions were
allocated to the smoldering SCCs listed in Table 2-20, while the lofted smoldering emissions were
assigned to the flaming emissions SCCs.
Figure 2-9 is a schematic of the data processing stream for the inventory of wildfire and prescribed burn
sources. The ptfire-rx and ptfire-wild inventory sources were estimated using Satellite Mapping
Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and Blue Sky
Pipeline. 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 2018 platform, the national and S/L/T fire information was input into SMARTFIRE2 and then
merged and associated based on user-defined weights for each fire information dataset. The output from
SMARTFIRE2 was daily acres burned by fire type, and latitude-longitude coordinates for each fire. The
fire type assignments were made using the fire information datasets. If the only information for a fire was
a satellite detect for fire activity, then the flow described in Figure 2-10 was used to make fire type
assignment by state and by month.
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Figure 2-9. Processing flow for fire emission estimates
Input Data Sets
(state/local/tribal and national data sets)
* ^
Data Preparation
Data Aggregation and Reconciliation
(SmartFire2)
Daily fire locations
with fire size and type
Fuel Moisture and
Fuel Loading Data
Smoke Modeling (BlueSky Framework)
Daily smoke emissions
for each fire
Emissions Post-Processing
Final Wildland Fire Emissions Inventory
-------
The second system used to estimate emissions is the BlueSky Modeling Pipeline. The framework supports
the calculation of fuel loading and consumption, and emissions using various models depending on the
available inputs as well as the desired results. The contiguous United States and Alaska, where Fuel
Characteristic Classification System (FCCS) fuel loading data are available, were processed using the
modeling chain described in Figure 2-11. The Fire Emissions Production Simulator (FEPS) in the
BlueSky Pipeline generates all the CAP emission factors for wildland fires used in the 2018 study. HAP
emission factors were obtained from Urbanski's (2014) work and applied by region and by fire type.
The FCCSv3 cross-reference was implemented along with the LANDFIREvl (at 200 meter resolution) to
provide better fuel bed information for the BlueSky Pipeline (BSP). The LANDFIREv2 was aggregated
from the native resolution and projection to 200 meter using a nearest-neighbor methodology.
Aggregation and reprojection was required for the proper function on BSP.
The final products from this process are annual and daily FFlO-formatted emissions inventories. These
SMOKE-ready inventory files contain both CAPs and HAPs. The BAFM HAP emissions from the
inventory were used directly in modeling and were not overwritten with VOC speciation profiles (i.e., an
"integrate HAP" use case).
Figure 2-11. Blue Sky Pipeline
2.5.2 Point source Agriculture Fires (ptagfire)
In the NEI, agricultural fires are stored as county-annual emissions and are part of the nonpoint data
category. For this study agricultural fires are modeled as day specific fires derived from satellite data for
the year 2018 in a similar way to the emissions in ptfire. The state of Florida provided their own
emissions (separate from the other states) for this study.
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Daily year-specific agricultural burning emissions are derived from HMS fire activity data, which
contains the date and location of remote-sensed anomalies. The activity is filtered using the 2018 USDA
cropland data layer (CDL). Satellite fire detects over agricultural lands are assumed to be agricultural
burns and assigned a crop type. Detects that are not over agricultural lands are output to a separate file for
use in the ptfire sector. Each detect is assigned an average size of between 40 and 80 acres based on crop
type. Grassland/pasture fires were moved to the ptfire sectors for this 2018 modeling platform. Depending
on their origin, grassland fires are in both ptfire-rx and ptfire-wild sectors because both fire types do
involve grassy fuels.
The point source agricultural fire (ptagfire) inventory sector contains daily agricultural burning emissions.
Daily fire activity was derived from the NOAA Hazard Mapping System (HMS) fire activity data. The
agricultural fires sector includes SCCs starting with '28015'. The first three levels of descriptions for
these SCCs are: 1) Fires - Agricultural Field Burning; Miscellaneous Area Sources; 2) Agriculture
Production - Crops - as nonpoint; and 3) Agricultural Field Burning - whole field set on fire. The SCC
2801500000 does not specify the crop type or burn method, while the more specific SCCs specify field or
orchard crops and, in some cases, the specific crop being grown. The SCCs for this sector listed are in
Table 2-23.
Table 2-23. SCCs included in the ptagfire sector
SCC
Description
2801500000
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Unspecified crop type and Burn Method
2801500141
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Field Crop is Bean (red): Headfire Burning
2801500150
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Field Crop is Corn: Burning Techniques Not Important
2801500151
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Double Crop Winter Wheat and Corn
2801500152
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; DoubleCrop Corn and Soybeans
2801500160
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Field Crop is Cotton: Burning Techniques Not Important
2801500171
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Fallow
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
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Another feature of the ptagfire database is that the satellite detections for 2018 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 2018 meteorological Weather Research Forecast (WRF) model simulation.
The locations of fire detections were then compared with this daily snow cover file. For any day in which
a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the
inventory. Due to the inconsistent reporting of fire detections from the Visible Infrared Imaging
Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as VIIRS or
Suomi National Polar-orbiting Partnership satellite were excluded. In addition, certain crop types (corn
and soybeans) have been excluded from these specific midwestern states: Iowa, Kansas, Indiana, Illinois,
Michigan, Missouri, Minnesota, Wisconsin, and Ohio. The reason for these crop types being excluded is
because states have indicated that these crop types are not burned.
Heat flux for plume rise was calculated using the size and assumed fuel loading of each daily agricultural
fire. This information is needed for a plume rise calculation within a chemical transport modeling system.
The daily agricultural and open burning emissions were converted from a tabular format into the
SMOKE-ready daily point flat file format. The daily emissions were also aggregated into annual values
by location and converted into the annual point flat file format.
For this modeling platform, a SMOKE update allows the use of HAP integration for speciation for
PTDAY inventories. The 2018 agricultural fire inventories include emissions for HAPs, so HAP
integration was used for this study.
2.6 Biogenic Sources (beis)
Biogenic emissions were computed based on the 18j version of the 2018 meteorology data used for the air
quality modeling and were developed using the Biogenic Emission Inventory System version 3.7
(BEIS3.7) within CMAQ. The BEIS3.7 creates gridded, hourly, model-species emissions from vegetation
and soils. It estimates CO, VOC (most notably isoprene, terpene, and sesquiterpene), and NO emissions
for the contiguous U.S. and for portions of Mexico and Canada. In the BEIS 3.7 two-layer canopy model,
the layer structure varies with light intensity and solar zenith angle (Pouliot and Bash, 2015). Both layers
include estimates of sunlit and shaded leaf area based on solar zenith angle and light intensity, direct and
diffuse solar radiation, and leaf temperature (Bash et al., 2015). 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 to convert WRF outputs to CMAQ inputs are shown
in Table 2-24.
Table 2-24. Meteorological variables required by BEIS 3.7
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2 m
RC
convective precipitation per met TSTEP
RGRND
solar rad reaching surface
RN
nonconvective precipitation per met TSTEP
RSTOMI
inverse of bulk stomatal resistance
SLYTP
soil texture type by USD A category
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Variable
Description
SOIM1
volumetric soil moisture in top cm
SOIT1
soil temperature in top cm
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 m
BEIS 3.7 was used in conjunction with Version 5 of the Biogenic Emissions Landuse Database (BELD5).
The BELD5 is based on an updated version of the USDA-USFS Forest Inventory and Analysis (FIA)
vegetation speciation-based data from 2001 to 2017 from the FIA version 8.0. Canopy coverage is based
on the 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). The FIA includes approximately 250,000 representative plots
of species fraction data that are within approximately 75 km of one another in areas identified as forest by
the MODIS canopy coverage. For land areas outside the conterminous United States, 500 meter grid
spacing land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is used.
BELDv5 also incorporates the following datasets:
Canadian BELD land use, updates to Version 4 of the Biogenic Emissions Landuse Database
(BELD4) for Canada and Impacts on Biogenic VOC Emissions
(https://www.epa.gov/sites/default/files/2019-08/documents/8Q0am zhang 2 O.pdf).
2017 30 meter USD A Cropland Data Layer (CDL) data
(http://www.nass.usda.gov/research/Cropland/Release/).
A minor bug correction was implemented in BEIS3 to correctly use a few agricultural landuse types in
BELD5 that resulted in a minor increase of 1.6% in nitric oxide emissions from soils for the CONUS
region. Additionally, a minor map projections issue was found in the BELD5 data used in 2018vl. This
was corrected in 2018v2 and resulted in a 0.1% increase in VOC in the CONUS region and a 2.3%
increase in VOC emissions in the Canadian provinces.
Biogenic emissions computed with BEIS were used to review and prepare summaries, but were left out of
the CMAQ-ready merged emissions in favor of inline biogenic emissions produced during the CMAQ
model run itself using the same algorithm described above but with finer time steps within the air quality
model.
2.7 Sources Outside of the United States
The emissions from Canada and Mexico are included as part of the emissions modeling sectors: othpt,
othar, othafdust, othptdust, onroadcan, and onroadmex. The "oth" refers to the fact that these emissions
are usually "other" than those in the U.S. state-county geographic FIPS, 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 (Canada only). The onroad emissions for
Canada and Mexico are in the onroad can and onroad mex sectors, respectively.
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Emissions in these sectors were taken from the EQUATES 2016 inventories. Environment and Climate
Change Canada (ECCC) provided the following inventories for use in EQUATES 2016 and 2017
modeling, which are described in more detail below:
Agricultural livestock and fertilizer, point source format (othpt sector)
CMV were provided as area sources but converted to point (not currently used)
Agricultural fugitive dust, point source format (othptdust sector)
Other area source dust (othafdust sector)
Onroad (onroad can sector)
- Nonroad and rail (othar sector)
Other area sources (othar sector)
Canadian CMV inventories that had been included in this sector in past modeling platforms are included
in the cmv_clc2 and cmv_c3 sectors as hourly point sources. The 2017 NEI CMV included most coastal
waters of Canada and Mexico with emissions derived from AIS data. These NEI emissions were used for
all areas of Canada and Mexico in which they were available and are included in the cmv_clc2 and
cmv_c3 sectors. Both the C1C2 and C3 emissions were developed in a point source format with point
locations at the center of the 12km grid cells. Activity and corresponding emissions along the St.
Lawrence Seaway were not included in the NEI. This region was gapfilled with emissions provided by
ECCC that were apportioned to point sources on the centroids of 12km grid cells using the Canadian
commercial marine vessel surrogate (CA 945). The Canadian emissions were held flat from 2017 to 2018.
In addition to emissions inventories, the ECCC 2015 dataset also included temporal profiles, and
shapefiles for creating spatial surrogates. These profiles and surrogates were used for this study. Other
than the CB6 species of NBAFM present in the speciated point source data, there are no explicit HAP
emissions in these Canadian inventories.
2.7.1 Point Sources in Canada and Mexico (othpt, canada_ag, canada_og2D)
Canadian point source inventories provided by ECCC for the EQUATES project for 2016 were used as-is
for 2018. These inventories include emissions for airports and other point sources. The Canadian point
source inventory is pre-speciated for the CB6 chemical mechanism. Point sources in Mexico were
compiled based on inventories projected to from the Inventario Nacional de Emisiones de Mexico, 2016
(Secretaria de Medio Ambiente y Recursos Naturales (SEMARNAT)). As in the EQUATES project, the
2016 Mexico emissions were projected to 2018 using trends from the Community Emissions Data System
(CEDS) dataset. The point source emissions were converted to English units and into the FF10 format that
could be read by SMOKE, missing stack parameters were gapfilled using SCC-based defaults, and
latitude and longitude coordinates were verified and adjusted if they were not consistent with the reported
municipality. Only CAPs are covered in the Mexico point source inventory.
2.7.2 Fugitive Dust Sources in Canada (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 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
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with a meteorology-based (precipitation and snow/ice cover) zero-out of emissions when the ground is
snow covered or wet. The EQUATES 2016 inventory was used as-is with 2018 meteorology applied.
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 (othar). The nonroad sources were monthly while the nonpoint and rail
emissions were annual. The 2016 Canada nonroad emissions were projected to 2018 using US MOVES-
based trends. For Mexico, 2016 Mexico nonpoint and nonroad inventories at the municipio resolution
provided by SEMARNAT were used, and were projected to 2018 using trends from the Community
Emissions Data System (CEDS) dataset. All Mexico inventories were annual resolution.
2.7.4 Onroad Sources in Canada and Mexico (onroad_can, onroad_mex)
The onroad emissions for Canada and Mexico are in the onroad can and onroadmex sectors,
respectively. Emissions for Canada come from the EQUATES 2016 (2016 was the latest year provided by
Environment and Climate Change Canada (ECCC)) and were projected from 2016 to 2018 using US
MOVES-based trends.
For Mexico onroad emissions, a version of the MOVES model for Mexico was run that provided the same
VOC HAPs and speciated VOCs as for the U.S. MOVES model (ERG, 2016a). This includes NBAFM
plus several other VOC HAPs such as toluene, xylene, ethylbenzene and others. Except for VOC HAPs
that are part of the speciation, no other HAPs are included in the Mexico onroad inventory (such as
particulate HAPs nor diesel particulate matter). Mexico onroad inventories were generated by MOVES
for the years 2017 and 2020, and then interpolated to 2018 for this study.
2.7.5 Fires in Canada and Mexico (ptfire_othna)
Annual 2018 wildland fire emissions for Mexico, Canada, Central America, and Caribbean nations are
included in the ptfireothna sector. Canadian fires, along with fires in Mexico, Central America, and the
Caribbean, were developed from Fire Inventory from NCAR (FINN) 2017 vl.5 daily fire emissions. For
FINN fires, listed vegetation type codes of 1 and 9 are defined as agricultural burning, all other fire
detections and assumed to be wildfires. All wildland fires that are not defined as agricultural are assumed
to be wildfires rather than prescribed. FINN fire detects of less than 50 square meters (0.012 acres) are
removed from the inventory. The locations of FINN fires are geocoded from latitude and longitude to
FIPS code.
2.7.6 Fires in Canada and Mexico (ptfire_othna)
Annual 2018 wildland fire emissions for Mexico, Canada, Central America, and Caribbean nations are
included in the ptfire othna sector. Canadian fires, along with fires in Mexico, Central America, and the
Caribbean, were developed from Fire Inventory from NCAR (FINN) 2017 vl.5 daily fire emissions. For
FINN fires, listed vegetation type codes of 1 and 9 are defined as agricultural burning, all other fire
detections and assumed to be wildfires. All wildland fires that are not defined as agricultural are assumed
to be wildfires rather than prescribed. FINN fire detects of less than 50 square meters (0.012 acres) are
removed from the inventory. The locations of FINN fires are geocoded from latitude and longitude to
FIPS code.
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2.7.7 Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb)
concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution
were available and were not modified other than the model-species name "CHLORINE" was changed to
"CL2" to support CMAQ modeling.
For mercury, the same volcanic mercury emissions were used as in the last several modeling platforms.
The emissions were originally developed for a 2002 multipollutant modeling platform with coordination
and data from Christian Seigneur and Jerry Lin for 2001 (Seigneur et. al, 2004 and Seigneur et. al, 2001).
Because of mercury bidirectional flux within the latest version of CMAQ, the only natural mercury
emissions included in the merge are from volcanoes.
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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.
3.1 Emissions modeling Overview
SMOKE version 4.8.1 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.
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.
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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 BEIS3.7
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
onroad can
Surrogates
Yes
monthly
onroad mex
Surrogates
Yes
monthly
othafdust adj
Surrogates
Yes
annual
79
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Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
othar
Surrogates
Yes
annual &
monthly
othpt
Point
Yes
annual &
monthly
in-line
othptdust adj
Point
Yes
monthly
None
ptagfire
Point
Yes
daily
in-line
pt oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire-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 within
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 2018v2, 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 and diesel for the 2032 case.
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.
For 2018gg, SMOKE was run for two modeling domains: a 36-km resolution CONtinental United States
"CONUS" modeling domain (36US3), and a 12-km resolution domain. For 2032, SMOKE was only run
at 12-km resolution. The domains are shown in Figure 3-1. More specifically, for each of the 12-km
resolution runs, SMOKE was run on the 12US1 domain and emissions were extracted from the 12US1
data files to create emissions for 12US2. Following the CMAQ run for 2018gg, the CMAQ outputs on the
36US3 grid were used to create boundary conditions for the 12US2 domain used for both 2018 and 2032.
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 each of the domains.
80
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Figure 3-1. Air quality modeling domains
Table 3-2. Descriptions of the platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig, xcell,
ycell, ncols, nrows, nthik
Continental
36km grid
36 km
Entire conterminous
US, almost all of
Mexico, most of
Canada (south of
60°N)
36US3
'LAM 40N97W', -2952000, -2772000,
36.D3, 36.D3, 172, 148, 1
Continental
12km grid
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_45 9X299
'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
81
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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 2018 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 2018v2, 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 2018v2 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
82
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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
83
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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 2018v2
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. These inventories were not supplemented
with missing species due to the minimal impact of supplementation.
Speciation updates made for the 2016v3 platform that are also in the 2018v2 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.
Updates to PM speciation cross references implemented in 2016v2 and carried into 2018v2 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.
84
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For additional information on speciation updates made in the prior platforms, see the 2016v3 platform
TSD (EPA, 2023a). Speciation profiles and cross references for this platform are available with the other
SMOKE input files for the platform. Emissions of VOC and PM2.5 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 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 CB6
chemical mechanism. 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 this 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 integration11). 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.12 SMOKE computes NONHAPTOG and then applies the speciation
profiles to allocate the NONHAPTOG to the remaining air quality model VOC species. 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. On the other hand, if certain
sources do not have the necessary HAPs, then an NHAPEXCLUDE file must be provided based on the
11 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.
12 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.
85
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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 the NONHAPTOG 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."
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation
i Emissions Ready for SMOKE
SMOKE
List of "no-integrate" sources
(NHAPEXCLUDE)
Speciation cross
reference file (GSREF)
NONHAPVOC to NONHAPTOG
factors (GSCNV)
NONHAPTOG speciation factors (GSPRO)
TOG speciation factors for which NBAFM
compounds removed prior to GSPRO creation
CMAQ-CB6 species
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 NBAFM pollutants in the inventory input into SMOKE are
automatically dropped. This approach 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
86
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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).
Table 3-4. Integration of naphthalene, benzene, acetaldehyde, formaldehyde and methanol
(NBAFM) for each 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)
ptfire othna
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptagfire
Full integration (NBAFM)
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
Full 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
87
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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 GSPROCOMBO file, has been in use since the
2005 platform; the second method (GSPRO with fraction) was used for the first time in the 2014v7.0
platform. The GSPRO COMBO method uses profile combinations specified in the GSPRO COMBO
ancillary file by pollutant (which can include emissions mode, e.g., EXH VOC), state and county (i.e.,
state/county FIPS code) and time period (i.e., month). Different GSPRO COMBO files can be used by
sector, allowing for different combinations to be used for different sectors; but within a sector, different
profiles cannot be applied based on SCC. The GSREF file indicates that a specific source uses a
combination file with the profile code "COMBO." SMOKE computes the resultant profile using the
fraction of each specific profile assigned by county, month and pollutant.
A GSPRO COMBO is used to specify a mix of E0 and E10 fuels in Canada. ECCC provided percentages
of ethanol use by province, and these were converted into E0 and E10 splits. For example, Alberta has
4.91% ethanol in its fuel, so we applied a mix of 49.1% E10 profiles (4.91% times 10, since 10% ethanol
would mean 100% E10), and 50.9% E0 fuel. Ethanol splits for all provinces in Canada are listed in Table
3-5. The Canadian onroad inventory includes four distinct FIPS codes in Ontario, allowing for application
of different E0/E10 splits in Southern Ontario versus Northern Ontario. In Mexico, only E0 profiles are
used.
Table 3-5. Ethanol percentages by volume by Canadian province
Province
Ethanol % by volume (E10 = 10%)
Alberta
4.91%
British Columbia
5.57%
Manitoba
9.12%
New Brunswick
4.75%
Newfoundland & Labrador
0.00%
Nova Scotia
0.00%
NW Territories
0.00%
Nunavut
0.00%
Ontario (Northern)
0.00%
Ontari o ( S outhern)
7.93%
Prince Edward Island
0.00%
Quebec
3.36%
Saskatchewan
7.73%
Yukon
0.00%
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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.
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).13 SMOKE essentially calculates the model-ready species by using the appropriate
emission factor without further speciation.14 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
13 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.
14 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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MOVES ID
Pollutant Name
26
Acetaldehyde
27
Acrolein
40
2,2,4-Trimethylpentane
41
Ethyl Benzene
42
Hexane
43
Propionaldehyde
44
Styrene
45
Toluene
46
Xylene
185
Naphthalene gas
For the nonroad sector, all sources are integrated using the same list of integrated pollutants as shown in
Table 3-6. 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 2018gc and carried into 2018v2, 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 2018gc and 2018v2, onroad emissions in California and Texas are speciated
consistently with the rest of the country.
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[1]-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.15
15 https://github.eom/USEPA/CMAO/blob/main/CCTM/src/bio g/beis4/gspro bio genics .txt.
90
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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 SPECIATE v5.1 and others
added to SPECIATE v5.2. These profiles are used in this 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
Southern Ute Indian Tribe Comprehensive Emissions Inventory for Criteria Pollutants, Hazardous
Air Pollutants, and Greenhouse Gases."16
• 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: 896-905."17 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 Quality18) 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 Section 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.
16 https://www.southernute-nsn.gov/wp-content/uploads/sites/15/2019/12/191203-SUIT-CY2017-Emissions-Inventorv-Report-
FINAL.pdf.
17 http://doi.Org/10.1016/i.scitotenv.2017.ll.161.
18 https://downloads.regulations.gov/EPA-HQ-OAR-2012-0133-0Q47/attachment 32.pdf.
91
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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. These fractions can vary by county FIPS, because they depend on the level of
controls, which is an input to the Oil and Gas Tool. The basin or region-specific profiles for oil and gas
sources used in this platform are shown in Table 3-7.
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
92
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Profile Code
Description
Region (if
not in profile
name)
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
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 categories is customized to
account for the impact of fuels, engine types, 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 Profiles for Nonroad Emissions
Profile
Profile Description
Engine
Type
Engine
Technology
Engine
Size
Horse-
power
category
Fuel
Fuel
Sub-
type
Emission
Process
95327
SI 2-stroke EO
SI 2-stroke
All
All
All
Gasoline
EO
exhaust
95328
SI 2-stroke E10
SI 2-stroke
All
All
All
Gasoline
E10
exhaust
95329
SI 4-stroke EO
SI 4-stroke
All
All
All
Gasoline
EO
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
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Profile
Profile Description
Engine
Type
Engine
Technology
Engine
Size
Horse-
power
category
Fuel
Fuel
Sub-
type
Emission
Process
95333a
19
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
EO Evap
SI
all
all
All
Gasoline
EO
evaporative
8754
E10 Evap
SI
all
all
All
Gasoline
E10
evaporative
8766
EO evap permeation
SI
all
all
All
Gasoline
EO
permeation
8769
E10 evap permeation
SI
all
all
All
Gasoline
E10
permeation
8869
EO Headspace
SI
all
all
All
Gasoline
EO
headspace
8870
E10 Headspace
SI
all
all
All
Gasoline
E10
headspace
1001
CNG Exhaust
All
all
all
All
CNG
All
exhaust
8860
LPG exhaust
All
all
all
All
LPG
All
exhaust
Speciation profiles for VOC in the nonroad sector account for the ethanol content of fuels across years. A
description of the actual fuel formulations 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 performed within 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 2018v2 platform, these sources get E10 speciation.
Table 3-9 summarizes the different profiles utilized for the fuel-related sources in each of the sectors. The
term "COMBO" indicates that a combination of the profiles listed was used to speciate that subcategory
using the GSPRO COMBO file.
Table 3-9. Select mobile-related VOC profiles
Sector
Sub-category
Profile
number
Profile Description
nonroad
non-US
Gasoline exhaust
COMBO
Pre-Tier 2 E0 exhaust (8750a) and
Pre-Tier 2 E10 exhaust (8751a)
nonpt/
ptnonipm
PFC and BTP
COMBO
E0 headspace (8869) and
E10 headspace (8870)
nonpt/
ptnonipm
Bulk plant storage (BPS) and refine-
to-bulk terminal (RBT) sources
8870
E10 Headspace
19 95333a replaced 95333. This correction was made to remove alcohols due to suspected contamination. Additional
information is available in SPECIATE.
