Technical Support Document (TSD)
Preparation of Emissions Inventories for the 2016vl North American
Emissions Modeling Platform
March 2021
Contacts:
Alison Eyth, Jeff Vukovich, Caroline Farkas
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Emissions Inventory and Analysis Group
Research Triangle Park, North Carolina
-------
TABLE OF CONTENTS
LIST OF TABLES IV
LIST OF FIGURES VII
LIST OF APPENDICES VIII
ACRONYMS IX
1 INTRODUCTION 12
2 EMISSIONS INVENTORIES AND APPROACHES 15
2.1 2016 POINT SOURCES (PTEGU, PT_OILGAS, PTNONIPM, AIRPORTS) 19
2.1.1 EGUsector (ptegu) 21
2.1.2 Point source oil and gas sector (pt oilgas) 22
2.1.3 Non-IPM sector (ptnonipm) 25
2.1.4 A ircraft and ground support equipment (airports) 28
2.2 2016 NONPOINT SOURCES (AFDUST, AG, NP_OILGAS, RWC, NONPT) 29
2.2.1 Area fugitive dust sector (afdust) 29
2.2.2 Agriculture Sector (ag) 36
2.2.2.1 Livestock Waste Emissions 37
2.2.2.2 Fertilizer Emissions 38
2.2.3 Nonpoint Oil and Gas Sector (np oilgas) 41
2.2.4 Residential Wood Combustion (rwc) 43
2.2.5 Nonpoint (nonpt) 44
2.3 2016 Onroad Mobile sources (onroad) 48
2.4 2016 Nonroad Mobile sources (cmv, rail, nonroad) 61
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 61
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3) 64
2.4.3 Rail Sources (rail) 68
2.4.4 Nonroad Mobile Equipment Sources (nonroad) 77
2.5 2016 Fires (ptfire, ptagfire) 83
2.5.1 Wild and Prescribed Fires (ptfire) 83
2.5.2 Point source Agriculture Fires (ptagfire) 90
2.6 2016 Biogenic Sources (beis) 93
2.7 Sources Outside of the United States 95
2.7.1 Point Sources in Canada and Mexico (othpt) 95
2.7.2 Fugitive Dust Sources in Canada (othafdust, othptdust) 95
2.7.3 Nonpoint and Nonroad Sources in Canada and Mexico (othar) 96
2.7.4 Onroad Sources in Canada and Mexico (onroad can, onroadjnex) 96
2.7.5 Fires in Canada and Mexico (ptfire othna) 96
2.7.6 Ocean Chlorine 96
3 EMISSIONS MODELING 97
3.1 Emissions modeling Overview 97
3.2 Chemical Speciation 101
3.2.1 VOC speciation 104
3.2.1.1 County specific profile combinations 107
3.2.1.2 Additional sector specific considerations for integrating HAP emissions from inventories into speciation 108
3.2.1.3 Oil and gas related speciation profiles Ill
3.2.1.4 Mobile source related VOC speciation profiles 112
3.2.2 PM speciation 117
3.2.2.1 Mobile source related PM2.5 speciation profiles 118
3.2.3 NO x speciation 120
3.2.4 Creation of Sulfuric Acid Vapor (SULF) 120
3.3 Temporal Allocation 122
3.3.1 Use ofFFlO format for finer than annual emissions 123
3.3.2 Electric Generating Utility temporal allocation (ptegu) 124
3.3.2.1 Base year temporal allocation of EGUs 124
ii
-------
3.3.2.2 Future year temporal allocation of EGUs 128
3.3.3 Airport Temporal allocation (airports) 134
3.3.4 Residential Wood Combustion Temporal allocation (rwc) 136
3.3.5 Agricultural Ammonia Temporal Profiles (ag) 140
3.3.6 Oil and gas temporal allocation (npoilgas) 141
3.3.7 Onroad mobile temporal allocation (onroad) 141
3.3.8 Nonroad mobile temporal allocation(nonroad) 146
3.3.9 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire) 147
3.4 Spatial Allocation 149
3.4.1 Spatial Surrogates for U.S. emissions 149
3.4.2 Allocation method for airport-related sources in the U.S. 155
3.4.3 Surrogates for Canada and Mexico emission inventories 155
3.5 Preparation of Emissions for the CAMx model 159
3.5.1 Development of CAMx Emissions for Standard CAMx Runs 159
3.5.2 Development of CAMx Emissions for Source Apportionment CAMx Runs 161
4 DEVELOPMENT OF FUTURE YEAR EMISSIONS 165
4.1 EGU Point Source Projections (ptegu) 169
4.2 Non-EGU Point and Nonpoint Sector Projections 172
4.2.1 Background on the Control Strategy Tool (CoST) 173
4.2.2 CoST Plant CLOSURE Packet (ptnonipm, ptoilgas) 177
4.2.3 CoST PROJECTION Packets (afdust, ag, cmv, rail, nonpt, np oilgas, ptnonipm, pt oilgas, rwc) 177
4.2.3.1 Fugitive dust growth (afdust) 178
4.2.3.2 Livestock population growth (ag) 179
4.2.3.3 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 179
4.2.3.4 Category 3 Commercial Marine Vessels (cmv_c3) 180
4.2.3.5 Oil and Gas Sources (pt oilgas, np oilgas) 182
4.2.3.6 Non-EGU point sources (ptnonipm) 184
4.2.3.7 Nonpoint Sources (nonpt) 186
4.2.3.8 Airport sources (airports) 187
4.2.4 CoST CONTROL Packets (nonpt, np oilgas, ptnonipm, pt oilgas) 187
4.2.4.1 Residential Wood Combustion (rwc) 189
4.2.4.2 Oil and Gas NSPS (np oilgas, pt oilgas) 191
4.2.4.3 LUCE NSPS (nonpt, ptnonipm, np oilgas, pt oilgas) 194
4.2.4.4 Fuel Sulfur Rules (nonpt, ptnonipm) 197
4.2.4.5 Natural Gas Turbines NOx NSPS (ptnonipm, pt oilgas) 198
4.2.4.6 Process Heaters NOx NSPS (ptnonipm, pt oilgas) 200
4.2.4.7 CLSWL (ptnonipm) 203
4.2.4.8 Petroleum Refineries NSPS Subpart JA (ptnonipm) 204
4.2.4.9 Ozone Transport Commission Rules (nonpt) 204
4.2.4.10 State-Specific Controls (ptnonipm) 205
4.3 Projections Computed Outside of CoST 206
4.3.1 Nonroad Mobile Equipment Sources (nonroad) 206
4.3.2 Onroad Mobile Sources (onroad) 207
4.3.3 Locomotives (rail) 209
4.3.1 Sources A dded in the 2021 fi Case 210
4.3.2 Sources Outside of the United States (onroadcan, onroadmex, othpt, ptfire othna, othar, othafdust, othptdust)
211
4.3.2.1 Canadian fugitive dust sources (othafdust, othptdust) 211
4.3.2.2 Point Sources in Canada and Mexico (othpt) 212
4.3.2.3 Nonpoint sources in Canada and Mexico (othar) 213
4.3.2.1 Onroad sources in Canada and Mexico (onroad can, onroad mex) 214
5 EMISSION SUMMARIES 215
6 REFERENCES 226
in
-------
List of Tables
Table 2-1. Platform sectors for the 2016 emissions modeling case 16
Table 2-2. Point source oil and gas sector NAICS Codes 22
Table 2-3. 2014NEIv2-to-2016 projection factors for pt_oilgas sector for 2016vl inventory 23
Table 2-4. 2016fh pt oilgas national emissions (excluding offshore) before and after 2014-to-2016
projections (tons/year) 24
Table 2-5. Pennsylvania emissions changes for natural gas transmission sources (tons/year) 24
Table 2-6. SCCs for Census-based growth from 2014 to 2016 25
Table 2-7. 2016vl platform SCCs for the airports sector 28
Table 2-8. Afdust sector SCCs 29
Table 2-9. Total impact of fugitive dust adjustments to unadjusted 2016 vl inventory 33
Table 2-10. 2016vl platform SCCs for the ag sector 36
Table 2-11. National back-projection factors for livestock: 2017 to 2016 37
Table 2-12. Source of input variables for EPIC 40
Table 2-13. 2014NEIv2-to-2016 oil and gas projection factors for CO and OK 42
Table 2-14. 2016 vl platform SCCs for RWC sector 43
Table 2-15. Projection factors for RWC by SCC 44
Table 2-16. 2016vl platform SCCs for Census-based growth 46
Table 2-17. MOVES vehicle (source) types 48
Table 2-18. Submitted data used to prepare onroad activity data 49
Table 2-19. Factors applied to project VMT from 2014 to 2016 to prepare default activity data 50
Table 2-20. Older Vehicle Adjustments Showing the Fraction of IHS Vehicle Populations to Retain for
2016\ 1 and 2017 Mil 58
Table 2-21. 2016vl platform SCCs for cmv_clc2 sector 61
Table 2-22. Vessel groups in the cmv_clc2 sector 63
Table 2-23. 2016vl platform SCCs for cmv_c3 sector 65
Table 2-24. 2017 to 2016 projection factors for C3 CMV 68
Table 2-25. 2016vl SCCs for the Rail Sector 69
Table 2-26. Class I Railroad Reported Locomotive Fuel Use Statistics for 2016 69
Table 2-27. 2016 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal) 71
Table 2-28. Surface Transportation Board R-l Fuel Use Data - 2016 72
Table 2-29. 2016 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)4 72
Table 2-30. Expenditures and fuel use for commuter rail 75
Table 2-31. Submitted nonroad input tables by agency 81
Table 2-32. Alaska counties/census areas for which nonroad equipment sector-specific emissions are
removed in 2016vl 82
Table 2-33. SCCs included in the ptfire sector for the 2016vl inventory 83
Table 2-34. National fire information databases used in 2016vl ptfire inventory 84
Table 2-35. List of S/L/T agencies that submitted fire data for 2016vl with types and formats 86
Table 2-36. Brief description of fire information submitted for 2016vl inventory use 86
Table 2-37. SCCs included in the ptagfire sector for the 2016vl inventory 90
Table 2-38. Assumed field size of agricultural fires per state(acres) 92
Table 2-39. Hourly Meteorological variables required by BEIS 3.61 94
Table 3-1. Key emissions modeling steps by sector 98
Table 3-2. Descriptions of the platform grids 100
Table 3-3. Emission model species produced for CB6 for CMAQ 102
Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM)
for each platform sector 106
Table 3-5. Ethanol percentages by volume by Canadian province 108
iv
-------
Table 3-6. MOVES integrated species in M-profiles 109
Table 3-7. Basin/Region-specific profiles for oil and gas Ill
Table 3-8. TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a used for the 2016
Platform 112
Table 3-9. Select mobile-related VOC profiles 2016 113
Table 3-10. Onroad M-profiles 114
Table 3-11. MOVES process IDs 115
Table 3-12. MOVES Fuel subtype IDs 116
Table 3-13. MOVES regclass IDs 116
Table 3-14. SPECIATE4.5 brake and tire profiles compared to those used in the 201 lv6.3 Platform 119
Table 3-15. Nonroad PM2.5 profiles 120
Table 3-16. NOx speciation profiles 120
Table 3-17. Sulfate split factor computation 121
Table 3-18. SO2 speciation profiles 121
Table 3-19. Temporal settings used for the platform sectors in SMOKE 122
Table 3-20. U.S. Surrogates available for the 2016vl modeling platforms 150
Table 3-21. Off-Network Mobile Source Surrogates 152
Table 3-22. Spatial Surrogates for Oil and Gas Sources 152
Table 3-23. Selected 2016 CAP emissions by sector for U.S. Surrogates (short tons in 12US1) 153
Table 3-24. Canadian Spatial Surrogates 156
Table 3-25. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3) 157
Table 3-26. Emission model species mappings for CMAQ and CAMx 160
Table 3-27. State tags for 2023llil. 2028fhl USSA modeling 162
Table 4-1. Overview of projection methods for the 2023 and 2028 regional cases 165
Table 4-2. EGU sector NOx emissions by State for the 2023 and 2028 regional cases 171
Table 4-3. Subset of CoST Packet Matching Hierarchy 174
Table 4-4. Summary of non-EGU stationary projections subsections 175
Table 4-5. Reductions from all facility/unit/stack-level closures in 2016vl 177
Table 4-6. Increase in total afdust PM2.5 emissions from projections in 2016vl 178
Table 4-7. National projection factors for livestock: 2016 to 2023 and 2028 179
Table 4-8. National projection factors for cmv_clc2 180
Table 4-9. California projection factors for cmv_clc2 180
Table 4-10. 2016-to-2023 and 2016-2028 CMV C3 projection factors outside of California 181
Table 4-11. 2016-to-2023 and 2016-2028 CMV C3 projection factors for California 181
Table 4-12. Year 2014-2017 high-level summary of national oil and gas exploration activity 184
Table 4-13. EIA's 2019 Annual Energy Outlook (AEO) tables used to project industrial sources 185
Table 4-14. Assumed retirement rates and new source emission factor ratios for NSPS rules 188
Table 4-15. Projection factors for RWC 190
Table 4-16. Non-point (npoilgas) SCCs in 2016vl modeling platform where Oil and Gas NSPS controls
applied 191
Table 4-17. Emissions reductions for np oilgas sector due to application of Oil and Gas NSPS 193
Table 4-18. Point source SCCs in ptoilgas sector where Oil and Gas NSPS controls were applied 193
Table 4-19. VOC reductions (tons/year) for the pt oilgas sector after application of the Oil and Gas NSPS
CONTROL packet for both future years 2023 and 2028 194
Table 4-20. SCCs and Engine Type in 2016vl modeling platform where RICE NSPS controls applied for
nonpt and ptnonipm sectors 194
Table 4-21. Non-point Oil and Gas SCCs in 2016vl modeling platform where RICE NSPS controls applied
195
Table 4-22. Nonpoint Emissions reductions after the application of the RICE NSPS 196
Table 4-23. Ptnonipm Emissions reductions after the application of the RICE NSPS 196
v
-------
Table 4-24. Oil and Gas Emissions reductions for np oilgas sector due to application of RICE NSPS 196
Table 4-25. Point source SCCs in ptoilgas sector where RICE NSPS controls applied 196
Table 4-26. Emissions reductions (tons/year) in pt oilgas sector after the application of the RICE NSPS
CONTROL packet for future years 2023 and 2028 197
Table 4-27. Summary of fuel sulfur rule impacts on nonpoint S02 emissions for 2023 and 2028 197
Table 4-28. Summary of fuel sulfur rule impacts on ptnonipm S02 emissions for 2023 and 2028 198
Table 4-29. Stationary gas turbines NSPS analysis and resulting emission rates used to compute controls. 198
Table 4-30. Ptnonipm SCCs in 2016vl modeling platform where Natural Gas Turbines NSPS controls
applied 199
Table 4-31. Ptnonipm emissions reductions after the application of the Natural Gas Turbines NSPS 199
Table 4-32. Point source SCCs in pt oilgas sector where Natural Gas Turbines NSPS control applied 200
Table 4-33. Emissions reductions (tons/year) for pt oilgas after the application of the Natural Gas Turbines
NSPS CONTROL packet for future years 2023 and 2028 200
Table 4-34. Process Heaters NSPS analysis and 2016vl new emission rates used to estimate controls 201
Table 4-35. Ptnonipm SCCs in 2016vl modeling platform where Process Heaters NSPS controls applied. 201
Table 4-36. Ptnonipm emissions reductions after the application of the Process Heaters NSPS 202
Table 4-37. Point source SCCs in pt oilgas sector where Process Heaters NSPS controls were applied 202
Table 4-38. NOx emissions reductions (tons/year) in ptoilgas sector after the application of the Process
Heaters NSPS CONTROL packet for futures years 2023 and 2028 203
Table 4-39. Summary of CISWI rule impacts on ptnonipm emissions for 2023 and 2028 203
Table 4-40. Summary of NSPS Subpart JA rule impacts on ptnonipm emissions for 2023 and 2028 204
Table 4-41. Factors used to Project 2016 VMT to 2023 and 2028 208
Table 4-42. Class I Line-haul Fuel Projections based on 2018 AEO Data 209
Table 4-43. Class I Line-haul Historic and Future Year Projected Emissions 210
Table 4-44. AEO growth rates for rail sub-groups 210
Table 4-45. Sources Added in the 2021fi Case 211
Table 5-1. National by-sector CAP emissions summaries for the 2016fh case, 12US1 grid (tons/yr) 216
Table 5-2. National by-sector CAP emissions summaries for the 2023fhl case, 12US1 grid (tons/yr) 217
Table 5-3. National by-sector CAP emissions summaries for the 2028fhl case, 12US1 grid (tons/yr) 218
Table 5-4. National by-sector CAP emissions summaries for the 2016fh case, 36US3 grid (tons/yr) 219
Table 5-5. National by-sector CAP emissions summaries for the 2023fhl case, 36US3 grid (tons/yr) 220
Table 5-6. National by-sector CAP emissions summaries for the 2028fhl case, 36US3 grid (tons/yr) 221
Table 5-7. National by-sector CAP emissions summaries for the 2016fi case, 12US1 grid (tons/yr) 222
Table 5-8. National by-sector CAP emissions summaries for the 2021fi case, 12US1 grid (tons/yr) 223
Table 5-9. National by-sector Ozone Season NOx emissions summaries 12US1 grid (tons/o.s.) 224
Table 5-10. National by-sector Ozone Season VOC emissions summaries 12US1 grid (tons/o.s.) 225
vi
-------
List of Figures
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and
cumulative 35
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions 39
Figure 2-3. Representative Counties in 2016vl 60
Figure 2-4. 2017NEI/2016 platform geographical extent (solid) and U.S. ECA (dashed) 62
Figure 2-5. 2016 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT) 70
Figure 2-6. Class I Railroads in the United States5 70
Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States 73
Figure 2-8. Class II and III Railroads in the United States5 74
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains 76
Figure 2-10. Processing flow for fire emission estimates in the 2016vl inventory 88
Figure 2-11. Default fire type assignment by state and month in cases where a satellite detect is only source
of fire information 89
Figure 2-12. Blue Sky Modeling Framework 89
Figure 2-13. Normbeis3 data flows 94
Figure 2-14. Tmpbeis3 data flow diagram 95
Figure 3-1. Air quality modeling domains 100
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation 106
Figure 3-3. Profiles composited for the new PM gas combustion related sources 117
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources 118
Figure 3-5. Eliminating unmeasured spikes in CEMS data 124
Figure 3-6. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification 126
Figure 3-7. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type 127
Figure 3-8. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type 127
Figure 3-9. Non-CEMS EGU Temporal Profile Application Counts 128
Figure 3-10. Future Year Emissions Follow the Pattern of Base Year Emissions 131
Figure 3-11. Excess Emissions Apportioned to Hours Less than the Historic Maximum 132
Figure 3-12. Regional Profile Applied due to not being able to Adjust below Historic Maximum 133
Figure 3-13. Regional Profile Applied, but Exceeds Historic Maximum in Some Hours 133
Figure 3-14. Diurnal Profile for all Airport SCCs 134
Figure 3-15. Weekly profile for all Airport SCCs 135
Figure 3-16. Monthly Profile for all Airport SCCs 135
Figure 3-17. Alaska Seaplane Profile 136
Figure 3-18. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold 137
Figure 3-19. RWC diurnal temporal profile 138
Figure 3-20. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr) 139
Figure 3-21. Day-of-week temporal profiles for OHH and Recreational RWC 139
Figure 3-22. Annual-to-month temporal profiles for OHH and recreational RWC 140
Figure 3-23. Example of animal NH3 emissions temporal allocation approach, summed to daily emissions
141
Figure 3-24. Example of temporal variability of NOx emissions 142
Figure 3-25. Sample onroad diurnal profiles for Fulton County, GA 143
Figure 3-26. Methods to Populate Onroad Speeds and Temporal Profiles by Road Type 144
Figure 3-27. Regions for computing Region Average Speeds and Temporal Profiles 144
Figure 3-28. Example of Temporal Profiles for Combination Trucks 145
Figure 3-29. Example Nonroad Day-of-week Temporal Profiles 146
Figure 3-30. Example Nonroad Diurnal Temporal Profiles 147
Figure 3-31. Agricultural burning diurnal temporal profile 148
vii
-------
Figure 3-32. Prescribed and Wildfire diurnal temporal profiles 149
Figure 4-1. EIA Oil and Gas Supply Regions as of AEO2019 183
List of Appendices
Appendix A: CB6 Assignment for New Species
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE4.5 that were used in the
2014 v7.2 Platform
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
viii
-------
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
FIPS
Federal Information Processing Standards
IX
-------
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
OTAQ
EPA's Office of Transportation and Air Quality
ORIS
Office of Regulatory Information System
OKI)
EPA's Office of Research and Development
OSAT
Ozone Source Apportionment Technology
PFC
Portable Fuel Container
PM2.5
Particulate matter less than or equal to 2.5 microns
PM10
Particulate matter less than or equal to 10 microns
PPm
arts per million
ppmv
Parts per million by volume
PSAT
Particulate Matter Source Apportionment Technology
RACT
Reasonably Available Control Technology
RBT
Refinery to Bulk Terminal
RIA
Regulatory Impact Analysis
RICE
Reciprocating Internal Combustion Engine
X
-------
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
XI
-------
1 Introduction
The U.S. Environmental Protection Agency (EPA), working in conjunction with the National Emissions
Inventory Collaborative, developed an air quality modeling platform for criteria air pollutants to represent
the years of 2016, 2023 and 2028. The starting point for the 2016 inventory was the 2014 National
Emissions Inventory (NEI), version 2 (2014NEIv2), although many inventory sectors were updated to
represent the year 2016 through the incorporation of 2016-specific state and local data along with
nationally-applied adjustment methods. The year 2023 and year 2028 inventories were developed starting
with the 2016 inventory using sector-specific methods as described below. The inventories support
several applications, including modeling in support of the Revised Cross State Air Pollution Rule
(CSAPR) Update for the 2008 Ozone National Ambient Air Quality Standards (NAAQS).
The air quality modeling platform consists of all the emissions inventories and ancillary data files used for
emissions modeling, as well as the meteorological, initial condition, and boundary condition files needed
to run the air quality model. This document focuses on the emissions modeling data and techniques
including the emission inventories, the ancillary data files, and the approaches used to transform
inventories for use in air quality modeling.
The National Emissions Inventory Collaborative is a partnership between state emissions inventory staff,
multi-jurisdictional organizations (MJOs), federal land managers (FLMs), EPA, and others to develop a
North American air pollution emissions modeling platform with a base year of 2016 for use in air quality
planning. The Collaborative planned for three versions of the 2016 platform: alpha, beta, and Version 1.0.
This numbering format for this platform is different from previous EPA platforms which had the first
number based on the version of the NEI, and the second number as a platform iteration for that NEI year
(e.g., 7.3 where 7 represents 2014 NEI-based platforms, and 3 means the third iteration of the platform).
For the emissions modeling documented in this technical support document (TSD), the emissions values
for most sectors are the same as those in the Inventory Collaborative 2016vl Emissions Modeling
Platform, available from http://views.cira.colostate.edu/wiki/wiki/10202. In the file packages for this
platform, the platform may sometimes be known as the 2016v7.3 platform. The specification sheets
posted on the 2016vl platform release page on the Wiki provide many details regarding the inventories
and emissions modeling techniques in addition to those addressed in this TSD.
Some updates were made to the 2016vl platform after the fall 2019 release that were included in the
Revised CSAPR Update modeling, including some minor revisions to commercial marine vessel (CMV)
emissions, and electric generating unit (EGU) emissions developed in January 2020. Updates to 2016vl
to correct airport emissions and 2016 EGU processing made in June and July of 2020 were not included
in the CSAPR Update modeling because the modeling was already complete by that time. The updated
data and a description of them are available on the EPA FTP site
ftp://newftp.epa.gov/air/emismod/2016/vl/postvl updates/. If you cannot access the FTP site through the
provided link, this link points to the same data:
https://gaftp.epa.gov/Air/emismod/2016/vl/postvl updates.
This 2016 emissions modeling platform includes all criteria air pollutants (CAPs) and precursors, and a
group of hazardous air pollutants (HAPs). The group of HAPs are those explicitly used by the chemical
mechanism in the Community Multiscale Air Quality (CMAQ) model (Appel et al., 2018) for
ozone/particulate matter (PM): chlorine (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 the Comprehensive Air
12
-------
Quality Model with Extensions (CAMx). However, the emissions modeling process first prepares outputs
in the format used by CMAQ, after which those emissions data are converted to the formats needed by
CAMx.
The 2016 platform used in this study consists of a 2016 base case, a 2023 case, and a 2028 case with the
abbreviations 2016fh_16j, 2023fhl_16j, and 2028fhl_16j, respectively. Additional cases that included
source apportionment by state and in some cases inventory sector were also developed. This platform
accounts for atmospheric chemistry and transport within a state-of-the-art photochemical grid model. In
the case abbreviation 2016fh_16j, 2016 is the year represented by the emissions; the "f' represents the
base year emissions modeling platform iteration, which here shows that it is 2014 NEI-based (whereas for
2011 NEI-based platforms, this letter was "e"); and the "h" stands for the eighth configuration of
emissions modeled for a 2014-NEI based modeling platform. The cases named 2023fhl_16j and
2028fhl_16j are the same as the original 2023fh and 2028fh future year cases, except that they include
EGU emissions that were developed in January 2020 and slightly updated commercial marine vessel
emissions. The case 2016fi was developed after some issues were identified with the 2016fh airport
emissions inventory and with the processing of EGU emissions at specific units when multiple units in the
NEI are mapped to multiple Continuous Emissions Modeling System (CEMS) units. The case 2021fi was
developed to provide a representation of emissions in 2021.
The 2016vl emissions modeling platform includes point sources, nonpoint sources, commercial marine
vessels (CMV), onroad and nonroad mobile sources, and fires for the U.S., Canada, and Mexico. Some
platform categories use more disaggregated data than are made available in the NEI. For example, in the
platform, onroad mobile source emissions are represented as hourly emissions by vehicle type, fuel type
process and road type while the NEI emissions are aggregated to vehicle type/fuel type totals and annual
temporal resolution. Temporal, spatial and other changes in emissions between the NEI and the emissions
input into the platform are described primarily in the platform specification sheets, although a full NEI
was not developed for the year 2016 because only point sources above a certain potential to emit must be
submitted for years between the full triennial NEI years (e.g., 2014, 2017, 2020). Emissions from Canada
and Mexico are used for the modeling platform but are not part of the NEI.
The primary emissions modeling tool used to create the air quality model-ready emissions was the Sparse
Matrix Operator Kernel Emissions (SMOKE) modeling system (http://www.smoke-model.org/), version
4.7 (SMOKE 4.7) with some updates. Emissions files were created for a 36-km national grid and for a
12-km national grid, both of which include the contiguous states and parts of Canada and Mexico as
shown in Figure 3-1.
The gridded meteorological model used to provide input data for the emissions modeling was developed
using the Weather Research and Forecasting Model (WRF,
https://ral.ucar.edu/solutions/products/weather-research-and-forecasting-model-wrf) version 3.8,
Advanced Research WRF core (Skamarock, et al., 2008). The WRF Model is a mesoscale numerical
weather prediction system developed for both operational forecasting and atmospheric research
applications. The WRF was run for 2016 over a domain covering the continental U.S. at a 12km
resolution with 35 vertical layers. The run for this platform included high resolution sea surface
temperature data from the Group for High Resolution Sea Surface Temperature (GHRSST) (see
https://www.ghrsst.org/) and is given the EPA meteorological case label "16j." The full case name
includes this abbreviation following the emissions portion of the case name to fully specify the name of
the case as "2016fh_16j."
13
-------
This document contains five sections and several appendices. Section 2 describes the 2016 inventories
input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used with the
emission inventories. Methods to develop future year emissions are described in Section 4. Data
summaries are provided in Section 5. Section 6 provides references. The Appendices provide additional
details about specific technical methods or data.
14
-------
2 Emissions Inventories and Approaches
This section summarizes the emissions data that make up the 2016vl platform. This section provides
details about the data contained in each of the platform sectors for the base year and the future year.
The original starting point for the emission inventories was the 2014NEIv2 although emissions for most
sectors have been updated to better represent the year 2016. Documentation for the 2014NEIv2, including
a TSD, is available at https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-
nei-technical-support-document-tsd. Documentation for each 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). In addition to the NEI-based data for the broad
categories of point, nonpoint, onroad, nonroad, and events (i.e., fires), emissions from the Canadian and
Mexican inventories and several other non-NEI data sources are included in the 2016 platform.
