* — 1
\ ' ef
PRO"^
Technical Support Document (TSD): Preparation of
Emissions Inventories for the 2016v2 North American
Emissions Modeling Platform
-------
-------
EPA-454/B-22-001
February 2022
Technical Support Document (TSD): Preparation of Emissions Inventories for the 2016v2 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
-------
Authors:
Alison Eyth (EPA/OAR)
Jeff Vukovich (EPA/OAR)
Caroline Farkas (EPA/OAR)
Janice Godfrey (EPA/OAR)
-------
TABLE OF CONTENTS
LIST OF TABLES VIII
LIST OF FIGURES XI
LIST OF APPENDICES XII
ACRONYMS XIII
1 INTRODUCTION 1
2 EMISSIONS INVENTORIES AND APPROACHES 3
2.1 2016 POINT SOURCES (PTEGU, PT_OILGAS, PTNONIPM, AIRPORTS) 8
2.1.1 EGUsector (ptegu) 9
2.1.2 Point source oil and gas sector (pt oilgas) 11
2.1.3 Non-IPM sector (ptnonipm) 15
2.1.4 Aircraft and ground support equipment (airports) 18
2.2 2016 Nonpoint sources (afdust, fertilizer, livestock, np_oilgas, rwc, solvents, nonpt) 19
2.2.1 Area fugitive dust sector (afdust) 19
2.2.2 Agricultural Livestock (livestock) 26
2.2.3 Agricultural Fertilizer (fertilizer) 27
2.2.4 Nonpoint Oil and Gas Sector (np oilgas) 31
2.2.5 Residential Wood Combustion (rwc) 32
2.2.6 Solvents (solvents) 33
2.2.7 Nonpoint (nonpt) 34
2.3 2016 Onroad Mobile sources (onroad) 35
2.3.1 Onroad Activity Data Development 36
2.3.2 MOVES Emission Factor Table Development 42
2.3.3 Onroad California Inventory Development (onroad ca) 45
2.4 2016 Nonroad Mobile sources (cmv, rail, nonroad) 46
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 46
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3) 50
2.4.3 Railway Locomotives (rail) 54
2.4.4 Nonroad Mobile Equipment (nonroad) 63
2.5 2016 Fires (ptfire-wild, ptfire-rx, ptagfire) 69
2.5.1 Wild and Prescribed Fires (ptfire) 69
2.5.2 Point source Agriculture Fires (ptagfire) 77
2.6 2016 Biogenic Sources (beis) 81
2.7 Sources Outside of the United States 83
2.7.1 Point Sources in Canada and Mexico (othpt, canadaag, canada_og2D) 84
2.7.2 Fugitive Dust Sources in Canada (othafdust, othptdust) 84
2.7.3 Nonpoint and Nonroad Sources in Canada and Mexico (othar) 84
2.7.4 Onroad Sources in Canada and Mexico (onroad can, onroadjnex) 85
2.7.5 Fires in Canada and Mexico (ptfire othna) 85
2.7.6 Ocean Chlorine and Sea Salt 85
3 EMISSIONS MODELING 86
3.1 Emissions modeling Overview 86
3.2 Chemical Speciation 90
3.2.1 VOC speciation 93
3.2.1.1 County specific profile combinations 96
3.2.1.2 Additional sector specific considerations for integrating HAP emissions from inventories into speciation 97
3.2.1.3 Oil and gas related speciation profiles 99
3.2.1.4 Mobile source related VOC speciation profiles 102
3.2.2 PM speciation 106
3.2.2.1 Mobile source related PM2.5 speciation profiles 109
3.2.3 NO x speciation 110
V
-------
3.2.4 Creation of Sulfuric Acid Vapor (SULF) Ill
3.3 Temporal Allocation 112
3.3.1 Use of FF10 format for finer than annual emissions 114
3.3.2 Electric Generating Utility temporal allocation (ptegu) 114
3.3.2.1 Base year temporal allocation of EGUs 114
3.3.2.2 Future year temporal allocation of EGUs 119
3.3.3 Airport Temporal allocation (airports) 125
3.3.4 Residential Wood Combustion Temporal allocation (rwc) 127
3.3.5 Agricultural Ammonia Temporal Profiles (ag) 131
3.3.6 Oil and gas temporal allocation (npoilgas) 132
3.3.7 Onroad mobile temporal allocation (onroad) 133
3.3.8 Nonroad mobile temporal allocation(nonroad) 137
3.3.9 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire) 138
3.4 Spatial Allocation 141
3.4.1 Spatial Surrogates for U.S. emissions 141
3.4.2 Allocation method for airport-related sources in the U.S. 147
3.4.3 Surrogates for Canada and Mexico emission inventories 148
3.5 Preparation of Emissions for the CAMx model 151
3.5.1 Development of CAMx Emissions for Standard CAMx Runs 151
3.5.2 Development of CAMx Emissions for Source Apportionment CAMx Runs 153
DEVELOPMENT OF FUTURE YEAR EMISSIONS 157
4.1 EGU Point Source Projections (ptegu) 162
4.2 Non-EGU Point and Nonpoint Sector Projections 165
4.2.1 Background on the Control Strategy Tool (CoST) 166
4.2.2 CoST Plant CLOSURE Packet (ptnonipm, ptoilgas) 170
4.2.3 CoST PROJECTION Packets (afdust, airports, cmv, livestock, nonpt, np oilgas, ptnonipm, pt oilgas, rail, rwc,
solvents) 170
4.2.3.1 Fugitive dust growth (afdust) 171
4.2.3.2 Livestock population growth (livestock) 172
4.2.3.3 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 173
4.2.3.4 Category 3 Commercial Marine Vessels (cmv_c3) 174
4.2.3.5 Oil and Gas Sources (pt oilgas, np oilgas) 176
4.2.3.6 Non-EGU point sources (ptnonipm) 180
4.2.3.7 A irport sources (airports) 184
4.2.3.8 Nonpoint Sources (nonpt) 184
4.2.3.9 Solvents (solvents) 186
4.2.3.10 Residential Wood Combustion (rwc) 187
4.2.4 CoST CONTROL Packets (nonpt, np oilgas, ptnonipm, pt oilgas) 189
4.2.4.1 Oil and Gas NSPS (np oilgas, pt oilgas) 191
4.2.4.2 LUCE NSPS (nonpt, ptnonipm, np oilgas, pt oilgas) 194
4.2.4.3 Fuel Sulfur Rules (nonpt, ptnonipm) 198
4.2.4.4 Natural Gas Turbines NOx NSPS (ptnonipm, pt oilgas) 200
4.2.4.5 Process Heaters NOx NSPS (ptnonipm, pt oilgas) 202
4.2.4.6 CLSWL (ptnonipm) 205
4.2.4.7 Petroleum Refineries NSPS Subpart JA (ptnonipm) 206
4.2.4.8 Ozone Transport Commission Rules (nonpt, solvents) 207
4.2.4.9 State-Specific Controls (ptnonipm) 207
4.3 Projections Computed Outside of CoST 209
4.3.1 Nonroad Mobile Equipment Sources (nonroad) 209
4.3.2 Onroad Mobile Sources (onroad) 210
4.3.3 Locomotives (rail) 213
4.3.4 Sources Outside of the United States (onroadcan, onroadjnex, othpt, canadaag, canada_og2D, ptfire othna,
othar, othafdust, othptdust) 215
4.3.4.1 Canadian fugitive dust sources (othafdust, othptdust) 215
4.3.4.2 Point Sources in Canada and Mexico (othpt, canada ag, canada_og2D) 216
4.3.4.3 Nonpoint sources in Canada and Mexico (othar) 216
4.3.4.4 Onroad sources in Canada and Mexico (onroad can, onroadjnex) 217
vi
-------
5 EMISSION SUMMARIES 219
6 REFERENCES 227
vii
-------
List of Tables
Table 2-1. Platform sectors for the 2016 emissions modeling case 4
Table 2-2. Point source oil and gas sector NAICS Codes 11
Table 2-3. Sources removed from ptoilgas due to Overlap with WRAP Oil and Gas Inventory 11
Table 2-4. Default stack parameter replacements 12
Table 2-5. 2014NEIv2-to-2016 projection factors for pt_oilgas sector for 2016vl inventory 13
Table 2-6. 2016fh pt oilgas national emissions (excluding offshore) before and after 2014-to-2016
projections in non-WRAP States (tons/year) 14
Table 2-7. Pennsylvania emissions changes for natural gas transmission sources (tons/year) 14
Table 2-8. SCCs for Census-based growth from 2014 to 2016 16
Table 2-9. 2016v2 platform SCCs for the airports sector 18
Table 2-10. Afdust sector SCCs 19
Table 2-11. Total impact of fugitive dust adjustments to unadjusted 2016v2 inventory 23
Table 2-12. SCCs for the livestock sector 26
Table 2-13. National back-projection factors for livestock: 2017 to 2016 27
Table 2-14. Source of input variables for EPIC 30
Table 2-15. 2014NEIv2-to-2016 oil and gas projection factors for OK 32
Table 2-16. 2016 vl platform SCCs for the residential wood combustion sector 32
Table 2-17. SCCs receiving Census-based adjustments to 2016 35
Table 2-18. MOVES vehicle (source) types 36
Table 2-19. Submitted data used to prepare 2016v2 onroad activity data 36
Table 2-20. State total differences between 2017 NEI and 2016v2 VMT data 38
Table 2-21. Fraction of IHS Vehicle Populations to Retain for 2016vl and 2017 NEI 44
Table 2-22. SCCs for cmv_clc2 sector 46
Table 2-23. Vessel groups in the cmv_clc2 sector 48
Table 2-24. SCCs for cmv_c3 sector 50
Table 2-25. 2017 to 2016 projection factors for C3 CMV 53
Table 2-26. 2016vl SCCs for the Rail Sector 54
Table 2-27. Class I Railroad Reported Locomotive Fuel Use Statistics for 2016 55
Table 2-28. 2016 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal) 56
Table 2-29. Surface Transportation Board R-l Fuel Use Data - 2016 58
Table 2-30. 2016 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)4 58
Table 2-31. Expenditures and fuel use for commuter rail 60
Table 2-32. Submitted nonroad input tables by agency 67
Table 2-33. Alaska counties/census areas for which nonroad equipment sector-specific emissions are
removed in 2016vl and 2016v2 68
Table 2-34. SCCs included in the ptfire sector for the 2016v2 inventory 70
Table 2-35. National fire information databases used in 2016vl ptfire inventory 70
Table 2-36. List of S/L/T agencies that submitted fire data for 2016vl with types and formats 72
Table 2-37. Brief description of fire information submitted for 2016vl inventory use 73
Table 2-38. SCCs included in the ptagfire sector for the 2016vl inventory 77
Table 2-39. Assumed field size of agricultural fires per state(acres) 79
Table 2-40. Hourly Meteorological variables required by BEIS 3.7 82
Table 3-1. Key emissions modeling steps by sector 87
Table 3-2. Descriptions of the platform grids 89
Table 3-3. Emission model species produced for CB6R3AE7 for CMAQ 90
Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM)
for each platform sector 95
viii
-------
Table 3-5. Ethanol percentages by volume by Canadian province 97
Table 3-6. MOVES integrated species in M-profiles 98
Table 3-7. Basin/Region-specific profiles for oil and gas 100
Table 3-8. TOG MOVES-SMOKE Speciation for nonroad emissions used for the 2016 Platform 102
Table 3-9. Select mobile-related VOC profiles 2016 103
Table 3-10. Onroad M-profiles 104
Table 3-11. MOVES process IDs 105
Table 3-12. MOVES Fuel subtype IDs 105
Table 3-13. MOVES regclass IDs 106
Table 3-14. Regional Fire Profiles 107
Table 3-15. Brake and tire PM2.5 profiles compared to those used in the 201 lv6.3 Platform 109
Table 3-16. Nonroad PM2.5 profiles 110
Table 3-17. NOx speciation profiles Ill
Table 3-18. Sulfate split factor computation Ill
Table 3-19. SO2 speciation profiles 112
Table 3-20. Temporal settings used for the platform sectors in SMOKE 113
Table 3-21. U.S. Surrogates available for the 2016vl and 2016v2 modeling platforms 142
Table 3-22. Off-Network Mobile Source Surrogates 143
Table 3-23. Spatial Surrogates for Oil and Gas Sources 144
Table 3-24. Selected 2016 CAP emissions by sector for U.S. Surrogates (short tons in 12US1) 145
Table 3-25. Canadian Spatial Surrogates 148
Table 3-26. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3) 149
Table 3-27. Emission model species mappings for CMAQ and CAMx (for CB6R3AE7) 152
Table 3-28. State tags for USA modeling 154
Table 4-1. Overview of projection methods for the future year cases 157
Table 4-2. EGU sector NOx emissions by State for 2016v2 cases 164
Table 4-3. Subset of CoST Packet Matching Hierarchy 167
Table 4-4. Summary of non-EGU stationary projections subsections 168
Table 4-5. Reductions from all facility/unit/stack-level closures in 2016v2 170
Table 4-6. Increase in total afdust PM2.5 emissions from projections in 2016v2 172
Table 4-7. National projection factors for livestock: 2017 to 2023, 2026, and 2030 173
Table 4-8. National projection factors for cmv_clc2 173
Table 4-9. California projection factors for cmv_clc2 174
Table 4-10. 2016-to-2023, 2016-to-2026, and 2016-to-2030 CMV C3 projection factors outside of California
175
Table 4-11. 2016-to-2023, 2016-to-2026, and 2016-to-2030 CMV C3 projection factors for California 175
Table 4-12. Year 2014-2017 high-level summary of national oil and gas exploration activity 178
Table 4-13. Point oil and gas sources held constant at 2018 levels 178
Table 4-14. Annual Energy Outlook (AEO) 2021 tables used to project industrial sources 181
Table 4-15. Ptnonipm sources held at recent emission levels 182
Table 4-16. Projection factors for RWC 188
Table 4-17. Assumed retirement rates and new source emission factor ratios for NSPS rules 190
Table 4-18. Non-point (npoilgas) SCCs in 2016vl and 2016v2 modeling platform where Oil and Gas NSPS
controls applied 192
Table 4-19. Emissions reductions for np oilgas sector due to application of Oil and Gas NSPS 193
Table 4-20. Point source SCCs in ptoilgas sector where Oil and Gas NSPS controls were applied 193
Table 4-21. VOC reductions (tons/year) for the pt oilgas sector after application of the Oil and Gas NSPS
CONTROL packet for both future years 2023, 2026 and 2032 194
Table 4-22. SCCs and Engine Types where RICE NSPS controls applied for nonpt and ptnonipm 195
ix
-------
Table 4-23. Non-point Oil and Gas SCCs in 2016v2 modeling platform where RICE NSPS controls applied
195
Table 4-24. Nonpoint Emissions reductions after the application of the RICE NSPS 196
Table 4-25. Ptnonipm Emissions reductions after the application of the RICE NSPS 196
Table 4-26. Oil and Gas Emissions reductions for np oilgas sector due to application of RICE NSPS 197
Table 4-27. Point source SCCs in ptoilgas sector where RICE NSPS controls applied 197
Table 4-28. Emissions reductions (tons/year) in pt oilgas sector after the application of the RICE NSPS
CONTROL packet for future years 2023, 2026, and 2032 197
Table 4-29. Summary of fuel sulfur rule impacts on nonpoint S02 emissions for 2023 198
Table 4-30. Summary of fuel sulfur rule impacts on ptnonipm S02 emissions for 2023 and 2028 199
Table 4-31. Stationary gas turbines NSPS analysis and resulting emission rates used to compute controls. 200
Table 4-32. Ptnonipm SCCs in 2016vl modeling platform where Natural Gas Turbines NSPS controls
applied 201
Table 4-33. Ptnonipm emissions reductions after the application of the Natural Gas Turbines NSPS 202
Table 4-34. Point source SCCs in pt oilgas sector where Natural Gas Turbines NSPS control applied 202
Table 4-35. Emissions reductions (tons/year) for ptoilgas after the application of the Natural Gas Turbines
NSPS CONTROL packet for future years 202
Table 4-36. Process Heaters NSPS analysis and 2016vl new emission rates used to estimate controls 203
Table 4-37. Ptnonipm SCCs in 2016vl modeling platform where Process Heaters NSPS controls applied. 203
Table 4-38. Ptnonipm emissions reductions after the application of the Process Heaters NSPS 204
Table 4-39. Point source SCCs in pt oilgas sector where Process Heaters NSPS controls were applied 205
Table 4-40. NOx emissions reductions (tons/year) in pt oilgas sector after the application of the Process
Heaters NSPS CONTROL packet for futures years 205
Table 4-41. Summary of CISWI rule impacts on ptnonipm emissions for 2023 206
Table 4-42. Summary of NSPS Subpart JA rule impacts on ptnonipm emissions for 2023 and 2028 207
Table 4-43. Factors used to Project VMT to future years 211
Table 4-44. Class I Line-haul Fuel Projections based on 2018 AEO Data 214
Table 4-45. Class I Line-haul Historic and Future Year Projected Emissions 214
Table 4-46. 2018 AEO growth rates for rail sub-groups 215
Table 5-1. National by-sector CAP emissions for the 20161j case, 12US1 grid (tons/yr) 220
Table 5-2. National by-sector CAP emissions for the 20231j case, 12US1 grid (tons/yr) 221
Table 5-3. National by-sector CAP emissions for the 20261j case, 12US1 grid (tons/yr) 222
Table 5-4. National by-sector CAP emissions for the 20321j case, 12US1 grid (tons/yr) 223
Table 5-5. National by-sector CAP emissions for the 20161j case, 36US3 grid (tons/yr) 224
Table 5-6. National by-sector CAP emissions for the 2023fj case, 36US3 grid (tons/yr) 225
Table 5-7. National by-sector Ozone Season NOx emissions summaries 12US1 grid (tons/o.s.) 226
Table 5-8. National by-sector Ozone Season VOC emissions summaries 12US1 grid (tons/o.s.) 226
x
-------
List of Figures
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and
cumulative 25
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions 29
Figure 2-3. Representative Counties in 2016v2 43
Figure 2-4. 2017NEI/2016 platform geographical extent (solid) and U.S. ECA (dashed) 47
Figure 2-5. 2016 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT) 55
Figure 2-6. Class I Railroads in the United States5 56
Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States 59
Figure 2-8. Class II and III Railroads in the United States5 60
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains 62
Figure 2-10. Processing flow for fire emission estimates in the 2016vl inventory 75
Figure 2-11. Default fire type assignment by state and month where data are only from satellites 76
Figure 2-12. Blue Sky Modeling Framework 77
Figure 2-13. Normbeis3 data flows 83
Figure 2-14. Tmpbeis3 data flow diagram 83
Figure 3-1. Air quality modeling domains 89
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation 95
Figure 3-3. Profiles composited for PM gas combustion related sources 108
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources 108
Figure 3-5. Eliminating unmeasured spikes in CEMS data 115
Figure 3-6. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification 116
Figure 3-7. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type 117
Figure 3-8. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type 118
Figure 3-9. Non-CEMS EGU Temporal Profile Application Counts 119
Figure 3-10. Future Year Emissions Follow the Pattern of Base Year Emissions 122
Figure 3-11. Excess Emissions Apportioned to Hours Less than the Historic Maximum 123
Figure 3-12. Regional Profile Applied due to not being able to Adjust below Historic Maximum 124
Figure 3-13. Regional Profile Applied, but Exceeds Historic Maximum in Some Hours 125
Figure 3-14. Diurnal Profile for all Airport SCCs 126
Figure 3-15. Weekly profile for all Airport SCCs 126
Figure 3-16. Monthly Profile for all Airport SCCs 127
Figure 3-17. Alaska Seaplane Profile 127
Figure 3-18. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold 129
Figure 3-19. RWC diurnal temporal profile 129
Figure 3-20. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr) 130
Figure 3-21. Day-of-week temporal profiles for OHH and Recreational RWC 131
Figure 3-22. Annual-to-month temporal profiles for OHH and recreational RWC 131
Figure 3-23. Example of animal NH3 emissions temporal allocation approach (daily total emissions) 132
Figure 3-24. Example of temporal variability of NOx emissions 134
Figure 3-25. Sample onroad diurnal profiles for Fulton County, GA 135
Figure 3-26. Methods to Populate Onroad Speeds and Temporal Profiles by Road Type 135
Figure 3-27. Regions for computing Region Average Speeds and Temporal Profiles 136
Figure 3-28. Example of Temporal Profiles for Combination Trucks 137
Figure 3-29. Example Nonroad Day-of-week Temporal Profiles 138
Figure 3-30. Example Nonroad Diurnal Temporal Profiles 138
Figure 3-31. Agricultural burning diurnal temporal profile 140
Figure 3-32. Prescribed and Wildfire diurnal temporal profiles 140
xi
-------
Figure 4-1. EIA Oil and Gas Supply Regions as of AEO2021
177
List of Appendices
Appendix A: CB6 Assignment for New Species
Appendix B: Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE versions 4.5
and later that were used in the 2016 alpha platforms
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
Xll
-------
Acronyms
AADT
Annual average daily traffic
AE6
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
AEO
Annual Energy Outlook
AERMOD
American Meteorological Society/Environmental Protection Agency
Regulatory Model
AIS
Automated Identification System
APU
Auxiliary power unit
BEIS
Biogenic Emissions Inventory System
BELD
Biogenic Emissions Land use Database
BenMAP
Benefits Mapping and Analysis Program
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAMD
EPA's Clean Air Markets Division
CAMx
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CB05
Carbon Bond 2005 chemical mechanism
CB6
Version 6 of the Carbon Bond mechanism
CBM
Coal-bed methane
CDB
County database (input to MOVES model)
CEMS
Continuous Emissions Monitoring System
CISWI
Commercial and Industrial Solid Waste Incinerators
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
CNG
Compressed natural gas
CO
Carbon monoxide
CONUS
Continental United States
CoST
Control Strategy Tool
CRC
Coordinating Research Council
CSAPR
Cross-State Air Pollution Rule
EO, 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
Xlll
-------
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
xiv
-------
RBT
Refinery to Bulk Terminal
RIA
Regulatory Impact Analysis
RICE
Reciprocating Internal Combustion Engine
RWC
Residential Wood Combustion
RPD
Rate-per-vehicle (emission mode used in SMOKE-MOVES)
RPH
Rate-per-hour (emission mode used in SMOKE-MOVES)
RPP
Rate-per-profile (emission mode used in SMOKE-MOVES)
RPV
Rate-per-vehicle (emission mode used in SMOKE-MOVES)
RVP
Reid Vapor Pressure
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
XV
-------
1 Introduction
The U.S. Environmental Protection Agency (EPA) has updated the 2016v l emissions modeling platform
developed by the National Emissions Inventory Collaborative to incorporate updated data, models, and
methods to create a 2016v2 emissions modeling platform. The 2016v2 platform is designed to be used
studies focused on criteria air pollutants and represents the years of 2016, 2023 2026, and 2032. The
2016v2 platform draws on data from the 2017 National Emissions Inventory (NEI), although the
inventory was updated to represent the year 2016 through the incorporation of 2016-specific state and
local data along with adjustment methods appropriate for each sector. The future year inventories were
developed starting with the base year 2016 inventory using sector-specific methods as described below.
The platform supports applications related to ozone transport and particulate matter.
The full air quality modeling platform consists of all the emissions inventories and ancillary data files
used for emissions modeling, as well as the meteorological, initial condition, and boundary condition files
needed to run the air quality model. This document focuses on the emissions modeling data and
techniques that comprise the emission modeling platform including the emission inventories, the ancillary
data files, and the approaches used to transform inventories for use in air quality modeling.
The National Emissions Inventory Collaborative is a partnership between state emissions inventory staff,
multi-jurisdictional organizations (MJOs), federal land managers (FLMs), EPA, and others to develop a
North American air pollution emissions modeling platform with a base year of 2016 for use in air quality
planning. The Collaborative planned for three versions of the 2016 platform: alpha, beta, and Version 1.0.
This numbering format for the 2016 platforms is different from previous EPA platforms which had the
first number based on the version of the NEI, and the second number as a platform iteration for that NEI
year (e.g., 7.3 where 7 represents 2014-2016 NEI-based platforms, and 3 means the third iteration of the
platform). As an evolution of the 2016vl platform, the 2016v2 platform is also known as the v7.4
platform. The specification sheets posted on the 2016vl platform release page
(http://views.cira.colostate.edu/wiki/wiki/10202) provide some additional details regarding the inventories
and emissions modeling techniques that are relevant for the 2016v2 platform in addition to those
addressed in this TSD.
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 Multi scale 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 2016v2 platform consists of cases that represent the years 2016, 2023 case, 2026, and 2032 case with
the abbreviations 2016fj_16j, 2023fj_16j, 2023fj_16j and 2032_16j, respectively. Derivatives of these
cases that included source apportionment by state and in some cases by inventory sector were also
developed. This platform accounts for atmospheric chemistry and transport within a state-of-the-art
photochemical grid model. In the case abbreviation 2016fh_16j, 2016 is the year represented by the
emissions; the "f' represents the base year emissions modeling platform iteration, where f is for the
1
-------
2016 platform that started with the 2014 NEI; and the "j" stands for the tenth configuration of emissions
modeled for that modeling platform.
The gridded meteorological model used to provide input data for the emissions modeling was developed
using the Weather Research and Forecasting Model (WRF,
https://ral.ucar.edu/solutions/products/weather-research-and-forecasting-model-wrf) version 3.8,
Advanced Research WRF core (Skamarock, et al., 2008). The WRF Model is a mesoscale numerical
weather prediction system developed for both operational forecasting and atmospheric research
applications. The WRF was run for 2016 over a domain covering the continental U.S. at a 12km
resolution with 35 vertical layers. The run for this platform included high resolution sea surface
temperature data from the Group for High Resolution Sea Surface Temperature (GHRSST) (see
https://www.ghrsst.org/) and is given the EPA meteorological case label "16j." The full case abbreviation
includes this suffix following the emissions portion of the case name to fully specify the abbreviation of
the case as "2016fj_16j."
The emissions modeling platform includes point sources, nonpoint sources, commercial marine vessels
(CMV), onroad and nonroad mobile sources, and fires for the U.S., Canada, and Mexico. Some platform
categories use more disaggregated data than are made available in the NEI. For example, in the platform,
onroad mobile source emissions are represented as hourly emissions by vehicle type, fuel type process
and road type while the NEI emissions are aggregated to vehicle type/fuel type totals and annual temporal
resolution. Temporal, spatial and other changes in emissions between the NEI and the emissions input
into the platform are described primarily in the platform specification sheets, although a full NEI was not
developed for the year 2016 because only point sources above a certain potential to emit must be
submitted for years between the full triennial NEI years (e.g., 2014, 2017, 2020). Emissions from Canada
and Mexico are used for the modeling platform but are not part of the NEI.
The primary emissions modeling tool used to create the air quality model-ready emissions was the Sparse
Matrix Operator Kernel Emissions (SMOKE) modeling system (http://www.smoke-model.org/), version
4.8.1 (SMOKE 4.8.1) with some updates. Emissions files were created for a 36-km national grid and for
a 12-km national grid, both of which include the contiguous states and parts of Canada and Mexico as
shown in Figure 3-1. Emissions at 36-km were only created for the inventory years 2016 and 2023.
This document contains six sections and several appendices. Section 2 describes the 2016 inventories
input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used to process the
emission inventories into air quality model-ready inputs. Methods to develop future year emissions are
described in Section 4. Data summaries are provided in Section 5. Section 6 provides references. The
Appendices provide additional details about specific technical methods or data.
2
-------
2 Emissions Inventories and Approaches
This section summarizes the emissions data that make up the 2016v2 platform. This section provides
details about the data contained in each of the platform sectors for the base year and the future year.
The original starting point for the emission inventories was the 2016vl platform. The 2016vl data were
updated with information and methods from the 2017 NEI, MOVES3, and updated inventory
methodologies. 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, 2021).
Documentation for each 2016vl emissions sector in the form of specification sheets is available on the
2016vl page of Inventory Collaborative Wiki (http://views.cira.colostate.edu/wiki/wiki/10202) provides
additional details of data provided for the 2016vl process. In addition to the NEI-based data for the broad
categories of point, nonpoint, onroad, nonroad, and events (i.e., fires), emissions from the Canadian and
Mexican inventories and several other non-NEI data sources are included in the 2016 platform. The
Canadian and Mexican inventories were updated in 2016v2.
The triennial year NEI data for CAPs are largely compiled from data submitted by state, local and tribal
(S/L/T) air agencies. A large proportion of HAP emissions data in the NEI are also from the S/L/T
agencies, but are augmented by the EPA when not available from S/L/Ts. The EPA uses the Emissions
Inventory System (EIS) to compile the NEI. The EIS includes hundreds of automated quality assurance
checks to help improve data quality and also supports tracking release point (e.g., stack) coordinates
separately from facility coordinates. The EPA collaborates extensively with S/L/T agencies to ensure a
high quality of data in the NEI. All emissions modeling sectors were modified in some way to better
represent the year 2016 for the 2016v2 platform.
For interim years other than triennial NEI years, point source data are pulled forward from the most recent
triennial NEI year for the sources that were not reported by S/L/Ts for the interim year. Thus, the 2016
point source emission inventories for the platform include emissions primarily from S/L/T-submitted
data, along with adjusted 2014 data pulled forward for sources under the annual reporting threshold with
the goal of better representing emissions in 2016. Most of the point sources in 2016v2 are consistent with
those in 2016vl. Agricultural and wildland fire emissions represent the year 2016 and are mostly
consistent with those in 2016vl. In 2016v2, emissions for nonpoint source sectors started with 2017 NEI
emissions and were adjusted to better represent the year 2016, as opposed to 2016vl where these sectors
were based on 2014 NEI data. Fertilizer emissions, nonpoint oil and gas emissions, and onroad and
nonroad mobile source emissions represent the year 2016 and were updated from 2016vl. CMV
emissions are consistent with 2016vl and were developed based on 2017 NEI CMV emissions and the
sulfur dioxide (SO2) emissions reflect rules that reduced sulfur emissions for CMV that took effect in the
year 2015. Locomotive emissions in the rail and ptnonipm sectors are consistent with those in 2016vl.
Nonpoint oil and gas emissions were developed using 2016-specific data for oil and gas wells and their
2016 production levels.
Onroad and nonroad mobile source emissions were developed using the Motor Vehicle Emission
Simulator (MOVES) and were updated from 2016vl. Onroad emissions for the platform were developed
based on emissions factors output from MOVES3 for the year 2016, run with inputs derived from the
2017NEI along with activity data (e.g., vehicle miles traveled and vehicle populations) provided by state
and local agencies for 2016vl or otherwise backcast to the year 2016. MOVES3 was also used to
generate nonroad emissions using spatial allocation factors updated for the 2016vl platform.
3
-------
For the purposes of preparing the air quality model-ready emissions, emissions from the five NEI data
categories are split into finer-grained sectors used for emissions modeling. The significance of an
emissions modeling or "platform sector" is that the data are run through the SMOKE programs
independently from the other sectors except for the final merge (Mrggrid). The final merge program
combines the sector-specific gridded, speciated, hourly emissions together to create CMAQ-ready
emission inputs. For studies that use CAMx, these CMAQ-ready emissions inputs are converted into the
file formats needed by CAMx.
In addition to the NEI-based sectors, emissions for Canada and Mexico are included. In 2016v2, these
emissions are based on updated data that represent the base year of 2016 for Canada from ECCC and for
Mexico from SEMARNAT.
Table 2-1 presents an overview the sectors in the emissions modeling platform and how they generally
relate to the NEI as their starting point. The platform sector abbreviations are provided in italics. These
abbreviations are used in the SMOKE modeling scripts, inventory file names, and throughout the
remainder of this document.
Table 2-1. Platform sectors for the 2016 emissions modeling case
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
EGU units:
ptegu
Point
Point source electric generating units (EGUs) for 2016 from the
Emissions Inventory System (EIS), based on 2016vl with minor
updates. Includes some adjustments to default stack parameters,
additional closures, and a few units that were previously in ptnonipm.
The inventory emissions are replaced with hourly 2016 Continuous
Emissions Monitoring System (CEMS) values for nitrogen oxides
(NOx) and SO2 for any units that are matched to the NEI, and other
pollutants for matched units are scaled from the 2016 point inventory
using CEMS heat input. Emissions for all sources not matched to
CEMS data come from the raw inventory. Annual resolution for
sources not matched to CEMS data, hourly for CEMS sources.
Point source oil and
gas:
ptoilgas
Point
Point sources for 2016 from 2016vl including S/L/T updates for oil
and gas production and related processes and updated from 2016vl
with the Western Regional Air Partnership (WRAP) 2014 inventory.
The sector includes sources from facilities with the following NAICS:
2111,21111,211111,211112 (Oil and Gas Extraction); 213111
(Drilling Oil and Gas Wells); 213112 (Support Activities for Oil and
Gas Operations); 2212, 22121, 221210 (Natural Gas Distribution);
48611, 486110 (Pipeline Transportation of Crude Oil); 4862, 48621,
486210 (Pipeline Transportation ofNatural Gas). Includes offshore
oil and gas platforms in the Gulf of Mexico (FIPS=85). Oil and gas
point sources that were not already updated to year 2016 in the
baseline inventory were projected from 2014 to 2016. Annual
resolution.
Aircraft and ground
support equipment:
airports
Point
Emissions from aircraft up to 3,000 ft elevation and emissions from
ground support equipment based on 2017 NEI data and backcast to
2016. Corrected from the 2016vl version which had some double
counting.
4
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Remaining non-
EGU point:
ptnonipm
Point
All 2016 point source inventory records not matched to the ptegu,
airports, or pt_oilgas sectors, including updates submitted by state and
local agencies for 2016vl and some additional sources that were not
operating in 2016 but did operate in later years. Updates from 2016vl
were minor in that a few sources moved to ptegu. NOx control
efficiencies were updated where new information was available. Year
2016 rail yard emissions were developed by the 2016vl rail
workgroup. Annual resolution.
Agricultural
fertilizer:
fertilizer
Nonpoint
Nonpoint agricultural fertilizer application emissions updated from
2016vl and including only ammonia and estimated for 2016 using the
FEST-C model and captured from a run of CMAQ for 2016. County
and monthly resolution.
Agricultural
Livestock:
livestock
Nonpoint
Nonpoint agricultural livestock emissions including ammonia and
other pollutants (except PM2 5) updated from 2016vl and backcast
from 2017NEI based on animal population data from the U.S.
Department of Agriculture (USDA) National Agriculture Statistics
Service Quick Stats, where available. County and annual resolution.
Agricultural fires
with point
resolution: ptagfire
Nonpoint
2016 agricultural fire sources based on EPA-developed data with state
updates, represented as point source day-specific emissions. They are
in the nonpoint NEI data category, but in the platform, they are treated
as point sources. Data are unchanged from 2016vl. Mostly at daily
resolution with some state-submitted data at monthly resolution.
Area fugitive dust:
afdust
Nonpoint
PM10 and PM2 5 fugitive dust sources updated from 2016vl and based
on the 2017 NEI nonpoint inventory, including building construction,
road construction, agricultural dust, and road dust. Agricultural dust,
paved road dust, and unpaved road dust were backcast to 2016 levels.
The NEI emissions are reduced during modeling according to a
transport fraction (computed for the 2016 platform) and a
meteorology-based (precipitation and snow/ice cover) zero-out.
Afdust emissions from the portion of Southeast Alaska inside the
36US3 domain are processed in a separate sector called 'afdust_ak'.
County and annual resolution.
Biogenic:
beis
Nonpoint
Year 2016, hour-specific, grid cell-specific emissions generated from
the BEIS3.7 model within SMOKE, including emissions in Canada
and Mexico using BELD5 land use data. Updated from 2016vl and
consistent with 2017NEI methods.
Category 1, 2 CMV:
cmv_clc2
Nonpoint
Category 1 and category 2 (C1C2) commercial marine vessel (CMV)
emissions sources backcast to 2016 from the 2017NEI using a
multiplier of 0.98. Emissions unchanged from 2016vl January 2020
version of CMV. Includes C1C2 emissions in U.S. state and Federal
waters along with all non-U.S. C1C2 emissions including those in
Canadian waters. Gridded and hourly resolution.
Category 3 CMV:
cmv_c3
Nonpoint
Category 3 (C3) CMV emissions converted to point sources based on
the center of the grid cells. Includes C3 emissions in U.S. state and
Federal waters, along with all non-U.S. C3 emissions including those
in Canadian waters. Emissions are consistent with 2016vl January
2020 version of CMV and are backcast to 2016 from 2017NEI
emissions based on factors derived from U.S. Army Corps of
Engineers Entrance and Clearance data and information about the
ships entering the ports. Gridded and hourly resolution.
5
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Locomotives :
rail
Nonpoint
Line haul rail locomotives emissions developed by the 2016vl rail
workgroup based on 2016 activity and emission factors and are
unchanged from 2016vl. Includes freight and commuter rail
emissions and incorporates state and local feedback. County and
annual resolution.
Solvents :
solvents
Nonpoint
(some
Point)
VOC emissions from solvents for 2016 derived using the VCPy
framework (Seltzer et al., 2021). Includes cleaners, personal care
products, adhesives, architectural coatings, and 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
2016 nonpoint oil and gas emissions updated from 2016vl. Based on
output from the 2017NEI version of the Oil and Gas tool along with
the 2014 WRAP oil and gas inventory and Pennsylvania's
unconventional well inventory. Specifically, for the seven WRAP
states we used the production-related emissions from the 2014 WRAP
inventory. For the exploration-related emissions for these seven
WRAP states we used the emissions from the 2017NEI version of the
Oil and Gas Tool. County and annual resolution.
Residential Wood
Combustion:
rwc
Nonpoint
2017 NEI nonpoint sources from residential wood combustion (RWC)
processes backcast to the year 2016 (updated from 2016vl). County
and annual resolution.
Remaining
nonpoint:
nonpt
Nonpoint
Nonpoint sources not included in other platform sectors and updated
from 2016vl with 2017NEI data. County and annual resolution.
Nonroad:
nonroad
Nonroad
2016 nonroad equipment emissions developed with MOVES3 using
the inputs that were updated for 2016vl. MOVES was used for all
states except California and Texas, which submitted emissions for
2016v 1. County and monthly resolution.
Onroad:
onroad
Onroad
2016 onroad mobile source gasoline and diesel vehicles from moving
and non-moving vehicles that drive on roads, along with vehicle
refueling. Includes the following modes: exhaust, extended idle,
auxiliary power units, off network idling, starts, evaporative,
permeation, refueling, and brake and tire wear. For all states except
California, developed using winter and summer MOVES emissions
tables produced by MOVES3 (updated from 2016vl) coupled with
activity data backcast from 2017NEI to year 2016 or provided for
2016vl by S/L/T agencies. SMOKE-MOVES was used to compute
emissions from the emission factors and activity data. Onroad
emissions for Alaska, Hawaii, Puerto Rico and the Virgin Islands were
held constant from 2016vl (based on MOVES2014b) and are part of
the onroad nonconus sector.
Onroad California:
onroadcaadj
Onroad
2016 California-provided CAP onroad mobile source gasoline and
diesel vehicles based on the EMFAC model, gridded and temporalized
using MOVES3 results updated from 2016vl. Volatile organic
compound (VOC) HAP emissions derived from California-provided
VOC emissions and MOVES-based speciation.
6
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Point source fires-
ptfire-rx
ptfire-wild
Events
Point source day-specific wildfires and prescribed fires for 2016
computed using Satellite Mapping Automated Reanalysis Tool for Fire
Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky
Framework (Sullivan, 2008 and Raffuse, 2007) for both flaming and
smoldering processes (i.e., SCCs 281XXXX002). Smoldering is
forced into layer 1 (by adjusting heat flux). Incorporates state inputs
and a few corrections from 2016vl. Daily resolution.
Non-US. Fires:
ptfireothna
N/A
Point source day-specific wildland fires for 2016 provided by
Environment Canada with data for missing months, and for Mexico
and Central America, filled in using fires from the Fire Inventory
(FINN) from National Center for Atmospheric Research (NCAR) fires
(NCAR, 2016 and Wiedinmyer, C., 2011). Includes any prescribed
fires although they are not distinguished from wildfires. Unchanged
from 2016v 1. Daily resolution.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
N/A
Fugitive dust sources of particulate matter emissions excluding land
tilling from agricultural activities, from Environment and Climate
Change Canada (ECCC) 2016 emission inventory updated for 2016vl.
A transport fraction adjustment is applied along with a meteorology-
based (precipitation and snow/ice cover) zero-out. County and annual
resolution.
Other Point Fugitive
dust sources not
from the NEI:
othptdust
N/A
Fugitive dust sources of particulate matter emissions from land tilling
from agricultural activities, ECCC 2016 emission inventory updated
for 2016vl, but wind erosion emissions were removed. A transport
fraction adjustment is applied along with a meteorology-based
(precipitation and snow/ice cover) zero-out. Data were originally
provided on a rotated 10-km grid for beta, but were smoothed so as to
avoid the artifact of grid lines in the processed emissions. Monthly
resolution.
Other point sources
not from the NEI:
othpt
N/A
Point sources from the ECCC 2016 emission inventory updated for
2016vl. Includes Canadian sources other than agricultural ammonia
and low-level oil and gas sources, along with emissions from
Mexico's 2016 inventory. Monthly resolution for Canada airport
emissions, annual resolution for the remainder of Canada and all of
Mexico.
Canada ag not from
the NEI:
canadaag
N/A
Agricultural point sources from the ECCC 2016 emission inventory
updated from 2016vl, including agricultural ammonia. Agricultural
data were originally provided on a rotated 10-km grid, but were
smoothed so as to avoid the artifact of grid lines in the processed
emissions. Data were forced into 2D low-level emissions to reduce the
size of othpt. Monthly resolution.
Canada oil and gas
2D not from the
NEI:
Canada og2D
N/A
Low-level point oil and gas sources from the ECCC 2016 emission
inventory updated from 2016vl. Data were forced into 2D low-level
emissions to reduce the size of othpt. Point oil and gas sources which
are subject to plume rise are in the othpt sector. Annual resolution.
Other non-NEI
nonpoint and
nonroad:
othar
N/A
Year 2016 Canada (province or sub-province resolution) emissions
from the ECCC inventory updated for 2016vl: monthly for nonroad
sources; annual for rail and other nonpoint Canada sectors. Year
2016 Mexico (municipio resolution) emissions from their 2016
inventory: annual nonpoint and nonroad mobile inventories.
7
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Other non-NEI
onroad sources:
onroad can
N/A
Year 2016 Canada (province resolution or sub-province resolution,
depending on the province) from the ECCC onroad mobile inventory
updated for 2016vl. Monthly resolution.
Other non-NEI
onroad sources:
onroad mex
N/A
Year 2016 Mexico (municipio resolution) onroad mobile inventory
based on MOVES-Mexico runs for 2014 and 2018 then interpolated to
2016 (unchanged from 2016vl). Monthly resolution.
Other natural emissions are also merged in with the above sectors: ocean chlorine and sea salt. The ocean
chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb) concentrations in
oceanic air masses (Bullock and Brehme, 2002). In CMAQ, the species name is "CL2". The sea salt
emissions were developed with version 4.1 of the OCEANIC pre-processor that comes with the CAMx
model. The preprocessor estimates time/space-varying emissions of aerosol sodium, chloride and sulfate;
gas-phase chlorine and bromine associated with sea salt; gaseous halo-methanes; and dimethyl sulfide
(DMS). These additional oceanic emissions are incorporated into the final model-ready emissions files for
CAMx.
The emission inventories in SMOKE input formats for the platform are available from EPA's Air
Emissions Modeling website: https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-
em issi on s-model in "-platforms. under the section entitled "2016v2 Platform". The platform informational
text file indicates the particular zipped files associated with each platform sector. A number of reports
(i.e., summaries) are available with the data files for the 2016 platform. The types of reports include state
summaries of inventory pollutants and model species by modeling platform sector and county annual
totals by modeling platform sector.
2.1 2016 point sources (ptegu, pt_oilgas, ptnonipm, airports)
Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude/longitude)
are specified, as in the case of an individual facility. A facility may have multiple emission release points
that may be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have
multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas).
This section describes NEI point sources within the contiguous U.S. and the offshore oil platforms which
are processed by SMOKE as point source inventories. A full NEI is compiled every three years including
2011, 2014 and 2017. In the intervening years, emissions information about point sources that exceed
certain potential to emit threshold are required to be submitted to the EIS that is used to compile the NEI.
A comprehensive description of how EGU emissions were characterized and estimated in the NEI is
located in Section 3.4 of the 2014 NEI TSD (EPA, 2018). The methods for emissions estimation are
similar for the interim year of 2016, but there is no TSD available specific to the 2016 point source NEI.
Information on state submissions for point sources through the 2016vl collaborative process are available
in the collaborative specification sheets.
The point source file used for the modeling platform is exported from EIS into the Flat File 2010 (FF10)
format that is compatible with SMOKE (see
https://www.cmascenter.Org/smoke/documentation/4.8.l/html/ch08s02s08.htmn. For the 2016v2
platform, the export of point source emissions, including stack parameters and locations from EIS, was
done on June 12, 2018, and specific modifications were made since that time. The flat file was modified
to remove sources without specific locations (i.e., their FIPS code ends in 777). Then the point source
FF10 was divided into four NEI-based platform point source sectors: the EGU sector (ptegu), point source
8
-------
oil and gas extraction-related emissions (pt oilgas), airport emissions were put into the airports sector,
and the remaining non-EGU sector also called the non-IPM (ptnonipm) sector. The split was done at the
unit level for ptegu and facility level for pt oilgas such that a facility may have units and processes in
both ptnonipm and ptegu, but units cannot be in both pt oilgas and any other point sector. Additional
information on updates made through the collaborative process is available in the collaborative
specification sheets.
The EGU emissions are split out from the other sources to facilitate the use of distinct SMOKE temporal
processing and future-year projection techniques. The oil and gas sector emissions (pt oilgas) were
processed separately for summary tracking purposes and distinct future-year projection techniques from
the remaining non-EGU emissions (ptnonipm).
The inventory pollutants processed through SMOKE for all point source sectors were: carbon monoxide
(CO), NOx, VOC, SO2, ammonia (NH3), particles less than 10 microns in diameter (PM10), and particles
less than 2.5 microns in diameter (PM2.5), and all of the air toxics listed in Table 3-3. The Naphthalene,
Benzene, Acetaldehyde, Formaldehyde, and Methanol (NBAFM) species are based on speciation in
2016v2. The resulting VOC in the modeling system may be higher or lower than the VOC emissions in
the NEI; they would only be the same if the HAP inventory and speciation profiles were exactly
consistent. For HAPs other than those in NBAFM, there is no concern for double-counting since CMAQ
handles these outside the CB6 mechanism.
The ptnonipm and pt oilgas sector emissions were provided to SMOKE as annual emissions. For those
ptegu sources with CEMS data that could be matched to the point inventory from EIS, hourly CEMS NOx
and SO2 emissions were used rather than the annual total NEI emissions. For all other pollutants at
matched units, the annual emissions were used as-is from the NEI, but were allocated to hourly values
using heat input from the CEMS data. For the sources in the ptegu sector not matched to CEMS data,
daily emissions were created using an approach described in Section 2.1.1. For non-CEMS units other
than municipal waste combustors and cogeneration units, IPM region- and pollutant-specific diurnal
profiles were applied to create hourly emissions.
2.1.1 EGU sector (ptegu)
The ptegu sector contains emissions from EGUs in the 2016 NEI point inventory that could be
matched to units found in the National Electric Energy Data System (NEEDS) v6.20 database
(https://www.epa.gov/airmarkets/national-electric-energy-data-svstem-needs-v6 dated 5/28/2021). The
matching was prioritized according to the amount of the emissions produced by the source. In the
SMOKE point flat file, emission records for sources that have been matched to the NEEDS database have
a value filled into the IPM YN column based on the matches stored within EIS. The 2016 NEI point
inventory consists of data submitted by S/L/T agencies and EPA to the EIS for Type A (i.e., large) point
sources. Those EGU sources in the 2014 NEIv2 inventory that were not submitted or updated for
2016 and not identified as retired were retained in 2016. The retained 2014 NEIv2 EGUs in CT, DE,
DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV were projected from 2014 to 2016
values using factors provided by the Mid-Atlantic Regional Air Management Association (MARAMA).
When possible, units in the ptegu sector are matched to 2016 CEMS data from EPA's Clean Air Markets
Division (CAMD) via ORIS facility codes and boiler ID. For the matched units, SMOKE replaces the
2016 emissions of NOx and SO2 with the CEMS emissions, thereby ignoring the annual values specified
in the NEI 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
9
-------
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 temporalization to hourly values.
In the SMOKE point flat file, emission records for point sources matched to CEMS data have values
filled into the ORIS FACILITY CODE and ORIS BOILER ID columns. The CEMS data in SMOKE-
ready format is available at http://ampd.epa.gov/ampd/ near the bottom of the "Prepackaged Data" tab.
Many smaller emitters in the CEMS program are not identified with ORIS facility or boiler IDs that can
be matched to the NEI due to inconsistencies in the way a unit is defined between the NEI and CEMS
datasets, or due to uncertainties in source identification such as inconsistent plant names in the two data
systems. Also, the NEEDS database of units modeled by IPM includes many smaller emitting EGUs that
do not have CEMS. Therefore, there will be more units in the NEEDS database than have CEMS data.
The temporal allocation of EGU units matched to CEMS is based on the CEMS data, whereas regional
profiles are used for most of the remaining units. More detail can be found in Section 3.3.2.
Some EIS units match to multiple CAMD units based on cross-reference information in the EIS alternate
identifier table. The multiple matches are used to take advantage of hourly CEMS data when a CAMD
unit specific entry is not available in the inventory. Where a multiple match is made the EIS unit is split
and the ORIS facility and boiler IDs are replaced with the individual CAMD unit IDs. The split EIS unit
NOX and 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,1 and IPM region. To allocate emissions to each hour
of the day, diurnal profiles were created using average CEMS data for heat input specific to fuel type and
IPM region. See Section 3.3.2 for more details on the temporal allocation approach for ptegu sources.
MWC and cogeneration units were specified to use uniform temporal allocation such that the emissions
are allocated to constant levels for every hour of the year. These sources do not use hourly CEMs, and
instead use a PTDAY file with the same emissions for each day, combined with a uniform hourly
temporal profile applied by SMOKE.
After the completion of 2016vl, it was determined that SMOKE was having an issue properly processing
CEMS emissions when there are multiple CEMS units mapped to the same NEI unit. This caused NOx
and S02 emissions in 2016vl to be higher at some units. This issue was corrected in 2016v2.
1 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.
10
-------
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 2-2. The use of NAICS to separate out the point oil
and gas emissions forces all sources within a facility to be in this sector, as opposed to ptegu where
sources within a facility can be split between ptnonipm and ptegu sectors. A major update in 2016v2 was
the incorporation of the WRAP oil and gas inventory for the states of Colorado, Montana, New Mexico,
North Dakota, South Dakota, Utah, and Wyoming. This inventory is described in more detail below and
in the WRAP Final report located here:
http://www.wrapair2.org/pdfAVRAP OGWG Report Baseline 17Sep2019.pdf (WRAP / Ramboll,
2019).
In addition, several New Mexico sources were removed from the ptnonipm sector because it was
determined they duplicated sources in the WRAP oil and gas inventory. The duplicate sources are listed
in Table 2-3. Finally, following a review of the incidence of default stack parameters in recent
inventories, stack parameters in the states of Louisiana, Illinois, Nebraska, Texas, Wisconsin, and
Wyoming were updated for sources with values found to be defaults. Release points for the agencies with
the values shown in Table 2-4were replaced with values from the PSTK file for the respective SCCs.
Comments for any impacted inventory records were appended in the FF10 inventory files with comments
of the form "stktemp replaced with ptsk default" so the updated records could be identified.
Table 2-2. Point source oil and gas sector NAICS Codes
NAICS
Type of point
source
NAICS description
2111, 21111
Production
Oil and Gas Extraction
211111
Production
Crude Petroleum and Natural Gas Extraction
211112
Production
Natural Gas Liquid Extraction
213111
Production
Drilling Oil and Gas Wells
213112
Support
Support Activities for Oil and Gas Operations
2212, 22121, 221210
Distribution
Natural Gas Distribution
4862, 48621, 486210
Transmission
Pipeline Transportation of Natural Gas
48611, 486110
Transmission
Pipeline Transportation of Crude Oil
Table 2-3. Sources removed from pt oilgas due to Overlap with WRAP Oil and Gas Inventory
State+county
FIPS
Facility ID
Facility Name
35015
7411811
Artesia Gas Plant
35015
17128911
Chaparral Gas Plant
35015
7761811
DCP Midstream - Peco
35015
7584511
Empire Abo Gas Plant
35015
7905211
Oxy - Indian Basin G
11
-------
State+county
FIPS
Facility ID
Facility Name
35025
5228911
DCP Midstream - Euni
35025
8091311
Denton Gas Plant
35025
8092311
Eunice Gas Processing Plant
35025
5226911
Jal No3 Gas Plant
35025
8241211
Linam Ranch Gas Plant
35025
5226611
Maljamar Gas Plant
35025
8241411
Saunders Gas Plant
35025
8241311
Targa - Monument Gas Plant
35045
7230311
Kutz Canyon Processing Plant
35045
8091911
San Juan River Gas Plant
35045
7992811
Val Verde Treatment Plant
Table 2-4. Default stack parameter replacements
Dataset ID
stkdiam
stkhgt
stktemp
stkvel
2014CODPHE
0.1 ft
1 ft
70 degF or 72 degF
2014PADEP
0.1 ft
1 ft
70 degF
0.1 ft/s or 1000
ft/s
2016LADEQ
0.3 ft
70 degF or 77 degF
0.1 ft/s
2016ILEPA
0.33 ft
33 ft or 35 ft
70 degF
2016TXCEQ
1 ft or 3 ft
40 ft
72 degF
0.1 ft/s
2014NVBAQ
32.8 ft
72 degF
2016WIDNR
20 ft
3.281 ft/s
2016MIDEQ
70 degF or 72 degF
2016MNPCA
70 degF
2016IADNR
68 degF or 70 degF
2014ORDEQ
72 degF
2014MSDEQ
72 degF
2016SCDEQ
72 degF
1 ft/s
2014NCDAQ
72 degF
0.2 ft/s
2016INDEM
0 degF
0 ft/s
2016NEDEQ
350 degF
1.6666 ft/s
2014KYDAQ
0 ft/s
2016WYDEQ
11.46 ft/s
The starting point for the 2016v2 emissions platform pt oilgas inventory was the 2016 point source NEI.
The 2016 NEI includes data submitted by S/L/T agencies and EPA to the EIS for Type A (i.e., large)
point sources. Point sources in the 2014 NEIv2 not submitted for 2016 were pulled forward from the 2014
NEIv2 unless they had been marked as shut down. For the federally-owned offshore point inventory of
oil and gas platforms, a 2014 inventory was developed by the U.S. Department of the Interior, Bureau of
Ocean and Energy Management, Regulation, and Enforcement (BOEM).
12
-------
The 2016 ptoilgas inventory includes sources with updated data for 2016 and sources carried forward
from the 2014NEIv2 point inventory. Each type of source can be identified based on the calc_year field in
the flat file 2010 (FF10) formatted inventory files, which is set to either 2016 or 2014. The pt oilgas
inventory was split into two components: one for 2016 sources, and one for 2014 sources. The 2016
sources were used in 2016vl platform without further modification. Updates were made to selected West
Virginia Type B facilities based on comments from the state.
For pt oilgas emissions that were carried forward from the 2014NEIv2, the emissions were projected to
represent the year 2016. Each state/SCC/NAICS combination in the inventory was classified as either an
oil source, a natural gas source, a combination of oil and gas, or designated as a "no growth" source.
Growth factors were based on historical state production data from the Energy Information
Administration (EIA) and are listed in Table 2-5. National 2016 pt oilgas emissions before and after
application of 2014-to-2016 projections are shown in Table 2-6. The historical production data for years
2014 and 2016 for oil and natural gas were taken from the following websites:
• https://www.eia.gov/dnav/pet/pet crd crpdn adc mbbl a.htm (Crude production)
• http://www.eia.gov/dnav/ng/ng sum lsum a epgO fgw mmcf a.htm (Natural gas production)
The "no growth" sources include all offshore and tribal land emissions, and all emissions with a NAICS
code associated with distribution, transportation, or support activities. As there were no 2015 production
data in the EIA for Idaho, no growth was assumed for this state; the only pt oilgas sources in Idaho were
pipeline transportation related. Maryland and Oregon had no oil production data on the EIA website. The
factors in Table 2-5 were applied to sources with NAICS = 2111,21111,211111, 211112, and 213111
and with production-related SCC processes. Table 2-5 provides a national summary of emissions before
and after this two-year projection for these sources in the pt oilgas sector. States for which the WRAP
inventory was used are included in this table for reference, but their factors were not used. Table 2-6
shows the national emissions for pt oilgas following the projection to 2016.
Table 2-5. 2014NEIv2-to-2016 projection factors for pt oilgas sector for 2016vl inventory
State
Natural Gas
growth
Oil growth
Combination gas/oil growth
Alabama
-9.0%
-17.5%
-13.2%
Alaska
1.9%
-1.1%
0.4%
Arizona
-55.7%
-85.7%
-70.7%
Arkansas
-26.7%
13.6%
-6.6%
California
-14.2%
-9.1%
-11.7%
Colorado (not used)
3.5%
22.0%
12.8%
Florida
8.0%
-13.2%
-2.6%
Idaho
0.0%
0.0%
0.0%
Illinois
13.2%
-9.5%
1.8%
Indiana
-6.2%
-27.5%
-16.9%
Kansas
-15.0%
-23.4%
-19.2%
Kentucky
-1.6%
-23.1%
-12.4%
Louisiana
-11.0%
-17.4%
-14.2%
Maryland
70.0%
N/A
N/A
Michigan
-12.6%
-23.4%
-18.0%
13
-------
State
Natural Gas
growth
Oil growth
Combination gas/oil growth
Mississippi
-10.9%
-16.3%
-13.6%
Missouri
-66.7%
-37.2%
-52.0%
Montana (not used)
-11.9%
-22.5%
-17.2%
Nebraska
27.3%
-25.0%
1.2%
Nevada
0.0%
-12.3%
-6.2%
New Mexico (not used)
1.4%
17.4%
9.4%
New York
-33.4%
-36.8%
-35.1%
North Dakota (not used)
31.4%
-4.3%
13.6%
Ohio
181.0%
44.4%
112.7%
Oklahoma
5.9%
6.9%
6.4%
Oregon
-18.0%
N/A
N/A
Pennsylvania
24.8%
-7.9%
8.5%
South Dakota (not used)
-33.9%
-21.7%
-27.8%
Tennessee
-31.9%
-22.1%
-27.0%
Texas
-6.1%
1.0%
-2.6%
Utah
-19.8%
-25.4%
-22.6%
Virginia
-10.0%
-50.0%
-30.0%
West Virginia
28.9%
0.7%
14.8%
Wyoming (not used)
-7.5%
-4.7%
-6.1%
Table 2-6. 2016fh ptoilgas national emissions (excluding offshore) before and after 2014-to-2016
projections in non-WRAP States (tons/year)
Pollutant
Before
projections
After projections
% change 2014 to 2016
CO
141,583
142,562
0.7%
NH3
292
283
-2.9%
NOX
325,703
326,870
0.4%
PM10-PRI
10,745
10,675
-0.7%
PM25-PRI
9,770
9,699
-0.7%
S02
24,983
24,691
-1.2%
VOC
90,482
91,435
1.1%
The state of Pennsylvania provided new emissions data for natural gas transmission sources for year
2016. The PA point source data replaced the emissions used in 2016beta. Table 2-7 illustrates the change
in emissions with this update.
Table 2-7. Pennsylvania emissions changes for natural gas transmission sources (tons/year).
State
2016
State
FIPS
NAICS
Pollutant
beta
2016 vl
2016vl - beta
Pennsylvania
42
486210
CO
2,787
2,385
403
Pennsylvania
42
486210
NOX
5,737
5,577
160
Pennsylvania
42
486210
PM10-PRI
400
227
173
Pennsylvania
42
486210
PM25-PRI
399
209
191
14
-------
State
State
FIPS
NAICS
Pollutant
2016
beta
2016 vl
2016vl - beta
Pennsylvania
42
486210
S02
30
33
-3
Pennsylvania
42
486210
VOC
1,221
1,149
71
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 2016v2 platform have
been updated from the 2016 NEI point inventory and 2016vl with the following changes.
Updates in 2016v2 platform as compared to 2016vl
For 2016v2, a review of stack parameters (i.e., height, diameter, velocity, temperature) was performed to
look for default values submitted for many stacks for the same type of source in the inventory. When
these parameters were substantially different from average values for that source type, the defaulted stack
parameters were replaced with the value from the SMOKE PSTK file for that SCC as shown in Table 2-4.
The affected states were Colorado, Illinois, Iowa, Kentucky, Louisiana, Michigan, Mississippi, Nebraska,
New Mexico, North Carolina, Oregon, Pennsylvania, South Carolina, Texas, Wisconsin, and Wyoming.
Other changes in 2016v2 ptnonipm from 2016vl were:
• Select municipal waste combustion (MWC) sources were moved from ptnonipm to ptegu as a
result of better matching with NEEDS. These include EIS unit identifiers 85563113, 87378913,
119255113, 112010313.
• Sources that were identified to overlap with the WRAP oil and gas inventory including a number
of gas plants were removed from ptnonipm.
• Sources that were identified as overlapping the new solvents sector were removed (i.e., SCCs
starting with 24 which have a Tier 1 description of "Solvent utilization" - including surface
coatings, graphic arts, personal care products, household products, and pesticide applications).
• Sources that were identified as not operating in 2016 but operating in other recent years were
added. These names (and EIS Facility IDs) of these sources were: COLOWYO COAL CO -
COLOWYO & COLLOM MINES (1839411), Northshore Mining Co - Silver Bay (6319411), US
Steel Corp - Keetac (13598411), United Taconite LLC - Fairlane Plant (6239611), MISSISSIPPI
SILICON LLC (17942211), TRIDENT (7766011), and WISCONSIN RAPIDS WWTF
(17658711). Year 2018 emissions were used for facilities 7766011, 17942211, and 1839411
because the 2018 inventory included CO and NOx, while year 2017 values were used for the
others. Although two of these sources were later found to have already been in the ptnonipm
inventory but with lower emissions, resulting in a double count in 2016 only.
• Emissions for specific rail yards in Georgia were updated at the request of the state. The specific
rail yards updated were: Austell, North Doraville, Krannert, Inman, Industry, Howells, and
Tilford.
• NOx control efficiencies were added to ptnonipm sources after a review of permitted limits was
conducted, but this does not impact base year emissions.
15
-------
The following subsections describe the development of the 2016vl ptnonipm sources.
Non-IPM Projection from 2014 to 2016 inside MARAMA resion
2014-to-2016 projection packets for all nonpoint sources were provided by MARAMA for the following
states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV. During the
development of 2016v2, some of these MARAMA factors were found to increase emissions by extremely
large amounts (e.g., over 100 times). These erroneous factors were backed out of the 2016v2 inventories.
The largest projections rolled back were for municipal waste combustors (MWC).
New Jersey provided their own projection factors for projection from 2014 to 2016 which were mostly the
same as those provided by MARAMA, except for three SCCs with differences (SCCs: 2302070005,
2401030000, 2401070000). For those three SCCs, the projection factors provided by New Jersey were
used instead of the MARAMA factors.
Non-IPM Projection from 2014 to 2016 outside MARAMA resion
In areas outside of the MARAMA states, historical census population, sometimes by county and
sometimes by state, was used to project select nonpt sources from the 2014NEIv2 to 2016vl platform.
The population data was downloaded from the US Census Bureau. Specifically, the "Population,
Population Change, and Estimated Components of Population Change: April 1, 2010 to July 1, 2017" file
(https://www2.census.gOv/programs-survevs/popest/datasets/2010-2017/counties/totals/co-est2017-
alldata.csv). A ratio of 2016 population to 2014 population was used to create a growth factor that was
applied to the 2014NEIv2 emissions with SCCs matching the population-based SCCs listed in Table 2-8
Positive growth factors (from increasing population) were not capped, but negative growth factors (from
decreasing population) were flatlined for no growth.
Table 2-8. SCCs for Census-based growth from 2014 to 2016
S( (
Tier 1
Tier 2 Description
Tier 3
Tier 4
Description
Description
Description
2302002100
Industrial
Food and Kindred
Commercial
Conveyorized
Processes
Products: SIC 20
Charbroiling
Charbroiling
2302002200
Industrial
Food and Kindred
Commercial
Under-fired
Processes
Products: SIC 20
Charbroiling
Charbroiling
2302003000
Industrial
Food and Kindred
Commercial Deep Fat
Total
Processes
Products: SIC 20
Frying
2302003100
Industrial
Food and Kindred
Commercial Deep Fat
Flat Griddle Frying
Processes
Products: SIC 20
Frying
2302003200
Industrial
Food and Kindred
Commercial Deep Fat
Clamshell Griddle
Processes
Products: SIC 20
Frying
Frying
2501011011
Storage and
Petroleum and Petroleum
Residential Portable
Permeation
Transport
Product Storage
Gas Cans
2501011012
Storage and
Petroleum and Petroleum
Residential Portable
Evaporation (includes
Transport
Product Storage
Gas Cans
Diurnal losses)
2501011013
Storage and
Petroleum and Petroleum
Residential Portable
Spillage During
Transport
Product Storage
Gas Cans
Transport
2501011014
Storage and
Petroleum and Petroleum
Residential Portable
Refilling at the Pump
Transport
Product Storage
Gas Cans
- Vapor Displacement
16
-------
S( (
Tier 1
Description
Tier 2 Description
Tier 3
Description
Tier 4
Description
250l0ll0l5
Storage and
Transport
Petroleum and IV H ole urn
Product Storage
Residential Portable
Gas Cans
Refilling al die Pump
- Spillage
2501012011
Storage and
Transport
Petroleum and Petroleum
Product Storage
Commercial Portable
Gas Cans
Permeation
2501012012
Storage and
Transport
Petroleum and Petroleum
Product Storage
Commercial Portable
Gas Cans
Evaporation (includes
Diurnal losses)
2501012013
Storage and
Transport
Petroleum and Petroleum
Product Storage
Commercial Portable
Gas Cans
Spillage During
Transport
2501012014
Storage and
Transport
Petroleum and Petroleum
Product Storage
Commercial Portable
Gas Cans
Refilling at the Pump
- Vapor Displacement
2501012015
Storage and
Transport
Petroleum and Petroleum
Product Storage
Commercial Portable
Gas Cans
Refilling at the Pump
- Spillage
2630020000
Waste Disposal
Treatment and Recovery
Wastewater Treatment,
Public Owned
Total Processed
2640000000
Waste Disposal
Treatment and Recovery
TSDFs, All TSDF
Types
Total: All Processes
2810025000
Miscellaneous
Area Sources
Other Combustion
Residential Grilling
Total
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Other non-IPM updates incorporated when develoyins 2016vl
New Jersey, emissions for SCCs for Industrial (2102004000) and Commercial/Institutional (2103004000)
Distillate Oil, Total: Boilers and Internal Combustion (IC) Engines were removed at that state's request.