94
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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). 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 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
9120
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
20 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.
95
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Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
8870M
E10 Headspace
1940-2050
18
12,13,14
10,20,30,40,41, 42,46,47,48
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 EO exhaust
1940-2000
1,2,15,16
10
20,30
8750aM
Pre-Tier 2 EO 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,
1821
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
10,20,30,40,41,42,46,47,48
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). 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
HotSoak Fuel Vapor Venting
21 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.
96
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Process ID
Process Name
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
97
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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.22
The newest PM profile used in the 2018v2 platform is the Sugar Cane Pre-Harvest Burning Mexico
profile (SUGP02). This profile falls under the sector ptagfire and are included in SPECIATE v5.2.
Additionally, a series of regional fire profiles were added to SPECIATE 5.1 and are used in 2018v2.
These fall under the sector ptfire and are as shown in Table 3-14.
Table 3-14. Regional fire PM speciation profiles used in ptfire sectors
Pollutant
Profile
Code
Profile Description
PM
95793
Forest Fire-Flaming-Oregon AE6
PM
95794
Forest Fire-Smoldering-Oregon AE6
PM
95798
Forest Fire-Flaming-North Carolina AE6
PM
95799
Forest Fire-Smoldering-North Carolina AE6
PM
95804
Forest Fire-Flaming-Montana AE6
PM
95805
Forest Fire-Smoldering-Montana AE6
PM
95807
Forest Fire Understory-Flaming-Minnesota AE6
PM
95808
Forest Fire Understory-Smoldering-Minnesota AE6
PM
95809
Grass Fire-Field-Kansas AE6
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., PM10 and PM2.5) and speciated PM (e.g., PEC, PFE). SMOKE essentially calculates the PM
components by using the appropriate EF without further speciation 23 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
22 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.
23 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/.
98
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Emissions from On-road Vehicles in MOVES2014" (EPA, 2015c). No changes to the mobile source PM
speciation profiles were made in the 2018v2 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). These formulas are based on brake wear profile 95462
and tire wear profile 95460 and 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 3-15.
Table 3-15. Nonroad PM2.5 profiles
SPECIATE4.5
Profile Code
SPECIATE4.5 Profile Name
Assigned to Nonroad
sources based on Fuel Type
8996
Diesel Exhaust - Heavy-heavy duty truck -
2007 model year with NCOM
Diesel
91106
HDDV Exhaust - Composite
Diesel
91113
Nonroad Gasoline Exhaust - Composite
Gasoline
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
99
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and ptnonipm, the profile "HONO" is used. Table 3-16 gives the split factor for these two profiles. The
onroad sector does not use the "HONO" profile to speciate NOx. MOVES2014 produces speciated NO,
NO2, and HONO by source, including emission factors for these species in the emission factor tables used
by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOx. The NO fraction
varies by heavy duty versus light duty, fuel type, and model year. The NO2 fraction = 1 - NO - HONO.
For more details on the NOx fractions within MOVES, see EPA report "Use of data from 'Development
of Emission Rates for the MOVES Model, 'Sierra Research, March 3, 2010" available at
https://nepis.epa. gov/Exe/ZyPDF.cgi?Dockev=Pl 00FlA5.pdf.
Table 3-16. 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 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 H2S04is 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 3-17; a summary of the profiles is provided
in Table 3-18.
Table 3-17. Sulfate split factor computation
Fuel
SCCs
Profile
Code
Fraction
as 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
100
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Fuel
SCCs
Profile
Code
Fraction
as S02
Fraction as
sulfate
Split factor (mass
fraction)
Subbituminous
1-0X-002-YY, where X is 1,
2 or 3 and YY is 21 thru 38
87514
.875
0.014
.014/.875 * 98/64 =
0.0245
Lignite
1-0X-003-YY, where X is 1,
2 or 3 and YY is 01 thru 18
and 21-ZZ-002-000 where
ZZ is 02,03 or 04
75014
0.75
0.014
.014/.75 * 98/64 =
0.0286
Residual oil
1-0X-004-YY, where X is 1,
2 or 3 and YY is 01 thru 06
and 21-ZZ-005-000 where
ZZ is 02,03 or 04
99010
0.99
0.01
.01/ 99 * 98/64 =
0.0155
Distillate oil
1-0X-005-YY, where X is 1,
2 or 3 and YY is 01 thru 06
and 21-ZZ-004-000 where
ZZ is 02,03 or 04
99010
0.99
0.01
Same as residual oil
Table 3-18. SO2 speciation profiles
Profile
pollutant
species
split factor
95014
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 2018v2, 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 3-19 summarizes the temporal aspects of emissions modeling by
comparing the key approaches used for temporal processing across the sectors. In the table, "Daily
temporal approach" refers to the temporal approach for getting daily emissions from the inventory using
the SMOKE Temporal program. The values given are the values of the SMOKE L TYPE setting. The
"Merge processing approach" refers to the days used to represent other days in the month for the merge
step. If this is not "all," then the SMOKE merge step runs only for representative days, which could
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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 3-19. Temporal settings used for the platform sectors in SMOKE
Platform sector
short name
Inventory
resolution(s)
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 temporal allocation.
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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, 2018, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2017). For most sectors, emissions from December 2018
(representative days) were used to fill in emissions for the end of December 2017. For biogenic
emissions, December 2017 emissions were processed using 2017 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.
For livestock, meteorological-based temporalization (described in section 3.3.5) is used for month-to-day
and day-to-hour temporalization. Monthly profiles for livestock are based on the daily data underlying the
EPA estimates from 2014NEIv2.
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 2018 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
103
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non-summer months. Prior to use of the CEMS data in SMOKE it is processed through the CEMCorrect
tool. The CEMCorrect tool identifies hours for which the data were not measured as indicated by the data
quality flags in the CEMS data files. Unmeasured data can be filled in with maximum values and thereby
cause erroneously high values in the CEMS data. When data were flagged as unmeasured and the values
were found to be more than three times the annual mean for that unit, the data for those hours 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 3-3 for an example).
Figure 3-3. Eliminating unmeasured spikes in CEMS data
2016 January CEMs for 6068 3
2016 Original CEMs
2016 Corrected CEMs
V\ AfPlftA/\A - A
,0V°
K.OV
,0V"
F..OV
A®
v-1°
f,0
Date
,0^
«^v
rt>
,0V°
<*nv
In modeling platforms prior to 2016 beta, unmatched EGUs were temporally allocated using daily and
diurnal profiles weighted by CEMS values within an IPM 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 2018
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 2018 base year and the two previous years (2016 and 2017). 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
a maximum capacity factor of less than 0.2 for every year (2016, 2017, and 2018) and a 3-year average
capacity factor of less than 0.1.
104
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Annual Unit Power Output
8760 Hourly HI ,MW\
(BTU) 1UUUlwi
NEEDS Heat Rate (~—¦)
nnual Unit Output (MW) = (B"':i __ggEquation 3-2
nnual Unit Output (MW) =
Unit Capacity Factor
8760 Hourly HI ,MW\
(BTU) \kW) Equation 3-3
NEEDS Heat Rate (f^)
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
in the 2016 platform are shown in Figure 3-4 by region, fuel, and for peaking/non-peaking. The counts
should be similar for this platform. There are 64 unique profiles available based on 8 regions, 4 fuels, and
2 for peaking unit status (peaking and non-peaking).
Figure 3-4. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification
(pcafc"j"»Tpejiiurq;
'rngjoVi ilB|
,9*:1W-2S~ TJ
al: 0 / 0 /
omer:0/ c
West North Certral.
(PMbrqftypeHunQ):
iewToy^i
|
"alfig; 07,(1
HWE-WI ,
jpMfcn^rongMjuM):
feasTA) ', 1 J
13 r—r2
¦west 1 J—
(peakiini'ronpesliiog}:
coS : 0/3
137
'oil: 0 / 0
otHer.O/i
StSARM ^ ^
(peaUig/nanpcako9):
axi: 11166~"r
w.mi-xs—"v-
ci:w/8 \
other: 0 / S3 \
South <
(peafcmg/norpealung):
coal: 0/97 |
9»?263VJ37<
O»:t8/0
other: 0/4 k
LAOCO
(pealurarixnpcjtonB)-
"foi'A/ 155
EGU Regions
¦ LADCO
¦ MANE-VU
~ Northwest
~ SESARM
I I South
~ Southwest
~ West
¦ West North Central
105
-------
The daily and diurnal profiles were calculated for each region, fuel, and peaking type group from the year
2018 CEMS heat input values. The heat input values were summed for each input group to the annual
level at each level of temporal resolution: monthly, month-of-day, and diurnal. The sum by temporal
resolution value was then divided by the sum of annual heat input in that group to get a set of
temporalization factors. Diurnal factors were created for both the summer and winter seasons to account
for the variation in hourly load demands between the seasons. For example, the sum of all hour 1 heat
input values in the group was divided by the sum of all heat inputs over all hours to get the hour 1 factor.
Each grouping contained 12 monthly factors, up to 31 daily factors per month, and two sets of 24 hourly
factors. The profiles were weighted by unit size where the units with more heat input have a greater
influence on the shape of the profile. Composite profiles were created for each region and type across all
fuels as a way to provide profiles for a fuel type that does not have hourly CEMS data in that region.
Figure 3-5 shows peaking and non-peaking daily temporal profiles for the gas fuel type in the LADCO
region. Figure 3-6 shows the diurnal profiles for the coal fuel type in the Mid-Atlantic Northeast Visibility
Union (MANE-VU) region.
Figure 3-5. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type
Daily Small EGU Profile for LADCO gas
2016
106
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Figure 3-6. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type
Diurnal Small EGU Profile for MANE-VU coal
SMOKE uses a cross reference file to select a monthly, daily, and diurnal profile for each source. For this
platform, the temporal profiles were assigned in the cross reference at the unit level to EGU sources
without hourly CEMS data. An inventory of all EGU sources without CEMS data was used to identify the
region, fuel type, and type (peaking/non-peaking) of each source. As with the input unit the 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 regions used to aggregate each profile group are shown in Figure 3-7. The
counts shown in this figure are from the 2016 platform. The numbers for this platform should be similar,
although not exactly the same.
107
-------
Figure 3-7. Non-CEMS EGU Temporal Profile Aggregation Regions
Small EGU 2016 Temporal Profile Application Counts
coal- 0 / 0
9K i
LADCO
(PN^W*pMli):
coal: 24/8
03/ 16
MANFVU
(pMfc/nonpetfk):
EGU
Regions
¦
LADCO
¦
MANE VU
~
Northwest
n
SESARM
~
South
~
Southwest
~
West
r~i
West North Central
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 separated from the winter months starting with the 2016v3 platform
and the approach has been retained for this 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., 2018)
is used as much as possible to temporally allocate the EGU emissions.
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 NET 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,
108
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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 2018 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., 2018) 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 3-7. 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 2018. 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 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.
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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 year 2016 CEMS
data for units in each of the 8 regions shown in Figure 3-4 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 3-5 provides an
example of daily profiles for gas fuel in the LADCO region for 2016. The EPA developed year-specific
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 3-6 provides an example of seasonal profiles that
allocate daily emissions to hours in the MANE-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 3-8). The year IPM output for 2030
maps to the year 2032 and was therefore used for the 2032 modeling case.
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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 2015-2019 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 emi ssions below the maximum,
whenever possible (see example in Figure 3-9).
Figure 3-8. Analytic Year Emissions Follow the Pattern of Base Year Emissions
2032 and 2018 Summer CEMs for 1379 4
Figure 3-9. Excess Emissions Apportioned to Hours Less than the Historic Maximum
2032 arwJ 2018 Summer CEMs for 55221 G1
2018 CEMS
2032 CEMS
2032 Adjusted CEMs
Annual unit max
mm
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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 2015-2019 maximum emissions level. For these units,
the regional fuel-specific average profiles were applied to all pollutants, including heat input, for the
respective season (see example in Figure 3-10). 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 2015-2019 annual maximum for the unit even
after regional profiles were applied (see example in Figure 3-11).
Figure 3-10. Regional Profile Applied due to not being able to Adjust below Historic Maximum
2032 and 2018 Summer CEMs for 6071 10
2018 CEMs
2032 C£Ms
2032 Adjusted CEMs
2032 Season Fuel
Annual unit max
May
2018
Figure 3-11. Regional Profile Applied, but Exceeds Historic Maximum in Some Hours
2032 and 2016 Summer CEMs for 55270.LM6
2018 CEMs
2032 CEMs
2032 Adjusted CEMs
2032 Season Fuel
Annual unit max
May Jun Jul Aug Sep
2018
Date
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3.3.3 Airport Temporal allocation (airports)
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. 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 3-12 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/svs/Terminal.asp). An overview of the Operations
Network data system is here: http://aspmhelp.faa.gov/index.php/Operations Network %28QPSNET%29.
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 3-12, Figure 3-13, and Figure 3-14. The weekly and monthly profiles from 2014 are used in this
platform. Note that Alaska seaplanes use the monthly profile shown in Figure 3-15. These were assigned
based on the facility ID.
Figure 3-12. Diurnal Profile for all Airport SCCs
Figure 3-13. Weekly profile for all Airport SCCs
Weekly Airport Profile
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Figure 3-14. Monthly Profile for all Airport SCCs
Monthly Airport Profile
0.05
0.04
0.03
0.02
0.01
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 3-15. 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
NH3; and a generic meteorology-based algorithm for other situations. Meteorological-based temporal
allocation was used for portions of the rwc sector and for agricultural livestock and fertilizer emissions.
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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 3-16 illustrates the impact of changing the temperature threshold for a warm climate county. The
plot shows the temporal fraction by day for Duval County, Florida, for the first four months of 2007. The
default 50 °F threshold creates large spikes on a few days, while the 60 °F threshold dampens these spikes
and distributes a small amount of emissions to the days that have a minimum temperature between 50 and
60 °F.
Figure 3-16. Example of RWC temporal allocation using a 50 versus 60 °F threshold
RWC temporal profile, Duval County, FL, Jan - Apr
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The diurnal profile used for most RWC sources (see Figure 3-17) places more of the RWC emissions in
the morning and the evening when people are typically using these sources. This profile is based on a
2004 MANE-VU survey based temporal profiles.24 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 3-17. RWC diurnal temporal profile
Comparison of RWC diurnal profile
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
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.
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 3-18, are based on a conventional single-
stage heat load unit burning red oak in Syracuse, New York. As shown in Figure 3-19, 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.
24 https://s3 .amazonaws.com/marama.org/wp-
content/uploads/2019/11/13093804/Qpen Burning Residential Areas Emissions Report-2004.pdf.
¦NEW
¦OLD
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Annual-to-month temporal allocation for OHH as well as recreational RWC were computed from the
MDNR 2008 survey and are illustrated in Figure 3-20. 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 3-18. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr)
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Figure 3-20. Annual-to-month temporal profiles for OHH and recreational RWC
3.3.5 Agricultural Ammonia Temporal Profiles (livestock)
For the agricultural livestock NFb 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 Tropospheric Emissions Spectrometer (TES) satellite instrument with the
GEOS-Chem model and its adjoint to estimate diurnal NFb emission variations from livestock as a
function of ambient temperature, aerodynamic resistance, and wind speed. The equations are:
Et.h = [161500/T, /, x eM380 x AR,,/, Equation 3-4
PE;,/; = Euh / Sum(E,,/,) 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 3-21 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.
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Although the GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the
same between the two approaches.
Figure 3-21. Example of animal NH3 emissions temporal allocation approach (daily total emissions)
2014fd Minnesota ag NH3 livestock daily temporal profiles
1600
1400
— 1200
;3 1000
1 800
-M
600
2 400
200 La
1/1/2014 2/1/2014 3/4/2014 4/4/2014 5/5/2014 6/5/2014 7/6/2014 S/6/2014 9/6/2014 10/7/201411/7/201412/8/2014
month^ hourly
approach approach
For the 2018 platform, the GenTPRO approach is applied to all sources in the livestock and fertilizer
sectors, NFb and non- NFb. Monthly profiles are based on the daily-based EPA livestock emissions from
the 2014 NEI. 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 2018 activity information
for the 2018 platform. Weekly and diurnal profiles are flat and are based on comments received on a
version of the 2011 platform.
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 3-19 consist of activity data for the onroad sector, not emissions.
For the off-network emissions from the rate-per-profile (RPP) and rate-per-vehicle (RPV) processes, the
VPOP activity data is annual and does not need temporal allocation. For rate-per-hour (RPH) processes
that result from hoteling of combination trucks, the HOTELING inventory is annual and was
temporalized to month, day of the week, and hour of the day through temporal profiles. 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.
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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 3-22 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 six 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.
Figure 3-22. Example of temporal variability of NOx emissions
2014v2 onroad RPD hourly NOX and VMT: Wake County, NC
IAAMAaaH:!
7/8/140:00 7/9/140:00 7/10/140:00 7/11/140:00 7/12/140:00 7/13/140:00 7/14/140:00 7/15/140:00
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
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VMT
NOX
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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 LOWSATSL'N 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 3-23. 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 3-24 shows which counties have temporal profiles specific to that county, and which counties use
MSA or regional average profiles. Figure 3-25 shows the regions used to compute regional average
profiles.
Monday
Figure 3-23. Sample onroad diurnal profiles for Fulton County, GA
Friday Fulton Co
Fulton Co
passenger
passenger
5 6 7 8 9 10 11 12 13 14 15 16 17 IS 19 20 21 22 23 24
road 2 road 3 road 4 road 5
9 10 11 12 13 14 15 16 17 IS 19 20 21 22 23 24
—road 3 road 4 road 5
Saturday
Fulton Co
passenger
Sunday
Fulton Co
passenger
5 6 7 S 9 10 11 12 13 14 15 16 17 IS 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
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Figure 3-24. Methods to Populate On road Speeds and Temporal Profiles by Road Type
Road Type 2
Legend
~ MSA Boundary {outlined in black)
| Individual
| MSA average of non-Core Counties
Region Average of MSA Core Counties
Region Average of MSA non-Core Counties
| Region Average of non-MSA Counties
Road Type 4
Legend
~ MSA Boundary (outlined in black)
| Individual
| MSA average of non-Core Counties
m Region Average of MSA Core Counties
! Region Average of MSA non-Core Counties
| Region Average of non-MSA Counties
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Figure 3-24 Methods to Populate Onroad Speeds and Temporal Profiles by Road Type (ctd).
Legend
| MSA Boundary (outlined in black)
J Individual
| MSA average of non-Core Counties
^ Region Average of MSA Core Counties
_ Region Average of MSA non-Core Counties
| Region Average of non-MSA Counties
Road Type 3
Legend
MSA Boundary (outlined in black)
| Individual
^ MSA average of non-Core Counties
Region Average of MSA Core Counties
Region Average of MSA non-Core Counties
B Region Average of non-MSA Counties
Road Type 5
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Figure 3-25. 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 3-26.
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
MOWS.
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Monday
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
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
0.06
0.05
0.04
0.03
0.02
0.01
0
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
Figure 3-26. Example of Temporal Profiles for Combination Trucks
Fulton Co combo Friday Fulton Co
0.08
0.07
combo
Saturday Fulton Co combo
0.08
Sunday Fulton Co combo
0.07
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
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
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 continuing into this platform, 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 3-27 shows two previously use temporal profiles (9 and 18) and the updated temporal profile (19)
that has lower emissions on weekends. In this platform, construction and commercial lawn and garden
sources use profile 19. Residental lawn and garden sources use profile 9 and agricultural sources use
profile 19.
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Figure 3-27. 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
monday tuesday Wednesday thursday friday saurday sundae
Figure 3-28 shows the previously existing temporal profiles 26 and 27 along with the temporal profiles
(25a and 26a) that have lower emissions overnight. In this platform, construction sources use profile 26a.
Commercial lawn and garden and agriculture sources use the profiles 26a and 25a, respectively.
Residental lawn and garden sources use profile 27.
Figure 3-28. Example Nonroad Diurnal Temporal Profiles
Hour of Day Profiles
0.11
o.i
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
hi h2 h3 h4 h5 to6 h7 h8 h9 hl0hllhl2hl3hl4hl5hl6hl7hlShl9h20h21h22h23h24
26a-New 27 25 a-New 26
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
126
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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 hour 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 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.
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 and continue to be used in
this 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 used 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
3-29 (McCarty et al., 2009). This puts most of the emissions during the workday and suppresses the
emissions during the middle of the night.
127
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Figure 3-29. 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 3-30 shows the profiles used for each state for the
2018gc and 2018v2 modeling platforms. The wildfire diurnal profiles are similar but vary according to the
average meteorological conditions in each state. The 2018gc and 2018v2 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 3-30. Prescribed and Wildfire diurnal temporal profiles
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3.4 Spatial Allocation
The methods used to perform spatial allocation are summarized in this section. The spatial factors are
typically applied by SCC to allocate emissions from a county or province-based emissions inventory to
specific grid cells. They are not used for point source data since those usually have specific locations
assigned. If a particular spatial dataset used to develop a spatial surrogate does not have data for all
counties (or provinces) for which there could be emissions assigned to use that surrogate, data are added
to the surrogate from other more comprehensive surrogates to ensure that emissions data are not lost when
the spatial surrogate is applied. Through gap-filling, data for entire counties or provinces are pulled from a
secondary or tertiary surrogate into the primary surrogate so that the gap-filled surrogate has entries for all
counties that may have a particular type of emissions.
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 2017 to 2018 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 this 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, thus special considerations are taken to include Alaska
emissions in 36-km modeling.
2018v2 platform uses the same surrogates and surrogate assignments as the 2016v3 platform, which were
essentially the same as those used for the 2016v2 platform. Documentation of the origin of the spatial
surrogates for the platform is provided in the 2018v2 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 80 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. Spatial surrogates are typically developed
based on nationally available data sources (e.g., census data, national land cover database). An exception
is when a regional inventory is used (e.g., the WRAP oil and gas inventory) and regional surrogates are
used in association with that inventory. As described in Section 3.4.2, an area-to-point approach overrides
the use of surrogates for airport refueling sources. Table 3-20 lists the codes and descriptions of the spatial
surrogates. In this table, surrogate names and codes listed in italics are not directly assigned to any
sources for this platform, but they may be used to gapfill other surrogates. The WRAP oil and gas
surrogates used in this platform are not listed in Table 3-20 but are instead listed in Table 3-22.
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).
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Issues were identified in the rail surrogates 261 and 271 that caused emissions to be allocated to cells far
from the county. Comparisons were made in which county-cell mappings from all surrogates, were
compared with the land area surrogate, and looked for county-cells that were two or more 36km cells
away from the nearest cell for each county in the land area surrogate. Several problem cells were
identified in 261 and 271. Therefore surrogates 261 and 271 were edited by removing the problem county-
cells, and renormalizing the remaining factors so they sum to one.
Some surrogates were updated or newly developed for this platform or for the 2016 platforms:
oil and gas surrogates represent activity during the year 2018;
onroad spatial allocation uses surrogates that do not distinguish between urban and rural road
types, correcting the issue arising in some counties due to the inconsistent urban and rural
definitions between MOVES, the activity data, and the surrogate data, and were further updated
for the 2016 platform;
spatial surrogates for on-roadway 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;
surrogate 508: "Public Schools" from 2018-2019 NCES public school was developed and is
assigned to school buses;
surrogate 259, used for transit bus off-network (onroad), was re-gapfilled using 306 (NLCD
Med+High) first and population second - this addressed the overallocation to rural areas noted
with the prior gapfilling approach;
surrogate 306 (NLCD Med+High) now used in place of 259 since intercity bus is now other bus;
- the use of 500 series surrogates (except for the new #508) were phased out;
rail surrogates 261 and 271 were updated to fix some misallocated emissions;
surrogate 535 was reassigned to 307 (NLCD All Development); and
surrogate 505 was reassigned to 306 (NLCD Med+High).
The surrogates for the U.S. were mostly generated using the Surrogate Tools DB tool, although a few
were developed using the Spatial Allocator. The tool and documentation for the Surrogate Tools DB is
available at https://www.cmascenter.org/surrogate tools db/.
Table 3-20. U.S. Surrogates available for this 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
130
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Code
Surrogate Description
Code
Surrogate Description
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
681
Spud Count - Oil Wells
221
Urban Unrestricted Road Miles
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 I
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
687
Feet Drilled at All Wells
242
All Restricted AADT
689
Gas Produced - Total
243
Total Unrestricted Road Miles
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 j
695
Well Count - Oil Wells
261
NT AD Total Railroad Density
696
Gas Production at Gas Wells
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
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 3-21. Emissions from the extended (i.e., overnight) idling of trucks were
assigned to surrogate 205, which is based on locations of overnight truck parking spaces. 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 those updates were carried into this
platform.