The triennial NEI data for CAPs are largely compiled from data submitted by state, local and tribal
(S/L/T) air agencies. HAP emissions data are also from the S/L/T agencies, but, are often augmented by
the EPA because they are voluntarily submitted. The EPA uses the Emissions Inventory System (EIS) to
compile the NEI. The EIS includes hundreds of automated quality assurance checks to help improve data
quality, and also supports tracking release point (e.g., stack) coordinates separately from facility
coordinates. The EPA collaborates extensively with S/L/T agencies to ensure a high quality of data in the
NEI. Using the 2014NEIv2 as a starting point, the National Inventory Collaborative worked to develop a
modeling platform that more closely represents the year 2016. All emissions modeling sectors were
modified in some way to better represent the year 2016 for the 2016vl platform.
The point source emission inventories for the platform include partially updated emissions to represent
2016 based on state-submitted data and adjustments to much of the remaining 2014 data to better
represent 2016. Agricultural and wildland fire emissions represent the year 2016. Most nonpoint source
sectors started with 2014NEIv2 emissions and were adjusted to better represent the year 2016. Fertilizer
emissions, nonpoint oil and gas emissions, and onroad and nonroad mobile source emissions represent the
year 2016. For CMV emissions, emissions were developed based on 2017 NEI CMV emissions and the
sulfur dioxide (SO2) emissions reflect rules that reduced sulfur emissions for CMV that took effect in the
year 2015. For fertilizer ammonia emissions, a 2016-specific emissions inventory is used in this platform.
Nonpoint oil and gas emissions were developed using 2016-specific data for oil and gas wells and their
2016 production levels.
Onroad and nonroad mobile source emissions were developed using the Motor Vehicle Emission
Simulator (MOVES). Onroad emissions for the platform were developed based on emissions factors
output from MOVES2014b for the year 2016, run with inputs derived from the 2014NEIv2 including
activity data (e.g., vehicle miles traveled and vehicle populations) provided by state and local agencies or
otherwise projected to the year 2016. MOVES2014b was also used to generate nonroad emissions
because it included important updates related to nonroad engine population growth rates and spatial
allocation factors.
For the purposes of preparing the air quality model-ready emissions, emissions from the five NEI data
categories are split into finer-grained sectors used for emissions modeling. The significance of an
emissions modeling or "platform sector" is that the data are run through the SMOKE programs
independently from the other sectors except for the final merge (Mrggrid). The final merge program
combines the sector-specific gridded, speciated, hourly emissions together to create CMAQ-ready
emission inputs. For studies that use CAMx, these CMAQ-ready emissions inputs are converted into the
file formats needed by CAMx.
15
-------
Table 2-1 presents an overview the sectors in the 2016 platform and how they generally relate to the
2014NEIv2 as their starting point. The platform sector abbreviations are provided in italics. These
abbreviations are used in the SMOKE modeling scripts, inventory file names, and throughout the
remainder of this document. Through the Collaborative workgroups, state and local agencies provided
data used in the development of most sectors.
Table 2-1. Platform sectors for the 2016 emissions modeling case
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
EGU units:
ptegu
Point
Point source electric generating units (EGUs) for 2016 from the
Emissions Inventory System (EIS), based on 2014NEIv2 with most
sources updated to 2016. Includes some specific S/L/T updates. The
inventory emissions are replaced with hourly 2016 Continuous
Emissions Monitoring System (CEMS) values for nitrogen oxides
(NOx) and SO2 for any units that are matched to the NEI, and other
pollutants for matched units are scaled from the 2016 point inventory
using CEMS heat input. Emissions for all sources not matched to
CEMS data come from the raw inventory. Annual resolution for
sources not matched to CEMS data, hourly for CEMS sources.
Point source oil and
gas:
ptoilgas
Point
Point sources for 2016 including S/L/T updates for oil and gas
production and related processes based on facilities with the following
NAICS: 2111,21111,211111,211112 (Oil and Gas Extraction);
213111 (Drilling Oil and Gas Wells); 213112 (Support Activities for
Oil and Gas Operations); 2212, 22121, 221210 (Natural Gas
Distribution); 48611, 486110 (Pipeline Transportation of Crude Oil);
4862, 48621, 486210 (Pipeline Transportation 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. Note that these
emissions were found to be overestimated in June 2020.
Remaining non-
EGU point:
ptnonipm
Point
All 2016 point source inventory records not matched to the ptegu,
airports, or pt_oilgas sectors, including updates submitted by state and
local agencies. Year 2016 rail yard emissions were developed by the
rail workgroup. Annual resolution.
Agricultural:
ag
Nonpoint
Nonpoint livestock and fertilizer application emissions. Livestock
includes ammonia and other pollutants (except PM2 5) and was
backcasted from a draft version of 2017NEI based on animal
population data from the U.S. Department of Agriculture (USDA)
National Agriculture Statistics Service Quick Stats, where available.
Fertilizer includes only ammonia and is estimated for 2016 using the
FEST-C model. County and monthly resolution.
Agricultural fires
with point
resolution: ptagfire
Nonpoint
2016 agricultural fire sources based on EPA-developed data with state
updates, represented as point source day-specific emissions. They are
in the nonpoint NEI data category, but in the platform, they are treated
as point sources. Mostly at daily resolution with some state-submitted
data at monthly resolution.
16
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Area fugitive dust:
afdust
Nonpoint
PMio and PM2 5 fugitive dust sources from the 2014NEIv2 nonpoint
inventory with paved road dust grown to 2016 levels; including
building construction, road construction, agricultural dust, and road
dust. The NEI emissions are reduced during modeling according to a
transport fraction (newly computed for the 2016 beta platform) and a
meteorology-based (precipitation and snow/ice cover) zero-out.
Afdust emissions from the portion of Southeast Alaska inside the
36US3 domain are processed in a separate sector called 'afdust_ak'.
County and annual resolution.
Biogenic:
beis
Nonpoint
Year 2016, hour-specific, grid cell-specific emissions generated from
the BEIS3.61 model within SMOKE, including emissions in Canada
and Mexico using BELD v4.1 "water fix" land use data (including
improved treatment of water grid cells).
Category 1, 2 CMV:
cmv_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. Includes C1C2 emissions in U.S. state
and Federal waters, and also all non-U.S. C1C2 emissions including
those in Canadian waters. Gridded and hourly resolution.
Category 3 CMV:
cmv_c3
Nonpoint
Category 3 (C3) CMV emissions converted to point sources based on
the center of the grid cells. Includes C3 emissions in U.S. state and
Federal waters, and also all non-U.S. C3 emissions including those in
Canadian waters. Emissions are backcast to 2016 from 2017NEI
emissions based on factors derived from U.S. Army Corps of
Engineers Entrance and Clearance data and information about the
ships entering the ports. Gridded and hourly resolution.
Locomotives :
rail
Nonpoint
Line haul rail locomotives emissions developed by the rail workgroup
based on 2016 activity and emission factors. Includes freight and
commuter rail emissions and incorporates state and local feedback.
County and annual resolution.
Remaining
nonpoint:
nonpt
Nonpoint
2014NEIv2 nonpoint sources not included in other platform sectors
with sources proportional to human population activity data grown to
year 2016; incorporates state and local feedback. County and annual
resolution.
Nonpoint source oil
and gas:
np oilgas
Nonpoint
2016 nonpoint oil and gas emissions output from the NEI oil and gas
tool along with state and local feedback. County and annual resolution.
Residential Wood
Combustion:
rwc
Nonpoint
2014NEIv2 nonpoint sources from residential wood combustion
(RWC) processes projected to the year 2016. County and annual
resolution.
Nonroad:
nonroad
Nonroad
2016 nonroad equipment emissions developed with the MOVES2014b
model which incorporates updated equipment growth rates. MOVES
was used for all states except California and Texas, which submitted
emissions. County and monthly resolution.
17
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Onroad:
onroad
Onroad
2016 onroad mobile source gasoline and diesel vehicles from moving
and non-moving vehicles that drive on roads, along with vehicle
refueling. Includes the following modes: exhaust, extended idle,
auxiliary power units, evaporative, permeation, refueling, and brake
and tire wear. For all states except California, developed using winter
and summer MOVES emissions tables produced by MOVES2014b
coupled with activity data projected to year 2016 or provided by S/L/T
agencies. SMOKE-MOVES was used to compute emissions from the
emission factors and activity data. Onroad emissions for Alaska,
Hawaii, Puerto Rico and the Virgin Islands were computed using the
same method as the continental U.S.,but are part of the
onroad nonconus sector.
Onroad California:
onroadcaadj
Onroad
2016 California-provided CAP onroad mobile source gasoline and
diesel vehicles based on the EMFAC model, which ere gridded and
temporalized using MOVES2014b results. Volatile organic compound
(VOC) HAP emissions derived from California-provided VOC
emissions and MOVES-based speciation.
Point source fires-
ptjire
Events
Point source day-specific wildfires and prescribed fires for 2016
computed using Satellite Mapping Automated Reanalysis Tool for Fire
Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky
Framework (Sullivan, 2008 and Raffuse, 2007) for both flaming and
smoldering processes (i.e., SCCs 281XXXX002). Smoldering is
forced into layer 1 (by adjusting heat flux). Incorporates state inputs.
Daily resolution.
Non-US. Fires:
ptfireothna
N/A
Point source day-specific wildfires and prescribed fires for 2016
provided by Environment Canada with data for missing months, and
for Mexico and Central America, filled in using fires from the Fire
Inventory (FINN) from National Center for Atmospheric Research
(NCAR) fires (NCAR, 2016 and Wiedinmyer, C., 2011). Daily
resolution.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
N/A
Fugitive dust sources of particulate matter emissions excluding land
tilling from agricultural activities, from Environment and Climate
Change Canada (ECCC) 2015 emission inventory, except that
construction dust emissions were reduced to levels compatible with
their 2010 inventory. A transport fraction adjustment is applied along
with a meteorology-based (precipitation and snow/ice cover) zero-out.
County and annual resolution.
Other Point Fugitive
dust sources not
from the NEI:
othptdust
N/A
Fugitive dust sources of particulate matter emissions from land tilling
from agricultural activities, ECCC 2015 emission inventory, but wind
erosion emissions were removed. A transport fraction adjustment is
applied along with a meteorology-based (precipitation and snow/ice
cover) zero-out. Data were originally provided on a rotated 10-km grid
for beta, but were smoothed so as to avoid the artifact of grid lines in
the processed emissions. Monthly resolution.
18
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Other point sources
not from the NEI:
othpt
N/A
Point sources from the ECCC 2015 emission inventory, including
agricultural ammonia, along with emissions from Mexico's 2008
inventory projected to 2014 and 2018 and then interpolated to 2016.
Agricultural data were originally provided on a rotated 10-km grid for
beta, but were smoothed so as to avoid the artifact of grid lines in the
processed emissions. Monthly resolution for Canada agricultural and
airport emissions, annual resolution for the remainder of Canada and
all of Mexico.
Other non-NEI
nonpoint and
nonroad:
othar
N/A
Year 2015 Canada (province or sub-province resolution) emissions
from the ECCC inventory: monthly for nonroad sources; annual for
rail and other nonpoint Canada sectors. Year 2016 Mexico (municipio
resolution) emissions, interpolated from 2014 and 2018 inventories
that were projected from their 2008 inventory: annual nonpoint and
nonroad mobile inventories.
Other non-NEI
onroad sources:
onroad can
N/A
Monthly year 2015 Canada (province resolution or sub-province
resolution, depending on the province) from the ECCC onroad mobile
inventory.
Other non-NEI
onroad sources:
onroad mex
N/A
Monthly year 2016 Mexico (municipio resolution) onroad mobile
inventory based on MOVES-Mexico runs for 2014 and 2018 then
interpolated to 2016.
Other natural emissions are also merged in with the above sectors: ocean chlorine and sea salt. The ocean
chlorine gas emission estimates are based on the build-up of molecular chlorine (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-
emissions-modelirig-platforms, under the section entitled "2016vl Platform". The platform "README"
file indicates the particular zipped files associated with each platform sector. A number of reports (i.e.,
summaries) are available with the data files for the 2016 platform. The types of reports include state
summaries of inventory pollutants and model species by modeling platform sector and county annual
totals by modeling platform sector. Additional types of data including outputs from SMOKE and inputs to
CAMx are available from the Intermountain West Data Warehouse.
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.
19
-------
A comprehensive description of how EGU emissions were characterized and estimated in the 2014 NEI is
located in Section 3.4 in the 2014NEIv2 TSD. The methods for emissions estimation are similar for the
interim year of 2016, but there is no TSD available specific to the 2016 point source submissions to EIS.
Additional information on state submissions through the collaborative process are available in the
collaborative specification sheets.
The point source file used for the modeling platform is exported from EIS into the Flat File 2010 (FF10)
format that is compatible with SMOKE (see
https://www.cmascenter.Org/smoke/documentation/4.7/html/ch08s02s08.htmn.
For the 2016vl platform, the export of point source emissions, including stack parameters and locations
from EIS, was done on June 12, 2018. The flat file was modified to remove sources without specific
locations (i.e., their FIPS code ends in 777). Then the point source FF10 was divided into four NEI-based
platform point source sectors: the EGU sector (ptegu), point source oil and gas extraction-related
emissions (pt oilgas), airport emissions were put into the airports sector, and the remaining non-EGU
sector also called the non-IPM (ptnonipm) sector. The split was done at the unit level for ptegu and
facility level for pt oilgas such that a facility may have units and processes in both ptnonipm and ptegu,
but cannot be in both pt oilgas and any other point sector. Additional information on updates made
through the collaborative process is available in the collaborative specification sheets.
The EGU emissions are split out from the other sources to facilitate the use of distinct SMOKE temporal
processing and future-year projection techniques. The oil and gas sector emissions (pt oilgas) were
processed separately for summary tracking purposes and distinct future-year projection techniques from
the remaining non-EGU emissions (ptnonipm).
The inventory pollutants processed through SMOKE for all point source sectors were: carbon monoxide
(CO), NOx, VOC, SO2, ammonia (NH3), particles less than 10 microns in diameter (PM10), and particles
less than 2.5 microns in diameter (PM2.5), and all of the air toxics listed in Table 3-3. The Naphthalene,
Benzene, Acetaldehyde, Formaldehyde, and Methanol (NBAFM) species are explicit in the CB6-CMAQ
chemical mechanism and are taken from the HAP emissions in the flat file (if present for a source) as
opposed to using emissions generated through VOC speciation, as is normally done for non-toxics
modeling applications. To prevent double counting of mass, NBAFM species are removed from VOC
speciation profiles, thus resulting in speciation profiles that may sum to less than 1. This is called the
"no-integrate" VOC speciation case and is discussed in detail in Section 3.2.1.1. The resulting VOC in
the modeling system may be higher or lower than the VOC emissions in the NEI; they would only be the
same if the HAP inventory and speciation profiles were exactly consistent. For HAPs other than those in
NBAFM, there is no concern for double-counting since CMAQ handles these outside the CB6
mechanism.
The ptnonipm and pt oilgas sector emissions were provided to SMOKE as annual emissions. For those
ptegu sources with CEMS data that could be matched to the point inventory from EIS, hourly CEMS NOx
and SO2 emissions were used rather than the annual total NEI emissions. For all other pollutants at
matched units, the annual emissions were used as-is from the NEI, but were allocated to hourly values
using heat input from the CEMS data. For the sources in the ptegu sector not matched to CEMS data,
daily emissions were created using an approach described in Section 2.1.1. For non-CEMS units other
than municipal waste combustors and cogeneration units, IPM region- and pollutant-specific diurnal
profiles were applied to create hourly emissions.
20
-------
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 database
(https://www.epa.gov/airmarkets/national-electric-energy-data-svstem-needs-v6). The matching was
prioritized according to the amount of the emissions produced by the source. In the SMOKE point flat
file, emission records for sources that have been matched to the NEEDS database have a value filled into
the IPMYN column based on the matches stored within EIS. The 2016 NEI point inventory consists of
data submitted by S/L/T agencies and EPA to the EIS for Type A (i.e., large) point sources. Those EGU
sources in the 2014 NEIv2 inventory that were not submitted or updated for 2016 and not identified as
retired were retained. The retained 2014 NEIv2 EGUs in CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC,
PA, RI, VT, VA, and WV were projected from 2014 to 2016 values using factors provided by the Mid-
Atlantic Regional Air Management Association (MARAMA).
Higher generation capacity units in the ptegu sector are matched to 2016 CEMS data from EPA's Clean
Air Markets Division (CAMD) via ORIS facility codes and boiler ID. For the matched units, SMOKE
replaces the 2016 emissions of NOx and SO2 with the CEMS emissions, thereby ignoring the annual
values specified in the NEI. For other pollutants at matched units, the hourly CEMS heat input data are
used to allocate the NEI annual emissions to hourly values. All stack parameters, stack locations, and
Source Classification Codes (SCC) for these sources come from the NEI or updates provided by data
submitters outside of EIS. Because these attributes are obtained from the NEI, the chemical speciation of
VOC and PM2.5 for the sources is selected based on the SCC or in some cases, based on unit-specific data.
If CEMS data exists for a unit, but the unit is not matched to the NEI, the CEMS data for that unit is not
used in the modeling platform. However, if the source exists in the NEI and is not matched to a CEMS
unit, the emissions from that source are still modeled using the annual emission value in the NEI
temporally allocated to hourly values. The EGU flat file inventory is split into a flat file with CEMS
matches and a flat file without CEMS matches to support analysis and temporalization.
In the SMOKE point flat file, emission records for point sources matched to CEMS data have values
filled into the ORIS FACILITY CODE and ORIS BOILER ID columns. The CEMS data in SMOKE-
ready format is available at http://ampd.epa.gov/ampd/ near the bottom of the "Prepackaged Data" tab.
Many smaller emitters in the CEMS program are not identified with ORIS facility or boiler IDs that can
be matched to the NEI due to inconsistencies in the way a unit is defined between the NEI and CEMS
datasets, or due to uncertainties in source identification such as inconsistent plant names in the two data
systems. Also, the NEEDS database of units modeled by IPM includes many smaller emitting EGUs that
do not have CEMS. Therefore, there will be more units in the NEEDS database than have CEMS data.
The temporal allocation of EGU units matched to CEMS is based on the CEMS data, whereas regional
profiles are used for most of the remaining units. More detail can be found in Section 3.3.2.
Some EIS units match to multiple CAMD units based on cross-reference information in the EIS alternate
identifier table. The multiple matches are used to take advantage of hourly CEMS data when a CAMD
unit specific entry is not available in the inventory. Where a multiple match is made the EIS unit is split
and the ORIS facility and boiler IDs are replaced with the individual CAMD unit IDs. The split EIS unit
NOX and 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.
21
-------
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.
The ptegu inventory for the 2016fi case includes an update that allows SMOKE to properly process
CEMS emissions when there are multiple CEMS units mapped to the same NEI unit. This caused NOx
and S02 emissions in 2016fi to be higher at some units.
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.
Table 2-2. Point source oil and gas sector NAICS Codes
NAICS
Type of point
source
NAICS description
2111, 21111
Production
Oil and Gas Extraction
211111
Production
Crude Petroleum and Natural Gas Extraction
211112
Production
Natural Gas Liquid Extraction
213111
Production
Drilling Oil and Gas Wells
213112
Support
Support Activities for Oil and Gas Operations
2212, 22121, 221210
Distribution
Natural Gas Distribution
4862, 48621, 486210
Transmission
Pipeline Transportation of Natural Gas
48611, 486110
Transmission
Pipeline Transportation of Crude Oil
The starting point for the 2016vl 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).
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.
22
-------
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. National 2016 pt oilgas emissions before and after
application of 2014-to-2016 projections are shown in Table 3. The historical production data for years
2014 and 2016 for oil and natural gas were taken from the following websites:
• https://www.eia.gov/dnav/pet/pet crd crpdn adc mbbl a.htm (Crude production)
• http://www.eia.gov/dnav/ng/ng sum lsum a epgO fgw mmcf a.htm (Natural gas production)
The "no growth" sources include all offshore and tribal land emissions, and all emissions with aNAICS
code associated with distribution, transportation, or support activities. As there were no 2015 production
data in the EIA for Idaho, no growth was assumed for this state; the only pt oilgas sources in Idaho were
pipeline transportation related. Maryland and Oregon had no oil production data on the EIA website. The
factors provided in Table 2-8 were applied to sources with NAICS = 2111,21111,211111,211112, and
213111 and with production-related SCC processes. Table 2-3 provides a national summary of emissions
before and after this 2 year projection for these sources in the pt oilgas sector. Table 2-4 shows the
national emissions for pt oilgas following the projection to 2016.
Table 2-3. 2014NEIv2-to-2016 projection factors for pt oilgas sector for 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
3.5%
22.0%
12.8%
Florida
8.0%
-13.2%
-2.6%
Idaho
0.0%
0.0%
0.0%
Illinois
13.2%
-9.5%
1.8%
Indiana
-6.2%
-27.5%
-16.9%
Kansas
-15.0%
-23.4%
-19.2%
Kentucky
-1.6%
-23.1%
-12.4%
Louisiana
-11.0%
-17.4%
-14.2%
Maryland
70.0%
N/A
N/A
Michigan
-12.6%
-23.4%
-18.0%
Mississippi
-10.9%
-16.3%
-13.6%
Missouri
-66.7%
-37.2%
-52.0%
Montana
-11.9%
-22.5%
-17.2%
23
-------
State
Natural Gas
growth
Oil growth
Combination gas/oil growth
Nebraska
27.3%
-25.0%
1.2%
Nevada
0.0%
-12.3%
-6.2%
New Mexico
1.4%
17.4%
9.4%
New York
-33.4%
-36.8%
-35.1%
North Dakota
31.4%
-4.3%
13.6%
Ohio
181.0%
44.4%
112.7%
Oklahoma
5.9%
6.9%
6.4%
Oregon
-18.0%
N/A
N/A
Pennsylvania
24.8%
-7.9%
8.5%
South Dakota
-33.9%
-21.7%
-27.8%
Tennessee
-31.9%
-22.1%
-27.0%
Texas
-6.1%
1.0%
-2.6%
Utah
-19.8%
-25.4%
-22.6%
Virginia
-10.0%
-50.0%
-30.0%
West Virginia
28.9%
0.7%
14.8%
Wyoming
-7.5%
-4.7%
-6.1%
Table 2-4. 2016fh ptoilgas national emissions (excluding offshore) before and after 2014-to-2016
projections (tons/year)
Pollutant
Before
projections
After projections
% change 2014 to 2016
CO
175,929
177,690
1.0%
NH3
4,347
4,338
-0.2%
NOX
377,517
379,866
0.6%
PM10-PRI
12,630
12,397
-1.8%
PM25-PRI
11,545
11,286
-2.2%
S02
35,236
34,881
-1.0%
VOC
127,242
129,253
1.6%
The state of Pennsylvania provided new emissions data for natural gas transmission sources for year
2016. The PA point source data replaced the emissions used in 2016beta. Table 2-5 illustrates the change
in emissions with this update.
Table 2-5. Pennsylvania emissions changes for natural gas transmission sources (tons/year).
State
2016
2016 vl -
State
FIPS
NAICS
Pollutant
beta
2016 vl
beta
Pennsylvania
42
486210
CO
2,787
2,385
403
Pennsylvania
42
486210
NOX
5,737
5,577
160
Pennsylvania
42
486210
PM10-PRI
400
227
173
Pennsylvania
42
486210
PM25-PRI
399
209
191
Pennsylvania
42
486210
S02
30
33
-3
Pennsylvania
42
486210
VOC
1,221
1,149
71
24
-------
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 2016vl platform have
been updated from the 2016 NEI point inventory with the following changes.
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.
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-est2Q17-
alldata.csv). A ratio of 2016 population to 2014 population was used to create a growth factor that was
applied to the 2014NEIv2 emissions with SCCs matching the population-based SCCs listed in Table 2-6
Positive growth factors (from increasing population) were not capped, but negative growth factors (from
decreasing population) were flatlined for no growth.
Table 2-6. SCCs for Census-based growth from 2014 to 2016
S( (
Tier 1
Tier 2 Description
Tier 3
Tier 4
Description
Description
Description
23020
Industrial
Food and Kindred Products:
Commercial Charbroiling
Conveyorized
02100
Processes
SIC 20
Charbroiling
23020
Industrial
Food and Kindred Products:
Commercial Charbroiling
Under-fired
02200
Processes
SIC 20
Charbroiling
23020
Industrial
Food and Kindred Products:
Commercial Deep Fat
Total
03000
Processes
SIC 20
Frying
23020
Industrial
Food and Kindred Products:
Commercial Deep Fat
Flat Griddle Frying
03100
Processes
SIC 20
Frying
23020
Industrial
Food and Kindred Products:
Commercial Deep Fat
Clamshell Griddle
03200
Processes
SIC 20
Frying
Frying
24010
Solvent
Surface Coating
Architectural Coatings
Total: All Solvent
01000
Utilization
Types
24010
Solvent
Surface Coating
Architectural Coatings -
Total: All Solvent
02000
Utilization
Solvent-based
Types
24010
Solvent
Surface Coating
Architectural Coatings -
Total: All Solvent
03000
Utilization
Water-based
Types
24011
Solvent
Surface Coating
Industrial Maintenance
Total: All Solvent
00000
Utilization
Coatings
Types
25
-------
S( (
Tier 1
Tier 2 Description
Tier 3
Tier 4
Description
Description
Description
24012
Solvent
Surface Coating
Other Special Purpose
Total: All Solvent
00000
Utilization
Coatings
Types
24250
Solvent
Graphic Arts
All Processes
Total: All Solvent
00000
Utilization
Types
24250
Solvent
Graphic Arts
Lithography
Total: All Solvent
10000
Utilization
Types
24250
Solvent
Graphic Arts
Letterpress
Total: All Solvent
20000
Utilization
Types
24250
Solvent
Graphic Arts
Rotogravure
Total: All Solvent
30000
Utilization
Types
24250
Solvent
Graphic Arts
Flexography
Total: All Solvent
40000
Utilization
Types
24400
Solvent
Miscellaneous Industrial
Adhesive (Industrial)
Total: All Solvent
20000
Utilization
Application
Types
24600
Solvent
Miscellaneous Non-industrial:
All Processes
Total: All Solvent
00000
Utilization
Consumer and Commercial
Types
24601
Solvent
Miscellaneous Non-industrial:
All Personal Care
Total: All Solvent
00000
Utilization
Consumer and Commercial
Products
Types
24602
Solvent
Miscellaneous Non-industrial:
All Household Products
Total: All Solvent
00000
Utilization
Consumer and Commercial
Types
24604
Solvent
Miscellaneous Non-industrial:
All Automotive
Total: All Solvent
00000
Utilization
Consumer and Commercial
Aftermarket Products
Types
24605
Solvent
Miscellaneous Non-industrial:
All Coatings and Related
Total: All Solvent
00000
Utilization
Consumer and Commercial
Products
Types
24606
Solvent
Miscellaneous Non-industrial:
All Adhesives and
Total: All Solvent
00000
Utilization
Consumer and Commercial
Sealants
Types
24608
Solvent
Miscellaneous Non-industrial:
All FIFRA Related
Total: All Solvent
00000
Utilization
Consumer and Commercial
Products
Types
24609
Solvent
Miscellaneous Non-industrial:
Miscellaneous Products
Total: All Solvent
00000
Utilization
Consumer and Commercial
(Not Otherwise Covered)
Types
24618
Solvent
Miscellaneous Non-industrial:
Pesticide Application: All
Total: All Solvent
00000
Utilization
Commercial
Processes
Types
24618
Solvent
Miscellaneous Non-industrial:
Pesticide Application: All
Surface Application
00001
Utilization
Commercial
Processes
24618
Solvent
Miscellaneous Non-industrial:
Pesticide Application: All
Soil Incorporation
00002
Utilization
Commercial
Processes
24618
Solvent
Miscellaneous Non-industrial:
Pesticide Application:
Not Elsewhere
70999
Utilization
Commercial
Non-Agricultural
Classified
24658
Solvent
Miscellaneous Non-industrial:
Pesticide Application
Total: All Solvent
00000
Utilization
Consumer
Types
25010
Storage and
Petroleum and Petroleum
Residential Portable Gas
Permeation
11011
Transport
Product Storage
Cans
25010
Storage and
Petroleum and Petroleum
Residential Portable Gas
Evaporation (includes
11012
Transport
Product Storage
Cans
Diurnal losses)
25010
Storage and
Petroleum and Petroleum
Residential Portable Gas
Spillage During
11013
Transport
Product Storage
Cans
Transport
25010
Storage and
Petroleum and Petroleum
Residential Portable Gas
Refilling at the Pump -
11014
Transport
Product Storage
Cans
Vapor Displacement
25010
Storage and
Petroleum and Petroleum
Residential Portable Gas
Refilling at the Pump -
11015
Transport
Product Storage
Cans
Spillage
26
-------
S( (
Tier 1
Tier 2 Description
Tier 3
Tier 4
Description
Description
Description
25010
Storage and
Petroleum and Petroleum
Commercial Portable Gas
Permeation
12011
Transport
Product Storage
Cans
25010
Storage and
Petroleum and Petroleum
Commercial Portable Gas
Evaporation (includes
12012
Transport
Product Storage
Cans
Diurnal losses)
25010
Storage and
Petroleum and Petroleum
Commercial Portable Gas
Spillage During
12013
Transport
Product Storage
Cans
Transport
25010
Storage and
Petroleum and Petroleum
Commercial Portable Gas
Refilling at the Pump -
12014
Transport
Product Storage
Cans
Vapor Displacement
25010
Storage and
Petroleum and Petroleum
Commercial Portable Gas
Refilling at the Pump -
12015
Transport
Product Storage
Cans
Spillage
26300
Waste Disposal
Treatment and Recovery
Wastewater Treatment,
Total Processed
20000
Public Owned
26400
Waste Disposal
Treatment and Recovery
TSDFs, All TSDF Types
Total: All Processes
00000
28100
Miscellane-ous
Other Combustion
Residential Grilling
Total
25000
Area Sources
28100
Miscellane-ous
Other Combustion
Cremation
Humans
60100
Area Sources
Other non-IPM updates in 2016vl
In New Jersey, emissions for SCCs for Industrial (2102004000) and Commercial/Institutional
(2103004000) Distillate Oil, Total: Boilers and Internal Combustion (IC) Engines were removed at that
state's request. These emissions were derived from EPA estimates, and double counted emissions that
were provided by New Jersey and assigned to other SCCs.