These emissions were derived from EPA estimates, and double counted emissions that were provided by
New Jersey and assigned to other SCCs.
The state of New Jersey also requested that animal waste NH3 emissions from the following SCCs be
removed: 2806010000 - Cats, 2806015000 - Dogs, 2807020001 - Black Bears, 2807020002 - Grizzly
Bears, 2807025000 - Elk, 2807030000 - Deer, and 2810010000 - Human Perspiration and Respiration.
These emissions existed in CA, DE, ME, NJ, and UT, and were removed from all states.
The state of Alaska reported several nonpoint sources that were missing in 2014NEIv2. Some of the
sources reported by Alaska were identified in our EGU inventory and removed from the new nonpoint
inventory. The rest of the stationary sources were converted to an FFlO-formatted nonpoint inventory and
included in 2016vl platform in the nonpt sector.
The state of Alabama requested that their Industrial, Commercial, Institutional (ICI) Wood emissions
(2102008000), which totaled more than 32,000 tons/year of PM2.5 emissions in the beta version of this
emissions modeling platform and were significantly higher than other states' ICI Wood emissions, be
removed from 2016vl platform.
The state of New York provided a new set of non-residential wood combustion emissions for inclusion in
2016vl platform. These new combustion emissions replace the emissions derived from the MARAMA
projection.
17
-------
The 2016fj case in the 2016v2 platform includes updates to a few specific ptnonipm units including the
closure of the Guardian Corp facility (#2989611), which closed in 2015, and adjusted the emissions at AV
RANCHOS WATER - WELL #4 to match those at WELL #9 because the emissions were determined to
be unrealistically high.
2.1.4 Aircraft and ground support equipment (airports)
The airport sector contains emissions of all pollutants from aircraft, categorized by their itinerant class
(i.e., commercial, air taxi, military, or general), as well as emissions from ground support equipment. The
starting point for the 2016 version 2 (v2) platform airport inventory is the airport emissions from the
January 2021 version of the 2017 NEI. The SCCs included in the airport sector are shown in Table 2-9.
Table 2-9. 2016v2 platform SCCs for the airports sector
S( (
Tier 1 description
Tier 2 (k'scriplion
Tier 3 (k'scriplion
Tier 4 (k'scriplion
2265008005
Mobile Sources
Off-highway Vehicle
Gasoline, 4-stroke
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2267008005
Mobile Sources
LPG
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2268008005
Mobile Sources
compressed natural gas
(CNG)
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2270008005
Mobile Sources
Off-highway Vehicle
Diesel
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2275001000
Mobile Sources
Aircraft
Military Aircraft
Total
2275020000
Mobile Sources
Aircraft
Commercial
Aircraft
Total: All Types
2275050011
Mobile Sources
Aircraft
General Aviation
Piston
2275050012
Mobile Sources
Aircraft
General Aviation
Turbine
2275060011
Mobile Sources
Aircraft
Air Taxi
Piston
2275060012
Mobile Sources
Aircraft
Air Taxi
Turbine
2275070000
Mobile Sources
Aircraft
Aircraft Auxiliary
Power Units
Total
40600307
Chemical
Evaporation
Transportation and
Marketing of Petroleum
Products
Gasoline Retail
Operations -
Stage I
Underground Tank
Breathing and
Emptying
20200102
Internal
Combustion
Engines
Industrial
Distillate Oil
(Diesel)
Reciprocating
18
-------
The 2016vl airport emissions inventory was created from the 2017 NEI airport emissions that were
estimated using the Federal Aviation Administration's (FAA's) Aviation Environmental Design Tool
(AEDT). Additional information about the 2017NEI airport inventory and the AEDT can be found in the
2017 National Emissions Inventory Technical Support Document (EPA. 2021). The 2017 NEI emissions
were adjusted from 2017 to represent year 2016 emissions using FAA data. Adjustment factors were
created using airport-specific numbers, where available, or the state default by itinerant class
(commercial, air taxi, and general) where there were not airport-specific values in the FAA data.
Emissions growth for facilities is capped at 500% and the state default growth is capped at 200%. Military
state default values were kept flat to reflect uncertainly in the data regarding these sources.
After the release of the April 2020 version of the 2017 NEI, an error in the computation of the NEI airport
emissions was identified and it was determined that they were overestimated. The error impacted
commercial aircraft emissions. The airport emissions in 2016v2 were recomputed based on corrected
2017 NEI emissions that were incorporated into the January 2021 release of 2017 NEI.
2.2 2016 Nonpoint sources (afdust, fertilizer, livestock, npoilgas,
rwc, solvents, nonpt)
This section describes the stationary nonpoint sources in the NEI nonpoint data category. Locomotives,
CI and C2 CMV, and C3 CMV are included in the NEI nonpoint data category, but are mobile sources
that are described in Section 2.4.
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 2-10 is a listing of the Source Classification Codes
(SCCs) in the afdust sector.
Table 2-10. Afdust sector SCCs
sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2275085000
Mobile Sources
Aircraft
Unpaved Airstrips
Total
2294000000
Mobile Sources
Paved Roads
All Paved Roads
Total: Fugitives
2294000002
Mobile Sources
Paved Roads
All Paved Roads
Total: Sanding/Salting -
Fugitives
2296000000
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
19
-------
sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2311000000
Industrial
Processes
Construction: SIC
15 -17
All Processes
Total
2311010000
Industrial
Processes
Construction: SIC
15 -17
Residential
Total
2311010070
Industrial
Processes
Construction: SIC
15 -17
Residential
Vehicle Traffic
2311020000
Industrial
Processes
Construction: SIC
15 -17
Industrial/Commercial/
Institutional
Total
2311030000
Industrial
Processes
Construction: SIC
15 -17
Road Construction
Total
2325000000
Industrial
Processes
Mining and
Quarrying: SIC 14
All Processes
Total
2325060000
Industrial
Processes
Mining and
Quarrying: SIC 10
Lead Ore Mining and Milling
Total
2801000000
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Total
2801000003
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Tilling
2801000005
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Harvesting
2801000007
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Loading
2801000008
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Transport
2805001000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Dust Kicked-up by Hooves
(use 28-05-020, -001, -002,
or -003 for Waste
2805001100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Confinement
2805001200
Miscellaneous
Area Sources
Agriculture
Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Manure handling and storage
2805001300
Miscellaneous
Area Sources
Agriculture
Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Land application of manure
2805002000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle production composite
Not Elsewhere Classified
2805003100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on pasture/range
Confinement
2805007100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
dry manure management systems
Confinement
2805007300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
dry manure management systems
Land application of manure
2805008100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Confinement
2805008200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Manure handling and storage
2805008300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Land application of manure
2805009100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Confinement
2805009200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Manure handling and storage
20
-------
sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2805009300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Land application of manure
2805010100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Confinement
2805010200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Manure handling and storage
2805010300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Land application of manure
2805018000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle composite
Not Elsewhere Classified
2805019100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Confinement
2805019200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Manure handling and storage
2805019300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Land application of manure
2805020002
Miscellaneous
Area Sources
Ag. Production -
Livestock
Cattle and Calves Waste
Emissions
Beef Cows
2805021100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Confinement
2805021200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Manure handling and storage
2805021300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Land application of manure
2805022100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Confinement
2805022200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Manure handling and storage
2805022300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Land application of manure
2805023100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Confinement
2805023200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Manure handling and storage
2805023300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Land application of manure
2805025000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production composite
Not Elsewhere Classified
(see also 28-05-039, -047, -
053)
2805030000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Not Elsewhere Classified
(see also 28-05-007, -008, -
009)
2805030007
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Ducks
2805030008
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Geese
2805035000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Horses and Ponies Waste
Emissions
Not Elsewhere Classified
2805039100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Confinement
2805039200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Manure handling and storage
21
-------
SCC
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2805039300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Land application of manure
2805040000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Sheep and Lambs Waste
Emissions
Total
2805045000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Goats Waste Emissions
Not Elsewhere Classified
2805047100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - deep-pit house
operations (unspecified animal
age)
Confinement
2805047300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - deep-pit house
operations (unspecified animal
age)
Land application of manure
2805053100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - outdoor
operations (unspecified animal
age)
Confinement
The starting point for the afdust emissions in 2016v2 is the 2017 NEI. The methodologies to estimate
emissions for each SCC in the preceding table are described in the 2017 NEI Technical Support
Document (EPA, 2021). The 2017 emissions were adjusted to better represent 2016 as described below.
For paved roads (SCC 2294000000) in non-MARAMA states, the 2017 NEI paved road emissions in
afdust were projected to year 2016 based on differences in county total vehicle miles traveled (VMT)
between 2017 and 2016:
2016 afdust paved roads = 2017 afdust paved roads * (2016 county total VMT) / (2017 county total VMT)
The development of the 2016 VMT is described in the onroad section. SCCs related to livestock
production were backcast using the same factors as were used for the livestock sector. All emissions
other than those for paved roads and livestock production are held constant with 2017 levels in the
2016v2 inventory, including unpaved roads.
Area Fusitive Dust Transport Fraction
The afdust sector is separated from other nonpoint sectors to allow for the application of a "transport
fraction," and meteorological/precipitation reductions. These adjustments are applied using a script that
applies land use-based gridded transport fractions based on landscape roughness, followed by another
script that zeroes out emissions for days on which at least 0.01 inches of precipitation occurs or there is
snow cover on the ground. The land use data used to reduce the NEI emissions determines the amount of
emissions that are subject to transport. This methodology is discussed in Pouliot, et al., 2010, and in
"Fugitive Dust Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). Both the
transport fraction and meteorological adjustments are based on the gridded resolution of the platform (i.e.,
12km grid cells); therefore, different emissions will result if the process were applied to different grid
resolutions. A limitation of the transport fraction approach is the lack of monthly variability that would
be expected with seasonal changes in vegetative cover. While wind speed and direction are not accounted
for in the emissions processing, the hourly variability due to soil moisture, snow cover and precipitation is
accounted for in the subsequent meteorological adjustment.
For the data compiled into the 2017 NEI, meteorological adjustments are applied to paved and unpaved
road SCCs but not transport adjustments. The meteorological adjustments that were applied (to paved
and unpaved road SCCs) in the 2017 NEI were backed out so that the entire sector could be processed
22
-------
consistently in SMOKE and the same grid-specific transport fractions and meteorological adjustments
could be applied sector-wide. Thus, the FF10 that is run through SMOKE consists of 100% unadjusted
emissions, and after SMOKE all afdust sources have both transport and meteorological adjustments
applied. The total impacts of the transport fraction and meteorological adjustments for 2016v2 are shown
in Table 2-11. Note that while totals from AK, HI, PR, and VI are included at the bottom of the table, they
are from non-continental U.S. (non-CONUS) modeling domains and are held constant from 2016vl.
Table 2-11. Total impact of fugitive dust adjustments to unadjusted 2016v2 inventory
Sliilc
I iiiidjiislcd
PMio
I iiiidjuslcd
PM2.5
Ch.ingi' in
PM10
('h.ingi* in
P\i: 5
PM10
Reduction
I'M:.?
Rcduclion
Alabama
301,220
40,516
-206,837
-27,820
69%
69%
Arizona
180,413
24,148
-65,952
-8,640
37%
36%
Arkansas
389,426
53,870
-261,601
-35,627
67%
66%
California
307,525
38,907
-133,858
-16,408
44%
42%
Colorado
276,798
40,283
-138,818
-19,548
50%
49%
Connecticut
24,307
4,007
-18,293
-3,032
75%
76%
Delaware
15,263
2,346
-9,201
-1,422
60%
61%
District of
Columbia
2,882
406
-1,804
-253
63%
62%
Florida
390,779
54,511
-208,568
-29,187
53%
54%
Georgia
290,522
41,465
-201,028
-28,482
69%
69%
Idaho
560,472
64,931
-295,880
-33,156
53%
51%
Illinois
1,107,780
159,636
-679,749
-97,634
61%
61%
Indiana
144,272
26,977
-95,341
-17,919
66%
66%
Iowa
385,014
56,805
-222,410
-32,650
58%
57%
Kansas
668,387
88,915
-300,638
-39,593
45%
45%
Kentucky
177,018
28,904
-128,875
-20,989
73%
73%
Louisiana
180,035
27,399
-115,251
-17,368
64%
63%
Maine
71,295
8,735
-59,096
-7,251
83%
83%
Maryland
74,347
11,904
-48,034
-7,748
65%
65%
Massachusetts
61,438
9,379
-47,183
-7,161
77%
76%
Michigan
292,345
38,470
-213,919
-27,925
73%
73%
Minnesota
423,012
59,575
-263,321
-36,486
62%
61%
Mississippi
448,193
54,854
-307,949
-37,331
69%
68%
Missouri
1,319,996
156,248
-858,902
-101,313
65%
65%
Montana
501,655
66,435
-277,120
-35,529
55%
53%
Nebraska
515,575
71,436
-246,621
-33,630
48%
47%
Nevada
138,466
18,305
-45,931
-6,047
33%
33%
New Hampshire
20,527
4,310
-16,979
-3,560
83%
83%
New Jersey
32,466
6,059
-21,778
-4,015
67%
66%
New Mexico
205,161
25,615
-80,428
-9,987
39%
39%
23
-------
Stale
I nad.jiislcd
PMu.
I nad.jiislcd
I'M:..*
Chanel' in
PMu.
Chanel' in
I'M:?
PMio
Reduction
PM:;
Reduction
New York
238,564
33,653
-178,529
-25,035
75%
74%
North Carolina
233,349
31,479
-160,106
-21,641
69%
69%
North Dakota
397,407
61,024
-211,752
-32,100
53%
53%
Ohio
273,211
42,880
-182,757
-28,709
67%
67%
Oklahoma
601,218
81,825
-313,021
-41,638
52%
51%
Oregon
605,831
68,330
-404,663
-44,666
67%
65%
Pennsylvania
135,564
24,365
-97,991
-17,891
72%
73%
Rhode Island
4,641
775
-3,308
-551
71%
71%
South Carolina
117,181
16,266
-77,402
-10,817
66%
66%
South Dakota
215,908
38,503
-106,792
-18,757
49%
49%
Tennessee
140,798
25,845
-95,578
-17,651
68%
68%
Texas
1,317,935
190,982
-632,794
-89,482
48%
47%
Utah
165,959
21,202
-84,561
-10,620
51%
50%
Vermont
76,398
8,509
-65,227
-7,237
85%
85%
Virginia
124,875
20,123
-90,751
-14,718
73%
73%
Washington
230,686
37,529
-128,255
-20,829
56%
56%
West Virginia
86,192
11,111
-72,997
-9,417
85%
85%
Wisconsin
182,302
30,984
-124,770
-21,188
68%
68%
Wyoming
542,620
60,863
-272,862
-30,182
50%
50%
Domain Total
(12km CONUS)
15,197,226
2,091,599
-8,875,481
-1,210,842
58%
58%
Alaska (vl)
112,025
11,562
-101,822
-10,508
91%
91%
Hawaii (vl)
109,120
11,438
-73,612
-7,673
67%
67%
Puerto Rico (vl)
5,889
1,313
-4,355
-984
74%
75%
Virgin Islands (vl)
3,493
467
-1,477
-195
42%
42%
Figure 2-1 illustrates the impact of each step of the adjustment. The reductions due to the transport
fraction adjustments alone are shown at the top of the figure. The reductions due to the precipitation
adjustments alone are shown in the middle of the figure. The cumulative emission reductions after both
transport fraction and meteorological adjustments are shown at the bottom of the figure. The top plot
shows how the transport fraction has a larger reduction effect in the east, where forested areas are more
effective at reducing PM transport than in many western areas. The middle plot shows how the
meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust
emissions.
24
-------
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction,
precipitation, and cumulative
2016fj (v2) afdust annual : PM2 5, xportfrac adjusted - unadjusted
2016fj (v2) afdust annual : PM2_5, precip adjusted - xportfrac adjusted
25
-------
2016fj (v2) afdust annual : PM2_5, xportfrac + precip adjusted - unadjusted
2.2.2 Agricultural Livestock (livestock)
The livestock sector includes NH3 emissions from fertilizer and emissions of all pollutants other than
PM2.5 from livestock in the nonpoint (county-level) data category of the 2017NEI. PM2.5 from livestock
are in the Area Fugitive Dust (afdust) sector. Combustion emissions from agricultural equipment, such as
tractors, are in the nonroad sector. The livestock sector includes VOC and HAP VOC in addition to NH3.
The 2016v2 uses a 2016 USDA-based county-level back-projection of 2017NEI livestock emissions. The
SCCs included in the ag sector are shown in Table 2-12.
Table 2-12. SCCs for the livestock sector
see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2805002000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Beef cattle production
composite
Not Elsewhere Classified
2805007100
Miscellaneous Area
Sources
Ag. Production -
Livestock
Poultry production - layers
with dry manure management
systems
Confinement
2805009100
Miscellaneous Area
Sources
Ag. Production -
Livestock
Poultry production - broilers
Confinement
2805010100
Miscellaneous Area
Sources
Ag. Production -
Livestock
Poultry production - turkeys
Confinement
2805018000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Daiiy 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)
26
-------
SCC
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2805035000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Horses and Ponies Waste
Emissions
Not Elsewhere Classified
2805040000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Sheep and Lambs Waste
Emissions
Total
2805045000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Goats Waste Emissions
Not Elsewhere Classified
The 2016v2 platform livestock emissions consist of a back-projection of 2017 NEI livestock emissions to
the year 2016 and include NH3 and VOC. The livestock waste emissions from 2017 NEI contain
emissions for beef cattle, dairy cattle, goats, horses, poultry, sheep, and swine. The data come from both
state-submitted emissions and EPA-calculated emission estimates. Further information about the 2017
NEI emissions can be found in the 2017 National Emissions Inventory Technical Support Document
(EPA, 2021). Back-projection factors for 2016 emission estimates are based on animal population data
from the USDA National Agriculture Statistics Service Quick Stats
(https://www.nass.usda.gov/Quick_Stats/). These estimates are developed by data collected from annual
agriculture surveys and the Census of Agriculture that is completed every five years. These data include
estimates for beef, layers, broilers, turkeys, dairy, swine, and sheep. Each SCC in the 2017 NEI livestock
inventory, except for 2805035000 (horses and ponies) and 2805045000 (goats), was mapped to one of
these USDA categories. Then, back-projection factors were calculated based on USDA animal
populations for 2016 and 2017. Emissions for animal categories for which population data were not
available (e.g., horses, goats) were held constant in the projection.
Back-projection factors were calculated at the county level, but only where county-level data were
available for a specific animal category. County-level factors were limited to a range of 0.833 to 1.2. Data
were not available for every animal category in every county. State-wide back-projection factors based on
state total animal populations were calculated and applied to counties where county-specific data was not
available for a given animal category. However, data were often not available for every animal category
in every state. For categories other than beef and dairy, data are not available for most states. In cases of
missing state-level data, a national back-projection factor was applied. Back-projection factors were not
pollutant-specific and were applied to all pollutants. The national back-projection factors, which were
only used when county or state data were not available, are shown in Table 2-13. The national factors
were created using a ratio between animal inventory counts for 2017 and 2016 from the USDA National
livestock inventory projections published in February 2018
(https://www.ers.usda. gov/webdocs/outlooks/87459/oce-2018-1.pdf?v=7587.1).
Table 2-13. National back-projection factors for livestock: 2017 to 2016
beef
-1.8%
swine
-3.6%
broilers
-2.0%
turkeys
-0.3%
layers
-2.3%
dairy
-0.4%
2.2.3 Agricultural Fertilizer (fertilizer)
Fertilizer emissions for 2016 are based on the Fertilizer Emission Scenario Tool for CMAQ (FEST-C)
model (https://www.cmascenter.org/fest-c/). These emissions are for SCC 2801700099 (Miscellaneous
27
-------
Area Sources; Ag. Production - Crops; Fertilizer Application; Miscellaneous Fertilizers). The
bidirectional version of CMAQ (v5.3.2) and the Fertilizer Emissions Scenario Tool for CMAQ FEST-C
(vl.4) were used to estimate ammonia (NFb) emissions from agricultural soils. The approach to estimate
year-specific fertilizer emissions consists of these steps:
• Run FEST-C to produce nitrate (N03), Ammonium (NH4+, including Urea), and organic
(manure) nitrogen (N) fertilizer usage estimates.
• Run the CMAQ model with bidirectional ("bidi") NFB exchange to generate gaseous ammonia
NFB emission estimates.
• Calculate county-level emission factors as the ratio of bidirectional CMAQ NFB fertilizer
emissions to FEST-C total N fertilizer application.
FEST-C is the software program that processes land use and agricultural activity data to develop inputs
for the CMAQ model when run with bidirectional exchange. FEST-C reads land use data from the
Biogenic Emissions Landuse Dataset (BELD), meteorological variables from the Weather Research and
Forecasting (WRF) model, and nitrogen deposition data from a previous or historical average CMAQ
simulation. FEST-C, then uses the Environmental Policy Integrated Climate (EPIC) modeling system
(https://epicapex.tamu.edu/epic/) to simulate the agricultural practices and soil biogeochemistry and
provides information regarding fertilizer timing, composition, application method and amount.
An iterative calculation was applied to estimate fertilizer emissions for the 2016 platform. First, fertilizer
application by crop type was estimated using FEST-C modeled data. Then CMAQ v5.3 was run with the
Surface Tiled Aerosol and Gaseous Exchange (STAGE) deposition option with bidirectional exchange to
estimate fertilizer and biogenic NFB emissions.
28
-------
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions
The Fertilizer Emission Scenario Tool for CMAQ
(FEST-C)
Fertilizer Activity Data
The following activity parameters were input into the EPIC model:
• Grid cell meteorological variables from WRF
• Initial soil profiles/soil selection
• Presence of 21 major crops: irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn,
silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans,
spring wheat, winter wheat, canola, and other crops (e.g., lettuce, tomatoes, etc.)
• Fertilizer sales to establish the type/composition of nutrients applied
• Management scenarios for the 10 USDA production regions. These include irrigation, tile
drainage, intervals between forage harvest, fertilizer application method (injected versus surface
applied), and equipment commonly used in these production regions.
The WRF meteorological model was used to provi de grid cell meteorological parameters for year 2016
using a national 12-km rectangular grid covering the continental U.S. The meteorological parameters in
Table 2-14 were used as EPIC model inputs.
29
-------
Table 2-14. Source of input variables for EPIC
EPIC input variable
Variable Source
Daily Total Radiation (MJ/m2 )
WRF
Daily Maximum 2-m Temperature (C)
WRF
Daily minimum 2-m temperature (C)
WRF
Daily Total Precipitation (mm)
WRF
Daily Average Relative Humidity (unitless)
WRF
Daily Average 10-m Wind Speed (m s"1 )
WRF
Daily Total Wet Deposition Oxidized N (g/ha)
CMAQ
Daily Total Wet Deposition Reduced N (g/ha)
CMAQ
Daily Total Dry Deposition Oxidized N (g/ha)
CMAQ
Daily Total Dry Deposition Reduced N (g/ha)
CMAQ
Daily Total Wet Deposition Organic N (g/ha)
CMAQ
Initial soil nutrient and pH conditions in EPIC were based on the 1992 USD A Soil Conservation Service
(CSC) Soils-5 survey. The EPIC model then was run for 25 years using current fertilization and
agricultural cropping techniques to estimate soil nutrient content and pH for the 2016 EPIC/WRF/CMAQ
simulation.
The presence of crops in each model grid cell was determined using USD A Census of Agriculture data
(2012) and USGS National Land Cover data (2011). These two data sources were used to compute the
fraction of agricultural land in a model grid cell and the mix of crops grown on that land.
Fertilizer sales data and the 6-month period in which they were sold were extracted from the 2014
Association of American Plant Food Control Officials (AAPFCO,
http://www.aapfco.org/publications.htmn. AAPFCO data were used to identify the composition (e.g.,
urea, nitrate, organic) of the fertilizer used, and the amount applied is estimated using the modeled crop
demand. These data were useful in making a reasonable assignment of what kind of fertilizer is being
applied to which crops.
Management activity data refers to data used to estimate representative crop management schemes. The
USD A Agricultural Resource Management Survey (ARMS,
https://www.nass.usda.gov/Surveys/Guide to NASS Surveys/A" 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.).
30
-------
2.2.4 Nonpoint Oil and Gas Sector (np_oilgas)
While the major emissions sources associated with oil and gas collection, processing, and distribution
have traditionally been included in the National Emissions Inventory (NEI) as point sources (e.g., gas
processing plants, pipeline compressor stations, and refineries), the activities occurring "upstream" of
these types of facilities have not been as well characterized in the NEI. Here, upstream activities refer to
emission units and processes associated with the exploration and drilling of oil and gas wells, and the
equipment used at the well site to then extract the product from the well and deliver it to a central
collection point or processing facility. The types of unit processes found at upstream sites include
separators, dehydrators, storage tanks, and compressor engines.
The nonpoint oil and gas (npoilgas) sector, which consists of oil and gas exploration and production
sources, both onshore and offshore (state-owned only). In the 2016vl platform, these emissions are
mostly based on the EPA Oil and Gas Tool run with data specific to the year 2016, with some states
submitting their own inventory data. Because of the growing importance of these emissions, special
consideration is given to the speciation, spatial allocation, and monthly temporalization of nonpoint oil
and gas emissions, instead of relying on older, more generalized profiles.
EPA Oil and Gas Tool
EPA developed the 2016 non-point oil and gas inventory for the 2016v2 platform using the 2017NEI
version of the Oil and Gas Emission Estimation Tool (the "Tool") with year 2016 oil and gas production
and exploration activity as input into the Tool. The Tool was previously used to estimate emissions for
the 2017 NEI. The 2016vl of the nonpoint oil and gas emissions were mainly generated using the 2014
NEI version of the Oil and Gas Tool. Year 2016 oil and gas activity data were supplied to EPA by some
state air agencies, and where state data were not supplied to EPA, EPA populated the 2016v2 inventory
with the best available data. The Tool is an Access database that utilizes county-level activity data (e.g.,
oil production and well counts), operational characteristics (types and sizes of equipment), and emission
factors to estimate emissions. The Tool creates a CSV-formatted emissions dataset covering all national
nonpoint oil and gas emissions. This dataset is then converted to FF10 format for use in SMOKE
modeling. A separate report named "2017 Nonpoint Oil and Gas Emission Estimation Tool
RevisionsVl 41 l_2019.docx" (ERG, 2019a) was generated that provides technical details of how the
tool was applied for the 2017NEI. This 2017 NEI Tool document can be found at:
https://gaftp.epa.gov/air/nei/2017/doc/supporting data/nonpoint/.
Nonpoint Oil and Gas Alternative Datasets
Some states provided, or recommended use of, a separate emissions inventory for use in 2016v2 platform
instead of emissions derived from the EPA Oil and Gas Tool. For example, the California Air Resources
Board (CARB) developed their own np oilgas emissions inventory for 2016 for California that were used
for the 2016vl and 2016v2 platforms.
In Pennsylvania for the 2016v2 modeling platform, the emissions associated with unconventional wells
for year 2016 were supplied by the Pennsylvania Department of Environmental Protection (PA DEP). The
Oil and Gas Tool was used to produce the conventional well emissions for 2016. Together these
unconventional and conventional well emissions represent the total non-point oil and gas emissions for
Pennsylvania.
31
-------
A major update in 2016v2 was the incorporation of the WRAP oil and gas inventory, which is described
in more detail below and in the WRAP Final report (WRAP / Ramboll, 2019). Specifically, production-
related emissions from the WRAP inventory were used, along with the exploration-related emissions from
the 2017NEI Oil and Gas Tool for the following states: CO, MT, ND, NM, SD, UT, and WY. The
exploration-related emissions were used from the Tool because they likely better align with exploration
activity in year 2016 vs the WRAP 2014 inventory which better represented exploration activity for year
2014.
Oklahoma Department of Environmental Quality requested that npoilgas emissions from 2014NEIv2 be
projected to 2016 for all source except lateral compressors. Projection factors for Oklahoma np oilgas
production, based on historical production data, are listed in Table 2-15. For lateral compressor emissions
in Oklahoma, the EPA Oil and Gas Tool inventory for 2016 was used, except with a 72% cut applied to
all emissions. Exploration np oilgas emissions in Oklahoma are based on the EPA Oil and Gas Tool
inventory for 2016, without modification.
Table 2-15. 2014NEIv2-to-2016 oil and gas projection factors for OK.
State/region
Emissions type
Factor
Pollutant(s)
Oklahoma
Oil Production
+6.9%
All
Oklahoma
Natural Gas Production
+5.9%
All
Oklahoma
Combination Oil + NG Production
+6.4%
All
Oklahoma
Coal Bed Methane Production
-30.0%
All
2.2.5 Residential Wood Combustion (rwc)
The RWC sector includes residential wood burning devices such as fireplaces, fireplaces with inserts, free
standing woodstoves, pellet stoves, outdoor hydronic heaters (also known as outdoor wood boilers),
indoor furnaces, and outdoor burning in firepits and chimneys. Free standing woodstoves and inserts are
further differentiated into three categories: 1) conventional (not EPA certified); 2) EPA certified,
catalytic; and 3) EPA certified, noncatalytic. Generally, the conventional units were constructed prior to
1988. Units constructed after 1988 had to meet EPA emission standards and they are either catalytic or
non-catalytic. The source classification codes (SCCs) in the RWC sector are listed in Table 2-16.
Table 2-16. 2016 vl platform SCCs for the residential wood combustion sector
see
Tier 1 Description
Tier 2
Description
Tier 3
Description
Tier 4 Description
2104008100
Stationary Source
Fuel Combustion
Residential
Wood
Fireplace: general
2104008210
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts;
non-EPA certified
2104008220
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts;
EPA certified; non-catalytic
2104008230
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts;
EPA certified; catalytic
2104008310
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding,
non-EPA certified
2104008320
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding,
EPA certified, non-catalytic
32
-------
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
For all states, RWC emissions from the 2017NEI were backcast to 2016 using a single projection factor
(+3.254%) based on data from EIA/SEDS.
2.2.6 Solvents (solvents)
The solvents sector is a diverse collection of emission sources for which emissions are driven by
evaporation. Included in this sector are everyday items such as cleaners, personal care products,
adhesives, architectural and aerosol coatings, printing inks, and pesticides. These sources exclusively emit
organic gases (i.e., VOCs) with origins spanning residential, commercial, institutional, and industrial
settings. The organic gases that evaporate from these sources often fulfill other functions than acting as a
traditional solvent (e.g., propellants, fragrances, emollients); as such, these emissions are frequently
described as volatile chemical products (VCPs). In the 2016v2 platform, these products comprise the
solvents sector.
The types of sources in the solvents sector include, but are not limited to:
• solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic
marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances,
and motor vehicles;
• solvent utilization for degreasing of furniture, metals, auto repair, electronics, and manufacturing;
• solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care
products, household products, adhesives and sealants; and
• solvent utilization for asphalt application, roofing, and pesticide application;
For the 2016v2 platform, emissions from the solvent 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
Transportation's 2012 Commodity Flow Survey (U.S. Department of Transportation, 2015) and the U.S.
Census Bureau's Paint and Allied Products Survey (U.S. Census Bureau, 2011), and producer price
indices, which scale commodity prices to target years, are retrieved from the Federal Reserve Bank of St.
33
-------
Louis (U.S. Bureau of Labor Statistics, 2020). In circumstances where the aforementioned datasets were
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). For oil
and gas solvent usage, the composition is assumed to be dominated by methanol and other hydrocarbon
blends. The physiochemical properties of all organic components are generated from the quantitative
structure-activity relationship model OPERA (Mansouri et al., 2018) and the characteristic evaporation
timescale of each component is estimated using previously published methods (Khare and Gentner, 2018;
Weschler and Nazaroff, 2008).
National-level emissions were allocated to the county-level using several proxies. Most emissions are
allocated using population as an allocation surrogate. This includes all cleaners, personal care products,
adhesives, architectural coatings, and aerosol coatings. Industrial coatings, allied paint products, printing
inks, and dry-cleaning emissions are allocated using county-level employment statistics from the U.S.
Census Bureau's County Business Patterns (U.S. Census Bureau, 2018) and follow the same mapping
scheme used in the EPA's 2017 National Emissions Inventory (EPA, 2021). Agricultural pesticides are
allocated using county-level agricultural pesticide use, as taken from the 2017 NEI and oil and gas
emissions are allocated using oil and gas well counts (U.S. EIA, 2019).
For 2016v2, point and nonpoint emissions with SCCs that overlap the solvents sector were removed from
the ptnonipm and nonpt sectors.
2.2.7 Nonpoint (nonpt)
The starting points for the 2016v2 nonpt inventory are the 2017 NEI and the 2014 NEI, including all
nonpoint sources that are not included in the sectors afdust, ag, cmv_clc2, cmv_c3, np oilgas, rail,
rwc, or solvents. The types of sources in the nonpt sector taken from 2016vl 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; and
• cellulosic biorefining.
For 2016v2, emissions were taken from 2017 NEI for waste disposal (including composting),
miscellaneous non-industrial sources such as cremation, hospitals, lamp breakage, and automotive repair
shops; bulk gasoline terminals; portable gas cans; and any construction agricultural dust or waste that is
not part of the afdust or livestock sectors. For biomass fuel combustion, 2017 NEI data were backcast to
2016 by applying a 4.27% reduction for industrial emissions, 0.15% reduction for commercial emissions.
Refueling emissions at gas stations that are in the nonpt sector were interpolated to 2016 between 2002
34
-------
and 2017 levels. Other nonpt emissions are the same as those in the 2016vl platform, except for solvents
that were moved to the solvents sector.
Adjustment of nonpt sources to 2016
Census population, sometimes by county and sometimes by state, was used to backcast select nonpt
sources from the 2017 NEI to 2016. The population data was downloaded from the US Census Bureau.
Specifically, the "Population, Population Change, and Estimated Components of Population Change:
April 1, 2010 to July 1, 2017" file (https://www2.census.gov/programs-survevs/popest/datasets/2010-
2017/counties/totals/co-est2017-alldata.csv). A ratio of 2016 population to 2014 population was used to
create a growth factor that was applied to the 2014NEIv2 emissions with SCCs matching the population-
based SCCs listed in Table 2-17. Positive growth factors (from increasing population) were not capped,
but negative growth factors (from decreasing population) were flatlined for no growth.
Table 2-17. SCCs receiving Census-based adjustments to 2016
S( (
Tier 1
Tier 2 Description
Tier 3
Tier 4
Description
Description
Description
2302002100
Industrial
Processes
Food and Kindred
Products: SIC 20
Commercial Charbroiling
Conveyorized Charbroiling
2302002200
Industrial
Processes
Food and Kindred
Products: SIC 20
Commercial Charbroiling
Under-fired Charbroiling
2302003000
Industrial
Food and Kindred
Commercial Deep Fat
Total
Processes
Products: SIC 20
Frying
2302003100
Industrial
Food and Kindred
Commercial Deep Fat
Flat Griddle Frying
Processes
Products: SIC 20
Frying
2302003200
Industrial
Food and Kindred
Commercial Deep Fat
Clamshell Griddle Frying
Processes
Products: SIC 20
Frying
2.3 2016 Onroad Mobile sources (onroad)
Onroad mobile source include emissions from motorized vehicles operating on public roadways. These
include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks, heavy-duty trucks,
and buses. The sources are further divided by the fuel they use, including diesel, gasoline, E-85, and
compressed natural gas (CNG) vehicles. The sector characterizes emissions from parked vehicle
processes (e.g., starts, hot soak, and extended idle) as well as from on-network processes (i.e., from
vehicles as they move along the roads). Except for California, all onroad emissions are generated using
the SMOKE-MOVES emissions modeling framework that leverages MOVES-generated emission factors,
county and SCC-specific activity data, and hourly meteorological data. The onroad source classification
codes (SCCs) in the modeling platform are more finely resolved than those in the National Emissions
Inventory (NEI). The NEI SCCs distinguish vehicles and fuels. The SCCs used in the model platform
also distinguish between emissions processes (i.e., off-network, on-network, and extended idle), and road
types.
Onroad emissions were computed with SMOKE-MOVES by multiplying specific types of vehicle activity
data by the appropriate emission factors. This section includes discussions of the activity data and the
emission factor development. The vehicles (aka source types) for which MOVES3 computes emissions
are shown in Table 2-18. SMOKE-MOVES was run for specific modeling grids. Emissions for the
contiguous U.S. states and Washington, D.C., were computed for a grid covering those areas. Emissions
for Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands were computed by running SMOKE-
MOVES for distinct grids covering each of those regions and are included in the onroad nonconus sector.
35
-------
In some summary reports these non-CONUS emissions are aggregated with emissions from the onroad
sector.
Table 2-18. 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
Other Bus
40
42
Transit Bus
40
43
School Bus
40
51
Refuse Truck
50
52
Single Unit Short-haul Truck
50
53
Single Unit Long-haul Truck
50
54
Motor Home
50
61
Combination Short-haul Truck
60
62
Combination Long-haul Truck
60
2.3.1 Onroad Activity Data Development
SMOKE-MOVES uses vehicle miles traveled (VMT), vehicle population (VPOP), vehicle starts, hours of
off-network idling (ONI), and hours of hoteling, to calculate emissions. These datasets are collectively
known as "activity data". For each of these activity datasets, first a national dataset was developed; this
national dataset is called the "EPA default" dataset. The default dataset started with the 2017 NEI activity
data, which was then scaled back to 2016 using Federal Highway Administration (FHWA) VM-2 trends.
Second, data submitted by state and local agencies were incorporated where available, in place of the EPA
default data. EPA default activity was used for California, but the emissions were scaled to California-
supplied values during the emissions processing. The agencies for which 2016 submitted data, or 2017
submitted VMT and VPOP data backcast to 2016, were used for the 2016v2 platform are shown in Table
2-19. Note that Florida and Rhode Island activity data were projected from 2014 to 2016.
Table 2-19. Submitted data used to prepare 2016v2 onroad activity data
Agency
2016 VMT
2016 VPOP
2017 NEI
Alaska
yes
Arizona - Maricopa
yes
Arizona - Pima
yes
yes
yes
Colorado
yes
yes
Connecticut
yes
yes
Delaware
yes
District of Columbia
yes
Georgia
yes
yes
Idaho
yes
Illinois - Chicago area
yes
yes
Illinois - rest of state
yes
yes
36
-------
Agency
2016 VMT
2016 VPOP
2017 NEI
Indiana - Louisville area
yes
Kentucky - lefferson
yes
yes
yes
Kentucky - Louisville exurbs
yes
Maine
yes
Maryland
yes
yes
yes
Massachusetts
yes
yes
Michigan - Detroit area
yes
yes
Michigan - rest of state
yes
yes
Minnesota
yes
yes
Missouri
yes
Nevada - Clark
yes
yes
yes
Nevada - Washoe
yes
New Hampshire
yes
yes
New lersey
yes
yes
New York
yes
North Carolina
yes
yes
Ohio
yes
Pennsylvania
yes
yes
South Carolina
yes
yes
Tennessee - Davidson
yes
Tennessee - Knox
yes
Texas
yes
Vermont
yes
Virginia
yes
yes
Washington
yes
West Virginia
yes
yes
Wisconsin
yes
yes
Vehicle Miles Traveled (VMT)
EPA calculated default 2016 VMT by backcasting the 2017 NEIVMT to 2016. The 2017 NEI Technical
Support Document has details on the development of the 2017 VMT (EPA, 2021). The data backcast to
2016 were used for states that did not submit 2016 VMT data. The factors to adjust VMT from 2017 to
2016 were based on VMT data from the FHWA county-level VM-2 reports similar to the state-level
reports at https://www.fhwa.dot.gov/policvinformation/statistics/2016/vm2.cfm and
https://www.fhwa.dot.gov/policvinformation/statistics/2017/vm2.cfm. For most states, EPA calculated
county-road type factors based on FHWA VM-2 County data for 2017 and 2016. Separate factors were
calculated by vehicle type for each of the MOVES road types. Some states have a very different
distribution of urban activity versus rural activity between 2017NEI and the FHWA data, due to
inconsistencies in the definition of urban versus rural. For those counties, a single county-wide projection
factor based on total FHWA VMT across all road types was applied to all VMT independent of road type.
County-total-based (instead of county+road-type) factors were used for all counties in IN, MS, MO, NM,
TN, TX, UT because many counties had large increases in one particular road type and decreases in
another road type. State-total-based factors were used for all counties in Alaska and Puerto Rico because
37
-------
county level data were questionable. Note that Alaska and Hawaii emissions have not yet been
recomputed using MOVES3-based emission factors. State total differences between the 2017 NEI and
2016v2 VMT data for all states are provided in Table 2-20.
Table 2-20. State total differences between 2017 NEI and 2016v2 VMT data
Slate
2017 \ i:i-20l6v2 %
Slate
2017 \EI-20I6\2 '5-
Alalxima
2,1"..
Montana
0 4"..
Alaska
4 o
Nebraska
1 5",.
Arizona
o 5" (i
\e\ ada
-4 (A,
Arkansas
1.8%
New Hampshire
1.6%
California
1.1%
New Jersey
0.8%
Colorado
2.3%
New Mexico
6.4%
Connecticut
0.6%
New York
1.3%
Delaware
2.8%
North Carolina
-0.2%
District of Columbia
2.5%
North Dakota
-0.2%
Florida
-8.4%
Ohio
0.7%
Georgia
4.2%
Oklahoma
0.8%
Hawaii
1.1%
Oregon
0.0%
Idaho
0.5%
Pennsylvania
0.3%
Illinois
-0.8%
Rhode Island
-2.7%
Indiana
-1.5%
South Carolina
0.9%
Iowa
0.4%
South Dakota
2.0%
Kansas
0.5%
Tennessee
1.4%
Kentucky
0.2%
Texas
7.0%
Louisiana
0.1%
Utah
0.5%
Maine
-0.7%
Vermont
0.1%
Maryland
1.6%
Virgin Islands
0.5%
Massachusetts
5.5%
Virginia
0.0%
Michigan
1.0%
Washington
2.1%
Minnesota
1.9%
West Virginia
0.7%
Mississippi
0.3%
Wisconsin
2.3%
Missouri
2.5%
Wyoming
2.2%
For the 2016 platform, VMT data submitted by state and local agencies were incorporated and used in
place of EPA defaults. Note that VMT data need to be provided to SMOKE for each county and SCC.
The onroad SCCs characterize vehicles by MOVES fuel type, vehicle (aka source) type, emissions
process, and road type. Any VMT provided at a different resolution than this were converted to a full
county-SCC resolution to prepare the data for processing by SMOKE. Details on pre-processing of
submitted VMT and VPOP are provided in the TSD Preparation of Emissions Inventories for the 2016vl
North American Emissions Modeling Platform (EPA, 2021b). Some of the provided data were adjusted
following quality assurance, as described below in the VPOP section.
38
-------
To ensure consistency in the 21/31/32 splits across the country, all state-submitted VMT for MOVES
vehicle types 21,31, and 32 (all of which are part of HPMS vehicle type 25) was summed, and then re-
split using the 21/31/32 splits from the EPA 2016v2 default VMT. VMT for each source type as a
percentage of total 21/31/32 VMT was calculated by county from the EPA default VMT. Then, state-
submitted VMT for 21/31/32 were summed and then resplit according to those percentages. This was
done for all states and counties listed above which submitted VMT for 2016. Most of the states listed
above did not provide VMT down to the source type, so splitting the light-duty vehicle VMT does not
create an inconsistency with state-provided data in those states. Exceptions are New Hampshire and
Pennsylvania: those two states provided SCC-level VMT, but these were reallocated to 21/31/32 so that
the splits are performed in a consistent way across the country. The 21/31/32 splits in the EPA default
VMT are based on the 2017 NEIVPOP data obtained from IHS-Polk through the Coordinating Research
Council (CRC) A-l 15 project (CRC, 2019).
Speed Activity (SPEED/SPDIST)
In SMOKE 4.7, SMOKE-MOVES was updated to use speed distributions similarly to how they are used
when running MOVES in inventory mode. This new speed distribution file, called SPDIST, specifies the
amount of time spent in each MOVES speed bin for each county, vehicle (aka source) type, road type,
weekday/weekend, and hour of day. This file contains the same information at the same resolution as the
Speed Distribution table used by MOVES but is reformatted for SMOKE. Using the SPDIST file results
in a SMOKE emissions calculation that is more consistent with MOVES than the old hourly speed profile
(SPDPRO) approach, because emission factors from all speed bins can be used, rather than interpolating
between the two bins surrounding the single average speed value for each hour as is done with the
SPDPRO approach.
As was the case with the previous SPDPRO approach, the SPEED inventory that includes a single overall
average speed for each county, SCC, and month, must still be read in by the SMOKE program Smkinven.
SMOKE requires the SPEED dataset to exist even when speed distribution data are available, even though
only the speed distribution data affects the selection of emission factors. The SPEED and SPDIST
datasets are carried over from 2017NEI and are based on a combination of the CRC A-100 (CRC, 2017)
project data and 2017 NEI MOVES CDBs.
Vehicle Population (VPOP)
The EPA default VPOP dataset was developed similarly to the default VMT dataset described above. In
the areas where we backcast 2017 NEI VMT:
2016v2 VPOP = 2016v2 VMT * (VPOP/VMT ratio by county-SCC6).
where the ratio by county-SCC is based on 2017NEI with MOVES3 fuel splits. In the areas where we
used 2016vl VMT resplit to MOVES3 fuels, 2016v2 VPOP = 2016vl VPOP with two resplits: First,
source types 21/31/32 were resplit according to 2017 NEI EPA default 21/31/32 splits so that the whole
country has consistent 21/31/32 splits. Next, fuels were resplit to MOVES3 fuels. There are some areas
where 2016 VMT was submitted but 2016 VPOP was not; those areas are using 2016vl VPOP (with
resplits). The same method was applied to the 2016 EPA default VMT to produce an EPA default VPOP
data set.
39
-------
Hotelina Hours (HOTELING)
Hoteling hours activity is used to calculate emissions from extended idling and auxiliary power units
(APUs) for heavy duty diesel vehicles. Many states have commented that EPA estimates of hoteling
hours, and therefore emissions resulting from hoteling, are higher than they could realistically be in reality
given the available parking spaces. Therefore, recent hoteling activity datasets, including the 2014NEIv2,
2016 beta, and 2016vl platforms, incorporate reductions to hoteling activity data based on the availability
of truck stop parking spaces in each county, as described below. Starting with 2016vl, hoteling hours
were recomputed using a new factor identified by EPA's Office of Transportation and Air Quality as
more appropriate based on recent studies.
The method used in 2016v2 is the following:
1 Start with 2016 VMT for source type 62 on restricted roads, by county.
2 Multiply that by 0.007248 hours/mile (EPA, 2020). (Note that this results in about 73.5%
less hoteling hours as compared to the 2014NEIv2 approach.)
3 Apply parking space reductions to keep hoteling within the estimated maximum hours by
county, except for states that requested we not do that (CO, ME, NJ, NY).
Hoteling hours were adjusted down in counties for which there were more hoteling hours assigned to the
county than could be supported by the known parking spaces. To compute the adjustment, we started
with the hoteling hours for the county as computed by the above method, and then we applied reductions
directly to the 2016 hoteling hours based on known parking space availability so that there were not more
hours assigned to the county than the available parking spaces could support if they were full every hour
of every day.
A dataset of truck stop parking space availability with the total number of parking spaces per county was
used in the computation of the adjustment factors. This same dataset is used to develop the spatial
surrogate for hoteling emissions. For the 2016vl platform, the parking space dataset included several
updates compared to 2016beta platform, based on information provided by some states (e.g., MD). Since
there are 8,784 hours in the year 2016; the maximum number of possible hoteling hours in a particular
county is equal to 8,784 * the number of parking spaces in that county. Hoteling hours for each county
were capped at that theoretical maximum value for 2016 in that county, with some exceptions as outlined
below.
Because the truck stop parking space dataset may be incomplete in some areas, and trucks may sometimes
idle in areas other than designated spaces, it was assumed that every county has at least 12 parking spaces,
even if fewer parking spaces are found in the parking space dataset. Therefore, hoteling hours are never
reduced below 105,408 hours for the year in any county. If the unreduced hoteling hours were already
below that maximum, the hours were left unchanged; in other words, hoteling activity are never increased
as a result of this analysis.
A handful of high activity counties that would otherwise be subject to a large reduction were analyzed
individually to see if their parking space count seemed unreasonably low. In the following counties, the
parking space count and/or the reduction factor was manually adjusted:
• 17043 / DuPage IL (instead of reducing hoteling by 89%, applied no adjustment)
40
-------
• 39061 / Hamilton OH (parking spot count increased to 20 instead of the minimum 12)
• 47147 / Robertson TN (parking spot count increased to 52 instead of just 26)
• 51015 / Augusta VA (parking space count increased to 48 instead of the minimum 12)
• 51059 / Fairfax VA (parking spot count increased to 20 instead of the minimum 12)
Georgia and New Jersey submitted hoteling activity for the 2016vl platform, which was carried through
to v2 with an updated APU factor for MOVES3 2016. For these states, the EPA default projection was
replaced with their state data. New Jersey provided their hoteling activity in a series of HotellingHours
MOVES-formatted tables, which include separate activity for weekdays and weekends and for each
month and which have units of hours-per-week. These data first needed to be converted to annual totals
by county.
Alaska Department of Natural Resources staff requested that we zero out hoteling activity in several
counties due to the nature of driving patterns in their region. In addition, there are no hoteling hours or
other emissions from long-haul combination trucks in Hawaii, Puerto Rico, or the Virgin Islands.
The states of Colorado, Maine, New Jersey, and New York requested that no reductions be applied to the
hoteling activity based on parking space availability. For these states, we did not apply any reductions
based on parking space availability and left the hours that were computed using the updated method for
2016vl; or in the case of New Jersey, their submitted activity data were unchanged.
Finally, the county total hoteling must be split into separate values for extended idling (SCC 2202620153)
and APUs (SCC 2202620191). Compared to earlier versions of MOVES, APU percentages have been
lowered for MOVES3. A 5.19% APU split was used for the year 2016, meaning that APUs are used for
5.19% of the hoteling hours. This APU percentage was applied nationwide, including in states where
hoteling activity was submitted.
For 2016v2, hoteling was calculated as: 2016v2 HOTELING = 2017NEI HOTELING * 2016v2
VMT/2017NEI VMT. This is effectively consistent with applying the 0.007248 factor directly to the
2016v2 VMT. Then, for counties that provided 2017 hoteling but did not have vehicle type 62 restricted
VMT in 2016 - that is, counties that should have hoteling, but do not have any VMT to calculate it from -
we backcast 2017 hoteling to 2016 using the FHWA-based county total 2017 to 2016 trend. Finally, the
annual parking-space-based caps for hoteling hours were applied. The same caps were used as for
2017NEI, except recalculated for a leap year (multiplied by 366/365).
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
41
-------
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
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.
2016v2 STARTS = 2016v2 VMT * (2017 STARTS/ 2017 VMT by county&SCC6)
Off-network Idling Hours
After creating VMT inputs for SMOKE-MOVES, Off-network idle (ONI) activity data were also needed.
ONI is defined in MOVES as time during which a vehicle engine is running idle and the vehicle is
somewhere other than on the road, such as in a parking lot, a driveway, or at the side of the road. This
engine activity contributes to total mobile source emissions but does not take place on the road network.
Examples of ONI activity include:
light duty passenger vehicles idling while waiting to pick up children at school or to pick up
passengers at the airport or train station,
single unit and combination trucks idling while loading or unloading cargo or making
deliveries, and
vehicles idling at drive-through restaurants.
Note that ONI does not include idling that occurs on the road, such as idling at traffic signals, stop signs,
and in traffic—these emissions are included as part of the running and crankcase running exhaust
processes on the other road types. ONI also does not include long-duration idling by long-haul
combination trucks (hoteling/extended idle), as that type of long duration idling is accounted for in other
MOVES processes.
ONI activity hours were calculated based on VMT. For each representative county, the ratio of ONI hours
to onroad VMT (on all road types) was calculated using the MOVES ONI Tool by source type, fuel type,
and month. These ratios are then multiplied by each county's total VMT (aggregated by source type, fuel
type, and month) to get hours of ONI activity.
2.3.2 MOVES Emission Factor Table Development
MOVES3 was run in emission rate mode to create emission factor tables using CB6 speciation for the
years 2016, 2023, 2026, and 2032, for all representative counties and fuel months. MOVES was run for
all counties in Alaska, Hawaii, and Virgin Islands, and for a single representative county in Puerto Rico.
The county databases CDBs used to run MOVES3 to develop the emission factor tables were derived
from those used for the 2017 NEI and therefore included any updated data provided and accepted for the
2017 NEI process. The 2017 NEI development included an extensive review of the various tables
including speed distributions were performed. Where state speed profiles, speed distributions, and
temporal profiles data were not accepted from S/L submissions, those data were obtained from the CRC
A-100 study. Once the data tables for 2017 NEI were incorporated into the CDBs, a new set of
representative counties was developed as part of the EQUATES project for the years 2002-2017 and was
42
-------
slightly expanded for 2016v22. Each county in the continental U.S. was classified according to its state,
altitude (high or low), fuel region, the presence of inspection and maintenance programs, the mean light-
duty age, and the fraction of ramps. A binning algorithm was executed to identify "like counties", and
then specific requests for representative county groups by states for the 2017 NEI were honored. The
result was 332 representative counties (up from 315 in 2016vl) as shown in Figure 2-3.
Figure 2-3. Representative Counties in 2016v2
For more information on the development of the 2016 age distributions and representative counties and
the review of the input data, see the memoranda "Onroad 2016-23-26-
32_ Documentation 20210824 _clean.docx" and
"CMAQ_Representative_Counties_Analysis_20201009_addFY23-26-32Parameters.xlsx" (ERG, 2021).
Age distributions are a key input to MOVES in determining emission rates. The base year CDB age
distributions were shifted back one year from 2017 to 2016 in all counties. The 2016 years were then
grown to each future year 2023, 2026, and 2032 everywhere except Alaska. Alaska age distributions were
not changed in the future years because the 2016 distributions did not show a recession dip around model
year 2009 and the vehicle populations looked sparse compared to other areas. The age distributions for
2016v2 were updated based on vehicle registration data obtained from the CRC A-l 15 project, subject to
2 One new representative county in Kentucky was added: Kenton County (FIPS code 21117) due to a change for year 2018.
Four new representative counties in North Carolina were added for the 2016, 2023, and 2026 runs: 37019, 37159, 37077, and
37135 due to inspection and maintenance programs changing in future years. In addition, one Nebraska county (FIPS code
31115) was moved into a similar group (representative county 31047) due to a small vehicle population and similar mean light-
duty vehicle age.
43
-------
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). The CRC project dealt with the
discrepancy by releasing datasets based on raw (unadjusted) information and adjusted sets of age
distributions, where the adjustments reflected the differences in population by model year of 2014 IHS
data and 2014 submitted data from a single state.
For the 2017 NEI, and for the 2016v2 platform, EPA repeated the CRC's assessment of IHS vs. state
vehicles by age, but with updated information from the 2017 NEI and for more states. The 2017 light-
duty vehicle (LDV) populations from the CRC A-l 15 project were compared by model year to the
populations submitted by state/local (S/L) agencies for the 2017 NEI. The comparisons by model year
were used to develop adjustment factors that remove older 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 2-21 below shows the fraction of vehicles to
keep by model year based on this analysis. The adjustments were applied to the 2016 IHS-based age
distributions from CRC project A-l 15 prior to use in 2016vl. 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 tails, the age distributions were renormalized to ensure they
summed to one (1). In addition, antique license plate vehicles were removed based on the registration
summary from IHS. Nationally, the prevalence of antique plates is only 0.8 percent, but as high as 6
percent in some states (e.g., Mississippi).
Table 2-21. Fraction of IHS Vehicle Populations to Retain for 2016vl and 2017 NEI
Model Year
Cars
Light
pre-1989
0.675
0.769
1989
0.730
0.801
1990
0.732
0.839
1991
0.740
0.868
1992
0.742
0.867
1993
0.763
0.867
1994
0.787
0.842
1995
0.776
0.865
1996
0.790
0.881
1997
0.808
0.871
44
-------
Model Year
(illS
l.ilihl
1998
0.819
0.870
1999
0.840
0.874
2000
0.838
0.896
2001
0.839
0.925
2002
0.864
0.921
2003
0.887
0.942
2004
0.926
0.953
2005
0.941
0.966
2006
1
0.987
2007-2017
1
1
In addition to removing the older and antique plate vehicles from the IHS data, 25 counties found to be
outliers because their fleet age was significantly younger than in typical counties. The outlier review was
limited to LDV source types 21,31, and 32. Many rural counties have outliers for low-population source
types such as Transit 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 final year 2016 age distributions were then grouped using a population-weighted average of the
source type populations of each county in the representative county group. The resulting end-product was
age distributions for each of the 13 source types in each of the 332 representative counties for 2016v2.
The long-haul truck source types 53 (Single Unit) and 62 (Combination Unit) are based on a nationwide
average due to the long-haul nature of their operation.
To create the emission factors, MOVES3 was run separately for each representative county and fuel
month and for each temperature bin needed for calendar year 2016. The CDBs used to run MOVES
include the state-specific control measures such as the California low emission vehicle (LEV) program,
except that fuels were updated to represent calendar year 2016. In addition, the range of temperatures run
along with the average humidities used were specific to the year 2016. The MOVES results were post-
processed into CSV-formatted emission factor tables that can be read by SMOKE-MOVES.
2.3.3 Onroad California Inventory Development (onroad_ca)
The California Air Resources Board (CARB) provided their own onroad emissions inventories based on
their EMFAC2017 model. EMFAC2017 was run by CARB for the years 2016, 2023, 2028, and 2035.
These inventories each include separate totals for on-network and off-network, but do not include NH3 or
refueling. California emissions were run through SMOKE-MOVES as a separate sector from the rest of
the country. The California onroad sector is called "onroad ca adj". Changes from 2016vl include:
45
-------
1) CARB refueling was backcast from 2017NEI to 2016 using MOVES trends, and then SMOKE-
MOVES was adjusted to match the backcast refueling.
2) California NH3 was set to MOVES state total NH3, distributed to county-SCC following the
distribution of carbon monoxide (CO) as a surrogate for activity.
3) For vehicle types other than 62 where CARB provided "idling" emissions, those emissions were
mapped to ONI. For vehicle type 62, the CARB-provided "idling" was split between hoteling and
ONI. For all other vehicle types (where CARB did not provide "idling" - generally LD vehicles),
CARB running exhaust was split between RPD and ONI. Using the updated ONI activity has
some effect on distributions of CARB emissions and the non-CARB portion of the emissions (e.g.,
NH3).
2.4 2016 Nonroad Mobile sources (cmv, rail, nonroad)
The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions (nonroad),
locomotive (rail), and CMV emissions.
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
The 2016v2 CMV emissions are based on the emissions developed for the 2017 NEI and are the same as
those used in the 2016vl platform. Sulfur dioxide (S02) emissions reflect rules that reduced sulfur
emissions for CMV that took effect in the year 2015. The cmv_clc2 inventory sector contains small to
medium-size engine CMV emissions. Category 1 and Category 2 (C1C2) marine diesel engines typically
range in size from about 700 to 11,000 hp. These engines are used to provide propulsion power on many
kinds of vessels including tugboats, towboats, supply vessels, fishing vessels, and other commercial
vessels in and around ports. They are also used as stand-alone generators for auxiliary electrical power on
many types of vessels. Category 1 represents engines up to 7 liters per cylinder displacement. Category 2
includes engines from 7 to 30 liters per cylinder.
The cmv_clc2 inventory sector contains sources that traverse state and federal waters along with
emissions from surrounding areas of Canada, Mexico, and international waters. The cmv_clc2 sources
are modeled as point sources but using plume rise parameters that cause the emissions to be released in
the ground layer of the air quality model.
The cmv_clc2 sources within state waters are identified in the inventory with the Federal Information
Processing Standard (FIPS) county code for the state and county in which the vessel is registered. The
cmv_clc2 sources that operate outside of state waters but within the Emissions Control Area (ECA) are
encoded with a state FIPS code of 85. The ECA areas include parts of the Gulf of Mexico, and parts of
the Atlantic and Pacific coasts. The cmv_clc2 sources in the 2016 inventory are categorized as operating
either in-port or underway and as main and auxiliary engines are encoded using the SCCs listed in Table
2-22.
Table 2-22. 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
46
-------
Category 1 and 2 CMV emissions were developed for the 2017 NEI,3 The 2017 NEI emissions were
developed based signals from Automated Identification System (AIS) transmitters. AIS is a tracking
system used by vessels to enhance navigation and avoid collision with other AIS transmitting vessels.
The USEPA Office of Transportation and Air Quality received AIS data from the U.S. Coast Guard
(USCG) in order to quantify all ship activity which occurred between January 1 and December 31, 2017.
The provided AIS data extends beyond 200 nautical miles from the U.S. coast (Figure 2-4). This
boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the North
American ECA, although some non-ECA activity are captured as well.
Figure 2-4. 2017NEI/2016 platform geographical extent (solid) and U.S. ECA (dashed)
The AIS data were compiled into five-minute intervals by the USCG, providing a reasonably refined
assessment of a vessel's movement. For example, using a five-minute average, a vessel traveling at 25
knots would be captured every two nautical miles that the vessel travels. For slower moving vessels, the
distance between transmissions would be less. The ability to track vessel movements through AIS data
and link them to attribute data, has allowed for the development of an inventory of very accurate emission
estimates. These AIS data were used to define the locations of individual vessel movements, estimate
hours of operation, and quantify propulsion engine loads. The compiled AIS data also included the
vessel's International Marine Organization (IMO) number and Maritime Mobile Service Identifier
(MMSI); which allowed each vessel to be matched to their characteristics obtained from the Clarksons
ship registry (Clarksons, 2018).
USEPA used the engine bore and stroke data to calculate cylinder volume. Any vessel that had a
calculated cylinder volume greater than 30 liters was incorporated into the USEPA's new Category 3
Commercial Marine Vessel (C3CMV) model. The remaining records were assumed to represent Category
3 Category 1 and 2 Commercial Marine Vessel 2017 Emissions Inventory (ERG, 2019b).
47
-------
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.
g
Emissionsinterval = Time (hr)interval x Power(kW) x x 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 unitless factor which reflects increasing propulsive emissions
during low load operations. Time indicates the activity duration time between consecutive intervals.
Next, vessels were identified in order determine their vessel type, and thus their vessel group, power
rating, and engine tier information which are required for the emissions calculations. See the 2017 NEI
documentation for more details on this process. Following the identification, 108 different vessel types
were matched to the C1C2 vessels. Vessel attribute data was not available for all these vessel types, so the
vessel types were aggregated into 13 different vessel groups for which surrogate data were available as
shown in Table 2-23. 11,302 vessels were directly identified by their ship and cargo number. The
remaining group of miscellaneous ships represent 13 percent of the AIS vessels (excluding recreational
vessels) for which a specific vessel type could not be assigned.
Table 2-23. 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 Ro
26
Tanker
100
Tug
3,994
Work Boat
77
Total in Inventory:
11.302
48
-------
As shown in Equation 2-1, power is an important component of the emissions computation. Vessel-
specific installed propulsive power ratings and service speeds were pulled from Clarksons ship registry
and adopted from the Global Fishing Watch (GFW) dataset when available. However, there is limited
vessel specific attribute data for most of the C1C2 fleet. This necessitated the use of surrogate engine
power and load factors, which were computed for each vessel group shown in Table 2. 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 2-22.
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
Inventory.4 Hazardous air pollutants and ammonia were added to the inventory according to multiplicative
factors applied either to VOC or PM2.5.
For more information on the emission computations for 2017, see the supporting documentation for the
2017 NEI C1C2 CMV emissions. The emissions from the 2017 NEI were adjusted to represent 2016 in
the cmv_clc2 sector using factors derived from U.S. Army Corps of Engineers national vessel Entrance
and Clearance data5 by applying a factor of 0.98 to all pollutants (based on EIA fuel use data). For
consistency, the same methods were used for California, Canadian, and other non-U.S. emissions. The
2017 emissions were mapped to 2016 dates so that the activity occurred on the same day of the week in
the same sequential week of the year in both years. Emissions that occurred on a federal holiday in 2017
were mapped to the same holiday on the corresponding 2016 date. Individual vessels that released
4 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.
5 U.S. Army Corps of Engineers [USACE], Foreign Waterborne Transportation: Foreign Cargo Inbound and Outbound
Vessel Entrances and Clearances. US Army Corps of Engineers, 2018.
49
-------
emissions within the same grid cell for over 400 hours were flagged as hoteling. The emissions from the
hoteling vessels were scaled to the 400-hour cap.
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3)
The cmv_c3 inventory are the same as those in the 2016vl platform and were developed in conjunction
with the CMV inventory for the 2017 NEI. This sector contains large engine CMV emissions. Category 3
(C3) marine diesel engines are those at or above 30 liters per cylinder, typically these are the largest
engines rated at 3,000 to 100,000 hp. C3 engines are typically used for propulsion on ocean-going vessels
including container ships, oil tankers, bulk carriers, and cruise ships. Emissions control technologies for
C3 CMV sources are limited due to the nature of the residual 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.
The cmv_c3 sources that operate outside of state waters but within the federal Emissions Control Area
(ECA) are encoded with a FIPS state code of 85, with the "county code" digits representing broad regions
such as the Atlantic, Gulf of Mexico, and Pacific. The ECA areas include parts of the Gulf of Mexico,
and parts of the Atlantic and Pacific coasts. CMV C3 sources around Puerto Rico, Hawaii and Alaska,
which are outside the ECA areas, are included in the 2016vl inventory but are in separate files from the
emissions around the continental United States (CONUS). The cmv_c3 sources in the 2016v2 inventory
are categorized as operating either in-port or underway and are encoded using the SCCs listed in Table
2-24. 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.7 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 2-24. SCCs for cmv c3 sector
sec
Tier 1 Description
l ii'i-2 Description
Tier 3 Dcscriplion
Tier 4 Dcscriplion
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
6 https://www.epa.gov/regulations-emissions-vehicles-and-engines/regulations-emissions-marine-vessels.