131
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Table 3-21. Off-Network Mobile Source Surrogates
Source type
Source Type name
Surrogate ID
Description
11
Motorcycle
307
NLCD All Development
21
Passenger Car
307
NLCD All Development
31
Passenger Truck
307
NLCD All Development
NLCD Low + Med +
32
Light Commercial Truck
308
High
41
Other Bus
306
NLCD Med + High
42
Transit Bus
259
Transit Bus Terminals
43
School Bus
508
Public Schools
51
Refuse Truck
306
NLCD Med + High
52
Single Unit Short-haul Truck
306
NLCD Med + High
53
Single Unit Long-haul Truck
306
NLCD Med + High
54
Motor Home
304
NLCD Open + Low
61
Combination Short-haul Truck
306
NLCD Med + High
62
Combination Long-haul Truck
306
NLCD Med + High
For the oil and gas sources in the np oilgas sector, the spatial surrogates were updated to those shown in
Table 3-22 using 2018 data consistent with what was used to develop the 2018gc nonpoint oil and gas
emissions. The exception was the use of WRAP spatial surrogates from 2016v2 platform for production
in New Mexico and North Dakota. The primary activity data source used for the development of the oil
and gas spatial surrogates was data from Drilling Info (DI) Desktop's HPDI database (Drilling Info,
2019). 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 2018. In total, over 1 million unique wells were compiled from the above
data sources (ERG, 2021). The wells cover 34 states and over 1,100 counties.
Table 3-22. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
670
Spud Count - CBM Wells
671
Spud Count - Gas Wells
672
Gas Production at Oil Wells
673
Oil Production at CBM Wells
674
Unconventional Well Completion Counts
676
Well Count - All Producing
677
Well Count - All Exploratory
678
Completions at Gas Wells
679
Completions at CBM Wells
132
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Surrogate Code
Surrogate Description
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
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 3-20 were not assigned to any SCCs, although many of the "unused"
surrogates are actually used to "gap fill" primary surrogates, as discussed above. Table 3-23 shows the
CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by sector assigned to each spatial surrogate.
For 36US3 modeling in this platform, 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
133
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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.
Table 3-23. Selected 2018 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
312,090
0
0
Afdust
304
NLCD Open + Low
0
0
842,116
0
0
Afdust
306
NLCD Med + High
0
0
52,278
0
0
Afdust
308
NLCD Low + Med + High
0
0
117,047
0
0
Afdust
310
NLCD Total Agriculture
0
0
791,881
0
0
fertilizer
310
NLCD Total Agriculture
1,636,229
0
0
0
0
livestock
310
NLCD Total Agriculture
2,582,189
0
0
0
226,398
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
297,798
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
23,668
272,514
245,871
131,592
112,049
Nonpt
307
NLCD All Development
85
25,798
110,610
8,169
69,262
Nonpt
308
NLCD Low + Med + High
884
156,033
15,683
10,076
10,037
Nonpt
310
NLCD Total Agriculture
0
0
38
0
0
Nonpt
319
NLCD Crop Land
0
0
97
72
299
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
596
Nonpt
801
Port Areas
0
0
0
0
6,730
Nonroad
261
NTAD Total Railroad Density
3
1,914
198
1
376
nonroad
304
NLCD Open + Low
4
1,690
144
4
2,488
nonroad
305
NLCD Low + Med
95
14,943
3,859
104
106,139
134
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Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nonroad
306
NLCD Med + High
326
166,683
10,459
297
89,752
nonroad
307
NLCD All Development
101
29,905
15,389
97
170,454
nonroad
308
NLCD Low + Med + High
551
286,527
23,894
234
47,904
nonroad
309
NLCD Open + Low + Med
121
21,137
1,246
135
45,692
nonroad
310
NLCD Total Agriculture
420
329,678
23,876
187
34,856
nonroad
320
NLCD Forest Land
15
3,954
558
8
3,731
nonroad
321
NLCD Recreational Land
83
12,636
5,805
76
215,471
nonroad
350
NLCD Water
191
114,414
4,918
212
293,014
nonroad
850
Golf Courses
13
2,066
118
14
5,685
nonroad
860
Mines
2
2,523
251
1
476
np oilgas
670
Spud Count - CBM Wells
0
0
0
0
183
np oilgas
671
Spud Count - Gas Wells
0
0
0
0
6,021
np oilgas
674
Unconventional Well Completion
Counts
31
25,368
618
30
1,110
np oilgas
678
Completions at Gas Wells
0
9,892
254
3,674
37,861
np oilgas
679
Completions at CBM Wells
0
5
0
237
700
np oilgas
681
Spud Count - Oil Wells
0
0
0
0
46,149
np oilgas
683
Produced Water at All Wells
0
22
0
0
868
np oilgas
685
Completions at Oil Wells
0
438
0
2,026
57,876
np oilgas
687
Feet Drilled at All Wells
0
84,073
2,261
115
3,834
np oilgas
689
Gas Produced - Total
0
569
28
2
32,663
np oilgas
691
Well Counts - CBM Wells
0
12,025
222
5
16,035
np oilgas
692
Spud Count - All Wells
0
365
12
42
34
np oilgas
693
Well Count - All Wells
0
0
0
0
2
np oilgas
694
Oil Production at Oil Wells
0
2,607
0
1,651
477,995
np oilgas
695
Well Count - Oil Wells
0
137,335
3,239
19,295
435,954
np oilgas
696
Gas Production at Gas Wells
0
40,240
0
4,249
235,302
np oilgas
697
Oil Production at Gas Wells
0
858
0
0
80,817
np oilgas
698
Well Count - Gas Wells
7
277,705
3,918
141
444,273
np oilgas
699
Gas Production at CBM Wells
0
29
5
0
3,531
np oilgas
2688
WRAP Gas production at oil wells
0
7,188
0
5,435
206,000
np oilgas
2689
WRAP Gas production at all wells
0
25,667
772
1,108
19,346
np oilgas
2691
WRAP Well count - CBM wells
0
190
15
0
1,269
np oilgas
2693
WRAP Well count - all wells
0
84
3
0
5
np oilgas
2694
WRAP Oil production at oil wells
0
31,299
446
17,337
70,025
np oilgas
2695
WRAP Well count - oil wells
0
1,233
124
4
55,343
np oilgas
2696
WRAP Gas production at gas wells
0
1,424
19
1
22,763
np oilgas
2697
WRAP Oil production at gas wells
0
29
0
0
10,273
np oilgas
2698
WRAP Well count - gas wells
0
728
56
0
49,283
np oilgas
2699
WRAP Gas production at CBM wells
0
9,026
268
8
6,984
np oilgas
6831
Produced water at CBM wells
0
0
0
0
740
np oilgas
6832
Produced water at gas wells
0
0
0
0
16,231
np oilgas
6833
Produced water at oil wells
0
0
0
0
74,707
135
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
np solvents
100
Population
0
0
0
0
1,354,437
np solvents
240
Total Road Miles
0
0
0
0
50,500
np solvents
306
NLCD Med + High
33
27
300
1
395,102
np solvents
307
NLCD All Development
24
6
19
5
365,628
np solvents
308
NLCD Low + Med + High
0
0
129
0
8,324
np solvents
310
NLCD Total Agriculture
0
0
0
0
162,850
onroad
205
Extended Idle Locations
333
31,740
616
17
3,337
onroad
242
All Restricted AADT
34,519
922,998
23,496
6,667
137,657
onroad
244
All Unrestricted AADT
63,741
1,538,528
53,194
14,424
387,798
onroad
259
Transit Bus Terminals
15
2,725
63
2
510
onroad
304
NLCD Open + Low
0
872
27
0
6,880
onroad
306
NLCD Med + High
927
94,894
3,461
86
19,921
Onroad
307
NLCD All Development
3,494
211,798
6,822
1,352
584,337
Onroad
308
NLCD Low + Med + High
206
21,756
549
78
31,605
Onroad
508
Public Schools
15
2,140
85
1
562
Rail
261
NT AD Total Railroad Density
15
35,364
988
32
1,704
Rail
271
NTAD Class 12 3 Railroad Density
350
535,605
14,016
695
23,244
Rwc
300
NLCD Low Intensity Development
16,143
34,093
299,278
7,988
323,969
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 this platform. 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 3-24 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 3-25.
136
-------
Table 3-24. 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
326
Plastics and rubber products manufacturing
1253
OFFR Other Construction not Urban
327
Non-metallic mineral product manufacturing
1254
OFFR Commercial Services
331
Primary Metal Manufacturing
1255
OFFR Oil Sands Mines
350
Water
1256
OFFR Wood industries CANVEC
412
Petroleum product wholesaler-distributors
1257
OFFR UNPAVED ROADS RURAL
448
clothing and clothing accessories stores
1258
OFFR Utilities
482
Rail transportation
1259
OFFR total dwelling
562
Waste management and remediation services
1260
OFFR water
901
AIRPORT
1261
OFFR ALL INDUST
902
Military LTO
1262
OFFR Oil and Gas Extraction
903
Commercial LTO
1263
OFFR ALLROADS
904
General Aviation LTO
1265
OFFR CANRAIL
921
Commercial Fuel Combustion
9450
Commercial Marine Vessel Ports
Table 3-25. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3)
Sector
Code
Mexican / 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
137
-------
Sector
Code
Mexican / Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
othafdust
955
CAN UNPAVEDROADSANDTRAILS
0
0
426,511
0
0
othar
11
MEX 2015 Population
0
0
0
0
628,869
othar
14
MEX Residential Heating - Wood
251
44,151
121,868
3,765
327,369
othar
16
MEX Residential Heating - Distillate Oil
4
121
0
0
5
othar
22
MEX Total Road Miles
1
236
5,247
1
5,900
othar
24
MEX Total Railroad Miles
0
53,191
1,141
492
2,003
othar
26
MEX Total Agriculture
573,834
74,104
47,068
1,866
16,648
othar
32
MEX Commercial Land
0
387
8,290
0
100,237
othar
34
MEX Industrial Land
176
4,104
4,022
13
100,682
othar
36
MEX Commercial plus Industrial Land
7
22,388
1,365
15
229,263
othar
40
MEX Residential (RES1-
4)+Comercial+Industrial+Institutional+Governme
nt
4
87
373
14
102,973
othar
42
MEX Personal Repair (COM3)
0
0
0
0
25,438
othar
44
MEX Airports Area
0
14,556
186
1,111
5,970
othar
48
MEX Brick Kilns
0
2,752
54,113
4,952
1,322
othar
50
MEX Mobile sources - Border Crossing
3
63
2
0
50
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
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
138
-------
Sector
Code
Mexican / Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
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 OFFRTOTFERT
77
57,573
3,951
52
5,312
othar
1252
CAN OFFR MINES
1
849
60
1
122
othar
1253
CAN OFFR Other Construction not Urban
70
33,981
4,176
44
11,227
othar
1254
CAN OFFR Commercial Services
43
15,106
2,335
33
36,291
othar
1255
CAN OFFR Oil Sands Mines
23
12,478
410
12
1,330
othar
1256
CAN OFFR Wood industries CANVEC
8
2,680
260
5
1,018
othar
1257
CAN OFFR Unpaved Roads Rural
26
11,193
656
20
28,180
othar
1258
CAN OFFRUtilities
9
4,169
200
6
873
othar
1259
CAN OFFR total dwelling
17
6,127
619
13
12,817
othar
1260
CAN OFFRwater
23
6,736
373
31
27,471
othar
1261
CAN OFFR ALL INDUST
4
5,287
157
2
1,081
othar
1262
CAN OFFR Oil and Gas Extraction
1
1,267
78
1
229
othar
1263
CAN OFFRALLROADS
3
1,548
150
2
474
othar
1265
CAN OFFRCANRAIL
0
541
17
0
42
onroad_can
200
CAN Urban Primary Road Miles
1,617
69,363
2,232
324
7,452
onroad_can
210
CAN Rural Primary Road Miles
667
41,473
1,255
137
3,276
onroad_can
220
CAN Urban Secondary Road Miles
3,036
110,302
4,484
681
19,873
onroad_can
230
CAN Rural Secondary Road Miles
1,764
78,435
2,467
369
9,127
onroad_can
240
CAN Total Road Miles
349
48,945
1,384
76
99,474
onroad_mex
11
MEX 2015 Population
0
299,194
1,737
567
298,729
onroad_mex
22
MEX Total Road Miles
10,795
1,204,621
59,899
27,420
245,504
onroad_mex
36
MEX Commercial plus Industrial Land
0
8,520
153
31
9,594
139
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4 Analytic Year Emissions Inventories and Approaches
The emission inventories for the analytic year of 2032 have been developed using projection methods that
are specific to the type of emissions source. Analytic year emissions are projected from the base year
either by running models to estimate analytic year emissions from specific types of emission sources (e.g.,
EGUs, and onroad and nonroad mobile sources), or for other types of sources by adjusting the base year
emissions according to the best estimate of changes expected to occur in the intervening years (e.g., non-
EGU point and nonpoint sources). For some sectors, the same emissions are used in the base and analytic
years, such as biogenic, all fire sectors, and fertilizer. Emissions for these sectors are held constant in
future years because the base year meteorological data are also used for the future year air quality model
runs, and emissions for these sectors are highly correlated with meteorological conditions. For the
remaining sectors, rules and specific legal obligations that go into effect in the intervening years, along
with changes in activity for the sector, are considered when possible. For sectors that were projected, the
methods used to project those sectors to 2032 are summarized in Table 4-1.
Table 4-1. Overview of projection methods for the analytic year cases
Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
EGU units:
ptegu
The Integrated Planning Model (IPM) outputs from the EPA's Post-IRA 2022
Reference Case were used. For 2032. the 2030 IPM output vear 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 2018 pt_oilgas sources. Production-
related sources were then grown from 2018 to 2032 using historic production data.
The production-related sources were then grown to 2032 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, reciprocating internal combustion engines
(RICE), and the Good Neighbor Plan for the 2015 Ozone NAAOS. These
projection factors were applied to 2018 emissions in the entire US, including the
WMP region.
Airports:
airports
Point source airport emissions were grown from 2016 to 2032 using factors
derived from the 2021 Terminal Area Forecast (TAF) released in lune 2022 (see
https://www.faa.gov/data rescarch/aviation/taf/). The 2016 emissions included
corrections to emissions for ATL from the state of Georgia, as well as some
corrections for specific airports in the state of Texas that were part of the 2016v3
platform.
Remaining non-
EGU point:
ptnonipm
2026gf from the 2016v3 platform was used as a starting point to project emissions
to 2032 using factors derived from AEO2022 to reflect growth from 2026 to 2032
(including railyards). 2026gf included controls to account for relevant NSPS for
RICE, gas turbines, refineries (subpart la), and process heaters. The Boiler MACT
is assumed to be fully implemented in 2018. Controls are reflected for the regional
haze program in Arizona and Good neighbor plan for the 2015 Ozone NAAQS. In
2026gf known closures as of that time those inventories were developed are
reflected and new sources were added based on 2019 NEI. Growth in MARAMA
states was derived from MARAMA spreadsheets after incorporating AEO 2022
data. Railyards in California were updated with CARB data for 2032. Point source
solvents are based on 2019 NEI and projected to 2032.
140
-------
Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
Category 1, 2 CMV:
cmv_clc2
Category 1 and category 2 (C1C2) CMV emissions sources outside of California
were projected to 2030 (2030 emissions were used to represent 2032) 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 calculated using new
2018->2030 factors based on interpolations of the same CARB data used to
calculate factors in 2016 platforms (2030 was used for 2032). Projection factors
for Canada emissions were calculated using 2018->2028 factors based on
interpolations of the ECCC data provided for the 2016 platforms, then multiplied
by the 2028-2030 US-based factors (same as in 2032fj in the 2016v2 platform).
Category 3 CMV:
cmv_c3
Category 3 (C3) CMV emissions were projected to 2030 using an EPA report on
projected bunker fuel demand that projects fuel consumption by region out to the
year 2030 (2030 was used for 2032). 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 2030 to arrive
at the final growth rates. Projection factors for Canada emissions were calculated
using 2018->2028 factors based on interpolations of the ECCC data provided for
the 2016 platforms, then multiplied by the 2028-2030 US-based factors (same as in
2032fj in the 2016v2 platform).
Locomotives:
rail
Rail was projected from 2026fj to 2032 using AEO2022-based growth factors, plus
ERTAC-based pollutant-specific factors for Class I. California rail used new
CARB 2032 inventory.
Area fugitive dust:
afdust
Paved road dust was grown to 2032 levels based on the growth in VMT from 2018
to 2032. Emissions for the remainder of the sector including building construction,
road construction, agricultural dust, and unpaved road dust were held constant at
2018 levels.
Livestock: livestock
Livestock were projected using factors developed for 2016v3 platform. Emissions
were projected from 2018 to 2032 based on factors created from USDA National
livestock inventory projections published in 2022
(https://www.ers.usda.gov/publications/pub-details/?pubid=103309).
Nonpoint source oil
and gas:
npoilgas
Exploration-related sources were based 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
2032 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. These projection factors were applied to 2018
production emissions in the entire US, including the WRAP region. Controls were
then applied to account for NSPS for oil and gas and RICE.
Residential Wood
Combustion:
rwc
2018 RWC emissions are the same as 2017 NEI. RWC emissions were projected
from 2018 to 2032 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.
141
-------
Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
Solvents:
solvents
The same projection and control factors to 2032 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. Applied the same OTC Rules
controls as 2016v3, but only included controls that took effect after 1/1/2018.
Remaining
nonpoint:
nonpt
Projected base year to 2032 using 2016v3-consistent projection and control
packets. For the purposes of the projection packets, 2016 was used as the base
year, because the base year nonpt inventory was from only one year later
(2017NEI) and so that projection packets from 2016 platform could be reused.
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 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 2032 after the AEO-based factors were updated to
AEO2022. Projection factors provided by North Carolina and New Jersey were
used through 2026, with MAR\MA-based projections used from 2026 to 2032.
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 MOVES3.0.3 model was newly run
for this case to create nonroad emissions for 2032. Fuels used in MOVES3 are
specific to 2032. Updated data from CARB were used for 2032. Texas nonroad
emissions were provided by TCEQ for 2023 and 2028, and interpolated to 2026;
they were then projected to 2032 using factors derived from MOVES.
Onroad:
onroad,
onroadnonconus
Activity data for 2018 were projected from the 2017 NEI. Activity data were then
projected to 2032 using factors derived from AEO2022. To create the emission
factors, MOVES3 was run for the year 2032 using 2018 meteorological data and
fuels, but with age distributions projected to represent 2032 and the remaining
inputs consistent with those used in 2017NEI. The 2032-specific activity data and
emission factors were then combined using SMOKE-MOVES to produce the 2032
emissions. Inspection and maintenance updates were included for NC and TN (this
changed the representative county groupings for 2032). Adjustments were applied
to reflect the Control of Air Pollution from New Motor Vehicles: Heavy-Duty
Engine and Vehicle Standards (2022) and the Final Rule to Revise Existing
National GHG Emissions Standards for Passenger Cars and Light Trucks Through
Model Year 2026 (2021). Section 4.3.2 describes the applicable rules that were
considered when projecting onroad emissions.
Onroad California:
onroad ca adj
CARB-provided emissions were used for 2032 in California.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
Area fugitive dust emissions were provided by ECCC prior to 2016vl. Projection
factors were derived from those inventories and applied to the 2016v2 inventory to
estimate the 2028 emissions and those emissions were used to represent 2032 in
this platform. Mexico emissions are not included in this sector.
Other Point Fugitive
dust sources not
from the NEI:
othptdust
Base year inventories from ECCC were held flat from 2018 for the analytic year
2032, including the same transport fraction as the base year and the meteorology-
based (precipitation and snow/ice cover) zero-out.
142
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Platform Sector:
abbreviation
Description of Projection Methods for Analytic Year Inventories
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 inventories to estimate 2028 emissions,
and those emissions were used to represent 2032. Canada projections were applied
by province-subclass where possible (i.e., where subclasses did not change
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 to 2028
(that inventory was used to represent 2032) using state and pollutant-specific
factors based on the 2016vl platform inventories.
Canada ag not from
the NEI:
Canada ag
Base year low-level agricultural sources were projected to 2028 (which was used
to represent 2032) using projection factors based on data provided by ECCC and
applied by province, pollutant, and ECCC sub-class code.
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. 2028 projections were used to represent 2032.
Other non-NEI
nonpoint and
nonroad:
othar
Analytic year Canada nonpoint inventories were provided by ECCC for 2016vl.
For Canadian nonpoint sources, factors were provided from which the analytic
year inventories could be derived. Projection factors for 2028 were derived from
those inventories and applied to the 2016v2 Canada nonpoint inventory to
represent 2032. For Canada nonroad, the previously generated 2026 data from
2016v2 platform was projected to 2032 using trends calculated from MOVES in
the US. For Mexico nonpoint and nonroad sources, state-pollutant projection
factors for 2028 were calculated from the 2016vl inventories, and then applied to
the 2016v2 base year inventories, with 2028 representing 2032.
Other non-NEI
onroad sources:
onroadcan
For Canadian mobile onroad sources, analytic year inventories were projected
from 2016 to 2026 using ECCC-provided projection data from vl platform at the
province and subclass (which is similar to SCC but not exactly) level. The
previously generated 2026 data from 2016v2 platform was projected to 2032 using
trends calculated from MOVES in the US.
Other non-NEI
onroad sources:
onroad mex
Monthly onroad mobile inventories were developed at municipio resolution based
on an interpolation of runs of MOVES-Mexico for 2028 and 2035.
143
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4.1 EGU Point Source Projections (ptegu)
The 2032 EGU emissions inventories relied on the EPA's Post-IRA 2022 Reference Case of the
Integrated Planning Model (IPM), with additional update of Final Good Neighbor Plan (GNP). 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 run (see the Final PM NAAQS web page
for more details, documentation of inputs and outputs to the modeling projections for this analysis):
• Final Good Neighbor Plan for 2015 Ozone NAAQS.
Inflation Reduction Act of 2021 (reflecting Tax Credits).
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 2030 and 2035. 2030 outputs were used in this analysis. All inputs,
outputs and full documentation of EPA's Post-IRA 2022 Reference Case and the associated EGU fleet
information (NEEDS for EPA Post-IRA 2022 Reference Case rev: are available on the Final
PM NAAQS modeling. Some of the key parameters used in the IPM run are:
• Demand: AEO 2021 + on-the-books OTAQ GHG Rules
• Gas and Coal Market assumptions: updated as of December 2021
• Cost and performance of fossil generation technologies: AEO 2021
• Cost and performance of renewable energy generation technologies: NREL ATB 2021 (mid-case)
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• Environmental rules and regulations (on-the-books): Final GNP, Revised CSAPR, MATS, BART,
CA AB 32, RGGI, various RPS and CES, non-air rules (Cooling Water Intake, ELC, CCR), State
Rules and mandates
• 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: NEEDS i >st-IRA 2022 Reference Case rev: 10-14-22
The 2030 outputs of the IPM projections were used for the 2032 inventory. 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 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
IPM outputs was "NEEDS NEIjcrej' 2016 2019stk 13apr22.xlsxn 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 base year, the NOX and S02 values in
the base year 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 the cases that comprise this platform.
The state total emissions in this table may not exactly match the sum of the emissions for each state in the
flat files for each season due to the process of apportioning seasonal total emissions to hours for input to
SMOKE followed by summing the daily emissions back up to annual. However, any difference should be
well within one percent of the state total emissions.