The state of New Jersey also requested that animal waste NH3 emissions from the following SCCs be
removed: 2806010000 - Cats, 2806015000 - Dogs, 2807020001 - Black Bears, 2807020002 - Grizzly
Bears, 2807025000 - Elk, 2807030000 - Deer, and 2810010000 - Human Perspiration and Respiration.
These emissions existed in CA, DE, ME, NJ, and UT, and were removed from all states.
The state of Alaska reported several nonpoint sources that were missing in 2014NEIv2. Some of the
sources reported by Alaska were identified in our EGU inventory and removed from the new nonpoint
inventory. The rest of the stationary sources were converted to an 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.
The 2016fi case 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.
27
-------
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 1 (vl) platform airport inventory is the airport emissions from the 2017
National Emissions Inventory (NEI). The SCCs included in the airport sector are shown in Table 2-7.
Table 2-7. 2016vl platform SCCs for the airports sector
S( (
Tier 1 description
Tier 2 (k'scriplion
Tier 3 (k'scriplion
Tier 4 (k'scriplion
2265008005
Mobile Sources
Off-highway Vehicle
Gasoline, 4-stroke
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2267008005
Mobile Sources
LPG
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2268008005
Mobile Sources
compressed natural gas
(CNG)
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2270008005
Mobile Sources
Off-highway Vehicle
Diesel
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2275001000
Mobile Sources
Aircraft
Military Aircraft
Total
2275020000
Mobile Sources
Aircraft
Commercial
Aircraft
Total: All Types
2275050011
Mobile Sources
Aircraft
General Aviation
Piston
2275050012
Mobile Sources
Aircraft
General Aviation
Turbine
2275060011
Mobile Sources
Aircraft
Air Taxi
Piston
2275060012
Mobile Sources
Aircraft
Air Taxi
Turbine
2275070000
Mobile Sources
Aircraft
Aircraft Auxiliary
Power Units
Total
40600307
Chemical
Evaporation
Transportation and
Marketing of Petroleum
Products
Gasoline Retail
Operations -
Stage I
Underground Tank
Breathing and
Emptying
Internal
Distillate Oil
(Diesel)
20200102
Combustion
Engines
Industrial
Reciprocating
The 2016vl airport emissions inventory was created from the 2017NEI airport emissions that were
estimated using the Federal Aviation Administration's (FAA's) Aviation Environmental Design Tool
(AEDT). Additional information about the 2017NEI airport inventory and the AEDT can be found in the
2017 National Emissions Inventory Technical Support Document (https://www.epa.gov/air-emissions-
inventories/2017-national-emissions-inventory-nei-technical-support-document-tsd). The 2017NEI
emissions were adjusted from 2017 to represent year 2016 emissions using FAA data. Adjustment factors
were created using airport-specific numbers, where available, or the state default by itinerant class
(commercial, air taxi, and general) where there were not airport-specific values in the FAA data.
Emissions growth for facilities is capped at 500% and the state default growth is capped at 200%. Military
state default values were kept flat to reflect uncertainly in the data regarding these sources.
28
-------
After the release of the April 2020 version of the 2017NEI, an error in the computation of the airport
emissions was identified and it was determined that they were overestimated. The error impacted
commercial aircraft emissions. The airport emission in the 2016fi case were recomputed based on
corrected 2017NEI emissions that were incorporated into the January 2021 release of 2017 NEI. The
corrected inventories and outputs from SMOKE were posted on the 2016vl FTP site
(ftp://newftp.epa.gov/air/emismod/2016/vl/postvl updates/ also available at
https://gaftp.epa.gov/Air/emismod/2016/vl/postvl updates).
2.2 2016 Nonpoint sources (afdust, ag, npoilgas, rwc, nonpt)
This section describes the stationary nonpoint sources in the NEI nonpoint data category. Locomotives,
CI and C2 CMV, and C3 CMV are included in the NEI nonpoint data category, but are mobile sources
that are described in Section 2.4.
The nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE due to the
configuration of the spatial surrogates. Part of the reason for this is to prevent possible double-counting
with county-level emissions and also because spatial surrogates for tribal data are not currently available.
These omissions are not expected to have an impact on the results of the air quality modeling at the 12-km
resolution used for this platform.
The following subsections describe how the sources in the NEI nonpoint inventory were separated into
modeling platform sectors, along with any data that were updated replaced with non-NEI data.
2.2.1 Area fugitive dust sector (afdust)
The area-source fugitive dust (afdust) sector contains PMio and PM2.5 emission estimates for nonpoint
SCCs identified by EPA as dust sources. Categories included in the afdust sector are paved roads,
unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production,
and mining and quarrying. It does not include fugitive dust from grain elevators, coal handling at coal
mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as
point sources so they are properly located. Table 2-8 is a listing of the Source Classification Codes
(SCCs) in the afdust sector.
Table 2-8. 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
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
29
-------
sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2325000000
Industrial
Processes
Mining and
Quarrying: SIC 14
All Processes
Total
2325060000
Industrial
Processes
Mining and
Quarrying: SIC 10
Lead Ore Mining and Milling
Total
2801000000
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Total
2801000003
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Tilling
2801000005
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Harvesting
2801000007
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Loading
2801000008
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Transport
2805001000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Dust Kicked-up by Hooves
(use 28-05-020, -001,-002,
or -003 for Waste
2805001100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Confinement
2805001200
Miscellaneous
Area Sources
Agriculture
Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Manure handling and storage
2805001300
Miscellaneous
Area Sources
Agriculture
Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Land application of manure
2805002000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle production composite
Not Elsewhere Classified
2805003100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on pasture/range
Confinement
2805007100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
dry manure management systems
Confinement
2805007300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
dry manure management systems
Land application of manure
2805008100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Confinement
2805008200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Manure handling and storage
2805008300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Land application of manure
2805009100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Confinement
2805009200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Manure handling and storage
2805009300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Land application of manure
2805010100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Confinement
2805010200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Manure handling and storage
2805010300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Land application of manure
2805018000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle composite
Not Elsewhere Classified
2805019100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Confinement
30
-------
sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2805019200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Manure handling and storage
2805019300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Land application of manure
2805020002
Miscellaneous
Area Sources
Ag. Production -
Livestock
Cattle and Calves Waste
Emissions
Beef Cows
2805021100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Confinement
2805021200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Manure handling and storage
2805021300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Land application of manure
2805022100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Confinement
2805022200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Manure handling and storage
2805022300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Land application of manure
2805023100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Confinement
2805023200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Manure handling and storage
2805023300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Land application of manure
2805025000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production composite
Not Elsewhere Classified
(see also 28-05-039, -047, -
053)
2805030000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Not Elsewhere Classified
(see also 28-05-007, -008, -
009)
2805030007
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Ducks
2805030008
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Geese
2805035000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Horses and Ponies Waste
Emissions
Not Elsewhere Classified
2805039100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Confinement
2805039200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Manure handling and storage
2805039300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Land application of manure
2805040000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Sheep and Lambs Waste
Emissions
Total
2805045000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Goats Waste Emissions
Not Elsewhere Classified
2805047100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - deep-pit house
operations (unspecified animal
age)
Confinement
2805047300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - deep-pit house
operations (unspecified animal
age)
Land application of manure
31
-------
SCC
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2805053100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - outdoor
operations (unspecified animal
age)
Confinement
The starting point for the afdust emissions is the 2014 National Emissions Inventory version 2. The
methodologies to estimate emissions for each SCC in the preceding table are described in the 2014 NEI
version 2 Technical Support Document.2 The 2014 emissions were adjusted to better represent 2016 as
described below.
MARAMA States area fugitive dust emissions
The MARAMA states include Connecticut, Delaware, the District of Columbia (DC), Maine, Maryland,
Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island,
Vermont, Virginia, and West Virginia. MARAMA submitted county-specific projection factors for their
states to project afdust emissions from the 2014NEI2 to 2016 for paved roads (SCC 2294000000),
residential construction dust (SCC 2311010000), industrial/commercial/institutional construction dust
(SCC 2311020000), road construction dust (SCC 2311030000), dust from mining and quarrying (SCC
2325000000), agricultural crop tilling dust (SCC 2801000003), and agricultural dust kick-up from beef
cattle hooves (SCC 2805001000). Other afdust emissions, including unpaved road dust emissions, were
held constant at 2014NEIv2 values.
Non-MARAMA States area fugitive dust emissions
For paved roads (SCC 2294000000) in non-MARAMA states, the 2014NEIv2 paved road emissions in
afdust were projected to year 2016 based on differences in county total vehicle miles traveled (VMT)
between 2014 and 2016:
2016 afdust paved roads = 2014 afdust paved roads * (2016 county total VMT) / (2014 county total VMT)
The development of the 2016 VMT is described in the onroad documentation. All emissions other than
those for paved roads are held constant in the 2016vl inventory, including unpaved roads for these states.
Area Fugitive Dust Transport Fraction
The afdust sector is separated from other nonpoint sectors to allow for the application of a "transport
fraction," and meteorological/precipitation reductions. These adjustments are applied using a script that
applies land use-based gridded transport fractions based on landscape roughness, followed by another
script that zeroes out emissions for days on which at least 0.01 inches of precipitation occurs or there is
snow cover on the ground. The land use data used to reduce the NEI emissions determines the amount of
emissions that are subject to transport. This methodology is discussed in Pouliot, et al., 2010, and in
"Fugitive Dust Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). Both the
transport fraction and meteorological adjustments are based on the gridded resolution of the platform (i.e.,
12km grid cells); therefore, different emissions will result if the process were applied to different grid
resolutions. A limitation of the transport fraction approach is the lack of monthly variability that would
be expected with seasonal changes in vegetative cover. While wind speed and direction are not accounted
for in the emissions processing, the hourly variability due to soil moisture, snow cover and precipitation is
accounted for in the subsequent meteorological adjustment.
2 https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventorv-nei-technical-support-document-tsd
32
-------
For the data compiled into the 2014NEIv2, meteorological adjustments are applied to paved and unpaved
road SCCs but not transport adjustments. For the 2014NEIvl, the meteorological adjustments were
inadvertently not applied. This created a large difference between the 2014NEIvl and 2014NEIv2 dust
emissions which did not impact the modeling platform because the modeling platform applies
meteorological adjustments and transport adjustments based on unadjusted NEI values (for both vl and
v2). Thus, for the 2014NEIv2, the meteorological adjustments that were applied (to paved and unpaved
road SCCs) had to be backed out so that the entire sector could be processed consistently in SMOKE and
the same grid-specific transport fractions and meteorological adjustments could be applied sector-wide.
Because it was determined that some counties in 2014NEIv2 did not have the adjustment applied, their
emissions were used as-is. Thus, the FF10 that is run through SMOKE consists of 100% unadjusted
emissions, and after SMOKE all afdust sources have both transport and meteorological adjustments
applied. The total impacts of the transport fraction and meteorological adjustments for 2016vl are shown
in Table 2-9. Note that while totals from AK, HI, PR, and VI are included at the bottom of the table, they
are from non-continental U.S. (non-CONUS) modeling domains.
Table 2-9. Total impact of fugitive dust adjustments to unadjusted 2016 vl inventory
SliUc
I iiiidjiislcd
PMio
I iiiidjiislcd
I'M:.?
< liiiniio in
PMio
< liiiniio in
I'M;?
PMio
Reduction
I'M:.?
Reduction
Alabama
535,218
63,682
-372,853
-44,336
70%
70%
Arizona
264,628
32,808
-96,814
-11,809
37%
36%
Arkansas
321,488
49,397
-211,050
-31,802
66%
64%
California
314,917
41,395
-134,347
-17,059
43%
41%
Colorado
242,327
36,848
-121,263
-17,718
50%
48%
Connecticut
23,740
3,385
-17,548
-2,510
74%
74%
Delaware
14,566
2,502
-8,843
-1,533
61%
61%
District of
Columbia
2,619
378
-1,627
-236
62%
62%
Florida
721,379
82,397
-412,621
-46,899
57%
57%
Georgia
557,354
66,609
-389,482
-46,272
70%
69%
Idaho
454,301
55,978
-241,373
-28,363
53%
51%
Illinois
997,748
143,992
-619,594
-88,735
62%
62%
Indiana
718,027
84,663
-498,442
-58,430
69%
69%
Iowa
387,029
60,253
-222,941
-34,557
58%
57%
Kansas
613,183
99,486
-277,007
-44,234
45%
44%
Kentucky
312,872
42,952
-233,163
-31,762
75%
74%
Louisiana
266,812
35,788
-172,875
-22,923
65%
64%
Maine
38,345
5,963
-31,893
-4,978
83%
83%
Maryland
105,892
16,672
-68,246
-10,824
64%
65%
Massachusetts
148,284
18,297
-112,998
-13,852
76%
76%
Michigan
390,994
48,838
-286,999
-35,560
73%
73%
Minnesota
405,052
61,723
-250,646
-37,609
62%
61%
Mississippi
434,575
53,546
-299,888
-36,494
69%
68%
Missouri
1,604,501
185,103
-1,084,830
-124,078
68%
67%
33
-------
Sialo
I nari.jiiMcri
PMu.
I nari.jiiMcri
I'M:..*
( haniie in
PIMio
Change in
PM:*
PMio
Reduction
PM: ?
Reduction
Montana
432,844
62,062
-236,341
-32,695
55%
53%
Nebraska
349,373
55,303
-165,083
-25,739
47%
47%
Nevada
161,820
23,360
-54,899
-7,953
34%
34%
New Hampshire
22,330
4,607
-18,436
-3,803
83%
83%
New Jersey
40,336
9,118
-26,776
-6,035
66%
66%
New Mexico
490,617
54,236
-200,695
-22,038
41%
41%
New York
264,041
44,137
-196,162
-32,785
74%
74%
North Carolina
206,465
30,017
-141,501
-20,610
69%
69%
North Dakota
473,241
82,478
-249,646
-43,138
53%
52%
Ohio
931,847
116,560
-638,127
-79,098
68%
68%
Oklahoma
450,904
67,915
-232,046
-33,983
51%
50%
Oregon
659,099
73,832
-456,949
-49,830
69%
67%
Pennsylvania
242,608
37,707
-179,647
-27,959
74%
74%
Rhode Island
4,935
785
-3,503
-556
71%
71%
South Carolina
164,477
22,016
-110,278
-14,795
67%
67%
South Dakota
339,195
63,248
-169,300
-31,302
50%
49%
Tennessee
295,092
43,414
-204,746
-29,995
69%
69%
Texas
1,264,131
180,314
-636,591
-87,931
50%
49%
Utah
209,800
26,453
-111,587
-13,771
53%
52%
Vermont
22,437
3,275
-18,644
-2,699
83%
82%
Virginia
286,237
37,007
-211,882
-27,348
74%
74%
Washington
242,907
41,851
-135,713
-23,281
56%
56%
West Virginia
123,003
15,127
-105,093
-12,911
85%
85%
Wisconsin
690,830
89,899
-486,508
-62,683
70%
70%
Wyoming
240,156
29,140
-123,388
-14,561
51%
50%
Domain Total
(12km CONUS)
18,484,575
2,506,516
11,280,883
-1,500,070
61%
60%
Alaska
112,025
11,562
-101,822
-10,508
91%
91%
Hawaii
109,120
11,438
-73,612
-7,673
67%
67%
Puerto Rico
5,889
1,313
-4,355
-984
74%
75%
Virgin Islands
3,493
467
-1,477
-195
42%
42%
Figure 2-1 illustrates the impact of each step of the adjustment. The reductions due to the transport
fraction adjustments alone are shown at the top of the figure. The reductions due to the precipitation
adjustments alone are shown in the middle of the figure. The cumulative emission reductions after both
transport fraction and meteorological adjustments are shown at the bottom of the figure. The top plot
shows how the transport fraction has a larger reduction effect in the east, where forested areas are more
effective at reducing PM transport than in many western areas. The middle plot shows how the
meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust
emissions.
34
-------
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction,
precipitation, and cumulative
2016fh (vl) afdust annual : PM2 5, xportfrac - unadjusted
%
\ It
m-
i &
r
¦ A
\J4
Y-v'v/^
fSKf v
\
* i
/¦ ¦ .'"~i ¦*¦?"*S \ V I ' V
—tv / %
^
#
: < /
./ I
it
I
t
A/
$4 >'
/ i \ /tz" /
) vy r
L )
\ \ \
, vtX
wl\
w \
M
7 x
/ \
a r
Max: 0.0 Mjrv -B20.4
•A\v
i . V J
M %y£® /'
1|
2016fh (vl) afdust annual : PM2 5, precip adjusted - xportfrac adjusted
m
/ [ft
F-
i
i
£3
l A
/r
L\
*,
W-^v'
A
\,
--JU _
\l
\ * "7?
\ KN;
V0 i
Jy, i
\
".. "j i .4^ .
—¦ I-.. O , v'-S , .
^:ey;S^/7"W
¦%
-J /¦" * 'V\ \y
V—¦ I ->
-jwy
ylr
\
v
Max: 0.0001373 Min: -405.6
Y« v, ^
U, \ c
X
/
/
) *-
\-i
\
J \ ^-3
irX -
% N -x -
y ¦
35
-------
2.2.2 Agriculture Sector (ag)
The ag sector includes NH3 emissions from fertilizer and emissions of all pollutants other than PM2.5
from livestock in the nonpoint (county-level) data category of the 2017NEI. PM2.5 from livestock are in
the Area Fugitive Dust (afdust) sector. Combustion emissions from agricultural equipment, such as
tractors, are in the Nonroad sector. The sector now includes VOC and HAP VOC in addition to NH3.
The 2016 version 1 (vl) platform uses a 2016-specific fertilizer inventory from the USDA's
Environmental Policy Integrated Climate (EPIC) model combined with a 2016 USDA-based county-level
back-projection of 2017NEI livestock emissions. The SCCs included in the ag sector are shown in Table
2-10.
Table 2-10. 2016vl platform SCCs for the ag sector
see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2801700099
Miscellaneous
Area Sources
Ag. Production
- Crops
Fertilizer Application
Miscellaneous
Fertilizers
2805002000
Miscellaneous
Area Sources
Ag. Production
- Livestock
Beef cattle production
composite
Not Elsewhere
Classified
2805007100
Miscellaneous
Area Sources
Ag. Production
- Livestock
Poultry production -
layers with dry manure
management systems
Confinement
2805009100
Miscellaneous
Area Sources
Ag. Production
- Livestock
Poultry production -
broilers
Confinement
2805010100
Miscellaneous
Area Sources
Ag. Production
- Livestock
Poultry production -
turkeys
Confinement
2805018000
Miscellaneous
Area Sources
Ag. Production
- Livestock
Dairy cattle composite
Not Elsewhere
Classified
36
-------
SCC
Tier 1 description
Tier 2 (k'scriplion
Tier 3 (k'scriplion
Tier 4 (k'scriplion
2805025000
Miscellaneous
Area Sources
Ag. Production
- Livestock
Swine production
composite
Not Elsewhere
Classified (see also
28-05-039,-047, -053)
2805035000
Miscellaneous
Area Sources
Ag. Production
- Livestock
Horses and Ponies
Waste Emissions
Not Elsewhere
Classified
2805040000
Miscellaneous
Area Sources
Ag. Production
- Livestock
Sheep and Lambs Waste
Emissions
Total
2805045000
Miscellaneous
Area Sources
Ag. Production
- Livestock
Goats Waste Emissions
Not Elsewhere
Classified
2.2.2.1 Livestock Waste Emissions
The 2016vl platform livestock emissions consist of a back-projection of 2017NEI livestock emissions to
the year 2016 and include NH3 and VOC. The livestock waste emissions from 2017NEI contain
emissions for beef cattle, dairy cattle, goats, horses, poultry, sheep, and swine. The data come from both
state-submitted emissions and EPA-calculated emission estimates. Further information about the 2017NEI
emissions can be found in the 2017 National Emissions Inventory Technical Support Document
(https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-technical-support-
document-tsd). Back-projection factors for 2016 emission estimates are based on animal population data
from the USDA National Agriculture Statistics Service Quick Stats
(https://www.nass.usda.gov/Quick_Stats/). These estimates are developed by data collected from annual
agriculture surveys and the Census of Agriculture that is completed every five years. These data include
estimates for beef, layers, broilers, turkeys, dairy, swine, and sheep. Each SCC in the 2017NEI livestock
inventory, except for 2805035000 (horses and ponies) and 2805045000 (goats), was mapped to one of
these USDA categories. Then, back-projection factors were calculated based on USDA animal
populations for 2016 and 2017. Emissions for animal categories for which population data were not
available (e.g. horses, goats) were held constant in the projection.
Back-projection factors were calculated at the county level, but only where county-level data was
available for a specific animal category. County-level factors were limited to a range of 0.8 to 1.2. Data
were not available for every animal category in every county. State-wide back-projection factors based on
state total animal populations were calculated and applied to counties where county-specific data was not
available for a given animal category. However, data were often not available for every animal category
in every state. For categories other than beef and dairy, data are not available for most states. In cases of
missing state-level data, a national back-projection factor was applied. Back-projection factors were not
pollutant-specific and were applied to all pollutants. The national back-projection factors, which were
only used when county or state data were not available, are shown in Table 2-11. The national factors
were created using a ratio between animal inventory counts for 2017 and 2016 from the USDA National
livestock inventory projections published in February 2018
(https://www.ers.usda.gov/webdocs/outlooks/87459/oce-2018-l.pdf?v=7587.1).
Table 2-11. National back-projection factors for livestock: 2017 to 2016
beef
-1.8%
swine
-3.6%
broilers
-2.0%
turkeys
-0.3%
layers
-2.3%
37
-------
dairy
-0.4%
sheep
+0.4%
2.2.2.2 Fertilizer Emissions
Fertilizer emissions for 2016 are based on the Fertilizer Emission Scenario Tool for CMAQ (FEST-C)
model (https://www.cmascenter.org/fest-c/). The bidirectional version of CMAQ (v5.3) and the Fertilizer
Emissions Scenario Tool for CMAQ FEST-C (vl.3) were used to estimate ammonia (NH3) emissions
from agricultural soils. The approach to estimate year-specific fertilizer emissions consists of these steps:
• Run FEST-C to produce nitrate (N03), Ammonium (NH4+, including Urea), and organic
(manure) nitrogen (N) fertilizer usage estimates
• Use USDA Economic Research Services crop specific fertilizer use data and state submitted data
to adjust the FEST-C fertilizer totals to match the USDA and State submitted.
• Run the CMAQ model with bidirectional ("bidi") NH3 exchange to generate gaseous ammonia
NH3 emission estimates.
• Calculate county-level emission factors as the ratio of bidirectional CMAQ NH3 fertilizer
emissions to FEST-C total N fertilizer application.
• Assign the NH3 emissions to one SCC: ".. .Miscellaneous Fertilizers" (2801700099).
FEST-C is the software program that processes land use and agricultural activity data to develop inputs
for the CMAQ model when run with bidirectional exchange. FEST-C reads land use data from the
Biogenic Emissions Landuse Dataset (BELD), meteorological variables from the Weather Research and
Forecasting model, and nitrogen deposition data from a previous or historical average CMAQ simulation.
FEST-C, then uses the EPIC modeling system (https://epicapex.tamu.edu/epic/) to simulate the
agricultural practices and soil biogeochemistry and provides information regarding fertilizer timing,
composition, application method and amount.
An iterative calculation was applied to estimate fertilizer emissions for the 2016 platform. We first
estimate fertilizer application by crop type using FEST-C modeled data. After receipt and addressing of
comments to the extent possible, we then adjusted the fertilizer application estimates using state submitted
data, (currently only Iowa), and USDA Economic Research Service state and crop specific survey data.
The USDA and state submitted annual fertilizer data was used to estimate the ratio of UDSA/state
fertilizer use to FEST-C annual total fertilizer estimates for each state and crop with USDA or state data.
This ratio is then applied to the FEST-C fertilizer application rates for each state and crop with data. A
maximum annual fertilization rate was estimated from the FEST-C simulation and annual adjusted totals
were limited to this rate to prevent unrealistically higher fertilization rates. Then we ran the CMAQ v5.3
model with the Surface Tiled Aerosol and Gaseous Exchange (STAGE) deposition option with
bidirectional exchange to estimate fertilizer and biogenic NH3 emissions. We use this approach for three
reasons: (1) FEST-C estimates fertilizer applications based on crop nutrient needs which is typically lower
than real world fertilization rates; (2) FEST-C fertilizer timing and application methods are assumed to be
correct; and (3) We desired a method to incorporate state submitted and USDA reported data into the final
fertilization emission estimates.
Example Calculation:
Adjustment of FEST-C fertilizer rates using state or USDA data:
38
-------
/
l'Crta justed,crop TTICIX
Fert submitted,crop ,,
I ; e/ r FEST-C,crop'' : max,crop
\ncrop
Z Per^FEST -C.crop
Where Fertadjusted,cropis the FEST-C 12km grid cell adjusted fertilization rate, Fertsubmittedxropis the USDA
or State submitted state mean annual application data for the specified crop, in kg ha"1, FERTfest-c.ctop is
the initial FEST-C 12km grid cell fertilization rate for the state being considered, ncrop is the number of
grid cells with fertilization use for the specified crop in the state, and Fertmax,crop is the maximum
fertilization rate estimated from EPIC for the crop.
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions
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 (see Table 3)
• Initial soil profiles/soil selection
• Presence of 21 major crops: irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn,
silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans,
spring wheat, winter wheat, canola, and other crops (e.g.. lettuce, tomatoes, etc.)
• Fertilizer sales to establish the type/composition of nutrients applied
39
-------
• Management scenarios for the 10 USDA production regions. These include irrigation, tile
drainage, intervals between forage harvest, fertilizer application method (injected versus surface
applied), and equipment commonly used in these production regions.
The WRF meteorological model was used to provide grid cell meteorological parameters for year 2016
using a national 12-km rectangular grid covering the continental U.S. The meteorological parameters in
Table 2-12 were used as EPIC model inputs.