7 https://www.epa.gOv/sites/production/files/2017-08/documents/2014v7.0 2014 emismod tsdvl.pdf.
50
-------
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.8 In addition, the USCG has mandated
that all commercial marine vessels continuously transmit AIS signals while transiting U.S. navigable
waters. As the vast majority of C3 vessels meet these requirements, any omitted from the inventory due to
lack of AIS adoption are deemed to have a negligible impact on national C3 emissions estimates. The
activity described by this inventory reflects ship operations within 200 nautical miles of the official U.S.
baseline. This boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the
North American ECA, although some non-ECA activity is captured as well (Figure 2-4).
The 2017 NEI data were computed based on the AIS data from the USGS for the year of 2017. The AIS
data were coupled with ship registry data that contained engine parameters, vessel power parameters, and
other factors such as tonnage and year of manufacture which helped to separate the C3 vessels from the
C1C2 vessels. Where specific ship parameters were not available, they were gap-filled. The types of
vessels that remain in the C3 data set include bulk carrier, chemical tanker, liquified gas tanker, oil tanker,
other tanker, container ship, cruise, ferry, general cargo, fishing, refrigerated vessel, roll-on/roll-off, tug,
and yacht.
Prior to use, the AIS data were reviewed - data deemed to be erroneous were removed, and data found to
be at intervals greater than 5 minutes were interpolated to ensure that each ship had data every five
minutes. The five-minute average data provide a reasonably refined assessment of a vessel's movement.
For example, using a five-minute average, a vessel traveling at 25 knots would be captured every two
nautical miles that the vessel travels. For slower moving vessels, the distance between transmissions
would be less.
The emissions were calculated for each C3 vessel in the dataset for each 5-minute time range and
allocated to the location of the message following to the interval. Emissions were calculated according to
Equation 2-2.
g
Emissionsinterval = Time (hr)interval x Power(kW) x x LLAF Equation 2-2
Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and
emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF
represents the low load adjustment factor, a unitless factor which reflects increasing propulsive emissions
during low load operations. Time indicates the activity duration time between consecutive intervals.
Emissions were computed according to a computed power need (kW) multiplied by the time (hr) and by
an engine-specific emission factor (g/kWh) and finally by a low load adjustment factor that reflects
increasing propulsive emissions during low load operations.
8 International Maritime Organization (IMO) Resolution MSC.99(73) adopted December 12th. 2000 and entered into force July
1st, 2002; as amended by SOLAS Resolution CONF.5/32 adopted December 13th, 2002.
51
-------
The resulting emissions were available at 5-minute intervals. Code was developed to aggregate these
emissions to modeling grid cells and up to hourly levels so that the emissions data could be input to
SMOKE for emissions modeling with SMOKE. Within SMOKE, the data were speciated into the
pollutants needed by the air quality model,9 but since the data were already in the form of point sources at
the center of each grid cell, and they were already hourly, no other processing was needed within
SMOKE. SMOKE requires an annual inventory file to go along with the hourly data, so 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 Environment Canada and the 2011 ECA-IMO C3
inventory.
For the gap-filled areas not covered by AIS selections or the Environment Canada inventory, the 2016
nonpoint C3 inventory was converted to a point inventory to support plume rise calculations for C3
vessels. The nonpoint emissions were allocated to point sources using a multi-step allocation process
because not all of the inventory components had a complete set of county-SCC combinations. In the first
step, the county-SCC sources from the nonpoint file were matched to the county-SCC points in the 2011
ECA-IMO C3 inventory. The ECA-IMO inventory contains multiple point locations for each county-
SCC. The nonpoint emissions were allocated to those points using the PM2.5 emissions at each point as a
weighting factor.
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
9 Ammonia (NH3) was also added by SMOKE in the speciation step.
52
-------
was 539.6 °F, and the velocity was 82.02 ft/s. Emissions were computed for each grid cell needed for
modeling.
Adjustment of the 2017 NEI CMV C3 to 2016
Because the NEI emissions data were for 2017, an analysis was performed of 2016 versus 2017 entrance
and clearance data (ERG, 2019c). Annual, monthly, and daily level data were reviewed. Annual ratios of
entrance and clearance activity were developed for each ship type as shown in Table 2-25. For vessel
types with low populations (C3 Yacht, tug, barge, and fishing vessels), an annual ratio of 0.98 was
applied. The 2017 emissions were mapped to 2016 dates so that the activity occurred on the same day of
the week in the same sequential week of the year in both years. Emissions that occurred on a federal
holiday in 2017 were mapped to the same holiday on the corresponding 2016 date. Individual vessels that
released emissions within the same grid cell for over 400 hours were flagged as hoteling. The emissions
from the hoteling vessels were scaled to the 400-hour cap.
Table 2-25. 2017 to 2016 projection factors for C3 CMV
Ship Type
Annual Ratio"
Barge
1.551
Bulk Carrier
1.067
Chemical Tanker
1.031
Container Ship
1.0345
Cruise
1.008
Ferry Ro Pax
1.429
General Cargo
0.888
Liquified Gas Tanker
1.192
Miscellaneous Fishing
0.932
Miscellaneous Other
1.015
Offshore
0.860
Oil Tanker
1.101
Other Tanker
1.037
Reefer
0.868
Ro Ro
1.007
Service Tug
1.074
a Above ratios were applied to the 2017 emission values to estimate 2016 values
The cmv_c3 projection factors were pollutant-specific and region-specific. Most states are mapped to a
single region with a few exceptions. Pennsylvania and New York were split between the East Coast and
Great Lakes, Florida was split between the Gulf Coast and East Coast, and Alaska was split between
Alaska East and Alaska West. The non-federal factors listed in this table were applied to sources outside
of U.S. federal waters (FIPS 98). Volatile Organic Compound (VOC) Hazardous Air Pollutant (HAP)
53
-------
emissions were projected using the VOC factors. NH3 emissions were computed by multiplying PM2.5
by 0.019247.
2.4.3 Railway Locomotives (rail)
There were no changes to the rail sector emissions inventories between 2016vl and 2016v2 aside from
updating emissions for seven rail yards in Georgia. The rail sector includes all locomotives in the NEI
nonpoint data category. The 2016vl inventory SCCs are shown in Table 2-26. This sector excludes
railway maintenance activities. Railway maintenance emissions are included in the nonroad sector. The
point source yard locomotives are included in the ptnonipm sector. In 2014NEIv2, rail yard locomotive
emissions were present in both the nonpoint (rail sector) and point (ptnonipm sector) inventories. For the
2016vl and 2016v2 platforms, rail yard locomotive emissions are only in the point inventory / ptnonipm
sector. Therefore, SCC 2285002010 is not present in the 2016vl platform rail sector, except in three
California counties. The California Air Resources Board (CARB) submitted rail emissions, including rail
yards, for 2016vl platform. In three counties, CARB's rail yard emissions could not be mapped to point
source rail yards, and so those counties' emissions were included in the rail sector.
Table 2-26. 2016vl SCCs for the Rail Sector
SCC
Sector
Description: Mobile Sources prefix lor all
2285002006
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
2285002007
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
2285002008
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains
(Amtrak)
2285002009
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
2285002010
rail
Railroad Equipment; Diesel; Yard Locomotives (nonpoint)
28500201
rail
Railroad Equipment; Diesel; Yard Locomotives (point)
Class I Line-haul Methodology
In 2008 air quality planners in the eastern US formed the Eastern Technical Advisory Committee
(ERTAC) for solving persistent emissions inventory issues. This work is the fourth inventory created by
the ERTAC rail group. For the 2016 inventory, the Class I railroads granted ERTAC Rail permission to
use the confidential link-level line-haul activity GIS data layer maintained by the Federal Railroad
Administration (FRA). In addition, the Association of American Railroads (AAR) provided national
emission tier fleet mix information. This allowed ERTAC Rail to calculate weighted emission factors for
each pollutant based on the percentage of the Class I line-haul locomotives in each USEPA Tier level
category. These two datasets, along with 2016 Class I line-haul fuel use data reported to the Surface
Transportation Board (Table 2-27), 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.
54
-------
Table 2-27. Class I Railroad Reported Locomotive Fuel Use Statistics for 2016
Class I Railroads
2016 R-l Reported Locomotive
Fuel Use (gal/year)
RFCI
(ton-miles/gal)
Adjusted
RFCI
(ton-miles/gal)
Line-Haul*
Switcher
BNSF
1,243,366,255
40,279,454
972
904
Canadian National
102,019,995
6,570,898
1,164
1,081
Canadian Pacific
56,163,697
1,311,135
1,123
1,445
CSX Transportation
404,147,932
39,364,896
1,072
1,044
Kansas City
Southern
60,634,689
3,211,538
989
995
Norfolk Southern
437,110,632
28,595,955
920
906
Union Pacific
900,151,933
85,057,080
1,042
1,095
Totals:
3,203,595,133
204,390,956
1,006
993
* Includes work trains; Adjusted RFCI values calculated from FRA gross ton-mile data. RFCI total is ton-mile weighted mean.
Annual default emission factors for locomotives based on operating patterns ("duty cycles") and the
estimated nationwide fleet mixes for both switcher and line-haul locomotives are available. However,
Tier level fleet mixes vary significantly between the Class I and Class II/III railroads. As can be seen in
Figure 2-5 and Figure 2-6, Class I railroad activity is highly regionalized in nature and is subject to
variations in terrain across the country which can have a significant impact on fuel efficiency and overall
fuel consumption.
Figure 2-5. 2016 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT)
Traffic Density
0.02 - 4.99 MGT
5.00 - 9.99 MGT
10.00 -19.99 MGT
20.00 - 39.99 MGT
40.00 - 59.99 MGT
60.00 - 99.99 MGT
55
-------
Figure 2-6. Class I Railroads in the United States5
For the 2016 inventory, the AAR provided a national line-haul Tier fleet mix profile representing the
entire Class I locomotive fleet. A locomotive's Tier level determines its allowable emission rates based
on the year when it was built and/or re-manufactured. The national fleet mix data was then used to
calculate weighted average in-use emissions factors for the line-haul locomotives operated by the Class I
railroads as shown in Table 2-28.
Table 2-28. 2016 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal)
AAR
Tier Level
Fleet Mix
Ratio
PMio
HC
NOx
CO
Uncontrolled (pre-1973)
0.047494
6.656
9.984
270.4
26.624
Tier 0 (1973-2001)
0.188077
6.656
9.984
178.88
26.624
Tier 0+ (Tier 0 rebuilds)
0.141662
4.16
6.24
149.76
26.624
Tier 1 (2002-2004)
0.029376
6.656
9.776
139.36
26.624
Tier 1 + (Tier 1 rebuilds)
0.223147
4.16
6.032
139.36
26.624
Tier 2 (2005-2011)
0.124536
3.744
5.408
102.96
26.624
Tier 2+ (Tier 2 rebuilds)
0.093607
1.664
2.704
102.96
26.624
Tier 3 (2012-2014)
0.123113
1.664
2.704
102.96
26.624
Tier 4 (2015 and later)
0.028988
0.312
0.832
20.8
26.624
2016 Weighted EF's
1.000000
4.117
6.153
138.631
26.624
Based on values in EPA Technical Highlights: Emission Factors for Locomotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April
2009.
Weighted Emission Factors (EF) per pollutant for each gallon of fuel used (grams/gal or lbs/gal) were
calculated for the US Class I locomotive fleet based on the percentage of line-haul locomotives certified
at each regulated Tier level (Equation 2-3).
56
-------
EFt — ^ EFiT X fT
7=1
Equation 2-3
where:
EFi = Weighted Emission Factor for pollutant i for Class I locomotive fleet (g/gal).
EFiT = Emission Factor for pollutant i for locomotives in Tier T (g/gal).
fr = Percentage of the Class I locomotive fleet in Tier T expressed as a ratio.
While actual engine emissions will vary within Tier level categories, the approach described above likely
provides reasonable emission estimates, as locomotive diesel engines are certified to meet the emission
standards for each Tier. It should be noted that actual emission rates may increase over time due to
engine wear and degradation of the emissions control systems. In addition, locomotives may be operated
in a manner that differs significantly from the conditions used to derive line-haul duty-cycle estimates.
Emission factors for other pollutants are not Tier-specific because these pollutants are not directly
regulated by USEPA's locomotive emission standards. PM2.5 was assumed to be 97% of PM10, the ratio
of volatile organic carbon (VOC) to (hydrocarbon) HC was assumed to be 1.053, and the emission factors
used for sulfur dioxide (SO2) and ammonia (NH3) were 0.0939 g/gal and 83.3 mg/gal, respectively. The
2016 SO2 emission factor is based on the nationwide adoption of 15 ppm ultra-low sulfur diesel (ULSD)
fuel by the rail industry.
The remaining steps to compute the Class 1 rail emissions involved calculating class I railroad-specific
rail fuel consumption index values and calculating emissions per link. The final link-level emissions for
each pollutant were then aggregated by state/county FIPS code and then converted into an FF10 file
format for input to SMOKE. More detail on these steps is described in the specification sheet for the
2016vl rail sector emissions.
Rail yard Methodology
Rail yard emissions were computed based on fuel use and/or yard switcher locomotive counts for the class
I rail companies for all of the rail yards on their systems. Three railroads provided complete rail yard
datasets: BNSF, UP, and KCS. CSX provided switcher counts for its 14 largest rail yards. This reported
activity data was matched to existing yard locations and data stored in USEPA's Emissions Inventory
System (EIS) database. All existing EIS yards that had activity data assigned for prior years, but no
reported activity data for 2016 were zeroed out. New yard data records were generated for reported
locations that were not found in EIS. Special care was made to ensure that the new yards added to EIS
did not duplicate existing data records. Data for non-Class I yards was carried forward from the 2014
NEI. Georgia provided updates on seven rail yards that were incorporated into 2016v2.
Since the railroads only supplied switcher counts, average fuel use per switcher values was calculated for
each railroad. This was done by dividing each company's 2016 R-l yard fuel use total by the number of
switchers reported for each railroad. These values were then used to allocate fuel use to each yard based
on the number of switchers reported for that location. Table 2-29 summarizes the 2016 yard fuel use and
-------
switcher data for each Class I railroad. The emission factors used for rail yard switcher engines are
shown in Table 2-30.
Table 2-29. Surface Transportation Board R-l Fuel Use Data - 2016
Kiiili'Oiid
2111(. IM Yard
I lK'l I NC (liilh
i:rt.\( ciilculiiicd
I'lK'l I SO (iiill)
Identified
Sm iichoi's
r.K 1 AC per Swilrhcr l-'ucl
I so i«sil)
BNSF
40,279,454
40,740,317
442
92,173
CSXT
39,364,896
43,054,795
455
94,626
CN
6,570,898
6,570,898
103
63,795
KCS
3,211,538
3,211,538
176
18,247
NS
28,595,955
28,658,528
458
62,573
CPRS
1,311,135
1,311,135
70
18,731
UP
85,057,080
85,057,080
1286
66,141
All Class I's
204,390,956
208,604,291
2,990
69,767
Table 2-30. 2016 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)4
Tier Level
AAU l leel
Mix Ualio
I'M in
IK
NOx
CO
Uncontrolled (pre-1973)
0.2601
6.688
15.352
264.48
27.816
Tier 0(1973-2001)
0.2361
6.688
15.352
191.52
27.816
Tier 0+ (Tier 0 rebuilds)
0.2599
3.496
8.664
161.12
27.816
Tier 1 (2002-2004)
0.0000
6.536
15.352
150.48
27.816
Tier 1+ (Tier 1 rebuilds)
0.0476
3.496
8.664
150.48
27.816
Tier 2 (2005-2011)
0.0233
2.888
7.752
110.96
27.816
Tier 2+ (Tier 2 rebuilds)
0.0464
1.672
3.952
110.96
27.816
Tier 3 (2012-2014)
0.1018
1.216
3.952
68.4
27.816
Tier 4 (2015 and later)
0.0247
0.228
1.216
15.2
27.816
2016 Weighted EF's
0.9999
4.668
11.078
178.1195
27.813
Based on values in EPA Technical Highlights: Emission Factors for Locomotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April
2009. AAR fleet mix ratios did not add up to 1.0000, which caused a small error for the CO weighted emission factor as shown above.
In addition to the Class I rail yards, Emission estimates were calculated for four large Class III railroad
hump yards which are among the largest classification facilities in the United States. These four yards are
located in Chicago (Belt Railway of Chicago-Clearing and Indiana Harbor Belt-Blue Island) and Metro-
East St. Louis (Alton & Southern-Gateway and Terminal Railroad Association of St. Louis-Madison).
Figure 2-7 shows the spatial distribution of active yards in the 2016vl and 2017 NEI inventories.
58
-------
Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States
FwfcraJ RaA-curJ AArmn»Vatah
Class II and III Methodology
There are approximately 560 Class II and 111 Railroads operating in the United States, most of which are
members of the American Short Line and Regional Railroad Association (ASLRRA). While there is a lot
of information about individual Class II and III railroads available online, a significant amount of effort
would be required to convert this data into a usable format for the creation of emission inventories. In
addition, the Class II and III rail sector has been in a constant state of flux ever since the railroad industry
was deregulated under the Staggers Act in 1980. Some states have conducted independent surveys of
their Class II and III railroads and produced emission estimates, but no national level emissions inventory
existed for this sector of the railroad industry prior to ERTAC Rail's work for the 2008 NEI.
Class II and III railroad activities account for nearly 4 percent of the total locomotive fuel use in the
combined ERTAC Rail emission inventories and for approximately 35 percent of the industry's national
freight rail track mileage. These railroads are widely dispersed across the country and often utilize older,
higher emitting locomotives than their Class I counterparts Class II and III railroads provide
transportation services to a wide range of industries. Individual railroads in this sector range from small
switching operations serving a single industrial plant to large regional railroads that operate hundreds of
miles of track. Figure 2-8 shows the distribution of Class II and III railroads and commuter railroads
across the country. This inventory will be useful for regional and local modeling, helps identify where
Class II and III railroads may need to be better characterized, and provides a strong foundation for the
59
-------
future development of a more accurate nationwide short line and regional railroad emissions inventory. A
picture of the locations of class II and III railroads is shown in Figure 2-8. The data sources, calculations,
and assumptions used to develop the Class II and III inventory are described in the 2016vl rail
specification sheet.
Figure 2-8. Class II and III Railroads in the United States3
Commuter Rail Methodology
Commuter rail emissions were calculated in the same way as the Class II and III railroads. The primary
difference is that the fuel use estimates were based on data collected by the Federal Transit
Administration (FTA) for the National Transit Database. 2016 fuel use was then estimated for each of the
commuter railroads shown in Table 2-31 by multiplying the fuel and lube cost total by 0.95, then dividing
the result by Metra's average diesel fuel cost of $1.93/gallon. These fuel use estimates were replaced
with reported fuel use statistics for MARC (Maryland), MBTA (Massachusetts), Metra (Illinois), and NJT
(New Jersey). The commuter railroads were separated from the Class II and III railroads so that the
appropriate SCC codes could be entered into the emissions calculation sheet.
Table 2-31. Expenditures and fuel use for commuter rail
IRA
Code
System
Cities Served
Propulsion
Type
DOT Fuel &
Lube Costs
Reported/Estimated
Fuel Use
ACEX
Altamont Corridor
Express
San Jose / Stockton
Diesel
$889,828
437,998.24
CV1RX
Capital MetroRail
Austin
Diesel
No data
n/a
DART
A-Train
Denton
Diesel
$0
0.00
60
-------
1 R\
Code
S\slem
Chios Sen I'd
Propulsion
1 > |H'
DOT 1 ik'I \
1 -iiho Cosls
Ki'pnrk'd/r.MiniiiU'd
1 m l I so
DRTD
Denver RTD: A&B
Lines
Denver
Electric
$0
0.00
JPBX
Caltrain
San Francisco / San Jose
Diesel
$7,002,612
3,446,881.55
LI
MTA Long Island Rail
Road
New York
Electric and
Diesel
$13,072,158
6,434,481.92
MARC
MARC Train
Baltimore / Washington, D.C.
Diesel and
Electric
$4,648,060
4.235.297.57
MBTA
MBTA Commuter Rail
Boston / Worcester / Providence
Diesel
$37,653,001
12.142.826.00
MNCW
MTA Metro-North
Railroad
New York / Yonkers / Stamford
Electric and
Diesel
$13,714,839
6,750,827.49
NICD
NICTD South Shore
Line
Chicago / South Bend
Electric
$181,264
0.00
NIRC
Metra
Chicago
Diesel and
Electric
$52,460,705
25.757.673.57
NJT
New Jersey Transit
New
York / Newark / Trenton / Philadelphia
Electric and
Diesel
$38,400,031
16.991.164.00
NMRX
New Mexico Rail
Runner
Albuquerque / Santa Fe
Diesel
$1,597,302
786,236.74
CFCR
SunRail
Orlando
Diesel
$856,202
421,446.58
MNRX
Northstar Line
Minneapolis
Diesel
$708,855
348,918.26
Not
Coded
SMART
San Rafael-Santa Rosa (Opened 2017)
Diesel
n/a
0.00
NRTX
Music City Star
Nashville
Diesel
$456,099
224,504.69
SCAX
Metrolink
Los Angeles / San Bernardino
Diesel
$19,245,255
9,473,052.98
SDNR
NCTD Coaster
San Diego / Oceanside
Diesel
$1,489,990
733,414.77
SDRX
Sounder Commuter
Rail
Seattle / Tacoma
Diesel
$1,868,019
919,491.22
SEPA
SEPTA Regional Rail
Philadelphia
Electric
$483,965
0.00
SLE
Shore Line East
New Haven
Diesel
No data
n/a
TCCX
Tri-Rail
Miami / Fort Lauderdale / West Palm
Beach
Diesel
$5,166,685
2,543,186.92
TREX
Trinity Railway
Express
Dallas / Fort Worth
Diesel
No data
n/a
UTF
UTA FrontRunner
Salt Lake City / Provo
Diesel
$4,044,265
1,990,700.39
VREX
Virginia Railway
Express
Washington, D.C.
Diesel
$3,125,912
1,538,661.35
WSTX
Westside Express
Service
Beaverton
Diesel
No data
n/a
*Reported fuel use values were used for MARC, MBTA, Metra, and New Jersey Transit.
61
-------
Intercity Passenger Methodology (Amtrak)
2016 marked the first time that a nationwide intercity passenger rail emissions inventory was created for
Amtrak. The calculation methodology mimics that used for the Class II and III and commuter railroads
with a few modifications. Since link-level activity data for Amtrak was unavailable, the default
assumption was made to evenly distribute Amtrak's 2016 reported fuel use across all of it diesel-powered
route-miles shown in Figure 2-9. Participating states were instructed that they could alter the fuel use
distribution within their jurisdictions by analyzing Amtrak's 2016 national timetable and calculating
passenger train-miles for each affected route. Illinois and Connecticut chose to do this and were able to
derive activity-based fuel use numbers for their states based on Amtrak's 2016 reported average fuel use
of 2.2 gallons per passenger train-mile. In addition, Connecticut provided supplemental data for selected
counties in Massachusetts, New Hampshire, and Vermont. Amtrak also submitted company-specific fleet
mix information and company-specific weighted emission factors were derived. Amtrak's emission rates
were 25% lower than the default Class II and III and commuter railroad emission rate. Details on the
computation of the Amtrak emissions are available in the rail specification sheet.
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains
Sort* FaMMndtamtoton < J«»l*
Other Data Sources
The California Air Resources Board (CARB) provided rail inventories for inclusion in the 2016vl
platform. CARB's rail inventories were used in California, in place of the national dataset described
above. For rail yards, the national point source rail yard dataset was used to allocate CARB-submitted rail
yard emissions to point sources where possible. That is, for each California county with at least one rail
yard in the national dataset, the emissions in the national rail yard dataset were adjusted so that county
62
-------
total rail yard emissions matched the CARB dataset. In other words, 2016vl and 2016v2 platforms
include county total rail yard emissions from CARB, but the locations of rail yards are based on the
national methodology. There are three counties with CARB-submitted rail yard emissions, but no rail
yard locations in the national dataset; for those counties, the rail yard emissions were included in the rail
sector using SCC 2285002010.
North Carolina separately provided passenger train (SCC 2285002008) emissions for use in the platform.
We used NC's passenger train emissions instead of the corresponding emissions from the Lake Michigan
Air Directors Consortium (LADCO) dataset.
None of these rail inventory sources included HAPs. For VOC speciation, the EPA preferred augmenting
the inventory with HAPs and using those HAPs for integration, rather than running the sector as a no-
integrate sector. So, Naphthalene, Benzene, Acetaldehyde, Formaldehyde, and Methanol (NBAFM)
emissions were added to all rail inventories, including the California inventory, using the same
augmentation factors as are used to augment HAPs in the NEI.
2.4.4 Nonroad Mobile Equipment (nonroad)
The mobile nonroad equipment sector includes all mobile source emissions that do not operate on roads,
excluding commercial marine vehicles, railways, and aircraft. Types of nonroad equipment include
recreational vehicles, pleasure craft, and construction, agricultural, mining, and lawn and garden
equipment. Nonroad equipment emissions were computed by running the MOVES3,10 which incorporates
the NONROAD model. MOVES3 and its predecessor MOVES2014b incorporated updated nonroad
engine population growth rates, nonroad Tier 4 engine emission rates, and sulfur levels of nonroad diesel
fuels. MOVES3 provides a complete set of HAPs and incorporates updated nonroad emission factors for
HAPs. MOVES3 was used for all states other than California and Texas, which developed their own
emissions using their own tools. VOC and PM speciation profile assignments are determined by MOVES
and applied by SMOKE. The fuels data in MOVES3 for nonroad vehicles is slightly updated from the
MOVES2014b fuels for nonroad vehicles.
MOVES3 provides estimates of NONHAPTOG along with the speciation profile code for the
NONHAPTOG emission source. This was accomplished by using NHTOG#### as the pollutant code in
the Flat File 2010 (FF10) inventory file that can be read into SMOKE, where #### is a speciation profile
code. One of the speciation profile codes is '95335a' (lowercase 'a'); the corresponding inventory
pollutant is NONHAPTOG95335A (uppercase 'A') because SMOKE does not support inventory
pollutant names with lowercase letters. Since speciation profiles are applied by SCC and pollutant, no
changes to SMOKE were needed to use the inventory file with this profile information. This approach
was not used for California or Texas, because the datasets in those states included VOC.
MOVES3, also provides estimates of PM2.5 by speciation profile code for the PM2.5 emission source,
using PM25_#### as the pollutant code in the FF10 inventory file, where #### is a speciation profile
code. To facilitate calculation of coarse particulate matter (PMC) within SMOKE, and to help create
emissions summaries, an additional pollutant representing total PM2.5 called PM25TOTAL was added to
the inventory. As with VOC / TOG, this approach is not used for California or Texas.
10 https://www.epa.gov/moves.
63
-------
M0VES3 outputs emissions data in county-specific databases, and a post-processing script converts the
data into FF10 format. Additional post-processing steps were performed as follows:
• County-specific FFlOs were combined into a single FF10 file.
• Emissions were aggregated from the more detailed SCCs modeled in MOVES to the SCCs
modeled in SMOKE. A list of the aggregated SMOKE SCCs is in Appendix A of the 2016vl
nonroad specification sheet.
• To reduce the size of the inventory, HAPs that are not needed for air quality modeling, such as
dioxins and furans, were removed from the inventory.
• To reduce the size of the inventory further, all emissions for sources (identified by county/SCC)
for which total CAP emissions are less than 1*10"10 were removed from the inventory. The
MOVES model attributes a very tiny amount of emissions to sources that are actually zero, for
example, snowmobile emissions in Florida. Removing these sources from the inventory reduces
the total size of the inventory by about 7%.
• Gas and particulate components of HAPs that come out of MOVES separately, such as
naphthalene, were combined.
• VOC was renamed VOC INV so that SMOKE does not speciate both VOC and NONHAPTOG,
which would result in a double count.
• PM25TOTAL, referenced above, was also created at this stage of the process.
• California and Texas emissions from MOVES were deleted and replaced with the CARB- and
TCEQ-supplied emissions, respectively.
Emissions for airport ground support vehicles (SCCs ending in -8005), and oil field equipment (SCCs
ending in -10010), were removed from the mobile nonroad inventory, to prevent a double count with the
ptnonipm and np oilgas sectors, respectively.
National Updates: Agricultural and Construction Equipment Allocation
The methodology for developing Agricultural equipment allocation data for the 2016vl platform was
developed by the North Carolina Department of Environmental Quality (NCDEQ). EPA updated the
Construction equipment allocation data used in MOVES for the 2016vl platform and the same updated
data were used in the 2016v2 platform.
NCDEQ compiled regional and state-level Agricultural sector fuel expenditure data for 2016 from the US
Department of Agriculture, National Agricultural Statistics Service (NASS), August 2018 publication,
"Farm Production Expenditures 2017 Summary."11 This resource provides expenditures for each of 5
major regions that cover the Continental U.S., as well as state-level data for 15 major farm producing
states. Because of the limited coverage of the NASS source relative to that in MOVES, it was necessary to
identify a means for estimating the 2016 Agricultural sector allocation data for the following States and
Territories from a different source: Alaska, Hawaii, Puerto Rico, and U.S. Virgin Islands. The approach
for these areas is described below.
11 Accessed from http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1066. November 2018.
64
-------
For the Continental U.S., NCDEQ first allocated the remainder of the regional fuel expenditures to states
in each region for which state-level data are not reported. For this allocation, NCDEQ relied on 2012 fuel
expenditure data from NASS' 2012 Census of Agriculture (note that 2017 data were not yet available at
the time of this effort).12 The next step to developing county-level allocation data for agricultural
equipment was to multiply the state-level fuel expenditure estimates by county-level allocation ratios.
These allocation ratios were computed from county-level fuel expenditure data from the NASS' 2012
Census of Agriculture. There were 17 counties for which fuel expenditure data were withheld in the
Census of Agriculture. For these counties, NCDEQ allocated the fuel expenditures that were not
accounted for in the applicable state via a surrogate indicator of fuel expenditures. For most states, the
2012 Census of Agriculture's total machinery asset value was the surrogate indicator used to perform the
allocation. This indicator was found to have the strongest correlation to agricultural sector fuel
expenditures based on analysis of 2012 state-level Census of Agriculture values for variables analyzed
(correlation coefficient of 0.87).13 Because the analyzed surrogate variables were not available for the two
counties in New York without fuel expenditure data, farm sales data from the 2012 Census of Agriculture
were used in the allocation procedure for these counties.
For Alaska and Hawaii, NCDEQ estimated 2016 state-level fuel production expenditures by first applying
the national change in fuel expenditures between 2012 and 2016 from NASS' "Farm Production
Expenditures" summary publications to 2012 state expenditure data from the 2012 Census of Agriculture.
Next, NCDEQ applied an adjustment factor to account for the relationship between national 2012 fuel
expenditures as reported by the Census of Agriculture and those reported in the Farm Production
Expenditures Summary. Hawaii's state-level fuel expenditures were allocated to counties using the same
approach as the states in the Continental U.S. (i.e., county-level fuel expenditure data from the NASS'
2012 Census of Agriculture). Alaska's fuel expenditures total was allocated to counties using a different
approach because the 2012 Census of Agriculture reports fuel expenditures data for a different list of
counties than the one included in MOVES. To ensure consistency with MOVES, NCDEQ allocated
Alaska's fuel expenditures based on the current allocation data in MOVES, which reflect 2002 harvested
acreage data from the Census of Agriculture.
Because NCDEQ did not identify any source of fuel expenditures data for Puerto Rico or the U.S. Virgin
Islands, the county allocation percentages that are represented by the 2002 MOVES allocation data were
used for these territories.14
For the Construction sector, by default MOVES2014b used estimates of 2003 total dollar value of
construction by county to allocate national Construction equipment populations to the state and local
levels.15 However, the 2016 Nonroad Collaborative Work Group sought to update the surrogate data used
to geographically allocate Construction equipment with a more recent data source thought to be more
reflective of emissions-generating Construction equipment activity at the county level: acres disturbed by
residential, non-residential, and road construction activity.
The nonpoint sector of the National Emissions Inventory (NEI) includes estimates of Construction Dust
(PM2.5), for which acreage disturbed by residential, non-residential, and road construction activity is a
12 Accessed from https://www.nass.usda.gov/Publications/AgCensus/2012/. November 2018.
13 Other variables analyzed were inventory of tractors and inventory of trucks.
14 For reference, these allocations were 0.0639 percent for Puerto Rico and 0.0002 percent for the U.S. Virgin Islands.
15 https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P1004LDX.pdf.
65
-------
function.16 The 2017 NEI Technical Support Document (EPA, 2021) includes a description of the
methods used to estimate acreage disturbed at the county level by residential, non-residential, and road
construction activity, for the 50 states.
Acreage disturbed by residential, non-residential, and road construction were summed together to arrive at
a single value of acreage disturbed by Construction activities at the county level. County-level acreage
disturbed were then summed together to arrive at acreage disturbed at the state level. State totals were
then summed to arrive at a national total of acreage disturbed by Construction activities.
Puerto Rico and the U.S. Virgin Islands are not included in the Construction equipment geographic
allocation update, so their relative share of the national population of Construction equipment remains the
same as MOVES2014b defaults.
For both the Agricultural and Construction equipment sectors, the surrogatequant and surrogateyearlD
fields in the model's nrstatesurrogate table, which allocates equipment from the state- to the county-level,
were populated with the county-level surrogates described above (fuel expenditures in 2016 for
Agricultural equipment; acreage disturbed by construction activity in 2014 for Construction equipment).
In addition, the nrbaseyearequippopulation table, which apportions the model's national equipment
populations to the state level, was adjusted so that each state's share of the MOVES base-year national
populations of Agricultural and Construction equipment is proportional to each state's share of national
acreage disturbed by construction activity (Construction equipment) and agricultural fuel expenditures
(Agricultural equipment). Additionally, the model"s nrsurrogate table, which defines the surrogate data
used in the nrstatesurrogate table, was updated to reflect the 2016vl changes to the Agricultural and
Construction equipment sectors.
Updated nrsurrogate, nrstatesurrogate, and nrbaseyearequippopulation tables, along with instructions for
utilizing these tables in MOVES runs, are available for download from EPA's ftp site:
ftp://newftp.epa.gov/air/emismod/2016/vl/reports/nonroad/ or at
https://gaftp.epa.gov/air/emismod/2016/vl/reports/nonroad/).
State-Supplied Nonroad Data
As shown Table 2-32. several state and local agencies provided nonroad inputs for use in the 2016vl
platform that were carried forward into the 2016v2 platform. Additionally, per the table footnotes, EPA
reviewed data submitted by state and local agencies for the 2014 and 2017 National Emissions Inventories
and utilized that information where appropriate (data specific to calendar years 2014 and 2017 were not
used in 2016vl). The nrfuelsupply table from MOVES3 was used in 2016v2 and is therefore not shown in
this table.
16 https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventorv-nei-data.
66
-------
Table 2-32. Submitted nonroad input tables by agency
sliiloid
Sliiio or
( uuiiMics) in
(ho A»enc\
=
— /.
= =
II
s -
— 0
5
C' 0
2 w
z
'j
a.
r —•
•3
3 -a
2
- :J
=
^ —
ZJ ?
.= 1
— _
i 2
~s. "=
•- —
= 0
Q.
O
r \
"5
- s
s —
i: 0
= TZ
'J
_2
z -
•= 2
'j
c
Jz
s =
c s
£ g
— 'J
z ±
^ _
¦f. —
= 'j
= '=
Z
1.
zl 5
£ 'J
z —
J/5 tSl
Ci -
It 2
V5 C3
CJ
z _2
2
C -z
£¦¦§
z -
z —
¦7
^ =
—
—
z —
Z '¦
- 0
"5 cj)
e I
= 7
3
• •
ts. =
~ -3
i i
= 3b
2
y.
4
ARIZONA -
Maricopa Co.
A
D
D
D
D
D
9
CONNECTIC
A
13
GEORGIA
D
16
IDAHO
C
17
ILLINOIS
E
18
INDIANA
c
E
19
IOWA
c
E
26
MICHIGAN
c
E
27
MINNESOTA
c
E
29
MISSOURI
E
36
NEW YORK
D
D
D
D
D
D
D
39
OHIO
C
E
49
UTAH
B
D
D
D
F
53
WASHINGT
D
D
D
55
WISCONSIN
E
A Submitted data with modification: updated the year ID to 2016.
r>
Submitted data with modification: deleted records that were not snowmobile source types 1002-1010.
c NEI 2014v2 data used for 2016vl platform.
D Submitted data.
17
Spreadsheet "ladco_nei2017_nrmonthallocation.xlsx."
17
Submitted data with modification: deleted records that were not the snowmobile surrogate ID 14.
Emissions Inside California and Texas
California nonroad emissions were provided by CARB for the years 2016, 2023, 2028, and 2035.
All California nonroad inventories are annual, with monthly temporalization applied in SMOKE.
Emissions for oil field equipment (SCCs ending in -10010) were removed from the California inventory
in order to prevent a double count with the np oilgas sector. VOC and PM2.5 emissions were allocated to
speciation profiles, and VOC HAPs were created, using MOVES data in California. For example, ratios
of VOC (PM2.5) by speciation profile to total VOC (PM2.5), and ratios of VOC HAPs to total VOC, were
calculated by county and SCC from the MOVES run in California, and then applied CARB-provided
VOC (PM2.5) in the inventory so that California nonroad emissions could be speciated consistently with
the rest of the country.
67
-------
Texas nonroad emissions were provided by the Texas Commission on Environmental Quality for the
years 2016, 2023, and 2028, using TCEQ's TexN2 tool.17 This tool facilitates the use of detailed Texas-
specific nonroad equipment population, activity, fuels, and related data as inputs for MOVES2014b, and
accounts for Texas-specific emission adjustments such as the Texas Low Emission Diesel (TxLED)
program. Texas nonroad emissions were provided seasonally; that is, total emissions for winter, spring,
summer and fall; those emissions were evenly distributed between the months in each season. As in
California, VOC and PM2.5 emissions were allocated to speciation profiles, and VOC HAPs were created,
using MOVES data in Texas. For example, ratios of VOC (PM2.5) by speciation profile to total VOC
(PM2.5), and ratios of VOC HAPs to total VOC, were calculated by county and SCC from the MOVES
run in Texas, and then applied TCEQ-provided VOC (PM2.5) in the inventory so that Texas nonroad
emissions could be speciated consistently with the rest of the country.
Nonroad Updates from State Comments
The 2016 Nonroad Collaborative workgroup received a small number of comments on the 2016beta
inventory, all of which were addressed and implemented in the 2016vl nonroad inventory and carried into
2016v2:
• Georgia Department of Natural Resources: utilize updated geographic allocation factors
(,nrstatesurrogate table) for the Commercial, Lawn & Garden (commercial, public, and
residential), Logging, Manufacturing, Golf Carts, Recreational, Railroad Maintenance Equipment
and A/C/Refrigeration sectors, using data from the U.S. Census Bureau and U.S. Forest Service.
• Lake Michigan Air Directors Consortium (LADCO): update seasonal allocation of agricultural
equipment activity (nrmonthallocation table) for Illinois, Indiana, Iowa, Michigan, Minnesota,
Missouri, Ohio, and Wisconsin.
• Texas Commission on Environmental Quality: replace MOVES nonroad emissions for Texas
with emissions calculated with TCEQ's TexN2 model.
• Alaska Department of Environmental Conservation: remove emissions as calculated by
MOVES for several equipment sector-county/census areas combinations in Alaska, due to an
absence of nonroad activity (see Table 2-33).
Table 2-33. Alaska counties/census areas for which nonroad equipment sector-specific emissions are
removed in 2016vl and 2016v2
Nonroiiil Kqiiipmenl Sector
Counties/Census Are.is (l-'ll'S) lor which equipment
sector emissions ;irc remo\eel in 2016
Agricultural
Aleutians East (02013), Aleutians West (02016), Bethel
Census Area (02050), Bristol Bay Borough (02060),
Dillingham Census Area (02070), Haines Borough (02100),
Hoonah-Angoon Census Area (02105), Ketchikan Gateway
(02130), Kodiak Island Borough (02150), Lake and
Peninsula (02164), Nome (02180), North Slope Borough
(02185), Northwest Arctic (02188), Petersburg Borough
(02195), Pr ofWales-Hyder Census Area (02198), Sitka
17 For more information on the TexN2 tool please see: ftp://amdaftp.tcea.texas.gov/EI/nonroad/TexN2/.
68
-------
Nonroiiil Kqiiipmenl Sector
Counties/Census Arc:is (I'll'S) lor which equipment
seeloi- emissions ;irc remo\oil in 20l(>
Borough (02220), Skagway Borough (02230), Valdez-
Cordova Census Area (02261), Wade Hampton Census Area
(02270), Wrangell City + Borough (02275), Yakutat City +
Borough (02282), Yukon-Koyukuk Census Area (02290)
Logging
Aleutians East (02013), Aleutians West (02016), Nome
(02180), North Slope Borough (02185), Northwest Arctic
(02188), Wade Hampton Census Area (02270)
Railway Maintenance
Aleutians East (02013), Aleutians West (02016), Bethel
Census Area (02050), Bristol Bay Borough (02060),
Dillingham Census Area (02070), Haines Borough (02100),
Hoonah-Angoon Census Area (02105), Juneau City +
Borough (02110), Ketchikan Gateway (02130), Kodiak
Island Borough (02150), Lake and Peninsula (02164), Nome
(02180),), North Slope Borough (02185), Northwest Arctic
(02188), Petersburg Borough (02195), Pr of Wales-Hyder
Census Area (02198), Sitka Borough (02220), Southeast
Fairbanks (02240), Wade Hampton Census Area (02270),
Wrangell City + Borough (02275), Yakutat City + Borough
(02282), Yukon-Koyukuk Census Area (02290)
2.5 2016 Fires (ptfire-wild, ptfire-rx, ptagfire)
Multiple types of fires are represented in the modeling platform. These include wild and prescribed fires
that are grouped into the ptfire-wild and ptfire-rx sectors, and agricultural fires that comprise the ptagfire
sector. All ptfire and ptagfire fires are in the United States. Fires outside of the United States are
described in the ptfire othna sector later in this document.
2.5.1 Wild and Prescribed Fires (ptfire)
Wildfires and prescribed burns that occurred during the inventory year are included in the year 2016
version 1 (2016vl) inventory as event and point sources. Only minor adjustments were made to ptfire for
2016v2. These minor adjustments consisted of correcting emissions for the Soberanes fire in California
that occurred in summer of 2016 and a few improvements to the spatial allocation of large wildfires (no
emissions changed in the cases). The wildfires and prescribed fires were broken up into two different
sectors, ptfire-wild and ptfire-rx respectively, for 2016v2. The point agricultural fires inventory (ptagfire)
is described in a separate section. For purposes of emission inventory preparation, wildland fire (WLF) is
defined as any non-structure fire that occurs in the wildland. The wildland is defined an area in which
human activity and development are essentially non-existent, except for roads, railroads, power lines, and
similar transportation facilities. Wildland fire activity is categorized by the conditions under which the
fire occurs. These conditions influence important aspects of fire behavior, including smoke emissions.
In the 2016v2 inventory, data processing was conducted differently depending on the fire type, as defined
below:
• Wildfire (WF): any fire started by an unplanned ignition caused by lightning; volcanoes; other acts
of nature; unauthorized activity; or accidental, human-caused actions, or a prescribed fire that has
developed into a wildfire.
69
-------
• Prescribed (Rx) fire: any fire intentionally ignited by management actions in accordance with
applicable laws, policies, and regulations to meet specific land or resource management
objectives. Prescribed fire is one type of fire fuels treatment. Fire fuels treatments are vegetation
management activities intended to modify or reduce hazardous fuels. Fuels treatments include
prescribed fires, wildland fire use, and mechanical treatment.
The SCCs used for the ptfire sources are shown in Table 2-34. The ptfire inventory includes separate
SCCs for the flaming and smoldering combustion phases for wildfire and prescribed burns. Note that
prescribed grassland fires or Flint Hills, Kansas have their own SCC in the 2016v2 inventory. The year
2016 fire season also included some major wild grassland fires. These wild grassland fires were assigned
the standard wildfire SCCs shown in Table 2-34.
Table 2-34. SCCs included in the ptfire sector for the 2016v2 inventory
SCC
Description
2801500170
Grassland fires; prescribed
2810001001
Forest Wildfires; Smoldering; Residual smoldering only (includes grassland
wildfires)
2810001002
Forest Wildfires; Flaming (includes grassland wildfires)
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming
National Fire Information Data
Numerous fire information databases are available from U.S. national government agencies. Some of the
databases are available via the internet while others must be obtained directly from agency staff. Table
2-35 provides the national fire information databases that were used for the 2016vl ptfire inventory,
including the website where the 2016 data were downloaded.
Table 2-35. National fire information databases used in 2016vl ptfire inventory
Dataset Name
Fire
Types
Form
at
Agenc
y
Coverage
Source
Hazard Mapping
System (HMS)
WF/R
NOA
North
https://www.ospo.noaa.gOv/Products/land/h
X
CSV
A
America
ms.html
Geospatial Multi-
Agency
Coordination(GeoM
AC)
WF
SHP
USGS
Entire US
httDs://www.geomac.gov/GeoMACTransiti
on.shtml
Incident Command
System Form 209:
Incident Status
Summary (ICS-209)
WF/R
X
CSV
Multi
Entire US
httDs://fam. nwcg.gov/fam-web/
National
Association of State
Foresters (NASF)
WF
CSV
Multi
Participati
ng US
states
httDs://fam.nwcg.gov/fam-web/ (see Public
Access Reports, Free Data Extract, then NASF State
Data Extract)
Monitoring Trends
in Burn Severity
(MTBS)
WF/R
X
SHP
USGS,
USFS
Entire US
httDs://www.mtbs.gov/direct-download
70
-------
Dataset Name
Fire
Types
Form
at
Agenc
y
Coverage
Source
Forest Service
Activity Tracking
System (FACTS)
RX
SHP
USFS
Entire US
Hazardous Fuel Treatment Reduction: Polygon
at httDs://data.fs.usda.aov/aeodata/edw/
datasets.php
US Fish and
Wildland Service
(USFWS) fire
database
WF/R
X
CSV
USFW
s
Entire US
Direct communication with USFWS
The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and
Atmospheric Administration's (NOAA) National Environmental Satellite and Data Information
Service (NESDIS) as a tool to identify fires over North America in an operational environment. The
system utilizes geostationary and polar orbiting environmental satellites. Automated fire detection
algorithms are employed for each of the sensors. When possible, HMS data analysts apply quality control
procedures for the automated fire detections by eliminating those that are deemed to be false and adding
hotspots that the algorithms have not detected via a thorough examination of the satellite imagery.
The HMS product used for the 2016vl inventory consisted of daily comma-delimited files containing fire
detect information including latitude-longitude, satellite used, time detected, and other information. The
Visible Infrared Imaging Radiometer Suite (VIIRS) satellite fire detects were introduced into the HMS in
late 2016. Since it was only available for a small portion of the year, the VIIRS fire detects were removed
for the entire year for consistency. In the 2016alpha inventory, the grassland fire detects were put in the
point agricultural fire sector (ptagfire). As there were a few significant grassland wildfires in Kansas and
Oklahoma in year 2016, all grassland fire detects were included in the ptfire sector for the 2016vl
inventory. These grassland fires were processed through Satellite Mapping Automated Reanalysis Tool
for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Framework.
GeoMAC (Geospatial Multi-Agency Coordination) is an online wildfire mapping application designed for
fire managers to access maps of current U.S. fire locations and perimeters. The wildfire perimeter data is
based upon input from incident intelligence sources from multiple agencies, GPS data, and infrared (IR)
imagery from fixed wing and satellite platforms.
The Incident Status Summary, also known as the "ICS-209" is used for reporting specific information on
significant fire incidents. The ICS-209 report is a critical interagency incident reporting tool giving daily
'snapshots' of the wildland fire management situation and individual incident information which include
fire behavior, size, location, cost, and other information. Data from two tables in the ICS-209 database
were merged and used for the 2016vl ptfire inventory: the
SIT209_HISTORY_INCIDENT_209_REPORTS table contained daily 209 data records for large fires,
and the SIT209 HISTORY INCIDENTS table contained summary data for additional smaller fires.
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
71
-------
States. The extent of coverage includes the continental U.S., Alaska, Hawaii, and Puerto Rico. Fire
occurrence and satellite data from various sources are compiled to create numerous MTBS fire products.
The MTBS Burned Areas Boundaries Dataset shapefiles include year 2016 fires and that are classified as
either wildfires, prescribed burns or unknown fire types. The unknown fire type shapes were omitted in
the 2016vl inventory development due to temporal and spatial problems found when trying to use these
data.
The US Forest Service (USFS) compiles a variety of fire information every year. Year 2016 data from the
USFS Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and
used for 2016vl emissions inventory development. This database includes information about activities
related to fire/fuels, silviculture, and invasive species. The FACTS database consists of shapefiles for
prescribed burns that provide acres burned, and start and ending time information.
The US Fish and Wildland Service (USFWS) also compiles wildfire and prescribed burn activity on their
federal lands every year. Year 2016 data were acquired from USFWS through direct communication with
USFWS staff and were used for 2016vl emissions inventory development. The USFWS fire information
provided fire type, acres burned, latitude-longitude, and start and ending times.
State/Local/Tribal Fire Information
During the 2016 emissions modeling platform development process, S/L/T agencies were invited by EPA
and 2016 Inventory Collaborative Fire Workgroup to submit all fire occurrence data for use in developing
the 2016vl fire inventory. A template form containing the desired format for data submittals was
provided to S/L/T air agencies. The list of S/L/T agencies that submitted fire data is provided in Table
2-36. Data from nine individual states and one Indian Tribe were used for the 2016vl ptfire inventory.
Table 2-36. List of S/L/T agencies that submitted fire data for 2016vl with types and formats.
Fire
S/L/T agency name
Types
Format
NCDEQ
WF/RX
CSV
KDHE
RX/AG
CSV
CO Smoke Mgmt
Program
RX
CSV
Idaho DEQ
AG
CSV
Nez Perce Tribe
AG
CSV
GADNR
ALL
EIS
MN
RX/AG
CSV
WA ECY
AG
CSV
NJDEP
WF/RX
CSV
Alaska DEC
WF/RX
CSV
The data provided by S/L/T agencies were evaluated by EPA and further feedback on the data submitted
by the state was requested at times. Table 2-37 provides a summary of the type of data submitted by each
S/L/T agency and includes spatial, temporal, acres burned and other information provided by the
agencies.
72
-------
Table 2-37. Brief description of fire information submitted for 2016vl inventory use.
S/L/T
agency
name
Fire
Types
Description
NCDEQ
WF/RX
Fire type, period-specific, latitude-longitude and acres burned
information. Technical direction was to remove all fire detects
that were not reconciled with any other national or state
agency database.
Kansas
DHE
RX/AG
Day-specific, county-centroid located, acres burned for Flint
Hills prescribed burns for Feb 27-May 4 time period.
Reclassified fuels for some agricultural burns. A grassland
gridding surrogate was used to spatially allocate the day-
specific grassland fire emissions.
Colorado
Smoke
Mgmt
Program
RX
Day-specific, latitude-longitude, and acres burned for
prescribed burns
Idaho DEQ
AG
Day-specific, latitude-longitude, acres burned for agricultural
burns. Total replacement of 2016 alpha fire inventory for
Idaho.
Nez Perce
Tribe
AG
Day-specific, latitude-longitude, acres burned for agricultural
burns. Total replacement of 2016 alpha fire inventory within
the tribal area boundary.
Georgia
DNR
ALL
Data submitted included all fires types via EIS. The wildfire
and prescribed burn data were provided as daily, point
emissions sources. The agricultural burns were provided as
day-specific point emissions sources.
Minnesota
RX/AG
Corrected latitude-longitude, day-specific and acres burned
for some prescribed and agricultural burns.
Washington
ECY
AG
Month-specific, latitude-longitude, acres burned, fuel loading
and emissions for agricultural burns. Not day-specific so
allocation to daily implemented by EPA. WA state direction
included to continue to use the 2014NEIv2 pile burns that
were included in the non-point sector for 2016vl.
New Jersey
DEP
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire
and prescribed burns.
Alaska DEC
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire
and prescribed burns.
Fire Emissions Estimation Methodology
The national and S/L/T data mentioned earlier were used to estimate daily wildfire and prescribed burn
emissions from flaming combustion and smoldering combustion phases for the 2016vl inventory.
Flaming combustion is more complete combustion than smoldering and is more prevalent with fuels that
have a high surface-to-volume ratio, a low bulk density, and low moisture content. Smoldering
73
-------
combustion occurs without a flame, is a less complete burn, and produces some pollutants, such as
PM2.5, VOCs, and CO, at higher rates than flaming combustion. Smoldering combustion is more
prevalent with fuels that have low surface-to-volume ratios, high bulk density, and high moisture content.
Models sometimes differentiate between smoldering emissions that are lofted with a smoke plume and
those that remain near the ground (residual emissions), but for the purposes of the 2016vl inventory the
residual smoldering emissions were allocated to the smoldering SCCs listed in Table 2-34. The lofted
smoldering emissions were assigned to the flaming emissions SCCs in Table 2-34.
Figure 2-10 is a schematic of the data processing stream for the 2016vl inventory for wildfire and
prescribe burn sources. The ptfire inventory sources were estimated using Satellite Mapping Automated
Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Framework.
SMARTFIRE2 is an algorithm and database system that operate within a geographic information system
(GIS). SMARTFIRE2 combines multiple sources of fire information and reconciles them into a unified
GIS database. It reconciles fire data from space-borne sensors and ground-based reports, thus drawing on
the strengths of both data types while avoiding double-counting of fire events. At its core, SMARTFIRE2
is an association engine that links reports covering the same fire in any number of multiple databases. In
this process, all input information is preserved, and no attempt is made to reconcile conflicting or
potentially contradictory information (for example, the existence of a fire in one database but not
another).
For the 2016vl inventory, the national and S/L/T fire information was input into SMARTFIRE2 and then
merged and associated based on user-defined weights for each fire information dataset. The output from
SMARTFIRE2 was daily acres burned by fire type, and latitude-longitude coordinates for each fire. The
fire type assignments were made using the fire information datasets. If the only information for a fire was
a satellite detect for fire activity, then the flow described in Figure 2-11 was used to make fire type
assignment by state and by month.
74
-------
Figure 2-10. Processing flow for fire emission estimates in the 2016vl inventory
Input Data Sets
(state/local/tribal and national data sets)
Fuel Moisture and
Fuel Loading Data
Smoke Modeling (BlueSky Framework)
Daily smoke emissions
for each fire
Emissions Post-Processing
Final Wildland Fire Emissions Inventory
Data Preparation
Data Aggregation and Reconciliation
(SmartFire2)
Daily fire locations
with fire size and type
75
-------
Figure 2-11. Default fire type assignment by state and month where data are only from satellites.
* _
Default Fire Type
Assignment
WF Months
¦ 4,5,6,7
~ 5,6,7,8
~ 5,6,7,8,9,10
~ 6,7,8
I None
The BlueSky Modeling Framework version 3.5 (revision #38169) was used to calculate fuel loading and
consumption, and emissions using various models depending on the available inputs as well as the desired
results. The contiguous United States and Alaska, where Fuel Characteristic Classification System
(FCCS) fuel loading data are available, were processed using the modeling chain described in Figure
2-12. The Fire Emissions Production Simulator (FEPS) in the BlueSky Framework generated the CAP
emission factors for wildland fires used in the 2016vl inventory. The HAPs were derived from regional
emissions factors from Urbanski (2014).
76
-------
Figure 2-12. BlueSky Modeling Framework
For the 2016vl inventory, the FCCSv2 spatial vegetation cover was upgraded to the LANDFIRE vl.4
fuel vegetation cover (See: https://www.landfire.gov/fccs.php). The FCCSv3 fuel bed characteristics were
implemented along with LANDFIREvl.4 to provide better fuel classification for the BlueSky Framework.
The LANDFIREv 1 .4 raster data were aggregated from the native resolution and projection to 200 meter
resolution using a nearest-neighbor methodology. Aggregation and reprojection was required to allow
these data to work in the BlueSky Framework.
2.5.2 Point Source Agricultural Fires (ptagfire)
The point source agricultural fire (ptagfire) inventory sector contains daily agricultural burning emissions.
Daily fire activity was derived from the NOAA Hazard Mapping System (HMS) fire activity data. The
agricultural fires sector includes SCCs starting with '28015'. The first three levels of descriptions for
these SCCs are: 1) Fires - Agricultural Field Burning; Miscellaneous Area Sources; 2) Agriculture
Production - Crops - as nonpoint; and 3) Agricultural Field Burning - whole field set on fire. The SCC
2801500000 does not specify the crop type or burn method, while the more specific SCCs specify field or
orchard crops and, in some cases, the specific crop being grown. The SCCs for this sector listed are in
Table 2-38.
Table 2-38. SCCs included in the ptagfire sector for the 2016vl inventory
SCC
Description
2801500000
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Unspecified crop type and Burn Method
2801500100
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crops Unspecified
2801500112
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set onfire;Field Crop is Alfalfa: Backfire Burning
2801500130
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Barley: Burning Techniques Not Significant
77
-------
see
Description
2801500141
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Bean (red): Headfire Burning
2801500150
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Corn: Burning Techniques Not Important
2801500151
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Double Crop Winter Wheat and Corn
2801500152
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;DoubleCrop Corn and Soybeans
2801500160
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Cotton: Burning Techniques Not Important
2801500170
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Grasses: Burning Techniques Not Important
2801500171
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Fallow
2801500182
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Hay (wild): Backfire Burning
2801500202
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Pea: Backfire Burning
2801500220
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Rice: Burning Techniques Not Significant
2801500250
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Sugar Cane: Burning Techniques Not Significant
2801500262
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Wheat: Backfire Burning
2801500263
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;DoubleCrop Winter Wheat and Cotton
2801500264
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;DoubleCrop Winter Wheat and Soybeans
2801500300
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop Unspecified
2801500320
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Apple
2801500350
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Cherry
2801500410
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Peach
2801500420
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Pear
2801500500
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire; Vine Crop Unspecified
2801500600
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Forest Residues Unspecified
78
-------
The EPA estimated biomass burning emissions using remote sensing data. These estimates were then
reviewed by the states and revised as resources allowed. As many states did not have the resources to
estimate emissions for this sector, remote sensing was necessary to fill in the gaps for regions where there
was no other source of data. Crop residue emissions result from either pre-harvest or post-harvest burning
of agricultural fields. The crop residue emission inventory for 2016 is day-specific and includes
geolocation information by crop type. The method employed and described here is based on the same
methods employed in the 2014 NEI with a few minor updates. It should be noted that grassland fires were
moved from the agricultural burning inventory sector to the prescribed and wildland fire sector for
2016beta and 2016vl inventories. This was done to prevent double-counting of fires and because the
largest fire (acres burned) in 2016 was a wild grassland fire in Kansas.
Daily, year-specific agricultural burning emissions were derived from HMS fire activity data, which
contains the date and location of remote-sensed anomalies. As point source inventories, the locations of
the fires are identified with latitude-longitude coordinates for specific fire events. The HMS activity data
were filtered using 2016 USD A cropland data layer (CDL). Satellite fire detects over agricultural lands
were assumed to be agricultural burns and assigned a crop type. Detects that were not over agricultural
lands were output to a separate file for use in the point source wildfire (ptfire) inventory sector. Each
detect was assigned an average size of between 40 and 80 acres based on crop type. The assumed field
sizes are found in Table 2-39.
Table 2-39. Assumed field size of agricultural fires per state(acres)
Sl;ite
Held Si/e
Alabama
40
Arizona
80
Arkansas
40
California
120
Colorado
80
Connecticut
40
Delaware
40
Florida
60
Georgia
40
Idaho
120
Illinois
60
Indiana
60
Iowa
60
Kansas
80
Kentucky
40
Louisiana
40
Maine
40
Maryland
40
Massachusetts
40
Michigan
40
Minnesota
60
Mississippi
40
Missouri
60
Montana
120
Nebraska
60
Nevada
40
New Hampshire
40
79
-------
Sl;ite
Held Si/e
New Jersey
40
New Mexico
80
New York
40
North Carolina
40
North Dakota
60
Ohio
40
Oklahoma
80
Oregon
120
Pennsylvania
40
Rhode Island
40
South Carolina
40
South Dakota
60
Tennessee
40
Texas
80
Utah
40
Vermont
40
Virginia
40
Washington
120
West Virginia
40
Wisconsin
40
Wyoming
80
Another feature of the ptagfire database is that the satellite detections for 2016 were filtered out to
exclude areas covered by snow during the winter months. To do this, the daily snow cover fraction per
grid cell was extracted from a 2016 meteorological Weather Research Forecast (WRF) model simulation.
The locations of fire detections were then compared with this daily snow cover file. For any day in which
a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the
inventory. Due to the inconsistent reporting of fire detections for year 2016 from the Visible Infrared
Imaging Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as
VIIRS or Suomi National Polar-orbiting Partnership satellite were excluded. In addition, certain crop
types (corn and soybeans) were excluded from the following states: Iowa, Kansas, Indiana, Illinois,
Michigan, Missouri, Minnesota, Wisconsin, and Ohio. Kansas was not included in this list in the 2014NEI
but added for 2016. The reason for these crop types being excluded is because states have indicated that
these crop types are not burned.
Crop type-specific emissions factors were applied to each daily fire to calculate criteria and hazardous
pollutant emissions. In all prior NEIs for this sector, the HAP emission factors and the VOC emission
factors were known to be inconsistent. The HAP emission factors were copied from the HAP emission
factors for wildfires in the 2014 NEI and in the 2016 beta and version 1 modeling platforms. The VOC
emission factors were scaled from the CO emission factors in the 2014 NEI and the 2016 beta and version
1 modeling platforms. See Pouliot et al, 2017 for a complete table of emission factors and fuel loading by
crop type.
80
-------
Heat flux values for computing fire plume rise were calculated using the size and assumed fuel loading of
each daily fire. Emission factors and fuel loading by crop type are available in Table 1 of Pouliot et al.
(2017). This information is needed for a plume rise calculation within a chemical transport modeling
system. In prior year modeling platforms including 2014, all the emissions were placed into layer 1 (i.e.
ground level).
The daily agricultural and open burning emissions were converted from a tabular format into the
SMOKE-ready daily point Flat File 2010 (FF10) format. The daily emissions were also aggregated into
annual values by location and converted into the annual point flat file format.
2.6 2016 Biogenic Sources (beis)
Biogenic emissions for the entire year 2016 were developed using the Biogenic Emission Inventory
System version 3.7 (BEIS3.7) within SMOKE. The landuse input into BEIS3.7 is the Biogenic Emissions
Landuse Dataset (BELD) version 5.
The BELD5 includes the following datasets:
• Newer version of the Forest Inventory and Analysis (FIA version 8.0
https://www.fia.fs.fed.us/library/database-documentation/index.php)
Agricultural land use from the 2017 US Department of Agriculture (USDA) crop data layer
(https://www.nass.usda.gov/Research_and_Science/Cropland/SARSla.php)
Global Moderate Resolution Imaging Spectroradiometer (MODIS) 20 category data with
enhanced lakes and Fraction of Photosynthetically Active Radiation (FPAR) for vegetation
coverage from National Center for Atmospheric Research (NCAR)
(https://www2.mmm.ucar.edu/wrf/users/download/get sources wps geog.html )
o Note BELD4.1 used 2011 USGS National Land Cover Data (NLCD) limited to the USA
and MODIS 20 category land use for the rest of the world.
Canadian BELD land use (https://www.epa.gov/sites/default/files/2019-
08/documents/800am zhang 2 O.pdf).
The FIA database reports on status and trends in forest area and location; in the species, size, and health
of trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates
by various products; and in forest land ownership. The FIA database version 8.0 includes recent updates
of these data through the year 2017 (from 2001). Earlier versions of BELD used an older version of the
FIA database that had included data only through the year 2014. Canopy coverage is based on the
MODIS 20 category data. 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 all land areas in the United States, 500-meter grid spacing land cover data
from the MODIS is used.
The processing of the BELD5 data follows the spatial allocation methods of Bash et al. 2016 like BELD
4. However, MODIS land use categories and FPAR are used in the place of NLCD land use and forest
coverage. MODIS land use has the additional broadleaf evergreen and deciduous needleleaf land use
types and only one developed land use type. BELD4.1 used lookup tables for species leaf biomass. In
BELD5, allometric relationships from the FIA v8.0 database (https://www.fia.fs.fed.us/library/database-
documentation/index.php) were utilized to estimate foliage biomass per species. This resulted in better
81
-------
agreement with measured foliage biomass. BVOC emissions are understood to originate from foliage
thus these biomass changes directly impacted the BEIS emission factors.
BEIS3.7 has some important updates from BEIS 3.61. These include the incorporation of Version 5 of
the Biogenic Emissions Landuse Database (BELD5), and updates to biomass emissions factors. The
biomass emissions factor updates take into account FIA updates. BEIS3.7 includes a two-layer canopy
model. Layer structure varies with light intensity and solar zenith angle. Both layers of the canopy model
include estimates of sunlit and shaded leaf area based on solar zenith angle and light intensity, direct and
diffuse solar radiation, and leaf temperature (Bash et al., 2016). The new algorithm requires additional
meteorological variables over previous versions of BEIS. The variables output from the Meteorology-
Chemistry Interface Processor (MCIP) that are used for BEIS3.7 processing are shown in Table 2-40.
The 2016 BEIS3 modeling for year 2016 included processing for both a 36km (36US3) and 12km domain
(12US1) (see Figure 3-1). The 12US2 modeling domain can also be supported by taking a subset or
window of the 12US1 BEIS3 emissions dataset.
Table 2-40. Hourly 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
RGRND
solar radiation reaching surface
RN
nonconvective precipitation
RSTOMI
inverse of bulk stomatal resistance
SLYTP
soil texture type by USD A category
SOIM1
volumetric soil moisture in top cm
SOIT1
soil temperature in top cm
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 m
SMOKE-BEIS3 modeling system consists of two programs named: 1) Normbeis3 and 2) Tmpbeis3.
Normbeis3 uses emissions factors and BELD5 landuse to compute gridded normalized emissions for
chosen model domain (see Figure 2-13). The emissions factor file (B360FAC) contains leaf-area-indices
(LAI), dry leaf biomass, winter biomass factor, indicator of specific leaf weight, and normalized emission
fluxes for 35 different species/compounds. The BELD5 file is the gridded landuse for many different
landuse types. The output gridded domain is the same as the input domain for the land use data. Output
emission fluxes (B3GRD) are normalized to 30 °C, and isoprene and methyl-butenol fluxes are also
normalized to a photosynthetic active radiation of 1000 |imol/m2s.