Table 4-2. EGU sector NOx emissions by State for the 2018v2 cases
State
2018gg
2032gg2
Alabama
27,026
10,596
Arizona
20,658
5,700
Arkansas
23,203
3,344
California
7,326
6,988
Colorado
20,016
2,146
Connecticut
3,818
2,415
Delaware
1,093
634
District of Columbia
NA
11
Florida
52,308
23,390
Georgia
29,172
7,530
Idaho
1,238
767
Illinois
34,258
7,023
Indiana
65,695
21,206
Iowa
25,880
20,056
Kansas
14,164
929
Kentucky
47,728
10,302
Louisiana
37,962
9,037
Maine
4,824
3,094
Maryland
8,691
2,478
Massachusetts
6,608
5,575
Michigan
47,391
16,734
Minnesota
21,469
3,090
Mississippi
16,380
4,672
Missouri
51,292
24,481
Montana
14,940
8,860
Nebraska
22,751
17,669
Nevada
4,788
2,558
New Hampshire
2,371
545
New Jersey
6,706
4,344
New Mexico
11,378
1,131
New York
15,512
10,653
North Carolina
36,939
5,064
North Dakota
34,009
19,602
Ohio
50,958
15,096
Oklahoma
22,084
2,689
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State
2018gg
2032gg2
Oregon
4,198
518
Pennsylvania
38,097
18,268
Rhode Island
577
567
South Carolina
15,132
4,628
South Dakota
1,193
1,205
Tennessee
9,132
1,489
Texas
110,843
22,197
Tribal Areas
23,755
2,762
Utah
25,601
6,111
Vermont
231
27
Virginia
23,233
8,070
Washington
10,096
2,398
West Virginia
41,410
16,532
Wisconsin
15,667
4,565
Wyoming
33,380
13,428
4.2 Sectors with Projections Computed using CoST
To project U.S. emissions for sectors other than EGUs, facility/unit closures information, growth
(projection) factors and/or controls were applied to certain categories within those sectors. Some facility
or sub-facility-level closure information was applied to the point sources. There are also a handful of
situations where new inventories were generated for sources that did not exist in the NEI (e.g., biodiesel
and cellulosic plants, yet-to-be constructed cement kilns). This subsection provides details on the data and
projection methods used to develop analytic year emissions for sectors other than EGUs that were
developed using the Control Strategy Tool.
Because the projection and control data are developed mostly independently from how the emissions
modeling sectors are defined, this section is organized primarily by the type of projections data, with
secondary consideration given to the emissions modeling sector (e.g., industrial source growth factors are
applicable to multiple emissions modeling sectors). The rest of this section is organized in the order that
the EPA uses the Control Strategy Tool (CoST) in combination with other methods to produce analytic
year inventories: 1) for point sources, apply facility or sub-facility-level closure information via CoST; 2)
apply all PROJECTION packets via CoST (these contain multiplicative factors that could cause increases
or decreases); 3) apply all percent reduction-based CONTROL packets via CoST; and 4) append any
other analytic-year inventories not generated via CoST. This organization allows consolidation of the
discussion of the emissions categories that are contained in multiple sectors, because the data and
approaches used across the sectors are consistent and do not need to be repeated. Sector names associated
with the CoST packets are provided in parentheses following the subsection titles.
The impacts of the projection and control factors on the emissions for each sector are shown in tables in
this section. In addition, the actual projection and control factors used to develop the analytic year
emissions are shown when they are general enough to fit into a table of reasonable length, although in
some cases, there are hundreds or thousands of factors used and the tables would be too large. To see
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these factors, visit the spreadsheets: 2032gg2 CoST_projection_packets llmay2023.xlsx and
2032gg2 CoST_projection_packets 1 lmay2023.xlsx on the FTP site for this platform.
4.2.1 Background on the Control Strategy Tool (CoST)
CoST is used to apply most non-EGU projection/growth factors, controls and facility/unit/stack-level
closures to the base year emissions modeling inventories to create analytic year inventories for the
following sectors: afdust, airports, cmv, livestock, nonpt, np oilgas, np solvents, pt oilgas, ptnonipm,
rail, and rwc. Information about CoST and related data sets is available from
https://www.epa.gov/economic-and-cost-analvsis-air-pollution-regulations/cost-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 inventories and applicable CoST packets. For reasons to be discussed later,
some emissions modeling sectors may require multiple CoST strategies to account for the compounding
of control programs that impact the same type of sources. There are also available linkages to existing and
user-defined control measure databases and it is up to the user to determine how control strategies are
developed and applied. The EPA typically creates individual CoST packets that represent specific
intended purposes (e.g., aircraft projections for airports are in a separate PROJECTION packet from
residential wood combustion sales/appliance turnover-based projections). CoST uses three packet types:
• CLOSURE: Closure packets are applied first in CoST. This packet can be used to zero-out (close)
point source emissions at resolutions as broad as a facility to as specific as a release point. The
EPA uses these types of packets for known post-base year controls as well as information on
closures provided by states on specific facilities, units or release points. This packet type is only
used for the ptnonipm and pt oilgas sectors.
• PROJECTION: Projection packets support the increase or decrease in emissions for virtually any
geographic and/or inventory source level. Projection factors are applied as multiplicative factors to
the base year emissions inventories prior to the application of any possible subsequent
CONTROLS. A PROJECTION packet is necessary whenever emissions increase from the base
year and is also desirable when information is based more on activity assumptions rather than on
known control measures. The EPA uses PROJECTION packet(s) for many modeling sectors.
• CONTROL: Control packets are applied after any/all CLOSURE and PROJECTION packet
entries. They support of similar level of specificity of geographic and/or inventory source level
application 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,
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then applies PROJECTION packet information, followed by CONTROL packets. A hierarchy is used by
CoST to separately apply PROJECTION and CONTROL packets. In short, in a separate process for
PROJECTION and CONTROL packets, more specific information is applied in lieu of less-specific
information in ANY other packets. For example, a facility-level PROJECTION factor will be replaced by
a unit-level, or facility and pollutant-level PROJECTION factor. It is important to note that this hierarchy
does not apply between packet types (e.g., CONTROL packet entries are applied irrespective of
PROJECTION packet hierarchies). A more specific example: a state/SCC-level PROJECTION factor will
be applied before a stack/pollutant-level CONTROL factor that impacts the same inventory record.
However, an inventory source that is subject to a CLOSURE packet record is removed from consideration
of subsequent PROJECTION and CONTROL packets.
The implication for this hierarchy and intra-packet independence is important to understand and quality
assure when creating future year strategies. For example, with consent decrees, settlements and state
comments, the goal is typically to achieve a targeted reduction (from the base year inventory) or a
targeted analytic-year emissions value. Therefore, controls due to consent decrees and state comments for
specific cement kilns (expressed as CONTROL packet entries) need to be applied instead of (not in
addition to) the more general approach of the PROJECTION packet entries for cement manufacturing. By
processing CoST control strategies with PROJECTION and CONTROL packets separated by the type of
broad measure/program, it is possible to show actual changes from the base year inventory to the future
(i.e., analytic) year inventory as a result of applying each packet.
Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a
hierarchal matching approach to assign PROJECTION factors to the inventory. For example, a packet
entry with Ranking=l will supersede all other potential inventory matches from other packets. CoST then
computes the projected emissions from all PROJECTION packet matches and then performs a similar
routine for all CONTROL packets. Therefore, when summarizing "emissions reduced" from CONTROL
packets, it is important to note that these reductions are not relative to the base year inventory, but rather
to the intermediate inventory after application of any/all PROJECTION packet matches (and
CLOSURES). A subset of the more than 70 hierarchy options is shown in Table 4-3, where the fields in
the table are similar to those used in the SMOKE FF10 inventories. For example, "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
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Rank
Matching Hierarchy
Inventory Type
14
REGION CD, NAICS, POLL
point, nonpoint
15
STATE, NAICS, SCC, POLL
point, nonpoint
16
STATE, NAICS, POLL
point, nonpoint
17
NAICS, SCC, POLL
point, nonpoint
18
NAICS, POLL
point, nonpoint
19
REGION CD, NAICS, SCC
point, nonpoint
20
REGION CD, NAICS
point, nonpoint
21
STATE, NAICS, SCC
point, nonpoint
22
STATE, NAICS
point, nonpoint
23
NAICS, SCC
point, nonpoint
24
NAICS
point, nonpoint
25
REGION CD, SCC, POLL
point, nonpoint
26
STATE, SCC, POLL
point, nonpoint
27
SCC, POLL
point, nonpoint
28
REGION CD, SCC
point, nonpoint
29
STATE, SCC
point, nonpoint
30
SCC
point, nonpoint
31
REGION CD, POLL
point, nonpoint
32
REGION CD
point, nonpoint
33
STATE, POLL
point, nonpoint
34
STATE
point, nonpoint
35
POLL
point, nonpoint
The contents of the controls, local adjustments and closures for the analytic year cases are described in the
following subsections. Year-specific projection factors (PROJECTION packets) for each analytic year
were used to create the analytic year cases, unless noted otherwise in the specific subsections. The
contents of a few of these projection packets (and control reductions) are provided in the following
subsections where feasible. However, most sectors used growth or control factors that varied
geographically, and their contents could not be provided in the following sections (e.g., facilities and units
subject to the Boiler MACT reconsideration has thousands of records). The remainder of Section 4.2 is
divided into subsections that are summarized in Table 4-4. Note that independent analytic year inventories
were used rather than projection or control packets for some sources.
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.
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Subsection
Title
Sector(s)
Brief Description
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.
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,
np solvents
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.
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Subsection
Title
Sector(s)
Brief Description
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.
4.2.2 CoST CLOSURE Packet (ptnonipm, pt_oilgas)
Packets:
CLOSURES2016v3_platform_ptnonipm_09j an2023_v 1
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-2018 permanent facility/unit shutdowns known in EIS as of the date of
the report). The starting point for the closures packet was the version from the 2016v3 platform. For
2018v2, additional closures were added and those are cumulative with the closures in 2018gc. 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 2018gc inventories
were incorporated in the 2018v2 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 2032.
Table 4-5. Reductions from all facility/unit/stack-level closures in 2032 from 2018 emissions levels
Pollutant
ptoilgas
CO
985
NH3
0
NOX
2,154
PM10
30
PM2.5
30
S02
1
VOC
193
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 to apply all of the PROJECTION packets. Regardless of
inventory type (point or nonpoint), the PROJECTION packets are applied prior to the CONTROL
packets. For several emissions modeling sectors (e.g., airports, np oilgas, pt oilgas), there is only one
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PROJECTION packet applied for each analytic year. For other sectors, there may be several different
sources of projection data and as a result there are multiple PROJECTION packets that are concatenated
by CoST during a control strategy run. The outputs are then quality-assured regarding duplicates and
applicability to the inventories in the CoST strategy. Similarly, CONTROL packets are kept in distinct
datasets for different control programs. Having the PROJECTION (and CONTROL) packets separated
into "key" projection and control programs allows for quick summaries of the impacts of these distinct
control programs on emissions.
Throughout the process of developing the 2016 platforms, MARAMA provided projection factors 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. Some other states also provided projection factors. Many of these were based on
data from the AEO available at the time the factors were generated. For the 2016v2 platform, MARAMA
provided new spreadsheets of projection factors to facilitate the incorporation of newer AEO data
available at that time, along with and other surrogate data used for projection factors. The new
spreadsheets also reflected sources affected by the Pennsylvania Reasonably Available Control
Technology (RACT) II. The data in these spreadsheets were further updated for the 2016v3 platform to
use factors based on AEO 2022. For some sectors, the 2016v3 inventories for the year 2026 were used as
the starting point for projection emissions to 2032 in this study. This facilitated the retention of some
state-provided data from the 2016 platforms in this platform. For states not covered by the MARAMA or
other state-provided packets, projection factors were developed using nationally available data and
methods.
Quantitative impacts of the projections on the emissions by sector nationally and by state are available in
the reports folder on the FTP site in the file 2032gg2projections by sector_packet.xlsx. Some excerpts
from this workbook are included in the subsections that follow.
4.2.3.1 Fugitive dust growth (afdust)
Packets:
Projection_2018_2032_afdust_paved_roads_for2032gg_14sep2022_v0
For paved roads (SCC 2294000000), the 2018 afdust emissions were projected to analytic year 2032
based on differences in county total VMT:
Analytic year afdust paved roads = 2018 afdust paved roads * (Analytic year county total VMT) /
(2018 county total VMT)
The VMT projections are described in the onroad section. Paved road dust emissions were projected this
way in all states, including MARAMA states. All emissions other than paved roads are held constant in
the analytic year projections. Unlike in 2016v3 platform, separate projection packets for the MARAMA
region were not use for this study for this sector. The impacts of the projections are shown in Table 4-6.
Table 4-6. Increase in PM2.5 emissions from projections in 2018v2
Sector
2018
Emissions
2032
Emissions
Percent Increase in
2032
Paved Roads
1,580,736
1,888,454
19.47%
All afdust
2,283,902
2,357,271
3.11%
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4.2.3.2 Airport sources (airports)
Packets:
airport_proj ections_itn_taf2021_2016_2032_25apr2022_v0
Airport emissions for 2016v3 were projected from the 2016 airport emissions to 2032 based on TAF 2021
based on the corrected 2017 NEI airport emissions (released in June 2022), and starting from the base
year 2016 instead of 2017. Year 2016 emissions were the starting point because they included corrections
to some airports in Georgia and Texas that were not in the 2017 NEI. 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 factors for facilities from 2016 to 2032 were limited to a range of 0.2 (80% reduction) to 5.0
(400% growth), and the state default projection factors were limited to a range of 0.5 (50% reduction) to
2.0 (100%) growth). Military state default projection values were kept flat (i.e., equal to 1.0) to reflect
uncertainly in the data regarding these sources. The projection factors for 25 major airports in the
Continental US are shown in Table 4-7. Separate projection factors are applied to commercial aviation,
general aviation, and air taxi SCCs. For airports without a projection factor specific to the air taxi
category, a state average projection factor is used. The national impact of the projections on airport
emissions from 2016 to 2032 is shown in Table 4-8.
Table 4-7. TAF 2021 growth factors for major airports, 2016 to 2032
Facility ID
State
Airport
Commercial
Aviation
General
Aviation
Air Taxi
10583311
Arizona
Phoenix (PHX)
1.5718
1.0276
0.5803
2255111
California
Los Angeles (LAX)
1.4171
0.7881
0.4868
9997011
California
San Francisco (SFO)
1.5497
0.9495
0.3167
9816811
Colorado
Denver (DEN)
1.6638
1.2331
0.3694
9762111
Florida
Orlando (MCO)
1.5906
1.0984
1.0474
9791511
Florida
Fort Lauderdale (FLL)
1.6579
1.0083
1.4303
9806211
Florida
Miami (MIA)
1.3662
0.9082
0.6174
9748811
Georgia
Atlanta (ATL)
1.4741
1.0626
n/a
2681611
Illinois
Chicago O'Hare (ORD)
1.8234
0.7652
n/a
9562811
Massachusetts
Boston (BOS)
1.5039
1.3743
0.9986
9535411
Michigan
Detroit (DTW)
1.5078
1.1021
n/a
6151711
Minnesota
Minneapolis (MSP)
1.4789
0.8776
0.2454
9392311
Nevada
Las Vegas (LAS)
1.3173
1.0173
1.0626
9376211
New Jersey
Newark (EWR)
1.5739
1.1233
n/a
9333211
New York
La Guardia (LGA)
1.2305
0.8187
n/a
9333311
New York
John F Kennedy (JFK)
1.4134
1.5807
0.2286
9279611
North Carolina
Charlotte (CLT)
1.7513
1.0288
n/a
154
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Commercial
General
Facility ID
State
Airport
Aviation
Aviation
Air Taxi
9246511
Oregon
Portland (PDX)
1.4561
0.9561
0.9602
9185011
Pennsylvania
Philadelphia (PHL)
1.5738
1.0464
n/a
9171111
Tennessee
Memphis (MEM)
1.4163
0.9277
0.5331
9076711
Texas
Dallas/Fort Worth (DFW)
1.6638
0.9549
n/a
9128911
Texas
Houston Intercontinental (IAH)
1.5991
0.9606
n/a
9076611
Utah
Salt Lake City (SLC)
1.6959
1.2681
0.5587
9063811
Virginia
Washington Dulles (IAD)
1.6017
0.957
0.4119
9093911
Washington
Seattle (SEA)
1.4455
0.7548
0.5497
Table 4-8. Impact of 2016 to 2032 factors on airport emissions
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
494,548
589,941
95,393
19.3%
NOX
128,306
170,662
42,356
33.0%
PM10-PRI
10,267
11,051
785
7.6%
PM25-PRI
8,969
9,711
742
8.3%
S02
15,472
20,874
5,402
34.9%
VOC
55,234
65,524
10,290
18.6%
CO
494,548
589,941
95,393
19.3%
4.2.3.3 Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
Packets:
Proj ecti on_2018_203 0_cmv_c 1 c2_for_2032gg_l 5 sep2022_v0
Proj ecti on_2018_203 0_cmv_Canada_for_2032gg_l 5 sep2022_v0
Category 1 and category 2 (C1C2) CMV emissions sources outside of California were projected to 2030
(with 2030 used for 2032) 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 2030 cmv_clc2 emissions for 2018v2 are based on the
same base year data as the 2018gc emissions. California cmv_clc2 emissions were projected based on
factors provided by the state. Table 4-9 lists the pollutant-specific projection factors to 2030 that were
used for cmv_clc2 sources outside of California. California sources were projected to 2030 using the
factors in Table 4-10, which are based on data provided by CARB.
Projection factors for Canada for 2030 were based on ECCC-provided 2023 and 2028 data projected to
2030.
155
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Table 4-9. National projection factors for cmv_clc2
Pollutant
U.S. 2018-to-2030 (%)
Canada 2028 to 2030 (%)
CO
+2.4%
+1.0%
NOX
-44.2%
-8.4%
PM10
-42.5%
-8.8%
PM2.5
-42.5%
-8.8%
S02
-46.7%
-0.6%
VOC
-46.0%
-7.8%
Table 4-10. California projection factors for cmv_clc2
Pollutant
2018-to-2030 (%)
CO
+19.6%
NOX
-15.8%
PM10
-29.8%
PM2.5
-29.8%
S02
+50.5%
VOC
+0.1%
4.2.3.4 Category 3 Commercial Marine Vessels (cmv_c3)
Packets:
Proj ecti on_2018_203 0_cmv_c3_for_2032gg_l 5 sep2022_v0
Proj ecti on_2018_203 0_cmv_Canada_for_2032gg_l 5 sep2022_v0
Growth rates for cmv_c3 emissions from 2018 to 2030 (with 2030 emissions used to represent 2032) were
projected using an EPA report on projected bunker fuel demand that included values through 2030.
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)25 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 2030 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 2030 cmv_c3 emissions for 2018v2 are based on the same base year data as the 2018gc emissions for
this sector. Projection factors for Canada for 2030 were based on ECCC-provided 2023 and 2028 data
projected to 2030.
25 https://nepis.epa.gov/Exe/ZvPURL.cgi?Dockev=P1005ZGH.TXT.
156
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The 2030 projection factors are shown in Table 4-11. Some regions for which 2018 projection factors
were available did not have 2030 projection factors specific to that region, so factors from another region
were used as follows:
• Alaska was proj ected 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 4-11 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 4-12
Table 4-11. 2018-to-2030 CMV C3 projection factors outside of California
Region
US 2018-to-
US 2018-to-2030
Canada 2028-to-
Canada 2028-to-
2030
other pollutants
2030
2030
NOX
NOX
other pollutants
US East Coast
-5.7%
+48.3%
-0.6%
+5.8%
US South Pacific
(excl. California)
-31.0%
+50.8%
n/a
n/a
US North Pacific
-2.9%
+41.0%
-0.3%
+4.6%
US Gulf
-13.1%
+35.7%
n/a
n/a
US Great Lakes
+23.0%
+29.3%
+3.7%
+4.3%
Other
+42.7%
+42.7%
n/a
n/a
Non-Federal Waters
2018-to-2030
S02
-73.6%
PM (main engines)
-25.9%
PM (aux. engines)
-30.1%
Other pollutants
+42.7%
Table 4-12. 2018-to-2030 CMV C3 projection factors for California
Pollutant
2018-to-2030
CO
+33.2%
NOx
+27.9%
PMio / PM2.5
+36.7%
S02
+32.3%
voc
+44.3%
4.2.3.5 Livestock population growth (livestock)
Packets:
Projection_2018gg_2032gg_ag_livestock_12sep2022_v0
157
-------
The 2018v2 livestock emissions were projected to year 2032 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 4-13, along
with the overall impacts on the livestock NH3 and VOC emissions. For emission projections to 2032, a
ratio was created between animal inventory counts for 2032 and 2018 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 base year emissions for the specific animal type to estimate 2032
NH3 and VOC emissions.
Table 4-13. National projection factors for livestock: 2018 to 2032
Animal
2018-to-2032
Beef
+0.57%
Swine
+10.54%
Broilers
+17.47%
Turkeys
+3.26%
Layers
+17.24%
Dairy
+0.09%
Overall NH3
+6.39%
Overall VOC
+6.12%
4.2.3.6 Nonpoint Sources (nonpt)
Packets:
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_PFC_version2_platform_MARAMA_noNC_l 6jul202 l_vl
Proj ection_2016_2026_nonpt_other_version3_platform_MARAMA_22aug2022_v0
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
Proj ection_2026_2028_finished_fuels_volpe_l 3aug202 l_v0
Projection_2026_2032_industrial_bySCC_version3_platform_12sep2022_nf_vl
Projection_2026_2032_nonpt_other_ver3_platform_MARAMA_for2032gg_12sep2022_v0
Proj ecti on_2026_2032_nonpt_PF C_version2_platform_M ARAM A_13 aug2021_v0
Projection_2026_2030_nonpt_population_version2_platform_noMARAMA_05aug2021_v0
In 2018v2, emissions sources in the nonpt sectors are based on 2017 NEI, and are projected to 2032 in
two parts. First, base year 2017NEI emissions were projected to 2026 using projection packets developed
for the 2016v3 platform. These projection packets reference 2016 as the base year because they are from
2016v3 platform, but for the nonpt sector in particular, these packets are applicable to the 2017NEI
emissions used in 2018v2 platform. Then, the newly projected 2026 emissions were projected to 2032
emissions using a second set of projection packets in which 2026 is the base year.
Inside MARAMA region
2016-to-2026 and 2026-to-2032 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 for portable fuel containers
158
-------
(PFCs), and a second projection packet per year for all other nonpt sources. The impacts of these factors
on nonpt emissions other than PFCs are shown in Table 4-14. The impacts of the factors on PFC sources
are shown in Table 4-15.
The MARAMA projection packets were used throughout the MARAMA region, except for 2016-to-2026
projections 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. NC- and NJ-provided projection packets were not available for
2032, so MARAMA projection factors were used in those two states beyond 2026. The impacts of the
North Carolina and New Jersey factors from 2016-2026 are shown in Table 4-16 and Table 4-17,
respectively.
Table 4-14. Impact of 2016-2026 factors on nonpt emissions in MARAMA states
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
405,690
410,106
4,416
1.1%
NH3
10,721
10,959
238
2.2%
NOX
183,170
186,704
3,534
1.9%
PM10-PRI
119,049
119,373
324
0.3%
PM25-PRI
106,750
107,056
306
0.3%
S02
22,668
22,028
-640
-2.8%
VOC
107,154
112,662
5,508
5.1%
Table 4-15. Impact of factors on nonpt PFC emissions in MARAMA states
Factor
Years
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
2016-2026
VOC
25,987
26,620
633
2.4%
2026-2032
VOC
20,879
21,112
233
1.1%
Table 4-16. Impact of 2016-2026 factors on nonpt emissions in North Carolina
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
25,506
26,730
1,224
4.8%
NH3
1,196
1,339
143
11.9%
NOX
9,463
10,423
960
10.1%
PM10-PRI
9,326
9,961
635
6.8%
PM25-PRI
8,506
9,087
581
6.8%
S02
418
434
16
3.8%
VOC
16,811
16,214
-597
-3.6%
159
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Table 4-17. Impact of 2016-2026 factors on nonpt emissions in New Jersey
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
19,492
19,924
432
2.2%
NH3
404
395
-9
-2.2%
NOX
22,302
22,544
242
1.1%
PM10-PRI
6,320
6,502
182
2.9%
PM25-PRI
5,759
5,924
165
2.9%
S02
367
367
0
0.0%
VOC
16,934
16,082
-852
-5.0%
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 by industrial sector using a series of AEOs to cover the period from 2016 through
2032: 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 2032. 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 2.25 for 2016 to 2026, and
1.75 for 2026 to 2032. Sources within the MARAMA region were not projected with these factors, but
with the MARAMA-provided growth factors. The impacts of these factors on emissions from 2016-2026
and 2025-2032 on nonpt emissions are shown in Table 4-18 and Table 4-19. The impacts of the factors
not associated with SCCs are shown in Table 4-20.
In response to comments, distillate emissions for SCCs 2103004000, 2103004001, and 2103004002 were
held flat with a 1.0 projection factor instead of showing increasing emissions in 2032.