Table 2-12. Source of input variables for EPIC
EPIC input variable
Variable Source
Daily Total Radiation (MJ m2)
WRF
Daily Maximum 2-m Temperature (C)
WRF
Daily minimum 2-m temperature (C)
WRF
Daily Total Precipitation (mm)
WRF
Daily Average Relative Humidity (unitless)
WRF
Daily Average 10-m Wind Speed (m s"1 )
WRF
Daily Total Wet Deposition Oxidized N (g/ha)
CMAQ
Daily Total Wet Deposition Reduced N (g/ha)
CMAQ
Daily Total Dry Deposition Oxidized N (g/ha)
CMAQ
Daily Total Dry Deposition Reduced N (g/ha)
CMAQ
Daily Total Wet Deposition Organic N (g/ha)
CMAQ
Initial soil nutrient and pH conditions in EPIC were based on the 1992 USDA Soil Conservation Service
(CSC) Soils-5 survey. The EPIC model then was run for 25 years using current fertilization and
agricultural cropping techniques to estimate soil nutrient content and pH for the 2016 EPIC/WRF/CMAQ
simulation.
The presence of crops in each model grid cell was determined through the use of USDA Census of
Agriculture data (2012) and USGS National Land Cover data (2011). These two data sources were used to
compute the fraction of agricultural land in a model grid cell and the mix of crops grown on that land.
Fertilizer sales data and the 6-month period in which they were sold were extracted from the 2014
Association of American Plant Food Control Officials (AAPFCO,
http://www.aapfco.org/publications.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
USDA Agricultural Resource Management Survey (ARMS,
https://www.nass.usda.gov/Surveys/Guide to NASS Surveys/Ag Resource Management/) was used to
provide management activity data. These data cover 10 USDA production regions and provide
40
-------
management schemes for irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn, silage corn,
cottonoats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans, spring wheat, winter
wheat, canola, and other crops (e.g. lettuce, tomatoes, etc.).
Fertilizer Emission Factors
The emission factors were derived from the 2016 CMAQ FEST-C outputs adjusted using USDA
Economic Research Service (ERS) state and crop specific reported annual fertilizer rates. Total fertilizer
emission factors for each month and county were computed by taking the ratio of total fertilizer NH3
emissions (short tons) to total nitrogen fertilizer application (short tons).
12 km by 12 km gridded NH3 emissions were mapped to a county shape file polygon. The cell was
assigned to a county if the grid centroid fell within the county boundary.
2.2.3 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 (np oilgas) 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 Nonpoint Oil and Gas Emission Estimation Tool (the "Tool") to estimate the
non-point oil and gas inventory for the 2016vl platform. The Tool was previously used to estimate
emissions for the 2014 NEI. Year 2016 oil and gas activity data were supplied to EPA by some state air
agencies, and where state data were not supplied to EPA, EPA populated the 2016vl inventory with the
best available data. The Tool is an Access database that utilizes county-level activity data (e.g. oil
production and well counts), operational characteristics (types and sizes of equipment), and emission
factors to estimate emissions. The Tool creates a CSV-formatted emissions dataset covering all national
nonpoint oil and gas emissions. This dataset is then converted to FF10 format for use in SMOKE
modeling. A separate report named "2016 Nonpoint Oil and Gas Emission Estimation Tool V1_0
December_2018.docx" was generated that provides technical details of how the tool was applied for the
2016vl platform (ERG, 2018).
In the 2016beta platform, it was found that the number of active wells in the state of Illinois was too high
(-48,000 total wells). After various discussions and other communications with the Illinois
Environmental Protection Agency (IEPA), a more accurate number of active of wells (-20,000 total
wells) was obtained and the new data were used in a rerun of the Oil and Gas Tool to produce new
emissions for the state of Illinois. These new emissions estimates for Illinois are in the 2016vl modeling
41
-------
platform. The reduction in total number of active wells resulted in NOX and VOC emissions being
reduced by about 14,000 tons and 48,000 tons, respectively, in 2016vl when compared to 2016beta
emissions.
Nonpoint Oil and Gas Alternative Datasets
Some states provided, or recommended use of, a separate emissions inventory for use in 2016vl platform
instead of emissions derived from the EPA Oil and Gas Tool. For example, the California Air Resources
Board (CARB) developed their own npoilgas emissions inventory for 2016 for California that were used
for the 2016vl platform.
In Pennsylvania for the 2016vl modeling platform, the emissions associated with unconventional wells
for year 2016 were supplied by the Pennsylvania Department of Environmental Protection (PA DEP). The
Oil and Gas Tool was used to produce the conventional well emissions for 2016. Together these
unconventional and conventional well emissions represent the total non-point oil and gas emissions for
Pennsylvania. The resulting NOX emissions for Pennsylvania were increased by about 16,000 tons in
2016vl when compared to the 2016beta emissions. The VOC emissions were reduced by about 56,000
tons in 2016vl due to these emissions changes in Pennsylvania.
Colorado Department of Public Health and Environment (CDPHE) requested that the 2014NEIv2 be
projected to 2016 instead of using data from the EPA Oil and Gas Tool. For Colorado projections were
applied to CO, NOX, PM, and S02, but not VOC. VOC emissions for year 2016 were assumed to equal
year 2014 levels for Colorado. Projection factors for Colorado are listed in Table 2-13 and are based on
historical production trends.
Oklahoma Department of Environmental Quality requested that np oilgas emissions from 2014NEIv2 be
projected to 2016 for all source except lateral compressors. Projection factors for Oklahoma np oilgas
production, based on historical production data, are listed in Table 2-13. For lateral compressor emissions
in Oklahoma, the EPA Oil and Gas Tool inventory for 2016 was used, except with a 72% cut applied to
all emissions. Exploration np oilgas emissions in Oklahoma are based on the EPA Oil and Gas Tool
inventory for 2016, without modification.
Table 2-13. 2014NEIv2-to-2016 oil and gas projection factors for CO and OK.
State/region
Emissions type
Factor
Pollutant(s)
Colorado
Oil
+22.0%
CO, NOX, S02
Colorado
Natural Gas
+3.5%
CO, NOX, PM, S02
Colorado
Combination Oil + NG
+12.8%
CO, NOX, PM, S02
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
42
-------
2.2.4 Residential Wood Combustion (rwc)
The RWC sector includes residential wood burning devices such as fireplaces, fireplaces with inserts, free
standing woodstoves, pellet stoves, outdoor hydronic heaters (also known as outdoor wood boilers),
indoor furnaces, and outdoor burning in firepits and chimneys. Free standing woodstoves and inserts are
further differentiated into three categories: 1) conventional (not EPA certified); 2) EPA certified,
catalytic; and 3) EPA certified, noncatalytic. Generally, the conventional units were constructed prior to
1988. Units constructed after 1988 had to meet EPA emission standards and they are either catalytic or
non-catalytic. The source classification codes (SCCs) in the RWC sector are listed in Table 2-14.
Table 2-14. 2016 vl platform SCCs for RWC sector
SCC
Tier 1 Description
Tier 2
Description
Tier 3
Description
Tier 4 Description
2104008100
Stationary Source
Fuel Combustion
Residential
Wood
Fireplace: general
2104008210
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace
inserts; non-EPA certified
2104008220
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace
inserts; EPA certified; non-
catalytic
2104008230
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace
inserts; EPA certified;
catalytic
2104008310
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding,
non-EPA certified
2104008320
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding,
EPA certified, non-catalytic
2104008330
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding,
EPA certified, catalytic
2104008400
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: pellet-fired,
general (freestanding or FP
insert)
2104008510
Stationary Source
Fuel Combustion
Residential
Wood
Furnace: Indoor,
cordwood-fired, non-EPA
certified
2104008610
Stationary Source
Fuel Combustion
Residential
Wood
Hydronic heater: outdoor
2104008700
Stationary Source
Fuel Combustion
Residential
Wood
Outdoor wood burning
device, NEC (fire-pits,
chimeas, etc)
2104009000
Stationary Source
Fuel Combustion
Residential
Firelog
Total: All Combustor
Types
For all states other than California, Washington, and Oregon RWC emissions from the NEI2014v2 were
projected to 2016 using projection factors derived by MARAMA based on implementing the projection
methodology from EPA's 2011 platform into a spreadsheet tool. Projection factors are by SCC and SCC-
pollutant; SCC-only factors (i.e., factors that do not specify a pollutant) are applied to all pollutants
without an SCC-pollutant factor. Table 2-15 lists the SCC-based projection factors applied to RWC
sources.
43
-------
Table 2-15. Projection factors for RWC by SCC
SCC
SCC description
I'olllllillll
2014-l<>-2016
2104008100
Fireplace: general
2.00%
2104008210
Woodstove
fireplace inserts; non-EPA certified
-3.40%
2104008220
Woodstove
fireplace inserts; EPA certified; non-catalytic
PM10-PRI
2.29%
2104008220
Woodstove
fireplace inserts; EPA certified; non-catalvtic
PM25-PRI
2.29%
2104008220
Woodstove
fireplace inserts; EPA certified; non-catalytic
5.25%
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
PM10-PRI
2.44%
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
PM25-PRI
2.44%
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
5.25%
2104008310
Woodstove
freestanding, non-EPA certified
CO
-2.35%
2104008310
Woodstove
freestanding, non-EPA certified
PM10-PRI
-2.17%
2104008310
Woodstove
freestanding, non-EPA certified
PM25-PRI
-2.17%
2104008310
Woodstove
freestanding, non-EPA certified
VOC
-2.06%
2104008310
Woodstove
freestanding, non-EPA certified
-2.35%
2104008320
Woodstove
freestanding, EPA certified, non-catalvtic
PM10-PRI
2.29%
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
PM25-PRI
2.29%
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
5.25%
2104008330
Woodstove
freestanding, EPA certified, catalytic
PM10-PRI
2.47%
2104008330
Woodstove
freestanding, EPA certified, catalytic
PM25-PRI
2.47%
2104008330
Woodstove
freestanding, EPA certified, catalytic
5.25%
2104008400
Woodstove
pellet-fired, general (freestanding or FP insert)
PM10-PRI
14.40%
2104008400
Woodstove
pellet-fired, general (freestanding or FP insert)
PM25-PRI
14.40%
2104008400
Woodstove
pellet-fired, general (freestanding or FP insert)
14.38%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
CO
-9.70%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM10-PRI
-6.15%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM25-PRI
-6.15%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
VOC
-9.74%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
-9.70%
2104008610
Hvdronic heater: outdoor
PM10-PRI
2.99%
2104008610
Hydronic heater: outdoor
PM25-PRI
2.99%
2104008610
Hydronic heater: outdoor
2.00%
2104008700
Outdoor wood burning device, NEC (fire-pits, chimineas, etc)
2.00%
2104009000
Fire log total
2.00%
For California, Oregon, and Washington, the RWC emissions were held constant atNEI2014v2 levels for
2016. This approach is consistent with the RWC projections used in the EPA's 2011 emissions modeling
platform.
After the 2014NEIv2 was published, it was determined that the 2014NEIv2 RWC inventory was missing
woodstove emissions for certain pollutants in Idaho. The missing emissions for woodstove SCCs
2104008210, 2104008230, 2104008310, 2104008330 were added to the inventory prior to projecting it to
2016 for the vl platform.
2.2.5 Nonpoint (nonpt)
The starting point for the 2016vl platform nonpt inventory is the 2014NEIv2, including all nonpoint
sources that are not included in the afdust, ag, cmv_clc2, cmv_c3, np oilgas, rail, or rwc sectors. The
types of sources in the nonpt sector include, but are not limited to:
44
-------
• stationary source fuel combustion, including industrial, commercial, and residential and orchard
heaters;
• commercial sources such as commercial cooking;
• industrial processes such as chemical manufacturing, metal production, mineral processes,
petroleum refining, wood products, fabricated metals, and refrigeration;
• solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic
marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances,
and motor vehicles;
• solvent utilization for degreasing of furniture, metals, auto repair, electronics, and manufacturing;
• solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care
products, household products, adhesives and sealants;
• solvent utilization for asphalt application and roofing, and pesticide application;
• storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline
service stations, aviation, and marine vessels;
• storage and transport of chemicals;
• waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;
• cellulosic biorefining;
• miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair
shops.
The nonpoint emissions in 2016vl platform are equivalent to those in the 2014NEIv2 except for the
following changes:
Nonyoint projection to 2016 inside MARAMA region
2014-to-2016 projection packets for all nonpoint sources were provided by MARAMA for the following
states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV.
New Jersey provided their own projection factors for projection from 2014 to 2016 which were mostly the
same as those provided by MARAMA, except for three SCCs with differences (SCCs: 2302070005,
2401030000, 2401070000). For those three SCCs, the projection factors provided by New Jersey were
used instead of the MARAMA factors.
Nonyoint projection to 2016 outside MARAMA region
In areas outside of the MARAMA states, historical census population, sometimes by county and
sometimes by state, was used to project select nonpt sources from the 2014NEIv2 to 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-est2Q17-
alldata.csv). A ratio of 2016 population to 2014 population was used to create a growth factor that was
applied to the 2014NEIv2 emissions with SCCs matching the population-based SCCs listed in Table 2-16.
Positive growth factors (from increasing population) were not capped, but negative growth factors (from
decreasing population) were flatlined for no growth.
45
-------
Table 2-16. 2016vl platform SCCs for Census-based growth
S( (
Tier 1
Description
Tier 2 Description
Tier 3
Description
Tier 4
Description
2302002100
Industrial
Processes
Food and Kindred
Products: SIC 20
Commercial Charbroiling
Conveyorized Charbroiling
2302002200
Industrial
Processes
Food and Kindred
Products: SIC 20
Commercial Charbroiling
Under-fired Charbroiling
2302003000
Industrial
Processes
Food and Kindred
Products: SIC 20
Commercial Deep Fat
Frying
Total
2302003100
Industrial
Processes
Food and Kindred
Products: SIC 20
Commercial Deep Fat
Frying
Flat Griddle Frying
2302003200
Industrial
Processes
Food and Kindred
Products: SIC 20
Commercial Deep Fat
Frying
Clamshell Griddle Frying
2401001000
Solvent
Utilization
Surface Coating
Architectural Coatings
Total: All Solvent Types
2401002000
Solvent
Utilization
Surface Coating
Architectural Coatings -
Solvent-based
Total: All Solvent Types
2401003000
Solvent
Utilization
Surface Coating
Architectural Coatings -
Water-based
Total: All Solvent Types
2401100000
Solvent
Utilization
Surface Coating
Industrial Maintenance
Coatings
Total: All Solvent Types
2401200000
Solvent
Utilization
Surface Coating
Other Special Purpose
Coatings
Total: All Solvent Types
2425000000
Solvent
Utilization
Graphic Arts
All Processes
Total: All Solvent Types
2425010000
Solvent
Utilization
Graphic Arts
Lithography
Total: All Solvent Types
2425020000
Solvent
Utilization
Graphic Arts
Letterpress
Total: All Solvent Types
2425030000
Solvent
Utilization
Graphic Arts
Rotogravure
Total: All Solvent Types
2425040000
Solvent
Utilization
Graphic Arts
Flexography
Total: All Solvent Types
2440020000
Solvent
Utilization
Miscellaneous
Industrial
Adhesive (Industrial)
Application
Total: All Solvent Types
2460000000
Solvent
Utilization
Miscellaneous Non-
industrial: Consumer
and Commercial
All Processes
Total: All Solvent Types
2460100000
Solvent
Utilization
Miscellaneous Non-
industrial: Consumer
and Commercial
All Personal Care Products
Total: All Solvent Types
2460200000
Solvent
Utilization
Miscellaneous Non-
industrial: Consumer
and Commercial
All Household Products
Total: All Solvent Types
2460400000
Solvent
Utilization
Miscellaneous Non-
industrial: Consumer
and Commercial
All Automotive Aftermarket
Products
Total: All Solvent Types
2460500000
Solvent
Utilization
Miscellaneous Non-
industrial: Consumer
and Commercial
All Coatings and Related
Products
Total: All Solvent Types
2460600000
Solvent
Utilization
Miscellaneous Non-
industrial: Consumer
and Commercial
All Adhesives and Sealants
Total: All Solvent Types
2460800000
Solvent
Utilization
Miscellaneous Non-
industrial: Consumer
and Commercial
All FIFRA Related Products
Total: All Solvent Types
46
-------
S( (
Tier 1
Tier 2 Description
Tier 3
Tier 4
Description
Description
Description
2460900000
Solvent
Miscellaneous Non-
Miscellaneous Products
Total: All Solvent Types
Utilization
industrial: Consumer
and Commercial
(Not Otherwise Covered)
2461800000
Solvent
Miscellaneous Non-
Pesticide Application: All
Total: All Solvent Types
Utilization
industrial:
Commercial
Processes
2461800001
Solvent
Miscellaneous Non-
Pesticide Application: All
Surface Application
Utilization
industrial:
Commercial
Processes
2461800002
Solvent
Miscellaneous Non-
Pesticide Application: All
Soil Incorporation
Utilization
industrial:
Commercial
Processes
2461870999
Solvent
Miscellaneous Non-
Pesticide Application: Non-
Not Elsewhere Classified
Utilization
industrial:
Commercial
Agricultural
2465800000
Solvent
Utilization
Miscellaneous Non-
industrial: Consumer
Pesticide Application
Total: All Solvent Types
2501011011
Storage and
Petroleum and
Residential Portable Gas
Permeation
Transport
Petroleum Product
Storage
Cans
2501011012
Storage and
Petroleum and
Residential Portable Gas
Evaporation (includes Diurnal
Transport
Petroleum Product
Storage
Cans
losses)
2501011013
Storage and
Petroleum and
Residential Portable Gas
Spillage During Transport
Transport
Petroleum Product
Storage
Cans
2501011014
Storage and
Petroleum and
Residential Portable Gas
Refilling at the Pump - Vapor
Transport
Petroleum Product
Storage
Cans
Displacement
2501011015
Storage and
Petroleum and
Residential Portable Gas
Refilling at the Pump -
Transport
Petroleum Product
Storage
Cans
Spillage
2501012011
Storage and
Petroleum and
Commercial Portable Gas
Permeation
Transport
Petroleum Product
Storage
Cans
2501012012
Storage and
Petroleum and
Commercial Portable Gas
Evaporation (includes Diurnal
Transport
Petroleum Product
Storage
Cans
losses)
2501012013
Storage and
Petroleum and
Commercial Portable Gas
Spillage During Transport
Transport
Petroleum Product
Storage
Cans
2501012014
Storage and
Petroleum and
Commercial Portable Gas
Refilling at the Pump - Vapor
Transport
Petroleum Product
Storage
Cans
Displacement
2501012015
Storage and
Petroleum and
Commercial Portable Gas
Refilling at the Pump -
Transport
Petroleum Product
Storage
Cans
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
47
-------
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 MOVES computes emissions are
shown in Table 2-17. SMOKE-MOVES was run for specific modeling grids. Emissions for the
contiguous U.S. states and Washington, D.C., were computed for a grid covering those areas. Emissions
for Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands were computed by running SMOKE-
MOVES for distinct grids covering each of those regions and are included in the onroad nonconus sector.
In some summary reports these non-CONUS emissions are aggregated with emissions from the onroad
sector.
Table 2-17. MOVES vehicle (source) types
MOYKS vehicle Ivpe
Description
II P.MS vehicle Ivpe
11
Motorcycle
10
21
Passenger Car
25
31
Passenger Truck
25
32
Light Commercial Truck
25
41
Intercity Bus
40
42
Transit Bus
40
43
School Bus
40
51
Refuse Truck
50
52
Single Unit Short-haul Truck
50
53
Single Unit Long-haul Truck
50
54
Motor Home
50
61
Combination Short-haul Truck
60
62
Combination Long-haul Truck
60
Onroad Activity Data Development
SMOKE-MOVES uses vehicle miles traveled (VMT), vehicle population (VPOP), and hours of hoteling,
to calculate emissions. These datasets are collectively known as "activity data". For each of these activity
datasets, first a national dataset was developed; this national dataset is called the "EPA default" dataset.
Second, data submitted by state agencies were incorporated where available, in place of the EPA default
data. EPA default activity was used for California, but the emissions were scaled to California-supplied
48
-------
values during the emissions processing. The agencies for which submitted VMT and VPOP data were
used for 2016 platforms are shown in Table 2-18 along with the timing of the submission: 2014vl or 2016
beta or 2016 vl. Data submitted for the 2014 NEI were adjusted before they were used for 2016
platforms.
Table 2-18. Submitted data used to prepare onroad activity data
Agency
2016 VMT
2016 VPOP
Alaska
yes(2014vl)
yes (2014vl)
Arizona - Maricopa
yes(2014vl)
yes (2014vl)
Arizona - Pima
yes (vl)
yes (vl)
Colorado
yes (beta)
yes (vl)
Connecticut
yes (beta)
yes (2014vl)
Delaware
yes(2014vl)
yes (2014vl)
District of Columbia
yes(2014vl)
yes (2014vl)
Georgia
yes (beta)
yes (beta)
Idaho
yes(2014vl)
yes (2014vl)
Illinois - Chicago area
yes(vl)
yes (vl)
Illinois - rest of state
yes (beta)
yes (2014vl)
Indiana - Louisville area
yes (vl)
Kentucky - Jefferson
yes (vl)
yes (2014vl)
Kentucky - Louisville exurbs
yes (vl)
Maine
yes (2014v2)
yes (2014v2)
Maryland
yes (beta)
yes (beta)
Massachusetts
yes (vl)
yes (vl)
Michigan - Detroit area
yes (beta)
yes (2014vl)
Michigan - rest of state
yes (beta)
yes (2014vl)
Minnesota
yes (beta)
yes (2014vl)
Missouri
yes (2014vl)
yes (2014vl)
Nevada - Clark
yes (beta)
yes (beta)
Nevada - Washoe
yes(2014vl)
yes (2014vl)
New Hampshire
yes (beta)
yes (beta)
New Jersey
yes (beta)
yes (vl)
New Mexico - Bernalillo
yes(2014vl)
yes (2014vl)
New York
yes(2014vl)
yes (2014vl)
North Carolina
yes (beta)
yes (beta)
Ohio
yes(2014vl)
yes (2014vl)
Oregon
yes(2014vl)
yes (2014vl)
Pennsylvania
yes (beta)
yes (beta)
Rhode Island
yes (2014vl)
yes (2014vl)
South Carolina
yes (beta)
yes (beta)
Tennessee - Davidson
yes(2014vl)
yes (2014vl)
Tennessee - Knox
yes(2014vl)
yes (2014vl)
Tennessee - rest of state
yes(2014v2)
yes (2014v2)
Texas
yes (2014vl)
yes (2014vl)
Vermont
yes(2014v2)
yes (2014v2)
Virginia
yes (beta)
yes (2014v2)
49
-------
Agency
2016 VMT
2016 VPOP
Washington
yes (2014v2)
yes (2014v2)
West Virginia
yes (beta)
yes (beta)
Wisconsin
yes (beta)
yes (beta)
Vehicle Miles Traveled (VMT)
EPA calculated default 2016 state VMT by projecting the 2014NEIv2 platform VMT to 2016. The
2014NEIv2 Technical Support Document has details on the development of those VMT
(https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventorv-nei-technical-support-
document-tsd). The data projected to 2016 were used for states that did not submit 2016 VMT data.
Projection factors to grow state VMT from 2014 to 2016 were based on state-level VMT data from the
Federal Highway Administration (FHWA) VM-2 reports
(https://www.fhwa.dot.gov/policvinformation/statistics/2014/vm2.cfm and
https://www.fhwa.dot.gov/policvinformation/statistics/2016/vm2.cfm). For most states, separate factors
were calculated for urban VMT and rural VMT. Some states have a very different distribution of urban
activity versus rural activity between 2014NEIv2 and the FHWA data, due to inconsistencies in the
definition of urban versus rural. For those states, a single state-wide projection factor based on total
FHWA VMT across all road types was applied to all VMT independent of road type. The following states
used a single state-wide projection factor to adjust the VMT to 2016 levels: AK, GA, IN, ME, MA, NE,
NM, NY, ND, TN, and WV. Also, state-wide projection factors in Texas and Utah were developed from
alternative VMT datasets provided by their respective Departments of Transportation. The VMT
projection factors for all states are provided in Table 2-19.
Table 2-19. Factors applied to project VMT from 2014 to 2016 to prepare default activity data
State
Rural roads
I rhan roads
Projection Kaclor Source
Alabama
5.36%
5.47%
FHWA VM-2 urban/rural
Alaska
8.27%
8.27%
FHWA VM-2 total
Arizona
1.07%
6.35%
FHWA VM-2 urban/rural
Arkansas
4.80%
5.36%
FHWA VM-2 urban/rural
California
1.06%
2.39%
FHWA VM-2 urban/rural
Colorado
5.97%
6.67%
FHWA VM-2 urban/rural
Connecticut
1.33%
1.45%
FHWA VM-2 urban/rural
Delaware
4.42%
6.75%
FHWA VM-2 urban/rural
District of Columbia
0.00%
2.68%
FHWA VM-2 urban/rural
Florida
10.27%
6.64%
FHWA VM-2 urban/rural
Georgia
10.10%
10.10%
FHWA VM-2 total
Hawaii
6.14%
4.21%
FHWA VM-2 urban/rural
Idaho
5.51%
7.80%
FHWA VM-2 urban/rural
Illinois
3.40%
1.96%
FHWA VM-2 urban/rural
Indiana
5.02%
5.02%
FHWA VM-2 total
Iowa
6.17%
6.05%
FHWA VM-2 urban/rural
Kansas
2.42%
6.52%
FHWA VM-2 urban/rural
Kentucky
2.52%
3.26%
FHWA VM-2 urban/rural
50
-------
Slsilc
Uursil roiids
I rhnii roads
Projection Kador Source
Louisiana
-5.49%
7.10%
FHWA VM-2 urban/rural
Maine
3.75%
3.75%
FHWA VM-2 total
Maryland
4.98%
4.75%
FHWA VM-2 urban/rural
Massachusetts
7.42%
7.42%
FHWA VM-2 total
Michigan
5.62%
0.66%
FHWA VM-2 urban/rural
Minnesota
2.66%
2.97%
FHWA VM-2 urban/rural
Mississippi
1.83%
4.96%
FHWA VM-2 urban/rural
Missouri
4.70%
4.17%
FHWA VM-2 urban/rural
Montana
3.32%
4.34%
FHWA VM-2 urban/rural
Nebraska
5.54%
5.54%
FHWA VM-2 total
Nevada
8.30%
5.30%
FHWA VM-2 urban/rural
New Hampshire
5.00%
3.65%
FHWA VM-2 urban/rural
New Jersey
5.41%
2.83%
FHWA VM-2 urban/rural
New Mexico
10.01%
10.01%
FHWA VM-2 total
New York
-4.90%
-4.90%
FHWA VM-2 total
North Carolina
7.47%
8.41%
FHWA VM-2 urban/rural
North Dakota
-7.35%
-7.35%
FHWA VM-2 total
Ohio
4.61%
5.42%
FHWA VM-2 urban/rural
Oklahoma
4.72%
1.23%
FHWA VM-2 urban/rural
Oregon
8.05%
4.84%
FHWA VM-2 urban/rural
Pennsylvania
-4.30%
4.73%
FHWA VM-2 urban/rural
Rhode Island
3.26%
3.26%
FHWA VM-2 urban/rural
South Carolina
9.70%
8.89%
FHWA VM-2 urban/rural
South Dakota
3.23%
2.64%
FHWA VM-2 urban/rural
Tennessee
6.29%
6.29%
FHWA VM-2 total
Texas
7.82%
7.82%
TxDOT3
Utah
11.62%
11.62%
UDOT4
Vermont
5.55%
2.24%
FHWA VM-2 urban/rural
Virginia
-4.93%
9.78%
FHWA VM-2 urban/rural
Washington
6.86%
4.43%
FHWA VM-2 urban/rural
West Virginia
2.21%
2.21%
FHWA VM-2 total
Wisconsin
4.15%
9.32%
FHWA VM-2 urban/rural
Wyoming
-1.38%
-1.53%
FHWA VM-2 urban/rural
Puerto Rico
0.00%
0.00%
No FHWA VM-2 data
Virgin Islands
0.00%
0.00%
No FHWA VM-2 data
For the 2016vl platform, VMT data submitted by state and local agencies were incorporated and used in
place of EPA defaults, as described below. Note that VMT data need to be provided to SMOKE for each
county and SCC. The onroad SCCs characterize vehicles by MOVES fuel type, vehicle (aka source) type,
3 2014: https://ftp.dot.state.tx.us/pub/txdot-info/trf7crash statistics/2014/01 .pdf
2016: https://ftp.dot.state.tx.us/pub/txdot-info/trf/crash statistics/2016/01 .pdf
4 2014: https://www.udot.utah.gov/main/uconowner.gf?n=27035817009129993
2016: https://www.udot.utah.gov/main/uconowner.gf?n=36418522778889648
51
-------
emissions process, and road type. Any VMT provided at a different resolution than this were converted to
a full county-SCC resolution to prepare the data for processing by SMOKE.