82
-------
Figure 2-13. Normbeis3 data flows
The normalized emissions output from Normbeis3 (B3GRD) are input into Tmpbeis3 along with the
MCIP meteorological data, chemical speciation profile to use for desired chemical mechanism, and
BIOSEASON file used to indicate how each day in year 2016 should be treated, either as summer or
winter. Figure 2-14 illustrates the data flows for the Tmpbeis3 program. The output from Tmpbeis
includes gridded, speciated, hourly emissions both in moles/second (B3GTS L) and tons/hour
(B3GTSS).
Figure 2-14. TmpbeisS data flow diagram.
^Program^^
Rte
Shows inpul or output
Biogenic emissions do not use an emissions inventory and do not have SCCs. The gridded land use data,
gridded meteorology, an emissions factor file, and a speciation profile are further described in the next
section.
2.7
Sources Outside of the United States
The emissions from Canada and Mexico and other areas outside of the U.S. are included in these
emissions modeling sectors: othpt, othar, othafdust, othptdust, onroad_can, onroad mex, and
ptfire_othna. The "oth" refers to the fact that these emissions are usually "other" than those in the NEI,
and the remaining characters provide the SMOKE source types: "pt" for point, "ar" for "area and nonroad
mobile," "afdust" for area fugitive dust (Canada only), and "ptdust" for point fugitive dust. Because
Canada and Mexico onroad mobile emissions are modeled differently from each other, they are separated
83
-------
into two sectors: onroad can and onroadmex. Emissions for Mexico are based on the Inventario
Nacional de Emisiones de Mexico, 2008 projected to year 2016 (ERG, 2014a). Additional details for
these sectors can be found in the 2016vl platform specification sheets.
2.7.1 Point Sources in Canada and Mexico (othpt, canada_ag,
canada_og2D)
Canadian point sources were taken from the ECCC 2016 emission inventory, which was new for the
2016v2 platform. The provided point source inventories include upstream oil and gas emissions,
agricultural ammonia and VOC. A new 2016 inventory was also provided by SEMARNAT of Mexico.
Due to the large number of points in the Canada inventories, the agricultural sources were split into a
separate sector called canada ag so that the sources could be placed into layer 1 as plume rise calculations
were not needed. Similarly, there were a very large number of Canadian oil and gas point sources, many
of which would be appropriate modeled in layer 1. These sources were placed into the canada_og2D
sector for layer 1 modeling. Reducing the size of the othpt sector sped up the air quality model run. The
Canadian point source inventory is pre-speciated for the CB6 chemical mechanism. Also for Canada,
agricultural data were originally provided on a rotated 10-km grid for the 2016beta platform. These were
smoothed out to avoid the artifact of grid lines in the processed emissions. The data were monthly
resolution for Canadian agricultural and airport emissions, along with some Canadian point sources, and
annual resolution for the remainder of Canada and all of Mexico.
2.7.2 Fugitive Dust Sources in Canada (othafdust, othptdust)
Fugitive dust sources of particulate matter emissions excluding land tilling from agricultural activities,
were provided by Environment and Climate Change Canada (ECCC) as part of their 2016 emission
inventory. Different source categories were provided as gridded point sources and area (nonpoint) source
inventories.
Gridded point source emissions resulting from land tilling due to agricultural activities were provided as
part of the ECCC 2016 emission inventory. The provided wind erosion emissions were removed. The
data were originally provided on a rotated 10-km grid for the 2016 beta platform, but these were
smoothed to avoid the artifact of grid lines appearing in the emissions output from SMOKE. The
othptdust emissions have a monthly resolution.
A transport fraction adjustment that reduces dust emissions based on land cover types was applied to both
point and nonpoint dust emissions, along with a meteorology-based (precipitation and snow/ice cover)
zero-out of emissions when the ground is snow covered or wet.
2.7.3 Nonpoint and Nonroad Sources in Canada and Mexico (othar)
ECCC provided year 2016 Canada province, and in some cases sub-province, resolution emissions from
for nonpoint and nonroad sources. The nonroad sources were monthly while the nonpoint and rail
emissions were annual. For Mexico, new year 2016 Mexico nonpoint and nonroad inventories from
SEMARNAT were used. All Mexico inventories were annual resolution. Canadian CMV inventories
that had been included in the othar sector in past modeling platforms are now included in the cmv_clc2
and cmv_c3 sectors as point sources.
84
-------
2.7.4 Onroad Sources in Canada and Mexico (onroad_can,
onroad_mex)
ECCC provided monthly year 2016 onroad emissions for Canada at the province resolution or sub-
province resolution depending on the province. For Mexico, monthly year 2016 onroad inventories at the
municipio resolution unchanged from the 2016vl inventories were used. The Mexico onroad emissions
are based on MOVES-Mexico runs for 2014 and 2018 that were interpolated to 2016.
2.7.5 Fires in Canada and Mexico (ptfire_othna)
Annual point source 2016 day-specific wildland emissions for Mexico, Canada, Central America, and
Caribbean nations were developed from a combination of the Fire Inventory from NCAR (FINN) daily
fire emissions and fire data provided by Environment Canada when available. Environment Canada
emissions were used for Canada wildland fire emissions for April through November and FINN fire
emissions were used to fill in the annual gaps from January through March and December. Only CAP
emissions are provided in the ptfire othna sector inventories. These emissions are unchanged from those
used in 2016vl.
For FINN fires, listed vegetation type codes of 1 and 9 are defined as agricultural burning, all other fire
detections and assumed to be wildfires. All wildland fires that are not defined as agricultural are assumed
to be wildfires rather than prescribed. FINN fire detects less than 50 square meters (0.012 acres) are
removed from the inventory. The locations of FINN fires are geocoded from latitude and longitude to
FIPS code.
2.7.6 Ocean Chlorine and Sea Salt
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb)
concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution
were available and were not modified other than the model-species name "CHLORINE" was changed to
"CL2" to support CMAQ modeling. The CL2 emissions are constant in all ocean grid cells. These data
are unchanged from the data in 2016vl and are passed to both CMAQ and CAMx. Separately from the
ocean chlorine, CMAQ computes sea salt particulate emissions inline during the model run.
For CAMx modeling, the OCEANIC preprocessor is used to compute emissions for the following
pollutants over ocean water: sodium (NA), chlorine (PCL), sulfate (PS04), dimethy sulfide (DMS), and
gas phase bromine (SSBR) and chlorine (SSCL). Additional information is provided in Section 3.5.
85
-------
3 Emissions Modeling
The CMAQ and CAMx air quality models require hourly emissions of specific gas and particle species
for the horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain). To
provide emissions in the form and format required by the model, it is necessary to "pre-process" the "raw"
emissions (i.e., emissions input to SMOKE) for the sectors described above in Section 2. In brief, the
process of emissions modeling transforms the emissions inventories from their original temporal
resolution, pollutant resolution, and spatial resolution into the hourly, speciated, gridded and vertical
resolution required by the air quality model. Emissions modeling includes temporal allocation, spatial
allocation, and pollutant speciation. Emissions modeling sometimes includes the vertical allocation (i.e.,
plume rise) of point sources, but many air quality models also perform this task because it greatly reduces
the size of the input emissions files if the vertical layers of the sources are not included.
As seen in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary across
sectors and may be hourly, daily, monthly, or annual total emissions. The spatial resolution may be
individual point sources; totals by county (U.S.), province (Canada), or municipio (Mexico); or gridded
emissions. This section provides some basic information about the tools and data files used for emissions
modeling as part of the modeling platform. For additional details that may not be covered in this section,
see the specification sheets provided with the 2016vl platform as many contain additional sector-specific
information in spatial allocation, temporal allocation, and speciation that is still relevant for 2016v2.
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 Tmpbeis3 program and not as a separate SMOKE step.
The "Inventory resolution" column shows the inventory temporal resolution from which SMOKE needs
to calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no input inventory;
86
-------
instead, activity data and emission factors are used in combination with meteorological data to compute
hourly emissions.
Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used. These
sectors are the only ones with emissions in aloft layers based on plume rise. The term "in-line" means
that the plume rise calculations are done inside of the air quality model instead of being computed by
SMOKE. In all of the "in-line" sectors, all sources are output by SMOKE into point source files which
are subject to plume rise calculations in the air quality model. In other words, no emissions are output to
layer 1 gridded emissions files from those sectors as has been done in past platforms. The air quality
model computes the plume rise using stack parameters, the Briggs algorithm, and the hourly emissions in
the SMOKE output files for each emissions sector. The height of the plume rise determines the model
layers into which the emissions are placed. The plume top and bottom are computed, along with the
plumes' distributions into the vertical layers that the plumes intersect. The pressure difference across each
layer divided by the pressure difference across the entire plume is used as a weighting factor to assign the
emissions to layers. This approach gives plume fractions by layer and source. Day-specific point fire
emissions are treated differently in CMAQ. After plume rise is applied, there are emissions in every layer
from the ground up to the top of the plume.
Table 3-1. Key emissions modeling steps by sector.
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust ad]
Surrogates
Yes
Annual
afdust ak adj
(36US3 only)
Surrogates
Yes
Annual
airports
Point
Yes
Annual
None
beis
Pre-gridded
land use
in BEIS 3 .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
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
87
-------
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
solvents
Surrogates
Yes
annual
Biogenic emissions can be modeled two different ways in the CMAQ model. The BEIS model in SMOKE
can produce gridded biogenic emissions that are then included in the gridded CMAQ-ready emissions
inputs, or alternatively, CMAQ can be configured to create "in-line" biogenic emissions within CMAQ
itself. For this platform, biogenic emissions were processed in SMOKE and included in the gridded
CMAQ-ready emissions. When CAMx is the targeted air quality model, BEIS is run within SMOKE and
the resulting emissions are included with the ground-level emissions input to CAMx.
In 2016v2 platform, 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.
SMOKE has the option of grouping sources so that they are treated as a single stack when computing
plume rise. For this platform, no grouping was performed because grouping combined with "in-line"
processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-
dimensional files). This occurs when stacks with different stack parameters or latitudes/longitudes are
grouped, thereby changing the parameters of one or more sources. The most straightforward way to get
the same results between in-line and offline is to avoid the use of grouping.
SMOKE was run for two modeling domains: a 36-km resolution CONtinental United States "CONUS"
modeling domain (36US3), and a 12-km resolution domain. Specifically, SMOKE was run on the 12US1
domain and emissions were extracted from 12US1 data files to create 12US2 emissions for 2016, 2023,
2026, and 2032. Emissions were developed for 36US3 for 2016 and 2023 only. The outputs of CAMx on
36US3 are used to create boundary conditions for the 12US2 domains. For 2026 and 2032, the 2023
boundary conditions were used. The domains are shown in Figure 3-1. All grids use a Lambert-Conformal
projection, with Alpha = 33°, Beta = 45° and Gamma = -97°, with a center of X = -97° and Y = 40°. Table
3-2 describes the grids for the three domains.
88
-------
Table 3-2. Descriptions of the platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig, xcell,
ycell, ncols, nrows, nthik
Continental
36km grid
36 km
Entire conterminous
US, almost all of
Mexico, most of
Canada (south of
60°N)
36US3
'LAM 40N97W, -2952000, -2772000,
36.D3, 36.D3, 172, 148, 1
Continental
12km grid
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_459X299
'LAM 40N97W',-2556000,-1728000,
12.D3, 12.D3, 459,299, 1
US 12 km or
"smaller"'
CONUS-12
12 km
Smaller 12km
CONUS plus some of
Mexico/Canada
12US2
'LAM 40N97W',-2412000,
-1620000, 12.D3, 12.D3, 396, 246, 1
Figure 3-1. Air quality modeling domains
89
-------
3.2 Chemical Speciation
The emissions modeling step for chemical speciation creates the "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual chemical
compounds (i.e., "explicit species") or groups of species (i.e., "lumped species"). The chemical
mechanism used for the 2016 platform is the CB6R3AE7 mechanism (Yarwood, 2010, Luecken, 2019).
In CB6R3AE7 the species added from compared to CB6 are 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 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).
Table 3-3 lists the model species produced by SMOKE in the platform used for this study. Updates to
species assignments for CB05 and CB6 were made for the 2014v7.1 platform and are described in
Appendix A.
Table 3-3. Emission model species produced for 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
N02
Nitrogen dioxide
HONO
Nitrous acid
S02
S02
Sulfur dioxide
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
NH3 FERT
Ammonia from fertilizer
VOC
AACD
Acetic acid
ACET
Acetone
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
APIN
Alpha pinene
BENZ
Benzene (not part of CB05)
CH4
Methane
ETH
Ethene
ETHA
Ethane
ETHY
Ethyne
ETOH
Ethanol
FACD
Formic acid
FORM
Formaldehyde
IOLE
Internal olefin carbon bond (R-C=C-R)
ISOP
Isoprene
IVOC
Intermediate volatility organic compounds
90
-------
Inventory Pollutant
Model Species
Model species description
KET
Ketone Groups
MEOH
Methanol
NAPH
Naphthalene
NVOL
Non-volatile compounds
OLE
Terminal olefin carbon bond (R-C=C)
PAR
Paraffin carbon bond
PRPA
Propane
SESQ
Sequiterpenes (from biogenics only)
SOAALK
Secondary Organic Aerosol (SOA) tracer
TERP
Terpenes (from biogenics only)
TOL
Toluene and other monoalkyl aromatics
UNR
Unreactive
XYLMN
Xylene and other polyalkyl aromatics, minus
naphthalene
Naphthalene
NAPH
Naphthalene from inventory
Benzene
BENZ
Benzene from the inventory
Acetaldehyde
ALD2
Acetaldehyde from inventory
Formaldehyde
FORM
Formaldehyde from inventory
Methanol
MEOH
Methanol from inventory
PM10
PMC
Coarse PM >2.5 microns and <10 microns
PM2.5
PEC
Particulate elemental carbon <2.5 microns
PN03
Particulate nitrate <2.5 microns
POC
Particulate organic carbon (carbon only) <2.5 microns
PS04
Particulate Sulfate <2.5 microns
PAL
Aluminum
PCA
Calcium
PCL
Chloride
PFE
Iron
PK
Potassium
PH20
Water
PMG
Magnesium
PMN
Manganese
PMOTHR
PM2.5 not in other AE6 species
PNA
Sodium
PNCOM
Non-carbon organic matter
PNH4
Ammonium
PSI
Silica
PTI
Titanium
One additional species in the emissions files but not on 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 were
developed from a draft version of 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.
91
-------
The SPECIATE database development and maintenance is a collaboration involving the EPA's Office of
Research and Development (ORD), Office of Transportation and Air Quality (OTAQ), and the Office of
Air Quality Planning and Standards (OAQPS), in cooperation with Environment Canada (EPA, 2016).
The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical
compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation profiles
for PM2.5.
As with previous platforms, some Canadian point source inventories are provided from Environment
Canada as pre-speciated emissions; although not all CB6 species are provided, the inventories were not
supplemented with missing species due to the minimal impact of supplementation.
Some updates to speciation profiles from previous platforms include the following:
• New profiles were incorporated for solvents;
• Additional oil and gas profiles were added (e.g., UTUBOGC, UTUBOGE, UTUBOGF);
• WRAP oil and gas profiles were used for the WRAP oil and gas inventory, although many WRAP
profiles were also used in the 2016vl platform.
Updates to the VOC speciation cross reference in 2016v2 included:
• solvents use the newly developed speciation profiles for that sector;
• changed all 8746 to G8746 (Profile name: Rice Straw and Wheat Straw Burning Composite of
G4420 and G4421);
• changed 2104008230/330 from 1084 to 4642 to match all other RWC SCCs (corrections_changes
.docx said 4462 but this was an obvious typo and should be 4642);
• changed 2680001000 from 0000 to G95241 TOG;
• updated cross reference to use Uinta Basin oil/gas profiles
• substituted profile 95417 with either UTUBOGC (2310010300, 2310011500, 2310111401,
2310010700, 2310010400, 31000107) or UTUBOGD (other SCCs);
• substituted profile 95418 with UTUBOGF;
• substituted profile 95419 with UTUBOGE;
• for Pennsylvania oil and gas profiles, substituted all 8949 with PAGAS01 (FIPS 42059 only),
PAGAS02 (FIPS 42019 only), PAGAS03 (FIPS 42125 only);
• for Colorado SCC 2310030300:,Set Archuleta/La Plata to SUIROGWT (counties are in Southern
Ute reservation), rest of Colorado to DJTFLR95;
• for Colorado SCC 2310030220: Set to DJTFLR95 (formerly FLR99);
• for Colorado 2310021010: Set Archuleta/La Plata to SUIROGCT (counties are in Southern Ute
reservation), rest of Colorado to 95398;
• for SCC 2310000551 (CBM produced water) use the new profile CBMPWWY.
Updates to PM speciation cross references included:
92
-------
• 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;
• Added new fire profiles for fire PM. Note that all US states (not DC7HI/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 4 representative states for forest fires and 2 representative states for grass
fires, and all states are mapped to one of the four representative forest states and one of the two
representative grass states. The GSREFs still have a non-FIPS-specific assignment to the previous
profile 3766AE6 for fires outside of the United States.
Speciation profiles and cross-references for this study platform are available in the SMOKE input files for
the 2016 platform. Emissions of VOC and PM2.5 emissions by county, sector and profile for all sectors
other than onroad mobile can be found in the sector summaries for the case. Totals of each model species
by state and sector can be found in the state-sector totals workbook for this case.
3.2.1 VOC speciation
The speciation of VOC includes HAP emissions from the NEI in the speciation process. Instead of
speciating VOC to generate all of the species listed in Table 3-3, emissions of five specific HAPs:
naphthalene, benzene, acetaldehyde, formaldehyde and methanol (collectively known as "NBAFM") from
the NEI were "integrated" with the NEI VOC. The integration combines these HAPs with the VOC in a
way that does not double count emissions and uses the HAP inventory directly in the speciation process.
The basic process is to subtract the specified HAPs emissions mass from the VOC emissions mass, and to
then use a special "integrated" profile to speciate the remainder of VOC to the model species excluding
the specific HAPs. The EPA believes that the HAP emissions in the NEI are often more representative of
emissions than HAP emissions generated via VOC speciation, although this varies by sector.
The NBAFM HAPs were chosen for integration because they are the only explicit VOC HAPs in the
CMAQ version 5.2. Explicit means that they are not lumped chemical groups like PAR, IOLE and
several other CB6 model species. These "explicit VOC HAPs" are model species that participate in the
modeled chemistry using the CB6 chemical mechanism. The use of inventory HAP emissions along with
VOC is called "HAP-CAP integration."
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats,
including PTDAY (the format used for the ptfire and ptagfire sectors). The ability to use integration with
the PTDAY format is used for the ptfire-rx and ptfire-wild sectors in the 2016 platform, but not for the
ptagfire sector which does not include HAPs. SMOKE allows the user to specify the particular HAPs to
integrate via the INVTABLE. This is done by setting the "VOC or TOG component" field to "V" for all
HAP pollutants chosen for integration. SMOKE allows the user to also choose the particular sources to
integrate via the NHAPEXCLUDE file (which actually provides the sources to be excluded from
integration18). 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
18 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.
93
-------
NONHAPVOC-to-NONHAPTOG factors and NONHAPTOG speciation profiles.19 SMOKE computes
NONHAPTOG and then applies the speciation profiles to allocate the NONHAPTOG to the other air
quality model VOC species not including the integrated HAPs. After determining if a sector is to be
integrated, if all sources have the appropriate HAP emissions, then the sector is considered fully
integrated and does not need a NHAPEXCLUDE file. If, on the other hand, certain sources do not have
the necessary HAPs, then an NHAPEXCLUDE file must be provided based on the evaluation of each
source's pollutant mix. The EPA considered CAP-HAP integration for all sectors in determining whether
sectors would have full, no or partial integration (see Figure 3-2). For sectors with partial integration, all
sources are integrated other than those that have either the sum of NBAFM > VOC or the sum of
NBAFM = 0.
In this platform, we create NBAFM species from the no-integrate source VOC emissions using speciation
profiles. Figure 3-2 illustrates the integrate and no-integrate processes for U.S. Sources. Since Canada
and Mexico inventories do not contain HAPs, we use the approach of generating the HAPs via speciation,
except for Mexico onroad mobile sources where emissions for integrate HAPs were available.
It should be noted that even though NBAFM were removed from the SPECIATE profiles used to create
the GSPRO for both the NONHAPTOG and no-integrate TOG profiles, there still may be small fractions
for "BENZ", "FORM", "ALD2", and "MEOH" present. This is because these model species may have
come from species in SPECIATE that are mixtures. The quantity of these model species is expected to be
very small compared to the BAFM in the NEI. There are no NONHAPTOG profiles that produce
"NAPH."
In SMOKE, the INVTABLE allows the user to specify the particular HAPs to integrate. Two different
INVTABLE files are used for different sectors of the platform. For sectors that had no integration across
the entire sector (see Table 3-4), EPA created a "no HAP use" INVTABLE in which the "KEEP" flag is
set to "N" for NBAFM pollutants. Thus, any NBAFM pollutants in the inventory input into SMOKE are
automatically dropped. This approach both avoids double-counting of these species and assumes that the
VOC speciation is the best available approach for these species for sectors using this approach. The
second INVTABLE, used for sectors in which one or more sources are integrated, causes SMOKE to keep
the inventory NBAFM pollutants and indicates that they are to be integrated with VOC. This is done by
setting the "VOC or TOG component" field to "V" for all five HAP pollutants. 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).
19 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.
94
-------
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation
; Emissions Ready for SMOKE
~
CMAQ-CB6 species
Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol
(NBAFM) for each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
ptegu
No integration, create NBAFM from VOC speciation
ptnonipm
No integration, create NBAFM from VOC speciation
ptfire-rx
Partial integration (NBAFM)
ptfire-wild
Partial integration (NBAFM)
ptfire othna
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptagfire
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
airports
No integration, create NBAFM from VOC speciation
afdust
N/A - sector contains no VOC
beis
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
cmv clc2
Full integration (NBAFM)
cmv c3
Full integration (NBAFM)
fertilizer
N/A - sector contains no VOC
livestock
Partial integration (NBAFM)
rail
Full integration (NBAFM)
nonpt
Partial integration (NBAFM)
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
95
-------
Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
pt oilgas
No integration, create NBAFM from VOC speciation
rwc
Partial integration (NBAFM)
onroad
Full integration (internal to MOVES); however, MOVES2014a speciation was CB6-CAMx,
not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-CMAQ
onroad can
No integration, no NBAFM in inventory, create NBAFM from speciation
onroadmex
Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation was
CB6-CAMx, not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-CMAQ
othafdust
N/A - sector contains no VOC
othptdust
N/A - sector contains no VOC
othar
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
Canada ag
No integration, no NBAFM in inventory, create NBAFM from speciation
Canada og2D
No integration, no NBAFM in inventory, create NBAFM from speciation
Integration for the mobile sources estimated from MOVES (onroad and nonroad sectors, other than for
California) is done differently. Briefly there are three major differences: 1) for these sources integration
is done using more than just NBAFM, 2) all sources from the MOVES model are integrated, and 3)
integration is done fully or partially within MOVES. For onroad mobile, speciation is done fully within
MOVES3 such that the MOVES model outputs emission factors for individual VOC model species along
with the HAPs. This requires MOVES to be run for a specific chemical mechanism. For this platform
MOVES was run for the CB6R3AE7 mechanism. Following the run of SMOKE-MOVES, NMOG
emissions were added to the data files through a post-SMOKE processor.
For nonroad mobile, speciation is partially done within MOVES such that it does not need to be run for a
specific chemical mechanism. For nonroad, MOVES outputs emissions of HAPs and NONHAPTOG are
split by speciation profile. Taking into account that integrated species were subtracted out by MOVES
already, the appropriate speciation profiles are then applied in SMOKE to get the VOC model species.
HAP integration for nonroad uses the same additional HAPs and ethanol as for onroad.
3.2.1.1 County specific profile combinations
SMOKE can compute speciation profiles from mixtures of other profiles in user-specified proportions via
two different methods. The first method, which uses a 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.
Starting with the 2016v7.2 beta and regional haze platforms, a GSPRO COMBO is used to specify a mix
of E0 and E10 fuels in Canada. ECCC provided percentages of ethanol use by province, and these were
converted into E0 and E10 splits. For example, Alberta has 4.91% ethanol in its fuel, so we applied a mix
of 49.1% E10 profiles (4.91% times 10, since 10% ethanol would mean 100%) E10), and 50.9% E0 fuel.
Ethanol splits for all provinces in Canada are listed in Table 3-5. The Canadian onroad inventory includes
96
-------
four distinct FIPS codes in Ontario, allowing for application of different E0/E10 splits in Southern
Ontario versus Northern Ontario. In Mexico, only EO profiles are used.
Table 3-5. Ethanol percentages by volume by Canadian province
Province
Ethanol % by volume (E10 = 10%)
Alberta
4.91%
British Columbia
5.57%
Manitoba
9.12%
New Brunswick
4.75%
Newfoundland & Labrador
0.00%
Nova Scotia
0.00%
NW Territories
0.00%
Nunavut
0.00%
Ontario (Northern)
0.00%
Ontari o ( S outhern)
7.93%
Prince Edward Island
0.00%
Quebec
3.36%
Saskatchewan
7.73%
Yukon
0.00%
A new method to combine multiple profiles became available in SMOKE4.5. It allows multiple profiles
to be combined by pollutant, state and county (i.e., state/county FIPS code) and SCC. This was used
specifically for the oil and gas sectors (pt oilgas and np oilgas) because SCCs include both controlled
and uncontrolled oil and gas operations which use different profiles.
3.2.1.2 Additional sector specific considerations for integrating HAP
emissions from inventories into speciation
The decision to integrate HAPs into the speciation was made on a sector-by-sector basis. For some
sectors, there is no integration and VOC is speciated directly; for some sectors, there is full integration
meaning all sources are integrated; and for other sectors, there is partial integration, meaning some
sources are not integrated and other sources are integrated. The integrated HAPs are either NBAFM or, in
the case of MOVES (onroad, nonroad, and MOVES-Mexico), a larger set of HAPs plus ethanol are
integrated. Table 3-4 above summarizes the integration method for each platform sector.
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).20 SMOKE essentially calculates the model-ready species by using the appropriate
20 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.
97
-------
emission factor without further speciation.21 Third, MOVES' internal speciation uses full integration of
an extended list of HAPs beyond NBAFM (called "M-profiles"). The M-profiles integration is very
similar to NBAFM integration explained above except that the integration calculation (see Figure 3-2) is
performed on emissions factors instead of on emissions, and a much larger set of pollutants are integrated
besides NBAFM. The list of integrated pollutants is described in Table 3-6. An additional run of the
Speciation Tool was necessary to create the M-profiles that were then loaded into the MOVES default
database. Fourth, for California, the EPA applied adjustment factors to SMOKE-MOVES to produce
California adjusted model-ready files. By applying the ratios through SMOKE-MOVES, the CARB
inventories are essentially speciated to match EPA estimated speciation. This resulted in changes to the
VOC HAPs from what CARB submitted to the EPA.
Table 3-6. MOVES integrated species in M-profiles
MOVES ID
Pollutant Name
5
Methane (CH4)
20
Benzene
21
Ethanol
22
MTBE
24
1,3-Butadiene
25
Formaldehyde
26
Acetaldehyde
27
Acrolein
40
2,2,4-Trimethylpentane
41
Ethyl Benzene
42
Hexane
43
Propionaldehyde
44
Styrene
45
Toluene
46
Xylene
185
Naphthalene gas
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 states provided emissions, MOVES-style speciation
has been implemented in 2016v2, with NONHAPTOG and PM2.5 pre-split by profiles and with all the
HAPs needed for VOC speciation augmented based on MOVES data in CA and TX. This means in
2016v2, onroad emissions in California and Texas are speciated consistently with the rest of the country,
while in 2016vl they were speciated using older speciation profiles.
21 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
-------
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 XYLMN and
SOAALK species in particular, so post-SMOKE we converted the emissions to CB6-CMAQ as follows:
• XYLMN = XYL[1]-0.966*NAPHTHALENE[1]
• PAR = PAR[1]-0.00001*NAPHTHALENE[1]
• SOAALK = 0.108*PAR[1]
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. BEIS3.7
includes the species (SESQ) that is mapped to the BEIS model species SESQT (Sesquiterpenes). The
profile code associated with BEIS3.7 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.22
3.2.1.3 Oil and gas related speciation profiles
Several oil and gas profiles were developed or assigned to sources in np oilgas and pt oilgas to better
reflect region-specific differences in VOC composition and whether the process SCC would include
controlled emissions, considering the controls are not part of the SCC. For example, SCC 2310030300
(Gas Well Water Tank Losses) in Colorado are controlled by a 95% efficient flare, so a profile
(DJTFLR95) was developed to represent the composition of the VOC exiting the flare. Region-specific
profiles were also available for several areas, some of which were included in SPECIATE5.1 and others
are slated to be added to SPECIATE5.2. These profiles are used in the 2016v2 platform and are listed in
Appendix B. Additional documentation is available in the SPECIATE database (for the SPECIATE5.1
profiles).
For the profiles planned to be released in SPECIATE 5.2:
1) 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."23
2) 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-
22 https://github.com/USEPA/CMAO/blob/main/CCTM/src/biog/beis3/gspro biogenics.txt.
23 https://www.southernute-nsn.gov/wp-content/uploads/sites/15/2019/12/191203-SUIT-CY2017-Emissions-Inventorv-Report-
FINAL.pdf.
99
-------
90524." 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 planned for SPECIATE 5.2.
3) 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 Quality,
https://www.tceq .texas. gov/assets/public/implementation/air/rules/Flare/201 Qflarestudv/2010-
flare-studv-final-report.pdf) 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 particular county/SCC
combinations using the SMOKE feature discussed in 3.2.1.1 that allows multiple profiles to be combined
within the chemical speciation cross reference file (GSREF) by pollutant, state/county, and SCC. Oil and
gas SCCs for associated gas, condensate tanks, crude oil tanks, dehydrators, liquids unloading and well
completions represent the total VOC from the process, including the portions of process that may be
flared or directed to a reboiler. For example, SCC 2310021400 (gas well dehydrators) consists of process,
reboiler, and/or flaring emissions. There are not separate SCCs for the flared portion of the process or the
reboiler. However, the VOC associated with these three portions can have very different speciation
profiles. Therefore, it is necessary to have an estimate of the amount of VOC from each of the portions
(process, flare, reboiler) so that the appropriate speciation profiles can be applied to each portion. The
Nonpoint Oil and Gas Emission Estimation Tool generates an intermediate file which provides flare, non-
flare (process), and reboiler (for dehydrators) emissions for six source categories that have flare
emissions: by county FIPS and SCC code for the U.S. From these emissions the fraction of the emissions
to assign to each profile was computed and incorporated into the 2016v2 platform. These fractions can
vary by county FIPS, because they depend on the level of controls, which is an input to the Speciation
Tool.
Table 3-7. Basin/Region-specific profiles for oil and gas
Profile Code
Description
Region
(if not in
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
24 http://doi.org/10.1016/i.scitotenv.2017.11.161.
100
-------
Profile Code
Description
Region
(if not in
profile
name)
PRM01 R
Permian Basin Produced Gas Composition for Non-CBM Wells
SSJCB R
South San Juan Basin Produced Gas Composition from CBM Wells
SSJCO R
South San Juan Basin Produced Gas Composition from Non-CBM Gas Wells
SWFLA R
SW Wyoming Basin Flash Gas Composition for Condensate Tanks
SWVNT R
SW Wyoming Basin Produced Gas Composition from Non-CBM Wells
UNT01 R
Uinta Basin Produced Gas Composition from CBM Wells
WRBCO R
Wind River Basin Produced Gagres Composition from Non-CBM Gas Wells
95087a
Oil and Gas - Composite - Oil Field - Oil Tank Battery Vent Gas
East
Texas
95109a
Oil and Gas - Composite - Oil Field - Condensate Tank Battery Vent Gas
East
Texas
95417
Uinta Basin, Untreated Natural Gas
95418
Uinta Basin, Condensate Tank Natural Gas
95419
Uinta Basin, Oil Tank Natural Gas
95420
Uinta Basin, Glycol Dehydrator
95398
Composite Profile - Oil and Natural Gas Production - Condensate Tanks
Denver-
Julesburg
95399
Composite Profile - Oil Field - Wells
California
95400
Composite Profile - Oil Field - Tanks
California
95403
Composite Profile - Gas Wells
San
Joaquin
UTUBOGC
Raw Gas from Oil Wells - Composite Uinta basin
UTUBOGD
Raw Gas from Gas Wells - Composite Uinta basin
UTUBOGE
Flash Gas from Oil Tanks - including Carbonyls - Composite Uinta basin
UTUBOGF
Flash Gas from Condensate Tanks - including Carbonyls - Composite Uinta basin
PAGAS01
Oil and Gas-Produced Gas Composition from Gas Wells-Greene Co, PA
PAGAS02
Oil and Gas-Produced Gas Composition from Gas Wells-Butler Co, PA
PAGAS03
Oil and Gas-Produced Gas Composition from Gas Wells-Washington Co, PA
SUIROGCT
Flash Gas from Condensate Tanks - Composite Southern Ute Indian Reservation
CMU01
Oil and Gas - Produced Gas Composition from Gas Wells - Central Montana Uplift
- Montana
WIL01
Oil and Gas - Flash Gas Composition from Tanks at Oil Wells - Williston Basin
North Dakota
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
101
-------
3.2.1.4 Mobile source related VOC speciation profiles
The VOC speciation approach for mobile source and mobile source-related source categories is
customized to account for the impact of fuels and engine type and technologies. The impact of fuels also
affects the parts of the nonpt and ptnonipm sectors that are related to mobile sources such as portable fuel
containers and gasoline distribution.
The VOC speciation profiles for the nonroad sector other than for California are listed in Table 3-8. They
include new profiles (i.e., those that begin with "953") for 2-stroke and 4-stroke gasoline engines running
on EO and E10 and compression ignition engines with different technologies developed from recent EPA
test programs, which also supported the updated toxics emission factor in MOVES2014a (Reichle, 2015
and EPA, 2015b).
Table 3-8. TOG MOVES-SMOKE Speciation for nonroad emissions used for the 2016 Platform
Profile
Profile Description
Engine
Type
Engine
Technology
Engine
Size
Horse-
power
category
Fuel
Fuel
Sub-
type
Emission
Process
95327
SI 2-stroke E0
SI 2-stroke
all
All
All
Gasoline
E0
exhaust
95328
SI 2-stroke E10
SI 2-stroke
all
All
All
Gasoline
E10
exhaust
95329
SI 4-stroke E0
SI 4-stroke
all
All
All
Gasoline
E0
exhaust
95330
SI 4-stroke E10
SI 4-stroke
all
All
All
Gasoline
E10
exhaust
95331
CI Pre-Tier 1
CI
Pre-Tier 1
All
All
Diesel
All
exhaust
95332
CI Tier 1
CI
Tier 1
All
All
Diesel
All
exhaust
95333
CI Tier 2
CI
Tier 2 and 3
all
All
Diesel
All
exhaust
95333a
25
CI Tier 2
CI
Tier 4
<56 kW
(75 hp)
S
Diesel
All
exhaust
8775
ACES Phase 1 Diesel
Onroad
CI Tier 4
Tier 4
>=56 kW
(75 hp)
L
Diesel
All
exhaust
8753
E0 Evap
SI
all
all
All
Gasoline
E0
evaporative
8754
E10 Evap
SI
all
all
All
Gasoline
E10
evaporative
8766
E0 evap permeation
SI
all
all
All
Gasoline
E0
permeation
8769
E10 evap permeation
SI
all
all
All
Gasoline
E10
permeation
8869
E0 Headspace
SI
all
all
All
Gasoline
E0
headspace
8870
E10 Headspace
SI
all
all
All
Gasoline
E10
headspace
1001
CNG Exhaust
All
all
all
All
CNG
All
exhaust
8860
LPG exhaust
All
all
all
All
LPG
All
exhaust
Speciation profiles for VOC in the nonroad sector account for the ethanol content of fuels across years. A
description of the actual fuel formulations can be found in NEITSD. For previous platforms, the EPA
used "COMBO" profiles to model combinations of profiles for EO and E10 fuel use, but beginning with
2014v7.0 platform, the appropriate allocation of EO and E10 fuels is done by MOVES.
25 95333a replaced 95333. This correction was made to remove alcohols due to suspected contamination. Additional
information is available in SPECIATE.
102
-------
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 2016v2 platform, all of these sources get E10
speciation.
Table 3-9 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for
2016. The term "COMBO" indicates that a combination of the profiles listed was used to speciate that
subcategory using the GSPROCOMBO file.
Table 3-9. Select mobile-related VOC profiles 2016
Sector
Sub-category
Profile
Nonroad non-US
gasoline exhaust
COMBO
8750a Pre-Tier 2 E0 exhaust
8751a Pre-Tier 2 E10 exhaust
nonpt/
ptnonipm
PFC and BTP
COMBO
8869 E0 Headspace
8870 E10 Headspace
nonpt/
ptnonipm
Bulk plant storage (BPS)
and refine-to-bulk terminal
(RBT) sources
8870 E10 Headspace
The speciation of onroad VOC occurs completely within MOVES. MOVES accounts for fuel type and
properties, emission standards as they affect different vehicle types and model years, and specific
emission processes. Table 3-10 describes the M-profiles available to MOVES depending on the model
year range, MOVES process (processID), fuel sub-type (fuelSubTypelD), and regulatory class
(regClassID). m While MOVES maps the liquid diesel profile to several processes, MOVES only
estimates emissions from refueling spillage loss (processID 19). The other evaporative and refueling
processes from diesel vehicles have zero emissions.
Table 3-11 through Table 3-13 describe the meaning of these MOVES codes. For a specific
representative county and future year, there will be a different mix of these profiles. For example, for HD
diesel exhaust, the emissions will use a combination of profiles 8774M and 8775M depending on the
proportion of HD vehicles that are pre-2007 model years (MY) in that particular county. As that county is
projected farther into the future, the proportion of pre-2007 MY vehicles will decrease. A second
example, for gasoline exhaust (not including E-85), the emissions will use a combination of profiles
8756M, 8757M, 8758M, 8750aM, and 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).
103
-------
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
9126
20,21,22
46,47
8774M
Pre-2007 MY HDD
exhaust
1940-2006
1,2,15,16
20,21,22
20,30
8775M
2007+ MY HDD
exhaust
2007-2050
1,2,15,16
20, 21, 22
20,30
8775M
2007+ MY HDD
exhaust
2007-2050
1,2,15,16,17,90
20, 21, 22
40,41,42,46,47,48
8855M
Tier 2 E85 Exhaust
1940-2050
1,2,15,16
50, 51, 52
10,20,30,40,41,
42,46,47,48
8869M
E0 Headspace
1940-2050
18
10
10,20,30,40,41,
42,46,47,48
8870M
E10 Headspace
1940-2050
18
12,13,14
10,20,30,40,41,
42,46,47,48
8871M
El5 Headspace
1940-2050
18
15,18
10,20,30,40,41,
42,46,47,48
8872M
El5 Evap
1940-2050
12,13,19
15,18
10,20,30,40,41,
42,46,47,48
8934M
E85 Evap
1940-2050
11
50,51,52
0
8934M
E85 Evap
1940-2050
12,13,18,19
50,51,52
10,20,30,40,41,
42,46,47,48
8750aM
Pre-Tier 2 E0 exhaust
1940-2000
1,2,15,16
10
20,30
8750aM
Pre-Tier 2 E0 exhaust
1940-2050
1,2,15,16
10
10,40,41,42,46,47,48
875 laM
Pre-Tier 2 E10 exhaust
1940-2000
1,2,15,16
11,12,13,14
20,30
875 laM
Pre-Tier 2 E10 exhaust
1940-2050
1,2,15,16
11,12,13,14,15,
1827
10,40,41,42,46,47,48
26 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.
27 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.
104
-------
Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
95120m
Liquid Diesel
19602060
11
20,21,22
0
95120m
Liquid Diesel
19602060
12,13,18,19
20,21,22
10,20,30,40,41,42,46,47,4
8
95335a
2010+MY HDD
exhaust
20102060
1,2,15,16,17,90
20,21,22
40,41,42,46,47,48
m While MOVES maps the liquid diesel profile to several processes, MOVES only estimates emissions from
refueling spillage loss (processID 19). The other evaporative and refueling processes from diesel vehicles have zero
emissions.
Table 3-11. MOVES process IDs
Process ID
Process Name
1
Running Exhaust*
2
Start Exhaust
9
Brakewear
10
Tirewear
11
Evap Permeation
12
Evap Fuel Vapor Venting
13
Evap Fuel Leaks
15
Crankcase Running Exhaust*
16
Crankcase Start Exhaust
17
Crankcase Extended Idle Exhaust
18
Refueling Displacement Vapor Loss
19
Refueling Spillage Loss
20
Evap Tank Permeation
21
Evap Hose Permeation
22
Evap RecMar Neck Hose Permeation
23
Evap RecMar Supply/Ret Hose Permeation
24
Evap RecMar Vent Hose Permeation
30
Diurnal Fuel Vapor Venting
31
HotSoak Fuel Vapor Venting
32
RunningLoss Fuel Vapor Venting
40
Nonroad
90
Extended Idle Exhaust
91
Auxiliary Power Exhaust
* Off-network idling is a process in MOVE S3 that is part ofprocesses 1 and 15
but assigned to road type 1 (off-network) instead of types 2-5
Table 3-12. MOVES Fuel subtype IDs
Fuel Subtype ID
Fuel Subtype Descriptions
10
Conventional Gasoline
11
Reformulated Gasoline (RFG)
12
Gasohol (E10)
105
-------
Fuel Subtype ID
Fuel Subtype Descriptions
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
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. Of particular note for
the 2016v7.2 beta and regional haze platforms, the nonroad PM2.5 speciation was updated as discussed
later in this section. Most of the PM profiles come from the 911XX series (Reff et. al, 2009), which
include updated AE6 speciation.28 Starting with the 2014v7.1 platform, profile 91112 (Natural Gas
28 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.
106
-------
Combustion - Composite) was replaced with 95475 (Composite -Refinery Fuel Gas and Natural Gas
Combustion). This updated profile is an AE6-ready profile based on the median of 3 SPECIATE4.5
profiles from which AE6 versions were made (to be added to SPECIATE5.0): boilers (95125a), process
heaters (95126a) and internal combustion combined cycle/cogen plant exhaust (95127a). As with profile
91112, these profiles are based on tests using natural gas and refinery fuel gas (England et al., 2007).
Profile 91112 which is also based on refinery gas and natural gas is thought to overestimate EC.
Profile 95475 (Composite -Refinery Fuel Gas and Natural Gas Combustion) is shown along with the
underlying profiles composited in Figure 3-3. Figure 3-4 shows a comparison of the new profile as of the
2014v7.1 platform with the one that we had been using in the 2014v7.0 and earlier platforms.
The newest PM profile for the 2016v2 platform is the Sugar Cane Pre-Harvest Burning Mexico profile
(SUGP02). This profile falls under the sector ptagfire and are included in SPECIATE 5.1.
Additionally, a series of regional fire profiles have been added to SPECIATE 5.1 and are used in 2016v2.
These fall under the sector ptfire and are as shown in Table 3-14.
Table 3-14. Regional Fire Profiles
Sector
Pollutan
t
Profile
Code
Profile Description
Ptfire
PM
95793
Forest Fire-Flaming-Oregon AE6
Ptfire
PM
95794
Forest Fire-Smoldering-Oregon AE6
Ptfire
PM
95798
Forest Fire-Flaming-North Carolina AE6
Ptfire
PM
95799
Forest Fire-Smoldering-North Carolina AE6
Ptfire
PM
95804
Forest Fire-Flaming-Montana AE6
Ptfire
PM
95805
Forest Fire-Smoldering-Montana AE6
Ptfire
PM
95807
Forest Fire Understory-Flaming-Minnesota AE6
Ptfire
PM
95808
Forest Fire Understory-Smoldering-Minnesota AE6
Ptfire
PM
95809
Grass Fire-Field-Kansas AE6
107
-------
Figure 3-3. Profiles composited for PIY1 gas combustion related sources
Zinc
Sulfate
Silicon
Potassium
Particulate Non-Carbon Organic Matter
Other Unspeciated PM2.5
Organic carbon
Nitrate
Nickel
Metal-bound Oxygen
Iron
Elemental Carbon
Copper
Chloride ion
Calcium
Bromine Atom
Ammonium
Aluminum
0 10 20 30 40 50
Weight Percent
I Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
Gas-fired process heater exhaust 95126a
s Gas-fired internal combustion combined cycle/cogeneration plant exhaust 95127a
Gas-fired boiler exhaust 95125a
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources
Zinc
Sulfate
Silicon
Potassium
Particulate Non-Carbon Organic Matter
Other Unspeciated PM2.5
Organic carbon
Nitrate
Nickel
Metal-bound Oxygen
Iron
Elemental Carbon
Copper
Chloride ion
Calcium
Bromine Atom
Ammonium
Aluminum
20 30 40
Weight Percent
I Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
Natural Gas Combustion - Composite 91112
108
-------
3.2.2.1 Mobile source related PM2.5 speciation profiles
For the onroad sector, for all processes except brake and tire wear, PM speciation occurs within MOVES
itself, not within SMOKE (similar to the VOC speciation described above). The advantage of using
MOVES to speciate PM is that during the internal calculation of MOVES, the model has complete
information on the characteristics of the fleet and fuels (e.g., model year, sulfur content, process, etc.) to
accurately match to specific profiles. This means that MOVES produces EF tables that include total PM
(e.g., PMio and PM2.5) and speciated PM (e.g., PEC, PFE). SMOKE essentially calculates the PM
components by using the appropriate EF without further speciation.29 The specific profiles used within
MOVES include two CNG profiles, 45219 and 45220, which were added to SPECIATE4.5. A list of
profiles is provided in the technical document, "Speciation of Total Organic Gas and Particulate Matter
Emissions from On-road Vehicles in MOVES2014" (EPA, 2015c).
For onroad brake and tire wear, the PM is speciated in the moves2smk postprocessor that prepares the
emission factors for processing in SMOKE. The formulas for this are based on the standard speciation
factors from brake and tire wear profiles, which were updated from the v6.3 platform based on data from
a Health Effects Institute report (Schauer, 2006). Table 3-15 shows the differences in the v7.1 (alpha) and
201 lv6.3 profiles.
Table 3-15. Brake and tire PM2.5 profiles compared to those used in the 2011v6.3 Platform
Inventory
Model
V6.3 platform
SPECIATE4.5
V6.3
SPECIATE4.5
Pollutant
Species
brakewear
brakewear profile:
platform
tirewear profile:
profile: 91134
95462 from
tirewear
95460 from
Schauer(2006)
profile:
Schauer(2006)
91150
PM2 5
PAL
0.00124
0.000793208
6.05E-04
3.32401E-05
PM2 5
PCA
0.01
0.001692177
0.00112
PM2 5
PCL
0.001475
0.0078
PM2 5
PEC
0.0261
0.012797085
0.22
0.003585907
PM2 5
PFE
0.115
0.213901692
0.0046
0.00024779
PM2 5
PH20
0.0080232
0.007506
PM2 5
PK
1.90E-04
0.000687447
3.80E-04
4.33129E-05
PM2 5
PMG
0.1105
0.002961309
3.75E-04
0.000018131
PM2 5
PMN
0.001065
0.001373836
1.00E-04
1.41E-06
PM2 5
PMOTHR
0.4498
0.691704999
0.0625
0.100663209
PM2 5
PNA
1.60E-04
0.002749787
6.10E-04
7.35312E-05
PM2 5
PNCOM
0.0428
0.020115749
0.1886
0.255808124
PM2 5
PNH4
3.00E-05
1.90E-04
PM2 5
PN03
0.0016
0.0015
PM2 5
POC
0.107
0.050289372
0.4715
0.639520309
PM2 5
PSI
0.088
0.00115
PM2 5
PS04
0.0334
0.0311
PM2 5
PTI
0.0036
0.000933341
3.60E-04
5.04E-06
29 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/.
109
-------
The formulas used based on brake wear profile 95462 and tire wear profile 95460 are as follows:
POC = 0.6395 * PM25TIRE + 0.0503 * PM25BRAKE
PEC = 0.0036 * PM25TIRE + 0.0128 * PM25BRAKE
PN03 = 0.000 * PM25TIRE + 0.000 * PM25BRAKE
PS04 = 0.0 * PM25TIRE + 0.0 * PM25BRAKE
PNH4 = 0.000 * PM25TIRE + 0.0000 * PM25BRAKE
PNCOM = 0.2558 * PM25TIRE + 0.0201 * PM25BRAKE
For California onroad emissions, adjustment factors were applied to SMOKE-MOVES to produce
California adjusted model-ready files. California did not supply speciated PM, therefore, the adjustment
factors applied to PM2.5 were also applied to the speciated PM components. By applying the ratios
through SMOKE-MOVES, the CARB inventories are essentially speciated to match EPA estimated
speciation.
For nonroad PM2.5, speciation is partially done within MOVES such that it does not need to be run for a
specific chemical mechanism. For nonroad, MOVES outputs emissions of PM2.5 split by speciation
profile. Similar to how VOC and NONHAPTOG are speciated, PM2.5 is now also speciated this way
starting with MOVES2014b. For California and Texas, PM2.5 emissions split by speciation profile are
estimated from total PM2.5 based on MOVES data in California and Texas, so that PM is speciated
consistently across the country. The PM2.5 profiles assigned to nonroad sources are listed in Table 3-16.
Table 3-16. Nonroad PM2.5 profiles
SPECIATE4.5
Profile Code
SPECIATE4.5 Profile Name
Assigned to Nonroad
sources based on Fuel
Type
8996
Diesel Exhaust - Heavy-heavy duty truck - 2007
model year with NCOM
Diesel
91106
HDDV Exhaust - Composite
Diesel
91113
Nonroad Gasoline Exhaust - Composite
Gasoline
95219
CNG Transit Bus Exhaust
CNG and LPG
3.2.3 NOx speciation
NOx emission factors and therefore NOx inventories are developed on a NO2 weight basis. For air quality
modeling, NOx is speciated into NO, NO2, and/or HONO. For the non-mobile sources, the EPA used a
single profile "NHONO" to split NOx into NO and NO2.
The importance of HONO chemistry, identification of its presence in ambient air and the measurements of
HONO from mobile sources have prompted the inclusion of HONO in NOx speciation for mobile
sources. Based on tunnel studies, a HONO to NOx ratio of 0.008 was chosen (Sarwar, 2008). For the
mobile sources, except for onroad (including nonroad, cmv, rail, othon sectors), and for specific SCCs in
othar and ptnonipm, the profile "HONO" is used. Table 3-17 gives the split factor for these two profiles.
The onroad sector does not use the "HONO" profile to speciate NOx. MOVES2014 produces speciated
NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables
used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOx. The NO
fraction varies by heavy duty versus light duty, fuel type, and model year.
110
-------
The NO2 fraction = 1 - NO - HONO. For more details on the NOx fractions within MOVES, see EPA
report "Use of data from 'Development of Emission Rates for the MOVES Model,'
Sierra Research, March 3, 2010" available at
https://nepis.epa. gov/Exe/ZyPDF.cgi?Dockev=Pl 00FlA5.pdf.
Table 3-17. NOx speciation profiles
Profile
pollutant
species
split factor
HONO
NOX
N02
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
N02
0.1
NHONO
NOX
NO
0.9
3.2.4 Creation of Sulfuric Acid Vapor (SULF)
Since at least the 2002 Platform, sulfuric acid vapor (SULF) has been estimated through the SMOKE
speciation process for coal combustion and residual and distillate oil fuel combustion sources. Profiles
that compute SULF from SO2 are assigned to coal and oil combustion SCCs in the GSREF ancillary file.
The profiles were derived from information from AP-42 (EPA, 1998), which identifies the fractions of
sulfur emitted as sulfate and SO2 and relates the sulfate as a function of S02.
Sulfate is computed from SO2 assuming that gaseous sulfate, which is comprised of many components, is
primarily H2SO4. The equation for calculating FhSO/ds given below.
Emissions of SULF (as H2S04) Equation 3-1
fraction of S emitted as sulfate MW H2S04
= S07 emissions x — - x
fraction of S emitted as S02 MW S02
In the above, MW is the molecular weight of the compound. The molecular weights of H2SO4 and SO2
are 98 g/mol and 64 g/mol, respectively.
This method does not reduce SO2 emissions; it solely adds gaseous sulfate emissions as a function of S02
emissions. The derivation of the profiles is provided in Table 3-18; a summary of the profiles is provided
in Table 3-19.
Table 3-18. Sulfate split factor computation
fuel
SCCs
Profile
Code
Fraction
as S02
Fraction as
sulfate
Split factor (mass
fraction)
Bituminous
1-0X-002-YY, where X is 1,
2 or 3 and YY is 01 thru 19
and 21-ZZ-002-000 where
ZZ is 02,03 or 04
95014
0.95
0.014
.014/.95 * 98/64 =
0.0226
Subbituminous
1-0X-002-YY, where X is 1,
2 or 3 and YY is 21 thru 38
87514
.875
0.014
.014/.875 * 98/64 =
0.0245
111
-------
Lignite
1-0X-003-YY, where X is 1,
2 or 3 and YY is 01 thru 18
and 21-ZZ-002-000 where
ZZ is 02,03 or 04
75014
0.75
0.014
.014/.75 * 98/64 =
0.0286
Residual oil
1-0X-004-YY, where X is 1,
2 or 3 and YY is 01 thru 06
and 21-ZZ-005-000 where
ZZ is 02,03 or 04
99010
0.99
0.01
.01/ 99 * 98/64 =
0.0155
Distillate oil
1-0X-005-YY, where X is 1,
2 or 3 and YY is 01 thru 06
and 21-ZZ-004-000 where
ZZ is 02,03 or 04
99010
0.99
0.01
Same as residual oil
Table 3-19. SO2 speciation profiles
Profile
pollutant
species
split factor
95014
S02
SULF
0.0226
95014
S02
S02
1
87514
S02
SULF
0.0245
87514
S02
S02
1
75014
S02
SULF
0.0286
75014
S02
S02
1
99010
S02
SULF
0.0155
99010
S02
S02
1
3.3 Temporal Allocation
Temporal allocation is the process of distributing aggregated emissions to a finer temporal resolution,
thereby converting annual emissions to hourly emissions as is required by CMAQ. While the total
emissions are important, the timing of the occurrence of emissions is also essential for accurately
simulating ozone, PM, and other pollutant concentrations in the atmosphere. Many emissions inventories
are annual or monthly in nature. Temporal allocation takes these aggregated emissions and distributes the
emissions to the hours of each day. This process is typically done by applying temporal profiles to the
inventories in this order: monthly, day of the week, and diurnal, with monthly and day-of-week profiles
applied only if the inventory is not already at that level of detail.
The temporal factors applied to the inventory are selected using some combination of country, state,
county, SCC, and pollutant. Table 3-20 summarizes the temporal aspects of emissions modeling by
comparing the key approaches used for temporal processing across the sectors. In the table, "Daily
temporal approach" refers to the temporal approach for getting daily emissions from the inventory using
the SMOKE Temporal program. The values given are the values of the SMOKE L TYPE setting. The
"Merge processing approach" refers to the days used to represent other days in the month for the merge
step. If this is not "all," then the SMOKE merge step runs only for representative days, which could
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).
112
-------
Table 3-20. Temporal settings used for the platform sectors in SMOKE
Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process holidays
as separate days
afdust adj
Annual
Yes
week
All
Yes
afdust ak adj
Annual
Yes
week
All
Yes
airports
Annual
Yes
week
week
Yes
beis
Hourly
No
n/a
All
No
Canada ag
Monthly
No
mwdss
mwdss
No
Canada og2D
Annual
Yes
mwdss
mwdss
No
cmv clc2
Annual
Yes
aveday
aveday
No
cmv c3
Annual
Yes
aveday
aveday
No
fertilizer
Monthly
No
all
all
No
livestock
Annual
Yes
all
all
No
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
No
mwdss
mwdss
Yes
np oilgas
Annual
Yes
aveday
aveday
No
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 adi
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 adi
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
solvents
Annual
Yes
aveday
aveday
No
'Note the annual and monthly "inventory" actually refers to the activity data (VMT, hoteling, and VPOP) for onroad.
VMT and hoteling is monthly and VPOP is annual. The actual emissions are computed on an hourly basis.
2Only units that do not have matching hourly CEMS data use monthly temporal profiles.
3Except for 2 SCCs that do not use met-based speciation
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
113
-------
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the
month. The value "mwdss" means hourly emissions for one representative Monday, representative
weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each month.
This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within
the month, but not week-to-week variation within the month. The value "aveday" means hourly
emissions computed for one representative day of each month, meaning emissions for all days within a
month are the same. Special situations with respect to temporal allocation are described in the following
subsections.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2016, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2015). For most sectors, emissions from December 2016
(representative days) were used to fill in emissions for the end of December 2015. For biogenic
emissions, December 2015 emissions were processed using 2015 meteorology.
3.3.1 Use of FF10 format for finer than annual emissions
The FF10 inventory format for SMOKE provides a consolidated format for monthly, daily, and hourly
emissions inventories. With the FF10 format, a single inventory file can contain emissions for all 12
months and the annual emissions in a single record. This helps simplify the management of numerous
inventories. Similarly, daily and hourly FF10 inventories contain individual records with data for all days
in a month and all hours in a day, respectively.
SMOKE prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual-to-month temporal allocation applied to it;
rather, it should only have month-to-day and diurnal temporal allocation. This becomes particularly
important when specific sectors have a mix of annual, monthly, daily, and/or hourly inventories. The
flags that control temporal allocation for a mixed set of inventories are discussed in the SMOKE
documentation. The modeling platform sectors that make use of monthly values in the FF10 files are
livestock, nonroad, onroad, onroad can, onroadmex, othar, and othpt.
3.3.2 Electric Generating Utility temporal allocation (ptegu)
3.3.2.1 Base year temporal allocation of EGUs
The temporal allocation procedure for EGUs in the base year is differentiated by whether or not the unit
could be directly matched to a unit with CEMS data via its ORIS facility code and boiler ID. Note that
for units matched to CEMS data, annual totals of their emissions input to CMAQ may be different than
the annual values in the 2016 annual inventory because the CEMS data replaces the NOx and SO2 annual
inventory data for the seasons in which the CEMS are operating. If a CEMS-matched unit is determined
to be a partial year reporter, as can happen for sources that run CEMS only in the summer, emissions
totaling the difference between the annual emissions and the total CEMS emissions are allocated to the
non-summer months. Prior to use of the CEMS data in SMOKE it is processed through the CEMCorrect
tool. The CEMCorrect tool identifies hours for which the data were not measured as indicated by the data
quality flags in the CEMS data files. Unmeasured data can be filled in with maximum values and thereby
cause erroneously high values in the CEMS data. When data were flagged as unmeasured and the values
were found to be more than three times the annual mean for that unit, the data for those hours are replaced
with annual mean values (Adelman et al., 2012). These adjusted CEMS data were then used for the
remainder of the temporal allocation process described below (see Figure 3-5 for an example).
114
-------
Figure 3-5. Eliminating unmeasured spikes in ("K.MS data
6000 -
5000 -
4000 -
.c
XI
g 3000 ¦
i/i
2000
1000
2016 January CEMs for 6068 3
2016 Original CEMs
2016 Corrected CEMs
U
>\_r*wVv—<\/A
W\n
K-0V
„.0V
A®
TP
„av
.V
„.ov
,.0V
.a"5
-.0^
,0V
-I0
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 2016vl
and 2016v2 platforms, the small EGU temporalization process was improved to also consider peaking
units.
The region, fuel, and type (peaking or non-peaking) were identified for each input EGU with CEMS data
that are used for generating profiles. The identification of peaking units was based on hourly heat input
data from the 2016 base year and the two previous years (2014 and 2015). The heat input was summed for
each year. Equation 3-2 shows how the annual heat input value is converted from heat units (BTU/year) to
power units (MW) using the unit-level heat rate (BTU/kWh) derived from the NEEDS v6 database. In
Equation 3-3 a capacity factor is calculated by dividing the annual unit MW value by the NEEDS v6 unit
capacity value (MW) multiplied by the hours in the year. A peaking unit was defined as any unit that had
a maximum capacity factor of less than 0.2 for every year (2014, 2015, and 2016) and a 3-year average
capacity factor of less than 0.1.
115
-------
Annual Unit Power Output
8760 Hourly HI ,MW\
Li=0 (BTU) \kW)
NEEDS Heat Rate (|^)
Annual Unit Output (MW) = —— Equation 3-2
Capacity Factor =
Unit Capacity Factor
Annual Unit Output (MW)
NEEDS Unit Capacity (~-) * 8760 (h) Equation 3-3
Input regions were determined from one of the eight EGU modeling regions based on MJO and climate
regions. Regions were used to group units with similar climate-based load demands. Region assignment is
made on a state level, where all units within a state were assigned to the appropriate region. Unit fuel
assignments were made using the primary NEEDS v6 fuel. Units fueled by bituminous, subbituminous, or
lignite are assigned to the coal fuel type. Natural gas units were assigned to the gas fuel type. Distillate
and residual fuel oil were assigned to the oil fuel type. Units with any other primary fuel were assigned
the "other" fuel type. The number of units used to calculate the daily and diurnal EGU temporal profiles
are shown in Figure 3-6 by region, fuel, and for peaking/non-peaking. Currently there are 64 unique
profiles available based on 8 regions, 4 fuels, and 2 for peaking unit status (peaking and non-peaking).
Figure 3-6. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification
Small EGU 2016 Temporal Profile Input Unit Counts
'ffcxtfwrsfj
(pcatonQ/y>onpqa*inq);
COd * 0 / 1 | V
.9as:ll-/-2S H
0.1:0/0 / ^
other Oj' 0
HorthQntral
KM4E-VU .
.. long rtcnp afc>oSl
'Wot ¦ J
(peatonj'nonpMiung!:
coal 0/3
5*4:99/137
'oil: 0 / 0
08«er 0/<
Southwest
(pealunjfnontwfcng):
G&X1JS
KSARM ^
(prakjng/Tonpwl ng):
coal: 1 /166"^
9»:376fW5—-v_
O): 74/8 \
ottwr:0/« \
Sooth
(pesakiog/'norpeakjog):
coal: 0/97 ]
9M.«3rJJ7-t
oi: 18/0 |
ether: 0/4 ^
LAOCO
_(pwlur9eUong)
'ij 155
EGU Regions
¦ LAOCO
¦ MANE-VU
~ Northwest
~ SESARM
~ Soutti
~ Southwest
~ West
~ West North Central
116
-------
The daily and diurnal profiles were calculated for each region, fuel, and peaking type group from the year
2016 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 3-7 shows peaking and non-peaking daily temporal profiles for the gas fuel type in the LADCO
region. Figure 3-8 shows the diurnal profiles for the coal fuel type in the Mid-Atlantic Northeast Visibility
Union (MANE-VU) region.
Figure 3-7. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type
2016
117
-------
Figure 3-8. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type
Diurnal Small EGU Profile for MANE-VU coal
SMOKE uses a cross-reference file to select a monthly, daily, and diurnal profile for each source. For the
2016 platforms, the temporal profiles were assigned in the cross-reference at the unit level to EGU
sources without hourly CEMS data. An inventory of all EGU sources without CEMS data was used to
identify the region, fuel type, and type (peaking/non-peaking) of each source. As with the input unit the
regions are assigned using the state from the unit FIPS. The fuel was assigned by SCC to one of the four
fuel types: coal, gas, oil, and other. A fuel type unit assignment is made by summing the VOC, NOX,
PM2.5, and S02 for all SCCs in the unit. The SCC that contributed the highest total emissions to the unit
for selected pollutants was used to assign the unit fuel type. Peaking units were identified as any unit with
an oil, gas, or oil fuel type with a NAICS of 22111 or 221112. Some units may be assigned to a fuel type
within a region that does not have an available input unit with a matching fuel type in that region. These
units without an available profile for their group were assigned to use the regional composite profile.
MWC and cogen units were identified using the NEEDS primary fuel type and cogeneration flag,
respectively, from the NEEDS v6 database. The number of EGU units assigned each profile group are
shown by region in Figure 3-9.
118
-------
Figure 3-9. Non-CEMS EGU Temporal Profile Application Counts
Small EGU 2016 Temporal Profile Application Counts
uuxo
(PN^iunsm*):
cosl: 2*/8
MANE VO
EGU Regions
¦ LADCO
¦ MANE VU
NorOiwest
~ SESARM
I I South
O Southwest
~ West
¦ West Horth Central
3.3.2.2 Future year temporal allocation of EGUs
For future 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
Integrated Planning Model (1PM) for all units. The winter shoulder was newly separated from the winter
months in 2016v2 platform. The seasonal emissions for the future year cases were produced by post
processing of the IPM outputs. The unit-level data were converted into hourly values through the
temporal allocation process using a 3-step methodology: annualized summer/winter value to month,
month to day, and day to hour. CEMS data from the air quality analysis year (e.g., 2016) is used as much
as possible to temporally allocate the EGU emissions.
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
future-year emission projections into future-year air quality modeling. The temporal allocation process is
applied to the seasonal emission projections for the three 1PM seasons: summer (May through
September), winter shoulder (October through November and March through April), and winter
(December through February). The Flat File used as the input to the temporal allocation process contains
unit-level emissions and stack parameters (i.e., stack location and other characteristics consistent with
information found in the NEI). When the flat file is produced from post-processed IPM outputs, a cross
reference is used to map the units in version 6 of the NEEDS database to the stack parameter and facility,
unit, release point, and process identifiers used in the NEI. This cross reference also maps sources to the
hourly CEMS data used to temporally allocate the emissions in the base year air quality modeling.
119
-------
All units have seasonal information provided in the future year Flat File, the monthly values in the Flat
File input to the temporal allocation process are computed by multiplying the average summer day,
average winter shield day, and average winter day emissions by the number of days in the respective
month. When generating seasonal emissions totals from the Flat File winter shield emissions are summed
with the winter emissions to create a total winter season. In summary, the monthly emission values shown
in the Flat File are not intended to represent an actual month-to-month emission pattern. Instead, they are
interim values that have translated IPM's seasonal projections into month-level data that serve as a
starting point for the temporal allocation process.
The monthly emissions within the Flat File undergo a multi-step temporal allocation process to yield the
hourly emission values at each unit, as is needed for air quality modeling: summer or winter value to
month, month to day, and day to hour. For sources not matched to unit-specific CEMS data, the first two
steps are done outside of SMOKE and the third step to get to hourly values is done by SMOKE using the
daily emissions files created from the first two steps. For each of these three temporal allocation steps,
NOx and SO2 CEMS data are used to allocate NOxand SO2 emissions, while CEMS heat input data are
used to allocate all other pollutants. The approach defined here gives priority to temporalization based on
the base year CEMS data to the maximum extent possible for both base and future year modeling.