Table 4-18. Impact of 2016-2026 industrial factors by SCC on nonpt emissions in non-MARAMA
states
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
291,269
307,431
16,163
5.5%
NH3
5,122
5,653
530
10.4%
NOX
300,170
319,970
19,800
6.6%
PM10-PRI
146,635
136,472
-10,163
-6.9%
PM25-PRI
97,608
98,086
478
0.5%
S02
128,668
93,840
-34,829
-27.1%
VOC
17,254
18,968
1,714
9.9%
160
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Table 4-19. Impact of 2026-2032 industrial factors by SCC on nonpt emissions in non-MARAMA
states
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
282,178
288,504
6,326
2.2%
NH3
5,653
5,780
128
2.3%
NOX
281,164
286,474
5,310
1.9%
PM10-PRI
136,472
140,194
3,722
2.7%
PM25-PRI
98,086
101,296
3,210
3.3%
S02
93,840
95,261
1,421
1.5%
VOC
18,968
19,298
330
1.7%
Table 4-20. Impact of 2026-2032 factors other than by SCC on nonpt emissions in non-MARAMA
states
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
456,758
457,537
779
0.2%
NH3
12,693
12,624
-70
-0.5%
NOX
218,267
215,622
-2,645
-1.2%
PM10-PRI
135,834
136,607
773
0.6%
PM25-PRI
122,067
122,762
695
0.6%
S02
14,089
13,886
-203
-1.4%
VOC
142,476
139,851
-2,625
-1.8%
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 2028 using the upstream
modules. For these sources, projection factors for 2028 were applied and the resulting emissions were
used to represent 2032. Sources within the MARAMA region were not projected with these factors, but
with the MARAMA-provided growth factors. The impact of the factors from 2016-2026 and 2026-2028
are shown in Table 4-21.
161
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Table 4-21. Impact of factors on nonpt finished fuel emissions
Factor
years
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
2016-2026
voc
405,952
336,366
-69,586
-17.1%
2026-2028
voc
336,366
317,038
-19,327
-5.7%
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, 2025, and 2030. These human population data were used to create modified
county-specific projection factors. The impacted SCCs are shown in Table 4-22. 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 population projections through the 2020s, and
was therefore not used. For example, rural areas of NC were projected to have more growth than urban
areas, which is the opposite of what has happened in recent years. 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. For these population-based projection factors, 2030 population was used to represent 2032.
Sources within the MARAMA region were not projected with these factors, but with the MARAMA-
provided growth factors. The impact of the population growth-based factors on the nonpt emissions is
shown in Table 4-23 and Table 4-24.
Table 4-22. SCCs in nonpt that use Human Population Growth for Projections
see
Description
2302002100
Industrial Processes;Food and Kindred Products: SIC 20;Commercial Cooking -
Charbroiling; Conveyorized 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)
162
-------
see
Description
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
Table 4-23. Impact of 2016-2026 population-based factors on nonpt emissions in non-MARAMA
states
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
105,731
117,336
11,605
11.0%
NH3
1,555
1,707
152
9.8%
NOX
1,747
1,942
195
11.2%
PM10-PRI
90,772
100,389
9,617
10.6%
PM25-PRI
83,068
91,860
8,792
10.6%
S02
92
102
9
10.3%
VOC
64,056
70,658
6,603
10.3%
Table 4-24. Impact of 2026-2030 population-based factors on nonpt emissions in non-MARAMA
states
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
117,336
121,978
4,641
4.0%
NH3
1,707
1,768
61
3.6%
NOX
1,942
2,020
78
4.0%
PM10-PRI
100,389
104,236
3,847
3.8%
PM25-PRI
91,860
95,377
3,517
3.8%
S02
102
105
4
3.7%
VOC
70,658
73,299
2,641
3.7%
163
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4.2.3.7 Solvents (np_solvents)
Packets:
Proj ection_2016_202X_solvents_v3platform_Idaho_asphalt_09aug2022_v0
Projection_2018_2032_np_solvents_for_2032gg_MARAMA_14sep2022_v0
Proj ection_2018_2030_np_solvents_for_2032gg_noMARAMA_l 3 sep2022_v0
The projection methodology for npsolvents is similar to the method used in the 2016v3 platform.
Projection factors from MARAMA were applied inside the MARAMA region, and projection factors
based on human population trends are applied for most solvent categories elsewhere. 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 4-25.
The following updates were made starting in 2016v3 platform 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.
For 2016v3, Idaho asphalt emissions (SCCs = 2461021000, 2461022000) were reduced by 14.2% based
on a comment from the state. The impact of the population-based factors on the np solvents sector
emissions outside of MARAMA states are shown in Table 4-26. The impacts of the factors on
np_solvents emissions in MARAMA states are shown in Table 4-27.
Table 4-25. 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
164
-------
see
SCC Descriptions
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
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
Table 4-26. Impact of population-based factors on np solvents emissions in non-MARAMA states
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
15
17
3
17.7%
NH3
58
65
7
12.6%
NOX
27
32
5
17.9%
PM10-PRI
450
508
58
12.9%
PM25-PRI
429
484
55
12.8%
S02
1
1
0
18.2%
VOC
1,435,256
1,613,518
178,262
12.4%
165
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Table 4-27. Impact of factors on npsolvents emissions in MARAMA states
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
voc
565,443
595,664
30,221
5.3%
4.2.3.8 Oil and Gas Sources (np_oilgas, pt_oilgas)
Packets:
Proj ection_2018_2032_np_oilgas_for_2032gg_21 sep2022_v0
Proj ection_2018_2032_pt_oilgas_for_2032gg_22sep2022_v0
Analytic year projections for the 2018v2 platform were generated for point oil and gas sources for the
year 2032. This projection consisted of three components: (1) applying facility closures to the ptoilgas
sector using the CoST CLOSURE packet (see Section 4.2.4); (2) using historical and/or forecast activity
data to generate analytic-year emissions before applicable control technologies are applied using the
CoST PROJECTION packet; and (3) estimating impacts of applicable control technologies on analytic-
year emissions using the CoST CONTROL packet. Applying the CLOSURE packet to the pt oilgas
sector resulted in small emissions changes to the national summary shown in Table 4-5.
For pt oilgas growth to 2032, the oil and gas sources were separated into production-related and pipeline-
related sources by NAICS and SCC. These sources were further subdivided by fuel-type and by NAICS
and SCC into either OIL, natural gas (NGAS), or BOTH (where oil or natural gas fuels are possible). The
next two subsections describe the growth component of the process.
For npoilgas growth to 2032, 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 2018 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 year of 2032. 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.
166
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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 2032. 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 2018 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 2018 to 2032. This process
was done using crude production forecast information to generate a factor to apply to oil-production
related SCCs or NAICS-SCC combinations and it was also done using natural gas production forecast
information to generate a factor to apply to natural gas-production related NAICS-SCC combinations. For
the SCC and NAICS-SCC combinations that are designated "BOTH" the average of the oil-production
and natural-gas production factors was calculated and applied to these specific combinations.
The state of Texas provided specific comments on the growth of production-related point sources. Texas
provided updated basin specific production for 2018 and 2021 to allow for a better calculation of the
estimated growth for this three-year period (http://webapps.rrc.texas.gov/PDO/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 2032. 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
mmarv.aspx ) so that a better estimate of growth from 2018 to 2021 for the AEO Supply Regions in New
Mexico could be calculated.
167
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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 2018-to-2032 growth for oil transmission was +21.2%, and the growth for natural gas
was +28.0%. The impact of the projection factors on the pt oilgas emissions is shown in Table 4-28.
Table 4-28. Impact of 2018-2032 projections on pt oilgas emissions
Pollutant
Inventory Emissions
Final Emissions
Emissions Change
Emissions % Change
CO
166,667
181,220
14,553
8.7%
NH3
365
283
-82
-22.5%
NOX
338,206
397,481
59,276
17.5%
PM10-PRI
11,400
12,705
1,305
11.4%
PM25-PRI
10,844
12,029
1,186
10.9%
S02
32,881
42,055
9,174
27.9%
VOC
209,368
218,922
9,554
4.6%
Exploration-related Sources (npoilgas)
Years 2017 through 2019 exploration emissions were generated using the 2017NEI version of the Oil and
Gas Tool. Table 4-29 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 2018v2 because they reflected the most
recent average of exploration activity and emissions. These averaged emissions were used for the 2032
analytic year. Note that CoST was not used to perform this projection step for exploration sources, but is
used to apply controls to exploration sources for 2032. The change in emissions from 2018 to 2032 due to
the impact of the projections is shown in Table 4-30.
Table 4-29. Year 2017-2019 high-level summary of national oil and gas exploration emissions
Pollutant
2017
emissions
2018
emissions
2019
emissions
Three Year avg
(2017-2019) (tons)
NOX
73,992
123,908
108,957
102,285
VOC
118,004
136,916
106,505
120,474
Table 4-30. Impact of 2018-2032 projections on np oilgas emissions
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
518,419
561,343
42,924
8.3%
NH3
7
2
-5
-71.9%
NOX
382,846
417,535
34,690
9.1%
PM10-PRI
7,177
7,503
326
4.5%
PM25-PRI
7,114
7,440
326
4.6%
S02
48,690
69,946
21,256
43.7%
VOC
1,920,896
2,209,159
288,264
15.0%
168
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4.2.3.1 Non-EGU point sources (ptnonipm)
Packets:
Proj ection_2026_2028_finished_fuels_volpe_l 3aug202 l_vO
Projection_2026_2032_industrial_byNAICS_SCC_version3_platform_22sep2022_v0
Projection_2026_2032_industrial_bySCC_version3_platform_12sep2022_nf_vl
Proj ection_2026_2032_ptnonipm_version2_platform_MARAMA_l 3aug202 l_vO
proj ection_2026_2028_corn_ethanol_E0B0_V olpe_l 3 aug202 l_vO
Projections to 2032 ptnonipm start with the year 2026 emissions from the 2016v3 platform and are
additionally projected to 2032. In 2016v3 platform, emissions for the 2023 ptnonipm sector were set equal
to emissions from the 2019 NEI point source emissions file dated March 25, 2022. This inventory was
projected to 2026 as part of 2016v3 platform, and then for 2018v2 platform, projected further into 2032.
This section describes the projections applied from 2026 to 2032. Details on projected ptnonipm
emissions through 2026 are available in the 2016v3 TSD.
The 2032 ptnonipm emissions were projected from the 2016v3 platform year 2026 point source emissions
using several growth and projection methods described as here. The projection of oil and gas sources is
explained in the oil and gas section.
2032 Point Inventory - inside MARAMA region
2026-to-2032 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 2026 for the base
year and 2032 for the projection year. Unlike in 2016v3 platform, additional projection packets were not
used in North Carolina, New Jersey, and Virginia, because those projection packets (originally provided
for 2016vl platform) do not extend beyond 2028. Instead, 2026-to-2032 projections in those three states
are based on the MARAMA projection tool. The impact of the MARAMA projection packet on ptnonipm
emissions from 2026 to 2032 is shown in Table 4-31.
Table 4-31. Impact of 2026-2032 MARAMA projections on ptnonipm emissions
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
97,728
99,785
2,057
2.1%
NH3
6,273
6,315
43
0.7%
NOX
88,133
89,211
1,077
1.2%
PM10-PRI
33,474
33,693
220
0.7%
PM25-PRI
23,681
23,874
192
0.8%
S02
50,072
50,021
-50
-0.1%
VOC
77,226
77,425
198
0.3%
169
-------
2032 Point Inventories - outside MARAMA region
Projection factors were developed by industrial sector from AEO 2022 in order to project emissions from
2026 to 2032. Emissions were mapped to AEO categories by NAICS and SCC (combination of NAICS
and SCC first, SCC only after that) and projection factors were created using a ratio between the base year
and projection year estimates from each specific AEO category. SCC/NAICS combinations with
emissions >100tons/year for any CAP26 were mapped to AEO sector and fuel. Table 4-32 below details
the AEO2022 tables used to map SCCs to AEO categories for the projections of industrial sources. The
impact of the projection packets specified by NAICS and SCC from 2026-2032 is shown in Table 4-33
and the impact of the projection packets specified by SCC is shown in Table 4-34.
Table 4-32. 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
Table 4-33. Impact of 2026-2032 industrial projections by NAICS and SCC on ptnonipm emissions
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
179,673
182,335
2,663
1.5%
NH3
2,697
2,761
63
2.3%
NOX
174,429
177,552
3,123
1.8%
PM10-PRI
27,752
28,660
909
3.3%
PM25-PRI
23,822
24,512
690
2.9%
S02
70,516
70,769
252
0.4%
VOC
12,296
12,662
367
3.0%
26 The "100 tpy" criterion for this purpose was based on emissions in the emissions values in the 2016 beta platform.
170
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Table 4-34. Impact of 2026-2032 industrial projections by SCC on ptnonipm emissions
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
464,198
476,609
12,411
2.7%
NH3
4,213
4,398
185
4.4%
NOX
321,324
333,674
12,349
3.8%
PM10-PRI
66,072
68,580
2,509
3.8%
PM25-PRI
47,440
49,148
1,708
3.6%
S02
99,096
104,118
5,023
5.1%
VOC
34,460
35,771
1,311
3.8%
Finished fuel and biorefinery factors
Factors were developed as part of the 2016 platform to project finished fuels and biorefineries to analytic
years. Estimates on growth of evaporative emissions from transporting finished fuels are not covered as
part of oil and gas projections, e.g., withdrawing fuel from tanks at bulk plants, filling tanks at service
stations, etc. For 2016vl platform, the AEO 2018 was used as a starting point for projecting volumes of
finished fuel that would be transported in the analytic years of 2023 and 2028. Then these volumes were
used to calculate inventories associated with evaporative emissions in 2016, 2023, and 2028 using the
upstream modules. Those emission inventories were mapped to the appropriate SCCs and projection
packets were generated using the upstream modules. Because the last analytic year available for the
2016vl platform was 2028, it was not possible to develop factors specific to 2032. Instead, the portion of
the factors effective from 2026 to 2028 were applied for this study and the resulting emissions were held
constant at 2028 levels. A set of 2026-to-2028 projection factors was interpolated from the 2023 and 2028
projection factors from 2016vl platform. Sources within the MARAMA region were projected with
MARAMA-provided growth factors. The impact of the finished fuels factors on ptnonipm emissions is
shown in Table 4-35 and the impact on biorefinery emissions is shown in Table 4-36.
Table 4-35. Impact of 2026-2028 factors on ptnonipm finished fuel emissions
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
VOC
13,936
13,092
-843
-6.1%
Table 4-36. Impact of 2026-2028 factors on ptnonipm biorefinery emissions
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
Emissions %
Change
CO
7,473
7,332
-141
-1.9%
NH3
297
291
-6
-1.9%
NOX
10,197
10,004
-192
-1.9%
PM10-PRI
5,659
5,552
-107
-1.9%
PM25-PRI
4,529
4,444
-85
-1.9%
S02
3,591
3,523
-68
-1.9%
VOC
13,708
13,449
-259
-1.9%
171
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4.2.3.2 Railroads (rail)
Packets:
Proj ection_2026_2032_rail_for_2032gg_23 sep2022_v0
The starting point for the 2032 rail emissions were the 2026 emissions from the 2016v3 platform. Those
emissions were projected from 2026 to 2032 based on AEO2022 growth rates as shown in Table 4-37.
Table 4-37. AEO2022 growth rates for rail sub-groups, 2026 to 2032
Sector
Pollutant
2032
Class I Railroads
NOx
-15.7%
Class I Railroads
PM
-22.9%
Class I Railroads
VOC
-27.3%
Class I Railroads
Others
+0.99%
Class II/III Railroads
All
+0.99%
Commuter/Passenger
All
+14.3%
Rail Yards
All
+0.99%
For 2018v2, CARB provided new locomotive emissions for 2032. For VOC speciation, the EPA preferred
augmenting the 2032 CARB inventory (which only included CAPs) with HAPs and using those HAPs for
integration, rather than running the California portion of the sector as no-integrate. 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. The overall impact of all
projections on the rail emissions are shown in Table 4-38.
Table 4-38. Impact of projections on rail emissions
Pollutant
2026
Emissions
2032
Emissions
Emissions
Change
CO
107,420
109,034
1,615
NH3
335
340
5.0
NOX
465,183
413,468
-51,715
PM10-PRI
12,460
10,429
-2,032
PM25-PRI
12,084
10,114
-1,977
S02
379
384
5.7
VOC
20,621
16,770
-3,851
4.2.3.3 Residential Wood Combustion (rwc)
Packets:
Proj ection_2017_2032gg_rwc_fromMARAMA_12sep2022_v0
For residential wood combustion, the growth and control factors are computed together into merged
factors in the same packet. Emissions for the states of California, Oregon, and Washington are held
172
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constant due to regulations in effect in those areas. For the remaining states, RWC emissions from
2017NEI were projected to 2032 using projection factors derived using the MARAMA tool that is based
on the projection methodology from EPA's 201 lv6.3 platform. The year 2017 was used to represent
2018. The development of projected growth in RWC emissions to year 2032 is based on 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 &
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 2025 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. All of the factors in the projection tool are
held constant with no additional changes after year 2025. 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. 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. The resulting growth factors for these appliance types varies by
appliance type and also by pollutant because the emission rates, from the 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.
Table 4-39 contains the factors to adjust the emissions from 2017 to 2032 outside of California, Oregon,
and Washington, where RWC emissions were held constant at 2017 NEI levels for the years 2017 and
2032 due to the unique control programs that those states have in place. Table 4-40 shows the overall
impact of projection on the sector.
Table 4-39. Projection factors for Residential Wood Combustion
see
SCC description
Pollutant*
2017-to-2032
2104008100
Fireplace: general
+15.36%
2104008210
Woodstove: fireplace inserts; non-EPA certified
-16.50%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic
PM10-PRI
+3.92%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic
PM25-PRI
+3.92%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic
+7.60%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic
PM10-PRI
+6.41%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic
PM25-PRI
+6.41%
173
-------
see
SCC description
Pollutant*
2017-to-2032
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
+12.47%
2104008310
Woodstove
freestanding, non-EPA certified
CO
-14.70%
2104008310
Woodstove
freestanding, non-EPA certified
PM10-PRI
-15.58%
2104008310
Woodstove
freestanding, non-EPA certified
PM25-PRI
-15.58%
2104008310
Woodstove
freestanding, non-EPA certified
VOC
-13.94%
2104008310
Woodstove
freestanding, non-EPA certified
-14.70%
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
PM10-PRI
+3.92%
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
PM25-PRI
+3.92%
2104008320
Woodstove
freestanding, EPA certified, non-catalvtic
+7.60%
2104008330
Woodstove
freestanding, EPA certified, catalytic
PM10-PRI
+6.41%
2104008330
Woodstove
freestanding, EPA certified, catalytic
PM25-PRI
+6.41%
2104008330
Woodstove
freestanding, EPA certified, catalytic
+12.47%
2104008400
Woodstove
pellet-fired, general (freestanding or FP insert)
PM10-PRI
+29.85%
2104008400
Woodstove
pellet-fired, general (freestanding or FP insert)
PM25-PRI
+29.85%
2104008400
Woodstove
pellet-fired, general (freestanding or FP insert)
+25.94%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
CO
-83.91%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM10-PRI
-82.31%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM25-PRI
-82.31%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
VOC
-84.03%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
-83.91%
2104008530
Furnace: Indoor, pellet-fired, general
PM10-PRI
+29.85%
2104008530
Furnace: Indoor, pellet-fired, general
PM25-PRI
+29.85%
2104008530
Furnace: Indoor, pellet-fired, general
+25.94%
2104008610
Hydronic heater: outdoor
PM10-PRI
-1.83%
2104008610
Hydronic heater: outdoor
PM25-PRI
-1.83%
2104008610
Hydronic heater: outdoor
-2.26%
2104008620
Hydronic heater: indoor
PM10-PRI
-1.83%
2104008620
Hydronic heater: indoor
PM25-PRI
-1.83%
2104008620
Hydronic heater: indoor
-2.26%
2104008630
Hydronic heater: pellet-fired
PM10-PRI
-1.83%
2104008630
Hydronic heater: pellet-fired
PM25-PRI
-1.83%
2104008630
Hydronic heater: pellet-fired
-2.26%
2104008700
Outdoor wood burning device, NEC (fire-pits, chimineas, etc)
+8.19%
2104009000
Fire log total
+8.19%
* If no pollutant is specified, facture is used for any pollutants that do not have a pollutant-specific factor
Table 4-40. Impact of projections on rwc emissions, 2017-2032
Pollutant
Inventory
Emissions
Final
Emissions
Emissions
Change
CO
2,317,024
2,259,199
-57,826
NH3
16,426
16,146
-280
NOX
37,382
38,599
1,217
PM10-PRI
301,157
291,135
-10,022
PM25-PRI
299,911
289,966
-9,945
S02
8,503
7,687
-816
VOC
319,313
313,588
-5,724
174
-------
4.2.4 CoST CONTROL Packets (nonpt, np_oilgas, ptnonipm, pt_oilgas,
np_sol vents)
The final step in the projection of emissions to an analytic year is the application of any control
technologies or programs. For analytic-year New Source Performance Standards (NSPS) controls (e.g.,
oil and gas, Reciprocating Internal Combustion Engines (RICE), Natural Gas Turbines, and Process
Heaters), we attempted to control only new sources/equipment using the following equation to account for
growth and retirement of existing sources and the differences between the new and existing source
emission rates.
Qn = Qo { [ (1 + Pf) t-l]Fn + (l-Ri)tFe + [l-(l-Ri)t]Fn]} Equation 4-1
where:
Qn = emissions in projection year
Qo = emissions in base year
Pf = growth rate expressed as ratio (e.g., 1.5=50 percent cumulative growth)
t = number of years between base and analytic years
Fn = emission factor ratio for new sources
Ri = retirement rate, expressed as whole number (e.g., 3.3 percent=0.033)
Fe = emission factor ratio for existing sources
The first term in Equation 4-1 represents new source growth and controls, the second term accounts for
retirement and controls for existing sources, and the third term accounts for replacement source controls.
For computing the CoST % reductions (Control Efficiency), the simplified Equation 4-2 was used for
analytic year projections:
Control. EfRctency2o2*(%) = 100 x ¦
[tP/2a2*-i)xFH+(i-si}12+fi-(i-Hi)12)xFnl\ Equation 4-2
PflOZX /
For example, to compute the control efficiency for 2032 from a base year of 2018 the existing source
emissions factor (Fe) is set to 1.0; 2032 (the analytic year) minus 2018 (the base year) is 14, and the new
source emission factor (Fn) is the ratio of the NSPS emission factor to the existing emission factor.
The NSPS are applied to sectors and with the specified retirement rates (R) as follows:
• The Oil and Gas NSPS is applied to the npoilgas and ptoilgas sectors with no assumed
retirement rate.
• The RICE NSPS is applied to the np oilgas, pt oilgas, nonpt, and ptnonipm sectors with an
assumed retirement rate of 40 years (2.5%).
• The Gas Turbines NSPS is applied to the pt oilgas and ptnonipm sectors with an assumed
retirement rate of 45 years (2.2%).
• The Process Heaters NSPS is applied to the pt oilgas and ptnonipm sectors with an assumed
retirement rate of 30 years (3.3%).
175
-------
Table 4-41 shows the values for the emission factors for new sources (Fn) with respect to each NSPS
regulation and other conditions within. Further information about the application of NSPS controls can be
found in Section 4 of the Additional Updates to Emissions Inventories for the Version 6.3, 2011
Emissions Modeling Platform for the Year 2023 technical support document (EPA, 2017).
Table 4-41. Assumed new source emission factor ratios for NSPS rules
NSPS
Pollutants
Applied where?