Air agencies from CO, CT, GA, IL, MD, NJ, NC, VA, WI, and Pima County (AZ) provided 2016 VMT
data by county and Highway Performance Monitoring Systems (HPMS) vehicle type to be used for the
2016beta and 2016vl platforms. That level of detail is sufficient for MOVES, but SMOKE also needs
VMT broken out by MOVES vehicle type (which is more detailed than HPMS vehicle type), and by fuel
type, and road type. To get VMT at the resolution needed by SMOKE, the county-HPMS VMT data
provided by the states were loaded into the county databases (CDBs) that are used to run MOVES.
MOVES CDBs include fuel type splits, road type splits, and VPOP by MOVES vehicle type. Using those
tables, county-HPMS VMT data were converted into the county-SCC VMT data that are needed by
SMOKE. One exception to the use of local data in these states was for North Carolina, where EPA default
VMT for buses was used along with state-submitted VMT for other vehicle types.
South Carolina and Massachusetts submitted VMT by county-HPMS using the same HPMS splits in
every county in the state. Unlike Massachusetts, South Carolina did not provide county-specific road type
splits. Instead, a new set of county-specific HPMS splits was developed from the EPA default VMT. For
all HPMS types except 25 (light cars and trucks), county-HPMS ratios were calculated from the EPA
default VMT, and then scaled up or down so that the overall state-HPMS ratio would match South
Carolina's state-HPMS ratio. For HPMS type 25, the county-HPMS ratios were set equal to the remainder
within each county so that all ratios within each county sum to 1.0. The new VMT by county-HPMS
varies by county while respecting the state-wide HPMS splits in South Carolina's original VMT dataset.
The VMT was then split to full SCC level using a similar procedure as other states that submitted VMT at
the county-HPMS level.
Pennsylvania and New Hampshire submitted VMT for the 2016beta platform at the full county-SCC
level, already in the FF10 format needed by SMOKE. These data were used directly for the 2016vl
platform, except for the redistribution of light duty VMT (see last item in this subsection).
Michigan and Minnesota submitted 2016 VMT by county and by road type for the 2016beta platform.
Fuel type and vehicle type distributions from the EPA default VMT were used to convert these data to full
SCC.
West Virginia submitted county total VMT only for the 2016beta platform. Fuel, vehicle, and road type
distributions from the EPA default VMT were used to convert their data to full SCC.
For the 2016beta platform, Clark County, NV, submitted VMT by county and MOVES vehicle type,
which is more detailed than HPMS vehicle type, but nevertheless cannot be imported into MOVES CDBs
as easily to facilitate the creation of VMT at the full SCC detail. Fuel type and road type distributions
from the EPA default VMT were used to convert these data to full SCC.
For the 2016vl platform, VMT was provided by:
• Massachusetts (by HPMS, to override what was provided for beta)
• Chicago area (8 counties, by HPMS/road; excluded motorcycles)
• Louisville area (5 counties, county totals restricted/unrestricted)
• Pima County AZ (by HPMS)
52
-------
Some of the provided data were adjusted following quality assurance, as described below in the VPOP
section.
A final step was performed on all state-submitted VMT. The distinction between a "passenger car"
(MOVES vehicle type 21) versus a "passenger truck" (MOVES vehicle type 31) versus a "light
commercial truck" (MOVES vehicle type 32) is not always consistent between different datasets. This
distinction can have a noticeable effect on the resulting emissions, since MOVES emission factors for
passenger cars are quite different than those for passenger trucks and light commercial trucks.
To ensure consistency in the 21/31/32 splits across the country, all state-submitted VMT for MOVES
vehicle types 21,31, and 32 (all of which are part of HPMS vehicle type 25) was summed, and then re-
split using the 21/31/32 splits from the EPA default VMT. VMT for each source type as a percentage of
total 21/31/32 VMT was calculated by county from the EPA default VMT. Then, state-submitted VMT
for 21/31/32 was summed and then resplit according to those percentages.
This was done for all states and counties listed above which submitted VMT for 2016. Most of the states
listed above did not provide VMT down to the source type, so splitting the light-duty vehicle VMT does
not create an inconsistency with state-provided data in those states. Exceptions are New Hampshire and
Pennsylvania: those two states provided SCC-level VMT, but these were reallocated to 21/31/32 so that
the splits are performed in a consistent way across the country. The 21/31/32 splits in the EPA default
VMT can be traced back to the 2014NEIv2 VPOP data obtained from IHS-Polk.
Speed Activity (SPEED/SPDIST)
In SMOKE 4.7, SMOKE-MOVES was updated to use speed distributions similarly to how they are used
when running MOVES in inventory mode. This new speed distribution file, called SPDIST, specifies the
amount of time spent in each MOVES speed bin for each county, vehicle (aka source) type, road type,
weekday/weekend, and hour of day. This file contains the same information at the same resolution as the
Speed Distribution table used by MOVES but is reformatted for SMOKE. Using the SPDIST file results
in a SMOKE emissions calculation that is more consistent with MOVES than the old hourly speed profile
(SPDPRO) approach, because emission factors from all speed bins can be used, rather than interpolating
between the two bins surrounding the single average speed value for each hour as is done with the
SPDPRO approach.
As was the case with the previous SPDPRO approach, the SPEED inventory that includes a single overall
average speed for each county, SCC, and month, must still be read in by the SMOKE program Smkinven.
SMOKE requires the SPEED dataset to exist even when speed distribution data are available, even though
only the speed distribution data affects the selection of emission factors. The SPEED dataset is carried
over from 2014NEIv2, while the SPDIST dataset is new for the 2016vl platform. Both are based on a
combination of the Coordinating Research Council (CRC) A-100 data and MOVES CDBs.
Vehicle Population (VPOP)
The EPA default VPOP dataset was based on the EPA default VMT dataset described above. For each
county, fuel type, and vehicle type, a VMT/VPOP ratio (miles per vehicle per year) was calculated based
on the 2014NEIv2 VMT and VPOP datasets. That ratio was applied to the 2016 EPA default VMT, to
produce an EPA default VPOP projection.
53
-------
As with VMT, several state and local agencies submitted VPOP data for the beta and vl platforms, and
those data were used in place of the EPA default VPOP. The VPOP SCCs used by SMOKE are similar to
the VMT SCCs, except the emissions process is represented as "00" because it is not relevant to vehicle
population data.
For the 2016 beta platform, GA, MD, MA, NJ, NC, WI, and Pima County AZ provided VPOP data for
the year 2016 by county and MOVES vehicle type. That level of detail is sufficient for MOVES, but
SMOKE also needs VPOP broken out by fuel type. To get VPOP by full SCC, the county-vehicle VPOP
data provided by the states were loaded into the MOVES CDBs. Using fuel type tables in the CDBs, it is
possible to take county-vehicle VPOP data and create county-SCC VPOP data at the resolution needed by
SMOKE. For Massachusetts, based on quality assurance checks, modifications to their VPOP like those
done for their VMT were not needed. Wisconsin provided VPOP for 2016 by county and HPMS vehicle
type instead of by MOVES vehicle type, but the same procedure was applied as for other states in this
group. For North Carolina, EPA default VPOP data were used for buses along with the state-submitted
VPOP for other vehicle types, consistent with the VMT.
West Virginia and Clark County, Nevada also provided VPOP for the 2016 beta platform by county and
MOVES vehicle type. Because they did not provide VMT by county-HPMS, these data were not put into
MOVES databases for splitting. Instead, the VPOP data were split to full SCC using county-vehicle to
county-SCC ratios calculated from the 2016 beta VMT - not the EPA default VMT, but the final VMT
incorporating state data and split to full SCC within MOVES CDBs. So effectively, MOVES CDBs were
used to split their VPOP to full SCC, but only indirectly. West Virginia's VPOP dataset did not include
any intercity buses (MOVES vehicle type 41), thus intercity bus VPOP data were taken from the EPA
default VPOP.
The FFlO-formatted county-SCC VPOP data provided by Pennsylvania and New Hampshire for the 2016
beta platform were used for the 2016vl platform.
EPA default VPOP data were used for the states that submitted VMT but did not submit VPOP (CT, IL,
MI, MN, and VA). The new VMT that South Carolina provided, in addition to the recalculation of HPMS
splits between counties, introduced some issues with VMT/VPOP ratios when comparing the 2016beta
VMT with EPA default beta VPOP. The largest VMT/VPOP ratio issues were for HD vehicles. Because
the light-duty (LD) VPOP data are based on the IHS-Polk registration data, only the heavy-duty (HD)
VPOP data were modified for South Carolina using the EPA defaults. For HD VPOP in South Carolina:
new VPOP = EPA default VPOP * (SC-submitted VMT / EPA default VMT). In other words, the same
changes that were made to the VMT as a result of the new state data were also made to the VPOP on a
percentage basis. This preserves VMT/VPOP ratios for HD vehicles in South Carolina compared to the
EPA default data. This procedure resulted in some changes to the overall HD VPOP total in South
Carolina, both at the county level and state level.
VPOP by source type was not re-split among the LD types 21/31/32. This is consistent with the 2016beta
platform, in which all state-submitted VMT was re-split, but state-submitted VPOP at the source type
level or better was not.
For 2016vl, VPOP data were provided for:
• Massachusetts (by HPMS)
• Chicago area (8 counties, by source type)
54
-------
• Colorado (by source type)
• New Jersey (by source type)
• Pima County, AZ (by source type)
The state-submitted VMT and VPOP data underwent several modifications based on quality assurance:
Colorado:
1. There was a lot of inconsistency between the VMT and VPOP when it was broken down into
individual vehicle types. Colorado indicated that we shouldn't put too much stock into the HPMS-
>vehicle breakdowns in their VPOP data. So, we summed their VPOP to HPMS type and re-split to
vehicle type based on splits from beta VPOP.
2. Due to concerns about VMT/VPOP ratios for long haul source types (41, 53, 62), we recalculated the
VPOP from VMT using average national VMT/VPOP ratios from 2014v2: 53,000 for 41s; 18,600 for
53s, and 68,000 for 62s. We also recalculated the 52 VPOP as old 52+53 VPOP minus new 53 VPOP.
In one county (08019), 52 VPOP ended up negative, so we increased the 53 VMT/VPOP ratio (which
decreased the VPOP) for that county only.
3. There were also some VMT/VPOP ratios at the county level for HPMS vehicle types 42, 43, and 61
that were greater than 150,000 miles/year. For these, we increased the VPOP for these county-vehicle
combinations so that the VMT/VPOP ratio would never exceed 150,000. This affected 6 county-
vehicle combinations, mostly with small VPOP.
Chicago area:
1. Chicago provided separate VMT for HPMS vehicle types 20 and 30, which were summed and re-
split based on 2016beta platform VMT to keep LD vehicle type distributions consistent.
2. Motorcycles VMT and VPOP were taken from the 2016beta platform.
3. Based on email communication and number comparison, the provided Chicago area bus VMT
(submitted as total buses), appear to include only data for bust types 41 and 42 only and not 43
(school). So, the bus VMT were allocated to the 4land 42 types and school bus VMT (43) were
carried forward from 2016beta.
4. For bus VPOP, Chicago did not provide intercity buses, so those were carried forward from
2016beta, but their transit and school bus VPOP values were retained.
5. The provided 50/60 VPOP appeared to be much too low, so we recalculated it based on their VMT
combined with average VMT/VPOP ratios: 24,000 for 51s; 10,000 for 52s; 18,600 for 53s; 4,000
for 54s; 57,000 for 61s and 68,000 for 62s.
6. Counties 17063 and 17093 had VPOP for 41/42 but no VMT. We added VMT from the 2016beta
platform for these county-vehicle combinations. The VMT for 41 was carried forward from
2016beta to 2016vl. For 42, the 2016vl VMT = beta VMT * (vl VPOP / beta VPOP).
Pima County: The provided 50/60 VPOP was not based on vehicle registrations, so we recalculated
based on their VMT combined with average VMT/VPOP ratios (as was done for Chicago).
Hotelina Hours (HOTEUNG)
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
55
-------
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. For 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 2016vl is the following:
1 Start with 2016vl VMT for 62 on restricted roads, by county.
2 Multiply that by 0.007248 hours/mile (Sonntag, 2018). This results in about 73.5% less
hoteling hours as compared to the 2014v2 approach.
3 Apply parking space reductions as has been done for 2016beta, except for states that
requested we not do that (CO, ME, NJ, NY).
Hoteling hours were adjusted down in counties for which there were more hoteling hours assigned to the
county than could be supported by the known parking spaces. To compute the adjustment, we started
with the hoteling hours for the county as computed by the above method, and then we applied reductions
directly to the 2016 hoteling hours based on known parking space availability so that there were not more
hours assigned to the county than the available parking spaces could support if they were full every hour
of every day.
A dataset of truck stop parking space availability with the total number of parking spaces per county was
used in the computation of the adjustment factors. This same dataset is used to develop the spatial
surrogate for hoteling emissions. For the 2016vl platform, the parking space dataset includes several
updates compared to 2016beta platform, based on information provided by some states (e.g., MD). Since
there are 8,784 hours in the year 2016; the maximum number of possible hoteling hours in a particular
county is equal to 8,784 * the number of parking spaces in that county. Hoteling hours for each county
were capped at that theoretical maximum value for 2016 in that county, with some exceptions as outlined
below.
Because the truck stop parking space dataset may be incomplete in some areas, and trucks may sometimes
idle in areas other than designated spaces, it was assumed that every county has at least 12 parking spaces,
even if fewer parking spaces are found in the parking space dataset. Therefore, hoteling hours are never
reduced below 105,408 hours for the year in any county. If the unreduced hoteling hours were already
below that maximum, the hours were left unchanged; in other words, hoteling activity are never increased
as a result of this analysis.
A handful of high activity counties that would otherwise be subject to a large reduction were analyzed
individually to see if their parking space count seemed unreasonably low. In the following counties, the
parking space count and/or the reduction factor was manually adjusted:
• 17043 / DuPage IL (instead of reducing hoteling by 89%, applied no adjustment)
• 39061 / Hamilton OH (parking spot count increased to 20 instead of the minimum 12)
• 47147 / Robertson TN (parking spot count increased to 52 instead of just 26)
• 51015 / Augusta VA (parking space count increased to 48 instead of the minimum 12)
• 51059 / Fairfax VA (parking spot count increased to 20 instead of the minimum 12)
56
-------
Georgia and New Jersey submitted hoteling activity for the 2016vl platform. For these states, the EPA
default projection was replaced with their state data. New Jersey provided their hoteling activity in a
series of HotellingHours MOVES-formatted tables, which include separate activity for weekdays and
weekends and for each month and which have units of hours-per-week. These data first needed to be
converted to annual totals by county.
For Georgia we were going to bring forward their beta HOTELING but found it was now much too large
compared to other states once the new hoteling factor was implemented. After discussion with Georgia
Department of Natural Resources staff, we agreed to recalculate from VMT for all counties except for
those where parking > 0 and restricted VMT = 0. In those counties, Georgia's 2016beta hoteling were
reduced by 73.5% (the same reduction factor applied to the rest of the country).
Alaska Department of Natural Resources staff requested that we zero out hoteling activity in several
counties due to the nature of driving patterns in their region. In addition, there are no hoteling hours or
other emissions from long-haul combination trucks in Hawaii, Puerto Rico, or the Virgin Islands.
All parking space counts are the same as 2016beta except Maryland, which submitted an update for
2016vl.
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; unchanged. Otherwise, the submitted data
from New Jersey would have been subject to reductions. The submitted data from Georgia did not exceed
the maximum value in any county, so their submitted data did not need to be reduced.
Finally, the county total hoteling must be split into separate values for extended idling (SCC 2202620153)
and APUs (SCC 2202620191). New Jersey's submittal of hoteling activity specified a 30% APU split,
and this was used for all New Jersey counties. For the rest of the country, a 12.4% APU split was used for
the year 2016, meaning that APUs are used for 12.4% of the hoteling hours.
Onroad Emission Factor Table Development
MOVES2014b was run in emission rate mode to create emission factor tables using CB6 speciation for
the years 2016, 2020, 2023, and 2028, for all representative counties and fuel months. MOVES was run
for all counties in Alaska, Hawaii, and Virgin Islands, and for a single representative county in Puerto
Rico. The county databases (CDBs) used to run MOVES to develop the emission factor tables were
updated from those used in the 2016beta platform.
Age distributions are a key input to MOVES in determining emission rates. The age distributions for
2016vl were updated based on vehicle registration data obtained from the CRC A-l 15 project, subject to
reductions for older vehicles determined according to CRC A-l 15 methods but using additional age
distribution data that became available as part of the 2017 NEI submitted input data. One of the findings
of CRC project A-l 15 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
57
-------
distributions, where the adjustments reflected the differences in population by model year of 2014 IHS
data and 2014 submitted data from a single state.
For the 2016 platform and 2017 NEI, EPA repeated the CRC's assessment of IHS vs. state discrepancies
but with updated 2017 information and for more states. The 2017 light-duty vehicle (LDV) populations
from the CRC A-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 data, 16
provided LDV population and age distributions with snapshot dates of January 2017, July 2017, or 2018.
The other 15 had either unknown or older (back to 2013) data pull dates, so were not a fair comparison to
the 2017 IHS data.
We reviewed the population by model year comparisons for each of the 16 geographic areas vs. IHS
separately for source type 21 and for source type 31 plus 32 together. We reallocated the S/L agency
populations of cars (source type 21) and light trucks (source types 31 and 32) to match IHS car and light-
duty truck splits by county for consistent VIN decoding. We also removed the state of Georgia from the
pool of S/L agencies used to calculate the adjustment factors to avoid its influence on a pooled geographic
adjustment. Georgia already works closely with IHS on VIN decoding, and as a result, their submittal
matched IHS. The IHS data are higher than the pooled state data by 6.5 percent for cars and 5.9 percent
for light trucks.
We calculated the vehicle age distribution adjustment factors as one minus the fraction of vehicles to
remove from IHS to equal the state data, with two exceptions. The model year range 2006/2007 to 2017
receives no adjustment and the model year 1987 receives a capped adjustment that equals the adjustment
to 1988. Table 2-20 below shows the fraction of vehicles to keep by model year based on this analysis.
The adjustments were applied to the 2016 IHS-based age distributions from CRC project A-l 15 prior to
use in 2016vl. In addition, we removed the county-specific fractions of antique license plate vehicles
present in the registration summary from IHS. Nationally, the prevalence of antique plates is only 0.8
percent, but as high as 6 percent in some states (e.g., Mississippi).
Table 2-20. Older Vehicle Adjustments Showing the Fraction of IHS Vehicle Populations to Retain
for 2016vl and 2017 NEI
Model Year
(illS
l.iiihl
pre-1989
0.675
0.769
1989
0.730
0.801
1990
0.732
0.839
1991
0.740
0.868
1992
0.742
0.867
1993
0.763
0.867
1994
0.787
0.842
1995
0.776
0.865
1996
0.790
0.881
1997
0.808
0.871
1998
0.819
0.870
1999
0.840
0.874
2000
0.838
0.896
2001
0.839
0.925
2002
0.864
0.921
2003
0.887
0.942
58
-------
Model Year
(illS
l.ilihl
2004
0.926
0.953
2005
0.941
0.966
2006
1
0.987
2007-2017
1
1
In addition to removing the older and antique plate vehicles from the IHS data, we accounted for 25
counties that were outliers because their fleet age was significantly younger than typical. We limited our
outlier identification to LDV source types 21,31, and 32, because they're the most important. Many rural
counties also have outliers for low-population source types such as Transit Bus and Refuse Truck; these
do not have much of an impact on the inventory overall and reflect sparse data in low-population areas
and therefore do not require correction.
The most extreme examples of LDV outliers were Light Commercial Truck age distributions where over
50 percent of the population in the entire county is 0 and 1 years old. These sorts of young fleets can
happen if the headquarters of a leasing or rental company is the owner/entity of a relatively large number
of vehicles relative to the county-wide population. While the business owner of thousands of new
vehicles may reside in a single county, the vehicles likely operate in broader areas without being
registered where they drive. To avoid creating artificial low spots of LDV emissions in these outlier
counties, we flagged all counties above a 0.35 fraction of new vehicles and excluded their age distribution
from the final set of grouped age distributions that went into the 2016vl CDBs.
The 2016 age distributions were then grouped using a population-weighted average of the source type
populations of each county in the representative county group. The end-product was age distributions for
each of the 13 source types in each of the 315 representative counties for 2016vl. It should be noted that
the long-haul truck source types 53 (Single Unit) and 62 (Combination Unit) are a nationwide average due
to the long-haul nature of their operation.
Input data tables provided by states were reviewed before they were used. Some submitted data tables
were found to be from previous emissions modeling platforms, primarily NEI 2014v2, 2016 alpha, or
2016 beta, and these were not explicitly used as most were already incorporated into the CDBs. All
average speed distributions in 2016vl came from the CRC A-100 study, and most age distributions (other
than accepted submittals for New Jersey, Pima County, Arizona, and Wisconsin) came from methods
described above for 2016 vl. The following submitted MOVES input data (other than the activity data
discussed above) were incorporated into the 2016vl base year MOVES CDBs:
• Chicago (IL) Metropolitan Agency for Planning: FF10 VMT, FF10 VPOP, Month/Day VMT
Fraction, Ramp Fractions
• Georgia Department of Natural Resources: Fuel Supply (county assignments to fuel type groups)
• Louisville (KY) Metro Air Pollution Control District: Road Type Distributions, Ramp Fractions
• Maryland Department of the Environment: Truck Stop Locations (these affect the spatial
surrogate but not the MOVES run)
• New Jersey Department of Environmental Protection: Age Distribution
• Pima (AZ) Association of Governments: Age Distribution, I/M Coverage, Day VMT Fraction,
Road Type Distribution
• Wisconsin Department of Natural Resources: Age Distribution, I/M Coverage
59
-------
Once the input data were incorporated into the CDBs, a new set of representative counties was developed.
Each county in the continental U.S. was classified according to its state, altitude (high or low), fuel
region, the presence of inspection and maintenance programs, the mean light-duty age, and the fracti on of
ramps. A binning algorithm was executed to identify "like counties", and then specific requests for
representative county groups by states were honored from the states of Maryland, New York, New J ersey,
Wisconsin, Michigan, and Georgia. The final result was 315 representative counties (up from 304 in
2016 beta) as shown in Figure 2-3. The representative counties themselves changed substantially; of the
315 representative counties, 145 were not representative counties in 2016 beta. The CDBs for these 145
counties were developed from the 2014NEIv2 counties and updated to represent the year 2016. For more
information on the development of the 2016 age distributions and representative counties and the review
of the input data, see the memoranda "Onroad 2016vl documentation_20191007" and
"RepCountiesFor2016v 1 -2017_13jun2019" (ERG, 2019).
Figure 2-3. Representative Counties in 2016vl
Reference County Groups 2016 V1
To create the 2016vl emission factors, MOVES was run separately for each representative county and
fuel month for each temperature bin needed for calendar year 2016. The CDBs used to run MOVES
include the state-specific control measures such as the California low emission vehicle (LEV) program,
except that fuels were updated to represent calendar year 2016. In addition, the range of temperatures rnn
60
-------
along with the average humidities used were specific to the year 2016. The MOVES results were post-
processed into CSV-formatted emission factor tables that can be read by SMOKE-MOVES.
Onroad California Inventory Development
The California Air Resources Board (CARB) provided their own onroad emissions inventories based on
their EMFAC2017 model. EMFAC2017 was run by CARB for model years 2016, 2023, 2028, and 2035.
Details on how SMOKE-MOVES emissions were adjusted to match the CARB-based 2016 inventory are
provided in the Emissions Processing Requirements section of this document.
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 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 that are in the
2017NEI 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 2016vl inventory are categorized as
operating either in-port or underway and as main and auxiliary engines are encoded using the SCCs listed
in Table 2-21.
Table 2-21. 2016vl platform 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
61
-------
Category 1 and 2 CMV emissions were developed for the 2017 NEI,5 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 US Exclusive Economic Zone and the North
American EC A, although some non-EC A activity are captured as well
Figure 2-4. 2017NEl/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 indivi dual vessel movements, estimate
hours of operation, and quantify propulsion engine loads. The compiled AIS data also included the
vessel's International Marine Organization (IMO) number and Maritime Mobile Service Identifier
(MMSI); which allowed each vessel to be matched to their characteristics obtained from the Clarksons
ship registry (Clarksons, 2018).
USEPA used the engine bore and stroke data to calculate cylinder volume. Any vessel that had a
calculated cylinder volume greater than 30 liters was incorporated into the USEPA's new Category 3
Commercial Marine Vessel (C3CMV) model. The remaining records were assumed to represent Category
1 and 2 (C1C2) or non-ship activity. The C1C2 AIS data were quality assured including the removal of
duplicate messages, signals from pleasure craft, and signals that were not from CMV vessels (e.g., buoys,
5 Category 1 and 2 Commercial Marine Vessel 2017 Emissions Inventory (ERG, 2019).
62
-------
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
Emissionsintervai = Time (hr)intervai x Power(kW) x £"F( ) 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 2-22. 11,302 vessels were directly identified by their ship and cargo number. The
remaining group of miscellaneous ships represent 13 percent of the AIS vessels (excluding recreational
vessels) for which a specific vessel type could not be assigned.
Table 2-22. Vessel groups in the cmv_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
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 Clarkson's ship registry
63
-------
and adopted from the Global Fishing Watch (GFW) dataset when available. However, there is limited
vessel specific attribute data for most of the C1C2 fleet. This necessitated the use of surrogate engine
power and load factors, which were computed for each vessel group shown in Table 2. In addition to the
power required by propulsive engines, power needs for auxiliary engines were also computed for each
vessel group. Emissions from main and auxiliary engines are inventoried with different SCCs as shown
in Table 2-21.
The final components of the emissions computation equation are the emission factors and the low load
adjustment factor. The emission factors used in this inventory take into consideration the EPA's marine
vessel fuel regulations as well as exhaust standards that are based on the year that the vessel was
manufactured to determine the appropriate regulatory tier. Emission factors in g/kWhr by tier for NOx,
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 factor were calculated per tier based on C1C2 population
distributions grouped by engine displacement. Boiler emission factors were obtained from an earlier Entec
study (Entec, 2004). If the year of manufacture was unknown then it was assumed that the vessel was
Tier 0, such that actual emissions may be less than those estimated in this inventory. Without more
specific data, the magnitude of this emissions difference cannot be estimated.
Propulsive emissions from low-load operations were adjusted to account for elevated emission rates
associated with activities outside the engines' optimal operating range. The emission factor adjustments
were applied by load and pollutant, based on the data compiled for the Port Everglades 2015 Emission
Inventory.6 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 data7 by applying a factor of 0.98 to all pollutants. For consistency, the same methods were
used for California, Canadian, and other non-U.S. emissions.
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3)
The cmv_c3 inventory is brand new for the 2016vl platform. It was developed in conjunction with the
CMV inventory for the 2017 NEI. This sector contains large engine CMV emissions. Category 3 (C3)
marine diesel engines are those at or above 30 liters per cylinder, typically these are the largest engines
rated at 3,000 to 100,000 hp. C3 engines are typically used for propulsion on ocean-going vessels
including container ships, oil tankers, bulk carriers, and cruise ships. Emissions control technologies for
6 USEPA. EPA and Port Everglades Partnership: Emission Inventories and Reduction Strategies. US Environmental
Protection Agency, Office of Transportation and Air Quality, June 2018.
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100UKV8.pdf.
7 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.
64
-------
C3 CMV sources are limited due to the nature of the residual fuel used by these vessels.8 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 2016vl inventory
are categorized as operating either in-port or underway and are encoded using the SCCs listed in Table
2-23 and distinguish between diesel and residual fuel, in port areas versus underway, and main and
auxiliary engines. In addition to C3 sources in state and federal waters, the cmv_c3 sector includes
emissions in waters not covered by the NEI (FIPS = 98) and taken from the "ECA-IMO-based" C3 CMV
inventory.9 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 2-23. 2016vl platform SCCs for cmv_c3 sector
see
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) in order to quantify all ship activity which occurred between January
1 and December 31, 2017. The International Maritime Organization's (IMO's) International Convention
for the Safety of Life at Sea (SOLAS) requires AIS to be fitted aboard all international voyaging ships
with gross tonnage of 300 or more, and all passenger ships regardless of size (IMO, 2002). In addition,
the USCG has mandated that all commercial marine vessels continuously transmit AIS signals while
transiting U.S. navigable waters. As the vast majority of C3 vessels meet these requirements, any omitted
from the inventory due to lack of AIS adoption are deemed to have a negligible impact on national C3
emissions estimates. The activity described by this inventory reflects ship operations within 200 nautical
miles of the official U.S. baseline. This boundary is roughly equivalent to the border of the U.S Exclusive
Economic Zone and the North American ECA, although some non-ECA activity is captured as well
(Figure 2-4).