Prior to using the 2016 CEMS data to develop monthly, daily, and hourly profiles, the CEMS data were
processed through the CEMCorrect tool to make adjustments for hours for which data quality flags
indicated the data were not measured and that the reported values were much larger than the annual mean
emissions for the unit. These adjusted CEMS data were used to compute the monthly, daily, and hourly
profiles described below.
For units that have CEMS data available and that have CEMS units matched to the NEI sources, the
emissions are temporalized according to the base year (i.e., 2016) CEMS data for that unit and pollutant.
For units that are not matched to the NEI or for which CEMS data are not available, the allocation of the
seasonal emissions to months is done using average fuel-specific season-to-month factors for both
peaking and non-peaking units generated for each of the eight regions shown in Figure 5. These factors
are based on a single year of CEMS data for the modeling base year associated with the air quality
modeling analysis being performed, such as 2016. The fuels used for creating the profiles for a region
were coal, natural gas, oil, and "other". The "other "fuels category is a broad catchall that includes fuels
such as wood and waste. Separate profiles are computed for NOx, SO2, and heat input, where heat input is
used to temporally allocate emissions for pollutants other than NOx and SO2. An overall composite
profile across all fuels is also computed and can be used in the event that a region has too few units of a
fuel type to make a reasonable average profile, or in the case when a unit changes fuels between the base
and future 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 future-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
120
-------
input into SMOKE, which uses temporal profiles to disaggregate the daily values into specific values for
each hour of the year.
For units without or not matched to CEMS data, or for which the CEMS data are found to be unsuitable
for use in the future 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 future 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 future year; (4) when there were no CEMS data for one season in the base year
but IPM runs the unit during both seasons; or (5) units experienced atypical conditions during the base
year, such as lengthy downtimes for maintenance or installation of controls.
The temporal profiles that map emissions from days to hours were computed based on the region and
fuel-specific seasonal (i.e., winter and summer) average day-to-hour factors derived from the CEMS data
for heat input for those fuels and regions and for that season. Heat input was used because it is the
variable that is the most complete in the CEMS data and should be present for all of the hours in which
the unit was operating. SMOKE uses these diurnal temporal profiles to allocate the daily emissions data
to hours of each day. Note that this approach results in each unit having the same hourly temporal
allocation for all the days of a season.
The emissions from units for which unit-specific profiles were not used were temporally allocated to
hours reflecting patterns typical of the region in which the unit is located. Analysis of CEMS data for
units in each of the 8 regions shown in Figure 3-6 revealed that there were differences in the temporal
patterns of historic emission data that correlate with fuel type (e.g., coal, gas, oil, and other), time of year,
pollutant, season (i.e., winter versus summer) and region of the country. The correlation of the temporal
pattern with fuel type is explained by the relationship of units' operating practices with the fuel burned.
For example, coal units take longer to ramp up and ramp down than natural gas units, and some oil units
are used only when electricity demand cannot otherwise be met. Geographically, the patterns were less
dependent on state location than they were on regional location. Figure 3-7 provides an example of daily
profiles for gas fuel in the LADCO region. The EPA developed seasonal average emission profiles, each
derived from base year CEMS data for each season across all units sharing both IPM region and fuel type.
Figure 3-8 provides an example of seasonal profiles that allocate daily emissions to hours in the MANE-
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 future year Flat File. Any
pollutants other than NOx and SO2 were temporally allocated using heat input. Through the temporal
allocation process, the future year emissions will have the same temporal pattern as the base year CEMS
data, where available, while the future-year seasonal total emissions for each unit match the future-year
unit-specific projection for each season (see example in Figure 3-10). The future year IPM output for
121
-------
2025 maps to the year 2026 and the IPM output for 2030 maps to year 2032 and were therefore used for
the respective 2026 and 2032 modeling cases.
In cases when the emissions for a particular unit are projected to be substantially higher in the future 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 future case, the maximum measured emissions of NOx and
SO2 in the period of 2014-2017 were computed. The temporally allocated emissions were then evaluated
at each hour to determine whether they were above this maximum. The amount of "excess emissions"
over the maximum were then computed. For units for which the "excess emissions" could be reallocated
to other hours, those emissions were distributed evenly to hours that were below the maximum. Those
hourly emissions were then reevaluated against the maximum, and the procedure of reallocating the
excess emissions to other hours was repeated until all of the hours had emissions below the maximum,
whenever possible (see example in Figure 3-11).
Figure 3-10. Future Year Emissions Follow the Pattern of Base Year Emissions
2030 and 2016 Summer CEMs for 2277 1
122
-------
Figure 3-11. Excess Emissions Apportioned to Hours Less than the Historic Maximum
12000
10000
8000
.c
§ 6000
(N
o
l/l
4000
2000
May Jun Jul Aug Sep
2016
Date
Using the above approach, it was not always possible to reallocate excess emissions to hours below the
historic maximum, such as when the total seasonal emissions of NOx or SO2 for a unit divided by the
number of hours of operation are greater than the 2014-2017 maximum emissions level. For these units,
the regional fuel-specific average profiles were applied to all pollutants, including heat input, for the
respective season (see example in Figure 3-12). It was not possible for SMOKE to use regional profiles
for some pollutants and adjusted CEMS data for other pollutants for the same unit and season, therefore,
all pollutants in the unit and season are assigned to regional profiles when regional profiles are needed.
For some units, hourly emissions values still exceed the 2014-2017 annual maximum for the unit even
after regional profiles were applied (see example in Figure 3-13).
2030 and 2016 Summer CEMs for 3943 2
2016 CEMs
2030 CEMs
2030 Adjusted CEMs
Annual unit max
123
-------
Figure 3-12. Regional Profile Applied due to not being able to Adjust below Historic Maximum
2030 and 2016 Summer CEMs for 6095 2
May Jun Jul Aug Sep
2016
Date
124
-------
Figure 3-13. Regional Profile Applied, but Exceeds Historic Maximum in Some Hours
2030 and 2016 Summer CEMs for 2103J.
•Q
2016 CEMS
2030 CEMS
2030 Adjusted CEMs
2030 Season Fuel
Annual unit max
May
2016
3000 -
2000 -
1000 -
3.3.3 Airport Temporal allocation (airports)
Airport temporal profiles were updated in 2014v7.0 and were kept the same for the 2016v2 platform. All
airport SCCs (i.e., 2275*, 2265008005, 2267008005, 2268008005 and 2270008005) were given the same
hourly, weekly and monthly profile for all airports other than Alaska seaplanes (which are not in the
CMAQ modeling domain). Hourly airport operations data were obtained from the Aviation System
Performance Metrics (ASPM) Airport Analysis website (https://aspm.faa.gov/apm/svs/AnalvsisAP asp).
A report of 2014 hourly Departures and Arrivals for Metric Computation was generated. An overview of
the ASPM metrics is at
http://aspmhelp.faa.gov/index.php/Aviation Performance Metrics %28APM%29. Figure 3-14 shows
the diurnal airport profile.
Weekly and monthly temporal profiles are based on 2014 data from the FAA Operations Network Air
Traffic Activity System (http://aspm.faa.gov/opsnet/sys/Terminal.asp). A report of all airport operations
(takeoffs and landings) by day for 2014 was generated. These data were then summed to month and day-
of-week to derive the monthly and weekly temporal profiles shown in Figure 3-14, Figure 3-15, and
Figure 3-16. An overview of the Operations Network data system is at
http://aspmhelp.faa.gov/index.php/Operations Network %28QPSNET%29. The weekly and monthly
profiles from 2014 are still used in the 2016 platforms.
Alaska seaplanes, which are outside the CONUS domain use the same monthly profile as in the 2011
platform shown in Figure 3-17. These were assigned based on the facility ID.
125
-------
Figure 3-14. Diurnal Profile for all Airport SCCs
Diurnal Airport Profile
Hour
Figure 3-15. Weekly profile for all Airport SCCs
Weekly Airport Profile
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
.6
¦JO
6
J?
126
-------
Figure 3-16. Monthly Profile for all Airport SCCs
Monthly Airport Profile
0.04
0.03
0.02
0.01
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 3-17. Alaska Seaplane Profile
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3.3.4 Residential Wood Combustion Temporal allocation (rwc)
There are many factors that impact the timing of when emissions occur, and for some sectors this includes
meteorology. The benefits of utilizing meteorology as a method for temporal allocation are: (1) a
meteorological dataset consistent with that used by the AQ model is available (e.g., outputs from WRF);
(2) the meteorological model data are highly resolved in terms of spatial resolution; and (3) the
meteorological variables vary at hourly resolution and can, therefore, be translated into hour-specific
temporal allocation.
The SMOKE program Gentpro provides a method for developing meteorology-based temporal allocation.
Currently, the program can utilize three types of temporal algorithms: annual-to-day temporal allocation
for residential wood combustion (RWC); month-to-hour temporal allocation for agricultural livestock
127
-------
NH3; and a generic meteorology-based algorithm for other situations. Meteorological-based temporal
allocation was used for portions of the rwc sector and for the entire ag sector.
Gentpro reads in gridded meteorological data (output from MCIP) along with spatial surrogates and uses
the specified algorithm to produce a new temporal profile that can be input into SMOKE. The
meteorological variables and the resolution of the generated temporal profile (hourly, daily, etc.) depend
on the selected algorithm and the run parameters. For more details on the development of these
algorithms and running Gentpro, see the Gentpro documentation and the SMOKE documentation at
http://www.cmascenter.Org/smoke/documentation/3.l/GenTPRQ Technical Summary Aug2012 Final.pd
f and https://www.cmascenter.Org/smoke/documentation/4.5/html/ch05s03s05.html. respectively.
For the RWC algorithm, Gentpro uses the daily minimum temperature to determine the temporal
allocation of emissions to days of the year. Gentpro was used to create an annual-to-day temporal profile
for the RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of
the year. On days where the minimum temperature does not drop below a user-defined threshold, RWC
emissions for most sources in the sector are zero. Conversely, the program temporally allocates the
largest percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total
annual emissions do not change, only the distribution of the emissions within the year is affected. The
temperature threshold for RWC emissions was 50 °F for most of the country, and 60 °F for the following
states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas. The algorithm is as follows:
If Td >= Tt: no emissions that day
If Td < Tt: daily factor = 0.79*(Tt -Td)
where (Td = minimum daily temperature; Tt = threshold temperature, which is 60 degrees F in
southern states and 50 degrees F elsewhere).
Once computed, the factors are normalized to sum to 1 to ensure that the total annual emissions are
unchanged (or minimally changed) during the temporal allocation process.
Figure 3-18 illustrates the impact of changing the temperature threshold for a warm climate county. The
plot shows the temporal fraction by day for Duval County, Florida, for the first four months of 2007. The
default 50 °F threshold creates large spikes on a few days, while the 60 °F threshold dampens these spikes
and distributes a small amount of emissions to the days that have a minimum temperature between 50 and
60 °F.
128
-------
Figure 3-18. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold
The diurnal profile used for most RWC sources (see Figure 3-19) places more of the RWC emissions in
the morning and the evening when people are typically using these sources. This profile is based on a
2004 MANE-VU survey based temporal profiles (https://s3.amazonaws.com/marama.org/wp-
content/uploads/2019/11/04184303/Qpen Burning Residential Areas Emissions Report-2004.pdf). This
profile was created by averaging three indoor and three RWC outdoor temporal profiles from counties in
Delaware and aggregating them into a single RWC diurnal profile. This new profile was compared to a
concentration-based analysis of aethalometer measurements in Rochester, New York (Wang et al. 2011)
for various seasons and days of the week and was found that the new RWC profile generally tracked the
concentration based temporal patterns.
Figure 3-19. RWC diurnal temporal profile
Comparison of RWC diurnal profile
The temporal allocation for "Outdoor Hydronic Heaters" (i.e., "OHH," SCC=2104008610) and "Outdoor
wood burning device, NEC (fire-pits, chimineas, etc.)" (i.e., "recreational RWC," SCC=21040087000) is
not based on temperature data, because the meteorologically-based temporal allocation used for the rest of
the rwc sector did not agree with observations for how these appliances are used.
129
-------
For OHH, the annual-to-month, day-of-week and diurnal profiles were modified based on information in
the New York State Energy Research and Development Authority's (NYSERDA) "Environmental,
Energy Market, and Health Characterization of Wood-Fired Hydronic Heater Technologies, Final Report"
(NYSERDA, 2012), as well as a Northeast States for Coordinated Air Use Management (NESCAUM)
report "Assessment of Outdoor Wood-fired Boilers" (NESCAUM, 2006). A Minnesota 2008 Residential
Fuelwood Assessment Survey of individual household responses (MDNR, 2008) provided additional
annual-to-month, day-of-week, and diurnal activity information for OHH as well as recreational RWC
usage.
Data used to create the diurnal profile for OHH, shown in Figure 3-20, are based on a conventional single-
stage heat load unit burning red oak in Syracuse, New York. As shown in Figure 3-21, the NESCAUM
report describes how for individual units, OHH are highly variable day-to-day but that in the aggregate,
these emissions have no day-of-week variation. In contrast, the day-of-week profile for recreational RWC
follows a typical "recreational" profile with emissions peaked on weekends.
Annual-to-month temporal allocation for OHH as well as recreational RWC were computed from the
MDNR 2008 survey and are illustrated in Figure 3-22. The OHH emissions still exhibit strong seasonal
variability, but do not drop to zero because many units operate year-round for water and pool heating. In
contrast to all other RWC appliances, recreational RWC emissions are used far more frequently during the
warm season.
Figure 3-20. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr)
130
-------
Figure 3-21. Day-of-week temporal profiles for OHH and Recreational RWC
Figure 3-22. Annual-to-month temporal profiles for OHH and recreational RWC
3.3.5 Agricultural Ammonia Temporal Profiles (ag)
For the agricultural livestock NH3 algorithm, the GenTPRO algorithm is based on an equation derived by
Jesse Bash of the EPA's ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation is
based on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to
estimate diurnal NH3 emission variations from livestock as a function of ambient temperature,
aerodynamic resistance, and wind speed. The equations are:
131
-------
Ea = [161500/Ti./, x e("1380/T,,/;)] x AR,-./,
PE;,/; = Ea / Sum(E;,/;)
Equation 3-4
Equation 3-5
where
• PE;,/; = Percentage of emissions in county i on hour h
• Eij, = Emission rate in county i on hour h
• Tin = Ambient temperature (Kelvin) in county i on hour h
• AR;,/; = Aerodynamic resistance in county i
GenTPRO was run using the "BASH NH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized
to the month. Figure 3-23 compares the daily emissions for Minnesota from the "old" approach (uniform
monthly profile) with the "new" approach (GenTPRO generated month-to-hour profiles) for 2014.
Although the GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the
same between the two approaches.
Figure 3-23. Example of animal NFb emissions temporal allocation approach (daily total emissions)
2014fd Minnesota ag NH3 livestock daily temporal profiles
1600
1/1/2014 2/1/2014 3/4/2014 4/4/2014 5/5/2014 6/5/2014 7/6/2014 B/6/2014 9/6/2014 10/7/2D1411/7/201412/8/2014
month^ hourly
approach approach
For the 2016 platform, the GenTPRO approach is applied to all sources in the livestock and fertilizer
sectors, NH3 and non- NH3. Monthly profiles are based on the daily-based EPA livestock emissions and
are the same as were used in 2014v7.0. Profiles are by state/SCC_category, where SCC_category is one
of the following: beef, broilers, layers, dairy, swine.
3.3.6 Oil and gas temporal allocation (np_oilgas)
Monthly oil and gas temporal profiles by county and SCC were updated to use 2016 activity information
for the 2016vl platform. Weekly and diurnal profiles are flat and are based on comments received on a
version of the 2011 platform.
132
-------
3.3.7 Onroad mobile temporal allocation (onroad)
For the onroad sector, the temporal distribution of emissions is a combination of traditional temporal
profiles and the influence of meteorology. This section will discuss both the meteorological influences
and the development of the temporal profiles for this platform.
The "inventories" referred to in Table 3-20 consist of activity data for the onroad sector, not emissions.
For the off-network emissions from the rate-per-profile (RPP) and rate-per-vehicle (RPV) processes, the
VPOP activity data is annual and does not need temporal allocation. For rate-per-hour (RPH) processes
that result from hoteling of combination trucks, the HOTELING inventory is annual and was
temporalized to month, day of the week, and hour of the day through temporal profiles.
For on-roadway rate-per-distance (RPD) processes, the VMT activity data is annual for some sources and
monthly for other sources, depending on the source of the data. Sources without monthly VMT were
temporalized from annual to month through temporal profiles. VMT was also temporalized from month
to day of the week, and then to hourly through temporal profiles. The RPD processes require a speed
profile (SPDPRO) that consists of vehicle speed by hour for a typical weekday and weekend day. For
onroad, the temporal profiles and SPDPRO will impact not only the distribution of emissions through
time but also the total emissions. Because SMOKE-MOVES (for RPD) calculates emissions based on the
VMT, speed and meteorology, if one shifted the VMT or speed to different hours, it would align with
different temperatures and hence different emission factors. In other words, two SMOKE-MOVES runs
with identical annual VMT, meteorology, and MOVES emission factors, will have different total
emissions if the temporal allocation of VMT changes. Figure 3-24 illustrates the temporal allocation of
the onroad activity data (i.e., VMT) and the pattern of the emissions that result after running SMOKE-
MOVES. In this figure, it can be seen that the meteorologically varying emission factors add variation on
top of the temporal allocation of the activity data.
Meteorology is not used in the development of the temporal profiles, but rather it impacts the calculation
of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the
hourly level by grid cell. More specifically, the on-network (RPD) and the off-network parked vehicle
(RPV, RPH, and RPP) processes use the gridded meteorology (MCIP) either directly or indirectly. For
RPD, RPV, and RPH, Movesmrg determines the temperature for each hour and grid cell and uses that
information to select the appropriate emission factor for the specified SCC/pollutant/mode combination.
For RPP, instead of reading gridded hourly meteorology, Movesmrg reads gridded daily minimum and
maximum temperatures. The total of the emissions from the combination of these four processes (RPD,
RPV, RPH, and RPP) comprise the onroad sector emissions. The temporal patterns of emissions in the
onroad sector are influenced by meteorology.
133
-------
Figure 3-24. Example of temporal variability of NOx emissions
A _
2014v2 onroad RPD hourly NOX and VMT: Wake County, NC
a
35 _
o
° =1 -
1
1
- 2.5
l/I
u 2.5 -
kA
A J\
A A
" ^ g
1 2 -
\/\
\v\
/-"v
IW\
1.5 £
5. VMT
£Z *-
O
== 15 .
Hi
r
i
\
A
r
E
¦*— i
i | \
i
1
L / A
, I \
\
" 1 O NOX
1- 1
§0.5-
0
V /
V i
V i
\ /
\ /
v /
0.5
\J
\J
n
7/8/1
10:00
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
homes were mapped to passenger vehicles for day-of-week and commercial trucks for hour-of-day; 3)
School buses and refuse trucks were mapped to commercial trucks for hour-of-day and use a new custom
day-of-week profile called LOWSATSUN that has a very low weekend allocation, since school buses and
refuse trucks operate primarily on business days. In addition to temporal profiles, CRC A-100 data were
also used to develop the average hourly speed data (SPDPRO) used by SMOKE-MOVES. In areas where
CRC A-100 data does not exist, hourly speed data is based on MOVES county databases.
The CRC A-100 dataset includes temporal profiles for individual counties, Metropolitan Statistical Areas
(MSAs), and entire regions (e.g., West, South). For counties without county or MSA temporal profiles
specific to itself, regional temporal profiles are used. Temporal profiles also vary by each of the MOVES
road types, and there are distinct hour-of-day profiles for each day of the week. Plots of hour-of-day
profiles for passenger vehicles in Fulton County, GA, are shown in Figure 3-25. Separate plots are shown
for Monday, Friday, Saturday, and Sunday, and each line corresponds to a particular MOVES road type
(i.e., road type 2 = rural restricted, 3 = rural unrestricted, 4 = urban restricted, and 5 = urban unrestricted).
Figure 3-26 shows which counties have temporal profiles specific to that county, and which counties use
MSA or regional average profiles. Figure 3-27 shows the regions used to compute regional average
profiles.
134
-------
Figure 3-25. Sample on road diurnal profiles for Fulton County, GA
Monday Fulton Co passenger
0.1
Friday Fulton Co passenger
0.09
Saturday Fulton Co passenger
0.09
Sunday Fulton Co passenger
o.i
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
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
Figure 3-26. Methods to Populate Onroad Speeds and Temporal Profiles by Road Type
135
-------
Figure 3-27. Regions for computing Region Average Speeds and Temporal Profiles
For hoteling, day-of-week profiles are the same as non-hoteling for combination trucks, while hour-of-day
non-hoteling profiles for combination trucks were inverted to create new hoteling profiles that peak
overnight instead of during the day. The combination truck profiles for Fulton County are shown in
Figure 3-28.
The CRC A-100 temporal profiles were used in the entire contiguous United States, except in California.
All California temporal profiles were carried over from 2014v7.0, although California hoteling uses CRC
A-100-based profiles just like the rest of the country, since CARB didn't have a hoteling-specific profile.
Monthly profiles in all states (national profiles by broad vehicle type) were also carried over from
2014v7.0 and applied directly to the VMT. For California, CARB supplied diurnal profiles that varied by
136
-------
vehicle type, day of the week,30 and air basin. These CARB-specific profiles were used in developing
EPA estimates for California. Although the EPA adjusted the total emissions to match California-
submitted emissions for 2016, the temporal allocation of these emissions took into account both the state-
specific VMT profiles and the SMOKE-MOYES process of incorporating meteorology.
Monday
Figure 3-28. Example of Temporal Profiles for Combination Trucks
Fulton Co combo Friday Fulton Co combo
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
Saturday
Fulton Co
combo
Sunday
Fulton Co
combo
5 6 7 8 9 10 11 12 13 14 15 16 17 13 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
3.3.8 Nonroad mobile temporal allocation(nonroad)
For nonroad mobile sources, temporal allocation is performed differently for different SCCs. Beginning
with the final 2011 platform and continued int the 2016 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 3-29 shows two previously existing temporal profiles (9 and 18) and a new temporal profile (19)
which has lower emissions on weekends. In the 2016 platform, construction and commercial lawn and
garden sources were updated from profile 18 to the new profile 19 which has lower emissions on
weekends. Residental lawn and garden sources continue to use profile 9 and agricultural sources
continue to use profile 19.
311 California's diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and
Tuesday, Wednesday, Thursday had the same profile.
137
-------
Figure 3-29. Example Nonroad Day-of-week Temporal 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
Day of Week Profiles
monday tuesday Wednesday thursday friday
s»urday
sunda/
Figure 3-30 shows the previously existing temporal profiles 26 and 27 along with new temporal profiles
(25a and 26a) which have lower emissions overnight. In the 2016 platform, construction sources
previously used profile 26 and were updated to use profile 26a. Commercial lawn and garden and
agriculture sources also previously used profile 26 but were updated to use the new profiles 26a and 25a,
respectively. Residental lawn and garden sources were updated from profile 26 to use profile 27.
Figure 3-30. Example Nonroad Diurnal Temporal Profiles
Hour of Day Profiles
0.11
o.i
0.09
0.08
0.07
0.06
0.05
0.04
0.0B
0.02
0.01
0
hi h2 h3 h4 h5 h6 h7 h8 h9 hl0hllhl2hl3hl4hl5hl6hl7hl8hl9h20h21h22h23h24
26a- New 27 25a-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
138
-------
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and
then subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the
ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are
subject to transport. This methodology is discussed in (Pouliot et al., 2010), and in "Fugitive Dust
Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). The precipitation adjustment is
applied to remove all emissions for hours where measurable rain occurs, or where there is snow cover.
Therefore, the afdust emissions vary day-to-day based on the precipitation and/or snow cover for each
grid cell and hour. Both the transport fraction and meteorological adjustments are based on the gridded
resolution of the platform; therefore, somewhat different emissions will result from different grid
resolutions. For this reason, to ensure consistency between grid resolutions, afdust emissions for the
36US3 grid are aggregated from the 12US1 emissions. Application of the transport fraction and
meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid modeling as
compared to ambient samples.
Biogenic emissions in the beis sector vary by every day of the year because they are developed using
meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions are
computed using appropriate emission factors according to the vegetation in each model grid cell, while
taking the meteorological data into account.
For the cmv sectors, most areas use hourly emission inventories derived from the 5-minute AIS data. In
some areas where AIS data are not available, such as in Canada between the St. Lawrence Seaway and the
Great Lakes and in the southern Caribbean, the flat temporal profiles are used for hourly and day-of-week
values. Most regions without AIS data also use a flat monthly profile, with some offshore areas using an
average monthly profile derived from the 2008 ECA inventory monthly values. These areas without AIS
data also use flat day of week and hour of day profiles.
For the rail sector, new monthly profiles were developed for the 2016 platform. Monthly temporal
allocation for rail freight emissions is based on AAR Rail Traffic Data, Total Carloads and Intermodal, for
2016. For passenger trains, monthly temporal allocation is flat for all months. Rail passenger miles data
is available by month for 2016 but it is not known how closely rail emissions track with passenger activity
since passenger trains run on a fixed schedule regardless of how many passengers are aboard, and so a flat
profile is chosen for passenger trains. Rail emissions are allocated with flat day of week profiles, and
most emissions are allocated with flat hourly profiles.
For the ptagfire sector, the inventories are in the daily point fire format FF10 PTDAY. The diurnal
temporal profile for ag fires reflects the fact that burning occurs during the daylight hours - see Figure
3-31 (McCarty et al., 2009). This puts most of the emissions during the work day and suppresses the
emissions during the middle of the night.
139
-------
Figure 3-31. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
Industrial processes that are not likely to shut down on Sundays, such as those at cement plants, use
profiles that include emissions on Sundays, while those that would shut down on Sundays use profiles that
reflect Sunday shutdowns.
For the ptfire sectors, the inventories are in the daily point fire format FF10 PTDAY. Separate hourly
profiles for prescribed and wildfires were used. Figure 3-32 below shows the profiles used for each state
for the 2016v2 modeling platform. The wildfire diurnal profiles are similar but vary according to the
average meteorological conditions in each state. The 2016v2 platform used updated diurnal profiles for
prescribed profile that better reflect flaming and residual smoldering phases and average burn practices.
These flaming and residual smoldering diurnal profiles do vary slightly by region.
Figure 3-32. Prescribed and Wildfire diurnal temporal profiles
140
-------
For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from MOVES. For California, CARB's annual inventory was temporalized to
monthly using monthly temporal profiles applied in SMOKE by SCC. This is an improvement over the
2011 platform, which applied monthly temporal allocation in California at the broader SCC7 level.
3.4 Spatial Allocation
The methods used to perform spatial allocation are summarized in this section. For the modeling
platform, spatial factors are typically applied by county and SCC. As described in Section 3.1, spatial
allocation was performed for national 36-km and 12-km domains. To accomplish this, SMOKE used
national 36-km and 12-km spatial surrogates and a SMOKE area-to-point data file. For the U.S., the EPA
updated surrogates to use circa 2014 to 2016 data wherever possible. For Mexico, updated spatial
surrogates were used as described below. For Canada, updated surrogates were provided by Environment
Canada for the 2016v7.2 platform. The U.S., Mexican, and Canadian 36-km and 12-km surrogates cover
the entire CONUS domain 12US1 shown in Figure 3-1. The 36US3 domain includes a portion of Alaska,
and since Alaska emissions are typically not included in air quality modeling, special considerations are
taken to include Alaska emissions in 36-km modeling.
Documentation of the origin of the spatial surrogates for the platform is provided in the workbook
US_SpatialSurrogate_Workbook_v07172018 which is available with the reports for the 2014v7.1
platform. The remainder of this subsection summarizes the data used for the spatial surrogates and the
area-to-point data which is used for airport refueling.
3.4.1 Spatial Surrogates for U.S. emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 36-km and 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-
to-point approach overrides the use of surrogates for an airport refueling sources. Table 3-21 lists the
codes and descriptions of the surrogates. Surrogate names and codes listed in italics are not directly
assigned to any sources for the 2016 platforms, but they are sometimes used to gapfill other surrogates, or
as an input for merging two surrogates to create a new surrogate that is used. The WRAP oil and gas
surrogates used in 2016v2 are not listed in Table 3-21 but are listed in Table 3-23
Many surrogates were updated or newly developed for use in the 2014v7.0 platform (Adelman, 2016).
They include the use of the 2011 National Land Cover Database (the previous platform used 2006) and
development of various development density levels such as open, low, medium high and various
combinations of these. These landuse 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. This and other surrogates
are described in a reference (Adelman, 2016).
Several surrogates were updated or developed as new surrogates for the 2016 platforms:
Oil and gas surrogates were updated to represent 2016;
Onroad spatial allocation uses surrogates that do not distinguish between urban and rural road
types, correcting the issue arising in some counties due to the inconsistent urban and rural
definitions between MOVES and the surrogate data and were further updated for the 2016
platform;
141
-------
Spatial surrogates 201 through 244, which concern road miles, annual average daily traffic
(AADT), and truck stops, were further updated for the 2016 beta and regional haze platforms.
Correction was made to the water surrogate to gap fill missing counties using the 2006 National
Land Cover Database (NLCD);
A public schools surrogate was added in 2016v2 (#508);
Aside from the new 508, the use of 500 series surrogates were phased out and
- Rail surrogates were updated to fix some misallocated emissions in 2016v2.
The surrogates for the U.S. were mostly generated using the Surrogate Tools DB tool. The tool and
documentation for the Surrogate Tool DB is available at https://www.cmascenter.org/surrogate tools db/.
Table 3-21. U.S. Surrogates available for the 2016vl and 2016v2 modeling platforms
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2)
318
NLCD Pasture Land
100
Population
319
NLCD Crop Land
110
Housing
320
NLCD Forest Land
131
urban Housing
321
NLCD Recreational Land
132
Suburban Housing
340
NLCD Land
134
Rural Housing
350
NLCD Water
137
Housing Change
508
Public Schools
140
Housing Change and Population
650
Refineries and Tank Farms
150
Residential Heating - Natural Gas
670
Spud Count - CBM Wells
160
Residential Heating - Wood ;
671
Spud Count - Gas Wells
170
Residential Heating - Distillate Oil
672
Gas Production at Oil Wells
180
Residential Heating - Coal
673
Oil Production at CBM Wells
190
Residential Heating - LP Gas
674
Unconventional Well Completion Counts
201
Urban Restricted Road Miles
676
Well Count - All Producing
202
Urban Restricted AADT
677
Well Count-All Exploratory
205
Extended Idle Locations
678
Completions at Gas Wells
211
Rural Restricted Road Miles
679
Completions at CBM Wells
212
Rural Restricted AADT i
681
Spud Count - Oil Wells
221
Urban Unrestricted Road Miles I
683
Produced Water at All Wells
222
Urban Unrestricted AADT
6831
Produced water at CBM wells
231
Rural Unrestricted Road Miles ;
6832
Produced water at gas wells
232
Rural Unrestricted AADT
6833
Produced water at oil wells
239
Total Road AADT
685
Completions at Oil Wells
240
Total Road Miles
686
Completions at All Wells
241
Total Restricted Road Miles s
687
Feet Drilled at All Wells
242
All Restricted AADT
689
Gas Produced - Total
243
Total Unrestricted Road Miles i
691
Well Counts - CBM Wells
244
All Unrestricted AADT
692
Spud Count-All Wells
258
Intercity Bus Terminals
693
Well Count - All Wells
259
Transit Bus Terminals
694
Oil Production at Oil Wells
260
Total Railroad Miles
695
Well Count - Oil Wells
261
NT AD Total Railroad Density
696
Gas Production at Gas Wells
271
NT AD Class 12 3 Railroad Density
697
Oil Production at Gas Wells
142
-------
Code
Surrogate Description
Code
Surrogate Description
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). On-network used AADT data and off network used land use
surrogates as shown in Table 3-22. Emissions from the extended (i.e., overnight) idling of trucks were
assigned to surrogate 205, which is based on locations of overnight truck parking spaces. This surrogate's
underlying data were updated for use in the 2016 platforms to include additional data sources and
corrections based on comments received.
Table 3-22. Off-Network Mobile Source Surrogates
Source type
Source Type name
Surrogate ID
Description
11
Motorcycle
307
NLCD All Development
21
Passenger Car
307
NLCD All Development
31
Passenger Truck
307
NLCD All Development
NLCD Low + Med +
32
Light Commercial Truck
308
High
41
Intercity Bus
306
NLCD Med + High
42
Transit Bus
259
Transit Bus Terminals
43
School Bus
508
Public Schools
51
Refuse Truck
306
NLCD Med + High
52
Single Unit Short-haul Truck
306
NLCD Med + High
53
Single Unit Long-haul Truck
306
NLCD Med + High
54
Motor Home
304
NLCD Open + Low
61
Combination Short-haul Truck
306
NLCD Med + High
62
Combination Long-haul Truck
306
NLCD Med + High
For the oil and gas sources in the np oilgas sector, the spatial surrogates were updated to those shown in
Table 3-23 using 2016 data consistent with what was used to develop the 2016v2 nonpoint oil and gas
emissions. The primary activity data source used for the development of the oil and gas spatial
surrogates was data from Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2017). This
database contains well-level location, production, and exploration statistics at the monthly level.
Due to a proprietary agreement with DI Desktop, individual well locations and ancillary
143
-------
production cannot be made publicly available, but aggregated statistics are allowed. These data were
supplemented with data from state Oil and Gas Commission (OGC) websites (Alaska, Arizona, Idaho,
Illinois, Indiana, Kentucky, Louisiana, Michigan, Mississippi, Missouri, Nevada, Oregon and
Pennsylvania, Tennessee). In cases when the desired surrogate parameter was not available (e.g., feet
drilled), data for an alternative surrogate parameter (e.g., number of spudded wells) was downloaded and
used. Under that methodology, both completion date and date of first production from HPDI were used to
identify wells completed during 2016. In total, over 1 million unique wells were compiled from the above
data sources. The wells cover 34 states and over 1,100 counties. (ERG, 2018).
Table 3-23. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
670
Spud Count - CBM Wells
671
Spud Count - Gas Wells
672
Gas Production at Oil Wells
673
Oil Production at CBM Wells
674
Unconventional Well Completion Counts
676
Well Count - All Producing
677
Well Count - All Exploratory
678
Completions at Gas Wells
679
Completions at CBM Wells
681
Spud Count - Oil Wells
683
Produced Water at All Wells
685
Completions at Oil Wells
686
Completions at All Wells
687
Feet Drilled at All Wells
689
Gas Produced - Total
691
Well Counts - CBM Wells
692
Spud Count - All Wells
693
Well Count - All Wells
694
Oil Production at Oil Wells
695
Well Count - Oil Wells
696
Gas Production at Gas Wells
697
Oil Production at Gas Wells
698
Well Count - Gas Wells
699
Gas Production at CBM Wells
2688
WRAP Gas production at oil wells
2689
WRAP Gas production at all wells
2691
WRAP Well count - CBM wells
2693
WRAP Well count - all wells
2694
WRAP Oil production at oil wells
2695
WRAP Well count - oil wells
144
-------
Surrogate Code
Surrogate Description
2696
WRAP Gas production at gas wells
2697
WRAP Oil production at gas wells
2698
WRAP Well count - gas wells
2699
WRAP Gas production at CBM wells
6831
Produced water at CBM wells
6832
Produced water at gas wells
6833
Produced water at oil wells
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-21 were not assigned to any SCCs, although many of the "unused"
surrogates are actually used to "gap fill" other surrogates that are used. When the source data for a
surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in
those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the
emission inventories. Table 3-24 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by
sector assigned to each spatial surrogate.
Table 3-24. Selected 2016 CAP emissions by sector for U.S. Surrogates (short tons in 12US1)
Sector
ID
Description
NH3
NOX
PM2 5
S02
VOC
afdust
240
Total Road Miles
0
0
303,187
0
0
afdust
304
NLCD Open + Low
0
0
826,942
0
0
afdust
306
NLCD Med + High
0
0
52,278
0
0
afdust
308
NLCD Low + Med + High
0
0
117,313
0
0
afdust
310
NLCD Total Agriculture
0
0
788,107
0
0
fertilizer
310
NLCD Total Agriculture
1,183,387
0
0
0
0
livestock
310
NLCD Total Agriculture
2,493,166
0
0
0
224,459
nonpt
100
Population
34,304
0
0
0
208
nonpt
150
Residential Heating - Natural Gas
42,973
219,189
3,632
1,442
13,296
nonpt
170
Residential Heating - Distillate Oil
1,563
31,048
3,356
41,193
1,051
nonpt
180
Residential Heating - Coal
20
101
53
1,086
111
nonpt
190
Residential Heating - LP Gas
111
33,230
175
705
1,292
nonpt
239
Total Road AADT
0
22
541
0
306,341
nonpt
242
All Restricted AADT
0
0
0
0
5,451
nonpt
244
All Unrestricted AADT
0
0
0
0
96,232
nonpt
271
NT AD Class 12 3 Railroad Density
0
0
0
0
2,252
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
20,531
184,856
208,027
64,947
104,310
nonpt
307
NLCD All Development
85
25,798
110,610
8,256
69,262
nonpt
308
NLCD Low + Med + High
1,029
172,195
16,762
13,578
9,849
nonpt
310
NLCD Total Agriculture
0
0
38
0
0
nonpt
319
NLCD Crop Land
0
0
95
71
293
nonpt
320
NLCD Forest Land
3,953
68
273
0
279
nonpt
650
Refineries and Tank Farms
0
16
0
0
106,401
145
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nonpt
711
Airport Areas
0
0
0
0
621
nonpt
801
Port Areas
0
0
0
0
8,194
nonroad
261
NT AD Total Railroad Density
3
2,154
227
1
426
nonroad
304
NLCD Open + Low
4
1,824
159
4
2,761
nonroad
305
NLCD Low + Med
94
15,985
3,832
119
115,955
nonroad
306
NLCD Med + High
305
183,591
11,839
328
94,299
nonroad
307
NLCD All Development
99
31,526
15,338
108
170,212
nonroad
308
NLCD Low + Med + High
498
338,083
28,486
241
51,957
nonroad
309
NLCD Open + Low + Med
119
21,334
1,256
151
45,828
nonroad
310
NLCD Total Agriculture
422
378,356
28,344
214
40,771
nonroad
320
NLCD Forest Land
15
5,910
699
9
3,944
nonroad
321
NLCD Recreational Land
83
11,616
6,517
89
246,560
nonroad
350
NLCD Water
188
115,168
5,952
232
355,808
nonroad
850
Golf Courses
13
2,001
117
16
5,647
nonroad
860
Mines
2
2,691
281
1
521
npoilgas
670
Spud Count - CBM Wells
0
0
0
0
116
npoilgas
671
Spud Count - Gas Wells
0
0
0
0
6,058
npoilgas
674
Unconventional Well Completion Counts
20
17,955
452
20
844
npoilgas
678
Completions at Gas Wells
0
5,397
136
2,980
31,452
npoilgas
679
Completions at CBM Wells
0
5
0
198
804
npoilgas
681
Spud Count - Oil Wells
0
0
0
0
15,200
npoilgas
683
Produced Water at All Wells
0
0
0
0
941
npoilgas
685
Completions at Oil Wells
0
262
0
889
30,398
npoilgas
687
Feet Drilled at All Wells
0
43,122
1,229
54
2,213
npoilgas
689
Gas Produced - Total
0
4,180
542
43
28,952
npoilgas
691
Well Counts - CBM Wells
0
12,811
242
5
16,129
npoilgas
694
Oil Production at Oil Wells
0
2,273
0
12,622
320,445
npoilgas
695
Well Count - Oil Wells
0
113,355
2,548
601
482,308
npoilgas
696
Gas Production at Gas Wells
0
1,841
0
17
258,236
npoilgas
698
Well Count - Gas Wells
0
258,985
4,836
239
448,848
npoilgas
699
Gas Production at CBM Wells
0
312
40
3
3,248
npoilgas
2688
WRAP Gas production at oil wells
0
7,747
0
5,487
221,806
npoilgas
2689
WRAP Gas production at all wells
0
26,613
782
1,133
22,687
npoilgas
2691
WRAP Well count - CBM wells
0
512
41
7
2,027
npoilgas
2693
WRAP Well count - all wells
0
8,376
110
20
3,345
npoilgas
2694
WRAP Oil production at oil wells
0
35,144
543
18,367
110,299
npoilgas
2695
WRAP Well count - oil wells
0
2,726
244
12
75,352
npoilgas
2696
WRAP Gas production at gas wells
0
4,316
42
2
36,853
npoilgas
2697
WRAP Oil production at gas wells
0
1,515
0
10
142,334
npoilgas
2698
WRAP Well count - gas wells
0
4,672
306
14
98,613
npoilgas
2699
WRAP Gas production at CBM wells
0
20,901
361
17
8,241
npoilgas
6831
Produced water at CBM wells
0
0
0
0
972
146
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
npoilgas
6832
Produced water at gas wells
0
0
0
0
12,662
npoilgas
6833
Produced water at oil wells
0
0
0
0
12,596
onroad
205
Extended Idle Locations
316
36,205
829
17
4,417
onroad
242
All Restricted AADT
36,294
1,175,588
31,434
7,652
166,637
onroad
244
All Unrestricted AADT
66,408
1,816,651
62,438
16,561
453,336
onroad
259
Transit Bus Terminals
12
2,634
65
2
486
onroad
304
NLCD Open + Low
864
27
0
6,330
onroad
306
NLCD Med + High
859
95,627
4,718
85
22,369
onroad
307
NLCD All Development
3,768
238,117
6,279
1,547
620,852
onroad
308
NLCD Low + Med + High
230
25,884
536
94
35,391
onroad
508
Public Schools
15
2,397
126
2
688
rail
261
NT AD Total Railroad Density
13
33,389
996
15
1,647
rail
271
NT AD Class 12 3 Railroad Density
313
525,992
14,823
442
24,435
rwc
300
NLCD Low Intensity Development
16,943
35,204
309,019
8,249
334,217
solvents
100
Population
0
0
0
0
1,487,737
solvents
306
NLCD Med + High
0
0
0
0
744,921
solvents
307
NLCD All Development
0
0
0
0
401,086
solvents
310
NLCD Total Agriculture
0
0
0
0
180,552
solvents
676
Well Count - All Producing
0
0
0
0
27,701
For 36US3 modeling in the 2016 platforms, most U.S. emissions sectors were processed using 36-km
spatial surrogates, and if applicable, 36-km meteorology. Exceptions include:
- For the onroad and onroad ca adj sectors, 36US3 emissions were aggregated from 12US1 by
summing emissions from a 3x3 group of 12-km cells into a single 36-km cell. Differences in 12-
km and 36-km meteorology can introduce differences in onroad emissions, and so this approach
ensures that the 36-km and 12-km onroad emissions are consistent. However, this approach means
that 36US3 onroad does not include emissions in Southeast Alaska; therefore, Alaska onroad
emissions are included in a separate sector called 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.
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
147
-------
equipment emissions as point sources. For the modeling platform, the EPA used the SMOKE "area-to-
point" approach for only jet refueling in the nonpt sector. The following SCCs use this approach:
2501080050 and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/rocket engine
firing and testing). The ARTOPNT approach is described in detail in the 2002 platform documentation:
http://www3.epa.gov/scram001/reports/Emissions%20TSD%20Voll 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 have been updated in the 2014v7.1
platform and carried forward into the 2016 platform. For the 2016 beta (v7.2) platform, a new set of
Canada shapefiles were provided by Environment Canada along with cross references spatially allocate
the year 2015 Canadian emissions. Gridded surrogates were generated using the Surrogate Tool
(previously referenced); Table 3-25 provides a list. Due to computational reasons, total roads (1263) were
used instead of the unpaved rural road surrogate provided. The population surrogate for Mexico;
surrogate code 11, uses 2015 population data at 1 km resolution and replaced the previous population
surrogate code 10. The other surrogates for Mexico are circa 1999 and 2000 and were based on data
obtained from the Sistema Municipal de Bases de Datos (SIMBAD) de INEGI and the Bases de datos del
Censo Economico 1999. Most of the CAPs allocated to the Mexico and Canada surrogates are shown in
Table 3-26.
Table 3-25. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
TOTAL INSTITUTIONAL AND
100
Population
923
GOVERNEMNT
101
total dwelling
924
Primary Industry
104
capped total dwelling
925
Manufacturing and Assembly
106
ALL INDUST
926
Distribution and Retail (no petroleum)
113
Forestry and logging
927
Commercial Services
200
Urban Primary Road Miles
932
CANRAIL
210
Rural Primary Road Miles
940
PAVED ROADS NEW
211
Oil and Gas Extraction
945
Commercial Marine Vessels
212
Mining except oil and gas
946
Construction and mining
220
Urban Secondary Road Miles
948
Forest
221
Total Mining
951
Wood Consumption Percentage
222
Utilities
955
UNPAVED ROADS AND TRAILS
230
Rural Secondary Road Miles
960
TOTBEEF
233
Total Land Development
970
TOTPOUL
240
capped population
980
TOTS WIN
308
Food manufacturing
990
TOTFERT
321
Wood product manufacturing
996
urban area
323
Printing and related support activities
1251
OFFR TOTFERT
324
Petroleum and coal products manufacturing
1252
OFFR MINES
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
148
-------
Code
Canadian Surrogate Description
Code
Description
412
Petroleum product wholesaler-distributors
1257
OFFR UNPAVED ROADS RURAL
448
clothing and clothing accessories stores
1258
OFFR Utilities
482
Rail transportation
1259
OFFR total dwelling
562
Waste management and remediation services
1260
OFFR water
901
AIRPORT
1261
OFFR ALL INDUST
902
Military LTO
1262
OFFR Oil and Gas Extraction
903
Commercial LTO
1263
OFFR ALLROADS
904
General Aviation LTO
1265
OFFR CANRAIL
921
Commercial Fuel Combustion
9450
Commercial Marine Vessel Ports
Table 3-26. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3)
Sector
Code
Mexican / Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
othafdust
106
CAN ALL INDUST
0
0
609
0
0
othafdust
212
CAN Mining except oil and gas
0
0
3,142
0
0
othafdust
221
CAN Total Mining
0
0
17,315
0
0
othafdust
222
CAN Utilities
0
0
2,792
0
0
othafdust
940
CAN Paved Roads New
0
0
29,862
0
0
othafdust
955
CAN UNPAVEDROADSANDTRAILS
0
0
426,511
0
0
othar
26
MEX Total Agriculture
560,091
82,958
48,439
1,987
18,052
othar
32
MEX Commercial Land
0
391
8,511
0
102,447
othar
34
MEX Industrial Land
164
4,244
4,135
11
102,903
othar
36
MEX Commercial plus Industrial Land
7
23,149
1,551
12
234,277
othar
40
MEX Residential (RES1-
4)+Comercial+Industrial+Institutional+Governmen
t
4
90
424
12
105,233
othar
42
MEX Personal Repair (COM3)
0
0
0
0
25,999
othar
44
MEX Airports Area
0
16,295
216
1,183
6,834
othar
48
MEX Brick Kilns
0
2,778
55,550
5,031
1,352
othar
50
MEX Mobile sources - Border Crossing
3
71
2
0
57
othar
100
CAN Population
795
52
622
15
225
othar
101
CAN total dwelling
0
0
0
0
151,094
othar
104
CAN Capped Total Dwelling
361
31,746
2,335
2,671
1,650
othar
113
CAN Forestry and logging
152
1,818
9,778
37
5,140
othar
211
CAN Oil and Gas Extraction
1
43
433
74
2,122
othar
212
CAN Mining except oil and gas
0
0
11
0
0
othar
221
CAN Total Mining
0
0
293
0
0
othar
222
CAN Utilities
57
3,439
166
464
65
othar
308
CAN Food manufacturing
0
0
19,253
0
17,468
othar
321
CAN Wood product manufacturing
873
4,822
1,646
383
16,605
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
149
-------
Sector
Code
Mexican / Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
othar
331
CAN Primary Metal Manufacturing
0
158
5,598
30
72
othar
412
CAN Petroleum product wholesaler-distributors
0
0
0
0
45,634
othar
448
CAN clothing and clothing accessories stores
0
0
0
0
143
othar
482
CAN Rail Transportation
1
4,106
89
1
258
othar
562
CAN Waste management and remediation services
247
1,981
2,747
2,508
9,654
othar
901
CAN Airport
0
108
10
0
11
othar
921
CAN Commercial Fuel Combustion
206
24,819
2,435
1,669
1,254
othar
923
CAN TOTAL INSTITUTIONAL AND
GOVERNEMNT
0
0
0
0
14,847
othar
924
CAN Primary Industry
0
0
0
0
40,409
othar
925
CAN Manufacturing and Assembly
0
0
0
0
70,468
othar
926
CAN Distribution and Retail (no petroleum)
0
0
0
0
7,475
othar
927
CAN Commercial Services
0
0
0
0
32,096
othar
932
CAN CANRAIL
52
91,908
1,822
48
3,901
othar
946
CAN Construction and Mining
0
0
0
0
10,211
othar
951
CAN Wood Consumption Percentage
1,010
11,223
113,852
1,603
161,174
othar
990
CAN TOTFERT
49
4,185
276
6,834
160
othar
996
CAN urbanarea
0
0
3,182
0
0
othar
1251
CAN OFFR TOTFERT
79
65,830
4,646
54
6,266
othar
1252
CAN OFFR MINES
1
905
67
1
134
othar
1253
CAN OFFR Other Construction not Urban
63
40,640
4,880
43
11,607
othar
1254
CAN OFFR Commercial Services
42
16,193
2,443
36
37,663
othar
1255
CAN OFFR Oil Sands Mines
23
12,478
410
12
1,330
othar
1256
CAN OFFR Wood industries CANVEC
8
3,180
288
6
1,102
othar
1257
CAN OFFR Unpaved Roads Rural
26
11,244
734
23
32,322
othar
1258
CAN OFFRUtilities
8
4,471
229
6
930
othar
1259
CAN OFFR total dwelling
17
6,485
649
15
13,317
othar
1260
CAN OFFRwater
23
6,495
493
33
34,204
othar
1261
CAN OFFR ALL INDUST
4
5,654
185
2
1,105
othar
1262
CAN OFFR Oil and Gas Extraction
1
1,291
77
1
212
othar
1263
CAN OFFRALLROADS
3
1,826
185
2
494
othar
1265
CAN OFFRCANRAIL
0
550
18
0
44
onroad_can
200
CAN Urban Primary Road Miles
1,742
84,596
2,810
367
8,888
onroad_can
210
CAN Rural Primary Road Miles
714
49,909
1,626
153
3,945
onroad_can
220
CAN Urban Secondary Road Miles
3,279
134,909
5,613
776
23,625
onroad_can
230
CAN Rural Secondary Road Miles
1,898
95,447
3,152
418
10,899
onroad_can
240
CAN Total Road Miles
346
63,465
1,500
88
117,123
onroad_mex
11
MEX 2015 Population
0
281,135
1,872
533
291,816
onroad_mex
22
MEX Total Road Miles
10,316
1,207,878
54,789
25,837
251,800
onroad_mex
36
MEX Commercial plus Industrial Land
0
7,971
142
29
9,187
150
-------
3.5
Preparation of Emissions for the CAMx model
3.5.1 Development of CAMx Emissions for Standard CAMx Runs
To perform air quality modeling with the Comprehensive Air Quality Model with Extensions (CAMx
model), the gridded hourly emissions output by the SMOKE model are output in the format needed by the
CMAQ model, but must be converted to the format required by CAMx. For "regular" CAMx modeling
(i.e., without two-way nesting), the CAMx conversion process consists of the following:
1) Convert all emissions file formats from the I/O API NetCDF format used by CMAQ to the UAM
format used by CAMx, including the merged, gridded low-level emissions files that include
biogenics
2) Shift hourly emissions files from the 25 hour format used by CMAQ to the averaged 24 hour
format used by CAMx
3) Rename and aggregate model species for CAMx
4) Convert 3D wildland and agricultural fire emissions into CAMx point format
5) Merge all inline point source emissions files together for each day, including layered fire
emissions originally from SMOKE
6) Add sea salt aerosol emissions to the converted, gridded low-level emissions files
Conversion of file formats from I/O API to UAM (i.e., CAMx) format is performed using a program
called "cmaq2uam". In the CAMx conversion process, all SMOKE outputs are passed through this step
first. Unlike CMAQ, the CAMx model does not have an inline biogenics option, and so for the purposes
of CAMx modeling, emissions from SMOKE must include biogenic emissions.
One difference between CMAQ-ready emissions files and CAMx-ready emissions files involves hourly
temporalization. A daily emissions file for CMAQ includes data for 25 hours, where the first hour is 0:00
GMT of a given day, and the last hour is 0:00 GMT of the following day. For the CAMx model, a daily
emissions file must only include data for 24 hours, not 25. Furthermore, to match the hourly configuration
expected by CAMx, each set of consecutive hourly timesteps from CMAQ-ready emissions files must be
averaged. For example, the first hour of a CAMx-ready emissions file will equal the average of the first
two hours from the corresponding CMAQ-ready emissions file, and the last (24th) hour of a CAMx-ready
emissions file will equal the average of the last two hours (24th and 25th) from the corresponding CMAQ-
ready emissions file. This time conversion is incorporated into each step of the CAMx-ready emissions
conversion process.
The CAMx model uses a slightly different version of the CB6 speciation mechanism than does the
CMAQ model. SMOKE prepares emissions files for the CB6 mechanism used by the CMAQ model
("CB6-CMAQ"), and therefore, the emissions must be converted to the CB6 mechanism used by the
CAMx model ("CB6-CAMx") during the CAMx conversion process. In addition to the mechanism
differences, CMAQ and CAMx also occasionally use different species naming conventions. For CAMx
modeling, we also create additional tracer species. A summary of the differences between CMAQ input
species and CAMx input species for CB6 (VOC), AE6 (PM2.5), and other model species, is provided in
Table 3-27. Each step of the CAMx-ready emissions conversion process includes conversion of CMAQ
species to CAMx species using a species mapping table which includes the mappings in Table 3-27.
151
-------
Table 3-27. Emission model species mappings for CMAQ and CAMx (for CB6R3AE7)
Inventory Pollutant
CMAQ Model Species
CAMx Model Species
Cl2
CL2
CL2
HC1
HCL
HCL
CO
CO
CO
NOx
NO
NO
N02
N02
HONO
HONO
S02
S02
S02
SULF
SULF
nh3
NH3
NH3
NH3 FERT
n/a (not used in CAMx)
voc
AACD
AACD
ACET
ACET
ALD2
ALD2
ALDX
ALDX
BENZ
BENZ and BNZA (duplicate species)
CH4
CH4
ETH
ETH
ETHA
ETHA
ETHY
ETHY
ETOH
ETOH
FACD
FACD
FORM
FORM
IOLE
IOLE
ISOP
ISOP and ISP (duplicate species)
IVOC
IVOA
KET
KET
MEOH
MEOH
NAPH + XYLMN (sum)
XYL and XYLA (duplicate species)
NVOL
n/a (not used in CAMx)
OLE
OLE
PAR
PAR
PRPA
PRPA
SESQ
SOT
SOAALK
n/a (not used in CAMx)
TERP + APIN (sum)
TERP and TRP (duplicate species)
TOL
TOL and TOLA (duplicate species)
UNR + NR (sum)
NR
PM10
PMC
CPRM
PM2.5
PEC
PEC
PN03
PN03
POC
POC
PS04
PS04
PAL
PAL
PCA
PCA
PCL
PCL
PFE
PFE
PK
PK
152
-------
Inventory Pollutant
CMAQ Model Species
CAMx Model Species
PH20
PH20
PMG
PMG
PMN
PMN
PMOTHR
FPRM
PNA
NA
PNCOM
PNCOM
PNH4
PNH4
PSI
PSI
PTI
PTI
POC + PNCOM (sum)
POA1
1 The POA species, which is the sum of POC and PNCOM, is passed to the CAMx model in addition to individual species POC
and PNCOM.
One feature which is part of CMAQ and is not part of CAMx involves plume rise for fires. For CMAQ
modeling, we process fire emissions through SMOKE as inline point sources, and plume rise for fires is
calculated within CMAQ using parameters from the inline emissions files (heat flux, etc). This is similar
to how non-fire point sources are handled, except that the fire parameters are used to calculate plume rise
instead of traditional stack parameters. The CAMx model supports inline plume rise calculations using
traditional stack parameters, but it does not support inline plume rise for fire sources. Therefore, for the
purposes of CAMx modeling, we must have SMOKE calculate plume rise for fires using the Laypoint
program. In this modeling platform, this must be done for the ptfire, ptfire othna, and ptagfire sectors. To
distinguish these layered fire emissions from inline fire emissions, layered fire emissions are processed
with the sector names "ptfire-wild3D", "ptfire-rx3D", "ptfire_othna3D", and "ptagfire3D". When
converting layered fire emissions files to CAMx format, stack parameters are added to the CAMx-ready
fire emissions files to force the correct amount of fire emissions into each layer for each fire location.
CMAQ modeling uses one gridded low-level emissions file, plus multiple inline point source emissions
files, per day. CAMx modeling also uses one gridded low-level emissions file per day - but instead of
reading multiple inline point source emissions files at once, CAMx can only read a single point source file
per day. Therefore, as part of the CAMx conversion process, all inline point source files are merged into a
single "mrgpt" file per day. The mrgpt file includes the layered fire emissions described in the previous
paragraph, in addition to all non-fire elevated point sources from the cmv_clc2, cmv_c3, othpt, ptegu,
ptnonipm, and pt oilgas sectors.
The remaining step in the CAMx emissions process is to generate sea salt aerosol emissions, which are
distinct from ocean chlorine emissions. Sea salt emissions do not need to be included in CMAQ-ready
emissions because they are calculated by the model, but they do need to be included in CAMx-ready
emissions. After the merged low-level emissions are converted to CAMx format, sea salt emissions are
generated using a program called "seasalt" and added to the low-level emissions. Sea salt emissions
depend on meteorology, vary on a daily and hourly basis, and exist for model species sodium (NA),
chlorine (PCL), sulfate (PS04), dimethy sulfide (DMS), and gas phase bromine (SSBR) and chlorine
(SSCL).
3.5.2 Development of CAMx Emissions for Source Apportionment CAMx
Runs
The CAMx model supports source apportionment modeling for ozone and PM sources using techniques
called Ozone Source Apportionment Technology (OSAT) and Particulate Matter Source Apportionment
153
-------
Technology (PSAT). These source apportionment techniques allow emissions from different types of
sources to be tracked through the CAMx model. Source apportionment model runs are most commonly
performed using one-way nesting (i.e., the inner grid takes boundary information from the outer grid but
the inner grid does not feed any concentration information back to the outer grid).
Source Apportionment modeling involves assigning tags to different categories of emissions. These tags
can be applied by region (e.g., state), by emissions type (e.g., SCC or sector), or a combination of the two.
For the Revised CSAPR Update study, emissions tagging was applied by state. All emissions from US
states, except for biogenics, fires, and fugitive dust (afdust), were assigned a state-specific tag. Emissions
from tribal lands are assigned a separate tag, as well as offshore emissions. Other tags include a tag for
biogenics and afdust; a tag for all fires, both inside and outside the US; and a tag for all anthropogenic
emissions from Canada and Mexico. A list of tags used in recent studies for state source apportionment
modeling is provided in Table 3-28. State-level tags 2 through 51 exclude emissions from biogenics,
fugitive dust, and fires, which are included in other tags.
Table 3-28. State tags for USA modeling
Tag
Emissions applied to tag
1
All biogenics (beis sector) and US fugitive dust (afdust sector)
2
Alabama
3
Arizona
4
Arkansas
5
California
6
Colorado
7
Connecticut
8
Delaware
9
District of Columbia
10
Florida
11
Georgia
12
Idaho
13
Illinois
14
Indiana
15
Iowa
16
Kansas
17
Kentucky
18
Louisiana
19
Maine
20
Maryland
21
Massachusetts
22
Michigan
23
Minnesota
24
Mississippi
25
Missouri
26
Montana
27
Nebraska
28
Nevada
29
New Hampshire
154
-------
Tag
Emissions applied to tag
30
New Jersey
31
New Mexico
32
New York
33
North Carolina
34
North Dakota
35
Ohio
36
Oklahoma
37
Oregon
38
Pennsylvania
39
Rhode Island
40
South Carolina
41
South Dakota
42
Tennessee
43
Texas
44
Utah
45
Vermont
46
Virginia
47
Washington
48
West Virginia
49
Wisconsin
50
Wyoming
51
Tribal Data
52
Canada and Mexico (except fires)
53
Offshore
54
All fires from US, Canada, and Mexico, including ag fires
For OSAT and PSAT modeling, all emissions must be input to CAMx in the form of a point source
(mrgpt) file, including low level sources that are found in gridded files for regular CAMx runs. In
addition, for any two-way nested modeling, all emissions must be input in a single mrgpt file, rather than
separate mrgpt files for each of the domains. Note that fire emissions require special consideration in two-
way nested model runs and for PSAT and OSAT modeling. That same consideration must be given to
any sector in which emissions are being gridded by SMOKE.
There are two main approaches for tagging emissions for CAMx modeling. One approach is to tag
emissions within SMOKE. Here, SMOKE will output tagged point source files (SGINLN files), which
can then be converted to CAMx point source format with the tags applied by SMOKE carried forward
into the CAMx inputs. The second approach is to, if necessary, depending on the nature of the tags, split
sectors into multiple components by tag so that each sector corresponds to a single tag. Then, the gridded
and/or point source format SMOKE outputs from those split sectors are converted to CAMx point source
format, and then merged into the full mrgpt file, with the tags applied at that last step. In some situations,
a mix of the two approaches is appropriate.
For ozone transport modeling runs, the first approach is used for most sectors, meaning tags are applied in
SMOKE. The exceptions are when the entire sector receives only one tag, e.g.: afdust, beis,
onroad ca adj, ptfire, ptagfire, ptfire othna, and all Canada and Mexico sectors. Afdust emissions are not
155
-------
tagged by state because the current tagging methodology does not support applying transportable fraction
and meteorological adjustments to tagged emissions.
Once the individual sector tagging is complete, the point source files for all of the sectors are merged
together to create the mrgpt file which includes all emissions, with the desired tags and appropriate
resolution throughout the domain for OSAT or PSAT modeling.
156
-------
4 Development of Future Year Emissions
The emission inventories for future years of 2023, 2026 and 2032 have been developed using projection
methods that are specific to the type of emissions source. Future emissions are projected from the 2016
base case either by running models to estimate future year emissions from specific types of emission
sources (e.g., EGUs, and onroad and nonroad mobile sources), or for other types of sources by adjusting
the base year emissions according to the best estimate of changes expected to occur in the intervening
years (e.g., non-EGU point and nonpoint sources). For some sectors, the same emissions are used in the
base and future years, such as biogenic, all fire sectors, and fertilizer. Emissions for these sectors are held
constant in future years because the 2016 meteorological data is used for the future year air quality model
runs, and emissions for these sectors are highly correlated with meteorological conditions. For the
remaining sectors, rules and specific legal obligations that go into effect in the intervening years, along
with changes in activity for the sector, are considered when possible. For sectors that were project, the
methods used to project those sectors to 2023, 2026 and 2032 are summarized in Table 4-1. For some
sectors, emissions were only projected to 2028 or 2030 instead of 2032 due to the availability of data for
projection factors and other factors.
Table 4-1. Overview of projection methods for the future year cases
Platform Sector:
abbreviation
Description of Projection Methods for Future Year Inventories
EGU units:
ptegu
The Integrated Planning Model (IPM) was run to create the future year EGU
emissions. IPM outputs from the Summer 2021 version of the IPM platform were
used (https://www.epa.20v/airmarkets/epas-power-sector-modelins-platform-v6-
usins-ipm-summer-2021 -reference-case). For 2023. the 2023 IPM output vear was
used, for 2026 the 2025 output year was used, and for 2032 the 2030 output year
was used because the year 2032 maps to the 2030 output year. Emission inventory
Flat Files for input to SMOKE were generated using post-processed IPM output
data. A list of included rules is provided in Section 4.1.
Point source oil and
gas:
ptoilgas
First, known closures were applied to the 2016 pt_oilgas sources. Production-
related sources were then grown from 2016 to 2019 using historic production data.
The production-related sources were then grown to 2023, 2026 and 2032 based on
growth factors derived from the Annual Energy Outlook (AEO) 2021 data for oil,
natural gas, or a combination thereof. The grown emissions were then controlled
to account for the impacts of New Source Performance Standards (NSPS) for oil
and gas sources, process heaters, natural gas turbines, and reciprocating internal
combustion engines (RICE). Some sources were held at 2018 levels. WRAP future
year inventories are used in the seven WRAP states (CO, MT, ND, NM, SD, UT
and WY). The future year WRAP inventories are the same for all future years.
Airports:
airports
Point source airport emissions were grown from 2016 to each future year using
factors derived from the 2019 Terminal Area Forecast (TAF) (see
https://www.faa.aov/data re search/aviation/taf/). Corrections to emissions for
ATL from the state of Georgia are included.
157
-------
Platform Sector:
abbreviation
Description of Projection Methods for Future Year Inventories
Remaining non-
EGU point:
ptnonipm
Known closures were applied to ptnonipm sources. Closures were obtained from
the Emission Inventory System (EIS) and also submitted by the states of Alabama,
North Carolina, Ohio, Pennsylvania, and Virginia. Industrial emissions were
grown according to factors derived from AEO2021 and for limited cases
AE02020 to reflect growth from 2020 onward. Data from earlier AEOs were used
to derive factors for 2016 through 2020. Rail yard emissions were grown using the
same factors as line haul locomotives in the rail sector. Controls were applied to
account for relevant NSPS for RICE, gas turbines, refineries (subpart Ja), and
process heaters. The Boiler MACT is assumed fully implemented in 2016 except
for North Carolina. Reductions due to consent decrees that had not been fully
implemented by 2016 were also applied, along with 2016vl comments received
from S/L/T agencies. Controls are reflected for the regional haze program in
Arizona. Changes to ethanol plants and biorefineries are included. In 2016v2,
additional closures were implemented, new sources were added based on
2018NEI, and growth in MARAMA states was updated using MARAMA
spreadsheets after incorporating AEO 2021 data. Where projections resulted in
significantly different emissions from historic levels, some sources were held at
2017, 2018, or 2019 levels.