New Source
Emission Factor (Fn)
Oil and Gas
VOC
Storage Tanks: 70.3% reduction in growth-only
(>1.0)
0.297
Oil and Gas
VOC
Gas Well Completions: 95% control (regardless)
0.05
Oil and Gas
VOC
Pneumatic controllers, not high-bleed >6scfm or
0.23
low-bleed: 77% reduction in growth-only (>1.0)
Oil and Gas
VOC
Pneumatic controllers, high-bleed >6scfm or low-
bleed: 100% reduction in growth-only (>1.0)
0.00
Oil and Gas
VOC
Compressor Seals: 79.9% reduction in growth-
only (>1.0)
0.201
Oil and Gas
VOC
Fugitive Emissions: 60% Valves, flanges,
connections, pumps, open-ended lines, and other
0.40
RICE
NOx
Lean burn: PA, all other states
0.25, 0.606
RICE
NOx
Rich Burn: PA, all other states
0.1, 0.069
RICE
NOx
Combined (average) LB/RB: PA, other states
0.175, 0.338
RICE
CO
Lean burn: PA, all other states
1.0 (n/a), 0.889
RICE
CO
Rich Burn: PA, all other states
0.15, 0.25
RICE
CO
Combined (average) LB/RB: PA, other states
0.575, 0.569
RICE
VOC
Lean burn: PA, all other states
0.125, n/a
RICE
VOC
Rich Burn: PA, all other states
0.1,n/a
RICE
VOC
Combined (average) LB/RB: PA, other states
0.1125, n/a
Gas Turbines
NOx
California and NOx SIP Call states
0.595
Gas Turbines
NOx
All other states
0.238
Process Heaters
NOx
Nationally to Process Heater SCCs
0.41
4.2.4.1 Oil and Gas NSPS (np_oilgas, pt_oilgas)
Packets:
Control_2018_2032_Oilgas_NSP S_withNMrule_np_oilgas_for_2032gg_21 sep2022_v0
Control_2018_2032_Oilgas_NSPS_withNMrule_pt_oilgas_for_2032gg_21 sep2022_v0
New packets to reflect the oil and gas NSPS were developed for the 2018 platform. For oil and gas NSPS
controls, except for gas well completions (a 95 percent control), the assumption of no equipment
retirements through year 2032 dictates that NSPS controls are applied to the growth component only of
any PROJECTION factors. For example, if a growth factor is 1.5 for storage tanks (indicating a 50
percent increase activity), then, using Table 4-41, 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
176
-------
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 4-42 shows the emission reductions for the oil and gas sectors as a result of applying the oil and gas
NSPS. Table 4-43 and Table 4-44 list the SCCs in the npoilgas and ptoilgas sectors for which the Oil
and Gas NSPS controls were. Note that controls are applied to both production and exploration-related
SCCs.)
Table 4-42. Emissions reductions for the oil and gas sectors due to applying the Oil and Gas NSPS
Sector
year
poll
2018gg
2018
pre-CoST
emissions
emissions
change from
2018
%
change
npoilgas
2032
VOC
2,425,264
2,400,100
-393,671
-16.4%
ptoilgas
2032
VOC
235,255
237,400
-9,022
-3.8%
Table 4-43. SCCs in np oilgas for which the Oil and Gas NSPS controls were applied
see
PRODUCT
OG_NSPS_SCC
TOOL
OR
STATE
Source category
SCC Description*
2310010300
OIL
3. Pneumatic
controllers: not
high or low bleed
TOOL
PRODUCTION
Crude Petroleum;Oil Well Pneumatic Devices
2310010700
OIL
5. Fugitives
TOOL
PRODUCTION
Crude Petroleum;Oil Well Fugitives
2310011020
OIL
1. Storage Tanks
TOOL
PRODUCTION
On-Shore Oil Production;Storage Tanks: Crude Oil
2310011500
OIL
5. Fugitives
TOOL
PRODUCTION
On-Shore Oil Production;Fugitives: All Processes
2310011501
OIL
5. Fugitives
TOOL
PRODUCTION
On-Shore Oil Production;Fugitives: Connectors
2310011502
OIL
5. Fugitives
TOOL
PRODUCTION
On-Shore Oil Production;Fugitives: Flanges
2310011503
OIL
5. Fugitives
TOOL
PRODUCTION
On-Shore Oil Production;Fugitives: Open Ended Lines
2310011505
OIL
5. Fugitives
TOOL
PRODUCTION
On-Shore Oil Production;Fugitives: Valves
2310011506
OIL
5. Fugitives
TOOL
PRODUCTION
On-Shore Oil Production;Fugitives: Other
2310020700
NGAS
5. Fugitives
TOOL
PRODUCTION
Natural Gas;Gas Well Fugitives
2310021010
NGAS
1. Storage Tanks
TOOL
PRODUCTION
On-Shore Gas Production;Storage Tanks: Condensate
2310021011
NGAS
1. Storage Tanks
TOOL
PRODUCTION
On-Shore Gas Production;Condensate Tank Flaring
2310021300
NGAS
3. Pneumatic
controllers: not
high or low bleed
TOOL
PRODUCTION
On-Shore Gas Production;Gas Well Pneumatic
Devices
2310021310
NGAS
6. Pneumatic
Pumps
TOOL
PRODUCTION
On-Shore Gas Production;Gas Well Pneumatic Pumps
2310021500
NGAS
2. Well
Completions
TOOL
EXPLORATION
On-Shore Gas Production;Gas Well Completion -
Flaring
2310021501
NGAS
5. Fugitives
TOOL
PRODUCTION
On-Shore Gas Production;Fugitives: Connectors
2310021502
NGAS
5. Fugitives
TOOL
PRODUCTION
On-Shore Gas Production;Fugitives: Flanges
2310021503
NGAS
5. Fugitives
TOOL
PRODUCTION
On-Shore Gas Production;Fugitives: Open Ended
Lines
2310021505
NGAS
5. Fugitives
TOOL
PRODUCTION
On-Shore Gas Production;Fugitives: Valves
2310021506
NGAS
5. Fugitives
TOOL
PRODUCTION
On-Shore Gas Production;Fugitives: Other
2310021509
NGAS
5. Fugitives
TOOL
PRODUCTION
On-Shore Gas Production;Fugitives: All Processes
177
-------
see
PRODUCT
OG_NSPS_SCC
TOOL
OR
STATE
Source category
SCC Description*
2310021601
NGAS
2. Well
Completions
TOOL
EXPLORATION
On-Shore Gas Production;Gas Well Venting - Initial
Completions
2310023000
CBM
6. Pneumatic
Pumps
TOOL
PRODUCTION
Coal Bed Methane Natural Gas;Dewatering Pump
Engines
2310023010
CBM
1. Storage Tanks
TOOL
PRODUCTION
Coal Bed Methane Natural Gas;Storage Tanks:
Condensate
2310023300
CBM
3. Pnuematic
controllers: not
high or low bleed
TOOL
PRODUCTION
Coal Bed Methane Natural Gas;Pneumatic Devices
2310023310
CBM
6. Pneumatic
Pumps
TOOL
PRODUCTION
Coal Bed Methane Natural Gas;Pneumatic Pumps
2310023509
CBM
5. Fugitives
TOOL
PRODUCTION
Coal Bed Methane Natural Gas;Fugitives
2310023511
CBM
5. Fugitives
TOOL
PRODUCTION
Coal Bed Methane Natural Gas;Fugitives: Connectors
2310023512
CBM
5. Fugitives
TOOL
PRODUCTION
Coal Bed Methane Natural Gas;Fugitives: Flanges
2310023513
CBM
5. Fugitives
TOOL
PRODUCTION
Coal Bed Methane Natural Gas;Fugitives: Open Ended
Lines
2310023515
CBM
5. Fugitives
TOOL
PRODUCTION
Coal Bed Methane Natural Gas;Fugitives: Valves
2310023516
CBM
5. Fugitives
TOOL
PRODUCTION
Coal Bed Methane Natural Gas;Fugitives: Other
2310023600
CBM
2. Well
Completions
TOOL
EXPLORATION
Coal Bed Methane Natural Gas;CBM Well
Completion: All Processes
2310030220
NGAS
1. Storage Tanks
TOOL
PRODUCTION
Natural Gas Liquids;Gas Well Tanks - Flashing &
Standing/Working/Breathing, Controlled
2310030300
NGAS
1. Storage Tanks
TOOL
PRODUCTION
Natural Gas Liquids;Gas Well Water Tank Losses
2310111401
OIL
6. Pneumatic
Pumps
TOOL
PRODUCTION
On-Shore Oil Exploration;Oil Well Pneumatic Pumps
2310111700
OIL
2. Well
Completions
TOOL
EXPLORATION
On-Shore Oil Exploration;Oil Well Completion: All
Processes
2310121401
NGAS
6. Pneumatic
Pumps
TOOL
PRODUCTION
On-Shore Gas Exploration;Gas Well Pneumatic Pumps
2310121700
NGAS
2. Well
Completions
TOOL
EXPLORATION
On-Shore Gas Exploration;Gas Well Completion: All
Processes
2310321010
NGAS
1. Storage Tanks
STATE
PRODUCTION
On-Shore Gas Production - Conventional;Storage
Tanks: Condensate
2310421010
NGAS
1. Storage Tanks
STATE
PRODUCTION
On-Shore Gas Production - Unconventional;Storage
Tanks: Condensate
* All SCC descriptions in this table start with "Industrial Processes;Oil and Gas Exploration and Production;"
Table 4-44. SCCs in ptoilgas for which the Oil and Gas NSPS controls were applied
SCC
Fuel
OG NSPS
SCC
NP or
PT
SCC Description*
30180010
NGAS
4. Compressor
Seals
PT
IP;Chemical Manufacturing;Equipment Leaks;Compressor Seals: Gas Stream
30600801
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Pipeline Valves and Flanges
30600802
OIL
5. Fugitives
PT
IP;Petroleum Industry ;Fugitive Emissions; Vessel Relief Valves
30600803
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Pump Seals w/o Controls
30600804
OIL
4. Compressor
Seals
PT
IP;Petroleum Industry;Fugitive Emissions;Compressor Seals
30600805
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Miscellaneous: Sampling/Non-Asphalt
Bio wing/Purging/etc.
30600806
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Pump Seals with Controls
30600811
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Pipeline Valves: Gas Streams
30600812
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Pipeline Valves: Light Liquid/Gas
Streams
178
-------
see
Fuel
OG NSPS
sec
NP or
PT
SCC Description*
30600813
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Pipeline Valves: Heavy Liquid Streams
30600815
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Open-ended Valves: All Streams
30600816
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Flanges: All Streams
30600817
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Pump Seals: Light Liquid/Gas Streams
30600818
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Pump Seals: Heavy Liquid Streams
30600819
OIL
4. Compressor
Seals
PT
IP;Petroleum Industry;Fugitive Emissions;Compressor Seals: Gas Streams
30600820
OIL
4. Compressor
Seals
PT
IP;Petroleum Industry;Fugitive Emissions;Compressor Seals: Heavy Liquid
Streams
30600822
OIL
5. Fugitives
PT
IP;Petroleum Industry ;Fugitive Emissions; Vessel Relief Valves: All Streams
30688801
OIL
5. Fugitives
PT
IP;Petroleum Industry;Fugitive Emissions;Specify in Comments Field
31000101
OIL
2. Well
Completions
PT
IP;Oil and Gas Production;Crude Oil Production;Well Completion
31000130
OIL
4. Compressor
Seals
PT
IP;Oil and Gas Production;Crude Oil Production;Fugitives: Compressor Seals
31000151
OIL
3. Pnuematic
controllers: high
or low bleed
PT
IP;Oil and Gas Production;Crude Oil Production;Pneumatic Controllers, Low
Bleed
31000152
OIL
3. Pnuematic
controllers: high
or low bleed
PT
IP;Oil and Gas Production;Crude Oil Production;Pneumatic Controllers High Bleed
>6 scfh
31000153
OIL
3. Pnuematic
controllers: not
high or low
bleed
PT
IP;Oil and Gas Production;Crude Oil Production;Pneumatic Controllers
Intermittent Bleed
31000207
NGAS
5. Fugitives
PT
IP;Oil and Gas Production;Natural Gas Production; Valves: Fugitive Emissions
31000220
NGAS
5. Fugitives
NP AN
D PT
IP;Oil and Gas Production;Natural Gas Production;All Equipt Leak Fugitives
(Valves, Flanges, Connections, Seals, Drains
31000225
NGAS
4. Compressor
Seals
PT
IP;Oil and Gas Production;Natural Gas Production;Compressor Seals
31000231
NGAS
5. Fugitives
PT
IP;Oil and Gas Production;Natural Gas Production;Fugitives: Drains
31000233
NGAS
3. Pnuematic
controllers: high
or low bleed
PT
IP;Oil and Gas Production;Natural Gas Production;Pneumatic Controllers, Low
Bleed
31000235
NGAS
3. Pnuematic
controllers: not
high or low
bleed
PT
IP;Oil and Gas Production;Natural Gas Production;Pneumatic Controllers
Intermittent Bleed
31000309
NGAS
4. Compressor
Seals
PT
IP;Oil and Gas Production;Natural Gas Processing;Compressor Seals
31000324
NGAS
3. Pnuematic
controllers: high
or low bleed
NP AN
D_PT
IP;Oil and Gas Production;Natural Gas Processing;Pneumatic Controllers Low
Bleed
31000325
NGAS
3. Pnuematic
controllers: high
or low bleed
NP AN
D_PT
IP;Oil and Gas Production;Natural Gas Processing;Pneumatic Controllers, High
Bleed >6 scfh
31000326
NGAS
3. Pnuematic
controllers: not
high or low
bleed
PT
IP;Oil and Gas Production;Natural Gas Processing;Pneumatic Controllers
Intermittent Bleed
31000506
OIL
1. Storage Tanks
PT
IP;Oil and Gas Production;Liquid Waste Treatment;Oil-Water Separation
Wastewater Holding Tanks
31088801
BOTH
5. Fugitives
PT
IP;Oil and Gas Production;Fugitive Emissions;Specify in Comments Field
31088811
BOTH
5. Fugitives
NP AN
D PT
IP;Oil and Gas Production;Fugitive Emissions;Fugitive Emissions
179
-------
see
Fuel
OG NSPS
sec
NP or
PT
SCC Description*
31700101
NGAS
3. Pnuematic
controllers: high
or low bleed
PT
IP;NGTS;Natural Gas Transmission and Storage Facilities;Pneumatic Controllers
Low Bleed
39090001
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Residual Oil: Breathing
Loss
39090002
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Residual Oil: Working
Loss
39090003
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Distillate Oil (No. 2):
Breathing Loss
39090004
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Distillate Oil (No. 2):
Working Loss
39090005
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Oil No. 6: Breathing Loss
39090006
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Oil No. 6: Working Loss
39090007
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Methanol: Breathing Loss
39090008
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Methanol: Working Loss
39090009
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Residual Oil/Crude Oil:
Breathing Loss
39090010
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Residual Oil/Crude Oil:
Working Loss
39090012
OIL
1. Storage Tanks
PT
IP;In-process Fuel Use;Fuel Storage - Fixed Roof Tanks;Dual Fuel (Gas/Oil):
Working Loss
40301001
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 13: Breathing Loss (67000 Bbl. Tank Size)
40301002
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 10: Breathing Loss (67000 Bbl. Tank Size)
40301003
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 7: Breathing Loss (67000 Bbl. Tank Size)
40301004
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 13: Breathing Loss (250000 Bbl. Tank Size)
40301005
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 10: Breathing Loss (250000 Bbl. Tank Size)
40301007
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 13: Working Loss (Tank Diameter Independent)
40301008
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 10: Working Loss (Tank Diameter Independent)
40301009
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Gasoline RVP 7: Working Loss (Tank Diameter Independent)
40301010
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refmeries;Fixed Roof Tanks (Varying
Sizes);Crude Oil RVP 5: Breathing Loss (67000 Bbl. Tank Size)
40301011
OIL
1. Storage Tanks
PT
CE;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
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Crude Oil RVP 5: Working Loss (Tank Diameter Independent)
40301013
OIL
1. Storage Tanks
PT
CE;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
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying Sizes);Jet
Naphtha (JP-4): Working Loss (Tank Diameter Independent)
40301019
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refmeries;Fixed Roof Tanks (Varying
Sizes);Distillate Fuel #2: Breathing Loss (67000 Bbl. Tank Size)
40301021
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Distillate Fuel #2: Working Loss (Tank Diameter Independent)
40301065
OIL
1. Storage Tanks
PT
CE;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
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Grade 6 Fuel Oil: Working Loss (Independent Tank Diameter)
40301079
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Grade 1 Fuel Oil: Working Loss (Independent Tank Diameter)
40301097
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Other Liquids: Breathing Loss (67000 Bbl. Tank Size)
180
-------
see
Fuel
OG NSPS
SCC
NP or
PT
SCC Description*
40301098
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Other Liquids: Breathing Loss (250000 Bbl. Tank Size)
40301099
OIL
1. Storage Tanks
PT
CE;Petroleum Product Storage at Refineries;Fixed Roof Tanks (Varying
Sizes);Other Liquids: Working Loss (Tank Diameter Independent)
40388801
OIL
5. Fugitives
PT
CE;Petroleum Product Storage at Refineries;Fugitive Emissions;General
40400300
OIL
1. Storage Tanks
PT
CE;Petroleum Liquids Storage (non-Refinery );Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank: Flashing Loss
40400301
OIL
1. Storage Tanks
PT
CE;Petroleum Liquids Storage (non-Refinery );Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank: Breathing Loss
40400302
OIL
1. Storage Tanks
PT
CE;Petroleum Liquids Storage (non-Refinery );Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank: Working Loss
40400311
OIL
1. Storage Tanks
NP AN
D PT
CE;Petroleum Liquids Storage (non-Refinery );Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Condensate, working+breathing+flashing losses
40400312
OIL
1. Storage Tanks
NP AN
D PT
CE;Petroleum Liquids Storage (non-Refinery );Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Crude Oil, working+breathing+flashing losses
40400313
OIL
1. Storage Tanks
PT
CE;Petroleum Liquids Storage (non-Refinery );Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Lube Oil, working+breathing+flashing losses
40400314
OIL
1. Storage Tanks
PT
CE;Petroleum Liquids Storage (non-Refinery );Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Specialty Chem-working+breathing+flashing
40400315
OIL
1. Storage Tanks
PT
CE;Petroleum Liquids Storage (non-Refinery );Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Produced Water, working+breathing+flashing
40400316
OIL
1. Storage Tanks
PT
CE;Petroleum Liquids Storage (non-Refinery );Oil and Gas Field Storage and
Working Tanks;Fixed Roof Tank, Diesel, working+breathing+flashing losses
40701613
OIL
1. Storage Tanks
PT
CE;Organic Chemical Storage;Fixed Roof Tanks - Alkanes (Paraffins);Petroleum
Distillate: Breathing Loss
40701614
OIL
1. Storage Tanks
PT
CE;Organic Chemical Storage;Fixed Roof Tanks - Alkanes (Paraffins);Petroleum
Distillate: Working Loss
* For all entries in this table, TOOL OR STATE = STATE and SRC CAT = PRODUCTION; In the SCC
description, IP is an abbreviation for Industrial Processes and CE is an abbreviation for Chemical Evaporation
4.2.4.2 RICE NSPS (nonpt, ptnonipm, np_oilgas, pt_oilgas)
Packets:
Control_2016_2026_RICE_NSPS_nonpt_v2_platform_l 6jul202 l_vO
Control_2026_2032_RICE_NSPS_nonpt_ptnonipm_v2_platform_13aug2021_v0
Control_2018_2032_RICE_NSPS_np_oilgas_for_2032gg_21 sep2022_v0
Control_2018_2032_RICE_NSPS_pt_oilgas_for_2032gg_22sep2022_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, 2026 emissions from 2016v3 platform were used as
the baseline for projections, so RICE NSPS controls only need to be applied beyond 2026 for that sector.
The 2026-to-2032 control packets were reused from 2016v2 platform.
For the pt_oilgas and np_oilgas sectors, year-specific RICE NSPS factors were generated for 2032. 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 2018gf to 2018v2. 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
181
-------
reduction was applied for lean burn, rich burn and "combined" engines using Equation 4-2 and
information listed in Table 4-41.
Table 4-45, Table 4-46, Table 4-47 and Table 4-48 show the reductions in emissions in the nonpt,
ptnonipm, and npoilgas and ptoilgas sectors after the application of the RICE NSPS CONTROL
packet. Note that for nonpoint oil and gas, VOC reductions were only appropriate in the state of
Pennsylvania. Table 4-49, Table 4-50, and Table 4-51 show the SCCs to which the NSPS controls are
applied in the nonpt, ptnonipm, np oilgas, and pt oilgas sectors.
Table 4-45. Emissions reductions in nonpt due to RICE NSPS
year
Poll
2018v2 (tons)
Emissions reductions
(tons)
% change
2032
CO
1,945,327
-32,620
-1.7%
2032
NOX
750,001
-52,059
-6.9%
Table 4-46. Emissions reductions in ptnonipm due to the RICE NSPS
year
poll
2026gf (tons)
Emissions
reductions (tons)
% change
2032
CO
1,380,825
-155
-0.01%
2032
NOX
860,031
-285
-0.03%
2032
VOC
760,436
-1.8
0.00%
Table 4-47. Emissions reductions in np oilgas due to the RICE NSPS
Year
Poll
2018v2 (tons)
2018 pre-CoST
emissions
Emissions
reduction
% change
2032
CO
664,681
661,330
-79,455
-12.0%
2032
NOX
670,576
648,890
-113,029
-17.4%
2032
VOC
2,425,264
2,400,113
-534
0.0%
Table 4-48. Emissions reductions in pt oilgas du to the RICE NSPS
Year
Pollutant
2018
Emissions Reductions
% change
2032
CO
208,810
-18,564
-8.9%
2032
NOX
424,313
-50,961
-12.0%
2032
VOC
235,255
-312
-0.1%
Table 4-49. 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
182
-------
see
Lean, Rich,
or Combined
SCCDESC
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 4-50. Non-point Oil and Gas SCCs where RICE NSPS controls are 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;;
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 Bum 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;;
183
-------
Table 4-51. 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)
4.2.4.3 Fuel Sulfur Rules (nonpt)
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 2018v2, states that had fully implemented their
controls by 2017 were removed from the control packet (namely Delaware, New York, and Pennsylvania)
because 2017 NEI was used for nonpoint emissions.
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 4-52, 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 pt oilgas sector. Note that ptnonipm
sources are not impacted in 2016v3 platform since the starting point for the analytic year emissions was
the 2019 NEI.
Table 4-52. Summary of fuel sulfur rule impacts on nonpoint S02 emissions for 2032
Pollutant
State
2032 pre-control
Emissions (tons)
2032 post-
control
Emissions (tons)
Change in
emissions
(tons)
Percent
change
NOX
Connecticut
3,356
3,112
-244
-7.3%
NOX
Maine
5,641
5,321
-320
-5.7%
NOX
Massachusetts
8,825
8,354
-472
-5.3%
NOX
New Hampshire
5,996
5,761
-235
-3.9%
NOX
Rhode Island
799
740
-59
-7.4%
NOX
Vermont
802
729
-73
-9.1%
NOX
Six state total
25,419
24,017
-1,402
-5.5%
S02
Connecticut
1,313
79
-1,234
-94.0%
S02
Maine
1,112
35
-1,078
-96.9%
184
-------
Pollutant
State
2032 pre-control
Emissions (tons)
2032 post-
control
Emissions (tons)
Change in
emissions
(tons)
Percent
change
S02
Massachusetts
2,090
83
-2,008
-96.0%
S02
New Hampshire
3,797
19
-3,778
-99.5%
S02
Rhode Island
336
38
-298
-88.7%
S02
Vermont
368
25
-344
-93.3%
S02
Six state total
9,017
279
-8,739
-96.9%
S02
ALL state total
167,825
159,086
-8,739
-5.2%
4.2.4.4 Natural Gas Turbines N0X NSPS (ptnonipm, pt_oilgas)
Packets:
Control_2018_2032_NG_Turbines_NSPS_pt_oilgas_for_2032gg_22sep2022_v0
Control_2026_2032_NG_Turbines_NSPS_ptnonipm_v2_platform_13aug2021_v0
For ptnonipm, the packet for 2032 was reused from the 2016v2 platform. For pt oilgas, the packet for
2018v2 is 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 year (2032). The control efficiency calculation methodology did not change
from the 2016v3 modeling platform to the 2018v2 platform.
Natural Gas Turbines NSPS controls were generated based on examination of emission limits for
stationary combustion turbines that are not in the power sector. In 2006, the EPA promulgated standards
of performance for new stationary combustion turbines in 40 CFR part 60, subpart KKKK. The standards
reflect changes in NOx emission control technologies and turbine design since standards for 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 4-53 compares 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 4-53. Stationary gas turbines NSPS analysis and resulting emission rates used to compute
controls
NOx Emission Limits for New Stationary Combustion Turbines
Firing Natural Gas
<50
MMBTU/hr
50-850
MMBTU/hr
>850
MMBTU/hr
Federal NSPS
100
25
15
ppm
State RACT Regulations
5-100
MMBTU/hr
100-250
MMBTU/hr
>250
MMBTU/hr
185
-------
NOx Emission Limits for New Stationary Combustion Turbines
Connecticut
225
75
75
ppm
Delaware
42
42
42
ppm
Massachusetts
65*
65
65
ppm
New Jersey
50*
50
50
ppm
New York
50
50
50
ppm
New Hampshire
55
55
55
ppm
* Only applies to 25-100 MMBTU/hr
Notes: The above state RACT table is from a 2001 analysis. The current NY State regulations have the
same emission limits.