8 https://www.epa.gov/regulations-emissions-vehicles-and-engines/regulations-emissions-marine-vessels
9 https://www.epa.gOv/sites/production/files/2017-08/documents/2014v7.0 2014 emismod tsdvl.pdf
65
-------
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
Emissionsintervai = Time (hr)intervai x Power(kW) x EF{x LLAF Equation 2-2
Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and
emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF
represents the low load adjustment factor, a unitless factor which reflects increasing propulsive emissions
during low load operations. Time indicates the activity duration time between consecutive intervals.
Emissions were computed according to a computed power need (kW) multiplied by the time (hr) and by
an engine-specific emission factor (g/kWh) and finally by a low load adjustment factor that reflects
increasing propulsive emissions during low load operations.
The resulting emissions were available at 5-minute intervals. Code was developed to aggregate these
emissions to modeling grid cells and up to hourly levels so that the emissions data could be input to
SMOKE for emissions modeling with SMOKE. Within SMOKE, the data were speciated into the
pollutants needed by the air quality model,10 but since the data were already in the form of point sources
at the center of each grid cell, and they were already hourly, no other processing was needed within
SMOKE. SMOKE requires an annual inventory file to go along with the hourly data, so those files were
also generated for each year.
10 Ammonia (NH3) was also added by SMOKE in the speciation step.
66
-------
On January 1st, 2015, the EC A 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. In 2016vl, 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 2016beta
nonpoint C3 inventory was converted to a point inventory to support plume rise calculations for C3
vessels. The nonpoint emissions were allocated to point sources using a multi-step allocation process
because not all of the inventory components had a complete set of county-SCC combinations. In the first
step, the county-SCC sources from the nonpoint file were matched to the county-SCC points in the 2011
ECA-IMO C3 inventory. The ECA-IMO inventory contains multiple point locations for each county-
SCC. The nonpoint emissions were allocated to those points using the PM2.5 emissions at each point as a
weighting factor.
Cmv_c3 underway emissions that did not have a matching FIPS in the ECA-IMO inventory were
allocated using the 12 km 2014 offshore shipping activity spatial surrogate (surrogate code 806). Each
county with underway emissions in the area inventory was allocated to the centroids of the cells
associated with the respective county in the surrogate. The emissions were allocated using the weighting
factors in the surrogate.
The resulting point emissions centered on each grid cell were converted to an annual point 2010 flat file
format (FF10). Pictures of the emissions are shown in Section 7 of this document. A set of standard stack
parameters were assigned to each release point in the cmv_c3 inventory. The assigned stack height was
65.62 ft, the stack diameter was 2.625 ft, the stack temperature was 539.6 °F, and the velocity was 82.02
ft/s. Emissions were computed for each grid cell needed for modeling.
Adjustment of the 2017 NEI CMV C3 to 2016
Because the NEI emissions data were for 2017, an analysis was performed of 2016 versus 2017 entrance
and clearance data (ERG, 2019a). Annual, monthly, and daily level data were reviewed. Annual ratios of
entrance and clearance activity were developed for each ship type as shown in Table 2-24. For vessel
types with low populations (C3 Yacht, tug, barge, and fishing vessels), an annual ratio of 0.98 was
applied.
67
-------
Table 2-24. 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 are applied to the 2017 emission values to estimate 2016 values
The cmv_c3 projection factors were pollutant-specific and region-specific. Most states are mapped to a
single region with a few exceptions. Pennsylvania and New York were split between the East Coast and
Great Lakes, Florida was split between the Gulf Coast and East Coast, and Alaska was split between
Alaska East and Alaska West. The non-federal factors listed in this table were applied to sources outside
of U.S. federal waters (FIPS 98). Volatile Organic Compound (VOC) Hazardous Air Pollutant (HAP)
emissions were projected using the VOC factors. NH3 emissions were held constant at 2014 levels.
2.4.3 Rail Sources (rail)
The rail sector includes all locomotives in the NEI nonpoint data category. The 2016vl inventory SCCs
are shown in Table 2-25. This sector excludes railway maintenance activities. Railway maintenance
emissions are included in the nonroad sector. The point source yard locomotives are included in the
ptnonipm sector. In 2014NEIv2, rail yard locomotive emissions were present in both the nonpoint (rail
sector) and point (ptnonipm sector) inventories. For the 2016vl platform, 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.
68
-------
Table 2-25. 2016vl SCCs for the Rail Sector
sec
Sector
Description: Mobile Sources prefix for all
2285002006
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
2285002007
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
2285002008
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains
(Amtrak)
2285002009
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
2285002010
rail
Railroad Equipment; Diesel; Yard Locomotives (nonpoint)
28500201
rail
Railroad Equipment; Diesel; Yard Locomotives (point)
Class I Line-haul Methodology
In 2008 air quality planners in the eastern US formed the Eastern Technical Advisory Committee
(ERTAC) for solving persistent emissions inventory issues. This work is the fourth inventory created by
the ERTAC rail group. For the 2016 inventory, the Class I railroads granted ERTAC Rail permission to
use the confidential link-level line-haul activity GIS data layer maintained by the Federal Railroad
Administration (FRA). In addition, the Association of American Railroads (AAR) provided national
emission tier fleet mix information. This allowed ERTAC Rail to calculate weighted emission factors for
each pollutant based on the percentage of the Class I line-haul locomotives in each USEPA Tier level
category. These two datasets, along with 2016 Class I line-haul fuel use data reported to the Surface
Transportation Board (Table 2-26), were used to create a link-level Class I emissions inventory, based on
a methodology recommended by Sierra Research. Rail Fuel Consumption Index (RFCI) is a measure of
fuel use per ton mile of freight. This link-level inventory is nationwide in extent, but it can be aggregated
at either the state or county level.
Table 2-26. Class I Railroad Reported Locomotive Fuel Use Statistics for 2016
Class 1 Railroads
2016 U-l Reported Locomotive
Kuel I se («al/vcar)
UK 1
(ton-ill ilcs/gal)
Adjusted
urc i
(lon-niilcs/gal)
Line-Maul"
Switcher
BNSF
1,243,366,255
40,279,454
972
904
Canadian National
102,019,995
6,570,898
1,164
1,081
Canadian Pacific
56,163,697
1,311,135
1,123
1,445
CSX Transportation
404,147,932
39,364,896
1,072
1,044
Kansas City
Southern
60,634,689
3,211,538
989
995
Norfolk Southern
437,110,632
28,595,955
920
906
Union Pacific
900,151,933
85,057,080
1,042
1,095
Totals:
3,203,595,133
204,390,956
1,006
993
* Includes work trains; Adjusted RFCI values calculated from FRA gross ton-mile data as described on page 7. RFCI total is ton-mile weighted mean.
Annual default emission factors for locomotives based on operating patterns ("duty cycles") and the
estimated nationwide fleet mixes for both switcher and line-haul locomotives are available. However,
Tier level fleet mixes vary significantly between the Class I and Class II/III railroads. As can be seen in
Figure 2-5 and Figure 2-6, Class I railroad activity is highly regionalized in nature and is subject to
69
-------
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
>= 100.00 MGT
Figure 2-6. Class I Railroads in the United States3
UP
Source: Federal Railroad Administration - December 2016
70
-------
For the 2016 inventory, the AAR provided a national line-haul Tier fleet mix profile representing the
entire Class I locomotive fleet. A locomotive's Tier level determines its allowable emission rates based
on the year when it was built and/or re-manufactured. The national fleet mix data was then used to
calculate weighted average in-use emissions factors for the line-haul locomotives operated by the Class I
railroads as shown in Table 2-27.
Table 2-27. 2016 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal)
AAU
Tier Level
Meet Mix
Uatio
I'M it.
IK
NOx
(O
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).
9
EFi = I EFiT x fr Equation 2-3
7=1
where:
EFi = Weighted Emission Factor for pollutant i for Class I locomotive fleet (g/gal).
EFiT = Emission Factor for pollutant i for locomotives in Tier T (g/gal) (Table 4).
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 PM104, the ratio
of volatile organic carbon (VOC) to (hydrocarbon) HC was assumed to be 1.053, and the emission factors
71
-------
used for sulfur dioxide (SO2) and ammonia (NH3)were 0.0939 g/gal4 and 83.3 mg/gal6, respectively. The
2016 SO2 emission factor is based on the nationwide adoption of 15 ppm ultra-low sulfur diesel (ULSD)
fuel by the rail industry.
The remaining steps to compute the Class 1 rail emissions involved calculating class I railroad-specific
rail fuel consumption index values and calculating emissions per link. The final
link-level emissions for each pollutant were then aggregated by state/county FIPS code and then
converted into an FF10 format used by SMOKE. More detail on these steps is described in the
specification sheet for the 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.
Since the railroads only supplied switcher counts, average fuel use per switcher values were calculated for
each railroad. This was done by dividing each company's 2016 R-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 2-28 summarizes the 2016 yard fuel use and
switcher data for each Class I railroad. The emission factors used for rail yard switcher engines are
shown in Table 2-29.
Table 2-28. Surface Transportation Board R-l Fuel Use Data - 2016
Kiiili'ttiid
2111(. IM Yard
l-licl I SO (liill)
i:rt.\( ciilculiiicd
I'lK'l I SO (liill)
1 (I011 li I'ied
S« ilchois
r.K 1 AC per Sm holier l-'uel
I SO l«!ll)
BiNSF
40,279,454
40,740,317
442
92,173
CSXT
39,364,896
43,054,795
455
94,626
CN
6,570,898
6,570,898
103
63,795
KCS
3,211,538
3,211,538
176
18,247
NS
28,595,955
28,658,528
458
62,573
CPRS
1,311,135
1,311,135
70
18,731
UP
85,057,080
85,057,080
1286
66,141
All Class I's
204,390,956
208,604,291
2,990
69,767
Table 2-29. 2016 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)4
Tier Level
AAU l leel
Mix K;i1 io
I'M HI
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
72
-------
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.
Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States
2016-2017 Active Rallyaids-
~ RailyjiiI LocataOn*
Clas-s I Railroads.
Sewrfeft Adnwiadrttf
Class II and III Methodology
There are approximately 560 Class II and III Railroads operating in the United States, most of which are
members of the American Short Line and Regional Railroad Association (ASLRRA). While there is a lot
of information about individual Class II and III railroads available online, a significant amount of effort
73
-------
would be required to convert this data into a usable format for the creation of emission inventories. In
addition, the Class II and III rail sector has been in a constant state of flux ever since the railroad industry
was deregulated under the Staggers Act in 1980. Some states have conducted independent surveys of
their Class II and III railroads and produced emission estimates, but no national level emissions inventory
existed for this sector of the railroad industry prior to ERTAC Rail's work for the 2008 NEI.
Class II and III railroad activities account for nearly 4 percent of the total locomotive fuel use in the
combined ERTAC Rail emission inventories and for approximately 35 percent of the industry's national
freight rail track mileage. These railroads are widely dispersed across the country and often utilize older,
higher emitting locomotives than their Class I counterparts Class II and III railroads provide
transportation services to a wide range of industries. Individual railroads in this sector range from small
switching operations serving a single industrial plant to large regional railroads that operate hundreds of
miles of track. Figure 2-8 shows the distribution of Class II and III railroads and commuter railroads
across the country. This inventory will be useful for regional and local modeling, helps identify where
Class II and III railroads may need to be better characterized, and provides a strong foundation for the
future development of a more accurate nationwide short line and regional railroad emissions inventory. A
picture of the locations of class II and III railroads is shown in Figure 2-8. The data sources, calculations,
and assumptions used to develop the Class II and III inventory are described in the 2016vl rail
specification sheet.
Figure 2-8. Class II and III Railroads in the United States5
Commuter Rail Methodology
Commuter rail emissions were calculated in the same way as the Class II and III railroads. The primary
difference is that the fuel use estimates were based on data collected by the Federal Transit
74
-------
Administration (FTA) for the National Transit Database. 2016 fuel use was then estimated for each of the
commuter railroads shown in Table 2-30 by multiplying the fuel and lube cost total by 0.95, then dividing
the result by Metra's average diesel fuel cost of $1.93/gallon. These fuel use estimates were replaced
with reported fuel use statistics for MARC (Maryland), MBTA (Massachusetts), Metra (Illinois), and NJT
(New Jersey). The commuter railroads were separated from the Class II and III railroads so that the
appropriate SCC codes could be entered into the emissions calculation sheet.
Table 2-30. Expenditures and fuel use for commuter rail
1 R\
(ode
S\s(i-m
( ilios Sencd
Propulsion
1 > pi-
DOT 1 ml A
1 -ii ho Cosls
Ki-porli-d/I'lsliniiili-d
I-iii-I I si-
ACEX
Altamont Corridor
Express
San Jose / Stockton
Diesel
$889,828
437,998.24
CMRX
Capital MetroRail
Austin
Diesel
No data
n/a
DART
A-Train
Denton
Diesel
$0
0.00
DRTD
Denver RTD: A&B
Lines
Denver
Electric
$0
0.00
JPBX
Caltrain
San Francisco / San Jose
Diesel
$7,002,612
3,446,881.55
LI
MTA Long Island Rail
Road
New York
Electric and
Diesel
$13,072,158
6,434,481.92
MARC
MARC Train
Baltimore / Washington, D.C.
Diesel and
Electric
$4,648,060
4.235.297.57
MBTA
MBTA Commuter Rail
Boston / Worcester / Providence
Diesel
$37,653,001
12.142.826.00
MNCW
MTA Metro-North
Railroad
New York / Yonkers / Stamford
Electric and
Diesel
$13,714,839
6,750,827.49
NICD
NICTD South Shore
Line
Chicago / South Bend
Electric
$181,264
0.00
NIRC
Metra
Chicago
Diesel and
Electric
$52,460,705
25.757.673.57
NJT
New Jersey Transit
New
York / Newark / Trenton / Philadelphia
Electric and
Diesel
$38,400,031
16.991.164.00
NMRX
New Mexico Rail
Runner
Albuquerque / Santa Fe
Diesel
$1,597,302
786,236.74
CFCR
SunRail
Orlando
Diesel
$856,202
421,446.58
MNRX
Northstar Line
Minneapolis
Diesel
$708,855
348,918.26
Not
Coded
SMART
San Rafael-Santa Rosa (Opened 2017)
Diesel
n/a
0.00
NRTX
Music City Star
Nashville
Diesel
$456,099
224,504.69
SCAX
Metrolink
Los Angeles / San Bernardino
Diesel
$19,245,255
9,473,052.98
SDNR
NCTD Coaster
San Diego / Oceanside
Diesel
$1,489,990
733,414.77
SDRX
Sounder Commuter
Rail
Seattle / Tacoma
Diesel
$1,868,019
919,491.22
SEPA
SEPTA Regional Rail
Philadelphia
Electric
$483,965
0.00
SLE
Shore Line East
New Haven
Diesel
No data
n/a
TCCX
Tri-Rail
Miami / Fort Lauderdale / West Palm
Beach
Diesel
$5,166,685
2,543,186.92
TREX
Trinity Railway
Express
Dallas / Fort Worth
Diesel
No data
n/a
UTF
UTA FrontRunner
Salt Lake City / Provo
Diesel
$4,044,265
1,990,700.39
VREX
Virginia Railway
Express
Washington, D.C.
Diesel
$3,125,912
1,538,661.35
WSTX
Westside Express
Service
Beaverton
Diesel
No data
n/a
*Reported fuel use values were used for MARC, MBTA, Metra, and New Jersey Transit.
75
-------
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
Seurc* Finktjl R*Aci*j Mnn*aj*n KI4
Other Data Sources
The California Air Resources Board (CARB) provided rail inventories for inclusion in the 2016vl
platform. C ARB'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
76
-------
total rail yard emissions matched the CARB dataset. In other words, 2016vl platform includes county
total rail yard emissions from CARB, but the locations of rail yards are based on the national
methodology. There are three counties with CARB-submitted rail yard emissions, but no rail yard
locations in the national dataset; for those counties, the rail yard emissions were included in the rail sector
using SCC 2285002010.
North Carolina separately provided passenger train (SCC 2285002008) emissions for use in the platform.
We used NC's passenger train emissions instead of the corresponding emissions from the Lake Michigan
Air Directors Consortium (LADCO) dataset.
None of these rail inventory sources included HAPs. For VOC speciation, the EPA preferred augmenting
the inventory with HAPs and using those HAPs for integration, rather than running the sector as a no-
integrate sector. So, Naphthalene, Benzene, Acetaldehyde, Formaldehyde, and Methanol (NBAFM)
emissions were added to all rail inventories, including the California inventory, using the same
augmentation factors as are used to augment HAPs in the NEI.
2.4.4 Nonroad Mobile Equipment Sources (nonroad)
The mobile nonroad equipment sector includes all mobile source emissions that do not operate on roads,
excluding commercial marine vehicles, railways, and aircraft. Types of nonroad equipment include
recreational vehicles, pleasure craft, and construction, agricultural, mining, and lawn and garden
equipment. Nonroad equipment emissions were computed by running the MOVES2014b model,11 which
incorporates the NONROAD2008 model. MOVES2014b replaced MOVES2014a in August 2018, and
incorporates updated nonroad engine population growth rates, nonroad Tier 4 engine emission rates, and
sulfur levels of nonroad diesel fuels. MOVES2014b provides a complete set of HAPs and incorporates
updated nonroad emission factors for HAPs. MOVES2014b was used for all states other than California
and Texas, which developed their own emissions using their own tools. VOC and PM speciation profile
assignments are determined by MOVES and applied by SMOKE.
MOVES2014b provides estimates of NONHAPTOG along with the speciation profile code for the
NONHAPTOG emission source. This was accomplished by using NHTOG#### as the pollutant code in
the Flat File 2010 (FF10) inventory file that can be read into SMOKE, where #### is a speciation profile
code. One of the speciation profile codes is '95335a' (lowercase 'a'); the corresponding inventory
pollutant is NONHAPTOG95335A (uppercase 'A') because SMOKE does not support inventory
pollutant names with lowercase letters. Since speciation profiles are applied by SCC and pollutant, no
changes to SMOKE were needed to use the inventory file with this profile information. This approach
was not used for California or Texas, because the datasets in those states included VOC.
MOVES2014b, unlike MOVES2014a, also provides estimates of PM2.5 by speciation profile code for the
PM2.5 emission source, using PM25_#### as the pollutant code in the FF10 inventory file, where #### is
a speciation profile code. To facilitate calculation of coarse particulate matter (PMC) within SMOKE, and
to help create emissions summaries, an additional pollutant representing total PM2.5 called PM25TOTAL
was added to the inventory. As with VOC / TOG, this approach is not used for California or Texas.
MOVES2014b outputs emissions data in county-specific databases, and a post-processing script converts
the data into FF10 format. Additional post-processing steps were performed as follows:
• County-specific FFlOs were combined into a single FF10 file.
11 https://www.epa. gov/moves
77
-------
• 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 for the vl 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."12 This resource provides expenditures for each of 5
major regions that cover the Continental U.S., as well as state-level data for 15 major farm producing
states. Because of the limited coverage of the NASS source relative to that in MOVES, it was necessary to
identify a means for estimating the 2016 Agricultural sector allocation data for the following States and
Territories from a different source: Alaska, Hawaii, Puerto Rico, and U.S. Virgin Islands. The approach
for these areas is described below.
For the Continental U.S., NCDEQ first allocated the remainder of the regional fuel expenditures to states
in each region for which state-level data are not reported. For this allocation, NCDEQ relied on 2012 fuel
expenditure data from NASS' 2012 Census of Agriculture (note that 2017 data were not yet available at
the time of this effort).13 The next step to developing county-level allocation data for agricultural
12 Accessed from htto://usda.mannlib.Cornell.edu/MannUsda/viewDocumentlnfo.do?documentID= 1066. November 2018.
13 Accessed from https://www.nass.usda.gov/Publications/AgCensus/2012/. November 2018.
78
-------
equipment was to multiply the state-level fuel expenditure estimates by county-level allocation ratios.
These allocation ratios were computed from county-level fuel expenditure data from the NASS' 2012
Census of Agriculture. There were 17 counties for which fuel expenditure data were withheld in the
Census of Agriculture. For these counties, NDEQ allocated the fuel expenditures that were not accounted
for in the applicable state via a surrogate indicator of fuel expenditures. For most states, the 2012 Census
of Agriculture's total machinery asset value was the surrogate indicator used to perform the allocation.
This indicator was found to have the strongest correlation to agricultural sector fuel expenditures based on
analysis of 2012 state-level Census of Agriculture values for variables analyzed (correlation coefficient of
0.87).14 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.15
For the Construction sector, MOVES2014b uses estimates of 2003 total dollar value of construction by
county to allocate national Construction equipment populations to the state and local levels.16 However,
the 2016 Nonroad Collaborative Work Group sought to update the surrogate data used to geographically
allocate Construction equipment with a more recent data source thought to be more reflective of
emissions-generating Construction equipment activity at the county level: acres disturbed by residential,
non-residential, and road construction activity.
The nonpoint sector of the 2014 National Emissions Inventory (NEI) includes estimates of Construction
Dust (PM2.5), for which acreage disturbed by residential, non-residential, and road construction activity is
a function.17 The 2014 NEI Technical Support Document18 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
14 Other variables analyzed were inventory of tractors and inventory of trucks.
15 For reference, these allocations were 0.0639 percent for Puerto Rico and 0.0002 percent for the U.S. Virgin Islands.
16 https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P1004LDX.pdf
17 https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventorv-nei-data
18 https://www.epa.gov/sites/production/files/2018-07/documents/nei2014v2 tsd 05iul2018.pdf
79
-------
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 nr state surrogate 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 MOVE-S2014b 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 nr surrogate 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 2-31 several state and local agencies provided nonroad inputs for use in the 2016vl
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).
80
-------
Table 2-31. Submitted nonroad input tables by agency
slsili'id
Sisilc or
( oiiniMii's) in
llii' Aiii'iio
=
— /.
= =
11
— 'j
5 j*
C' o
2 w
z
Q.
r —•
3 s
2
2
= _2
~
5T <—i
=
& zz
~ 2
s —
-
s _2
* —
.5 lb
.= -
i 2
Tj. "=
- a.
= s
a.
o
r E.
'v *z
rE -5
•- s
s —
-E 2
= TZ
'J
_2
s -
.= 2
:Z :j
jz
s =
c s
= k
— o
s *
^ _
.«*.
Tt "
x —
= 'j
i* —
= h
i.
zl 5
*_ 'J
•I s
= —
V5
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, and 2028.
All California nonroad inventories are annual, with monthly temporalization applied in SMOKE.
Emissions for oil field equipment (SCCs ending in -10010) were removed from the California inventory
in order to prevent a double count with the np oilgas sector.
Texas nonroad emissions were provided by the Texas Commission on Environmental Quality for the
years 2016, 2023, and 2028, using TCEQ's TexN2 tool.19 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.
19 For more information on the TexN2 tool please see: ftp://amdaftp.tcea.texas.gov/EI/nonroad/TexN2/
81
-------
Nonroad Updates from State Comments
The 2016 Nonroad Collaborative Work group received a small number of comments on the 2016beta
inventory, all of which were addressed and implemented in the 2016vl nonroad inventory:
• Georgia Department of Natural Resources: incorporate updated fuel supply {nrfuelsupply
table) for 45 Georgia counties, to reflect the removal of summer Reid Vapor Pressure restrictions
in 2016; utilize updated geographic allocation factors (nr state surrogate table) for the
Commercial, Lawn & Garden (commercial, public, and residential), Logging, Manufacturing,
Golf Carts, Recreational, Railroad Maintenance Equipment and A/C/Refrigeration sectors, using
data from the U.S. Census Bureau and U.S. Forest Service.
• Lake Michigan Air Directors Consortium (LADCO): update seasonal allocation of agricultural
equipment activity (nrmonthallocation table) for Illinois, Indiana, Iowa, Michigan, Minnesota,
Missouri, Ohio, and Wisconsin.
• Texas Commission on Environmental Quality: replace MOVES2014b nonroad emissions for
Texas with emissions calculated with TCEQ's TexN2 model.
• Alaska Department of Environmental Conservation: remove emissions as calculated by
MOVES2014b for several equipment sector-county/census areas combinations in Alaska, due to
an absence of nonroad activity (see Table 2-32).
Table 2-32. Alaska counties/census areas for which nonroad equipment sector-specific emissions
are removed in 2016vl
Nonroad Kqiiipmcnl Sector
Counties/Census Areas (I IPS) lor which equipment
sector emissions are rcmo\ed in 20I6\ 1
Agricultural
Aleutians East (02013), Aleutians West (02016), Bethel
Census Area (02050), Bristol Bay Borough (02060),
Dillingham Census Area (02070), Haines Borough (02100),
Hoonah-Angoon Census Area (02105), Ketchikan Gateway
(02130), Kodiak Island Borough (02150), Lake and
Peninsula (02164), Nome (02180), North Slope Borough
(02185), Northwest Arctic (02188), Petersburg Borough
(02195), Pr of Wales-Hyder Census Area (02198), Sitka
Borough (02220), Skagway Borough (02230), Valdez-
Cordova Census Area (02261), Wade Hampton Census Area
(02270), Wrangell City + Borough (02275), Yakutat City +
Borough (02282), Yukon-Koyukuk Census Area (02290)
Logging
Aleutians East (02013), Aleutians West (02016), Nome
(02180), North Slope Borough (02185), Northwest Arctic
(02188), Wade Hampton Census Area (02270)
Railway Maintenance
Aleutians East (02013), Aleutians West (02016), Bethel
Census Area (02050), Bristol Bay Borough (02060),
Dillingham Census Area (02070), Haines Borough (02100),
Hoonah-Angoon Census Area (02105), Juneau City +
Borough (02110), Ketchikan Gateway (02130), Kodiak
Island Borough (02150), Lake and Peninsula (02164), Nome
82
-------
Nonroiul Kqiiipmenl Sector
Counties/Census A rests (MI'S) lor which equipment
sector emissions ;irc remo\oil in 20I6\ 1
((PI SO), ). North Slope Borough (<) "* 1S5) Norllnwsl Arctic
(02188), Petersburg Borough (02195), Pr ofWales-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, ptagfire)
Multiple types of fires are represented in the modeling platform. These include wild and prescribed fires
that are grouped into the ptfire sector, and agricultural fires that comprise the ptagfire sector. All ptfire
and ptagfire fires are in the United States. Fires outside of the United States are described in the
ptfire othna sector later in this document.
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. 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 2016vl inventory, data processing was conducted differently depending on the fire type, as defined
below:
• Wildfire (WF): any fire started by an unplanned ignition caused by lightning; volcanoes; other acts
of nature; unauthorized activity; or accidental, human-caused actions, or a prescribed fire that has
developed into a wildfire.
• Prescribed (Rx) fire: any fire intentionally ignited by management actions in accordance with
applicable laws, policies, and regulations to meet specific land or resource management
objectives. Prescribed fire is one type of fire fuels treatment. Fire fuels treatments are vegetation
management activities intended to modify or reduce hazardous fuels. Fuels treatments include
prescribed fires, wildland fire use, and mechanical treatment.
The SCCs used for the ptfire sources are shown in Table 2-33. The ptfire inventory includes separate
SCCs for the flaming and smoldering combustion phases for wildfire and prescribed burns. Note that
prescribed grassland fires or Flint Hills, Kansas have their own SCC in the 2016vl inventory. The year
2016 fire season also included some major wild grassland fires. These wild grassland fires were assigned
the standard wildfire SCCs shown in Table 2-33.
Table 2-33. SCCs included in the ptfire sector for the 2016vl inventory
SCC
Description
2801500170
Grassland fires; prescribed
2810001001
Forest Wildfires; Smoldering; Residual smoldering only (includes grassland
wildfires)
83
-------
see
Description
2810001002
Forest Wildfires; Flaming (includes grassland wildfires)
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming
National Fire Information Data
Numerous fire information databases are available from U.S. national government agencies. Some of the
databases are available via the internet while others must be obtained directly from agency staff. Table
2-34 provides the national fire information databases that were used for the 2016vl ptfire inventory,
including the website where the 2016 data were downloaded.