Category 1, 2 CMV:
cmv_clc2
Category 1 and category 2 (C1C2) CMV emissions sources outside of California
were projected to 2023, 2026, and 2030 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 The 2023 emissions are unchanged from 2016vl and the 2026 emissions
are equivalent to interpolating 2016vl emissions between 2023 and 2028. For the
2032 case, factors were derived in the same way but taken only to 2030. California
emissions were projected based on factors provided by the state. Projection factors
for Canada for 2026 were based on ECCC-provided 2023 and 2028 data
interpolated to 2026. For 2032, a 2028->2030 trend based on US factors was
applied on top of the ECCC-based 2016->2028 projections that differed by
province.
Category 3 CMV:
cmv_c3
Category 3 (C3) CMV emissions were projected to 2023, 2026, and 2030 using an
EPA report on projected bunker fuel demand that projects 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. 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 the new bunker fuel demand growth rates for 2023, 2026,
and 2030 to arrive at the final growth rates. Projections were taken only to 2030
(used for 2032) as it was the last year of data in the report. The 2023 emissions are
unchanged from 2016vl and the 2026 emissions are equivalent to interpolating
2016vl emissions between 2023 and 2028. Projection factors for Canada for 2026
were based on ECCC-provided 2023 and 2028 data interpolated to 2026. For 2032,
a 2028->2030 trend based on US factors was applied on top of the ECCC-based
2016->2028 projections that differed by province.
158
-------
Platform Sector:
abbreviation
Description of Projection Methods for Future Year Inventories
Locomotives:
rail
Passenger and freight were projected using separate factors. Freight emissions
were computed for future years based on future year fuel use values for 2023 and
2026. Specifically, they were based on AEO2018 freight rail energy use growth
rate projections along with emission factors based on historic emissions trends that
reflect the rate of market penetration of new locomotive engines. The 2023
emissions are unchanged from 2016vl platform. The future year 2026 was
interpolated from the 2016vl future years of 2023 and 2028. The future year 2032
emissions are projected based on AEO2018 growth rates from 2026 to 2030.
Area fugitive dust:
afdust, afdust ak
Paved road dust was grown to 2023, 2026, and 2032 levels based on the growth in
VMT from 2016. The remainder of the sector including building construction, road
construction, agricultural dust, and unpaved road dust was held constant, except in
the MARAMA region and NC where some factors were provided for categories
other than paved roads. The projected emissions are reduced during modeling
according to a transport fraction (newly computed for the beta platform) and a
meteorology-based (precipitation and snow/ice cover) zero-out as they are for the
base year.
Livestock: livestock
Livestock were projected to 2023, 2026, and 2030 based on factors created from
USDA National livestock inventory projections published in March 2019
(https://www.ers.usda.eov/publications/pub-details/?pubid=92599). The latest vear
available in the report was 2030.
Nonpoint source oil
and gas:
npoilgas
Production-related sources were grown starting from an average of 2014 and 2016
production data. Emissions were initially projected to 2019 using historical data
and then grown to 2023, 2026 and 2032 based on factors generated from AEO2021
reference case. Based on the SCC, factors related to oil, gas, or combined growth
were used. Coalbed methane SCCs were projected independently. Controls were
then applied to account for NSPS for oil and gas and RICE. WRAP future year
inventories are used in seven WRAP states. The future year WRAP inventories for
are the same for all future years.
Residential Wood
Combustion:
rwc
RWC emissions were projected from 2016 to 2023, 2026 and 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 growth is held
constant after 2026 in the tool for all sources except fireplaces. RWC emissions in
California, Oregon, and Washington were held constant.
Solvents:
solvents
Solvents are based on a new method for 2016v2, while in 2016vl these emissions
part of nonpt. The same projection and control factors were applied to solvent
emissions as if these SCCs were in nonpt. Additional SCCs in the new inventory
that correlate with human population were also projected. Solvent emissions
associated with oil and gas activity were projected using the same projection
factors as the oil and gas sectors. The 2016vl NC and NJ nonpoint packets were
used for 2023 and interpolated to 2026, and updated to apply to more SCCs.
Outside of the MARAMA region, 2032 projections are proportional to growth in
human population to 2030. The MARAMA nonpt tool was used to project 2026
emissions to 2032 after updating the AEO-based factors to use AEO2021. OTC
controls for solvents are applied.
159
-------
Platform Sector:
abbreviation
Description of Projection Methods for Future Year Inventories
Remaining
nonpoint:
nonpt
Industrial emissions were grown according to factors derived from AEO2021 to
reflect growth from 2020 onward. Data from earlier AEOs were used to derive
factors for 2016 through 2020. Portions of the nonpt sector were grown using
factors based on expected growth in human population. The MARAMA projection
tool was used to project emissions to 2023 and 2026 after the AEO-based factors
were updated to AEO2021. Factors provided by North Carolina and New Jersey
were preserved. The 2026 emissions were projected 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 to account for fuel sulfur rules in the mid-Atlantic and northeast. OTC
controls for PFCs are included. In general, controls and projection methods are
consistent with those used in 2016vl.
Nonroad:
nonroad
Outside California and Texas, the MOVES3 model was run to create nonroad
emissions for 2023, 2026, and 2032. The fuels used are specific to the future year,
but the meteorological data represented the year 2016. For California and Texas,
existing 2016vl emissions were retained for 2023, and 2026 emissions were
interpolated from 2016vl 2023 and 2028. For 2032, California emissions were
interpolated between the years 2028 and 2035, submitted by the state. For 2032,
for Texas, 2026 was projected to 2032 using MOVES trends.
Onroad:
onroad,
onroadnonconus
Activity data for 2016 were backcast from the 2017 NEI then projected from 2016
to 2019 based on trends in FHWA VM-2 trends. Projection from 2019 to 2023,
2026, and 2032 were done using factors derived from AE02020 (for years 2019 to
2020) and AEO 2021 (for years 2020 to 2023 and 2023 to 2026 and 2032). Where
S/Ls provided activity data for 2023, those data were used. To create the emission
factors, MOVES3 was run forthe years 2023, 2026, and 2032, with 2016
meteorological data and fuels, but with age distributions projected to represent
future years, and the remaining inputs consistent with those used in 2017. The
future year activity data and emission factors were then combined using SMOKE-
MOVES to produce the 2023, 2026, and 2032 emissions. Section 4.3.2 describes
the applicable rules that were considered when projecting onroad emissions.
Onroad California:
onroadcaadj
CARB-provided emissions were used for California, but temporally allocated
with MOVES3-based data. CARB inventories for 2026 and 2032 were
interpolated from existing CARB years.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
Othafdust emissions for future years were provided by ECCC in 2016vl.
Projection factors were derived from those 2023 and 2028 inventories and applied
to the 2016v2 inventory. 2026 projection factors were interpolated from 2023 and
2028, and 2032 projections were set to the 2016vl 2028 inventory values. Mexico
emissions are not included in this sector.
Other Point Fugitive
dust sources not
from the NEI:
othptdust
Wind erosion emissions were removed from the point fugitive dust inventories.
Base year 2016 inventories with the rotated grid pattern removed were held flat for
the future years, including the same transport fraction as the base year and the
meteorology-based (precipitation and snow/ice cover) zero-out.
160
-------
Platform Sector:
abbreviation
Description of Projection Methods for Future Year Inventories
Other point sources
not from the NEI:
othpt
Canada emissions for future years were provided by ECCC in 2016vl. Projection
factors were derived from those 2023 and 2028 inventories and applied to the
2016v2 inventory. 2026 projection factors were interpolated from 2023 and 2028,
and 2032 projections were set to 2028. Canada projections were applied by
province-subclass where possible (i.e., where subclasses did not change from
2016vl to 2016v2). For inventories where that was not possible, including airports
and most stationary point sources except for oil and gas, projections were applied
by province. For Mexico sources, Mexico's 2016 inventory was grown using to the
future years 2023, 2026, and 2028 (representing 2032) using state+pollutant
factors based on the 2016vl platform inventories.
Canada ag not from
the NEI:
canadaag
Reallocated base year emissions low-level agricultural sources that were originally
developed on the rotated 10-km grid were projected to 2023, 2026, and 2028 (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 future years based on province-subclass changes in the ECCC-
provided data used for 2016vl. 2026 projection factors were interpolated from
2023 and 2028, and 2032 emissions were set to 2028 levels.
Other non-NEI
nonpoint and
nonroad:
othar
Future year Canada nonpoint inventories were provided by ECCC for 2016vl. For
Canadian nonroad sources, factors were provided from which the future year
inventories could be derived. Projection factors were derived from those 2023 and
2028 inventories and applied to the 2016v2 inventory. 2026 projection factors
were interpolated from 2023 and 2028. For 2032, Canada nonroad and rail
emissions were projected from 2026 to 2032 based on US trends, while 2028
emissions were used to represent 2032 for the rest of the sector.
For Mexico nonpoint and nonroad sources, state-pollutant projection factors for
2023 and 2028 were calculated from the 2016vl inventories, and then applied to
the 2016v2 base year inventories. 2026 projection factors were interpolated from
2023 and 2028, and 2028 emissions were used to represent 2032 in Mexico.
Other non-NEI
onroad sources:
onroadcan
For Canadian mobile onroad sources, future year inventories were projected from
2016 to 2023 and 2026 using ECCC-provided projection data from vl platform at
the province and subclass (which is similar to SCC but not exactly) level, with
2026 interpolated from 2023 and 2028. 2032 was projected from 2026 using US-
based onroad trends.
Other non-NEI
onroad sources:
onroadmex
Monthly year Mexico (municipio resolution) onroad mobile inventories were
developed based runs of MOVES-Mexico for 2023, 2028, and 2035. 2023 was
reused from the 2016vl platform; 2026 was interpolated between 2023 and 2028
and 2032 was interpolated between 2028 and 2035.
161
-------
4.1 EGU Point Source Projections (ptegu)
The 2023, 2026, and 2032 EGU emissions inventories were developed from the output of the v6 platform
using the Summer 2021 Reference Case run of the Integrated Planning Model (IPM). IPM is a linear
programming model that accounts for variables and information such as energy demand, planned unit
retirements, and planned rules to forecast unit-level energy production and configurations. The following
specific rules and regulations are included in the IPM v6 platform run:
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 regulation requires
states to submit revised State Implementation Plans (SIPs) that include (1) goals for improving
visibility in Class I areas on the 20% worst days and allowing no degradation on the 20% best
days and (2) assessments and plans for achieving Best Available Retrofit Technology (BART)
emission targets for sources placed in operation between 1962 and 1977. Since 2010, EPA has
approved SIPs or, in a few cases, put in place regional haze Federal Implementation Plans for
several states. The BART limits approved in these plans (as of summer 2020) that will be in place
for EGUs are represented in the 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 the 2023, 2025 (used for the 2026 case), and 2030 (used for the
2032 case31). All inputs, outputs and full documentation of EPA's IPM v6 Summer 2021 Reference Case
and the associated NEEDS version is available on the power sector modeling website
(https://www.epa.gov/airmarkets/documentation-epas-power-sector-modeling-platform-v6-summer-2Q21-
reference-case). Some of the key parameters used in the IPM run are:
31 Planned retirements for 2030 and 203 lare adjusted so that 2030 outputs are reflective of the 2032 calendar year.
162
-------
• Demand: AEO 2020
• Gas and Coal Market assumptions: updated as of September 2020
• Cost and performance of fossil generation technologies: AEO 2020
• Cost and performance of renewable energy generation technologies: NREL ATG 2020 (mid-case)
• Nuclear unit operational costs: AEO 2020 with some adjustments
• Environmental rules and regulations (on-the-books): Revised CSAPR, MATS, BART, CA AB 32,
RGGI, various RPS and CES, non-air rules (Cooling Water Intake, ELC, CCR), State Rules
• Financial assumptions: 2016-2020 data, reflects tax credit extensions from Consolidated
Appropriations Act of 2021
• Transmission: updated data with build options
• Retrofits: carbon capture and sequestration option for CCs
• Operating reserves (in select runs): Greater detail in representing interaction of load, wind, and
solar, ensuring availability of quick response of resources at higher levels of RE penetration
• Fleet: Summer 2021 reference case NEEDS
The EGU emissions are calculated for the inventory using the output of the IPM model for the forecast
year. Units that are identified to have a primary fuel of landfill gas, fossil waste, non-fossil waste, residual
fuel oil, or distillate fuel oil may be missing emissions values for certain pollutants in the generated
inventory flat file. Units with missing emissions values are gapfilled using projected base year values. The
projections are calculated using the ratio of the future year seasonal generation in the IPM parsed file and
the base year seasonal generation at each unit for each fuel type in the unit as derived from the 2018 EIA-
923 tables and the 2018 NEI. New controls identified at a unit in the IPM parsed file are accounted for
with appropriate emissions reductions in the gapfill projection values. When base year unit-level
generation data cannot be obtained no gapfill value is calculated for that unit. Additionally, some units,
such as landfill gas, may not be assigned a valid SCC in the initial flat file. The SCCs for these units are
updated based on the base year SCC for the unit-fuel type.
Combined cycle units produce some of their energy from process steam that turns a steam turbine. The
IPM model assigns a fraction of the total combined cycle production to the steam turbine. When the
emissions are calculated these steam units are assigned emissions values that come from the combustion
portion of the process. In the base year NEI steam turbines are usually implicit to the total combined cycle
unit. To achieve the proper plume rise for the total combined cycle emissions, the stack parameters for the
steam turbine units are updated with the parameters from the combustion release point.
Large EGUs in the IPM-derived flat file inventory are associated with hourly CEMS data for NOX and
S02 emissions values in the base year. To maintain a temporal pattern consistent with the 2016 base year,
the NOX and S02 values in the hourly CEMS inventories are projected to match the total seasonal
emissions values in the future years.
The EGU sector NOx emissions by state are listed in Table 4-2 for each of the 2016v2 cases.
163
-------
Table 4-2. EGU sector NOx emissions by State for 2016v2 cases
State
2016fj
2023fj
2026fj
2032fj
Alabama
28,596
8,043
9,319
9,726
Arizona
21,716
3,806
3,416
5,817
Arkansas
27,224
10,014
9,258
11,583
California
7,123
14,292
16,286
12,885
Colorado
30,152
12,437
12,725
14,268
Connecticut
4,088
3,798
3,740
3,883
Delaware
1,487
311
320
464
District of Columbia
NA
38
39
39
Florida
64,682
22,004
22,451
21,423
Georgia
29,479
6,388
5,937
9,056
Idaho
1,369
738
705
737
Illinois
32,140
17,861
16,777
21,755
Indiana
83,485
37,165
36,007
35,951
Iowa
22,971
21,736
17,946
22,293
Kansas
14,959
3,824
4,351
8,115
Kentucky
57,583
25,679
25,207
22,992
Louisiana
48,021
15,888
16,949
18,053
Maine
4,935
3,743
3,063
3,171
Maryland
10,448
3,025
3,008
2,824
Massachusetts
8,605
4,625
4,566
4,652
Michigan
43,291
24,603
22,378
25,355
Minnesota
21,737
14,360
9,442
11,155
Mississippi
16,525
4,508
5,208
4,972
Missouri
57,647
40,766
34,935
44,534
Montana
15,832
8,796
8,760
9,060
Nebraska
20,738
24,712
20,274
22,011
Nevada
3,969
3,049
3,017
3,081
New Hampshire
2,158
507
483
547
New Jersey
6,626
3,915
4,032
4,052
New Mexico
20,222
1,834
1,987
1,417
New York
18,415
12,097
11,693
11,129
North Carolina
35,326
19,002
15,984
22,560
North Dakota
38,400
20,787
19,276
22,895
Ohio
55,581
33,865
27,031
34,326
164
-------
State
2016fj
2023fj
2026fj
2032fj
Oklahoma
25,084
3,814
3,426
5,745
Oregon
4,150
2,194
2,145
4,129
Pennsylvania
84,086
20,793
23,965
22,131
Rhode Island
524
490
476
508
South Carolina
14,231
10,512
7,134
8,808
South Dakota
1,109
1,090
1,054
1,152
Tennessee
19,173
2,474
2,100
1,957
Texas
111,612
46,370
27,164
39,437
Tribal Data
35,057
2,940
2,970
5,637
Utah
27,450
20,588
10,915
16,478
Vermont
302
111
4
8
Virginia
27,953
6,431
7,270
6,554
Washington
8,860
2,319
2,532
2,848
West Virginia
52,265
29,445
21,450
23,343
Wisconsin
16,250
6,102
4,304
6,678
Wyoming
36,095
10,855
11,036
12,507
4.2 Non-EGU Point and Nonpoint Sector Projections
To project all U.S. non-EGU stationary sources, facility/unit closures information and growth
(PROJECTION) factors and/or controls were applied to certain categories within the afdust, ag, cmv, rail,
nonpt, np oilgas, ptnonipm, pt oilgas and rwc platform sectors. Some facility or sub-facility-level
closure information was also applied to the point sources. There are also a handful of situations where
new inventories were generated for sources that did not exist in the NEI (e.g., biodiesel and cellulosic
plants, yet-to-be constructed cement kilns). This subsection provides details on the data and projection
methods used for the non-EGU point and nonpoint sectors.
Because the projection and control data are developed mostly independently from how the emissions
modeling sectors are defined, this section is organized primarily by the type of projections data, with
secondary consideration given to the emissions modeling sector (e.g., industrial source growth factors are
applicable to four emissions modeling sectors). The rest of this section is organized in the order that the
EPA uses the Control Strategy Tool (CoST) in combination with other methods to produce future 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 future-year inventories not generated via CoST. This organization allows consolidation of the
discussion of the emissions categories that are contained in multiple sectors, because the data and
approaches used across the sectors are consistent and do not need to be repeated. Sector names associated
with the CoST packets are provided in parentheses following the subsection titles. The projection and
control factors applied by CoST to prepare the future year emissions are provided with other 2016v2 input
data and reports on the 2016v2 FTP site.
165
-------
4.2.1 Background on the Control Strategy Tool (CoST)
CoST is used to apply most non-EGU projection/growth factors, controls and facility/unit/stack-level
closures to the 2016-based emissions modeling inventories to create future year inventories for the
following sectors: afdust, airports, cmv, livestock, nonpt, np oilgas, pt oilgas, ptnonipm, rail, rwc, and
solvents. 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 future year (i.e., projected) inventories. Future year inventories are created for each emissions
modeling sector by applying a CoST control strategy type called "Project future year inventory" and each
strategy includes all base year 2016 inventories and applicable CoST packets. For reasons to be discussed
later, some emissions modeling sectors may require multiple CoST strategies to account for the
compounding of control programs that impact the same type of sources. There are also available linkages
to existing and user-defined control measure databases and it is up to the user to determine how control
strategies are developed and applied. The EPA typically creates individual CoST packets that represent
specific intended purposes (e.g., aircraft projections for airports are in a separate PROJECTION packet
from residential wood combustion sales/appliance turnover-based projections). CoST uses three packet
types:
1. CLOSURE: Closure packets are applied first in CoST. This packet can be used to zero-out (close)
point source emissions at resolutions as broad as a facility to as specific as a release point. The
EPA uses these types of packets for known post-2016 controls as well as information on closures
provided by states on specific facilities, units or release points. This packet type is only used for
the ptnonipm and pt oilgas sectors.
2. 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.
3. 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, inventory
controls are first backed out prior to the application of a more-stringent replacement control).
These packets are stored as data sets within the Emissions Modeling Framework and use comma-
delimited formats. As mentioned above, CoST first applies any/all CLOSURE information for point
sources, then applies PROJECTION packet information, followed by CONTROL packets. A hierarchy is
used by CoST to separately apply PROJECTION and CONTROL packets. In short, in a separate process
for PROJECTION and CONTROL packets, more specific information is applied in lieu of less-specific
information in ANY other packets. For example, a facility-level PROJECTION factor will be replaced by
a unit-level, or facility and pollutant-level PROJECTION factor. It is important to note that this hierarchy
does not apply between packet types (e.g., CONTROL packet entries are applied irrespective of
166
-------
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 future-year emissions value. Therefore, as encountered with this future year base case, consent
decrees and state comments for specific cement kilns (expressed as CONTROL packet entries) needed to
be applied instead of (not in addition to) the more general approach of the PROJECTION packet entries
for cement manufacturing. By processing CoST control strategies with PROJECTION and CONTROL
packets separated by the type of broad measure/program, it is possible to show actual changes from the
base year inventory to the future year inventory as a result of applying each packet.
Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a
hierarchal matching approach to assign PROJECTION factors to the inventory. For example, a packet
entry with Ranking=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, although the fields
in the table are not necessarily named the same in CoST, but rather are similar to those in the SMOKE
FF10 inventories. For example, "REGIONCD" is the county-state-county FIPS code (e.g., Harris
county Texas is 48201) and "STATE" would be the 2-digit state FIPS code with three trailing zeroes
(e.g., Texas is 48000).
Table 4-3. Subset of CoST Packet Matching Hierarchy
Rank
Matching Hierarchy
Inventory Type
1
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, SCC, POLL
point
2
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, POLL
point
3
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, POLL
point
4
REGION CD, FACILITY ID, UNIT ID, POLL
point
5
REGION CD, FACILITY ID, SCC, POLL
point
6
REGION CD, FACILITY ID, POLL
point
7
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, SCC
point
8
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID
point
9
REGION CD, FACILITY ID, UNIT ID, REL POINT ID
point
10
REGION CD, FACILITY ID, UNIT ID
point
11
REGION CD, FACILITY ID, SCC
point
12
REGION CD, FACILITY ID
point
13
REGION CD, NAICS, SCC, POLL
point, nonpoint
14
REGION CD, NAICS, POLL
point, nonpoint
15
STATE, NAICS, SCC, POLL
point, nonpoint
16
STATE, NAICS, POLL
point, nonpoint
17
NAICS, SCC, POLL
point, nonpoint
18
NAICS, POLL
point, nonpoint
167
-------
Rank
Matching Hierarchy
Inventory Type
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 future year cases are described in the
following subsections. Year-specific projection factors (PROJECTION packets) for each future year
were used to create the future year cases, unless noted otherwise in the specific subsections. The contents
of a few of these projection packets (and control reductions) are provided in the following subsections
where feasible. However, most sectors used growth or control factors that varied geographically, and
their contents could not be provided in the following sections (e.g., facilities and units subject to the
Boiler MACT reconsideration has thousands of records). The remainder of Section 4.2 is divided into
several subsections that are summarized in Table 4-4. Note that independent future year inventories were
used rather than projection or control packets for some sources.
Table 4-4. Summary of non-EGU stationary projections subsections
Subsection
Title
Sector(s)
Brief Description
4.2.2
CoST Plant CLOSURE
packet
ptnonipm,
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 future year.
4.2.3.1
Fugitive dust growth
afdust
PROJECTION packet: county-level resolution,
primarily based on VMT growth.
4.2.3.2
Livestock population
growth
livestock
PROJECTION packet: national, by-animal type
resolution, based on animal population projections.
4.2.3.3
Category 1 and 2
commercial marine
vessels
cmv clc2
PROJECTION packet: Category 1 & 2: CMV uses
SCC/poll for all states except Calif.
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.
168
-------
Subsection
Title
Sector(s)
Brief Description
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 AE02020 and
AEO2021 were used for nonpt and ptnonipm. Data
derived from EIA state historical data and AEO2021
for 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, EIA-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 EIA-based factors for industrial
sources. Evaporative Emissions from Finished Fuels
projected using EIA-based factors. Human
population used as growth for applicable sources.
4.2.3.9
Solvents
solvents
Several PROJECTION packets including
population-based, MARAMA state factors, and oil
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
packets
ptnonipm,
nonpt,
npoilgas,
pt oilgas
Introduces and summarizes national impacts of all
CoST CONTROL packets to the future year.
4.2.4.1
Oil and Gas NSPS
npoilgas,
ptoilgas
CONTROL packets: reflect the impacts of the NSPS
for oil and gas sources.
4.2.4.2
RICE NSPS
ptnonipm,
nonpt,
npoilgas,
pt oilgas
CONTROL packets apply reductions for lean burn,
rich burn, and combined engines for identified
SCCs.
4.2.4.3
Fuel Sulfur Rules
ptnonipm,
nonpt
CONTROL packet: updated by MARAMA, applies
reductions to specific units in ten states.
4.2.4.4
Natural Gas Turbines
NOx NSPS
ptnonipm
CONTROL packets apply NOx emission reductions
established by the NSPS for turbines.
4.2.4.5
Process Heaters NOx
NSPS
ptnonipm
CONTROL packet: applies NOx emission limits
established by the NSPS for process heaters.
4.2.4.6
CISWI
ptnonipm
CONTROL packet: applies controls to specific
CISWI units in 11 states.
4.2.4.7
Petroleum Refineries
NSPS Subpart JA
ptnonipm
CONTROL packet: control efficiencies are applied
to identified delayed coking and storage tank units.
169
-------
Subsection
Title
Sector(s)
Brief Description
4.2.4.89
Ozone Transport
Commission Rules
nonpt,
solvents
CONTROL packets reflecting rules for solvents and
portable fuel containers.
4.2.4.8
State-Specific Controls
ptnonipm
CONTROL packets and comments submitted by
individual states for rules that may only impact their
state or corrections noted from previous reviews.
Includes consent decrees and Arizona regional haze
controls.
4.2.2 CoST Plant CLOSURE Packet (ptnonipm, pt_oilgas)
Packets:
CLOSURES2016v2_platform_ptnonipm_l 8jun202 l_v2
The CLOSURES packet contains facility, unit and stack-level closure information derived from an
Emissions Inventory System (EIS) unit-level report from June 9, 2021, with closure status equal to
"PS" (permanent shutdown; i.e., post-2016 permanent facility/unit shutdowns known in EIS as of the
date of the report). In addition, comments on past modeling platforms received by states and other
agencies specified additional closures, as well as some previously specified closures which should
remain open, in the following states: Alabama, North Carolina, Ohio, Pennsylvania, and Virginia. The
list of closures also includes two Pennsylvania facilities that were only partially closed in prior runs, but
have since completely closed: Pittsburgh Corning Corp - Port Allegany (ID 3025211), and Osram
Sylvania Inc. - Wellsboro Plant (ID 5490611). Ultimately, all data were updated to match the SMOKE
FF10 inventory key fields, with all duplicates removed, and a single CoST packet was generated. These
changes impact sources in the ptnonipm and ptoilgas sectors. The cumulative reduction in emissions
for ptnonipm and pt oilgas are shown in Table 4-5.
Table 4-5. Reductions from all facility/unit/stack-level closures in 2016v2
Pollutant
Ptnonipm
ptoilgas
CO
12,147
187
NH3
508
0
NOX
14,009
284
PM10
10,891
9
PM2.5
7,104
9
S02
24,103
178
VOC
7,181
106
4.2.3 CoST PROJECTION Packets (afdust, airports, cmv, livestock, nonpt,
np_oilgas, ptnonipm, pt_oilgas, rail, rwc, solvents)
For point inventories, after the application of any/all CLOSURE packet information, the next step CoST
performs when running a control strategy is the application of all PROJECTION packets. Regardless of
inventory type (point or nonpoint), the PROJECTION packets are applied prior to the CONTROL
packets. For several emissions modeling sectors (i.e., airports, np oilgas, pt oilgas), there is only one
170
-------
PROJECTION packet applied for each future year. For all other sectors, there are several different
sources of projection data and as a result there are multiple PROJECTION packets that are concatenated
by CoST during a control strategy run and quality-assured regarding duplicates and applicability to the
inventories in the CoST strategy. Similarly, CONTROL packets are kept in distinct datasets for different
control programs. Having the PROJECTION (and CONTROL) packets separated into "key" projection
and control programs allows for quick summaries of these distinct control programs.
For the 2016vl platform MARAMA provided PROJECTION and CONTROL packets for years 2023 and
2028 for states including: Connecticut, Delaware, Maryland, Massachusetts, New Hampshire, New York,
New Jersey, North Carolina, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia, Maine, and
the District of Columbia. MARAMA only provided pt oilgas and np oilgas packets for Rhode Island,
Maryland and Massachusetts. For 2016v2, new spreadsheets of projection factors were provided that
facilitated the incorporation of data from the AEO 2021 and other surrogate data for projection factors.
The new spreadsheets also to reflect sources affected by the Pennsylvania Reasonably Available Control
Technology (RACT) II including 2023 emissions for one Pennsylvania facility (Anchor Hocking LLC,
Monaca Plant) affected by the rule. For that facility, emissions values were swapped in after applying all
other projections and controls. For states not covered by the MARAMA packets, projection factors were
developed using nationally available data and methods.
4.2.3.1 Fugitive dust growth (afdust)
Packets:
Proj ection_2016_2023_afdust_versionl_platform_MARAMA_l 5jul2 l_v2
Proj ection_2016_2023_afdust_versionl_platform_NJ_20aug202 l_vl
Proj ection_2016_2023_afdust_versionl_platform_national_24jun202 l_v0
Proj ection_2016_2023_all_nonpoint_versionl_platform_NC_24jun202 l_nf_v5
Proj ection_2016_2026_afdust_version l_platform_MARAMA_nopavedroads_noNCNJ_l 5jul202 l_v0
Proj ection_2016_2026_afdust_versionl_platform_NJ_nopavedroads_20jul202 l_v0
Proj ection_2016_2026_afdust_versionl_platform_national_20jul202 l_v0
Proj ection_2016_2026_all_nonpoint_versionl_platform_NC_l 9jul202 l_v0
Projection_2026_2032_afdust_version2_platform_MARAMA_nopavedroads_05aug2021_v0
Projection_2026_2032_afdust_version2_platform_national_05aug2021_v0
MARAMA States
MARAMA provided a spreadsheet tool that could be used to compute projection factors for their states to
project 2016 afdust emissions to future years 2023, 2026, and 2032. These county-specific projection
factors impacted paved roads (SCC 2294000000), residential construction dust (SCC 2311010000),
industrial/commercial/institutional construction dust (SCC 2311020000), road construction dust (SCC
2311030000), dust from mining and quarrying (SCC 2325000000), agricultural crop tilling dust (SCC
2801000003), and agricultural dust kick-up from beef cattle hooves (SCC 2805001000). Other afdust
emissions, including unpaved road dust emissions, were held constant in future year projections. North
Carolina and New Jersey provided their own packets for this sector for 2023 and 2028, which were
interpolated to 2026. Projections for 2032 used a 2026 baseline and were based on MARAMA-provided
data, including in NC and NJ. For paved roads, new VMT-based projection factors based on 2016v2
VMT were used in place of projection factors provided by MARAMA, NC, and NJ for all years, since
their factors were based on older VMT.
171
-------
Non-MARAMA States
For paved roads (SCC 2294000000), the 2016 afdust emissions were projected to future years 2023 and
2026 based on differences in county total VMT:
Future year afdust paved roads = 2016 afdust paved roads * (Future year county total VMT) /
(2016 county total VMT)
EPA used a similar method to develop factors to project the afdust emissions from 2026 to 2032. The
VMT projections are described in the onroad section. Paved road dust emissions were projected this way
in all states, including MARAMA states.
In non-MARAMA states, all emissions other than paved roads are held constant in the future year
projections. The impacts of the projections are shown in Table 4-6.
Table 4-6. Increase in total afdust PM2.5 emissions from projections in 2016v2
2016
Emissions
2023
Emissions
percent
Increase
2023
2026
Emissions
percent
Increase
2026
2032
Emissions
percent
Increase
2032
2,254,168
2,313,089
2.61%
2,332,376
3.47%
2,353,763
4.42%
4.2.3.2 Livestock population growth (livestock)
Packets:
Proj ection_2016_2023_all_nonpoint_versionl_platform_NC_24jun202 l_nf_v5
Proj ection_2016_2026_all_nonpoint_version2_platform_NC_l 9jul202 l_nf_vl
Proj ection_2017_2023_ag_livestock_version2_platform_23jun202 l_v0
Proj ecti on_2017_2023_ag_version 1 _platform_NJ_20aug2021_v 1
Proj ection_2017_2026_ag_livestock_version2_platform_23jun202 l_v0
Proj ection_2017_2026_livestock_version2_platform_NJ_l 6jul202 l_v0
Proj ection_2026_2032_ag_livestock_version2_platform_05aug2021_v0
The 2017NEI livestock emissions were projected to year 2023 and 2028 using projection factors created
from USDA National livestock inventory projections published in March 2019
(https://www.ers.usda.gov/publications/pub-details/?pubid=92599) and are shown in Table 4-7. For
emission projections to 2023, a ratio was created between animal inventory counts for 2023 and 2017 to
create a projection factor. This process was completed for the animal categories of beef, dairy, broilers,
layers, turkeys, and swine. The projection factor was then applied to the 2017NEI base emissions for the
specific animal type to estimate 2023 NH3 and VOC emissions. For emission projections to 2026 and
2030, the same method was used to develop and apply the factors. Note that 2030 is the latest year
available in this report so the projection inventory used in the 2032 case was for 2030 for this sector.
New Jersey (NJ) provided NJ-specific projection factors that were used to grow livestock waste emissions
from 2017 to 2023 and 2028. The factors were interpolated to obtain factors for 2026. North Carolina
(NC) provided NC-specific projection factors that used a 2016-based projection, therefore, NC's livestock
waste emissions are projected from the 2016 back-casted base year emissions to 2023 and 2026. As in
New Jersey, North Carolina provided projection factors for 2023 and 2028, which were interpolated to
2026.
172
-------
The EPA developed factors using the USDA data to project livestock emissions from 2026 to 2030 and
applied these in all states.
Table 4-7. National projection factors for livestock: 2017 to 2023, 2026, and 2030
Animal
2017-to-2023
2017-to-2026
2017-to-2030
beef
-0.27%
+0.61%
+1.51%
swine
+8.93%
+12.50%
+15.17%
broilers
+8.30%
+12.67%
+18.77%
turkeys
+1.22%
+2.52%
+4.29%
layers
+6.88%
+12.60%
+20.22%
dairy
+0.62%
+1.28%
+2.16%
4.2.3.3 Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
Packets:
Proj ecti on_2016_2023_cmv_c 1 c2_version 1 _platform_04oct2019_v 1
Proj ection_2016_2023_cmv_Canada_versionl_platform_24sep2019_v0
Proj ection_2016_2026_cmv_c 1 c2_version2_platform_14jul202 l_v0
Proj ection_2016_2026_cmv_Canada_version2_platform_l 5jul202 l_v0
Proj ecti on_2016_203 0_cmv_c 1 c2_version2_platform_04aug2021_v0
Proj ection_2016_2030_cmv_Canada_version2_platform_04aug202 l_v0
Category 1 and category 2 (C1C2) CMV emissions sources outside of California were projected to 2023,
2026, and 2030 based on factors derived from the Regulatory Impact Analysis (RIA) Control of
Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less
than 30 Liters per Cylinder (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-
control-emissions-air-pollution-locomotive). The 2023 emissions are unchanged from 2016vl and the
2026 emissions are equivalent to interpolating 2016vl emissions between 2023 and 2028. Projections
were taken only to 2030 and those were used for the 2032 case. California emissions were projected based
on factors provided by the state. Table 4-8 lists the pollutant-specific projection factors to 2023, and 2028
that were used for cmv_clc2 sources outside of California. California sources were projected to 2023 and
2028 using the factors in Table 4-9, which are based on data provided by CARB.
Projection factors for Canada for 2026 were based on ECCC-provided 2023 and 2028 data interpolated to
2026. For 2032, a 2028 to 2030 trend based on US factors was applied on top of the ECCC-based 2016 to
2028 projections that differed by province
Table 4-8. National projection factors for cmv_clc2
Pollutant
20l6-to-2023 (%)
20I6-1O-2026 (%)
20I6-1O-2030 (%)
CO
-1.3%
-0.4%
+1.4%
NOX
-29.3%
-39.0%
-49.3%
PM10
-28.3%
-37.8%
-48.3%
PM2.5
-28.3%
-37.8%
-48.3%
S02
-65.3%
-65.7%
-66.1%
VOC
-31.5%
-42.0%
-51.3%
173
-------
Table 4-9. California projection factors for cmv_clc2
Pollutant
20l6-to-2023 (%)
20I6-1O-2026 (%)
20I6-1O-2030 (%)
CO
+20.1%
+23.2%
+26.5%
NOX
-15.0%
-16.6%
-19.4%
PM10
-29.9%
-32.1%
-35.8%
PM2.5
-29.9%
-32.1%
-35.8%
S02
+24.1%
+38.9%
+61.0%
VOC
+1.5%
+1.7%
+0.5%
4.2.3.4 Category 3 Commercial Marine Vessels (cmv_c3)
Packets:
Proj ection_2016_2023_cmv_c3_versionl_platform_04oct2019_v2_Mexico32
Proj ection_2016_2023_cmv_c3_version l_platform_24sep2019_v 1
Proj ection_2016_2023_cmv_Canada_versionl_platform_24sep2019_v0
Proj ection_2016_2026_cmv_c3_version2_platform_l 5jul202 l_vO
Proj ection_2016_2026_cmv_Canada_version2_platform_l 5jul202 l_vO
Proj ection_2016_2030_cmv_c3_version2_platform_04aug202 l_vO
Proj ection_2016_2030_cmv_Canada_version2_platform_04aug202 l_vO
Growth rates for cmv_c3 emissions from 2016 to 2023, 2026 and 2030 were projected using an EPA
report on projected bunker fuel demand. Bunker fuel usage was used as a surrogate for marine vessel
activity. 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. The year 2030 was used for 2032 due to uncertainty in future fuel use data.
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)33 were refactored to use the new bunker fuel usage growth rates. The assumptions of
changes in fleet composition and emissions rates from the C3 RIA were preserved and applied to the new
bunker fuel demand growth rates for 2023, 2026, and 2030 to arrive at the final growth rates. Projections
were taken only to 2030 (used for 2032) as it was the last year of data in the report. The Category 3 marine diesel
engines Clean Air Act and International Maritime Organization standards from April, 2010
(https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-emissions-new-
marine-compression-0) were also considered when computing the emissions.
The 2023 emissions are unchanged from 2016vl and the 2026 emissions are equivalent to interpolating
2016vl emissions between 2023 and 2028. Projection factors for Canada for 2026 were based on ECCC-
provided 2023 and 2028 data interpolated to 2026. For 2032, a 2028 to 2030 trend based on US factors
was applied on top of the ECCC-based 2016 to 2028 projections that differed by province.
32 2023 has a Mexico packet is because the Mexico CMV inventory covers some ports, but no offshore underway. This
inventory has emissions in the 36US3 domain only, not 12US1 and was not projected to 2026 or 2032.
33 https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockev=P1005ZGH.TXT.
174
-------
The 2023, 2026, and 2030 projection factors are shown in Table 4-10. Some regions for which 2016
projection factors were available did not have 2023 or 2026 projection factors specific to that region, so
factors from another region were used as follows:
• Alaska was projected using North Pacific factors.
• Hawaii was projected using South Pacific factors.
• Puerto Rico and Virgin Islands were projected using Gulf Coast factors.
• Emissions outside Federal Waters (FIPS 98) were projected using the factors given in
Table 4-10 for the region "Other".
• California was projected using a separate set of state-wide projection factors based on
CMV emissions data provided by the California Air Resources Board (CARB). These
factors are shown in Table 4-11
Table 4-10. 2016-to-2023, 2016-to-2026, and 2016-to-2030 CMV C3 projection factors outside of
California
Region
2016-1 o-2023
2016-lo-2023
2016-1 <>-2026
2016-lo-2026
20I6-1O-2030
20I6-1O-2030
\()\
oilier
\()\
oilier
\()\
oilier
polluliinls
polluliinls
polluliinls
US East Coast
-0.1%
+27.7%
-0.9"u
41.4%
-8.1%
58.4%
US South Pacific
(ex. California)
-24.8%
+20.9%
-30.3%
+36.6%
-37.6%
+55.2%
US North Pacific
-3.4%
+22.6%
-3.8%
+34.6%
-4.4%
+47.8%
US Gulf
-6.9%
+20.8%
-10.2%
+29.8%
-14.6%
+42.5%
US Great Lakes
+8.7%
+14.6%
+15.4%
+22.7%
+24.2%
+33.9%
Other
+23.1%
+23.1%
+35.0%
+35.0%
+50.1%
+50.1%
Non-l-'edenil \Yiilers
20l6-lo-2023
2016-lo-2026
2016-1 (>-2030
S02
-77.2%
-75.0%
-72.2%
PM (main engines)
-36.1%
-29.9%
-22.0%
PM (aux. engines)
-39.7%
-33.9%
-26.5%
Other pollutants
+23.1%
+35.0%
+50.1%
Table 4-11. 2016-to-2023, 2016-to-2026, and 2016-to-2030 CMV C3 projection factors for California
Polliiliinl
20I6-1O-2023
20I6-1O-2026
20I6-1O-2030
CO
1.180
1.276
1.401
Nox
1.156
1.259
1.336
PMio / PM2.5
1.205
1.311
1.447
S02
1.183
1.272
1.392
VOC
1.242
1.373
1.542
175
-------
4.2.3.5 Oil and Gas Sources (pt_oilgas, np_oilgas)
Packets:
Proj ection_2016_2023_oilgas_version2_platform_30jun202 l_vl
Proj ection_2016_2026_oilgas_version2_platform_14jul202 l_vO
Proj ection_2016_2032_oilgas_version2_platform_14jul202 l_vO
Future year inventories for seven of the WRAP states were provided by WRAP. The details about these
non-point and point source oil and gas data can be found here:
http://www.wrapair2.org/pdfAVRAP OGWG 2028 OTB RevFinalReport 05March2020.pdf (WRAP /
Ramboll, 2020). This future year WRAP data for npoilgas and ptoilgas are the same for all future
years, 2023 = 2026 = 2032.
For areas outside of the WRAP states, future year projections for the 2016v2 platform were generated for
point oil and gas sources for years 2023, 2026 and 2032. These projections consisted of three
components: (1) applying facility closures to the pt oilgas sector using the CoST CLOSURE packet; (2)
using historical and/or forecast activity data to generate future-year emissions before applicable control
technologies are applied using the CoST PROJECTION packet; and (3) estimating impacts of applicable
control technologies on future-year emissions using the CoST CONTROL packet. Applying the
CLOSURE packet to the pt oilgas sector resulted in small emissions changes to the national summary
shown inTable 4-5. Note the closures for years 2023, 2026 and 2032 are the same.
For pt_oilgas growth to 2023, 2026 and 2032, the oil and gas sources were separated into production-
related and exploration-related sources by SCC. These sources were further subdivided by fuel-type by
SCC into either OIL, natural gas (NGAS), BOTH oil-natural gas fuels possible, or coal-bed methane
(CBM). The next two subsections describe the growth component process.
For np_oilgas growth to 2023, 2026 and 2032, oil and gas sources were separated into production-related,
transmission-related, and all other point sources by NAICS. These sources are further subdivided by fuel-
type by SCC into either OIL, natural gas (NGAS), or BOTH oil-natural gas fuels possible.
Production-related Sources (pt oilgas, np oilgas)
The growth factors for the production-related NAICS-SCC combinations were generated in a two-step
process. The first step used historical production data at the state-level to get state-level short-term trends
or factors from 2016 to year 2019.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) 2021 reference case for the Lower 48
forecast production tables to project from year 2019 to the years of 2023 and 2028. Specifically, AEO
2021 Table 59 "Lower 48 Crude Oil Production and Wellhead Prices by Supply Region " and AEO 2021
Table 60 "Lower 48 Natural Gas Production and Supply Prices by Supply Region " were used in this
projection process. The AEO2021 forecast production is supplied for each EIA Oil and Gas Supply
region shown in Figure 4-1.
176
-------
Figure 4-1. EIA Oil and Gas Supply Regions as of AEO2021
Pacific
The result of this second step is a growth factor for each Supply Region from 2019 to 2023 and from 2019
to 2026. A Supply Region mapping to FIPS cross-walk was developed so the regional growth factors
could be applied for each FIPS (for pt_oilgas) or to the county-level np_oilgas inventories. Note that
portions of Texas are in three different Supply Regions and portions of New Mexico are in two different
supply regions. The state-level historical factor (2016 to 2019) was then multiplied by the Supply Region
factor (2019 to future years) to produce a state-level or FlPS-level factor to grow from 2016 to 2023 and
from 2016 to 2026. This process was done using crude production forecast information to generate a
factor to apply to oil-production related SCCs or NAICS-SCC combinations and it was also done using
natural gas production forecast information to generate a factor to apply to natural gas-production related
NAICS-SCC combinations. For the NAICS-SCC combinations that are designated "BOTH" the average
of the oil-production and natural-gas production factors was calculated and applied to these specific
combinations.
The state of Texas provided specific technical direction for growth of production-related point sources.
Texas provided updated basin specific production for 2016 and 2019 to allow for a better calculation of
the estimated growth for this three-year period
(http://webapps.rrc.texas.gov/PDO/generalReportAction.do). The AEO2021 was used as described above
for the three AEO Oil and Gas Supply Regions that include Texas counties to grow from 2019 to 2023
and 2026. However, Texas only wanted these growth factors applied to sources in the Permian and Eagle
Ford basins and the oil and gas production point sources in the other basins in Texas were not grown.
After the 2023 run, it was discovered that Texas CBM emissions in "no growth" counties were incorrectly
grown (reduced by 19%) in 2023. This was fixed for 2026 and 2032. Texas gas and oil emissions in "no
growth" counties were correctly held flat (plus controls if applicable) in 2023.
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
177
-------
(https://wwwapps.emnrd.state.nm.us/ocd/ocdpermitting/Reporting/Production/CountvProductionIniection
Summary.aspx) so that a better estimate of growth from 2016 to 2019 for the AEO Supply Regions in
New Mexico could be calculated.
Transmission-related Sources (ptoilgas)
Projection factors were generated using the same AEO2021 tables used for production sources. The
growth factors for transmission sources were developed solely using AEO 2021 data for the entire lower
48 states (one national factor for oil transmission and one national factor for natural gas transmission).
Exploration-related Sources (npoilgas)
Due to Year 2016 being a low exploration activity year when compared to exploration activity in other
recent years, Years 2014 through 2017 exploration emissions were generated using the 2017NEI version
of the Oil and Gas Tool. Table 4-12provides a high-level national summary of the activity data for the
four years. This four-year average (2014-2017) emissions data were used because they were readily
available for use with the 2016v2 platform. These averaged emissions were used for both the 2023, 2026
and 2032 future years in the 2016v2 emissions modeling platform. Note CoST was not used for this
projection step for exploration sources.
Table 4-12. Year 2014-2017 high-level summary of national oil and gas exploration activity
Parameter (all US states)
Year2014
Year2015
Year2016
Year2017
4-year
average
Total Well Completions
40,306
22,754
15,605
21,850
25,129
Unconventional Well
Completions
20,896
11,673
7,610
11,617
12,949
Total Oil Spuds
36,104
17,240
7,014
14,322
18,670
Total Natural Gas Spuds
4,750
3,168
4,244
4,025
4,047
Total Coalbed Methane Spuds
239
130
141
222
183
Total Spuds
41,093
20,538
11,399
18,569
22,900
Total Feet Drilled
327,832,580
178,297,779
106,468,774
181,164,800
198,440,983
Projection overrides (pt oilgas)
A draft set of projected point oil and gas emissions were reviewed and compared to recent emissions data
from 2018. In cases where the recent and projected emissions were substantially different, projected
emissions were instead taken from a recent year of emissions and held constant through the future years.
The affected sources are shown in Table 4-13.
Table 4-13. Point oil and gas sources held constant at 2018 levels
County
FIPS
State
County
Facility
ID
Facility Name
01091
Alabama
Marengo Co
1041811
Transcontinental Gas Pipe Line Company L
01129
Alabama
Washington Co
1028711
American Midstream Chatom, LLC
04005
Arizona
Coconino Co
1115011
EPNG - WILLIAMS COMPRESSOR
STATION
13195
Georgia
Madison Co
2803411
Transcontinental Gas Pipe Line Company,
178
-------
County
FIPS
State
County
Facility
ID
Facility Name
18003
Indiana
Allen Co
4544011
PANHANDLE EASTERN PIPE LINE CO
EDGERT
18075
Indiana
Jay Co
7957111
ANR PIPELINE COMPANY PORTLAND
COMPRES
19181
Iowa
Warren Co
2962011
NATURAL GAS PIPELINE CO OF
AMERICA - STA
20057
Kansas
Ford Co
3839911
Natural Gas Pipeline of America - Minneo
20097
Kansas
Kiowa Co
5027511
Northern Natural Gas - Mullinville Stati
21089
Kentucky
Greenup Co
6096911
TN Gas Pipeline Co LLC - Station 200
21197
Kentucky
Powell Co
5787411
TN Gas Pipeline Co LLC - Station 106
21217
Kentucky
Taylor Co
5727111
TN Gas Pipeline Co LLC - Station 871
22001
Louisiana
Acadia Par
6082411
ANR Pipeline Co - Eunice Compressor Stat
22011
Louisiana
Beauregard Par
5998611
Transcontinental Gas Pipe Line Co LLC (T
22013
Louisiana
Bienville Par
6000211
Southern Natural Gas Co - Bear Creek Sto
22021
Louisiana
Caldwell Par
6426511
Texas Gas Transmission LLC - Columbia Co
22023
Louisiana
Cameron Par
13610511
Sabine Pass LNG LP - Sabine Pass Liquefa
22075
Louisiana
Plaquemines Par
7449511
East Bay Central Facility
22079
Louisiana
Rapides Par
5740911
Texas Gas Transmission LLC - Pineville C
22083
Louisiana
Richland Par
5607811
ANR Pipeline Co - Delhi Compressor Stati
22113
Louisiana
Vermilion Par
5064311
Sea Robin Pipeline Co LLC - Erath Compre
28063
Mississippi
Jefferson Co
7035611
Texas Eastern Transmission LP, Union Chu
28067
Mississippi
Jones Co
7035911
TRANSCONTINENTAL GAS PIPE LINE
COMPANY L
28137
Mississippi
Tate Co
6952811
Trunkline Gas Company, LLC, Independence
31131
Nebraska
Otoe Co
7767611
Northern Natural Gas Company
39039
Ohio
Defiance Co
7938111
ANR Pipeline Company (0320010169)
39045
Ohio
Fairfield Co
8259811
CRAWFORD COMPRESSOR STATION
(0123000137)
40007
Oklahoma
Beaver Co
8131911
BEAVER COMPRESSOR STATION
40139
Oklahoma
Texas Co
8402511
TYRONE CMPSR STA
48103
Texas
Crane Co
4163111
BLOCK 31 GAS PLANT
48195
Texas
Hansford Co
6534211
EG HILL COMPRESSOR
48371
Texas
Pecos Co
5765911
COYANOSA GAS PLANT
48501
Texas
Yoakum Co
6648711
PLAINS COMPRESSOR STATION
51143
Virginia
Pittsylvania Co
4005411
Transco Gas Pipe Line Corp Station 165
54083
West Virginia
Randolph Co
6790711
Columbia Gas - FILES CREEK 6C4340
54099
West Virginia
Wayne Co
6341411
Columbia Gas - CEREDO 4C3360
179
-------
4.2.3.6 Non-EGU point sources (ptnonipm)
Packets:
Proj ection_2016_202X_ptnonipm_versionl_platform_WI_supplement_25 sep2019_v0
Proj ection_2016_2023_corn_ethanol_E0B0_Volpe_27sep2019_v0
Proj ection_2016_2023_finished_fuels_volpe_04oct2019_v2
Proj ection_2016_2023_industrial_byNAICS_SCC_version2_platform_23jun202 l_vO
Proj ection_2016_2023_industrial_by SCC_version2_platform_28jun202 l_nf_vl
Projection_2016_2023_ptnonipm_airports_railyards_versionl_platform_NC_nopoll_26sep2019_v0
Proj ection_2016_2023_ptnonipm_version2_platform_MARAMA_20aug202 l_vl
Proj ection_2016_2023_ptnonipm_versionl_platform_NJ_l 0sep2019_v0
Proj ection_2016_2023_ptnonipm_version l_platform_VA_04oct2019_v 1
Proj ection_2023_2026_finished_fuels_volpe_l 6jul202 l_vO
Projection_2023_2026_industrial_byNAICS_SCC_version2_platform_23jul2021_v0
Projection_2023_2026_industrial_bySCC_version2_platform_23jul2021_nf_vl
Projection_2023_2026_ptnonipm_version2_platform_MARAMA_23jul2021_nf_vl
projection_2023_2026interp_corn_ethanol_E0B0_Volpe_23jul2021_v0
Projection_2023_2026interp_ptnonipm_version2_platform_NC_23jul2021_v0
Projection_2023_2026interp_ptnonipm_version2_platform_NJ_23jul2021_v0
Projection_2023_2026interp_ptnonipm_version2_platform_VA_23jul2021_v0
proj ection_2026_2028_corn_ethanol_E0B0_V olpe_l 3 aug202 l_vO
Proj ection_2026_2028_finished_fuels_volpe_l 3aug202 l_vO
Projection_2026_2032_industrial_byNAICS_SCC_version2_platform_13aug2021_v0
Projection_2026_2032_industrial_bySCC_version2_platform_13aug2021_nf_vl
Proj ection_2026_2032_ptnonipm_version2_platform_MARAMA_l 3aug202 l_vO
The 2023, 2026, and 2032 ptnonipm projections involved several growth and projection methods
described here. The projection of oil and gas sources is explained in the oil and gas section.
2023 and 2026 Point Inventory - inside MARAMA region
2016-to-2023 and 2016-to-2026 projection packets for point sources were provided by MARAMA for the
following states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV.
The MARAMA projection packets were used throughout the MARAMA region, except in North
Carolina, New Jersey, and Virginia. Those three states provided their own projection packets for the
ptnonipm sector in 2016vl, and those projection packets were used instead of the MARAMA packets in
those states in 2016v2 as well. The Virginia growth factors for one facility were edited to incorporate
emissions limits provided by MARAMA for that facility. A separate adjustment was made to emissions a
Pennsylvania source (process ID 13629614) based on updated information provided by MARAMA.
2023 and 2026 Point Inventories - outside MARAMA region
Projection factors were developed by industrial sector from a series of AEOs to cover the period from
2016 through 2023: AEO2018 was used to go from 2016 to 2017; AEO2019 to go from 2017 to 2020;
and either AE02020 or AEO2021 to go from 2020 to 2023 and 2026. AE02020 was used for Process
Flow categories - paper, aluminum, glass, cement/lime, iron/steel - due to reported issues with AEO2021
that affected these categories. All other source categories used AEO2021. The SCCs were mapped to
180
-------
AEO categories and projection factors were created using a ratio between the base year and projection
year estimates from each specific AEO category. Table 4-14 below details the AEO2021 tables used to
map SCCs to AEO categories for the projections of industrial sources. Depending on the category, a
projection factor may be national or regional. The maximum projection factor was capped at 1.25.
MARAMA states were not projected using this method. Also in 2016v2, more SCCs were mapped to the
AEO categories for SCCs that had not been projected in 2016vl. For example, SCCs for the cement kilns
that did not specify a fuel are now mapped to the "Value of Shipments" as a generic indicator for
projected growth.
An SCC-NAICS projection was also developed using AEO2021. SCC/NAICS combinations with
emissions >100tons/year for any CAP34 were mapped to AEO sector and fuel. Projection factors for this
method were capped at a maximum of 2.5.
New units were added for 2016v2 based on 2018NEI analysis, although these are also added in 2016 as
described in Section 2.1.3.
Any control efficiencies that were set to 100 in the 2016 base year inventory were identified and adjusted
prior to projecting the inventories. Note that a control efficiency equal to 100 means that there would be
no emissions, so control efficiencies equal to 100 are assumed to be in error.
Table 4-14. Annual Energy Outlook (AEO) 2021 tables used to project industrial sources
AEO 2021 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
The state of Wisconsin provided source-specific growth factors for four facilities in the state. For those
facilities, the growth factors provided by Wisconsin were used instead of those derived from the AEO.
A draft set of projected ptnonipm emissions were reviewed and compared to recent emissions data from
2017 through 2019. In cases where the recent and projected emissions were substantially different, the
2023 emissions were instead taken from a recent year of emissions and were then projected from 2023
to later future years. The affected sources are shown in Table 4-15.
34 The "100 tpy" criterion for this purpose was based on emissions in the emissions values in the 2016 beta platform.
181
-------
Table 4-15. Ptnonipm sources held at recent emission levels
County
FIPS
State
County
Facility
ID
Facility Name
Override
year
01053
AL
Escambia Co
7440111
Georgia-Pacific Brewton LLC
2018
01097
AL
Mobile Co
1060511
Kimberly-Clark Corporation
2018
01099
AL
Monroe Co
1019211
GP Cellulose Alabama River Cellulose LLC
2018
01099
AL
Monroe Co
1019211
GP Cellulose Alabama River Cellulose LLC
2018
04013
AZ
Maricopa Co
1121211
Oak Canyon Manufacturing Inc
2017
05063
AR
Independence Co
1082811
FUTUREFUEL CHEMICAL COMPANY
2018
06037
CA
Los Angeles Co
15995211
ROBERTSONS READY MIX -
PALMDALE
2018
06071
CA
San Bernardino Co
706411
NTC - DIR. OF PUBLIC WORKS,
MISSION RELA
2018
06071
CA
San Bernardino Co
706411
NTC - DIR. OF PUBLIC WORKS,
MISSION RELA
2018
06083
CA
Santa Barbara Co
7064311
IMERYS FILTRATION MINERALS, INC.
2018
08069
CO
Larimer Co
4363211
AVAGO TECHNOLOGIES WIRELESS
(USA) MANUF.
2018
12057
FL
Hillsborough Co
716311
MOSAIC FERTILIZER, LLC
2017
12105
FL
Polk Co
535211
CITROSUCO NORTH AMERICA, INC.
2018
13021
GA
Bibb Co
7414811
Graphic Packaging Macon Mill
2018
13067
GA
Cobb Co
554511
Caraustar Industries Inc
2018
13095
GA
Dougherty Co
3709811
MillerCoors LLC
2018
13103
GA
Effingham Co
536311
Georgia-Pacific Consumer Operations LLC
2018
13185
GA
Lowndes Co
555311
PCA Valdosta Mill
2018
13193
GA
Macon Co
8352311
International Paper - Flint River Mill
2018
13245
GA
Richmond Co
554311
DSM Chemicals North America, Inc.
2018
17031
IL
Cook Co
3205411
Bimbo QSR Chicago LLC
2017
17103
IL
Lee Co
7792411
St. Marys Cement Inc
2018
17103
IL
Lee Co
7792411
St. Marys Cement Inc
2018
17103
IL
Lee Co
7792411
St. Marys Cement Inc
2018
18165
IN
Vermillion Co
8223611
Elanco US Incorporated Clinton Laborato
2018
20209
KS
Wyandotte Co
15089511
Reconserve Inc.
2017
21019
KY
Boyd Co
5060111
Ak Steel Corp
2018
21019
KY
Boyd Co
5060111
Ak Steel Corp
2018
21019
KY
Boyd Co
5060111
Ak Steel Corp
2018
21059
KY
Daviess Co
5892411
Owensboro Grain Co
2018
21145
KY
Mc Cracken Co
6050611
Four Rivers Nuclear Partnership LLC - Pa
2018
21205
KY
Rowan Co
7382011
Guardian Automotive Trim, SRG Global Inc
2017
22033
LA
East Baton Rouge
Par
7228811
ExxonMobil Chemical Company - Baton
Roug
2018
22033
LA
East Baton Rouge
Par
8214811
Georgia-Pacific Consumer Operations LLC
2019
182
-------
County
FIPS
State
County
Facility
ID
Facility Name
Override
year
22089
LA
St Charles Par
8020911
Rain CII Carbon LLC - Norco Coke Calcini
2018
22093
LA
St James Par
5273111
Rain CII Carbon LLC - Gramercy Coke
Plan
2018
22093
LA
St James Par
7205911
Mosaic Fertilizer LLC - Faustina Plant
2018
26103
MI
Marquette Co
17688311
Marquette Branch Prison
2018
26115
MI
Monroe Co
7888111
Guardian Industries-Carleton
2018
26125
MI
Oakland Co
6664511
EAGLE VALLEY RECYCLE AND
DISPOSAL FACILI
2018
26147
MI
St Clair Co
7239111
ST. CLAIR / BELLE RIVER POWER
PLANT
2018
28131
MS
Stone Co
15334111
MF WIGGINS LLC
2018
36001
NY
Albany Co
8105211
LAFARGE BUILDING MATERIALS INC
2018
36089
NY
St. Lawrence Co
7968211
ALCOA MASSENA OPERATIONS
(WEST PLANT)
2018
36089
NY
St. Lawrence Co
17890211
ALCOA USA Corp
2018
37027
NC
Caldwell Co
7961211
Hamilton Square Lenoir Casegoods Plant
2018
37049
NC
Craven Co
8504911
Marine Corps Air Station - Cherry Point
2018
37049
NC
Craven Co
8504911
Marine Corps Air Station - Cherry Point
2018
37133
NC
Onslow Co
8424011
MCIEAST-Marine Corps Base Camp
Lejeune
2018
41029
OR
Jackson Co
8056111
Roseburg Forest Products - Medford MDF
2018
41029
OR
Jackson Co
8056211
Biomass One, L P.
2018
42007
PA
Beaver Co
8141411
ALLEGHENY & TSINGSHAN
STAINLESS LLC MIDL
2018
42007
PA
Beaver Co
8141411
ALLEGHENY & TSINGSHAN
STAINLESS LLC MIDL
2018
42071
PA
Lancaster Co
4951311
BUCK CO INC/QUARRYVILLE
2018
45035
SC
Dorchester Co
4797811
SHOWA DENKO CARBON INC
2018
47037
TN
Davidson Co
4700711
Vanderbilt University
2017
47157
TN
Shelby Co
5723011
Cargill Corn Milling
2018
48057
TX
Calhoun Co
5846711
POINT COMFORT PLANT
2018
51085
VA
Hanover Co
6310111
Bear Island Paper Company
2017
51085
VA
Hanover Co
6310111
Bear Island Paper Company
2018
51121
VA
Montgomery Co
5748611
Radford Army Ammunition Plant
2019
51680
VA
Lynchburg
6648111
Griffin Pipe Products Company LLC
2018
51700
VA
Newport News
4938811
Huntington Ingalls Incorporated -NN Ship
2018
53011
WA
Clark Co
4986811
Georgia-Pacific Consumer Operations LLC
2018
54039
WV
Kanawha Co
5782411
BAYER CROPSCIENCE - Institute
2018
55009
WI
Brown Co
4943911
Ahlstrom-Munksjo NA Specialty Solutions
2018
55031
WI
Douglas Co
4864411
Superior Refining Company LLC
2018
55133
WI
Waukesha Co
12694411
PROHEALTH CARE WAUKESHA
MEMORIAL
2018
183
-------
2032 Point Inventories
The 2032 point inventory was created by projecting 2026 to 2032 to simplify the procedure and to keep
these factors consistent throughout the platform.
All Volpe packets stopped at 2028 because that was the last year available.
The MARAMA and PFC projection packets were developed from the MARAMA tool including updated
AEO values and VMT to get the factors from 2026 to 2032.
A new 2026 to 2032 projection packet was created based on human population. The human population
dataset does not contain population estimates beyond 2030, to 2030 population was used to represent
2032. A new 2026 to 2032 projection packet was created for industrial sources based on AEO2021.
Rail yards were projected from 2026 to 2032 based on AEO 2021 using the same factors as were used for
the rail sector class II and III commuter trains.
4.2.3.7 Airport sources (airports)
Packets:
airport_proj ections_itn_taf2019_2016_2023_04jun202 l_v0
airport_proj ecti ons_itn_taf2019_2016_2026_04j un2021_v0
airport_proj ecti ons_itn_taf2019_2016_2032_04j un2021_v0
Airport emissions were projected from the 2016 airport emissions based on the corrected 2017 NEI
airport emissions to 2023, 2026, and 2032, using the same projection approach as for 2016vl, but using
TAF 2019 instead of TAF 2018, and starting from the base year 2016 instead of 2017. The Terminal Area
Forecast (TAF) data available from the Federal Aviation Administration
(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. For airports not matching a unit in the TAF data, state default
growth factors by itinerant class (commercial, air taxi, and general) were created from the collection of
airports unmatched. Emission growth for facilities is capped at 500% and the state default growth is
capped at 200%. Military state default projection values were kept flat (i.e., equal to 1.0) to reflect
uncertainly in the data regarding these sources.
4.2.3.8 Nonpoint Sources (nonpt)
Packets:
Proj ection_2016_2023_all_nonpoint_versionl_platform_NC_24jun202 l_nf_v5
Proj ection_2016_2023_finished_fuels_volpe_04oct2019_v2
Proj ection_2016_2023_industrial_by SCC_version2_platform_28jun202 l_nf_vl
Proj ection_2016_2023_nonpt_other_version2_platform_MARAMA_02jul202 l_nf_vl
Proj ection_2016_2023_nonpt_PF C_version l_platform_MARAMA_20sep2019_v 1
Proj ection_2016_2023_nonpt_population_beta_platform_ext_20sep2019_v 1
Proj ection_2016_2023_nonpt_version l_platform_NJ_04oct2019_v 1
Proj ection_2016_2026_all_nonpoint_version2_platform_NC_l 9jul202 l_nf_vl
Proj ection_2016_2026_finished_fuels_volpe_l 6jul202 l_v0
Proj ection_2016_2026_industrial_by SCC_version2_platform_l 6jul202 l_vl
Proj ection_2016_2026_nonpt_other_version2_platform_MARAMA_noNCNJ_l 6jul202 l_v0
Proj ection_2016_2026_nonpt_PFC_version2_platform_MARAMA_noNC_l 6jul202 l_vl
184
-------
Proj ection_2016_2026_nonpt_population_version2_platform_noMARAMA_l 6jul202 l_vO
Proj ection_2016_2026_nonpt_version2_platform_NJ_l 6jul202 l_vO
Proj ection_2026_2028_finished_fuels_volpe_l 3aug202 l_vO
Projection_2026_2030_nonpt_population_version2_platform_noMARAMA_05aug2021_v0
Projection_2026_2032_industrial_bySCC_version2_platform_13aug2021_nf_vl
Projection_2026_2032_nonpt_other_version2_platform_MARAMA_05aug2021_v0
Proj ecti on_2026_2032_nonpt_PF C_version2_platform_MARAM A_13 aug2021_v0
Inside MARAMA region
2016-to-2023 and 2016-to-2026 projection packets for all nonpoint sources were provided by MARAMA
for the following states after updated data for AEO2021: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC,
PA, RI, VT, VA, and WV. MARAMA provided one projection packet per year for portable fuel
containers (PFCs), and a second projection packet per year for all other nonpt sources.
The MARAMA projection packets were used throughout the MARAMA region, except in North Carolina
and New Jersey. Both NC and NJ provided separate projection packets for the nonpt sector for 2016vl
and those projection packets were used instead of the MARAMA packets in those two states. New Jersey
did not provide projection factors for PFCs, and so NJ PFCs were projected using the MARAMA PFC
growth packet.