New source emission rate (Fn)
NOx ratio (Fn)
Control (%)
NOx SIP Call states plus CA
= 25 / 42 =
0.595
40.5%
Other states
= 25 / 105 =
0.238
76.2%
For control factor development, the existing source emission ratio was set to 1.0 for combustion turbines.
The new source emission ratio for the NOx SIP Call states and California is the ratio of state NOx
emission limit to the Federal NSPS. A complicating factor in the above is the lack of size information in
the stationary source SCCs. Plus, the size classifications in the NSPS do not match the size differentiation
used in state air emission regulations. We accepted a simplifying assumption that most industrial
applications of combustion turbines are in the 100-250 MMBtu/hr size range and computed the new
source emission rates as the NSPS emission limit for 50-850 MMBtu/hr units divided by the state
emission limits. We used a conservative new source emission ratio by using the lowest state emission
limit of 42 ppmv (Delaware). This yields a new source emission ratio of 25/42, or 0.595 (40.5 percent
reduction) for states with existing combustion turbine emission limits. States without existing turbine
NOx limits would have a lower new source emission ratio: the uncontrolled emission rate (105 ppmv via
AP-42) divided into 25 ppmv = 0.238 (76.2 percent reduction). This control was then plugged into
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 2032. The Natural Gas
Turbines control packet for pt oilgas also includes additional controls for the EPNG Williams facility in
Arizona, in order to reduce the post-control facility total of 584.77 tons/yr NOx.
Table 4-54 shows the reduction in NOx emissions after the application of the Natural Gas Turbines NSPS
CONTROL packet and include emissions both inside and outside the MARAMA region. Table 4-55 and
Table 4-56 list the point source SCCs for which Natural Gas Turbines NSPS controls were applied.
Table 4-54. Emissions reductions due to the Natural Gas Turbines NSPS
Sector
Year
Pollutant
2026gf (tons)
Emissions
reduction (tons)
0/
/O
change
ptnonipm
2032
NOX
860,031
-726
-0.08%
pt oilgas
2032
NOX
424,313
-13,984
-3.3%
Table 4-55. SCCs in ptnonipm for which Natural Gas Turbines NSPS controls were applied
see
SCC Description
20200201
Internal Combustion Engines; Industrial; Natural Gas; Turbine
20200203
Internal Combustion Engines; Industrial; Natural Gas; Turbine: Cogeneration
186
-------
see
SCC Description
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 4-56. SCCs in ptoilgas for which Natural Gas Turbines NSPS controls were 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
4.2.4.5 Process Heaters NOx NSPS (ptnonipm, pt_oilgas)
Packets:
Control_2018_2032_Process_Heaters_NSPS_pt_oilgas_for_2032gg_22sep2022_v0
Control_2026_2032_Process_Heaters_NSPS_ptnonipm_v2_platform_13aug2021_v0
For ptnonipm, the packet for 2026 to 2032 was reused from the 2016v2 platform. For pt oilgas, the
packets were newly developed for 2018v2 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 2018, 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 4-57.
Table 4-57. Process Heaters NSPS analysis and 2018v2 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
187
-------
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.
The impact on emissions from applying the process heaters NSPS is shown in Table 4-58. Table 4-59 and
Table 4-60 list the point source SCCs for which the Process Heaters NSPS controls were applied.
Table 4-58. Emissions reductions due to the application of the Process Heaters NSPS
Sector
Year
Pollutant
2026gf
(tons)
Emissions
reduction (tons)
0/
/o
change
ptnonipm
2032
NOX
860,031
-5,923
-0.7%
pt oilgas
2032
NOX
424,313
-2,224
-0.5%
Table 4-59. SCCs in ptnonipm for which Process Heaters NSPS controls were applied
see
SCC Description*
30190003
IP; Chemical Manufacturing; Fuel Fired Equipment; Process Heater: Natural Gas
30190004
IP; Chemical Manufacturing; Fuel Fired Equipment; Process Heater: Process Gas
30590002
IP; Mineral Products; Fuel Fired Equipment; Residual Oil: Process Heaters
30590003
IP; Mineral Products; Fuel Fired Equipment; Natural Gas: Process Heaters
30600101
IP; Petroleum Industry; Process Heaters; Oil-fired
30600102
IP; Petroleum Industry; Process Heaters; Gas-fired
30600103
IP; Petroleum Industry; Process Heaters; Oil
30600104
IP; Petroleum Industry; Process Heaters; Gas-fired
30600105
IP; Petroleum Industry; Process Heaters; Natural Gas-fired
30600106
IP; Petroleum Industry; Process Heaters; Process Gas-fired
30600107
IP; Petroleum Industry; Process Heaters; Liquified Petroleum Gas (LPG)
30600199
IP; Petroleum Industry; Process Heaters; Other Not Classified
30990003
IP; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas: Process Heaters
31000401
IP; Oil and Gas Production; Process Heaters; Distillate Oil (No. 2)
31000402
IP; Oil and Gas Production; Process Heaters; Residual Oil
31000403
IP; Oil and Gas Production; Process Heaters; Crude Oil
31000404
IP; Oil and Gas Production; Process Heaters; Natural Gas
31000405
IP; Oil and Gas Production; Process Heaters; Process Gas
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see
SCC Description*
31000406
IP; Oil and Gas Production; Process Heaters; Propane/Butane
31000413
IP; Oil and Gas Production; Process Heaters; Crude Oil: Steam Generators
31000414
IP; Oil and Gas Production; Process Heaters; Natural Gas: Steam Generators
31000415
IP; Oil and Gas Production; Process Heaters; Process Gas: Steam Generators
39900501
IP; Miscellaneous Manufacturing Industries; Process Heater/Furnace; Distillate Oil
39900601
IP; Miscellaneous Manufacturing Industries; Process Heater/Furnace; Natural Gas
39990003
IP; Miscellaneous Manufacturing Industries; Miscellaneous Manufacturing Industries;
Natural Gas: Process Heaters
* IP = Industrial Processes
Table 4-60. SCCs in ptoilgas for which Process Heaters NSPS controls were applied
SCC
SCC Description
30190003
Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Process Heater: Natural Gas
30600102
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600104
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600105
Industrial Processes; Petroleum Industry; Process Heaters; Natural Gas-fired
30600106
Industrial Processes; Petroleum Industry; Process Heaters; Process Gas-fired
30600199
Industrial Processes; Petroleum Industry; Process Heaters; Other Not Classified
30990003
Industrial Processes; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas: Process Heaters
31000401
Industrial Processes; Oil and Gas Production; Process Heaters; Distillate Oil (No. 2)
31000402
Industrial Processes; Oil and Gas Production; Process Heaters; Residual Oil
31000403
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil
31000404
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas
31000405
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas
31000413
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil: Steam Generators
31000414
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas: Steam Generators
31000415
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas: Steam Generators
39900501
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace; Distillate Oil
39900601
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace; Natural Gas
4.2.4.6 Ozone Transport Commission Rules (np_solvents)
Packets:
Control_2016_202X_solvents_OTC_v3_platform_MARAMA_l 4sep2022_nf_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 np solvents sector. The new SCCs in the solvents sector were added to the packet.
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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 2018v2 OTC packet
is based on the packet from the 2016v3 platform, except with controls enacted prior to 2018 (and
therefore already reflected in the base year inventory) removed from the packet.
4.2.4.7 Good Neighbor Plan 2015 Ozone NAAQS (ptnonipm, pt_oilgas)
The Good Neighbor Plan for the 2015 ozone NAAQS includes NOx controls for both EGU and non-EGU
sources. The regulation ensures that 23 states meet the Clean Air Act's "Good Neighbor" requirements by
reducing pollution that significantly contributes to problems attaining and maintaining EPA's health-
based air quality standard for ground-level ozone (or "smog"), known as the 2015 Ozone National
Ambient Air Quality Standards (NAAQS), in downwind states. The estimated impact of the rule on the
non-EGUs modeled in this study is reflect in Table 4-61.
Table 4-61. NOx emissions reductions after application of Good Neighbor Plan control packet
Year
Sector
Pollutant
Uncontrolled
Emissions
Emissions Reductio
(tons/yr)n
%
change
2032
ptnonipm
NOX
90,630
-36,417
-40.2%
2032
pt oilgas
NOX
51,408
-10,315
-20.1%
4.3 Sectors with Projections Computed Outside of CoST
Projections for sectors not calculated using CoST are discussed in this section.
4.3.1 Nonroad Mobile Equipment Sources (nonroad)
Outside of California and Texas, the MOVES3 model (version 3.0.3) was run for 2032. The fuels used are
specific to the analytic year, but the meteorological data represented the year 2018. The 2032 nonroad
emissions include all nonroad control programs finalized as of the date of the MOVES3.0.3 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
2018v2, CARB provided new nonroad inventories for 2032. In Texas, a 2026 nonroad inventory was
interpolated from TCEQ-provided 2023 and 2028 inventories, and then the interpolated 2026 emissions
were projected to 2032 using 2026-to-2032 trends calculated from MOVES3 emissions in Texas. 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 2018 inventory, but based on the analytic year
MOVES runs instead of the 2018 MOVES run.
4.3.2 Onroad Mobile Sources (onroad)
For 2018v2, MOVES3 was run for 2032 to obtain onroad emission factors that account for 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 2020 or
later years.
An update in 2018v2 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).27 The adjustment factors that reflect the impacts
of the rule on CAPs are shown in Table 4-62. These adjustment factors are intended to represent not only
27 https ://www. govinfo. gov/content/pkg/FR-2021-12-30/pdf/2021-27854.pdf.
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the effects of the rule on onroad emissions in 2032, but also ancillary effects on stationary emissions such
as increased electricity production for electric vehicles.
Table 4-62. Light duty greenhouse gas rule adjustments for 2032 onroad emissions
Year
Source Type
Fuel Type
CO
voc
NOx
S02
PM
2032
Light Truck
Diesel
-13.36%
-35.97%
-27.45%
-31.63%
-50.52%
2032
Light Truck
E85
+0.74%
-0.56%
+1.79%
+119.02%
+3.97%
2032
Light Truck
Gasoline
-0.67%
-10.50%
+1.59%
+169.88%
+0.06%
2032
Passenger Car
Diesel
+2.02%
+2.70%
+6.44%
+351.16%
+6.92%
2032
Passenger Car
E85
+1.89%
+3.21%
+7.61%
+540.54%
+12.99%
2032
Passenger Car
Gasoline
-1.60%
-14.14%
-3.66%
+63.41%
-10.14%
The 2032 emission factors for 2018v2 are the same as those from 2016v2 platform, with the following
exceptions. For 2018v2, MOVES3 was run for combination long haul trucks only for 2032 using an
updated age distribution, and the resulting emission factors were used. For 2018v2, 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).
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 2018. 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 2032 were adjusted to match
the temporal and spatial patterns of the SMOKE-MOVES based emissions.
Analytic year 2032 VMT was developed as follows:
• VMT were projected from 2018 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. EPA
calculated county-road type factors based on FHWA VM-2 county-level data for 2018 to 2019,
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 2032 using AEO2022.
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 AEO2022 tables were used:
• Light Duty (LD): Light-Duty VMT by Technology Type (table #41):
https://www.eia. gov/outlooks/aeo/data/browser/#/?id=51-AEQ2022&sourcekev=0
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• Heavy Duty (HD): Freight Transportation Energy Use (table #49):
https://www.eia. gov/outlooks/aeo/data/browser/#/?id=58-
AE02022&cases=ref2022~aeo2020ref&sourcekev=0
To develop the VMT projection factors, total VMT for each MOVES fuel and vehicle grouping was
calculated for the years 2021 and 2032 based on the AEO-to-MOVES mappings above. From these totals,
2021-2032 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 2018v2 platform are provided in Table 4-63. 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 2021 and 203228
(https://www.woodsandpoole.com/. circa 2015). The purpose of this adjustment based on population
changes helps account for areas of the country that are growing more than others.
Table 4-63. Factors used to Project VMT to analytic years
SCC6
description
2021 to 2032 factor
220111
LD gas
1.187
220121
LD gas
1.187
220131
LD gas
1.187
220132
LD gas
1.187
220141
Buses gas
1.296
220142
Buses gas
1.296
220143
Buses gas
1.296
220151
MHD gas
1.296
220152
MHD gas
1.296
220153
MHD gas
1.296
220154
MHD gas
1.296
220161
HHD gas
0.486
220221
LD diesel
1.221
220231
LD diesel
1.221
220232
LD diesel
1.221
220241
Buses diesel
1.131
220242
Buses diesel
1.131
220243
Buses diesel
1.131
220251
MHD diesel
1.131
220252
MHD diesel
1.131
220253
MHD diesel
1.131
220254
MHD diesel
1.131
220261
HHD diesel
1.077
220262
HHD diesel
1.077
220341
Buses CNG
1.108
220342
Buses CNG
1.108
220343
Buses CNG
1.108
28 The final year of the population dataset used is 2030, and so 2030 population was used to represent 2032.
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SCC6
description
2021 to 2032 factor
220351
MHDCNG
1.108
220352
MHDCNG
1.108
220353
MHDCNG
1.108
220354
MHDCNG
1.108
220361
HHD CNG
1.046
220521
LD E-85
0.746
220531
LD E-85
0.746
220532
LD E-85
0.746
220921
LD Electric
6.707
220931
LD Electric
6.707
220932
LD Electric
6.707
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. 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 2018v2 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-
haul truck restricted road VMT in 2018v2; in those counties, the national AEO-based projection factor for
diesel combination trucks was used to project 2018v2 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; for 2032, the APU
percentage is 31.72%.
Analytic year starts were calculated using 2018v2-based VMT ratios:
Analytic year STARTS = Analytic year VMT * (2018 STARTS / 2018 VMT by county+SCC6)
Analytic year ONI activity was calculated using a similar formula:
Analytic year ONI = Analytic year VMT * (2018 ONI / 2018 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 2018v2 platform, CARB provided new EMFAC
emissions for 2032.
4.3.3 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. The
Canada and Mexico projections for 2032 are mostly the same as those in the 2016v2 platform, except
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with new SMOKE runs which map the emissions to 2018 calendar dates. The 2016v2 platform and
2018v2 platform use similar base year inventories in Canada and Mexico, allowing the previously
generated 2032 projections from 2016v2 platform to be reused for this study.
For the 2016vl platform, ECCC provided data from which Canadian analytic year projections could be
derived. These data includes emissions 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, 2028 emissions inventories were projected from the 2016v2 base year
inventory using projection factors based on the ECCC sub-class level data from the 2016vl platform,
except with the 2015-to-2028 trend reduced to a 2016-to-2028 trend (i.e., reduce the total change by
1/13). Some Canadian emissions inventories then received an additional projection from 2028 to 2032,
with methodology for the 2032 projections varying by sector. 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.
4.3.3.1 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 2028, and emissions from 2028 were used to represent the year
2032. 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)
For this study, the base year emissions from the othptdust sector were held flat from the base year to the
analytic year.
4.3.3.2 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 2028, which was then used to represent
the year 2032. 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, projection factors to 2028 were based on total
airport emissions from the 2016vl Canada inventory by province and pollutant. 2028 emissions were then
used to represent 2032.
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During the development of 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
2016vl platform for 2015 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. 2028 emissions
were used to represent 2032 for these point sources.
Mexico
The othpt sector includes a general point source inventory in Mexico which was updated for 2016v2
platform. Similar to the procedure for projecting Canadian stationary point sources, factors for projecting
from 2016 to 2028 were calculated from the 2016vl platform Mexico point source inventories by state
and pollutant and were then applied to the updated base year inventory to create a 2028 point source
inventory. Mexico point source emissions for 2028 were used to represent 2032.
4.3.3.3 Nonpoint sources in Canada and Mexico (othar)
Canadian stationary sources
For 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 2016vl platform for 2015 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.
For 2016vl platform, ECCC provided an additional stationary area source inventory for 2028
representing electric power generation (EPG). According to ECCC, this inventory's emissions do not
double count the 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 platform area source EPG inventory was included in the 2018v2 platform analytic year case, with
2028 emissions used to represent 2032.
Canadian mobile sources
Projection information for mobile nonroad sources, including rail and CMV, is covered by the ECCC sub-
class level data for 2015 and 2028. ECCC sub-class level data from 2016vl platform was used to project
the 2016v2 base year inventory to 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.
Instead of using 2028 mobile source emissions to represent 2032, additional projections out to 2032 were
applied to the Canada nonroad and rail inventories. For nonroad, national projection factors by fuel,
nonroad equipment sector, and pollutant were calculated from the 2016v2 platform US MOVES runs for
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2026 and 2032 (excluding California and Texas for which we did not use MOVES data) and applied to
the interpolated 2026 Canada nonroad inventory from 2016v2 platform. The 2026 Canada nonroad
inventory was used as the baseline for the 2032 projection rather than 2028, because we did not have a
MOVES run for 2028 which is consistent with the 2026 and 2032 MOVES3 runs performed for 2016v2
platform. For rail, factors for projecting 2026 Canadian rail from 2016v2 platform to 2032 were the same
as the factors used to project US rail emissions from 2026 to 2030 (used to represent 2032) in that
platform, which was based on the 2018 AEO.
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 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. 2028 emissions were used to represent
2032, including for nonroad (unlike in Canada).
4.3.3.4 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 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.
Instead of using 2028 mobile source emissions to represent 2032, additional projections out to 2032 were
applied to the Canada onroad inventory. National projection factors distinguishing gas from diesel, light
duty from heavy duty, refueling from non-refueling, and pollutant were calculated from the 2016v2
platform US MOVES runs for 2026 and 2032 (excluding California for which we did not use MOVES
data) and applied to the interpolated 2026 Canada onroad inventory. The 2026 Canada onroad inventory
was used as the baseline for the 2032 projection rather than 2028, because we did not have a MOVES3
run for 2028 which is consistent with the 2026 and 2032 MOVES runs performed for 2016v2 platform.
For Mexican mobile onroad sources, MOVES-Mexico was run to create emissions inventories for years
2028 and 2035, with 2032 emissions interpolated between 2028 and 2035. The 2035 MOVES-Mexico run
included diesel refueling whereas 2028 did not; thus diesel refueling emissions were excluded from the
2032 interpolation.
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5 Emission Summaries
Table 5-1 and Table 5-2 summarize annual emissions by sector for the 2018gg and 2032gg2 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 5-3 provides similar summaries for the 36-
km domain (36US3) for 2018 only, as boundary conditions based on 2018 emissions were also used in
2032. 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."
State totals and other summaries are available in the reports area on the FTP site for the 2018v2 platform
(https://gaftp.epa.gov/Air/emismod/2018/v2/reports).
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Table 5-1. National by-sector CAP emissions for the 2018gg case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdustadj
5,691,832
789,322
airports
489,039
0
131,779
9,813
8,576
16,492
54,939
cmv_clc2
24,431
86
168,566
4,622
4,480
655
6,674
cmv_c3
15,068
42
114,722
2,380
2,189
4,891
9,311
fertilizer
1,636,229
livestock
2,582,189
226,398
nonpt
1,927,267
102,898
739,250
572,589
475,154
166,399
818,185
nonroad
10,473,047
1,925
988,078
96,182
90,714
1,372
1,016,057
npoilgas
661,167
38
668,403
12,669
12,508
55,360
2,414,209
npsolvents
36
58
34
469
448
5
2,336,842
onroad
17,043,371
103,249
2,827,564
207,714
88,309
22,628
1,172,608
ptoilgas
200,740
434
385,649
13,663
13,047
39,003
232,995
ptagfire
421,836
93,685
17,935
59,968
38,050
7,451
63,726
ptegu
573,335
21,576
1,143,179
157,107
127,072
1,314,836
32,612
ptfire-rx
10,873,070
177,629
182,473
1,168,800
1,001,912
91,868
2,617,765
ptfire-wild
10,275,916
168,798
147,585
1,051,942
891,476
79,478
2,426,465
ptnonipm
1,378,771
60,793
894,993
388,475
242,515
561,234
766,021
rail
117,171
365
570,969
15,494
15,005
727
24,947
rwc
2,160,529
16,413
34,093
300,139
299,278
7,988
323,969
beis
3,902,690
974,463
25,755,648
Con. U.S. Total + beis
60,537,483
4,966,406
9,989,734
9,753,858
4,100,054
2,370,387
40,299,369
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
492,798
105,145
Canada oil and gas 2D
666
7
3,232
185
185
3,933
509,228
Canada othafdust
580,703
90,421
Canada othar
2,182,369
3,815
306,078
223,090
174,668
16,318
725,957
Canada onroadcan
1,661,932
7,156
331,485
23,592
11,282
1,531
134,046
Canada othpt
1,115,125
19,472
650,660
90,023
43,036
989,829
148,163
Canada othptdust
132,266
46,401
Canada ptfireothna
4,679,983
93,406
195,209
671,858
565,668
38,759
1,343,696
Canada CMV
11,104
37
96,622
1,716
1,594
2,941
5,409
Mexico othar
111,429
114,444
54,457
102,675
33,595
1,659
353,294
Mexico onroad mex
1,821,182
2,918
447,430
15,744
11,158
6,638
159,185
Mexico othpt
140,473
1,168
182,265
50,809
35,368
368,023
37,066
Mexico ptfire othna
438,065
8,465
17,524
57,762
49,343
3,612
126,265
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
34,428
133
292,670
7,437
6,886
29,127
16,779
Offshore cmv outside
Federal waters
24,283
457
267,502
25,810
23,752
189,097
11,528
Offshore pt oilgas
51,872
8
49,962
636
635
462
38,833
Non-U.S. Total
12,272,911
744,285
2,895,097
1,984,306
1,093,993
1,651,929
3,714,595
199
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Table 5-2. National by-sector CAP emissions for the 2032gg2 case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
5,780,588
809,684
airports
570,574
0
164,926
10,686
9,373
20,039
63,302
cmv clc2
25,299
50
97,145
2,695
2,611
374
3,779
cmv c3
21,045
59
112,190
3,331
3,064
6,812
13,167
fertilizer
1,636,229
livestock
2,741,401
239,799
nonpt
1,939,962
104,043
712,933
581,503
492,884
123,628
735,427
nonroad
11,602,438
2,249
565,879
53,184
49,329
1,127
827,539
np oilgas
621,290
26
568,387
12,497
12,350
76,245
2,283,192
np solvents
8,409,394
98,770
784,553
188,234
49,249
18,159
518,947
onroad
197,719
352
339,405
15,329
14,443
47,938
237,476
pt oilgas
421,836
93,685
17,935
59,968
38,050
7,451
63,726
ptagfire
308,100
28,078
383,178
73,294
66,046
294,886
32,246
ptegu
10,873,070
177,629
182,473
1,168,800
1,001,912
91,868
2,617,765
ptfire-rx
10,275,916
168,798
147,585
1,051,942
891,476
79,478
2,426,465
ptfire-wild
1,393,746
68,428
847,781
377,960
240,155
501,935
757,877
ptnonipm
111,045
347
405,629
10,069
9,733
394
15,427
rail
2,091,084
16,135
35,602
290,785
289,904
7,223
318,652
rwc
38
65
38
527
503
6
2,524,685
beis
3,902,690
974,463
25,755,648
Con. U.S. Total + beis
52,765,247
5,136,344
6,340,103
9,681,390
3,980,767
1,277,563
39,435,120
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
667,454
104,909
Canada oil and gas 2D
510
7
1,205
136
136
3,703
470,211
Canada othafdust
711,618
110,490
Canada other
2,204,204
3,696
224,546
214,031
155,763
16,178
753,048
Canada onroad can
1,176,889
6,506
156,141
25,861
8,339
847
67,063
Canada othpt
1,169,373
23,880
456,857
77,938
46,049
868,773
163,728
Canada othptdust
132,266
46,401
Canada ptfire othna
4,679,983
93,406
195,209
671,858
565,668
38,759
1,343,696
Canada CMV
12,884
43
81,922
1,957
1,816
3,415
6,188
Mexico other
132,253
110,416
75,376
109,103
37,151
2,090
434,481
Mexico onroad mex
1,595,367
4,193
383,169
20,996
14,140
9,390
173,311
Mexico othpt
136,038
1,524
209,202
66,914
46,178
306,258
51,730
Mexico ptfire othna
438,065
8,465
17,524
57,762
49,343
3,612
126,265
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
47,504
177
244,007
9,995
9,221
42,425
23,002
Offshore cmv outside
Federal waters
34,333
333
377,167
18,817
17,315
50,004
16,272
Offshore ptoilgas
51,872
8
49,962
636
635
462
38,833
Non-U.S. Total
11,679,275
920,109
2,472,288
2,119,885
1,108,644
1,345,918
3,772,736
200
-------
Table 5-3. National by-sector CAP emissions for the 2018gg case, 36US3 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2_5
S02
voc
afdustadj
5,696,028
789,743
airports
489,904
0
131,938
9,843
8,602
16,517
55,044
cmv_clc2
24,434
86
168,585
4,623
4,480
655
6,674
cmv_c3
15,289
43
116,700
2,410
2,218
4,964
9,426
fertilizer
1,636,229
livestock
2,582,191
226,399
nonpt
1,927,717
102,919
740,303
572,647
475,204
166,409
818,460
nonroad
10,477,852
1,925
988,242
96,215
90,744
1,372
1,016,923
npoilgas
661,167
38
668,403
12,669
12,508
55,360
2,414,209
npsolvents
36
58
34
469
448
5
2,336,846
onroad
17,049,876
103,264
2,828,221
207,767
88,334
22,629
1,173,091
ptoilgas
200,740
434
385,649
13,663
13,047
39,003
232,995
ptagfire
421,836
93,685
17,935
59,968
38,050
7,451
63,726
ptegu
573,370
21,576
1,143,369
157,110
127,073
1,314,836
32,616
ptfire-rx
10,873,070
177,629
182,473
1,168,800
1,001,912
91,868
2,617,765
ptfire-wild
10,275,916
168,798
147,585
1,051,942
891,476
79,478
2,426,465
ptnonipm
1,378,776
60,793
895,044
388,517
242,526
561,234
766,024
rail
117,171
365
570,969
15,494
15,005
727
24,947
rwc
2,184,698
16,441
34,564
301,273
300,412
8,062
324,549
beis
3,987,520
996,046
26,186,779
36US3 U.S. Total + beis
60,659,372
4,966,472
10,016,061
9,759,438
4,101,784
2,370,572
40,732,937
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
508,077
107,843
Canada oil and gas 2D
730
7
3,538
203
203
4,420
604,562
Canada othafdust
583,720
90,878
Canada othar
2,342,203
4,111
340,782
236,933
186,083
17,052
763,150
Canada onroadcan
1,731,621
7,433
348,542
24,601
11,819
1,587
139,162
Canada othpt
1,378,449
21,382
831,679
102,194
50,204
1,123,746
203,319
Canada othptdust
129,213
45,052
Canada ptfireothna
7,465,807
151,948
311,054
1,066,014
902,171
61,148
2,114,343
Canada CMV
13,594
45
119,577
2,128
1,974
4,035
6,724
Mexico othar
1,638,884
574,281
216,199
451,850
243,750
12,232
1,546,910
Mexico onroad mex
6,240,630
10,795
1,512,464
80,530
61,809
28,020
553,804
Mexico othpt
426,418
3,532
463,774
198,039
132,579
1,538,614
115,851
Mexico ptfire othna
5,958,233
101,557
285,718
955,132
629,885
38,914
1,853,619
Mexico CMV
64,665
1
205,403
16,300
15,100
109,886
8,832
Offshore cmv in Federal
waters
36,343
161
312,748
9,053
8,374
40,877
17,652
Offshore cmv outside
Federal waters
91,453
1,236
1,040,036
95,917
88,271
709,026
41,730
Offshore pt oilgas
51,872
8
49,962
636
635
462
38,833
Annual Total
27,440,903
1,384,575
6,041,477
3,952,464
2,468,786
3,690,018
8,116,334
201
-------
6 References
Adelman, Z. 2012. Memorandum: Fugitive Dust Modeling for the 2008 Emissions Modeling Platform.