Table 2-34. 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
X
CSV
NOA
A
North
America
httDs://www.osDo.noaa.gov/Products/land/h
ms.html
Geospatial Multi-
Agency
Coordination(GeoM
AC)
WF
SHP
USGS
Entire US
https://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
https://fam.nwcg.gov/fam-web/ (see Public
Access Reports, Free Data Extract, then NASF State
Data Extract)
Monitoring Trends
in Burn Severity
(MTBS)
WF/R
X
SHP
USGS,
USFS
Entire US
httDs://www.mtbs.gov/direct-download
Forest Service
Activity Tracking
System (FACTS)
RX
SHP
USFS
Entire US
Hazardous Fuel Treatment Reduction: Polygon
at https://data.fs.usda.aov/aeodata/edw/
datasets.DhD
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.
84
-------
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
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.
85
-------
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-
35. Data from nine individual states and one Indian Tribe were used for the 2016vl ptfire inventory.
Table 2-35. 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 2-36 provides a summary of the type of data submitted by each
S/L/T agency and includes spatial, temporal, acres burned and other information provided by the
agencies.
Table 2-36. Brief description of fire information submitted for 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
86
-------
S/L/T
agency
name
Fire
Types
Description
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
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 2-33. SCCs
included in the ptfire sector for the 2016vl inventoryTable 2-33. The lofted smoldering emissions
were assigned to the flaming emissions SCCs in Table 2-33.
Figure 2-10 is a schematic of the data processing stream for the 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 Blue Sky Framework.
SMARTFIRE2 is an algorithm and database system that operate within a geographic information system
(GIS). SMARTFIRE2 combines multiple sources of fire information and reconciles them into a unified
GIS database. It reconciles fire data from space-borne sensors and ground-based reports, thus drawing on
the strengths of both data types while avoiding double-counting of fire events. At its core, SMARTFIRE2
87
-------
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.
Figure 2-10. Processing flow for fire emission estimates in the 2016vl inventory
Input Data Sets
(state/local/tribal and national data sets)
O
Data Preparation
Data Aggregation and Reconciliation
(SmartFire2)
Daily fire locations
with fire size and type
Fuel Moisture and
Fuel Loading Data
Smoke Modeling (BlueSky Framework)
Daily smoke emissions
for each fire
Emissions Post-Processing
Final Wildland Fire Emissions Inventory
88
-------
Figure 2-11. Default fire type assignment by state and month in cases where a satellite detect is only
source of fire information.
4 -
Default Fire Type
Assignment
WF Months
~ 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 all of the
CAP emission factors for wildland fires used in the 2016vl inventory. The FIAPs were derived from
regional emissions factors from Urbanski (2014).
Figure 2-12. Blue Sky Modeling Framework
Location
Dates
Type
Size
Fuels
(FCCS v3;
~L
Consumption
Emission
LF v1.4)
(Consume v4)
*-»¦ Factors
(FEPS v2)
Emissions
Bluesky Framework v3.5.0
89
-------
For the 2016vl inventory, the FCCSv2 spatial vegetation cover was upgraded to the LANDFIRE vl.4
fuel vegetation cover (See: https://www.landfire.gov/fccs.php). The FCCSv3 fuel bed characteristics were
implemented along with LANDFIREvl.4 to provide better fuel classification for the BlueSky Framework.
The LANDFIREvl.4 raster data were aggregated from the native resolution and projection to 200 meter
resolution using a nearest-neighbor methodology. Aggregation and reprojection was required to allow
these data to work in the BlueSky Framework.
2.5.2 Point source Agriculture Fires (ptagfire)
The point source agricultural fire (ptagfire) inventory sector contains daily agricultural burning emissions.
Daily fire activity was derived from the NOAA Hazard Mapping System (HMS) fire activity data. The
agricultural fires sector includes SCCs starting with '28015'. The first three levels of descriptions for
these SCCs are: 1) Fires - Agricultural Field Burning; Miscellaneous Area Sources; 2) Agriculture
Production - Crops - as nonpoint; and 3) Agricultural Field Burning - whole field set on fire. The SCC
2801500000 does not specify the crop type or burn method, while the more specific SCCs specify field or
orchard crops and, in some cases, the specific crop being grown. The SCCs for this sector listed are in
Table 2-37.
Table 2-37. SCCs included in the ptagfire sector for the 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 on fire;Field Crop is Alfalfa: Backfire Burning
2801500130
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Barley: Burning Techniques Not Significant
2801500141
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Bean (red): Headfire Burning
2801500150
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Corn: Burning Techniques Not Important
2801500151
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Double Crop Winter Wheat and Corn
2801500152
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;DoubleCrop Corn and Soybeans
2801500160
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Cotton: Burning Techniques Not Important
2801500170
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Grasses: Burning Techniques Not Important
2801500171
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Fallow
2801500182
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Hay (wild): Backfire Burning
2801500202
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Pea: Backfire Burning
90
-------
see
Description
2801500220
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Rice: Burning Techniques Not Significant
2801500250
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Sugar Cane: Burning Techniques Not Significant
2801500262
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Field Crop is Wheat: Backfire Burning
2801500263
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;DoubleCrop Winter Wheat and Cotton
2801500264
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;DoubleCrop Winter Wheat and Soybeans
2801500300
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop Unspecified
2801500320
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Apple
2801500350
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Cherry
2801500410
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Peach
2801500420
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Orchard Crop is Pear
2801500500
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire; Vine Crop Unspecified
2801500600
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole
field set on fire;Forest Residues Unspecified
The EPA estimated biomass burning emissions using remote sensing data. These estimates were then
reviewed by the states and revised as resources allowed. As many states did not have the resources to
estimate emissions for this sector, remote sensing was necessary to fill in the gaps for regions where there
was no other source of data. Crop residue emissions result from either pre-harvest or post-harvest burning
of agricultural fields. The crop residue emission inventory for 2016 is day-specific and includes
geolocation information by crop type. The method employed and described here is based on the same
methods employed in the 2014 NEI with a few minor updates. It should be noted that grassland fires were
moved from the agricultural burning inventory sector to the prescribed and wildland fire sector for
2016beta and 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 USDA cropland data layer (CDL). Satellite fire detects over agricultural lands
were assumed to be agricultural burns and assigned a crop type. Detects that were not over agricultural
lands were output to a separate file for use in the point source wildfire (ptfire) inventory sector. Each
detect was assigned an average size of between 40 and 80 acres based on crop type. The assumed field
sizes are found in Table 2-38.
91
-------
Table 2-38. Assumed field size of agricultural fires per state(acres)
State
Field Size
Alabama
40
Arizona
80
Arkansas
40
California
120
Colorado
80
Connecticut
40
Delaware
40
Florida
60
Georgia
40
Idaho
120
Illinois
60
Indiana
60
Iowa
60
Kansas
80
Kentucky
40
Louisiana
40
Maine
40
Maryland
40
Massachusetts
40
Michigan
40
Minnesota
60
Mississippi
40
Missouri
60
Montana
120
Nebraska
60
Nevada
40
New Hampshire
40
New Jersey
40
New Mexico
80
New York
40
North Carolina
40
North Dakota
60
Ohio
40
Oklahoma
80
Oregon
120
Pennsylvania
40
Rhode Island
40
South Carolina
40
South Dakota
60
Tennessee
40
Texas
80
Utah
40
Vermont
40
Virginia
40
Washington
120
West Virginia
40
Wisconsin
40
Wyoming
80
92
-------
Another feature of the ptagfire database is that the satellite detections for 2016 were filtered out to
exclude areas covered by snow during the winter months. To do this, the daily snow cover fraction per
grid cell was extracted from a 2016 meteorological Weather Research Forecast (WRF) model simulation.
The locations of fire detections were then compared with this daily snow cover file. For any day in which
a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the
inventory. Due to the inconsistent reporting of fire detections for year 2016 from the Visible Infrared
Imaging Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as
VIIRS or Suomi National Polar-orbiting Partnership satellite were excluded. In addition, certain crop
types (corn and soybeans) were excluded from the following states: Iowa, Kansas, Indiana, Illinois,
Michigan, Missouri, Minnesota, Wisconsin, and Ohio. Kansas was not included in this list in the 2014NEI
but added for 2016. The reason for these crop types being excluded is because states have indicated that
these crop types are not burned.
Crop type-specific emissions factors were applied to each daily fire to calculate criteria and hazardous
pollutant emissions. In all prior NEIs for this sector, the HAP emission factors and the VOC emission
factors were known to be inconsistent. The HAP emission factors were copied from the HAP emission
factors for wildfires in the 2014 NEI and in the 2016 beta and version 1 modeling platforms. The VOC
emission factors were scaled from the CO emission factors in the 2014 NEI and the 2016 beta and version
1 modeling platforms. See Pouliot et al, 2017 for a complete table of emission factors and fuel loading by
crop type.
Heat flux values for computing fire plume rise were calculated using the size and assumed fuel loading of
each daily fire. Emission factors and fuel loading by crop type are available in Table 1 of Pouliot et al.
(2017). This information is needed for a plume rise calculation within a chemical transport modeling
system. In prior NEIs including the 2014 NEI, all the emissions were placed into layer 1 (i.e. ground
level).
The daily agricultural and open burning emissions were converted from a tabular format into the
SMOKE-ready daily point Flat File 2010 (FF10) format. The daily emissions were also aggregated into
annual values by location and converted into the annual point flat file format.
2.6 2016 Biogenic Sources (beis)
Biogenic emissions for the entire year 2016 were developed using the Biogenic Emission Inventory
System version 3.61 (BEIS3.61) within SMOKE. The landuse input into BEIS3.61 is the Biogenic
Emissions Landuse Dataset (BELD) version 4.1 which is based on an updated version of the USDA-
USFS Forest Inventory and Analysis (FIA) vegetation speciation-based data from 2001 to 2014 from the
FIA version 5.1.
BEIS3.61 has some important updates from BEIS 3.14. These include the incorporation of Version 4.1 of
the Biogenic Emissions Landuse Database (BELD4), and the incorporation of a canopy model to estimate
leaf-level temperatures (Pouliot and Bash, 2015). BEIS3.61 includes a two-layer canopy model. Layer
structure varies with light intensity and solar zenith angle. Both layers of the canopy model include
estimates of sunlit and shaded leaf area based on solar zenith angle and light intensity, direct and diffuse
solar radiation, and leaf temperature (Bash et al., 2016). The new algorithm requires additional
meteorological variables over previous versions of BEIS. The variables output from the Meteorology-
Chemistry Interface Processor (MCIP) that are used for BEIS3.61 processing are shown in Table 2-39.
The 2016 version 1 of the BEIS3 modeling for year 2016 included processing for both a 36km (36US3)
93
-------
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 2-39. Hourly Meteorological variables required by BEIS 3.61
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2 m
RC
convective precipitation
RGRND
solar rad reaching sfc
RN
nonconvective precipitation
RSTOMI
inverse of bulk stomatal resistance
SLYTP
soil texture type by 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 BELD4 landuse to compute gridded normalized emissions for
chosen model domain (see Figure 2-13). The emissions factor file (B360FAC) contains leaf-area-indices
(LAI), dry leaf biomass, winter biomass factor, indicator of specific leaf weight, and normalized emission
fluxes for 35 different species/compounds. The BELD4 file is the gridded landuse for 276 different
landuse types. The output gridded domain is the same as the input domain for the land use data. Output
emission fluxes (B3GRD) are normalized to 30 °C, and isoprene and methyl-butenol fluxes are also
normalized to a photosynthetic active radiation of 1000 |imol/m2s.
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).
94
-------
Figure 2-14. Tmpbeis3 data flow diagram.
^ Program ^
Shows inpul or outojl
QolionaJ
'J
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, onroadcan, onroadmex, and
ptfire othna. The "oth" refers to the fact that these emissions are usually "other" than those in the NEI,
and the remaining characters provide the SMOKE source types: "pt" for point, "ar" for "area and nonroad
mobile," "afdust" for area fugitive dust (Canada only), and "ptdust" for point fugitive dust. Because
Canada and Mexico onroad mobile emissions are modeled differently from each other, they are separated
into two sectors: onroad can and onroad mex. 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)
Canadian point sources were taken from the ECCC 2015 emission inventory, including upstream oil and
gas emissions, agricultural ammonia and VOC, along with point source emissions from Mexico's 2008
inventory projected to 2014 and 2018 and then interpolated to 2016. The Canadian point source inventory
is pre-speciated for the CB6 chemical mechanicsm. Also for Canada, agricultural data were originally
provided on a rotated 10-km grid for the 2016beta platform. These were smoothed out so as to avoid the
artifact of grid lines in the processed emissions. The data were monthly resolution for Canadian
agricultural and airport emissions, along with some Canadian point sources, and annual resolution for the
remainder of Canada and all of Mexico.
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 2015 emission
inventory. Different source categories were provided as gridded point sources and area (nonpoint) source
95
-------
inventories. Following consultation with ECCC, construction dust emissions in the othafdust inventory
were reduced to levels compatible with their 2010 inventory.
Gridded point source emissions resulting from land tilling due to agricultural activities were provided as
part of the ECCC 2015 emission inventory. The provided wind erosion emissions were removed. The
data were originally provided on a rotated 10-km grid for the 2016 beta platform, but these were
smoothed so as to avoid the artifact of grid lines appearing in the emissions output from SMOKE. The
othptdust emissions have a monthly resolution.
A transport fraction adjustment that reduces dust emissions based on land cover types was applied to both
point and nonpoint dust emissions, along with a meteorology-based (precipitation and snow/ice cover)
zero-out of emissions when the ground is snow covered or wet.
2.7.3 Nonpoint and Nonroad Sources in Canada and Mexico (othar)
ECCC provided year 2015 Canada province, and in some cases sub-province, resolution emissions from
for nonpoint and nonroad sources. The nonroad sources were monthly while the nonpoint and rail
emissions were annual. For Mexico, year 2016 Mexico nonpoint and nonroad inventories at the
municipio resolution were interpolated from 2014 and 2018 inventories that were projected from their
2008 inventory. All Mexico inventories were annual resolution. Canadian CMV inventories that had
been included in this sector in past modeling platforms are now included in the cmv_clc2 and cmv_c3
sectors as point sources.
2.7.4 Onroad Sources in Canada and Mexico (onroad_can, onroad_mex)
ECCC provided monthly year 2015 onroad emissions for Canada at the province resolution or sub-
province resolution depending on the province. For Mexico, monthly year 2016 onroad inventories at the
municipio resolution were used. The Mexico onroad emissions are based on MOVES-Mexico runs for
2014 and 2018 that were then interpolated to 2016
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.
For FINN fires, listed vegetation type codes of 1 and 9 are defined as agricultural burning, all other fire
detections and assumed to be wildfires. All wildland fires that are not defined as agricultural are assumed
to be wild fires rather than prescribed. FINN fire detects less than 50 square meters (0.012 acres) are
removed from the inventory. The locations of FINN fires are geocoded from latitude and longitude to
FIPS code.
2.7.6 Ocean Chlorine
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.
96
-------
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 resolution
required by the air quality model. Emissions modeling includes temporal allocation, spatial allocation,
and pollutant speciation. Emissions modeling sometimes includes the vertical allocation of point sources,
but many air quality models also perform this task because it greatly reduces the size of the input
emissions files if the vertical layers of the sources are not included.
As seen in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary across
sectors and may be hourly, daily, monthly, or annual total emissions. The spatial resolution may be
individual point sources; totals by county (U.S.), province (Canada), or municipio (Mexico); or gridded
emissions. This section provides some basic information about the tools and data files used for emissions
modeling as part of the modeling platform. For additional details that may not be covered in this section,
see the specification sheets provided with the 2016vlplatform as many will contain additional sector-
specific information.
3.1 Emissions modeling Overview
SMOKE version 4.7 was used to process the raw emissions inventories into emissions inputs for each
modeling sector into a format compatible with CMAQ, which were then converted to CAMx. For sectors
that have plume rise, the in-line plume rise capability allows for the use of emissions files that are much
smaller than full three-dimensional gridded emissions files. For quality assurance of the emissions
modeling steps, emissions totals by specie for the entire model domain are output as reports that are then
compared to reports generated by SMOKE on the input inventories to ensure that mass is not lost or
gained during the emissions modeling process.
When preparing emissions for the air quality model, emissions for each sector are processed separately
through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sector-
specific 2-D gridded emissions across sectors. The SMOKE settings in the run scripts and the data in the
SMOKE ancillary files control the approaches used by the individual SMOKE programs for each sector.
Table 3-1 summarizes the major processing steps of each platform sector with the columns as follows.
The "Spatial" column shows the spatial approach used: "point" indicates that SMOKE maps the source
from a point location (i.e., latitude and longitude) to a grid cell; "surrogates" indicates that some or all of
the sources use spatial surrogates to allocate county emissions to grid cells; and "area-to-point" indicates
that some of the sources use the SMOKE area-to-point feature to grid the emissions (further described in
Section 3.4.2).
The "Speciation" column indicates that all sectors use the SMOKE speciation step, though biogenics
speciation is done within the Tmpbeis3 program and not as a separate SMOKE step.
The "Inventory resolution" column shows the inventory temporal resolution from which SMOKE needs
to calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no input inventory;
97
-------
instead, activity data and emission factors are used in combination with meteorological data to compute
hourly emissions.
Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used. These
sectors are the only ones with emissions in aloft layers based on plume rise. The term "in-line" means
that the plume rise calculations are done inside of the air quality model instead of being computed by
SMOKE. The air quality model computes the plume rise using stack parameters, 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. The othpt sector has only "in-line" emissions, meaning that all of the emissions are treated as
elevated sources and there are no emissions for those sectors in the two-dimensional, layer-1 files created
by SMOKE. Other inline-only sectors are: cmv_c3, ptegu, ptfire, ptfire othna, ptagfire. Day-specific
point fire emissions are treated differently in CMAQ. After plume rise is applied, there are emissions in
every layer from the ground up to the top of the plume. Note that SMOKE has the option of grouping
sources so that they are treated as a single stack when computing plume rise. For the modeling cases
discussed in this document, no grouping was performed because grouping combined with "in-line"
processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-
dimensional files). This occurs when stacks with different stack parameters or latitude and longitudes are
grouped, thereby changing the parameters of one or more sources. The most straightforward way to get
the same results between in-line and offline is to avoid the use of stack grouping.
Table 3-1. Key emissions modeling steps by sector.
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust adj
Surrogates
Yes
annual
afdust ak adj
(36US3 only)
Surrogates
Yes
annual
ag
Surrogates
Yes
monthly
airports
Point
Yes
annual
None
beis
Pre-gridded
land use
in BEIS3 .61
computed hourly
cmv clc2
Surrogates
Yes
annual
cmv c3
Point
Yes
annual
in-line
nonpt
Surrogates &
area-to-point
Yes
annual
nonroad
Surrogates &
area-to-point
Yes
monthly
np oilgas
Surrogates
Yes
annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
onroadcaadj
Surrogates
Yes
monthly activity,
computed hourly
onroad nonconus
(36US3 only)
Surrogates
Yes
monthly activity,
computed hourly
onroad can
Surrogates
Yes
monthly
98
-------
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
onroad mex
Surrogates
Yes
monthly
othafdust ad]
Surrogates
Yes
annual
othar
Surrogates
Yes
annual &
monthly
othpt
Point
Yes
annual &
monthly
in-line
othptdust ad]
Point
Yes
monthly
None
ptagfire
Point
Yes
daily
in-line
pt oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire
Point
Yes
daily
in-line
ptfire othna
Point
Yes
daily
in-line
ptnonipm
Point
Yes
annual
in-line
rail
Surrogates
Yes
annual
rwc
Surrogates
Yes
annual
Biogenic emissions can be modeled two different ways in the CMAQ model. The BEIS model in SMOKE
can produce gridded biogenic emissions that are then included in the gridded CMAQ-ready emissions
inputs, or alternatively, CMAQ can be configured to create "in-line" biogenic emissions within CMAQ
itself. For this platform, biogenic emissions were processed in SMOKE and included in the gridded
CMAQ-ready emissions. When CAMx is the targeted air quality modeling, BEIS is run within SMOKE
and the resulting emissions are included with the ground-level emissions input to CAMx.
SMOKE has the option of grouping sources so that they are treated as a single stack when computing
plume rise. For this platform, no grouping was performed because grouping combined with "in-line"
processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-
dimensional files). This occurs when stacks with different stack parameters or latitudes/longitudes are
grouped, thereby changing the parameters of one or more sources. The most straightforward way to get
the same results between in-line and offline is to avoid the use of grouping.
SMOKE was run for two modeling domains: a 36-km resolution CONtinental United States "CONUS"
modeling domain (36US3), and the 12-km resolution domain. 12US2. More specifically, SMOKE was
run on the 12US1 domain and emissions were extracted from 12US1 data files to create 12US2 emission.
The domains are shown in Figure 3-1. All grids use a Lambert-Conformal projection, with Alpha = 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.
99
-------
Table 3-2. Descriptions of the platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig, xcell,
ycell, ncols, nrows, nthik
Continental
36km grid
36 km
Entire conterminous
US, almost all of
Mexico, most of
Canada (south of
60°N)
36US3
'LAM 40N97W', -2952000, -2772000,
36.D3, 36.D3, 172, 148, 1
Continental
12km grid
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_45 9X299
'LAM 40N97W', -2556000, -1728000,
12.D3, 12.D3, 459,299, 1
US 12 km or
"smaller"
CONUS-12
12 km
Smaller 12km
CONUS plus some of
Mexico/Canada
12US2
'LAM 40N97W', -2412000 , -
1620000, 12.D3, 12.D3, 396, 246, 1
Figure 3-1. Air quality modeling domains
100
-------
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 CB6 mechanism (Yarwood, 2010). We used a particular
version of CB6 that we refer to as "CMAQ CB6" that breaks out naphthalene from model species XYL,
resulting in explicit model species NAPH and XYLMN instead of XYL and uses SOAALK. This
platform generates the PM2.5 model species associated with the CMAQ Aerosol Module version 6 (AE6).
Table 3-3 lists the model species produced by SMOKE in the platform used for this study. Updates to
species assignments for CB05 and CB6 were made for the 2014v7.1 platform and are described in
Appendix A.
101
-------
Table 3-3. Emission model species produced for CB6 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
nh,
NH3
Ammonia
NH3 FERT
Ammonia from fertilizer
voc
ACET
Acetone
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
BENZ
Benzene (not part of CB05)
CH4
Methane
ETH
Ethene
ETHA
Ethane
ETHY
Ethyne
ETOH
Ethanol
FORM
Formaldehyde
IOLE
Internal olefin carbon bond (R-C=C-R)
ISOP
Isoprene
KET
Ketone Groups
MEOH
Methanol
NAPH
Naphthalene
NVOL
Non-volatile compounds
OLE
Terminal olefin carbon bond (R-C=C)
PAR
Paraffin carbon bond
PRPA
Propane
SESQ
Sequiterpenes (from biogenics only)
SOAALK
Secondary Organic Aerosol (SOA) tracer
TERP
Terpenes (from biogenics only)
TOL
Toluene and other monoalkyl aromatics
UNR
Unreactive
XYLMN
Xylene and other polyalkyl aromatics, minus
naphthalene
Naphthalene
NAPH
Naphthalene from inventory
Benzene
BENZ
Benzene from the inventory
Acetaldehyde
ALD2
Acetaldehyde from inventory
Formaldehyde
FORM
Formaldehyde from inventory
Methanol
MEOH
Methanol from inventory
PM10
PMC
Coarse PM >2.5 microns and <10 microns
PM2.5
PEC
Particulate elemental carbon <2.5 microns
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
102
-------
Inventory Pollutant
Model Species
Model species description
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
Sea-salt species (non -
PCL
Particulate chloride
anthropogenic)20
PNA
Particulate sodium
The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were
developed from the SPECIATE 4.5 database (https://www.epa.gov/air-emissions-modeling/speciate-2).
which is the EPA's repository of TOG and PM speciation profiles of air pollution sources. The
SPECIATE database development and maintenance is a collaboration involving the EPA's Office of
Research and Development (ORD), Office of Transportation and Air Quality (OTAQ), and the Office of
Air Quality Planning and Standards (OAQPS), in cooperation with Environment Canada (EPA, 2016).
The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical
compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation profiles
for PM2.5.
Some key features and recent updates to speciation from previous platforms include the following:
• VOC speciation profile cross reference assignments for point and nonpoint oil and gas sources
were updated to (1) make corrections to the 201 lv6.3 cross references, (2) use new and revised
profiles that were added to SPECIATE4.5 and (3) account for the portion of VOC estimated to
come from flares, based on data from the Oil and Gas estimation tool used to estimate emissions
for the NEI. The new/revised profiles included oil and gas operations in specific regions of the
country and a national profile for natural gas flares;
• the Western Regional Air Partnership (WRAP) speciation profiles used for the np oilgas sector
are the SPECIATE4.5 revised versions (profiles with "_R" in the profile code);
• the VOC and PM speciation process for nonroad mobile has been updated - profiles are now
assigned within MOVES2014b which outputs the emissions with those assignments; also the
nonroad profiles themselves were updated;
• VOC and PM speciation for onroad mobile sources occurs within MOVES2014a except for brake
and tirewear PM speciation which occurs in SMOKE;
• speciation for onroad mobile sources in Mexico is done within MOVES and is more consistent
with that used in the United States;
20 These emissions are created outside of SMOKE.
103
-------
• the PM speciation profile for C3 ships in the US and Canada was updated to a new profile,
5675AE6; and
• As with previous platforms, some Canadian point source inventories are provided from
Environment Canada as pre-speciated emissions; however for the 2015 inventory, not all CB6-
CMAQ species were provided; missing species were supplemented by speciating VOC which was
provided separately.
Speciation profiles and cross-references for this study platform are available in the SMOKE input files for
the 2016 platform. Emissions of VOC and PM2.5 emissions by county, sector and profile for all sectors
other than onroad mobile can be found in the sector summaries for the case. Totals of each model species
by state and sector can be found in the state-sector totals workbook for this case.
3.2.1 VOC speciation
The speciation of VOC includes HAP emissions from the 2014NEIv2 in the speciation process. Instead
of speciating VOC to generate all of the species listed in Table 3-3, emissions of five specific HAPs:
naphthalene, benzene, acetaldehyde, formaldehyde and methanol (collectively known as "NBAFM") from
the NEI were "integrated" with the NEI VOC. The integration combines these HAPs with the VOC in a
way that does not double count emissions and uses the HAP inventory directly in the speciation process.
The basic process is to subtract the specified HAPs emissions mass from the VOC emissions mass, and to
then use a special "integrated" profile to speciate the remainder of VOC to the model species excluding
the specific HAPs. The EPA believes that the HAP emissions in the NEI are often more representative of
emissions than HAP emissions generated via VOC speciation, although this varies by sector.
The NBAFM HAPs were chosen for integration because they are the only explicit VOC HAPs in the
CMAQ version 5.2. Explicit means that they are not lumped chemical groups like PAR, IOLE and
several other CB6 model species. These "explicit VOC HAPs" are model species that participate in the
modeled chemistry using the CB6 chemical mechanism. The use of inventory HAP emissions along with
VOC is called "HAP-CAP integration."
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats,
including PTDAY (the format used for the ptfire and ptagfire sectors). The ability to use integration with
the PTDAY format was made available in the version of SMOKE used for the 2014v7.1 platform, but this
new feature is not used for the 2016 platform because the ptfire and ptagfire inventories for 2016 do not
include HAPs. SMOKE allows the user to specify the particular HAPs to integrate via the INVTABLE.
This is done by setting the "VOC or TOG component" field to "V" for all HAP pollutants chosen for
integration. SMOKE allows the user to also choose the particular sources to integrate via the
NHAPEXCLUDE file (which actually provides the sources to be excluded from integration21). For the
"integrated" sources, SMOKE subtracts the "integrated" HAPs from the VOC (at the source level) to
compute emissions for the new pollutant "NONHAPVOC." The user provides NONHAPVOC-to-
NONHAPTOG factors and NONHAPTOG speciation profiles.22 SMOKE computes NONHAPTOG and
then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC
species not including the integrated HAPs. After determining if a sector is to be integrated, if all sources
21 Since SMOKE version 3.7, the options to specify sources for integration are expanded so that a user can specify the
particular sources to include or exclude from integration, and there are settings to include or exclude all sources within a sector.
In addition, the error checking is significantly stricter for integrated sources. If a source is supposed to be integrated, but it is
missing NBAFM or VOC, SMOKE will now raise an error.