Industrial Sources outside MARAMA region
Projection factors were developed by industrial sector from a series of AEOs to cover the period from
2016 through 2023: AEO2018 was used to go from 2016 to 2017; AEO2019 to go from 2017 to 2020;
and either AE02020 or AEO2021 to go from 2020 to 2023 and 2026. AE02020 was used for Process
Flow categories - paper, aluminum, glass, cement/lime, iron/steel - due to reported issues with AEO2021
that affected these categories. All other source categories used AEO2021. 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 nonpoint sector, only AEO Table 2 was used to map
SCCs to AEO categories for the projections of industrial sources. Depending on the category, a projection
factor may be national or regional. The maximum projection factor was capped at a factor of 1.25.
Sources within the MARAMA region were not projected with these factors, but with the MARAMA-
provided growth factors.
Evaporative Emissions from Transport of Finished Fuels outside MARAMA region
Estimates on growth of evaporative emissions from transporting finished fuels are partially covered in the
nonpoint and point oil and gas projection packets. However, there are some processes with evaporative
emissions from storing and transporting finished fuels which are not included in the nonpoint and point
oil and gas projection packets, e.g., withdrawing fuel from tanks at bulk plants, filling tanks at service
stations, etc., and those processes are included in nonpoint other. The EIA's AEO for year 2018 was used
as a starting point for projecting volumes of finished fuel that would be transported in future years, i.e.,
2023 and 2026. Then these volumes were used to calculate inventories associated with evaporative
emissions in 2016, 2023, and 2026 using the upstream modules. Those emission inventories were
mapped to the appropriate SCCs and projection packets were generated from 2016 to 2023 and 2016 to
2026 using the upstream modules. Sources within the MARAMA region were not projected with these
factors, but with the MARAMA-provided growth factors.
185
-------
Human Population Growth outside MARAMA region
For SCCs that are projected based on human population growth, population projection data were available
from the Benefits Mapping and Analysis Program (BenMAP) model by county for several years,
including 2017, 2023, and 2026. These human population data were used to create modified county-
specific projection factors. Note that 2017 is being used as the base year since 2016 human population is
not available in this dataset. A newer human population dataset was assessed but it did not have
trustworthy near-term (e.g., 2023/2026) projections, and was not used; for example, rural areas of NC
were projected to have more growth than urban areas, which is the opposite of what one would expect.
Growth factors were limited to 5% cumulative annual growth (e.g. 35% annual growth over 7 years), but
none of the factors fell outside that range. Sources within the MARAMA region were not projected with
these factors, but with the MARAMA-provided growth factors.
2032 inventory
The 2032 nonpt inventory was created by projecting 2026 to 2032 to simplify the procedure and to keep
these factors consistent throughout the platform.
All Volpe packets and the Cellulosic inventories stopped at 2028 because that was the last year available.
The MARAMA and PFC projection packets were developed from the MARAMA tool including updated
AEO values and VMT to get the factors from 2026 to 2032.
A new 2026 to 2030 projection packet was created based on human population. The human population
dataset used for projections does not contain population estimates beyond 2030, to 2030 population was
used to represent 2032. A new 2026 to 2032 projection packet was created for industrial sources based on
AEO 2021.
4.2.3.9 Solvents (solvents)
Packets:
Proj ection_2016_2023_solvents_v2platform_from_oilgas_30jun202 l_v0
Proj ection_2016_2023_solvents_v2platform_MARAMA_20aug202 l_v 1
Proj ection_2016_2023_solvents_v2platform_NC_20aug202 l_vl
Proj ection_2016_2023_solvents_v2platform_NJ_30jun202 l_v0
Proj ection_2016_2023_solvents_v2platform_population_30jun202 l_v0
Proj ection_2016_2026_solvents_v2platform_from_oilgas_l 9jul202 l_v0
Proj ection_2016_2026_solvents_v2platform_MARAMA_noNCNJ_l 9jul202 l_v0
Proj ection_2016_2026_solvents_v2platform_NC_l 9jul202 l_v0
Proj ection_2016_2026_solvents_v2platform_NJ_l 9jul202 l_v0
Proj ection_2016_2026_solvents_v2platform_population_noMARAMA_l 9jul202 l_v0
Projection_2026_2030_solvents_v2platform_population_noMARAMA_05aug2021_v0
Projection_2026_2032_solvents_v2platform_from_oilgas_05aug2021_v0
Proj ecti on_2026_2032_solvents_v2platform_M ARAM A_20aug2021_v 1
The projection methodology for solvents is the same as it was in 2016vl platform when solvents were
part of nonpt. The MARAMA, NC, and NJ nonpt projection packets all affect solvents. Elsewhere,
solvents are projected using human population trends for most solvent categories. All of these packets
were checked to confirm they cover all SCCs in the solvents sector, and packets were supplemented with
additional SCCs as needed, copied from factors for existing SCCs.
186
-------
The following updates were made to supplement the SCCs in the projection packets:
- changed 2461800001 to 2461800000;
all 2460- SCCs and 2402000000 use human population (copied from an existing 2460- SCC);
2477777777 uses gas/oil average ("BOTH") production growth factors from np_oilgas;
all other SCCs were already covered; two SCCs do not use projection factors and are held flat
(2420000000 / dry cleaning held flat outside MARAMA region; 2461850000 / ag pesticide
application held flat everywhere except North Carolina as NC provided their own factors).
The 2026 projection packets were interpolated from 2023 and 2028 for NC/NJ.
For 2032, the projections start from 2026. Separate NC/NJ packets were not available; so we just had the
oil/gas, MARAMA-tool-based, and non-MARAMA pop-based. The population dataset used for the non-
MARAMA populated-based packet only goes out to 2030, but the other packets are 2032.
4.2.3.10 Residential Wood Combustion (rwc)
Packets:
Proj ection_2016_2023_all_nonpoint_versionl_platform_NC_24jun202 l_nf_v5
Proj ection_2016_2023_rwc_version2_platform_fromMARAMA_22jun202 l_v0
Proj ection_2016_2026_all_nonpoint_version2_platform_NC_l 9jul202 l_nf_vl
Proj ection_2016_2026_rwc_version2_platform_fromMARAMA_l 9jul202 l_v0
Projection_2026_2032_rwc_version2_platform_fromMARAMA_05aug2021_v0
For residential wood combustion, the growth and control factors are computed together into merged
factors in the same packets. For states other than California, Oregon, and Washington, RWC emissions
from 2016 were projected to 2023 and 2026 using projection factors derived using the MARAMA tool
that is based on the projection methodology from EPA's 201 lv6.3 platform. The development of
projected growth in RWC emissions to year 2023 starts with the projected growth in RWC appliances
derived from year 2012 appliance shipments reported in the Regulatory Impact Analysis (RIA) for
Proposed Residential Wood Heaters NSPS Revision Final Report available at:
http://www2.epa.gov/sites/production/files/2013-12/documents/ria-20140103.pdf. The 2012 shipments
are based on 2008 shipment data and revenue forecasts from a Frost & Sullivan Market Report (Frost &
Sullivan, 2010). Next, to be consistent with the RIA, growth rates for new appliances for certified wood
stoves, pellet stoves, indoor furnaces and OHH were based on forecasted revenue (real GDP) growth rate
of 2.0% per year from 2013 through 2023 and 2026 and 2032 as predicted by the U.S. Bureau of
Economic Analysis (BEA, 2012). While this approach is not perfectly correlated, in the absence of
specific shipment projections, the RIA assumes the overall trend in the projection is reasonable. The
growth rates for appliances not listed in the RIA (fireplaces, outdoor wood burning devices (not elsewhere
classified) and residential fire logs) are estimated based on the average growth in the number of houses
between 2002 and 2012, about 1% (U.S. Census, 2012).
In addition to new appliance sales and forecasts extrapolating beyond 2012, assumptions on the
replacement of older, existing appliances are needed. Based on long lifetimes, no replacement of
fireplaces, outdoor wood burning devices (not elsewhere classified) or residential fire logs is assumed. It
is assumed that 95% of new woodstoves will replace older non-EPA certified freestanding stoves (pre-
1988 NSPS) and 5% will replace existing EPA-certified catalytic and non-catalytic stoves that currently
meet the 1988 NSPS (Houck, 2011).
187
-------
Equation 4-1 was applied with RWC-specific factors from the rule. The EPA RWC NSPS experts assume
that 10% of new pellet stoves and OHH replace older units and that because of their short lifespan, that
10% of indoor furnaces are replaced each year; these are the same assumptions used since the 2007
emissions modeling platform (EPA, 2012d). The resulting growth factors for these appliance types varies
by appliance type and also by pollutant because the emission rates, from EPA RWC tool (EPA, 2013rwc),
vary by appliance type and pollutant. For EPA certified units, the projection factors for PM are lower
than those for all other pollutants. The projection factors also vary because the total number of existing
units in 2016 varies greatly between appliance types.
Table 4-16 contains the factors to adjust the emissions from 2016 to 2026 and 2032. California, Oregon,
and Washington RWC were held constant at NEI2014v2 levels for 2016, 2026, and 2032 due to the
unique control programs those states have in place.
Table 4-16. Projection factors for RWC
S( (
SC'C description
I'olliii.inr
2016-1 o-
2023
2016-to-
2026
2016-to-
2032
2104008100
Fireplace: general
7.19%
10.29%
16.49%
2104008210
Woodstove: fireplace inserts; non-EPA
certified
-13.92%
-17.97%
-17.97%
2104008220
Woodstove: fireplace inserts; EPA
certified; non-catalytic
PM10-PRI
4.09%
5.08%
5.08%
2104008220
Woodstove: fireplace inserts; EPA
certified; non-catalytic
PM25-PRI
4.09%
5.08%
5.08%
2104008220
Woodstove: fireplace inserts; EPA
certified; non-catalytic
8.34%
10.28%
10.28%
2104008230
Woodstove: fireplace inserts; EPA
certified; catalytic
PM10-PRI
6.06%
7.68%
7.68%
2104008230
Woodstove: fireplace inserts; EPA
certified; catalytic
PM25-PRI
6.06%
7.68%
7.68%
2104008230
Woodstove: fireplace inserts; EPA
certified; catalytic
12.08%
15.27%
15.27%
2104008310
Woodstove: freestanding, non-EPA
certified
CO
-12.09%
-15.72%
-15.72%
2104008310
Woodstove: freestanding, non-EPA
certified
PM10-PRI
-12.67%
-16.52%
-16.52%
2104008310
Woodstove: freestanding, non-EPA
certified
PM25-PRI
-12.67%
-16.52%
-16.52%
2104008310
Woodstove: freestanding, non-EPA
certified
VOC
-11.40%
-14.84%
-14.84%
2104008310
Woodstove: freestanding, non-EPA
certified
-12.09%
-15.72%
-15.72%
2104008320
Woodstove: freestanding, EPA certified,
non-catalytic
PM10-PRI
4.09%
5.08%
5.08%
2104008320
Woodstove: freestanding, EPA certified,
non-catalytic
PM25-PRI
4.09%
5.08%
5.08%
2104008320
Woodstove: freestanding, EPA certified,
non-catalytic
8.34%
10.28%
10.28%
2104008330
Woodstove: freestanding, EPA certified,
catalytic
PM10-PRI
6.07%
7.69%
7.69%
188
-------
SCC
SC'C description
I'oNiiI.iiU"
2016-1 o-
2023
2016-1 o-
2026
2016-10-
2032
Woodstove: freestanding, EPA certified,
2104008330
catalytic
PM25-PRI
6.07%
7.69%
7.69%
Woodstove: freestanding, EPA certified,
2104008330
catalytic
12.08%
15.27%
15.27%
2104008400
Woodstove: pellet-fired, general
(freestanding or FP insert)
PM10-PRI
30.09%
38.02%
38.02%
2104008400
Woodstove: pellet-fired, general
(freestanding or FP insert)
PM25-PRI
30.09%
38.02%
38.02%
2104008400
Woodstove: pellet-fired, general
(freestanding or FP insert)
26.96%
33.85%
33.85%
Furnace: Indoor, cordwood-fired, non-EPA
2104008510
certified
CO
-64.93%
-84.78%
-84.78%
Furnace: Indoor, cordwood-fired, non-EPA
2104008510
certified
PM10-PRI
-62.99%
-82.89%
-82.89%
Furnace: Indoor, cordwood-fired, non-EPA
2104008510
certified
PM25-PRI
-62.99%
-82.89%
-82.89%
Furnace: Indoor, cordwood-fired, non-EPA
2104008510
certified
VOC
-65.02%
-84.89%
-84.89%
Furnace: Indoor, cordwood-fired, non-EPA
2104008510
certified
-64.93%
-84.78%
-84.78%
2104008530
Furnace: Indoor, pellet-fired, general
PM10-PRI
30.09%
38.02%
38.02%
2104008530
Furnace: Indoor, pellet-fired, general
PM25-PRI
30.09%
38.02%
38.02%
2104008530
Furnace: Indoor, pellet-fired, general
26.96%
33.85%
33.85%
2104008610
Hydronic heater: outdoor
PM10-PRI
0.06%
-0.40%
-0.40%
2104008610
Hydronic heater: outdoor
PM25-PRI
0.06%
-0.40%
-0.40%
2104008610
Hydronic heater: outdoor
-0.73%
-1.30%
-1.30%
2104008620
Hydronic heater: indoor
PM10-PRI
0.06%
-0.40%
-0.40%
2104008620
Hydronic heater: indoor
PM25-PRI
0.06%
-0.40%
-0.40%
2104008620
Hydronic heater: indoor
-0.73%
-1.30%
-1.30%
2104008630
Hydronic heater: pellet-fired
PM10-PRI
0.06%
-0.40%
-0.40%
2104008630
Hydronic heater: pellet-fired
PM25-PRI
0.06%
-0.40%
-0.40%
2104008630
Hydronic heater: pellet-fired
-0.73%
-1.30%
-1.30%
Outdoor wood burning device, NEC (fire-
2104008700
pits, chimineas, etc)
7.19%
9.25%
9.25%
2104009000
Fire log total
7.19%
9.25%
9.25%
* If no pollutant is specified, facture is used for any pollutants that do not have a pollutant-specific factor
4.2.4 CoST CONTROL Packets (nonpt, np_oilgas, ptnonipm, pt_oilgas)
The final step in the projection of emissions to a future year is the application of any control technologies
or programs. For future-year New Source Performance Standards (NSPS) controls (e.g., oil and gas,
Reciprocating Internal Combustion Engines (RICE), Natural Gas Turbines, and Process Heaters), we
attempted to control only new sources/equipment using the following equation to account for growth and
retirement of existing sources and the differences between the new and existing source emission rates.
Qn = Qo { [ (1 + Pf) t-l]Fn + (l-Ri)tFe + [l-(l-Ri)t]Fn]} Equation 4-1
189
-------
where:
Qn = emissions in projection year
Qo = emissions in base year
Pf = growth rate expressed as ratio (e.g., 1.5=50 percent cumulative growth)
t = number of years between base and future years
Fn = emission factor ratio for new sources
Ri = retirement rate, expressed as whole number (e.g., 3.3 percent=0.033)
Fe = emission factor ratio for existing sources
The first term in 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
2023, 2026 and 2032 projections:
^ t , r.«- ¦ fn/\ -.nn (-, \(Pf202x-i)xFn+(i-Ri)12+(i-(i-Ri)12)xFnl\ Equation 4-2
Control Efficiency202*(%) = 100 x 1 - L J202x—- —^ ±1
V Pj202X '
For example, to compute the control efficiency for 2028 from a base year of 2015 the existing source
emissions factor (Fe) is set to 1.0, 2028 (future year) minus 2016 (base year) is 12, and new source
emission factor (Fn) is the ratio of the NSPS emission factor to the existing emission factor. Table 4-17
shows the values for Retirement rate and new source emission factors (Fn) for new sources with respect to
each NSPS regulation and other conditions within. For the nonpt sector, the RICE NSPS control program
was applied when estimating year 2023 and 2028 emissions for the 2016vl modeling platform. Further
information about the application of NSPS controls can be found in Section 4 of the Additional Updates
to Emissions Inventories for the Version 6.3, 2011 Emissions Modeling Platform for the Year 2023
technical support document (EPA, 2017).
Table 4-17. Assumed retirement rates and new source emission factor ratios for NSPS rules
NSPS Rule
Sector(s)
Retirement
Rate years
(%/year)
Pollutant
Impacted
Applied where?
New Source
Emission Factor
(Fn)
Storage Tanks: 70.3% reduction in
0.297
growth-only (>1.0)
Gas Well Completions: 95% control
0.05
(regardless)
Pneumatic controllers, not high-bleed
0.23
Oil and
>6scfm or low-bleed: 77% reduction in
Gas
np_oilgas,
pt_oilgas
No
assumption
growth-only (>1.0)
VOC
Pneumatic controllers, high-bleed
>6scfm or low-bleed: 100% reduction in
growth-only (>1.0)
0.00
Compressor Seals: 79.9% reduction in
0.201
growth-only (>1.0)
Fugitive Emissions: 60% Valves, flanges,
0.40
connections, pumps, open-ended lines,
and other
190
-------
NSPS Rule
Sector(s)
Retirement
Rate years
(%/year)
Pollutant
Impacted
Applied where?
New Source
Emission Factor
(Fn)
Pneumatic Pumps: 71.3%; Oil and Gas
0.287
Lean burn: PA, all other states
0.25, 0.606
NOx
Rich Burn: PA, all other states
0.1, 0.069
Combined (average) LB/RB: PA, other
states
0.175, 0.338
np_oilgas,
Lean burn: PA, all other states
1.0 (n/a), 0.889
RICE
pt_oilgas,
40, (2.5%)
CO
Rich Burn: PA, all other states
0.15, 0.25
nonpt,
Combined (average) LB/RB: PA, other
0.575, 0.569
ptnonipm
states
Lean burn: PA, all other states
0.125, n/a
VOC
Rich Burn: PA, all other states
0.1, n/a
Combined (average) LB/RB: PA, other
states
0.1125,n/a
Gas
pt_oilgas,
45 (2.2%)
NOx
California and NOx SIP Call states
0.595
Turbines
ptnonipm
All other states
0.238
Process
pt_oilgas,
30 (3.3%)
NOx
Nationally to Process Heater SCCs
0.41
Heaters
ptnonipm
4.2.4.1 Oil and Gas NSPS (np_oilgas, pt_oilgas)
Packets:
Control_2016_2023_OilGas_NSPS_np_oilgas_v2_platform_23jun202 l_vO
Control_2016_2023_OilGas_NSPS_pt_oilgas_v2_platform_23jun202 l_vO
Control_2016_2026_OilGas_NSPS_np_oilgas_v2_platform_23jun202 l_vO
Control_2016_2026_OilGas_NSPS_pt_oilgas_v2_platform_23jun202 l_vO
Control_2016_2032_OilGas_NSPS_np_oilgas_v2_platform_23jun202 l_vO
Control_2016_2032_OilGas_NSPS_pt_oilgas_v2_platform_23jun202 l_vO
New packets to reflect the oil and gas NSPS were developed for the 2016v2 platform. For oil and gas
NSPS controls, except for gas well completions (a 95 percent control), the assumption of no equipment
retirements through year 2028 dictates that NSPS controls are applied to the growth component only of
any PROJECTION factors. For example, if a growth factor is 1.5 for storage tanks (indicating a 50
percent increase activity), then, using Table 4-17, the 70.3 percent VOC NSPS control to this new growth
will result in a 23.4 percent control: 100 *(70.3 * (1.5 -1) / 1.5); this yields an "effective" growth rate
(combined PROJECTION and CONTROL) of 1.1485, or a 70.3 percent reduction from 1.5 to 1.0. The
impacts of all non-drilling completion VOC NSPS controls are therefore greater where growth in oil and
gas production is assumed highest. Conversely, for oil and gas basins with assumed negative growth in
activity/production, VOC NSPS controls will be limited to well completions only. These reductions are
year-specific because projection factors for these sources are year-specific. Table 4-18 (npoilgas) and
Table 4-20 (ptoilgas) list the SCCs where Oil and Gas NSPS controls were applied; note controls are
applied to production and exploration-related SCCs. Table 4-19 (np oilgas) and Table 4-21 (pt oilgas)
shows the reduction in VOC emissions in states other than the WRAP states after the application of the
Oil and Gas NSPS CONTROL packet for future years.
191
-------
Table 4-18. Non-point (npoilgas) SCCs in 2016vl and 2016v2 modeling platform where Oil and
Gas NSPS controls applied
see
SRC TYPE
OILGAS NSPS
CATEGORY
TOOL OR
STATE
see
SRC CAT TYPE
SCCDESC
2310010200
OIL
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; Crude Petroleum; Oil Well Tanks -
Flashing & Standing/Working/Breathing
2310010300
OIL
3. Pnuematic
controllers: not high
or low bleed
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; Crude Petroleum; Oil Well Pneumatic
Devices
2310011500
OIL
5. Fugitives
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Production; Fugitives: All
Processes
2310011501
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Production; Fugitives:
Connectors
2310011502
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Production; Fugitives:
Flanges
2310011503
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Production; Fugitives: Open
Ended Lines
2310011505
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Production; Fugitives:
Valves
2310021010
NGAS
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Storage Tanks:
Condensate
2310021300
NGAS
3. Pnuematic
controllers: not high
or low bleed
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Gas Well
Pneumatic Devices
2310021310
NGAS
6. Pneumatic Pumps
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Gas Well
Pneumatic Pumps
2310021501
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives:
Connectors
2310021502
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives:
Flanges
2310021503
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives: Open
Ended Lines
2310021505
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives:
Valves
2310021506
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives:
Other
2310021509
NGAS
5. Fugitives
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives: All
Processes
2310021601
NGAS
2. Well Completions
STATE
EXPLORATION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Gas Well
Venting - Initial Completions
192
-------
see
SRC TYPE
OILGAS NSPS
CATEGORY
TOOL OR
STATE
see
SRC CAT TYPE
SCCDESC
2310030300
NGAS
1. Storage Tanks
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; Natural Gas Liquids; Gas Well Water Tank
Losses
2310111401
OIL
6. Pneumatic Pumps
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Exploration; Oil Well
Pneumatic Pumps
2310111700
OIL
2. Well Completions
TOOL
EXPLORATION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Exploration; Oil Well
Completion: All Processes
2310121401
NGAS
6. Pneumatic Pumps
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Exploration; Gas Well
Pneumatic Pumps
2310121700
NGAS
2. Well Completions
TOOL
EXPLORATION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Exploration; Gas Well
Completion: All Processes
2310421010
NGAS
1. Storage Tanks
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production -
Unconventional; Storage Tanks: Condensate
2310421700
NGAS
2. Well Completions
STATE
EXPLORATION
Gas Well Completion: All Processes Unconventional
Table 4-19. Emissions reductions for npoilgas sector due to application of Oil and Gas NSPS
year
poll
2016v2
2016
pre-CoST
emissions
emissions
change from
2016
%
change
2023
VOC
2405032
2467173
-519753
-21.1%
2026
VOC
2405032
2467173
-677742
-27.5%
2032
VOC
2405032
2467173
-715125
-29.0%
Table 4-20. Point source SCCs in pt oilgas sector where Oil and Gas NSPS controls were applied.
FUEL
see
PRODUCED
OILGAS NSPS CATEGORY
SCCDESC
Industrial Processes; Oil and Gas Production; Crude Oil
31000101
Oil
2. Well Completions
Production; Well Completion
Industrial Processes; Oil and Gas Production; Crude Oil
31000130
Oil
4. Compressor Seals
Production; Fugitives: Compressor Seals
Industrial Processes; Oil and Gas Production; Crude Oil
31000133
Oil
1. Storage Tanks
Production; Storage Tank
3. Pnuematic controllers:
Industrial Processes; Oil and Gas Production; Crude Oil
31000151
Oil
high or low bleed
Production; Pneumatic Controllers, Low Bleed
3. Pnuematic controllers:
Industrial Processes; Oil and Gas Production; Crude Oil
31000152
Oil
high or low bleed
Production; Pneumatic Controllers High Bleed >6 scfh
Industrial Processes; Oil and Gas Production; Natural Gas
31000207
Gas
5. Fugitives
Production; Valves: Fugitive Emissions
Industrial Processes; Oil and Gas Production; Natural Gas
Production; All Equipt Leak Fugitives (Valves, Flanges,
31000220
Gas
5. Fugitives
Connections, Seals, Drains
193
-------
FUEL
see
PRODUCED
OILGAS NSPS CATEGORY
SCCDESC
Industrial Processes; Oil and Gas Production; Natural Gas
31000222
Gas
2. Well Completions
Production; Well Completions
Industrial Processes; Oil and Gas Production; Natural Gas
31000225
Gas
4. Compressor Seals
Production; Compressor Seals
3. Pnuematic controllers:
Industrial Processes; Oil and Gas Production; Natural Gas
31000233
Gas
high or low bleed
Production; Pneumatic Controllers, Low Bleed
Industrial Processes; Oil and Gas Production; Natural Gas
31000309
Gas
4. Compressor Seals
Processing; Compressor Seals
3. Pnuematic controllers:
Industrial Processes; Oil and Gas Production; Natural Gas
31000324
Gas
high or low bleed
Processing; Pneumatic Controllers Low Bleed
3. Pnuematic controllers:
Industrial Processes; Oil and Gas Production; Natural Gas
31000325
Gas
high or low bleed
Processing; Pneumatic Controllers, High Bleed >6 scfh
Industrial Processes; Oil and Gas Production; Fugitive Emissions;
31088811
Both
5. Fugitives
Fugitive Emissions
Table 4-21. VOC reductions (tons/year) for the ptoilgas sector after application of the Oil and Gas
NSPS CONTROL packet for both future years 2023, 2026 and 2032.
Year
Pollutant
2016v2
Emissions Reductions
% change
2023
VOC
226,805
-2,228
-1.0%
2026
VOC
226,805
-2,828
-1.2%
2032
VOC
226,805
-2,975
-1.3%
4.2.4.2 RICE NSPS (nonpt, ptnonipm, np_oilgas, pt_oilgas)
Packets:
CONTROL_2016_2023_RICE_N SPS_nonpt_ptnonipm_beta_platform_extended_04oct2019_v 1
CONTROL2016_2023_RICE_NSPS_ptnonipm_vl_platform_MARAMA_l 0sep2019_v0
Control_2016_2023_RICE_NSPS_pt_oilgas_v2_platform_23jun202 l_v0
Control_2016_2026_RICE_NSPS_nonpt_v2_platform_l 6jul202 l_v0
Control_2016_2026_RICE_NSPS_pt_oilgas_v2_platform_23jun202 l_v0
Control_2016_2026_RICE_NSPS_np_oilgas_v2_platform_23jun202 l_v0
Control_2016_2023_RICE_NSPS_np_oilgas_v2_platform_23jun202 l_v0
Control_2023_2026interp_RICE_NSPS_ptnonipm_v2_platform_MARAMA_22jul2021_v0
Control_2023_2026interp_RICE_NSPS_ptnonipm_v2_platform_noMARAMA_22jul2021_v0
Control_2026_2032_RICE_NSPS_nonpt_ptnonipm_v2_platform_13aug2021_v0
Control_2016_2032_RICE_NSPS_pt_oilgas_v2_platform_23jun202 l_v0
Control_2016_2032_RICE_NSPS_np_oilgas_v2_platform_23jun202 l_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, the 2023 packets were reused from the 2016vl
platform. The 2026 packets were interpolated between 2023 and 2028. The 2026 to 2032 packets were
developed using consistent methods to the other 2016v2 packets. For the pt oilgas and np oilgas sectors,
year-specific RICE NSPS factors were generated for all 3 specific years 2023, 2026 and 2032. New
growth factors based on AEO2021 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. The actual control
efficiency calculation methodology did not change from 2016vl to 2016v2. For RICE NSPS controls,
194
-------
the EPA emission requirements for stationary engines differ according to whether the engine is new or
existing, whether the engine is located at an area source or major source, and whether the engine is a
compression ignition or a spark ignition engine. Spark ignition engines are further subdivided by power
cycle, two-stroke versus four-stroke, and whether the engine is rich burn or lean burn. The NSPS
reduction was applied for lean burn, rich burn and "combined" engines using Equation 4-2 and
information listed in Table 4-17. Table 4-22, Table 4-23, and Table 4-27 list the SCCs where RICE
NSPS controls were applied for the 2016v2 platform. Table 4-24, Table 4-25, Table 4-26 and Table 4-28.
Emissions reductions (tons/year) in pt oilgas sector after the application of the RICE NSPS CONTROL
packet for future years 2023, 2026, and 2032.show the reductions in emissions in the nonpoint, ptnonipm,
and point and nonpoint oil and gas sectors after the application of the RICE NSPS CONTROL packet for
the future years. Note that for nonpoint oil and gas, VOC reductions were only appropriate in the state of
Pennsylvania.
Table 4-22. SCCs and Engine Types where RICE NSPS controls applied for nonpt and ptnonipm
see
Lean, Rich, or
Combined
SCCDESC
20200202
Combined
Internal Combustion Engines; Industrial; Natural Gas; Reciprocating
20200253
Rich
Internal Combustion Engines; Industrial; Natural Gas; 4-cycle Rich Burn
20200254
Lean
Internal Combustion Engines; Industrial; Natural Gas; 4-cycle Lean Burn
20200256
Lean
Internal Combustion Engines; Industrial; Natural Gas; 4-cycle Clean Burn
20300201
Combined
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Reciprocating
2102006000
Combined
Stationary Source Fuel Combustion; Industrial; Natural Gas; Total: Boilers and IC
Engines
2102006002
Combined
Stationary Source Fuel Combustion; Industrial; Natural Gas; All IC Engine Types
2103006000
Combined
Stationary Source Fuel Combustion; Commercial/Institutional; Natural Gas; Total:
Boilers and IC Engines
Table 4-23. Non-point Oil and Gas SCCs in 2016v2 modeling platform where RICE NSPS controls
applied
see
Lean, Rich,
or Combined
category
SRC_TYPE
TOOL OR
STATE
see
SRC CAT TYPE
SCCDESC
2310000220
Combined
BOTH
TOOL
EXPLORATION
Industrial Processes; Oil and Gas Exploration and
Production; All Processes; Drill Rigs
2310000660
Combined
BOTH
TOOL
EXPLORATION
Industrial Processes; Oil and Gas Exploration and
Production; All Processes; Hydraulic Fracturing
Engines
2310020600
Combined
NGAS
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; Natural Gas; Compressor Engines
2310021202
Lean
NGAS
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Natural
Gas Fired 4Cycle Lean Burn Compressor Engines
50 To 499 HP
2310021251
Lean
NGAS
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Lateral
Compressors 4 Cycle Lean Burn
2310021302
Rich
NGAS
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Natural
195
-------
see
Lean, Rich,
or Combined
category
SRC_TYPE
TOOL OR
STATE
see
SRC CAT TYPE
SCCDESC
Gas Fired 4Cycle Rich Burn Compressor Engines
50 To 499 HP
2310021351
Rich
NGAS
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Lateral
Compressors 4 Cycle Rich Burn
2310023202
Lean
CBM
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; Coal Bed Methane Natural Gas;
CBM Fired 4Cycle Lean Burn Compressor
Engines 50 To 499 HP
2310023251
Lean
CBM
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; Coal Bed Methane Natural Gas;
Lateral Compressors 4 Cycle Lean Burn
2310023302
Rich
CBM
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; Coal Bed Methane Natural Gas;
CBM Fired 4Cycle Rich Burn Compressor Engines
50 To 499 HP
2310023351
Rich
CBM
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; Coal Bed Methane Natural Gas;
Lateral Compressors 4 Cycle Rich Burn
2310400220
Combined
BOTH
STATE
EXPLORATION
Industrial Processes; Oil and Gas Exploration and
Production; All Processes - Unconventional; Drill
Rigs
Table 4-24. Nonpoint Emissions reductions after the application of the RICE NSPS
year
Poll
2016v2 (tons)
Emissions reductions
(tons)
% change
2023
CO
1,897,760
-17,374
-0.9%
2023
NOX
692,492
-24,339
-3.5%
2026
CO
1,897,760
-21,639
-1.1%
2026
NOX
692,492
-31,207
-4.5%
2032
CO
1,897,760
-28,129
-1.5%
2032
NOX
692,492
-42,028
-6.1%
Table 4-25. Ptnonipm Emissions reductions after the application of the RICE NSPS
year
poll
2016v2 (tons)
Emissions
reductions (tons)
% change
2023
CO
1,411,093
-1,994
-0.1%
2023
NOX
945,768
-2,513
-0.3%
2023
VOC
597,842
-2
0.0%
2026
CO
1,411,093
-2,258
-0.2%
2026
NOX
945,768
-2,894
-0.3%
2026
VOC
597,842
-2
0.0%
2032
CO
1,411,093
-2,691
-0.2%
2032
NOX
945,768
-3,535
-0.4%
2032
VOC
597,842
-3
0.0%
196
-------
Table 4-26. Oil and Gas Emissions reductions for npoilgas sector due to application of RICE NSPS
year
Poll
2016v2
2016pre-CoST
emissions
Emissions
reduction
% change
2023
CO
770832
748563
-90213
-12.1%
2023
NOX
575272
605920
-85510
-14.1%
2023
VOC
2405032
2467173
-497
0.0%
2026
CO
770832
748563
-119278
-15.9%
2026
NOX
575272
605920
-113547
-18.7%
2026
VOC
2405032
2467173
-686
0.0%
2032
CO
770832
748563
-150866
-20.2%
2032
NOX
575272
605920
-147020
-24.3%
2032
VOC
2405032
2467173
-827
0.0%
Table 4-27. Point source SCCs in ptoilgas sector where RICE NSPS controls applied.
see
Lean, Rich, or
Combined
SCCDESC
20200202
Combined
Internal Combustion Engines; Industrial; Natural Gas; Reciprocating
20200253
Rich
Internal Combustion Engines; Industrial; Natural Gas;4-cycle Rich Burn
20200254
Lean
Internal Combustion Engines; Industrial; Natural Gas;4-cycle Lean Burn
20200256
Combined
Internal Combustion Engines; Industrial; Natural Gas;4-cycle Clean Burn
20300201
Combined
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Reciprocating
31000203
Combined
Industrial Processes; Oil and Gas Production; Natural Gas Production; Compressors
(See also 310003-12 and -13)
Table 4-28. Emissions reductions (tons/year) in pt oilgas sector after the application of the RICE
NSPS CONTROL packet for future years 2023, 2026, and 2032.
Year
Pollutant
2016v2
Emissions Reductions
% change
2023
CO
205,547
-17,027
-8.3%
2023
NOX
409,699
-46,451
-11.3%
2023
VOC
226,805
-311
-0.1%
2026
CO
205,547
-22,259
-10.8%
2026
NOX
409,699
-62,219
-15.2%
2026
VOC
226,805
-430
-0.2%
2032
CO
205,547
-26,970
-13.1%
2032
NOX
409,699
-76,086
-18.6%
2032
VOC
226,805
-527
-0.2%
197
-------
4.2.4.3 Fuel Sulfur Rules (nonpt, ptnonipm)
Packets:
Control_2016_202X_MANEVU_Sulfur_fromMARAMA_v l_platform_23 sep2019_v0
The control packet for fuel sulfur rules is reused from the 2016vl platform and is the same for all future
years. Fuel sulfur rules controls are reflected for the following states: Connecticut, Delaware, Maine,
Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. The
fuel limits for these states are incremental starting after year 2012, but are fully implemented by July 1,
2018, in these states. The control packet representing these controls was updated by MARAMA for the
2016vl platform.
Summaries of the sulfur rules by state, with emissions reductions relative to the entire sector emissions
and relative to the future year emissions for the affected SCCs are provided in Table 4-29 and Table
4-30. These tables reflect the impacts of the MARAMA packet only, as these reductions are not estimated
in non-MARAMA states. Most of these reductions occur in the nonpt sector; a small amount of
reductions occurs in the ptnonipm sector, and a negligible amount of reductions occur in the pt oilgas
sector.
Table 4-29. Summary of fuel sulfur rule impacts on nonpoint S02 emissions for 2023
year
poll
2016v2
(tons)
emissions
reductions
(tons)
%
change
(nonpt)
2023
S02
135,604
-30,267
-22.3%
Table 4-29 (ctd.) Change in nonpoint emissions of affected SCCs due to fuel sulfur rule impacts by state
Pollutant
State
2023 pre-control
Emissions (tons)
2023 post-
control
Emissions (tons)
Change in
emissions
(tons)
Percent
change
NOX
Connecticut
3,778
3,505
-273
-7.2%
NOX
Delaware
441
413
-28
-6.3%
NOX
Maine
2,817
2,506
-311
-11.0%
NOX
Massachusetts
7,917
7,477
-440
-5.6%
NOX
New Hampshire
5,554
5,305
-249
-4.5%
NOX
New Jersey
2,111
1,868
-243
-11.5%
NOX
Pennsylvania
5,953
5,852
-101
-1.7%
NOX
Rhode Island
826
767
-59
-7.1%
NOX
Vermont
825
748
-77
-9.3%
NOX
TOTAL
30,221
28,441
-1,780
-5.9%
S02
Connecticut
7,660
268
-7,392
-96.5%
S02
Delaware
432
2
-430
-99.5%
S02
Maine
5,711
83
-5,629
-98.6%
S02
Massachusetts
6,776
242
-6,534
-96.4%
S02
New Hampshire
4,043
20
-4,022
-99.5%
S02
New Jersey
663
20
-643
-97.0%
198
-------
Pollutant
State
2023 pre-control
Emissions (tons)
2023 post-
control
Emissions (tons)
Change in
emissions
(tons)
Percent
change
S02
Pennsylvania
7,244
2,206
-5,038
-69.5%
S02
Rhode Island
210
21
-189
-89.8%
S02
Vermont
432
41
-391
-90.6%
S02
TOTAL
33,170
2,903
-30,267
-91.2%
Table 4-30. Summary of fuel sulfur rule impacts on ptnonipm S02 emissions for 2023 and 2028
year
Poll
2016v2 (tons)
emissions
reductions
(tons)
% change
(ptnonipm)
2023
S02
648,529
-1,177
-0.2%
Table 4-30 (ctd). Change in ptnonipm emissions of affected SCCs due to fuel sulfur rule impacts by state
Pollutant
State
2023 pre-control
emissions (tons)
2023 post-
control
emissions (tons)
Change in
emissions
(tons)
Percent
change
NOX
Connecticut
72
70
-2
-3.5%
NOX
Delaware
167
162
-5
-3.0%
NOX
Maine
316
307
-9
-2.8%
NOX
Massachusetts
293
286
-7
-2.5%
NOX
New Hampshire
21
19
-1
-6.4%
NOX
New Jersey
208
200
-8
-3.7%
NOX
Pennsylvania
298
289
-9
-3.1%
NOX
Rhode Island
118
115
-3
-2.7%
NOX
Vermont
0
0
0
-15.0%
NOX
TOTAL
1,493
1,448
-45
-3.0%
S02
Connecticut
6
0
-5
-94.0%
S02
Delaware
111
48
-62
-56.3%
S02
Maine
470
106
-363
-77.4%
S02
Massachusetts
349
144
-205
-58.7%
S02
New Hampshire
350
75
-275
-78.6%
S02
New Jersey
15
0
-15
-97.0%
S02
Pennsylvania
180
79
-101
-56.0%
S02
Rhode Island
236
111
-125
-53.0%
S02
Vermont
34
9
-26
-75.0%
S02
TOTAL
1,750
573
-1,177
-67.3%
199
-------
4.2.4.4 Natural Gas Turbines N0X NSPS (ptnonipm, pt_oilgas)
Packets:
CONTROL_2016_2023_Natural_Gas_Turbines_NSPS_ptnonipm_beta_platform_extended_04oct2019_v 1
CONTROL_2016_2023_NG_Turbines_NSPS_ptnonipm_v l_platform_MARAMA_l 0sep2019_v0
Control_2016_2023_NG_Turbines_NSPS_pt_oilgas_v2_platform_28jun202 l_nf_v 1
Control_2023_2026interp_NG_Turbines_NSPS_ptnonipm_v2_platform_MARAMA_22jul2021_v0
Control_2023_2026interp_NG_Turbines_NSPS_ptnonipm_v2_platform_nonMARAMA_22jul2021_v0
Control_2016_2026_NG_Turbines_NSPS_pt_oilgas_v2_platform_28jun202 l_nf_v 1
Control_2026_2032_NG_Turbines_NSPS_ptnonipm_v2_platform_13aug2021_v0
Control_2016_2032_NG_Turbines_NSPS_pt_oilgas_v2_platform_20aug202 l_v 1
For ptnonipm, the packets for 2023 were reused from the 2016vl platform; the packets for 2026 were
interpolated between the 2023 and 2028 packets for the 2016vl platform; and the packet from 2026 to
2032 was developed using methods consistent with how the 2023 and 2028 packets were developed. For
pt oilgas, the packets for 2016v2 are based on updated growth information for that sector from state-
historical production data and the AEO2021 production forecast database. The new growth factors were
to calculate the new control efficiencies for all future years (2023, 2026, and 2032). The control
efficiency calculation methodology did not change from 2016vl to 2016v2 modeling platform.
Natural Gas Turbines NSPS controls were generated based on examination of emission limits for
stationary combustion turbines that are not in the power sector. In 2006, the EPA promulgated standards
of performance for new stationary combustion turbines in 40 CFR part 60, subpart KKKK. The standards
reflect changes in NOx emission control technologies and turbine design since standards for these units
were originally promulgated in 40 CFR part 60, subpart GG. The 2006 NSPSs affecting NOx and SO2
were established at levels that bring the emission limits up-to-date with the performance of current
combustion turbines. Stationary combustion turbines were also regulated by the NOx State
Implementation Plan (SIP) Call, which required affected gas turbines to reduce their NOx emissions by
60 percent. Table 4-3 lcompares the 2006 NSPS emission limits with the NOx Reasonably Available
Control Technology (RACT) regulations in selected states within the NOx SIP Call region. The map
showing the states and partial-states in the NOx SIP Call Program can be found at:
https://www.epa.gov/airmarkets/programs and more recently https://www.epa.gov/airmarkets/final-
update-nox-sip-call-regulations. The state NOx RACT regulations summary (Pechan, 2001) is from a year
2001 analysis, so some states may have updated their rules since that time.
Table 4-31. Stationary gas turbines NSPS analysis and resulting emission rates used to compute
controls
NOx Emission Limits for New Stationary Combustion Turbines
Firing Natural Gas
<50 MMBTU/hr
50-850
MMBTU/hr
>850
MMBTU/hr
Federal NSPS
100
25
15
Ppm
State RACT Regulations
5-100
MMBTU/hr
100-250
MMBTU/hr
>250
MMBTU/hr
Connecticut
225
75
75
Ppm
Delaware
42
42
42
Ppm
Massachusetts
65*
65
65
Ppm
New Jersey
50*
50
50
Ppm
200
-------
NOx Emission Limits for New Stationary Combustion Turbines
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 2023 and 2026, but not for
2032 since MARAMA factors were not available beyond 2028.
Table 4-32 and Table 4-34 list the point source SCCs where Natural Gas Turbines NSPS controls were
applied for the 2016vl platform. Table 4-33 and Table 4-35 show the reduction in NOx emissions after
the application of the Natural Gas Turbines NSPS CONTROL packet to the future years. The values in
Table 4-33 and Table 4-35 include emissions both inside and outside the MARAMA region.
Table 4-32. Ptnonipm SCCs in 2016vl modeling platform where Natural Gas Turbines NSPS
controls applied
see
SCC description
20200201
Internal Combustion Engines
Industrial; Natural Gas; Turbine
20200203
Internal Combustion Engines
Industrial; Natural Gas; Turbine: Cogeneration
20200209
Internal Combustion Engines
Industrial; Natural Gas; Turbine: Exhaust
20200701
Internal Combustion Engines
Industrial; Process Gas; Turbine
20200714
Internal Combustion Engines
Industrial; Process Gas; Turbine: Exhaust
20300202
Internal Combustion Engines
Commercial/Institutional; Natural Gas; Turbine
20300203
Internal Combustion Engines
Cogeneration
Commercial/Institutional; Natural Gas; Turbine:
201
-------
Table 4-33. Ptnonipm emissions reductions after the application of the Natural Gas Turbines NSPS
year
poll
2016v2 (tons)
emissions
reduction (tons)
0/
/O
change
2023
NOX
945,768
-2,098
-0.2%
2026
NOX
945,768
-2,440
-0.3%
2032
NOX
945,768
-3,165
-0.3%
Table 4-34. Point source SCCs in ptoilgas sector where Natural Gas Turbines NSPS control
applied.
see
SCC description
20200201
Internal Combustion Engines; Industrial; Natural Gas; Turbine
20200209
Internal Combustion Engines; Industrial; Natural Gas; Turbine: Exhaust
20300202
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine
20300209
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine: Exhaust
20200203
Internal Combustion Engines; Industrial; Natural Gas; Turbine: Cogeneration
20200714
Internal Combustion Engines; Industrial; Process Gas; Turbine: Exhaust
20300203
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Turbine:
Cogeneration
Table 4-35. Emissions reductions (tons/year) for pt oilgas after the application of the Natural Gas
Turbines NSPS CONTROL packet for future years.
Year
Pollutant
2016v2
Emissions
Reduction
%
change
2023
NOX
409,699
-8,160
-2.0%
2026
NOX
409,699
-11,357
-2.8%
2032
NOX
409,699
-14,039
-3.4%
4.2.4.5 Process Heaters NOx NSPS (ptnonipm, pt_oilgas)
Packets:
Control_2016_2023_Process_Heaters_NSPS_ptnonipm_beta_platform_ext_25 sep2019_v0
Control_2023_2026interp_Process_Heaters_NSPS_ptnonipm_v2_platform_22jul2021_v0
Control_2016_2023_Process_Heaters_NSPS_pt_oilgas_v2_platform_23jun2021_v0
Control_2016_2026_Process_Heaters_NSPS_pt_oilgas_v2_platform_23jun2021_v0
Control_2026_2032_Process_Heaters_NSPS_ptnonipm_v2_platform_13aug2021_v0
Control_2016_2032_Process_Heaters_N SPS_pt_oilgas_v2_platform_23jun202 l_vO
For ptnonipm, the control packet for 2023 was reused for 2016vl platform; the packet for 2023 to 2026
was developed based on an interpolation between the 2023 and 2028 factors for 2016vl platform; and the
2026 to 2032 packet was developed using methods consistent with how the 2023 and 2028 packets were
developed. For pt oilgas, the packets were newly developed for 2016v2 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
202
-------
because they can be used to control process streams by recovering the fuel value while destroying the
VOC. The criteria pollutants of most concern for process heaters are NOx and SO2.
In 2016, it is assumed that process heaters have not been subject to regional control programs like the
NOx SIP Call, so most of the emission controls put in-place at refineries and chemical plants have
resulted from RACT regulations that were implemented as part of SIPs to achieve ozone NAAQS in
specific areas, and refinery consent decrees. The boiler/process heater NSPS established NOx emission
limits for new and modified process heaters. These emission limits are displayed in Table 4-36.
Table 4-36. Process Heaters NSPS analysis and 2016vl new emission rates used to estimate controls
NOx emission rate Existing (Fe)
Fraction at this rate
Average
PPMV
Natural
Draft
Forced
Draft
80
0.4
0
100
0.4
0.5
150
0.15
0.35
200
0.05
0.1
240
0
0.05
Cumulative, weighted: Fe
104.5
134.5
119.5
NSPS Standard
40
60
New Source NOx ratio (Fn)
0.383
0.446
0.414
NSPS Control (%)
61.7
55.4
58.6
For computations, the existing source emission ratio (Fe) was set to 1.0. The computed (average) NOx
emission factor ratio for new sources (Fn) is 0.41 (58.6 percent control). The retirement rate is the inverse
of the expected unit lifetime. There is limited information in the literature about process heater lifetimes.
This information was reviewed at the time that the Western Regional Air Partnership (WRAP) developed
its initial regional haze program emission projections, and energy technology models used a 20-year
lifetime for most refinery equipment. However, it was noted that in practice, heaters would probably have
a lifetime that was on the order of 50 percent above that estimate. Therefore, a 30-year lifetime was used
to estimate the effects of process heater growth and retirement. This yields a 3.3 percent retirement rate.
This control was then plugged into Equation 4-2 as a function of the year-specific projection factor. Table
4-37 and Table 4-39 list the point source SCCs where Process Heaters NSPS controls were applied for the
2016vl platform. Table 4-38 and Table 4-40 show the reduction in NOx emissions after the application
of the Process Heaters NSPS CONTROL packet for the future years.
Table 4-37. Ptnonipm SCCs in 2016vl modeling platform where Process Heaters NSPS controls
applied.
see
Sccdesc
30190003
Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Process Heater:
Natural Gas
30190004
Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Process Heater:
Process Gas
30590002
Industrial Processes; Mineral Products; Fuel Fired Equipment; Residual Oil: Process
Heaters
203
-------
see
Sccdesc
30590003
Industrial Processes; Mineral Products; Fuel Fired Equipment; Natural Gas: Process
Heaters
30600101
Industrial Processes; Petroleum Industry; Process Heaters; Oil-fired
30600102
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600103
Industrial Processes; Petroleum Industry; Process Heaters; Oil
30600104
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600105
Industrial Processes; Petroleum Industry; Process Heaters; Natural Gas-fired
30600106
Industrial Processes; Petroleum Industry; Process Heaters; Process Gas-fired
30600107
Industrial Processes; Petroleum Industry; Process Heaters; Liquified Petroleum Gas (LPG)
30600199
Industrial Processes; Petroleum Industry; Process Heaters; Other Not Classified
30990003
Industrial Processes; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas:
Process Heaters
31000401
Industrial Processes; Oil and Gas Production; Process Heaters; Distillate Oil (No. 2)
31000402
Industrial Processes; Oil and Gas Production; Process Heaters; Residual Oil
31000403
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil
31000404
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas
31000405
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas
31000406
Industrial Processes; Oil and Gas Production; Process Heaters; Propane/Butane
31000413
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil: Steam
Generators
31000414
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas: Steam
Generators
31000415
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas: Steam
Generators
39900501
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Distillate Oil
39900601
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Natural Gas
39990003
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous
Manufacturing Industries; Natural Gas: Process Heaters
Table 4-38. Ptnonipm emissions reductions after the application of the Process Heaters NSPS
year
pollutant
2016v2
(tons)
emissions
reduction (tons)
0/
/O
change
2023
NOX
945,768
-9,311
-1.0%
2026
NOX
945,768
-11,286
-1.2%
2032
NOX
945,768
-16,371
-1.7%
204
-------
Table 4-39. Point source SCCs in ptoilgas sector where Process Heaters NSPS controls were
applied
see
SCC Description
30190003
Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Process Heater:
Natural Gas
30600102
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600104
Industrial Processes; Petroleum Industry; Process Heaters; Gas-fired
30600105
Industrial Processes; Petroleum Industry; Process Heaters; Natural Gas-fired
30600106
Industrial Processes; Petroleum Industry; Process Heaters; Process Gas-fired
30600199
Industrial Processes; Petroleum Industry; Process Heaters; Other Not Classified
30990003
Industrial Processes; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas:
Process Heaters
31000401
Industrial Processes; Oil and Gas Production; Process Heaters; Distillate Oil (No. 2)
31000402
Industrial Processes; Oil and Gas Production; Process Heaters; Residual Oil
31000403
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil
31000404
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas
31000405
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas
31000413
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil: Steam
Generators
31000414
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas: Steam
Generators
31000415
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas: Steam
Generators
39900501
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Distillate Oil
39900601
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Natural Gas
Table 4-40. NOx emissions reductions (tons/year) in pt oilgas sector after the application of the
Process Heaters NSPS CONTROL packet for futures years.
Year
Pollutant
2016v2
Emissions Reduction
%
change
2023
NOX
409,699
-1,592
-0.4%
2026
NOX
409,699
-2,095
-0.5%
2032
NOX
409,699
-2,599
-0.6%
4.2.4.6 CISWI (ptnonipm)
Packets:
Control_2016_202X_CISWI_ptnonipm_beta_platform_ext_25sep2019_v0
The 2016vl packet for CISWI was reused in the 2016v2 platform and is the same for all future years.
On March 21, 2011, the EPA promulgated the revised NSPS and emission guidelines for Commercial and
Industrial Solid Waste Incineration (CISWI) units. This was a response to the voluntary remand that was
granted in 2001 and the vacatur and remand of the CISWI definition rule in 2007. In addition, the
205
-------
standards redevelopment included the 5-year technology review of the new source performance standards
and emission guidelines required under Section 129 of the Clean Air Act. The history of the CISWI
implementation is documented here: https://www.epa.gov/stationary-sources-air-pollution/commercial-
and-industrial-solid-waste-incineration-units-ciswi-new. Baseline and CISWI rule impacts associated with
the CISWI rule are documented here: https://www.regulations.gov/document?D=EPA-HO-OAR-20Q3-
0119-2559. The EPA mapped the units from the CISWI baseline and controlled dataset to the 2014 NEI
inventory and computed percent reductions such that our future year emissions matched the CISWI
controlled dataset values. Table 4-41 summarizes the total impact of CISWI controls for 2023 and 2028.
Note that this rule applies to specific units in 11 states: Alaska, Arkansas, Illinois, Iowa, Louisiana,
Maine, Oklahoma, Oregon, Pennsylvania, Tennessee, and Texas for CO, S02, and NOX.
Table 4-41. Summary of CISWI rule impacts on ptnonipm emissions for 2023
year
pollutant
2016v2
(tons)
emissions
reductions
(tons)
%
change
2023
CO
1,411,093
-2,791
-0.2%
2023
NOX
945,768
-2,002
-0.2%
2023
S02
648,529
-1,815
-0.3%
4.2.4.7 Petroleum Refineries NSPS Subpart JA (ptnonipm)
Packets:
Control_2016_202X_NSPS_Subpart_Ja_ptnonipm_beta_platform_ext_25 sep2019_v0
The 2016vl packet for Subpart JA was reused in the 2016v2 platform and is the same for all future years.
On June 24, 2008, EPA issued final amendments to the Standards of Performance for Petroleum
Refineries. This action also promulgated separate standards of performance for new, modified, or
reconstructed process units after May 14, 2007 at petroleum refineries. The final standards for new
process units included emissions limitations and work practice standards for fluid catalytic cracking units,
fluid coking units, delayed coking units, fuel gas combustion devices, and sulfur recovery plants. In 2012,
EPA finalized the rule after some amendments and technical corrections. See
https://www.epa.gov/stationarv-sources-air-pollution/petroleum-refineries-new-source-performance-
standards-nsps-40-cfr for more details on NSPS - 40 CFR 60 Subpart Ja. These NSPS controls were
applied to petroleum refineries in the ptnonipm sector for years 2023 and 2028. Units impacted by this
rule were identified in the 2016vl inventory. For delayed coking units, an 84% control efficiency was
applied and for storage tanks, a 49% control efficiency was applied. The analysis of applicable units was
completed prior to the 2014v2 NEI and the 2016vl platform. Therefore, to ensure that a control was not
applied to a unit that was already in compliance with this rule, we compared emissions from the 2016vl
inventory and the 2011 en inventory (the time period of the original analysis). Any unit that demonstrated
a 55+%> reduction in VOC emissions from 201 len to 2016vl would be considered compliant with the rule
and therefore not subject to this control. Table 4-42 below reflects the impacts of these NSPS controls on
the ptnonipm sector. This control is applied to all pollutants; Table 4-42 summarizes reductions for the
future years for NOX, S02, and VOC.
206
-------
Table 4-42. Summary of NSPS Subpart JA rule impacts on ptnonipm emissions for 2023 and 2028
2016vl
emissions
year
pollutant
(tons)
reductions (tons)
% change
2023
NOX
945,768
-1
0.0%
2023
S02
648,529
-3
0.0%
2023
VOC
597,842
-5,269
-0.9%
4.2.4.8 Ozone Transport Commission Rules (nonpt, solvents)
Packets:
Control_2016_202X_nonpt_OTC_v l_platform_MARAMA_04oct2019_v 1
Control_2016_202X_nonpt_PF C_v l_platform_MARAMA_04oct2019_v 1
The 2016vl packets are reused and are the same for all years.
Several MARAMA states have adopted rules reflecting the recommendations of the Ozone Transport
Commission (OTC) for reducing VOC emissions from consumer products, architectural and industrial
maintenance coatings, and various other solvents. The rules affected 27 different SCCs in the surface
coatings (2401xxxxxx), degreasing (2415000000), graphic arts (2425010000), miscellaneous industrial
(2440020000), and miscellaneous non-industrial consumer and commercial (246xxxxxxx) categories.
The packet applies only to MARAMA states and not all states adopted all rules. This packet applies to
emissions in the new solvents sector. The new SCCs in the solvents sector were added to the packet.
The OTC also developed a model rule to address VOC emissions from portable fuel containers (PFCs) via
performance standards and phased-in PFC replacement that was implemented in two phases. Some states
adopted one or both phases of the OTC rule, while others relied on the Federal rule. MARAMA
calculated control factors to reflect each state's compliance dates and, where states implemented one or
both phases of the OTC requirements prior to the Federal mandate, accounted for the early reductions in
the control factors. The rules affected permeation, evaporation, spillage, and vapor displacement for
residential (250101 lxxx) and commercial (2501012xxx) portable gas can SCCs. This packet applies to
the nonpt sector.
MARAMA provided control packets to apply the solvent and PFC rule controls.
4.2.4.9 State-Specific Controls (ptnonipm)
Packets:
Control_2016_202X_ptnonipm_NC_BoilerMACT_beta_platform_ext_25 sep2019_v0
Control_2016_202X_AZ_Regional_Haze_ptnonipm_beta_platform_ext_25 sep2019_v0
CONTROL_2016_202X_Consent_Decrees_ptnonipm_v l_platform_MARAMA_10sep2019_v0
CONTROL_2016_202X_DC_supplemental_ptnonipm_v l_platform_04oct2019_v 1
CONTROL_2016_202X_Consent_Decrees_other_state_comments_beta_platform_extended_20aug202 l_v2
ICI Boilers - North Carolina
The Industrial/Commercial/Institutional Boilers and Process Heaters MACT Rule, hereafter simply
referred to as the "Boiler MACT," was promulgated on January 31, 2013, based on reconsideration.
207
-------
Background information on the Boiler MACT can be found at: https://www.epa.gov/stationary-sources-
air-pollution/industrial-commercial-and-institutional-boilers-and-process-heaters. The Boiler MACT
promulgates national emission standards for the control of HAPs (NESHAP) for new and existing
industrial, commercial, and institutional (ICI) boilers and process heaters at major sources of HAPs. The
expected cobenefit for CAPs at these facilities is significant and greatest for S02 with lesser impacts for
direct PM, CO and VOC. This control addresses only the expected cobenefits to existing ICI boilers in the
State of North Carolina. All other states previously considered for this rule are assumed to be in
compliance with the rule and therefore the emissions need no further estimated controls applied. The
control factors applied here were provided by North Carolina.
Arizona Regional Haze Controls
U.S. EPA Region 9 provided regional haze FIP controls for a few industrial facilities. Information on
these controls are available in the docket https://www.regulations.gov/document?D=EPA-R09-OAR-
2013-0588-0072. These non-EGU controls have implementation dates between September 2016 and
December 2018.
Consent Decrees
MARAMA provided a list of controls relating to consent decrees to be applied to specific units within the
MARAMA region. This list includes sources in North Carolina that were subject to controls in the beta
version of this emission modeling platform. Outside of the MARAMA region, controls related to consent
decrees were applied to several sources, including the LaFarge facility in Michigan (8127411), for which
NOX emissions must be reduced by 18.633% to meet the decree; and the Cabot facilities in Louisiana and
Texas, which had been subject to consent decree controls in the 2011 platforms, and 2016 emissions
values suggest controls have not yet taken effect. Other facilities subject to a consent decree were
determined to already be in compliance based on 2016 emissions values.
For 2016v2, an update to the NOx control efficiencies for the Minntac facility (6927911) was
implemented based on reduction information from Minnesota Pollution Control Agency. In 2016vl, the
reduction was nearly 95% for the full facility and has been updated to reduce five units with the majority
of the NOX emissions by 33-37%) each.
State Comments
A comment from the State of Illinois that was included in the 2011 platform was carried over for the
2016vl platform. The data accounts for three coal boilers being replaced by two gas boilers not in the
inventory and results in a large S02 reduction.
The State of Ohio reported that the P. H. Glatfelter Company facility (8131111) has switched fuels after
2016, and so controls related to the fuel switch were applied. This is a new control for version 1 platform.
Comments relating to Regional Haze in the 2011 platform were analyzed for potential use in the 2016vl
platform. For those comments that are still applicable, control efficiencies were recalculated so that
2016vl post-control emissions (without any projections) would equal post-control emissions for the 2011
platform (without any projections). This is to ensure that controls which may already be applied are
accounted for. Some facilities' emissions were already less than the 2011 post-control value in 2016vl
and therefore did not need further controls here. For facility 3982311 (Eastman Chemical in Tennessee),
one unit has a control efficiency of 90 in 2016vl and the others have no control; a replacement control of
91.675 was applied for this facility so that the unit with control efficiency=90 is not double controlled.
208
-------
Wisconsin provided alternate emissions to use as input to 2023vl/2028vl CoST. Wisconsin provided new
emissions totals for three facilities and requested that these new totals be used as the basis for 2023vl and
2028vl projections, instead of 2016vl. The provided emissions were facility-level only, therefore 2016vl
emissions were scaled at these facilities to match the new provided totals.
The District of Columbia provided a control packet to be applied to three ptnonipm facilities in all 2016vl
platform projections.
4.3 Projections Computed Outside of CoST
Projections for some sectors are not calculated using CoST. These are discussed in this section.
4.3.1 Nonroad Mobile Equipment Sources (nonroad)
Outside California and Texas, the MOVES3 model was run separately for each future year, including
2023, 2026, and 2032, resulting in a separate inventory for each year. The fuels used are specific to each
future year, but the meteorological data represented the year 2016. The 2023, 2026, and 2032 nonroad
emission factors account for regulations such the Emissions Standards for New Nonroad Spark-Ignition
Engines, Equipment, and Vessels (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-
rule-control-emissions-nonroad-spark-ignition). Locomotives and Marine Compression-Ignition Engines
Less than 30 Liters per Cylinder (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-
rul e-control-emissions-air-pollution-locomotive). and Clean Air Nonroad Diesel Final Rule - Tier 4
(https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-emissions-air-
pollution-nonroad-dieseD. The resulting future year inventories were processed into the format needed by
SMOKE in the same way as the base year emissions.
Inside California and Texas, CARB and TCEQ provided separate datasets for 2023 and 2028. CARB also
provided a nonroad dataset for 2035. The 2023 California and Texas datasets were used as provided. For
2026, we interpolated the 2023 and 2028 datasets in both California and Texas. The California 2032
nonroad dataset is an interpolation of 2028 and 2035. Since we do not have any TCEQ datasets beyond
2028, the 2032 Texas nonroad dataset was projected from the TCEQ-based 2026 dataset using projection
factors based on the MOVES runs from 2026 and 2032 by county, SCC, and pollutant. The 2032 Texas
nonroad projection was built from 2026 rather than 2028 because we did not have a 2028 MOVES run
consistent with the 2026 and 2032 MOVES runs for 2016v2 platform. VOC and PM2.5 by speciation
profile, and VOC HAPs, were added to all future year California and Texas nonroad inventories using the
same procedure as for the 2016 inventory, but based on the future year MOVES runs instead of the 2016
MOVES run.
The nonroad inventories include all nonroad control programs finalized as of the date of the MOVES3.0.0
release, including most recently:
• Emissions Standards for New Nonroad Spark-Ignition Engines, Equipment, and Vessels: October,
2008;
• Growth and control from Locomotives and Marine Compression-Ignition Engines Less than 30
Liters per Cylinder: March, 2008; and
• Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004.
209
-------
4.3.2 Onroad Mobile Sources (onroad)
The MOVES3 model was run separately for each future year, including 2023, 2026, and 2032, resulting
in separate emission factors for each year. The 2023, 2026, and 2032 onroad emission factors account for
changes in activity data and the impact of on-the-books rules that are implemented into MOVES3. These
include regulations such as:
• Safer Affordable Fuel Efficient (SAFE) Vehicles Final Rule for Model Years 2021-2026 (March,
2020);
• Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy -
Duty Engines and Vehicles - Phase 2 (October, 2016);
• Tier 3 Vehicle Emission and Fuel Standards Program (March, 2014)
(https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-air-pollution-
motor-vehicles-tier-3);
• 2017 and Later Model Year Light-Duty Vehicle GHG Emissions and Corporate Average Fuel
Economy Standards (October 2012);
• Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy -
Duty Engines and Vehicles (September, 2011);
• Regulation of Fuels and Fuel Additives: Modifications to Renewable Fuel Standard Program
(RFS2) (December, 2010); and
• Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy
Standards Final Rule for Model-Year 2012-2016 (May, 2010).
Local inspection and maintenance (I/M) and other onroad mobile programs are included such as:
California LEVIII, the National Low Emissions Vehicle (LEV) and Ozone Transport Commission (OTC);
LEV regulations (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-air-
pol 1 ution-new-motor-vehicles-and-2). local fuel programs, and Stage II refueling control programs.
The fuels used are specific to each future year, the age distributions were projected to the future year, and
the meteorological data represented the year 2016. The resulting emission factors were combined with
future year activity data using SMOKE-MOVES run in a similar way as the base year. The development
of the future year activity data is described later in this section. CARB provided separate emissions
datasets for each future year. The CARB-provided emissions were adjusted to match the temporal and
spatial patterns of the SMOKE-MOVES based emissions. Additional information about the development
of future year onroad emission and on how SMOKE was run to develop the emissions can be found in the
2016vl platform onroad sector specification sheet.
Future year VMT was developed as follows:
• VMT were projected from 2016 to 2019 using VMT data from the FHWA county-level VM-2
reports. At the time of this study, these reports were available for each year up through 2019. As
with the original 2016 backcasting, EPA calculated county-road type factors based on FHWA
VM-2 County data for each of the three years, and county total factors were applied instead of
county-road factors in states with significant changes in road type classifications from year to
year.
210
-------
• 2019 VMT were projected to 2023 using a combination of AE02020 and AEO2021 reference case
tables. AEO2021 starts with the year 2020, so AE02020 was used to project from 2019 to 2020,
and AEO2021 was used to project from 2020 to 2023.
• VMT data submitted by state and local agencies for the year 2023 for the 2016 version 1 platform
were were incorporated where available, in place of the EPA default 2023 projection. The
following states or agencies submitted 2023 VMT: Connecticut, Georgia, Massachusetts, New
Jersey, North Carolina, Ohio, Wisconsin, Louisville metro (KY/IN), Pima County AZ, and Clark
County NV.