UNC Institute for the Environment, Chapel Hill, NC. September 28, 2012.
Adelman, Z. 2016. 2014 Emissions Modeling Platform Spatial Surrogate Documentation. UNC Institute
for the Environment, Chapel Hill, NC. October 1, 2016. Available at
https://gaftp.epa.gov/Air/emismod/2014/vl/spatial surrogates/.
Adelman, Z., M. Omary, Q. He, J. Zhao and D. Yang, J. Boylan, 2012. "A Detailed Approach for
Improving Continuous Emissions Monitoring Data for Regulatory Air Quality Modeling."
Presented at the 2012 International Emission Inventory Conference, Tampa, Florida. Available
from http://www.epa.gOv/ttn/chief/conference/ei20/index.html#ses-5.
Appel, K.W., Napelenok, S., Hogrefe, C., Pouliot, G., Foley, K.M., Roselle, S.J., Pleim, J.E., Bash, J.,
Pye, H.O.T., Heath, N., Murphy, B., Mathur, R., 2018. Overview and evaluation of the
Community Multiscale Air Quality Model (CMAQ) modeling system version 5.2. In Mensink C.,
Kallos G. (eds), Air Pollution Modeling and its Application XXV. ITM 2016. Springer
Proceedings in Complexity. Springer, Cham. Available at https://doi.org/10.1007/978-3-319-
57645-9 11.
Bash, J.O., Baker, K.R., Beaver, M.R., Park, J.-H., Goldstein, A.H., 2016. Evaluation of improved land
use and canopy representation in BEIS with biogenic VOC measurements in California. Available
from http J/www, geosci-model-dev. net/9/2191/2016/.
BEA, 2012. "2013 Global Outlook projections prepared by the Conference Board in November 2012".
U.S. Bureau of Economic Analysis. Available from: http://www.conference-
b oard. org/data/ gl ob aloutl ook. cfm.
Bullock Jr., R, and K. A. Brehme (2002) "Atmospheric mercury simulation using the CMAQ model:
formulation description and analysis of wet deposition results." Atmospheric Environment 36, pp
2135-2146. Available at https://doi.org/10.1016/S1352-2310(02^)00220-0.
California Air Resources Board (CARB): ORGPROF - Organic chemical profiles for source categories,
2018. https://ww2.arb.ca.gov/speciation-profiles-used-carb-modeling .
California Air Resources Board (CARB): 2005 Architectural Coatings Survey - Final Report, 2007.
California Air Resources Board (CARB): 2010 Aerosol Coatings Survey Results, 2012.
California Air Resources Board (CARB): 2014 Architectural Coatings Survey - Draft Data Summary,
2014.
California Air Resources Board (CARB): Final 2015 Consumer & Commercial Product Survey Data
Summaries, 2019.
Coordinating Research Council (CRC). Report A-100. Improvement of Default Inputs for MOVES and
SMOKE-MOVES. Final Report. February 2017. Available at http://crcsite.wpengine.com/wp-
content/uploads/2019/05/ERG FinalReport CRCA100 28Feb2017.pdf.
202
-------
Coordinating Research Council (CRC). Report A-l 15. Developing Improved Vehicle Population Inputs
for the 2017 National Emissions Inventory. Final Report. April 2019. Available at
http://crcsite.wpengine.eom/wp-content/uploads/2019/05/CRC-Proiect-A-115-Final-
Report 20190411.pdf.
Drillinginfo, Inc. 2015. "DI Desktop Database powered by HPDI." Currently available from
https://www.enverus.com/.
England, G., Watson, J., Chow, J., Zielenska, B., Chang, M., Loos, K., Hidy, G., 2007. "Dilution-Based
Emissions Sampling from Stationary Sources: Part 2— Gas-Fired Combustors Compared with
Other Fuel-Fired Systems," Journal of the Air & Waste Management Association, 57:1, 65-78,
DOI: 10.1080/10473289.2007.10465291. Available at
https://www.tandfonline.eom/doi/abs/10.1080/10473289.2007.10465291.
EPA, 2017. Light-Duty Vehicle, Light-Duty Truck, and Medium-Duty Passenger Vehicle Tier 2 Exhaust
Emission Standards. Office of Transportation and Air Quality, Ann Arbor, MI 48105. Available
at: https://www.epa.gov/emission-standards-reference-guide/epa-emission-standards-light-dutv-
vehicles-and-trucks-and.
EPA, 2008. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines
and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder. EPA420-R-08-001.
Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi/P10023 S4.PDF?Dockev=P 10023S4.PDF.
EPA, 2012d. Preparation of Emission Inventories for the Version 5.0, 2007 Emissions Modeling Platform
Technical Support Document. Available from: https://www.epa.gov/air-emissions-modeling/2007-
version-50-technical-support-document.
EPA, 2013rwc. "2011 Residential Wood Combustion Tool version 1.1, September 2013", available from
US EPA, OAQPS, EIAG.
EPA, 2015b. Draft Report Speciation Profiles and Toxic Emission Factors for Nonroad Engines. EPA-
420-R-14-028. Available at
https://cfpub.epa.gov/si/si public record Report.cfm?dirEntryId=309339&CFID=83476290&CF
TOKEN=35281617.
EPA, 2015c. Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles in
MOVES2014. EPA-420-R-15-022. Available at
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockev=Pl OONOJG.pdf.
EPA, 2016. SPECIATE Version 4.5 Database Development Documentation, U.S. Environmental
Protection Agency, Office of Research and Development, National Risk Management Research
Laboratory, Research Triangle Park, NC 27711, EPA/600/R-16/294, September 2016. Available at
https://www.epa.gov/sites/production/files/2016-Q9/documents/speciate 4.5.pdf.
EPA, 2017. Additional Updates to Emissions Inventories for the Version 6.3, 2011 Emissions Modeling
Platform for the Year 2023 technical support document. Available at:
https://www.epa.gov/sites/production/files/2Q17-
ll/documents/2011v6.3 2023en update emismod tsd oct2017.pdf.
EPA, 2018. AERMOD Model Formulation and Evaluation Document. EPA-454/R-18-003. U.S.
Environmental Protection Agency, Research Triangle Park, North Carolina 27711. Available at
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockev=P100UT95.PDF.
203
-------
EPA, 2018. 2014 National Emission Inventory, version 2 Technical Support Document. U.S.
Environmental Protection Agency, OAQPS, Research Triangle Park, NC 27711. Available at:
https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-nei-technical-
support-document-tsd.
EPA, 2019. Final Report, SPECIATE Version 5.0, Database Development Documentation, Research
Triangle Park, NC, EPA/600/R-19/988. Available at https://www.epa.gov/air-emissions-
modeling/ speciate-51 -and-5 O-addendum-and-final-report.
EPA, 2020. Population and Activity of Onroad Vehicles in MOVES3. EPA-420-R-20-023. Office of
Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. November
2020. Available under the MOVES3 section at https://www.epa.gov/moves/moves-technical-
reports.
EPA, 2021. Technical Support Document (TSD) Preparation of Emissions Inventories for the 2016v2
North American Emissions Modeling Platform. Available at https://www.epa.gov/air-emissions-
modeling/2016v2-platform.
EPA, 2021b. 2017 National Emission Inventory: January 2021 Updated Release, Technical Support
Document. U.S. Environmental Protection Agency, OAQPS, Research Triangle Park, NC 27711.
Available at: https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-
nei-technical-support-document-tsd.
EPA, 2021c. Technical Support Document (TSD) Preparation of Emissions Inventories for the 2016vl
North American Emissions Modeling Platform. Available at https://www.epa.gov/air-emissions-
modeling/2016-version-l-technical-support-document.
EPA, 2021d. 2017 National Emissions Inventory (NEI), Research Triangle Park, NC, January 2021.
https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-data.
EPA, 2022a. Technical Support Document (TSD) Preparation of Emissions Inventories for the 2018
North American Emissions Modeling Platform. EPA-454/B-22-005. Available at
https://www.epa.gov/air-emissions-modeling/2018-emissions-modeling-platform.
EPA, 2022b. 2019 National Emissions Inventory (NEI) Technical Support Document: Point Data
Category, Research Triangle Park, NC. EPA-454/R-22-001. Available at:
https://www.epa.gov/air-emissions-modeling/2019-nei-technical-support-documentation.
EPA, 2023a. Technical Support Document (TSD) Preparation of Emissions Inventories for the 2016v3
North American Emissions Modeling Platform. EPA-454/B-23-002. Available at
https://www.epa.gov/air-emissions-modeling/2016v3-platform.
EPA, 2023b. 2020 National Emissions Inventory (NEI), Research Triangle Park, NC, March 2023.
https://www.epa.gov/air-emissions-inventories/2020-national-emissions-inventory-nei-data.
ERG, 2014a. Develop Mexico Future Year Emissions Final Report. Available at
https://gaftp.epa.gov/air/emismod/201 l/v2platform/2011 emissions/Mexico Emissions WA%204-
09 final report 121814.pdf.
ERG, 2016b. "Technical Memorandum: Modeling Allocation Factors for the 2014 Oil and Gas Nonpoint
Tool." Available at https://gaftp.epa.gov/air/emismod/2014/vl/spatial surrogates/oil and gas/.
ERG, 2017. "Technical Report: Development of Mexico Emission Inventories for the 2014 Modeling
Platform." Available at https://gaftp.epa.gov/Air/ernismod/2014/v2/2014fd/ernissions/EPA%205-
18%20Report Clean%20Final 01042017.pdf.
204
-------
ERG, 2018. Technical Report: "2016 Nonpoint Oil and Gas Emission Estimation Tool Version 1.0".
Available at
https://gaftp.epa.gov/air/emismod/2016/vl/reports/2016%20Nonpoint%200il%20and%20Gas%2
0Emission%20Estimation%20Tool%20Vl 0%20December 2018.pdf.
ERG, 2019a. "2017 Nonpoint Oil and Gas Emission Estimation Tool Revisions" Available from:
https://gaftp.epa.gov/air/nei/2017/doc/supporting data/nonpoint/2017%200il%20and%20Gas%20
Memos.zip.
ERG, 2019b. Category 1 and 2 Commercial Marine Emissions Inventory. Available from:
https://www.epa.gov/sites/default/files/2019-l 1/cmv methodology documentation.zip.
ERG, 2019c. 2016 versus 2017 entrance and clearance data. Available
from:https://gaftp.epa.gov/Air/emismod/2016/v2/reports/cmv/EandC 2016 to 2017 Activity Rat
ios.pdf.
ERG, 2021. "Historical Nonpoint Oil and Gas Emission Inventory Development for 2018".
The Freedonia Group, 2016. Solvents, Industry Study #3429.
Foley et al., 2023. 2002-2017 anthropogenic emissions data for air quality modeling over the United
States. Available at
https://www.sciencedirect.com/science/article/pii/S23523409230014037via%3Dihub.
Frost & Sullivan, 2010. "Project: Market Research and Report on North American Residential Wood
Heaters, Fireplaces, and Hearth Heating Products Market (P.O. # PO1-IMP403-F&S). Final
Report April 26, 2010", pp. 31-32. Prepared by Frost & Sullivan, Mountain View, CA 94041.
Gkatzelis, G.I., Coggon, M.M., McDonald, B.C., Peischl, J., Aikin, K.C., Gilman, J.B., Trainer, M.,
Warneke, C. Identifying Volatile Chemical Product Tracer Compounds in US Cities. Environ. Sci.
Technol. 2021, 55 (1), 188-199.
Houck, 2011. "Dirty- vs. Clean-Burning? What percent of freestanding wood heaters in use in the U.S.
today are still old, uncertified units?" Hearth and Home, December 2011.
Hutchins, M.L., Holkzworth, R.H., Brundell, J.B., and Rodger, C.J., 2012. Relative detection efficiency
of the World Wide Lightning Location Network. Available from
http://wwlln.net/publications/Hutchins Detection Efficiency RadioSci 2012.pdf.
Kang et al., 2022. Assessing the Impact of Lightning NOx Emissions in CMAQ using Lightning Flash
Data from WWLLN over the Contiguous United States. Available from
https://doi.org/10.3390/atmosl3081248.
Khare, P., and Gentner, D. R., 2018. Considering the future of anthropogenic gas-phase organic
compound emissions and the increasing influence of non-combustion sources on urban air quality,
Atmos ChemPhys, 18, 5391-5413, 10.5194/acp-18-5391-2018.
Luecken D., Yarwood G, Hutzell WT, 2019. Multipollutant modeling of ozone, reactive nitrogen and
HAPs across the continental US with CMAQ-CB6. Atmospheric environment. 2019 Mar
15;201:62-72.
205
-------
Mansouri, K., Grulke, C. M., Judson, R. S., and Williams, A. J., 2018. OPERA models for predicting
physicochemical properties and environmental fate endpoints, J Cheminformatics, 10,
10.1186/sl 3321-018-0263-1.
McCarty, J.L., Korontzi, S., Jutice, C.O., and T. Loboda. 2009. The spatial and temporal distribution of
crop residue burning in the contiguous United States. Science of the Total Environment, 407 (21):
5701-5712. Available at
https://www.sciencedirect.com/science/article/abs/pii/S13522310080001377via%3Dihub.
MDNR, 2008. "A Minnesota 2008 Residential Fuelwood Assessment Survey of individual household
responses". Minnesota Department of Natural Resources. Available from
http://files.dnr.state.mn.us/forestry/um/residentialfuelwoodassessment07 08.pdf.
NCAR, 2016. FIRE EMISSION FACTORS AND EMISSION INVENTORIES, FINN Data, downloaded
2014 SAPRC99 version from https://www.acom.ucar.edu/Data/fire/.
NESCAUM, 2006. "Assessment of Outdoor Wood-fired Boilers". Northeast States for Coordinated Air
Use Management (NESCAUM) report. Available from
http://www.nescaum.org/documents/assessment-of-outdoor-wood-fired-boilers/2006-1031-owb-
report revised-iune2006-appendix.pdf.
NYSERDA, 2012. "Environmental, Energy Market, and Health Characterization of Wood-Fired Hydronic
Heater Technologies, Final Report". New York State Energy Research and Development
Authority (NYSERDA). Available from: https://www-nyserda-ny-gov.webpkgcache.com/doc/-
/s/www.nyserda.ny.gov/-
/media/Proiect/Nvserda/Files/Publications/Research/Environmental/Wood-Fired-Hvdronic-
Heater-Tech-Summary.pdf.
Pechan, 2001. E.H. Pechan & Associates, Inc., Control Measure Development Support—Analysis of
Ozone Transport Commission Model Rules, Springfield, VA, prepared for the Ozone Transport
Commission, Washington, DC, March 31, 2001. Available at
https://otcair.Org/upload/Documents/Reports/Control%20Measure%20Development%20Support.p
df.
Pouliot, G., H. Simon, P. Bhave, D. Tong, D. Mobley, T. Pace, and T. Pierce. 2010. "Assessing the
Anthropogenic Fugitive Dust Emission Inventory and Temporal Allocation Using an Updated
Speciation of Particulate Matter." International Emission Inventory Conference, San Antonio, TX.
Available at http://www3.epa.gov/ttn/chief/conference/eil9/session9/pouliot pres.pdf.
Pouliot, G. and J. Bash, 2015. Updates to Version 3.61 of the Biogenic Emission Inventory System
(BEIS). Presented at Air and Waste Management Association conference, Raleigh, NC, 2015.
Pouliot G, Rao V, McCarty JL, Soja A. Development of the crop residue and rangeland burning in the
2014 National Emissions Inventory using information from multiple sources. Journal of the Air &
Waste Management Association. 2017 Apr 27;67(5):613-22.
Pye, H. O. T.; Pouliot, G. A., 2012. Modeling the role of alkanes, polycyclic aromatic hydrocarbons, and
their oligomers in secondary organic aerosol formation. Environ. Sci. Technol. 2012, 46,
6041-6047.
206
-------
Raffuse, S., D. Sullivan, L. Chinkin, S. Larkin, R. Solomon, A. Soja, 2007. Integration of Satellite-
Detected and Incident Command Reported Wildfire Information into BlueSky, June 27, 2007.
Available at: http://getbluesky.org/smartfire/docs.cfm.
Ramboll (Shah, T., Yarwood G.,) and EPA (Eyth, A., Strum, M), 2017. COMPOSITION OF ORGANIC
GAS EMISSIONS FROM FLARING NATURAL GAS, Presented at the 2017 International
Emission Inventory Conference, August 18, 2017. Available at
https://www.epa.gov/sites/production/files/2017-ll/documents/organic gas.pdf. Additional Memo
from Ramboll Environ to EPA (same title as presentation) dated September 23, 2016.
Ramboll, 2020. https://github.com/CMASCenter/Speciation-
Tool/blob/master/docs/Ramboll sptool mapping updates AE7 AE8 24Mar2020 final full.pdf.
Reichle, L., R. Cook, C. Yanca, D. Sonntag, 2015. "Development of organic gas exhaust speciation
profiles for nonroad spark-ignition and compression-ignition engines and equipment", Journal of
the Air & Waste Management Association, 65:10, 1185-1193, DOI:
10.1080/10962247.2015.1020118. Available at https://doi.org/10.1080/10962247.2015.102Q118.
Reff, A., Bhave, P., Simon, H., Pace, T., Pouliot, G., Mobley, J., Houyoux. M. "Emissions Inventory of
PM2.5 Trace Elements across the United States", Environmental Science & Technology 2009 43
(15), 5790-5796, DOI: 10.1021/es802930x. Available at https://doi.org/10.1021/es802930x.
Sarwar, G., S. Roselle, R. Mathur, W. Appel, R. Dennis, "A Comparison of CMAQ HONO predictions
with observations from the Northeast Oxidant and Particle Study", Atmospheric Environment 42
(2008) 5760-5770). Available at https://doi.Org/10.1016/i.atmosenv.2007.12.065.
Schauer, J., G. Lough, M. Shafer, W. Christensen, M. Arndt, J. DeMinter, J. Park, "Characterization of
Metals Emitted from Motor Vehicles," Health Effects Institute, Research Report 133, March 2006.
Available at https://www.healtheffects.org/publication/characterization-metals-emitted-motor-
vehicles.
Seltzer, K. M., Pennington, E., Rao, V., Murphy, B. N., Strum, M., Isaacs, K. K., and Pye, H. O. T., 2021.
Reactive organic carbon emissions from volatile chemical products, Atmos. Chem. Phys., 21,
5079-5100, https://doi.org/10.5194/acp-21-5079-2021.
Skamarock, W., J. Klemp, J. Dudhia, D. Gill, D. Barker, M. Duda, X. Huang, W. Wang, J. Powers, 2008.
A Description of the Advanced Research WRF Version 3. NCAR Technical Note. National Center
for Atmospheric Research, Mesoscale and Microscale Meteorology Division, Boulder, CO. June
2008. Available at: https://opensky.ucar.edU/islandora/obiect/technotes:500.
Sullivan D.C., Raffuse S.M., Pryden D.A., Craig K.J., Reid S.B., Wheeler N.J.M., Chinkin L.R., Larkin
N.K., Solomon R., and Strand T. (2008) Development and applications of systems for modeling
emissions and smoke from fires: the BlueSky smoke modeling framework and SMARTFIRE: 17th
International Emissions Inventory Conference, Portland, OR, June 2-5.
Swedish Environmental Protection Agency, 2004. Swedish Methodology for Environmental Data;
Methodology for Calculating Emissions from Ships: 1. Update of Emission Factors.
207
-------
U.S. Census Bureau, Economy Wide Statistics Division, 2018. County Business Patterns, 2018.
https://www.census.gov/programs-survevs/cbp/data/datasets.html.
U.S. Bureau of Labor Statistics, 2020. Producer Price Index by Industry, retrieved from FRED, Federal
Reserve Bank of St. Louis. https://fred.stlouisfed.Org/categories/31.
U.S. Census Bureau, 2011 Paint and Allied Products - 2010, MA325F(10).
https://www.census.gov/data/tables/time-series/econ/cir/ma325f.html.
U.S. Census Bureau, 2021. 2018 Annual Survey of Manufacturers (ASM), Washington D.C., USA.
https://www.census.gov/data/developers/data-sets/Annual-Survev-of-Manufactures.html.
U.S. Department of Transportation and the U.S. Department of Commerce, 2015. 2012 Commodity Flow
Survey, EC12TCF-US. https://www.census.gov/library/publications/2015/econ/ecl2tcf-us.html.
U.S. Energy Information Administration, 2019. The Distribution of U.S. Oil and Natural Gas Wells by
Production Rate, Washington, DC. https://www.eia.gov/petroleum/wells/.
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/es2013984.
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.
208
-------
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.50166.
209
-------
210
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-23-003
Environmental Protection Air Quality Assessment Division September 2023
Agency Research Triangle Park, NC
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