• The resulting 2023 VMT data, including VMT submitted by local agencies, were projected to
2026 and 2032 using AEO2021. Thus the 2026 and 2032 projected VMT used 2023 as the
baseline and incorporated submitted 2023 VMT.
Annual VMT data from the AE02020 and AEO2021 reference cases by fuel and vehicle type were used
to project VMT from 2019 to future years. Specifically, the following two AEO2021 tables were used:
• Light Duty (LD): Light-Duty VMT by Technology Type (table #41:
https://www.eia. gov/outlooks/aeo/data/browser/#/?id=51-AEQ2021&sourcekev=0
• Heavy Duty (HD): Freight Transportation Energy Use (table #49:
http s ://www. ei a. gov/outl ooks/aeo/data/browser/#/?i d=5 8 -
AEO2021 &cases=ref2021 ~aeo2020ref&sourcekey=0
To develop the VMT projection factions, total VMT for each MOVES fuel and vehicle grouping was
calculated for the years 2019, 2023, 2026, and 2032 based on the AEO-to-MOVES mappings above.
From these totals, 2019-2023, 2023-2026, and 2023-2032 VMT trends were calculated for each fuel and
vehicle grouping. Those trends became the national VMT projection factors. The AEO2021 tables include
data starting from the year 2020. Since we were using AEO data to project from 2019, 2019-to-2020
projection factors were calculated from AE02020, and then multiplied by 2020-to-future projection
factors from AEO2021. MOVES fuel and vehicle types were mapped to AEO fuel and vehicle classes.
The resulting 2019-to-future year national VMT projection factors used for the 2016v2 platform are
provided in Table 4-43 These factors were adjusted to prepare county-specific projection factors for light
duty vehicles based on human population data available from the BenMAP model by county for the years
2023, 2026, and 203035 (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 4-43. Factors used to Project VMT to future years
SCC6
description
2019 to 2023
factor
2023 to 2026
factor
2023 to 2032
factor
220111
LD gas
1.13
1.04
1.09
220121
LD gas
1.13
1.04
1.09
220131
LD gas
1.13
1.04
1.09
220132
LD gas
1.13
1.04
1.09
35 The final year of the population dataset used is 2030
211
-------
2019 to 2023
2023 to 2026
2023 to 2032
SCC6
description
factor
factor
factor
220142
Buses gas
1.03
1.01
1.12
220143
Buses gas
1.03
1.01
1.12
220151
MHDgas
1.03
1.01
1.12
220152
MHDgas
1.03
1.01
1.12
220153
MHDgas
1.03
1.01
1.12
220154
MHDgas
1.03
1.01
1.12
220161
HHDgas
0.67
0.80
0.68
220221
LD diesel
1.37
1.17
1.40
220231
LD diesel
1.37
1.17
1.40
220232
LD diesel
1.37
1.17
1.40
220241
Buses diesel
1.091
1.05
1.11
220242
Buses diesel
1.09
1.05
1.11
220243
Buses diesel
1.09
1.05
1.11
220251
MHD diesel
1.09
1.05
1.11
220252
MHD diesel
1.09
1.05
1.11
220253
MHD diesel
1.09
1.05
1.11
220254
MHD diesel
1.09
1.05
1.11
220261
HHD diesel
1.08
1.04
1.06
220262
HHD diesel
1.08
1.04
1.06
220342
Buses CNG
1.12
0.99
0.98
220521
LD E-85
1.05
0.96
0.85
220531
LD E-85
1.05
0.96
0.85
220532
LD E-85
1.05
0.96
0.85
220921
LD Electric
2.28
1.40
2.64
220931
LD Electric
2.28
1.40
2.64
220932
LD Electric
2.28
1.40
2.64
In areas where the EPA default future year VMT projection were used, future 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 future year projected VMT to estimate future year
VPOP. Future year VPOP data submitted by state and local agencies were incorporated into the VPOP
projections for 2023. Future year VPOP data for 2023 were provided by state and local agencies in NH,
NJ, NC, WI, Pima County, AZ, and Clark County, NV. In addition, 2023 VPOP was carried forward from
version 1 platform in CT, GA, MA, and the Louisville metro areas; as those areas only submitted VMT
for 2023 and not VPOP, but keeping the 2016 version 1 VPOP in those areas ensures consistency between
the VMT and VPOP. Additionally, North Carolina bus VMT and VPOP, which was an EPA default
projection in version 1 platform, was carried forward from version 1 platform so that all VMT and VPOP
in North Carolina would be the same as in version 1. Both VMT and VPOP were redistributed between
the LD car and truck vehicle types (21/31/32) based on splits from the EPA computed default projection.
Hoteling hours were projected to the future years by calculating 2016 inventory HOTELING/VMT ratios
for each county for combination long-haul trucks on restricted roads only. Those ratios were then applied
to the future year projected VMT for combination long-haul trucks on restricted roads to calculate future
212
-------
year hoteling. Some counties had hoteling activity but did not have combination long-haul truck restricted
road VMT in 2016; in those counties, the national AEO-based projection factor for diesel combination
trucks was used to project 2016 hoteling to the future years. This procedure gives county-total hoteling for
the future years. Each future year also has a distinct APU percentage based on MOVES input data that
was used to split county total hoteling to each SCC: 12.91% APU for 2023, 20.46% for 2026, and 31.72%
APU for 2032. New Jersey provided 2023 hoteling data for 2016vl and those data were used for the
2016v2, using the new APU fraction for MOVES3 2023 (12.91%). As in the 2016 backcast, for counties
that had 2017 hoteling, but do not have vehicle type 62 VMT on restricted road type - that is, counties that
should have hoteling, but do not have any VMT to calculate it from - we projected 2016 to 2019 using the
FHWA-based county total 2016 to 2019 trend, and then used the AEO-based factors for heavy duty diesel
to project beyond 2019.
Future year starts were calculated using 2017NEI-based VMT ratios, similar to how 2016 starts were
calculated:
Future year STARTS = Future year VMT * (2017 STARTS / 2017 VMT by county+SCC6)
Future year ONI activity was calculated using a similar formula, but with 2016-based ratios rather than
2017-based ratios, in order to reflect the new method used to calculate ONI activity for 2016:
Future year ONI = Future year VMT * (2016 ONI / 2016 VMT by county+SCC6)
In California, onroad emissions in SMOKE-MOVES are adjusted to match CARB-provided data
using the same procedure described in Section 2.3.3. EMFAC2017 was run by CARB for the years
2016, 2023, 2028, and 2035. California onroad emissions for 2026 were interpolated from CARB
2023 and 2028, and emissions for 2032 were interpolated from CARB 2028 and 2035.
4.3.3 Locomotives (rail)
For 2023, rail emissions are unchanged from 2016vl, including rail yards (which already included the
Georgia-provided update for 2023 in 2016vl). Rail emissions for 2026 were interpolated from the 2023
and 2028 emissions in 2016vl. Factors to compute emissions for future year of 2030 were based on future
year fuel use values from the Energy Information Administration's 2018 Annual Energy Outlook (AEO)
freight rail energy use growth rate projections for 2016 thru 2030 (see Table 4-44) and emission factors
based on historic emissions trends that reflect the rate of market penetration of new locomotive engines.
The locomotive projections only go to 2030 to be consistent with the out year for the commercial marine
vessel projections.
A correction factor was added to adjust the AEO projected fuel use for 2017 to match the actual 2017 R-l
fuel use data. The additive effect of this correction factor was carried forward for each subsequent year
from 2018 thru 2030. The modified AEO growth rates were used to calculate future year Class I line-haul
fuel use totals for 2020, 2023, 2026, and 2030. As shown in Table 4-44 the future year fuel use values
ranged between 3.2 and 3.4 billion gallons, which matched up well with the long-term line-haul fuel use
trend between 2005 and 2018. The emission factors for NOx, PM10 and VOC were derived from trend
lines based on historic line-haul emission factors from the period of 2007 through 2017.
213
-------
Table 4-44. Class I Line-haul Fuel Projections based on 2018 AEO Data
Year
AEO Freight
Factor
Projection
Factor
Corrected AEO Fuel
Uaw AEO Fuel
2016
1
1
3,203,595,133
3,203,595,133
2017
1.0212
1.0346
3,314,384,605
3,271,393,249
2018
1.0177
1.0311
3,303,215,591
3,260,224,235
2019
1.0092
1.0226
3,275,939,538
3,232,948,182
2020
1.0128
1.0262
3,287,479,935
3,244,488,580
2021
1.0100
1.0235
3,278,759,301
3,235,767,945
2022
0.9955
1.0090
3,232,267,591
3,189,276,235
2023
0.9969
1.0103
3,236,531,624
3,193,540,268
2024
1.0221
1.0355
3,317,383,183
3,274,391,827
2025
1.0355
1.0489
3,360,367,382
3,317,376,026
2026
1.0410
1.0544
3,377,946,201
3,334,954,845
2027
1.0419
1.0553
3,380,697,189
3,337,705,833
2028
1.0356
1.0490
3,360,491,175
3,317,499,820
2029
1.0347
1.0529
3,373,114,601
3,314,913,891
2030
1.0319
1.0561
3,383,235,850
3,305,890,648
The projected fuel use data was combined with the emission factor estimates to create future year link-
level emission inventories based on the MGT traffic density values contained in the FRA's 2016
shapefile. The link-level data created for 2020, 2023, 2026 and 2030 was aggregated to create county,
state, and national emissions estimates (see Table 4-45) which were then converted into FF10 format for
use in the 2016v2 emissions platform.
Table 4-45. Class I Line-haul Historic and Future Year Projected Emissions
Inventory
CO
IIC
MI3
NOx
PM10
PM2.5
S()2
2007 (2008 NEI)
110,969
37,941
347
754,433
25,477
23,439
7,836
2014 NEI
107,995
29,264
338
609,295
19,675
18,101
381
2016 v2
96,068
22,991
301
492,999
14,351
13,889
427
2017 NEI
97,272
21,560
304
492,385
14,411
13,979
343
2023 Projected
97,514
17,265
305
403,207
10,816
10,477
431
2026 Projected
99,840
15,524
312
375,121
9,714
9,412
438
2030 Projected
99,338
12,512
311
349,868
8,014
7,766
436
Other rail emissions were projected based on AEO growth rates as shown in Table 4-46. See the 2016vl
rail specification sheet for additional information on rail projections.
214
-------
Table 4-46. 2018 AEO growth rates for rail sub-groups
Sector
2016
2023
2026
2030
Rail Yards
1.0
0.9969
1.0410
1.0284
Class II/III Railroads
1.0
0.9969
1.0410
1.0284
Commuter/Passenger
1.0
1.0879
1.1310
1.2220
4.3.4 Sources Outside of the United States (onroad_can, onroad_mex, othpt,
canada_ag, canada_og2D, ptfire_othna, othar, othafdust, othptdust)
This section discusses the projection of emissions from Canada and Mexico. Information about the base
year inventory used for these projections or the naming conventions can be found in Section 2.7. Most of
the Canada and Mexico projections are based on inventories and other data from 2016vl platform,
applied to the 2016v2 platform base year inventories.
For 2016vl platform, ECCC provided data from which Canadian future year projections could be derived
in a file called "Projected_CAN2015_2023_2028.xlsx", which includes emissions data for 2015, 2023,
and 2028 by pollutant, province, ECCC sub-class code, and other source categories. ECCC sub-class
codes are present in most Canadian inventories and are similar to SCC, but more detailed for some types
of sources and less detailed for other types of sources. For most Canadian inventories, 2023 and 2026
inventories were projected from the new 2016 base year inventory using projection factors based on the
ECCC sub-class level data from the 2016vl platform, except with the 2015-to-2023 trend reduced to a
2016-to-2023 trend (reduce the total change by 1/8), and with 2026 interpolated between 2023 and 2028.
Exceptions to this general procedure are noted below. For example, ECCC sub-class level data could not
be used to project inventories where the sub-class codes changed from 2016vl to 2016v2. As noted
below, inventories projected to 2028 were often used to represent the year 2032 due to lack of information
for later years. Fire emissions in Canada and Mexico in the ptfire othna sector, were not projected.
4.3.4.1 Canadian fugitive dust sources (othafdust, othptdust)
Canadian area source dust (othafdust)
For Canadian area source dust sources, ECCC sub-class level data from 2016vl platform was used to
project the 2016v2 base year inventory to 2023 and 2028. Emissions for 2026 were interpolated between
the 2023 and 2028 emissions, and emissions from 2028 were used to represent the year 2032. As with the
base year, the future year dust emissions are pre-adjusted, so future year othafdust follows the same
emissions processing methodology as the base year with respect to the transportable fraction and
meteorological adjustments.
Canadian point source dust (othptdust)
In 2016vl platform, ECCC provided sub-class level emissions data for the othptdust sector for the base
and future years. Since the othptdust projections in 2016vl were nearly flat, we decided to not project
othptdust for the v2 platform (i.e., the 2016fj othptdust emissions were reused for all future year cases).
215
-------
4.3.4.2 Point Sources in Canada and Mexico (othpt, canada_ag,
canada_og2D)
Canada point agriculture and oil and gas emissions
For Canadian agriculture and upstream oil and gas sources, ECCC sub-class level data from 2016vl
platform was used to project the 2016v2 base year inventory to 2023 and 2028. Emissions for 2026 were
interpolated between 2023 and 2028, and emissions from 2028 were 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, the ECCC sub-class codes changed from 2016vl to
2016v2 platform. Therefore, the ECCC sub-class level data from 2016vl platform could not be used to
project the 2016v2 base year inventory. Instead, projection factors were based on total airport emissions
from the 2016vl Canada inventory by province and pollutant. As with other sectors, 2026 emissions were
interpolated between 2023 and 2028, and 2028 emissions were used to represent 2032.
In 2016vl platform, future 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, 2023,
and 2028, by province and pollutant. This is the same procedure that was used for airports, except using
different projection factors based on only the stationary sources.
Mexico
The othpt sector includes a general point source inventory in Mexico which was updated for 2016v2
platform. Similar to the procedure for projecting Canadian stationary point sources, factors for projecting
from 2016 to 2023 and 2028 were calculated from the 2016vl platform Mexico point source inventories
by state and pollutant. Mexico point source emissions for 2026 were interpolated between 2023 and 2028,
and 2028 emissions were used to represent 2032.
4.3.4.3 Nonpoint sources in Canada and Mexico (othar)
Canadian stationary sources
In 2016vl platform, future 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, 2023, and 2028, by province and pollutant. This is
the same procedure that was used for airports and stationary point sources, except using different
projection factors based on only the stationary area sources.
216
-------
For 2016vl platform, ECCC provided an additional stationary area source inventory for 2023 and 2028
representing electric power generation (EPG). According to ECCC, this inventory's emissions do not
double count the 2023 and 2028 point source inventories, and it is appropriate to include this area source
EPG inventory in the othar sector as an additional standalone inventory in the future years. Therefore, the
2016vl area source EPG inventory was included in the 2016v2 platform future year cases. Emissions for
2026 were interpolated from 2023 and 2028, and 2028 emissions were 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, 2023, and 2028. ECCC sub-class level data from 2016vl platform was used to
project the 2016v2 base year inventory to 2023 and 2028. Emissions for 2026 were interpolated from
2023 and 2028. For the nonroad inventory, the sub-class code is analogous to the SCC7 level in U.S.
inventories. For example, there are separate sub-class codes for fuels (e.g., 2-stroke gasoline, diesel, LPG)
and nonroad equipment sector (e.g., construction, lawn and garden, logging, recreational marine) but not
for individual vehicle types within each category (e.g., snowmobiles, tractors). For rail, the sub-class code
is closer to full SCCs in the NEI.
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 US MOVES runs for 2026 and 2032
(excluding California and Texas for which we did not use MOVES data) and applied to the interpolated
2026 Canada nonroad inventory. 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 to 2032 were the same as the factors used to project US rail emissions from 2026 to
2030 (used to represent 2032), 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 2023 and 2028 were
calculated from the 2016vl platform Mexico area and nonroad inventories by state and pollutant. Separate
proejctions were calculated for the area and nonroad inventories. Emissions for 2026 were interpolated
between 2023 and 2028, and 2028 emissions were used to represent 2032, including for nonroad (unlike
in Canada).
4.3.4.4 Onroad sources in Canada and Mexico (onroad_can,
onroad_mex)
For Canadian mobile onroad sources, projection information is covered by the ECCC sub-class level data
for 2015, 2023, and 2028. ECCC sub-class level data from 2016vl platform was used to project the
2016v2 base year inventory to 2023 and 2028. Emissions for 2026 were interpolated from 2023 and 2028.
For the onroad inventory, the sub-class code is analogous to the SCC6+process level in U.S. inventories,
in that it specifies fuel type, vehicle type, and process (e.g., brake, tire, exhaust, refueling), but not road
type.
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 US MOVES
runs for 2026 and 2032 (excluding California for which we did not use MOVES data) and applied to the
217
-------
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
2023, 2028, and 2035. The emissions for 2023 were reused from the 2016vl platform, 2026 emissions
were interpolated between 2023-2028, and 2032 emissions were interpolated between 2028-2035.
MOVES-Mexico emissions for 2035 were not available from the 2016vl platform, so a new MOVES-
Mexico run was performed for 2035 to support the 2032 interpolation. The 2035 MOVES-Mexico run
included diesel refueling whereas 2016/2023/2028 did not; we excluded diesel refueling from the 2032
interpolation.
218
-------
5 Emission Summaries
Tables 5-1 through Table 5-4 summarize emissions by sector for the 2016fj, 2023fj, and 2032fj 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 5-5 and Table 5-6 provide similar
summaries for the 36-km domain (36US3) for 2016 and 2023. Note that totals for the 12US2 domain are
not available here, but the sum of the U.S. sectors would be essentially the same and only the Canadian
and Mexican emissions would change according to how far north/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 includes CMV emissions at U.S. ports. "Offshore" represents CMV emissions that are
outside of U.S. state waters. Canadian CMV emissions are included in the other sector. The total of all US
sectors is listed as "Con U.S. Total."
Table 5-7 and Table 5-8 summarize ozone season NOx and VOC emissions, respectively, for the 20161j,
2023fj, 2026fj and 2032fj cases.
State totals and other summaries are available in the reports area on the web and FTP sites for the 2016v2
platform (https://www.epa.gov/air-emissions-modeling/2016v2-platform,
https://gaftp.epa.gov/Air/emismod/2016/v2 )
219
-------
Table 5-1. National by-sector CAP emissions for the 2016fj case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,314,612
880,002
airports
486,237
0
126,713
10,011
8,733
15,245
54,191
cmv clc2
23,548
83
162,502
4,457
4,320
634
6,436
cmv c3
13,956
39
110,462
2,201
2,025
4,528
8,600
fertilizer
1,183,387
livestock
2,493,166
224,459
nonpt
1,878,357
109,393
685,856
517,279
438,112
134,178
823,345
nonroad
10,593,504
1,845
1,110,243
109,008
103,047
1,513
1,134,711
np oilgas
767,276
20
573,037
12,540
12,454
42,741
2,394,024
onroad
18,309,739
107,903
3,394,103
225,510
106,447
25,960
1,310,505
pt oilgas
195,388
283
369,113
13,003
12,453
44,162
225,116
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
658,496
24,039
1,319,734
164,090
133,543
1,565,675
33,748
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
ptnonipm
1,403,822
62,974
933,618
391,071
249,030
644,852
593,789
rail
104,551
326
559,381
16,344
15,819
457
26,082
rwc
2,230,849
16,943
35,204
309,908
309,019
8,249
334,217
solvents
0
0
0
0
0
0
2,841,997
beis
3,973,014
983,247
26,791,907
CONUS + beis
54,639,227
4,291,614
10,600,953
9,592,386
3,537,687
2,603,295
39,934,957
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
491,788
104,968
Canada oil and gas 2D
667
7
3,241
186
186
3,944
510,623
Canada othafdust
696,793
108,328
Canada othar
2,191,451
3,819
323,152
225,620
177,134
16,294
740,566
Canada onroad can
1,849,517
7,685
407,423
26,017
14,012
1,739
158,429
Canada othpt
1,116,192
19,482
651,451
90,042
43,051
990,049
148,216
Canada othptdust
152,566
53,684
Canada ptfire othna
761,402
13,032
16,359
84,481
71,749
6,731
185,476
Canada CMV
10,741
37
93,456
1,682
1,563
2,984
5,184
Mexico othar
115,887
112,005
60,196
105,146
34,788
1,733
362,643
Mexico onroad mex
1,828,101
2,789
442,410
15,151
10,836
6,247
158,812
Mexico othpt
109,015
1,096
190,997
54,044
37,491
355,883
35,768
Mexico ptfire othna
383,162
7,436
16,604
44,994
38,178
2,785
131,499
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
33,224
128
293,102
7,188
6,658
28,060
16,209
Offshore cmv outside
Federal waters
23,338
440
257,615
24,827
22,847
181,941
11,083
Offshore ptoilgas
50,052
15
48,691
668
667
502
48,210
Non-U.S. Total
8,472,751
659,759
2,804,698
1,529,403
621,172
1,598,894
2,617,684
220
-------
Table 5-2. National by-sector CAP emissions for the 2023fj case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,401,391
899,185
airports
517,268
0
145,795
10,055
8,806
17,694
57,943
cmv clc2
23,570
59
116,344
3,191
3,093
242
4,527
cmv c3
17,076
48
107,609
2,699
2,483
5,537
10,602
fertilizer
1,183,387
livestock
2,626,271
235,783
nonpt
1,891,033
110,651
694,255
521,019
443,557
102,467
778,316
nonroad
10,581,631
2,032
737,604
70,997
66,494
974
863,250
np oilgas
768,609
30
586,759
14,862
14,735
61,972
2,389,864
onroad
13,148,561
100,915
1,655,937
191,255
61,836
10,813
831,291
pt oilgas
225,150
309
403,961
17,092
16,178
64,753
223,469
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
427,367
36,995
594,744
114,785
98,246
634,036
37,919
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
ptnonipm
1,432,679
61,885
908,799
382,640
244,237
534,410
588,194
rail
105,988
330
469,157
12,778
12,376
460
20,436
rwc
2,207,381
16,741
36,863
302,976
302,069
7,705
330,560
solvents
0
0
0
0
0
0
2,972,209
beis
3,973,014
983,247
26,791,907
Con. U.S. Total + beis
49,319,818
4,430,866
7,678,812
9,548,093
3,435,978
1,556,166
39,267,692
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
583,282
104,584
Canada oil and gas 2D
477
7
1,920
128
128
3,305
412,111
Canada othafdust
782,334
121,430
Canada othar
2,196,835
3,729
267,788
219,440
164,701
16,198
740,364
Canada onroad can
1,590,905
6,850
254,786
26,537
11,305
937
102,118
Canada othpt
1,129,621
22,315
553,839
72,613
42,672
877,388
154,137
Canada othptdust
152,566
53,684
Canada ptfire othna
761,402
13,032
16,359
84,481
71,749
6,731
185,476
Canada CMV
11,597
40
67,837
1,819
1,690
3,158
5,525
Mexico other
126,192
109,995
69,552
107,496
36,249
1,953
404,664
Mexico onroad mex
1,772,026
3,266
427,900
17,023
11,764
7,556
161,115
Mexico othpt
123,814
1,321
187,731
59,146
40,987
292,546
44,668
Mexico ptfire othna
383,162
7,436
16,604
44,994
38,178
2,785
131,499
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
39,846
150
257,244
8,460
7,815
34,951
19,345
Offshore cmv outside
Federal waters
28,551
277
314,614
15,643
14,396
41,490
13,542
Offshore ptoilgas
50,052
15
48,691
668
667
502
48,210
Non-U.S. Total
8,214,481
751,715
2,484,865
1,593,349
617,415
1,289,500
2,527,359
221
-------
Table 5-3. National by-sector CAP emissions for the 2026fj case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,428,543
905,256
Airports
533,307
0
152,022
10,214
8,952
18,502
59,667
cmv clc2
23,816
52
101,403
2,788
2,702
243
3,889
cmv c3
18,598
52
107,790
2,941
2,705
6,021
11,587
Fertilizer
1,183,387
Livestock
2,676,214
240,237
Nonpt
1,901,236
110,845
697,001
525,570
448,103
101,118
750,750
Nonroad
10,751,235
2,075
654,121
62,250
58,069
993
823,108
np oilgas
759,656
30
572,137
14,987
14,859
64,530
2,420,875
Onroad
11,585,277
101,412
1,349,183
191,676
56,943
10,458
712,159
pt oilgas
228,771
326
410,387
17,670
16,735
66,401
227,428
Ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
Ptegu
375,426
37,372
524,517
105,766
91,749
527,497
38,012
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
Ptnonipm
1,447,666
62,195
922,900
385,646
246,557
538,782
589,066
Rail
108,411
338
441,525
11,683
11,317
468
18,709
Rwc
2,197,235
16,670
37,264
300,528
299,616
7,523
329,169
Solvents
0
0
0
0
0
0
3,061,634
Beis
3,973,014
983,247
26,791,907
Con. U.S. Total + beis
47,904,137
4,482,184
7,191,237
9,562,611
3,426,245
1,457,639
39,209,617
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
632,182
104,570
Canada oil and gas 2D
497
7
1,493
133
133
3,550
447,884
Canada othafdust
825,908
128,106
Canada other
2,206,851
3,717
254,419
218,350
161,430
16,174
755,871
Canada onroad can
1,504,701
6,461
210,090
26,684
10,386
893
82,677
Canada othpt
1,154,185
23,274
495,903
75,829
44,714
872,534
159,956
Canada othptdust
152,566
53,684
Canada ptfire othna
761,402
13,032
16,359
84,481
71,749
6,731
185,476
Canada CMV
11,987
41
70,985
1,880
1,747
3,280
5,709
Mexico other
130,146
110,429
73,150
108,612
36,855
2,038
423,290
Mexico onroad mex
1,677,896
3,546
407,181
18,048
12,307
8,141
163,311
Mexico othpt
131,373
1,445
200,959
63,917
44,176
301,303
48,989
Mexico ptfire othna
383,162
7,436
16,604
44,994
38,178
2,785
131,499
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
43,378
163
247,179
9,172
8,466
38,601
21,050
Offshore cmv outside
Federal waters
31,251
304
344,269
17,136
15,769
45,504
14,821
Offshore ptoilgas
50,052
15
48,691
668
667
502
48,210
Non-U.S. Total
8,086,882
802,052
2,387,283
1,648,376
628,367
1,302,035
2,593,311
222
-------
Table 5-4. National by-sector CAP emissions for the 2032fj case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdustadj
6,458,049
912,011
airports
567,555
0
165,344
10,576
9,283
20,226
63,334
cmv_clc2
24,263
43
85,429
2,338
2,266
246
3,319
cmv_c3
20,561
58
107,190
3,253
2,992
6,651
12,856
fertilizer
1,183,387
livestock
2,732,952
245,305
nonpt
1,903,520
110,928
687,427
528,664
452,252
97,673
730,932
nonroad
11,248,705
2,165
562,189
52,589
48,733
1,043
801,700
npoilgas
729,184
30
538,818
14,811
14,683
64,244
2,408,435
onroad
8,679,801
102,102
1,019,701
191,468
50,703
9,770
585,930
ptoilgas
225,894
321
401,808
17,779
16,835
67,026
226,979
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
451,570
36,761
604,700
116,719
100,420
713,590
40,632
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
ptnonipm
1,448,923
62,326
919,014
386,807
247,646
538,255
588,692
rail
108,120
337
417,808
10,029
9,716
466
15,775
rwc
2,210,901
16,833
37,501
302,689
301,777
7,559
331,058
solvents
0
0
0
0
0
0
3,152,515
beis
3,973,014
983,247
26,791,907
Con. U.S. Total + beis
45,592,497
4,539,456
6,767,916
9,598,120
3,431,999
1,641,852
39,130,792
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
666,106
104,732
Canada oil and gas 2D
511
7
1,208
136
136
3,713
471,499
Canada othafdust
854,957
132,557
Canada othar
2,207,683
3,708
224,883
214,775
156,436
16,153
755,356
Canada onroadcan
1,176,830
6,505
156,013
25,852
8,338
846
67,108
Canada othpt
1,170,310
23,893
457,199
77,957
46,065
868,995
163,782
Canada othptdust
152,566
53,684
Canada ptfireothna
761,402
13,032
16,359
84,481
71,749
6,731
185,476
Canada CMV
12,790
43
71,970
1,970
1,829
3,531
6,061
Mexico othar
132,782
110,719
75,549
109,362
37,260
2,095
435,707
Mexico onroad mex
1,595,504
4,195
383,146
20,987
14,132
9,392
173,325
Mexico othpt
136,413
1,528
209,778
67,100
46,303
307,141
51,870
Mexico ptfire othna
383,162
7,436
16,604
44,994
38,178
2,785
131,499
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
47,889
179
234,630
10,084
9,300
43,171
23,268
Offshore cmv outside
Federal waters
34,680
337
381,968
19,029
17,510
50,589
16,450
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non-U.S. Total
7,710,009
837,703
2,277,997
1,684,916
634,144
1,315,644
2,634,342
223
-------
Table 5-5. National by-sector CAP emissions for the 2016fj case, 36US3 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,318,693
880,413
Airports
486,976
0
126,863
10,036
8,756
15,268
54,284
cmv clc2
22,299
79
154,053
4,230
4,100
608
6,126
cmv c3
13,634
38
107,651
2,137
1,966
4,394
8,426
Fertilizer
1,183,387
Livestock
2,493,168
224,459
Nonpt
1,879,030
109,453
686,374
517,360
438,157
134,419
823,601
Nonroad
10,598,518
1,845
1,110,424
109,045
103,082
1,514
1,135,706
np oilgas
767,276
20
573,037
12,540
12,454
42,741
2,394,024
onroad
18,316,814
107,918
3,394,861
225,566
106,476
25,961
1,311,039
pt oilgas
195,388
283
369,113
13,003
12,453
44,162
225,116
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
Ptegu
658,548
24,039
1,319,935
164,096
133,548
1,565,684
33,754
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
ptnonipm
1,403,836
62,974
933,635
391,098
249,038
644,852
593,790
Rail
104,551
326
559,381
16,344
15,819
457
26,082
Rwc
2,255,921
16,972
35,693
314,353
313,464
8,325
334,819
Solvents
0
0
0
0
0
0
2,842,494
Beis
4,135,928
997,794
27,766,644
36US3 U.S. Total + beis
54,839,208
4,291,714
10,606,555
9,600,852
3,542,409
2,603,488
40,911,786
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
507,030
107,661
Canada oil and gas 2D
732
7
3,548
203
203
4,432
606,218
Canada othafdust
722,629
112,358
Canada othar
2,352,757
4,115
358,976
239,649
188,729
17,031
779,607
Canada onroad can
1,926,698
7,980
428,161
27,152
14,692
1,802
164,479
Canada othpt
1,379,994
21,394
832,840
102,218
50,224
1,124,153
203,402
Canada othptdust
152,834
52,953
Canada ptfire othna
6,282,821
104,683
134,301
685,169
580,963
60,914
1,501,988
Canada CMV
13,768
49
121,623
2,288
2,122
5,165
6,733
Mexico othar
1,699,433
562,057
235,176
465,425
252,429
12,630
1,588,164
Mexico onroad mex
6,273,194
10,319
1,497,028
74,169
56,782
26,400
552,952
Mexico othpt
319,500
3,314
485,613
213,413
141,638
1,453,380
111,716
Mexico ptfire othna
7,133,496
120,584
346,990
1,155,563
745,860
45,208
2,259,747
Mexico CMV
64,730
0
204,997
16,286
15,087
109,778
8,817
Offshore cmv in Federal
waters
36,315
163
322,278
9,143
8,466
40,887
17,403
Offshore cmv outside
Federal waters
88,395
1,175
1,006,880
92,499
85,125
683,740
40,266
Offshore ptoilgas
50,052
15
48,691
668
667
502
48,210
Annual Total
27,621,887
1,342,884
6,027,100
3,959,307
2,308,297
3,586,022
7,997,363
224
-------
Table 5-6. National by-sector CAP emissions for the 2023fj case, 36US3 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,405,476
899,596
Airports
518,068
0
145,956
10,083
8,833
17,720
58,047
cmv clc2
22,224
56
109,865
3,030
2,937
225
4,273
cmv c3
16,709
46
104,555
2,623
2,413
5,380
10,397
fertilizer
1,183,387
livestock
2,626,273
235,783
Nonpt
1,891,745
110,722
694,794
521,086
443,601
102,705
778,566
Nonroad
10,586,164
2,032
737,740
71,022
66,517
975
863,939
np oilgas
768,609
30
586,759
14,862
14,735
61,972
2,389,864
Onroad
13,153,476
100,929
1,656,397
191,305
61,855
10,814
831,668
pt oilgas
225,150
309
403,961
17,092
16,178
64,753
223,469
Ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
Ptegu
427,367
36,995
594,744
114,785
98,246
634,036
37,919
ptfire-rx
7,094,333
130,849
127,470
778,864
655,354
58,690
1,546,840
ptfire-wild
6,643,510
109,088
100,030
684,798
580,377
52,719
1,567,400
ptnonipm
1,432,698
61,885
908,821
382,667
244,245
534,410
588,195
Rail
106,036
331
469,545
12,789
12,387
460
20,454
Rwc
2,229,940
16,769
37,302
306,911
306,005
7,774
331,137
Solvents
0
0
0
0
0
0
2,972,706
Beis
4,135,928
997,794
27,766,644
36US3 U.S. Total + beis
49,514,603
4,430,978
7,685,971
9,556,085
3,440,230
1,556,326
40,244,484
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada ag
600,883
107,266
Canada oil and gas 2D
527
7
2,115
142
142
3,714
489,811
Canada othafdust
810,859
125,871
Canada othar
2,356,241
4,019
308,601
232,951
175,488
17,180
780,201
Canada onroad can
1,655,613
7,109
268,025
27,680
11,859
971
106,159
Canada othpt
1,364,416
24,576
686,691
80,094
48,582
993,177
214,520
Canada othptdust
152,834
52,953
Canada ptfire othna
6,282,821
104,683
134,301
685,169
580,963
60,914
1,501,988
Canada CMV
14,789
52
88,545
2,463
2,285
5,507
7,134
Mexico other
1,821,647
552,207
263,072
483,534
266,265
13,459
1,731,394
Mexico onroad mex
6,053,503
12,083
1,447,199
94,407
72,468
31,838
560,284
Mexico othpt
381,638
4,088
537,165
251,989
167,147
1,416,350
141,037
Mexico ptfire othna
7,133,496
120,584
346,990
1,155,563
745,860
45,208
2,259,747
Mexico CMV
79,677
0
252,331
20,046
18,571
19,304
10,853
Offshore cmv in Federal
waters
43,338
191
280,425
10,740
9,920
50,540
20,650
Offshore cmv outside
Federal waters
108,334
741
1,234,211
58,174
53,534
155,668
49,468
Offshore ptoilgas
50,052
15
48,691
668
667
502
48,210
Non-U.S. Total
8,214,481
751,715
2,484,865
1,593,349
617,415
1,289,500
2,527,359
225
-------
Table 5-7. National by-sector Ozone Season NOx emissions summaries 12US1 grid (tons/o.s.)
Sector
20l(,lj
2023lj
2026lj
2032lj
airports
56,300
64,779
67,546
73,465
cmv clc2 12
90,624
64,719
56,294
47,300
cmv c3 12
264,816
277,635
287,826
300,207
nonpt
193,886
196,857
198,442
195,724
nonroad
566,188
377,891
334,265
284,630
np oilgas
239,247
244,056
238,015
224,204
onroad
1,341,526
650,732
523,684
387,755
onroad ca adj
99,730
48,303
44,880
41,490
pt oilgas
175,250
189,944
192,640
189,043
ptagfire
3,193
3,193
3,193
3,193
ptegu
605,014
264,200
239,930
265,088
ptnonipm
391,374
381,066
386,919
385,113
rail
236,771
198,559
186,854
176,801
rwc
4,280
4,528
4,596
4,601
Total U.S. Anthro
4,268,199
2,966,463
2,765,084
2,578,614
beis
587,057
587,057
587,057
587,057
ptfire-rx
20,531
20,531
20,531
20,531
ptfire-wild
55,500
55,500
55,500
55,500
Grand Total
4,931,288
3,629,551
3,428,173
3,241,702
Table 5-8. National by-sector Ozone Season VOC emissions summaries 12US1 grid (tons/o.s.)
Sector
20l(,lj
2023fj
2026lj
20321]
airports
24,078
25,745
26,511
28,140
cmv clc2 12
3,538
2,476
2,121
1,805
cmv c3 12
14,553
17,965
19,716
21,943
livestock
156,077
164,112
167,229
170,725
nonpt
344,481
324,891
313,572
305,544
nonroad
573,637
421,807
398,145
383,526
np oilgas
980,746
979,486
992,390
986,718
onroad
552,899
348,610
293,979
235,488
onroad ca ad)
44,432
27,229
24,394
19,788
pt oilgas
114,505
113,824
115,484
115,296
ptagfire
6,314
6,314
6,314
6,314
ptegu
16,215
17,999
18,313
18,934
ptnonipm
248,145
245,742
246,081
245,868
rail
11,039
8,648
7,917
6,674
rwc
36,554
37,983
38,361
38,408
solvents
1,194,840
1,249,563
1,287,153
1,325,357
Total U.S. Anthro
4,322,053
3,992,395
3,957,681
3,910,526
beis
20,896,708
20,896,708
20,896,708
20,896,708
ptfire-rx
277,019
277,019
277,019
277,019
ptfire-wild
1,005,261
1,005,261
1,005,261
1,005,261
Grand Total
26.501.041
26.171.383
26.136.669
26.089.515
226
-------
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.
227
-------
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-Project-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/P10023S4.PDF?Dockey=P10023S4.PDF.
EPA, 2012d. Preparation of Emission Inventories for the Version 5.0, 2007 Emissions Modeling Platform
Technical Support Document. Available from:
https://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?Dockey=P100UT95.PDF .
228
-------
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-50-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/air-emissions-
modeling/speciate-51-and-50-addendum-and-final-report.
EPA, 2021. 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 -techni cal - support-document-tsd.
EPA, 2021. 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, 2021b. 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-1 -technical-support-document.
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 finaly 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/emismod/2014/v2/2014fd/emissions/EPA%
205-18%20Report Clean%20Final 01042017.pdf.
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%20Qil%20and%20Gas%20
Emission%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%
20Memos.zip.
ERG, 2019b. Category 1 and 2 Commercial Marine Emissions Inventory. Available from:
https://www.epa.gov/sites/default/files/2019-ll/cmv_methodology_documentation.zip.
ERG, 2019c. 2016 versus 2017 entrance and clearance data. Available from:
https://gaftp.epa.gov/Air/emismod/2016/v2/reports/EandC_2016_to_2017_Activity_Ratios.pdf.
The Freedonia Group, 2016. Solvents, Industry Study #3429.
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.
229
-------
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.
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 Chem Phys, 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.
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.eom/science/article/abs/pii/S 1352231008000137?via
%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://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: http://www.nvserda.ny.gov/Publications/Case-Studies/-
/media/Files/Publications/Research/Environmental/Wood-Fired-Hvdronic-Heater-Tech.ashx.
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.
230
-------
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.
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.
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/object/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.
231
-------
U.S. Census Bureau, Economy Wide Statistics Division, 2018. County Business Patterns, 2018.
https://www.census.gov/programs-surveys/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-Survey-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/librarv/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.
Wiedinmver. C.. S.K. Akagi. R.J. Yokelson. L.K. Emmons. J.A. Al-Saadi3. J. J. Orlando1, and A. J. Soia.
(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.
WRAP / Ramboll, 2019. Revised Final Report: Circa-2014 Baseline Oil and Gas Emission Inventory for
the WESTAR-WRAP Region, September 2019. Available at:
http://www.wrapair2.org/pdf/WRAP OGWG Report Baseline 17Sep2019.pdf.
WRAP / Ramboll, 2020. Revised Final Report: 2028 Future Year Oil and Gas Emission Inventory for
WESTAR-WRAP States - Scenario #1: Continuation of Historical Trends
http://www.wrapair2.org/pdf/WRAP OGWG 2028 OTB RevFinalReport 05March2020.pdf.
Yarwood, G., J. Jung,, G. Whitten, G. Heo, J. Mellberg, and M. Estes,2010: Updates to the Carbon Bond
Chemical Mechanism for Version 6 (CB6). Presented at the 9th Annual CMAS Conference,
Chapel Hill, NC. Available at
https://www.cmascenter.org/conference/2010/abstracts/emery updates carbon 2010.pdf.
Zhu, Henze, et al, 2013. "Constraining U.S. Ammonia Emissions using TES Remote Sensing
Observations and the GEOS-Chem adjoint model", Journal of Geophysical Research:
Atmospheres, 118: 1-14. Available at https://doi.org/10.1002/igrd.5Q166.
232
-------
Appendix A: CB6 Assignment for New Species
233
-------
ENVIRON
September 27, Z016
MEMORANDUM
To: Alison Eyth and Madeleine Strum, OAQPS, EPA
From: Ross BeardsleYand Greg Yarwcod, RamboJI Environ
Subject: Species Mappings for CSS and CBC5 for use with SPECIATE 4.5
Summary
Ram ball Environ (REJ reviewed version 4.5 of the SPECIATE database, and created CEOS and C56
mechanism species mappings far newly added compounds. In addition, the mapping guicelines for
Carbon Bond (CB] mechanisms were expanded to promote consistency in current and future work
Background
The Environmental Protection Agency's SPECIATE repository contains gas and particulate matter
speciation profiles of sir pollution sources, which are used in the generation cf emissions data for air
quality models |AO.M' such as CMAQ. {http://www.crna5centBr.org/cmaq/"] and CAMjs
{http://www.camx.comJ. However, the condensed chemical mechanisms used within these
photochemical models utilize fewer species than SPECIATE to rep resent gas phase chemistry, and
thus the SPECIATE compounds must be assigned to the AQM model species cf the condensed
mechanisms. A chemical mapping is used to show the representation of organic chemical species by
the model compounds of the condensed mechanisms.
This memorandum describes how chemical mappings were developed from SPECIATE 4.5
compounds to model species of the CE mechanism, specifically CEC5
Ihttp://wvAV.camx.com/publ/pdfs/CE05_Fi nail_Report_120BD5.prff) and CBS
(http://aqrp.ceer.Lrtexas.edu/projectinfoFYL2_L3/12-012/12-012%20FiaaJ%2DReport.pdf).
Methods
CB Model Species
Organic gases are mapped to the CE mechanism either as explicitly represented individual
compounds (e.g. ALD2 for acetaldehyde>, or as a combination of model species that represent
common structural groups (e.g. ALDX for other aldehydes, PAR for alkyl groups). Table 1 lists all of
the explicit and structural model species in CBC5 and CBS mechanisms, each of which represents a
defined number of carbon atoms allowing for carbon to be conserved in all cases. CBS contains fDur
more explicit model species than CB05 and an additional structu ral group to represent ketones. The
CBC5 representation of the five additional CB6 species is provided in the 'Included in CB05' column of
Table 1.
Emboli Bwirarv 771 San Mann Drive, Surti 2113, Nowtsa CA MH3B
V-H4U.SSe07DQ F414n.S9S.07D7
234
-------
ENVIRON
in addition to the explicit and structural species, there are two model species that are used to
represent organic gases that are not treated by the CB mechanism:
NV'OL— Very low volatility 5PEC1ATE compounds that reside predominantly in the particle phase and
should be excluded from the gas phase mechanism. These compounds are mapped by setting
NVOL equal to the molecular weight (e.g. decabramodiphenyl oxide is mapped as 959.2
NVOL}, which a Hows for the total mass of all NVOL to be determined.
UN K- Compounds that are unable to be mapped to CB using the available model species. This
approach should be avoided unless absolutely necessary, and will tead to a warning messa|e
in the speciation tool.
Table 1. Model spedes in the CBQ5 and CB6 chemical mechanisms.
Included in
Model
Number
CBOS
Species
Hi
(structural
Included
Maine
Oesra'jjtiwn
Cars o*i 5
mapping]
in CUE
Expliiit model spec es
ACET
Acetar.e i^proporione)
1
No 13PAR|
res
ald2
Acet: dehyde [etharialp
2
res
res
3ENZ
3er,iene
6
Mo(l PAR. 3
UNR|
res
CH4
Methane
1
yes
res
ETH
Sihene (ethylene]
2
res
res
ET HA
Ethane
2
Yes
res
ET HT
Ethyne [acetylene)
2
Mo (1 PAR,. 1
JNR|
res
ETOH
Ethanol
2
res
res
=ORM
:ormaldehvde (methanol}
1
'r'ts
res
SOP
saprene [2-mBthyl-1..3-butBdiene)
5
res
res
y EOH
Methanol
1
>es
res
PRPA
=rnparis
3
Mo. (1.3 PA3,
1.5 UNR'i
res
Coinmofl Structural groups
ALDX
-gher: dehyde graup |-C-CHO|
2
res
res
OLE
ntsmal cielin group R.,>C=CC=0|
1
Mo (1 PAR|
res
OLE
Termini' c stir group (H R >C=C|
2
res
res
°AR
^arsfnic group (R -Zirto, CA9499S 2
V*i413.B39jCCTH F+l 4U.E99jQr7D(7
235
-------
236
-------
ENVIRON
Mapping guidelines for nan-explicit organic gases using C6 model species
SPECIATE compound; that = re not treated explicitly are mapped to CB model species that represent
common structural groups. Table 2 lists the carbon number and general mapping guide lines for each
of the structure model species.
Table 2. General Guidelines for mapping using CB6 structural model species.
CB4
Species
Name
Number of
Cart] his
RepiesErts
ALOX
2
Aldehyde group. ALIJX represents 2 carbons and additional cartons are rEp^esEnted a;
alkyl groups [mostly PAR), E.g. propionaldehyds is ALOX + PAR
'OLE
4
-itamal clefin grc jp. IOLE represents 4 carbons and adcatic-iBl carbons are represented as
alkyl groups [mostly PAR}, E.g. 2-pentene isomers are IOLE + PAR.
S-tcfprbni:
* OLE with 2 carton brands or, both sides of the double Send are downgraded to
OLE
-------
T^fTl
ENVIRON
Table 3. Mapping guidelines for same difficult to map compound classes and structural groups
Compound
Class/S'ructufal
group
CB model species representation
Chlorotemeres and
other h alogenated
benienes-
Guideline:
* 3 or less halogens - i PAS, 3 JNR
* 4 or more halogen: -6 U^JR
Example!:
* 1,3,3-CMorctsmene -1 PAR, 3 UN 3
* Tetrachlorotenier.es - 6 UMR
QtifHKWfe
Guideline:
* 1 OLE with additional carton: represented » alkyl greens (generally
PAH)
Examples:
* MelfiylCYdopentadiene-1 OLE, 2 PAS
* ¦¦fetiffl&pQQwjli/LrsC,- 1 IC .E. 3 PAR
:uranSi,:!-pTrjiEj
Guideline:
* 2 OLE with additional carbcrts represented as alKyl gruLps (generally
PAR)
Examples:
* i-ButylfLrsn — 2 C .E, 4 PAR
* 2-Pentylftiran - 2 OLE, 3 PAR
* ^pTTOle -iO.E
* i-Meth>1p»mMc - 2 OLE, 1 PAR
-eterocyclit aromatic
compounds
containing 2 non-
carton atams
Guideline:
* 1 OLE with remaining cartons represented is alfeyl groups (generally
PAR)
Examples:
* Ettvjlpyraiine — 1 OLE, 4 3AS
* l-fietiylpyraiole — 1 OLE. 2 PAR
* 4.3-Oirnethyloiazole -1 C .E, 3 PAR
Tnple borid(s]>
Guideline:
* Tnple bond: are treated a: =AS unless they are the only reactive
functional group, ra compound contains mors than one triple tond
and ra other readme functional groups, then one ctthe trole tonds
s treated as OLE with additional cartons treated as alleyl groups.
Examples:
* l-Penter-a-me — 1 C.E 3 PAR
* IJ5-HBKadien-3-jrie- 2 C.E. 2 PAR
* 1,6— eotadivne - 1 OLE. 3 PAR
These guide! ines we re usee tc nr a p tfie new species from SPEICATE45, s nd alsc tc revise some
previously mapped compounds. Overall, a total of 175 new species from 5PECIATEV4.5 were mapped
and 7 previously mapped species were revised based on the new guidelines.
3apfccl1 Environ US Cnporation, 773 San Marin Drwe, Sute 2113, Hmrrtor&3®M 4
V+1413.099J57CH F-U413.SSSJ37B7
238
-------
Recommendation
1, complete a systematic review of the mapping of ail species to ensure conformity with cxirre r t
mapping guidelines. The assignments c* """sting c?n"po«»ds are "mi1!*** new species wire
••evc'.,ed 5*: -svi- ee tc ore ""its :?nii=T=rc-. r rs-pi-s =z:r-:s:ir€-. the ~5.:r tv r
existing species mappings wets not -ev ewed as t was cutsicE the scops af:Ws »v:-rk.
2. [ = -.e:pi 'lit^ccr'Csv ssssryi\i a •: :¦=:!¦ <¦§ l;r-er 0zzz*"Z3-~?i tassd :n :r.eir
v-c =ti tv iirf--' rti'^iec ~Zi, :r o*.v -.-c it tv. tc ifrp"rv = :.p;:r fj=- : = corodary organic aerosol
, Z-C j ircc= :ng ntf; =ti tr'bssis set (V55' SCA tic:=.. •yh':!-- • avs^sc = i" bcf CVACt
3-3 camx. A preliminary bvgit.aiL:n of the possibility of r:i~g zz hails** psrf;fnsa, s*-i is
discussed1 in a -=psrate memorandum.
nd¥hi», ZA'smm
239
-------
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE versions 4.5 and
later that were used in the 2016 platforms
Table B-l Profiles first used in 2016beta, 2016vl, and 2016v2 platforms
Sector
Pollutant
Profile code
Profile description
SPECIATE
version
ptfire,
ptagfire
VOC
G8746
Rice Straw and Wheat Straw Burning Composite of G4420
and G4421
5.0
livestock
VOC
G95241TOG
Swine Farm and Animal Waste with gapfilled methane and
ethane
5.0
npoilgas.
pt oilgas
VOC
UTUBOGC
Raw Gas from Oil Wells - Composite Uinta basin
5.1
npoilgas.
pt oilgas
VOC
UTUBOGD
Raw Gas from Gas Wells - Composite Uinta basin
5.1
npoilgas.
pt oilgas
VOC
UTUBOGE
Flash Gas from Oil Tanks - including Carbonyls -
Composite Uinta basin
5.1
npoilgas.
pt oilgas
VOC
UTUBOGF
Flash Gas from Condensate Tanks - including Carbonyls -
Composite Uinta basin
5.1
npoilgas.
pt oilgas
VOC
PAGAS01
Oil and Gas-Produced Gas Composition from Gas Wells-
Greene Co, PA
5.1
npoilgas.
pt oilgas
VOC
PAGAS02
Oil and Gas-Produced Gas Composition from Gas Wells-
Butler Co, PA
5.1
npoilgas.
pt oilgas
VOC
PAGAS03
Oil and Gas-Produced Gas Composition from Gas Wells-
Washington Co, PA
5.1
npoilgas.
pt oilgas
VOC
SUIROGCT
Flash Gas from Condensate Tanks - Composite Southern Ute
Indian Reservation
5.2
npoilgas.
pt oilgas
VOC
CBMPWWY
Coal Bed Methane Produced Water Profile - WY ponds
5.2
npoilgas.
pt oilgas
VOC
DJTFLR95
DJ Condensate Flare Profile with DRE 95%
5.2
npoilgas.
pt oilgas
VOC
CMU01
Oil and Gas - Produced Gas Composition from Gas Wells -
Central Montana Uplift - Montana
5.1
npoilgas.
pt oilgas
VOC
WIL01
Oil and Gas - Flash Gas Composition from Tanks at Oil
Wells - Williston Basin North Dakota
5.1
npoilgas.
pt oilgas
VOC
WIL02
Oil and Gas - Flash Gas Composition from Tanks at Oil
Wells - Williston Basin Montana
5.1
npoilgas.
pt oilgas
VOC
WIL03
Oil and Gas - Produced Gas Composition from Oil Wells -
Williston Basin North Dakota
5.1
npoilgas.
pt oilgas
VOC
WIL04
Oil and Gas - Produced Gas Composition from Oil Wells -
Williston Basin Montana
5.1
cmv_clc2,
cmv c3
VOC
95331NEIHP
Marine Vessel - 95331 blend with CMV HAP
5.1
ptagfire
PM
SUGP02
Sugar Cane Pre-Harvest Burning Mexico
5.1
ptfire
PM
95793
Forest Fire-Flaming-Oregon AE6
5.1
ptfire
PM
95794
Forest Fire-Smoldering-Oregon AE6
5.1
ptfire
PM
95798
Forest Fire-Flaming-North Carolina AE6
5.1
240
-------
Sector
Pollutant
Profile code
Profile description
SPECIATE
version
ptfire
PM
95799
Forest Fire-Smoldering-North Carolina AE6
5.1
ptfire
PM
95804
Forest Fire-Flaming-Montana AE6
5.1
ptfire
PM
95805
Forest Fire-Smoldering-Montana AE6
5.1
ptfire
PM
95807
Forest Fire Understory-Flaming-Minnesota AE6
5.1
ptfire
PM
95808
Forest Fire Understory-Smoldering-Minnesota AE6
5.1
ptfire
PM
95809
Grass Fire-Field-Kansas AE6
5.1
Table B-2 Profiles first used in 2016 alpha platform
Profile
SPECIATE
Comment
Sector
Pollutant
code
Profile description
version
5.0
Replacement for v4.5
profile 95223; Used 70%
methane, 20% ethane,
and the 10% remaining
Poultry Production - Average of Production
VOC is from profile
nonpt
voc
G95223TOG
Cycle with gapfilled methane and ethane
95223
5.0
Replacement for v4.5
profile 95240. Used 70%
methane, 20% ethane;
Nonpt,
Beef Cattle Farm and Animal Waste with
the 10% remaining VOC
ptnonipm
voc
G95240TOG
gapfilled methane and ethane
is from profile 95240.
5.0
Replacement for v4.5
profile 95241. Used 70%
methane, 20% ethane;
the 10% remaining VOC
nonpt
voc
G95241TOG
Swine Farm and Animal Waste
is from profile 95241
nonpt,
5.0
Composite of AE6-ready
ptnonipm,
versions of SPECIATE4.5
pt_oilgas,
Composite -Refinery Fuel Gas and Natural
profies 95125, 95126,
ptegu
PM2.5
95475
Gas Combustion
and 95127
Spark-Ignition Exhaust Emissions from 2-
4.5
stroke off-road engines - E10 ethanol
nonroad
VOC
95328
gasoline
Spark-Ignition Exhaust Emissions from 4-
4.5
stroke off-road engines - E10 ethanol
nonroad
VOC
95330
gasoline
Diesel Exhaust Emissions from Pre-Tier 1
4.5
nonroad
VOC
95331
Off-road Engines
Diesel Exhaust Emissions from Tier 1 Off-
4.5
nonroad
VOC
95332
road Engines
Diesel Exhaust Emissions from Tier 2 Off-
4.5
nonroad
VOC
95333
road Engines
Oil and Gas - Composite - Oil Field - Oil
4.5
nP_oilgas
VOC
95087a
Tank Battery Vent Gas
Oil and Gas - Composite - Oil Field -
4.5
nP_oilgas
voc
95109a
Condensate Tank Battery Vent Gas
Composite Profile - Oil and Natural Gas
4.5
nP_oilgas
voc
95398
Production - Condensate Tanks
241
-------
Profile
SPECIATE
Comment
Sector
Pollutant
code
Profile description
version
nP_oilgas
voc
95403
Composite Profile - Gas Wells
4.5
Oil and Gas Production - Composite Profile
4.5
nP_oilgas
voc
95417
- Untreated Natural Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95418
- Condensate Tank Vent Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
nP_oilgas
voc
95419
- Oil Tank Vent Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95420
- Glycol Dehydrator, Uinta Basin
Oil and Gas -Denver-Julesburg Basin
4.5
Produced Gas Composition from Non-CBM
np_oilgas
voc
DJVNT R
Gas Wells
np_oilgas
voc
FLR99
Natural Gas Flare Profile with DRE >98%
4.5
Oil and Gas -Piceance Basin Produced Gas
4.5
np_oilgas
voc
PNC01 R
Composition from Non-CBM Gas Wells
Oil and Gas -Piceance Basin Produced Gas
4.5
nP_oilgas
voc
PNC02 R
Composition from Oil Wells
Oil and Gas -Piceance Basin Flash Gas
4.5
np_oilgas
voc
PNC03 R
Composition for Condensate Tank
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
PNCDH
- Glycol Dehydrator, Piceance Basin
Oil and Gas -Powder River Basin Produced
4.5
np_oilgas
voc
PRBCB R
Gas Composition from CBM Wells
Oil and Gas -Powder River Basin Produced
4.5
np_oilgas
voc
PRBCO R
Gas Composition from Non-CBM Wells
Oil and Gas -Permian Basin Produced Gas
4.5
np_oilgas
voc
PRM01 R
Composition for Non-CBM Wells
Oil and Gas -South San Juan Basin
4.5
Produced Gas Composition from CBM
np_oilgas
voc
SSJCB R
Wells
Oil and Gas -South San Juan Basin
4.5
Produced Gas Composition from Non-CBM
np_oilgas
voc
SSJCO R
Gas Wells
Oil and Gas -SW Wyoming Basin Flash Gas
4.5
np_oilgas
voc
SWFLA R
Composition for Condensate Tanks
Oil and Gas -SW Wyoming Basin Produced
4.5
np_oilgas
voc
SWVNT R
Gas Composition from Non-CBM Wells
Oil and Gas -Uinta Basin Produced Gas
4.5
np_oilgas
voc
UNT01 R
Composition from CBM Wells
Oil and Gas -Wind River Basin Produced
4.5
np_oilgas
voc
WRBCO R
Gas Composition from Non-CBM Gas Wells
Chemical Manufacturing Industrywide
4.5
pt_oilgas
voc
95325
Composite
pt_oilgas
voc
95326
Pulp and Paper Industry Wide Composite
4.5
pt_oilgas,
4.5
ptnonipm
voc
95399
Composite Profile - Oil Field - Wells
pt_oilgas
voc
95403
Composite Profile - Gas Wells
4.5
Oil and Gas Production - Composite Profile
4.5
pt_oilgas
voc
95417
- Untreated Natural Gas, Uinta Basin
242
-------
Profile
SPECIATE
Comment
Sector
Pollutant
code
Profile description
version
Oil and Gas -Denver-Julesburg Basin
4.5
Produced Gas Composition from Non-CBM
pt_oilgas
voc
DJVNT R
Gas Wells
pt_oilgas,
4.5
ptnonipm
voc
FLR99
Natural Gas Flare Profile with DRE >98%
Oil and Gas -Piceance Basin Produced Gas
4.5
pt_oilgas
voc
PNC01 R
Composition from Non-CBM Gas Wells
Oil and Gas -Piceance Basin Produced Gas
4.5
pt_oilgas
voc
PNC02 R
Composition from Oil Wells
Oil and Gas Production - Composite Profile
4.5
pt_oilgas
voc
PNCDH
- Glycol Dehydrator, Piceance Basin
pt_oilgas,
Oil and Gas -Powder River Basin Produced
4.5
ptnonipm
voc
PRBCO R
Gas Composition from Non-CBM Wells
pt_oilgas,
Oil and Gas -Permian Basin Produced Gas
4.5
ptnonipm
voc
PRM01 R
Composition for Non-CBM Wells
Oil and Gas -South San Juan Basin
4.5
pt_oilgas,
Produced Gas Composition from Non-CBM
ptnonipm
voc
SSJCO R
Gas Wells
pt_oilgas,
Oil and Gas -SW Wyoming Basin Produced
4.5
ptnonipm
voc
SWVNT R
Gas Composition from Non-CBM Wells
Composite Profile - Prescribed fire
4.5
ptfire
voc
95421
southeast conifer forest
Composite Profile - Prescribed fire
4.5
ptfire
voc
95422
southwest conifer forest
Composite Profile - Prescribed fire
4.5
ptfire
voc
95423
northwest conifer forest
Composite Profile - Wildfire northwest
4.5
ptfire
voc
95424
conifer forest
ptfire
voc
95425
Composite Profile - Wildfire boreal forest
4.5
Chemical Manufacturing Industrywide
4.5
ptnonipm
voc
95325
Composite
ptnonipm
voc
95326
Pulp and Paper Industry Wide Composite
4.5
onroad
PM2.5
95462
Composite - Brake Wear
4.5
Used in SMOKE-MOVES
onroad
PM2.5
95460
Composite - Tire Dust
4.5
Used in SMOKE-MOVES
243
-------
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
The table below provides a crosswalk between fuel distribution SCCs and classification type for portable
fuel containers (PFC), fuel distribution operations associated with the bulk-plant-to-pump (BTP), refinery
to bulk terminal (RBT) and bulk plant storage (BPS).
see
Typ
e
Description
40301001
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Breathing Loss (67000 Bbl. Tank Size)
40301002
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 10: Breathing Loss (67000 Bbl. Tank Size)
40301003
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 7: Breathing Loss (67000 Bbl. Tank Size)
40301004
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Breathing Loss (250000 Bbl. Tank Size)
40301006
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 7: Breathing Loss (250000 Bbl. Tank Size)
40301007
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Working Loss (Tank Diameter Independent)
40301101
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 13: Standing Loss (67000 Bbl. Tank Size)
40301102
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 10: Standing Loss (67000 Bbl. Tank Size)
40301103
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 7: Standing Loss (67000 Bbl. Tank Size)
40301105
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 10: Standing Loss (250000 Bbl. Tank Size)
40301151
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline: Standing Loss - Internal
40301202
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor
Space; Gasoline RVP 10: Filling Loss
40301203
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor
Space; Gasoline RVP 7: Filling Loss
40400101
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400102
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400103
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400104
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank
40400105
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank
40400106
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Breathing Loss (250000 Bbl Capacity) - Fixed Roof Tank
40400107
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Working Loss (Diam. Independent) - Fixed Roof Tank
40400108
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Working Loss (Diameter Independent) - Fixed Roof Tank
40400109
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Working Loss (Diameter Independent) - Fixed Roof Tank
40400110
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
244
-------
see
Typ
e
Description
40400111
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
40400112
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss (67000 Bbl Capacity)- Floating Roof Tank
40400113
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400114
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400115
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400116
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float Rf Tnk
40400117
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (250000 Bbl Cap.) - Float Rf Tnk
40400118
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400119
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400120
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400130
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
40400131
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400132
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400133
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
40400140
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Ext. Float Roof Tank w/ Secondy Seal
40400141
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400142
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400143
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400148
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
40400149
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
40400150
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Miscellaneous Losses/Leaks: Loading Racks
40400151
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Valves, Flanges, and Pumps
40400152
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Collection Losses
40400153
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Control Unit Losses
40400160
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
40400161
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
40400162
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
245
-------
see
Typ
e
Description
40400163
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
40400170
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400171
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400172
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400173
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400178
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal)
40400179
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)
40400199
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
40400201
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400202
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400203
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400204
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400205
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400206
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400207
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank
40400208
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank
40400210
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float Rf Tnk
40400211
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400212
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400213
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
246
-------
see
Typ
e
Description
40400230
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
40400231
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400232
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400233
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
40400240
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400241
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400248
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10/13/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
40400249
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
40400250
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Loading Racks
40400251
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Valves, Flanges, and Pumps
40400252
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Collection Losses
40400253
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Control Unit Losses
40400260
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
40400261
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
40400262
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
40400263
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
40400270
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400271
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400272
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
247
-------
see
Typ
e
Description
40400273
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400278
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10/13/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal)
40400279
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)
40400401
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 13: Breathing Loss
40400402
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 13: Working Loss
40400403
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 10: Breathing Loss
40400404
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 10: Working Loss
40400405
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 7: Breathing Loss
40400406
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 7: Working Loss
40600101
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading
40600126
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading
40600131
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Normal Service)
40600136
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading (Normal Service)
40600141
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Balanced Service)
40600144
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading (Balanced Service)
40600147
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Clean Tanks)
40600162
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Loaded with Fuel (Transit Losses)
40600163
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Return with Vapor (Transit Losses)
248
-------
see
Typ
e
Description
40600199
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Not Classified
40600231
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Cleaned and Vapor Free Tanks
40600232
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers
40600233
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Cleaned and Vapor Free Tanks
40600234
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Ballasted Tank
40600235
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Ocean Barges Loading - Ballasted Tank
40600236
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Uncleaned Tanks
40600237
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Ocean Barges Loading - Uncleaned Tanks
40600238
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Uncleaned Tanks
40600239
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tankers: Ballasted Tank
40600240
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Average Tank Condition
40600241
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tanker Ballasting
40600299
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Not Classified
40600301
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Splash Filling
40600302
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Submerged Filling w/o Controls
40600305
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Unloading
40600306
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Balanced Submerged Filling
40600307
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Underground Tank Breathing and Emptying
40600399
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Not Classified **
40600401
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling
Vehicle Gas Tanks - Stage II; Vapor Loss w/o Controls
40600501
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Leaks
249
-------
see
Typ
e
Description
40600502
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Venting
40600503
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station
40600504
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station Leaks
40600602
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage II; Liquid Spill Loss w/o Controls
40600701
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Splash Filling
40600702
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Submerged Filling w/o Controls
40600706
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Balanced Submerged Filling
40600707
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Underground Tank Breathing and Emptying
40688801
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Fugitive
Emissions; Specify in Comments Field
2501050120
RBT
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Terminals: All Evaporative
Losses; Gasoline
2501055120
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Plants: All Evaporative
Losses; Gasoline
2501060050
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Total
2501060051
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Submerged Filling
2501060052
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Splash Filling
2501060053
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Balanced Submerged Filling
2501060200
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Total
2501060201
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Breathing and Emptying
2501995000
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; All Storage Types: Working
Loss; Total: All Products
2505000120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; All Transport Types; Gasoline
2505020120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline
250
-------
see
Typ
e
Description
2505020121
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline -
Barge
2505030120
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Transport; Truck; Gasoline
2505040120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Pipeline; Gasoline
2660000000
BTP
/BPS
Waste Disposal, Treatment, and Recovery; Leaking Underground Storage Tanks; Leaking
Underground Storage Tanks; Total: All Storage Types
251
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-22-001
Environmental Protection Air Quality Assessment Division February 2022
Agency Research Triangle Park, NC
252
------- |