22 These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a specified list of
pollutants, for example NBAFM.
104
-------
have the appropriate HAP emissions, then the sector is considered fully integrated and does not need a
NHAPEXCLUDE file. If, on the other hand, certain sources do not have the necessary HAPs, then an
NHAPEXCLUDE file must be provided based on the evaluation of each source's pollutant mix. The
EPA considered CAP-HAP integration for all sectors in determining whether sectors would have full, no
or partial integration (see Figure 3-2. Process of integrating NBAFM with VOC for use in VOC
Speciation). For sectors with partial integration, all sources are integrated other than those that have
either the sum of NBAFM > VOC or the sum of NBAFM = 0.
In this platform, we create NBAFM species from the no-integrate source VOC emissions using speciation
profiles. Figure 3-2 illustrates the integrate and no-integrate processes for U.S. Sources. Since Canada
and Mexico inventories do not contain HAPs, we use the approach of generating the HAPs via speciation,
except for Mexico onroad mobile sources where emissions for integrate HAPs were available.
It should be noted that even though NBAFM were removed from the SPECIATE profiles used to create
the GSPRO for both the NONHAPTOG and no-integrate TOG profiles, there still may be small fractions
for "BENZ", "FORM", "ALD2", and "MEOH" present. This is because these model species may have
come from species in SPECIATE that are mixtures. The quantity of these model species is expected to be
very small compared to the BAFM in the NEI. There are no NONHAPTOG profiles that produce
"NAPH."
In SMOKE, the INVTABLE allows the user to specify the particular HAPs to integrate. Two different
INVTABLE files are used for different sectors of the platform. For sectors that had no integration across
the entire sector (see Table 3-4), EPA created a "no HAP use" INVTABLE in which the "KEEP" flag is
set to "N" for NBAFM pollutants. Thus, any NBAFM pollutants in the inventory input into SMOKE are
automatically dropped. This approach both avoids double-counting of these species and assumes that the
VOC speciation is the best available approach for these species for sectors using this approach. The
second INVTABLE, used for sectors in which one or more sources are integrated, causes SMOKE to keep
the inventory NBAFM pollutants and indicates that they are to be integrated with VOC. This is done by
setting the "VOC or TOG component" field to "V" for all five HAP pollutants. Note for the onroad
sector, "full integration" includes the integration of benzene, 1,3 butadiene, formaldehyde, acetaldehyde,
naphthalene, acrolein, ethyl benzene, 2,2,4-Trimethylpentane, hexane, propionaldehyde, styrene, toluene,
xylene, and methyl tert-butyl ether (MTBE).
105
-------
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation
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
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptfire othna
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptagfire
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
airport
No integration, create NBAFM from VOC speciation
ag
Partial integration (NBAFM)
afdust
N/A - sector contains no VOC
beis
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
cmv clc2
Full integration (NBAFM)
cmv c3
Full integration (NBAFM)
rail
Partial integration (NBAFM)
nonpt
Partial integration (NBAFM)
nonroad
Full integration (NBAFM in California, internal to MOVES elsewhere)
np oilgas
Partial integration (NBAFM)
othpt
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
pt oilgas
No integration, create NBAFM from VOC speciation
rwc
Partial integration (NBAFM)
106
-------
Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
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
Integration for the mobile sources estimated from MOVES (onroad and nonroad sectors, other than for
California) is done differently. Briefly there are three major differences: 1) for these sources integration
is done using more than just NBAFM, 2) all sources from the MOVES model are integrated, and 3)
integration is done fully or partially within MOVES. For onroad mobile, speciation is done fully within
MOVES2014a such that the MOVES model outputs emission factors for individual VOC model species
along with the HAPs. This requires MOVES to be run for a specific chemical mechanism. MOVES was
run for the CB6-CAMx mechanism rather than CB6-CMAQ, so post-SMOKE onroad emissions were
converted to CB6-CMAQ. More specifically, the CB6-CAMx mechanism excludes XYLMN, NAPH,
and SOAALK. After SMOKE processing, we converted the onroad and onroadmex emissions to CB6-
CMAQ as follows:
• XYLMN = XYL[1]-0.966*NAPHTHALENE[1]
• PAR = PAR[l]-0.00001 *NAPHTHALENE[1]
• SOAALK = 0.108*PAR[1]
For nonroad mobile, speciation is partially done within MOVES such that it does not need to be run for a
specific chemical mechanism. For nonroad, MOVES outputs emissions of HAPs and NONHAPTOG are
+split by speciation profile. Taking into account that integrated species were subtracted out by MOVES
already, the appropriate speciation profiles are then applied in SMOKE to get the VOC model species.
HAP integration for nonroad uses the same additional HAPs and ethanol as for onroad.
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.
In previous platforms, the GSPRO COMBO feature was used to speciate nonroad mobile and gasoline-
related stationary sources that use fuels with varying ethanol content. In these cases, the speciation
profiles require different combinations of gasoline profiles, e.g., 0% ethanol (E0) and 10% ethanol (E10)
profiles. Since the ethanol content varied spatially (e.g., by state or county), temporally (e.g., by month),
and by modeling year (future years have more ethanol), the GSPRO COMBO feature allowed
107
-------
combinations to be specified at various levels for different years. The GSPROCOMBO is no longer
needed for nonroad sources outside of California because nonroad emissions within MOVES have the
speciation profiles built into the results, so there is no need to assign them via the GSREF or
GSPRO COMBO feature. For the 2016 alpha platform, GSPRO COMBO is still used for nonroad
sources in California and for certain gasoline-related stationary sources nationwide. The fractions
combining the E0 and E10 profiles are based on year 2010 regional fuels and do not vary by month.
GSPRO COMBO is not needed for inventory years after 2016, because the vast majority of fuel is
projected to be E10 in future years.
Starting with the 2016v7.2 beta and regional haze platforms, a GSPRO COMBO is used to specify a mix
of E0 and E10 fuels in Canada. ECCC provided percentages of ethanol use by province, and these were
converted into E0 and E10 splits. For example, Alberta has 4.91% ethanol in its fuel, so we applied a mix
of 49.1% E10 profiles (4.91% times 10, since 10% ethanol would mean 100% E10), and 50.9% E0 fuel.
Ethanol splits for all provinces in Canada are listed in Table 3-5. The Canadian onroad inventory includes
four distinct FIPS codes in Ontario, allowing for application of different E0/E10 splits in Southern
Ontario versus Northern Ontario. In Mexico, only E0 profiles are used.
Table 3-5. Ethanol percentages by volume by Canadian province
Province
Ethanol % by volume (E10 = 10%)
Alberta
4.91%
British Columbia
5.57%
Manitoba
9.12%
New Brunswick
4.75%
Newfoundland & Labrador
0.00%
Nova Scotia
0.00%
NW Territories
0.00%
Nunavut
0.00%
Ontario (Northern)
0.00%
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.
108
-------
For the rail sector, the EPA integrated NBAFM for most sources. Some SCCs had zero BAFM and,
therefore, they were not integrated. These were SCCs provided by states for which EPA did not do HAP
augmentation (2285002008, 2285002009 and 2285002010) because EPA does not create emissions for
these SCCs. The VOC for these sources sum to 272 tons, and most of the mass is in California (189 tons)
and Washington state (62 tons).
Speciation for the onroad sector is unique. First, SMOKE-MOVES is used to create emissions for these
sectors and both the MEPROC and INVTABLE files are involved in controlling which pollutants are
processed. Second, the speciation occurs within MOVES itself, not within SMOKE. The advantage of
using MOVES to speciate VOC is that during the internal calculation of MOVES, the model has complete
information on the characteristics of the fleet and fuels (e.g., model year, ethanol content, process, etc.),
thereby allowing it to more accurately make use of specific speciation profiles. This means that MOVES
produces emission factor tables that include inventory pollutants (e.g., TOG) and model-ready species
(e.g., PAR, OLE, etc).23 SMOKE essentially calculates the model-ready species by using the appropriate
emission factor without further speciation.24 Third, MOVES' internal speciation uses full integration of
an extended list of HAPs beyond NBAFM (called "M-profiles"). The M-profiles integration is very
similar to NBAFM integration explained above except that the integration calculation (see Figure 3-2.
Process of integrating NBAFM with VOC for use in VOC Speciation) is performed on emissions factors
instead of on emissions, and a much larger set of pollutants are integrated besides NBAFM. The list of
integrated pollutants is described in Table 3-6. An additional run of the Speciation Tool was necessary to
create the M-profiles that were then loaded into the MOVES default database. Fourth, for California, the
EPA applied adjustment factors to SMOKE-MOVES to produce California adjusted model-ready files.
By applying the ratios through SMOKE-MOVES, the CARB inventories are essentially speciated to
match EPA estimated speciation. This resulted in changes to the VOC HAPs from what CARB submitted
to the EPA. Finally, MOVES speciation used the CAMx version of CB6 which does not split out
naphthalene.
Table 3-6. MOVES integrated species in M-profiles
MOVES ID
Pollutant Name
5
Methane (CH4)
20
Benzene
21
Ethanol
22
MTBE
24
1,3-Butadiene
25
Formaldehyde
26
Acetaldehyde
27
Acrolein
40
2,2,4-Trimethylpentane
41
Ethyl Benzene
42
Hexane
43
Propionaldehyde
23 Because the EF table has the speciation "baked" into the factors, all counties that are in the county group (i.e., are mapped to
that representative county) will have the same speciation.
24 For more details on the use of model-ready EF, see the SMOKE 3.7 documentation:
https://www.cmascenter.Org/smoke/documentation/3.7/html/.
109
-------
MOVES ID
Pollutant Name
44
Styrene
45
Toluene
46
Xylene
185
Naphthalene gas
For the nonroad sector, all sources are integrated using the same list of integrated pollutants as shown in
Table 3-6. Outside of California, the integration calculations are performed within MOVES. For
California, integration calculations are handled by SMOKE. The CARB-based nonroad inventory
includes VOC HAP estimates for all sources, so every source in California was integrated as well. Some
sources in the original CARB inventory had lower VOC emissions compared to sum of all VOC HAPs.
For those sources, VOC was augmented to be equal to the VOC HAP sum, ensuring that every source in
California could be integrated. The CARB-based nonroad data includes exhaust and evaporative mode-
specific data for VOC, but, does not contain refueling.
MOVES-MEXICO for onroad used the same speciation approach as for the U.S. in that the larger list of
species shown in Table 3-6 was used. However, MOVES-MEXICO used CB6-CAMx, not CB6-CMAQ,
so post-SMOKE we converted the emissions to CB6-CMAQ as follows:
• XYLMN = XYL[1]-0.966*NAPHTHALENE[1]
• PAR = PAR[1]-0.00001*NAPHTHALENE[1]
• SOAALK = 0.108*PAR[1]
For most sources in the rwc sector, the VOC emissions were greater than or equal to NBAFM, and
NBAFM was not zero, so those sources were integrated, although a few specific sources that did not meet
these criteria could not be integrated. In all cases, these sources have SCC= 2104008400 (pellet stoves),
and NBAFM > VOC, but not by a significant amount. This results from the sum of NBAFM emission
factors exceeding the VOC emission factor. In total, the no-integrate rwc sector sources sum to 4.4 tons
VOC and 66 tons of NBAFM. Since for the NATA case the NBAFM are used from the inventory, these
no-integrate NBAFM emissions were used in the speciation.
For the nonpt sector, sources for which VOC emissions were greater than or equal to NBAFM, and
NBAFM was not zero, were integrated. There is a substantial amount of mass in the nonpt sector that is
not integrated: 731,000 tons which is about 20% of the VOC in that sector. It is likely that there would be
sources in nonpt that are not integrated because the emission source is not expected to have NBAFM. In
fact, 390,000 tons of the no-integrate VOC have no NBAFM in the speciation profiles used for these no-
integrate sources. Of the portion of no-integrate VOC with NBAFM there is 3,900 tons NBAFM in the
profiles (that are dropped from the profiles per the procedure in Figure 3-2. Process of integrating
NBAFM with VOC for use in VOC Speciation) for these no-integrate sources.
For the biog sector, the speciation profiles used by BEIS are not included in SPECIATE. BEIS3.61
includes the species (SESQ) that is mapped to the BEIS model species SESQT (Sesquiterpenes). The
profile code associated with BEIS3.61 for use with CB05 is "B10C5," while the profile for use with CB6
is "B10C6." The main difference between the profiles is the explicit treatment of acetone emissions in
B10C6.
110
-------
3.2.1.3 Oil and gas related speciation profiles
Most of the recently added VOC profiles from SPECIATE4.5 (listed in Appendix B) are in the oil and gas
sector. A new national flare profile, FLR99, Natural Gas Flare Profile with DRE >98% was developed
from a Flare Test study and used in the v7.0 platform. For the oil and gas sources in the np oilgas and
pt oilgas sectors, several counties were assigned to newly available basin or area-specific profiles in
SPECIATE4.5 that account for measured or modeled, from measured compositions specific to a particular
region of the country. In the 2011 platform, the only county-specific profiles were for the WRAP, but in
the 2014 and 2016 platforms, several new profiles were added for other parts of the country. The 2016
platform uses the latest version of the WRAP profiles. These profiles are denoted with an _R suffix, and
reflect newer data and corrections to older WRAP profiles. All WRAP profile codes were renamed to
include an "_R" to distinguish between the previous set of profiles (even those that did not change). For
the Uintah basin and Denver-Julesburg Basin, Colorado, more updated profiles were used instead of the
WRAP profiles. Table 3-7 lists the region-specific profiles assigned to particular counties or groups of
counties. Although this platform increases the use of regional profiles, many counties still rely on the
national profiles. A minor change in 2016vl was to use county-specific profile assignments from SCC
2310121700 for the SCCs 2310021500, 2310421700 in Pennsylvania.
In addition to region-specific assignments, multiple profiles were assigned to particular county/SCC
combinations using the SMOKE feature discussed in 3.2.1.1. Oil and gas SCCs for associated gas,
condensate tanks, crude oil tanks, dehydrators, liquids unloading and well completions represent the total
VOC from the process, including the portions of process that may be flared or directed to a reboiler. For
example, SCC 2310021400 (gas well dehydrators) consists of process, reboiler, and/or flaring
emissions. There are not separate SCCs for the flared portion of the process or the reboiler. However, the
VOC associated with these three portions can have very different speciation profiles. Therefore, it is
necessary to have an estimate of the amount of VOC from each of the portions (process, flare, reboiler) so
that the appropriate speciation profiles can be applied to each portion. The Nonpoint Oil and Gas
Emission Estimation Tool generates an intermediate file which provides flare, non-flare (process), and
reboiler (for dehydrators) emissions for six source categories that have flare emissions: by county FIPS
and SCC code for the U.S. From these emissions we can compute the fraction of the emissions to assign
to each profile. These fractions can vary by county FIPS, because they depend on the level of controls,
which is an input to the Speciation Tool.
Table 3-7. Basin/Region-specific profiles for oil and gas
Profile
Code
Description
Region (if not in
the profile name)
DJVNT R
Denver-Julesburg Basin Produced Gas Composition from Non-
CBM Gas Wells
PNC01 R
Piceance Basin Produced Gas Composition from Non-CBM Gas
Wells
PNC02 R
Piceance Basin Produced Gas Composition from Oil Wells
PNC03 R
Piceance Basin Flash Gas Composition for Condensate Tank
PNCDH
Piceance Basin, Glycol Dehydrator
PRBCB R
Powder River Basin Produced Gas Composition from CBM Wells
PRBCO R
Powder River Basin Produced Gas Composition from Non-CBM
Wells
PRM01 R
Permian Basin Produced Gas Composition for Non-CBM Wells
SSJCB R
South San Juan Basin Produced Gas Composition from CBM
Wells
111
-------
Profile
Code
Description
Region (if not in
the profile name)
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-Jule sburg
Basin
95399
Composite Profile - Oil Field - Wells
State of California
95400
Composite Profile - Oil Field - Tanks
State of California
95403
Composite Profile - Gas Wells
San Joaquin Basin
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). California nonroad source profiles are presented in Table 3-9.
Table 3-8. TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a used for the
2016 Platform
Profile
Profile Description
Engine
Type
Engine
Technology
Engine
Size
Horse-
power
category
Fuel
Fuel
Sub-
type
Emission
Process
95327
SI 2-stroke E0
SI 2-stroke
all
All
all
Gasoline
E0
exhaust
95328
SI 2-stroke E10
SI 2-stroke
all
All
all
Gasoline
E10
exhaust
95329
SI 4-stroke E0
SI 4-stroke
all
All
all
Gasoline
E0
exhaust
95330
SI 4-stroke E10
SI 4-stroke
all
All
all
Gasoline
E10
exhaust
95331
CI Pre-Tier 1
CI
Pre-Tier 1
All
all
Diesel
all
exhaust
112
-------
Profile
Profile Description
Engine
Type
Engine
Technology
Engine
Size
Horse-
power
category
Fuel
Fuel
Sub-
type
Emission
Process
95332
CI Tier 1
CI
Tier 1
All
all
Diesel
all
exhaust
95333
CI Tier 2
CI
Tier 2 and 3
all
all
Diesel
all
exhaust
95333
CI Tier 2
CI
Tier 4
<56 kW
(75 hp)
S
Diesel
all
exhaust
8775
ACES Phase 1 Diesel
Onroad
CI Tier 4
Tier 4
>=56 kW
(75 hp)
L
Diesel
all
exhaust
8753
E0 Evap
SI
all
all
all
Gasoline
E0
evaporative
8754
E10 Evap
SI
all
all
all
Gasoline
E10
evaporative
8766
E0 evap permeation
SI
all
all
all
Gasoline
E0
permeation
8769
E10 evap permeation
SI
all
all
all
Gasoline
E10
permeation
8869
E0 Headspace
SI
all
all
all
Gasoline
E0
headspace
8870
E10 Headspace
SI
all
all
all
Gasoline
E10
headspace
1001
CNG Exhaust
All
all
all
all
CNG
all
exhaust
8860
LPG exhaust
All
all
all
all
LPG
all
exhaust
Speciation profiles for VOC in the nonroad sector account for the ethanol content of fuels across years. A
description of the actual fuel formulations for 2014 can be found in the 2014NEIv2 TSD. For previous
platforms, the EPA used "COMBO" profiles to model combinations of profiles for E0 and E10 fuel use,
but beginning with 2014v7.0 platform, the appropriate allocation of E0 and E10 fuels is done by MOVES.
Combination profiles reflecting a combination of E10 and E0 fuel use are still used for sources upstream
of mobile sources such as portable fuel containers (PFCs) and other fuel distribution operations associated
with the transfer of fuel from bulk terminals to pumps (BTP), which are in the nonpt sector. They are also
used for California nonroad sources. For these sources, ethanol may be mixed into the fuels, in which
case speciation would change across years. The speciation changes from fuels in the ptnonipm sector
include BTP distribution operations inventoried as point sources. Refinery-to-bulk terminal (RBT) fuel
distribution and bulk plant storage (BPS) speciation does not change across the modeling cases because
this is considered upstream from the introduction of ethanol into the fuel. The mapping of fuel
distribution SCCs to PFC, BTP, BPS, and RBT emissions categories can be found in Appendix C.
Table 3-9 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for
2016. The term "COMBO" indicates that a combination of the profiles listed was used to speciate that
subcategory using the GSPRO COMBO file.
Table 3-9. Select mobile-related VOC profiles 2016
Sector
Sub-category
2014
Nonroad- California & non US
gasoline exhaust
COMBO
8750a Pre-Tier 2 E0 exhaust
8751a Pre-Tier 2 E10 exhaust
Nonroad-California
gasoline evaporative
COMBO
8753 E0 evap
8754 E10 evap
Nonroad-California
gasoline refueling
COMBO
8869 E0 Headspace
113
-------
Sector
Sub-category
2014
8870
E10 Headspace
Nonroad-California
diesel exhaust
8774
Pre-2007 MY HDD exhaust
Nonroad-California
diesel evap-
orative and diesel refueling
4547
Diesel Headspace
nonpt/
ptnonipm
PFC and BTP
COMBO
8869
8870
E0 Headspace
E10 Headspace
nonpt/
ptnonipm
Bulk plant storage (BPS)
and refine-to-bulk terminal
(RBT) sources
8869
E0 Headspace
The speciation of onroad VOC occurs completely within MOVES. MOVES accounts for fuel type and
properties, emission standards as they affect different vehicle types and model years, and specific
emission processes. Table 3-10 describes all of the M-profiles available to MOVES depending on the
model year range, MOVES process (processID), fuel sub-type (fuelSubTypelD), and regulatory class
(regClassID). Table 3-11 through Table 3-13 describe the meaning of these MOVES codes. For a
specific representative county and future year, there will be a different mix of these profiles. For
example, for HD diesel exhaust, the emissions will use a combination of profiles 8774M and 8775M
depending on the proportion of HD vehicles that are pre-2007 model years (MY) in that particular county.
As that county is projected farther into the future, the proportion of pre-2007 MY vehicles will decrease.
A second example, for gasoline exhaust (not including E-85), the emissions will use a combination of
profiles 8756M, 8757M, 8758M, 8750aM, and 875laM. Each representative county has a different mix
of these key properties and, therefore, has a unique combination of the specific M-profiles. More detailed
information on how MOVES speciates VOC and the profiles used is provided in the technical document,
"Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles in
MOVES2014" (EPA, 2015c).
Table 3-10. Onroad M-profiles
Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
1001M
CNG Exhaust
1940-2050
1,2,15,16
30
48
4547M
Diesel Headspace
1940-2050
11
20,21,22
0
4547M
Diesel Headspace
1940-2050
12,13,18,19
20,21,22
10,20,30,40,41,
42,46,47,48
8753M
E0 Evap
1940-2050
12,13,19
10
10,20,30,40,41,42,
46,47,48
8754M
E10 Evap
1940-2050
12,13,19
12,13,14
10,20,30,40,41,
42,46,47,48
8756M
Tier 2 E0 Exhaust
2001-2050
1,2,15,16
10
20,30
8757M
Tier 2 E10 Exhaust
2001-2050
1,2,15,16
12,13,14
20,30
8758M
Tier 2 El5 Exhaust
1940-2050
1,2,15,16
15,18
10,20,30,40,41,
42,46,47,48
8766M
E0 evap permeation
1940-2050
11
10
0
8769M
E10 evap permeation
1940-2050
11
12,13,14
0
8770M
El5 evap permeation
1940-2050
11
15,18
0
8774M
Pre-2007 MY HDD
exhaust
1940-2006
1,2,15,16,17,90
20, 21, 22
40,41,42,46,47, 48
114
-------
Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
8774M
Pre-2007 MY HDD
exhaust
1940-2050
9125
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, 1826
10,40,41,42,46,47,48
Table 3-11. MOVES process IDs
Process ID
Process Name
1
Running Exhaust
2
Start Exhaust
9
Brakewear
10
Tire wear
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
25 91 is the processed for APUs which are diesel engines not covered by the 2007 Heavy-Duty Rule, so the older technology
applieds to all years.
26 The profile assingments 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 combinate is already assigned to profile 8758.
115
-------
23
Evap RecMar Supply/Ret Hose Permeation
24
Evap RecMar Vent Hose Permeation
30
Diurnal Fuel Vapor Venting
31
HotSoak Fuel Vapor Venting
32
RunningLoss Fuel Vapor Venting
40
Nonroad
90
Extended Idle Exhaust
91
Auxiliary Power Exhaust
Table 3-12. MOVES Fuel subtype IDs
Fuel Subtype ID
Fuel Subtype Descriptions
10
Conventional Gasoline
11
Reformulated Gasoline (RFG)
12
Gasohol (E10)
13
Gasohol (E8)
14
Gasohol (E5)
15
Gasohol (E15)
18
Ethanol (E20)
20
Conventional Diesel Fuel
21
Biodiesel (BD20)
22
Fischer-Tropsch Diesel (FTD100)
30
Compressed Natural Gas (CNG)
50
Ethanol
51
Ethanol (E85)
52
Ethanol (E70)
Table 3-13. MOVES regclass IDs
Reg. Class ID
Regulatory Class Description
0
Doesn't Matter
10
Motorcycles
20
Light Duty Vehicles
30
Light Duty Trucks
40
Class 2b Trucks with 2 Axles and 4 Tires (8,500 lbs < GVWR <= 10,000 lbs)
41
Class 2b Trucks with 2 Axles and at least 6 Tires or Class 3 Trucks (8,500 lbs < GVWR <= 14,000
lbs)
42
Class 4 and 5 Trucks (14,000 lbs < GVWR <= 19,500 lbs)
46
Class 6 and 7 Trucks (19,500 lbs < GVWR <= 33,000 lbs)
47
Class 8a and 8b Trucks (GVWR > 33,000 lbs)
48
Urban Bus (see CFR Sec 86.091 2)
For portable fuel containers (PFCs) and fuel distribution operations associated with the bulk-plant-to-
pump (BTP) distribution, ethanol may be mixed into the fuels; therefore, county- and month-specific
COMBO speciation was used (via the GSPROCOMBO file). Refinery to bulk terminal (RBT) fuel
distribution and bulk plant storage (BPS) speciation are considered upstream from the introduction of
116
-------
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.27 Starting with the 2014v7.1 platform, we replaced profile 91112
(Natural Gas Combustion - Composite) with 95475 (Composite -Refinery Fuel Gas and Natural Gas
Combustion). This updated profile is an AE6-ready profile based on the median of 3 SPECIATE4.5
profiles from which AE6 versions were made (to be added to SPECIATE5.0): boilers (95125a), process
heaters (95126a) and internal combustion combined cycle/cogen plant exhaust (95127a). As with profile
91112, these profiles are based on tests using natural gas and refinery fuel gas (England et al., 2007).
Profile 91112 which is also based on refinery gas and natural gas is thought to overestimate EC.
Profile 95475 (Composite -Refinery Fuel Gas and Natural Gas Combustion) is shown along with the
underlying profiles composited in Figure 3-3. Figure 3-4 shows a comparison of the new profile as of the
2014v7.1 platform with the one that we had been using in the 2014v7.0 and earlier platforms.
Figure 3-3. Profiles composited for the new PM gas combustion related sources
Zinc
Sulfate
Silicon
Potassium !
Particulate Non-Carbon Organic Matter _
Other Unspeciated PM2.5
Organic carbon
Nitrate
Nickel
Metal-bound Oxygen — 1
Iron P-
Elemental Carbon
Copper
Chloride ion
Calcium P"
Bromine Atom
Ammonium
Aluminum
0 10 20 30 40 50 60
Weight Percent
¦ Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
¦ Gas-fired process heater exhaust 95126a
¦ Gas-fired internal combustion combined cycle/cogeneration plant exhaust 95127a
¦ Gas-fired boiler exhaust 95125a
27 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.
117
-------
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
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, etc). SMOKE essentially calculates the PM
components by using the appropriate EF without further speciation.28 The specific profiles used within
MOVES include two CNG profiles, 45219 and 45220, which were added to SPECIATE4.5. A list of
profiles is provided in the technical document, "Speciation of Total Organic Gas and Particulate Matter
Emissions from On-road Vehicles in MOVES2014" (EPA, 2015c).
For onroad brake and tire wear, the PM is speciated in the moves2smk postprocessor that prepares the
emission factors for processing in SMOKE. The formulas for this are based on the standard speciation
factors from brake and tire wear profiles, which were updated from the v6.3 platform based on data from
a Health Effects Institute report (Schauer, 2006). Table 3-14 shows the differences in the v7.1 and v6.3
profiles.
28 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.0rg/smoke/documentation/3.7/l1tml/.
118
-------
Table 3-14. SPECIATE4.5 brake and tire profiles compared to those used in the 2011v6.3 Platform
Inventory
Pollutant
Model
Species
V6.3 platform
brakewear profile:
91134
SPECIATE4.5 brakewear
profile: 95462 from
Schauer (2006)
V6.3 platform
tirewear
profile: 91150
SPECIATE4.5 tirewear
profile: 95460 from
Schauer (2006)
PM2 5
PAL
0.00124
0.000793208
6.05E-04
3.32401E-05
PM2 5
PCA
0.01
0.001692177
0.00112
PM2 5
PCL
0.001475
0.0078
PM2 5
PEC
0.0261
0.012797085
0.22
0.003585907
PM2 5
PFE
0.115
0.213901692
0.0046
0.00024779
PM2 5
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
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. California nonroad emissions, which are not from MOVES, continue to be
speciated the traditional way with speciation profiles assigned by SMOKE using the GSREF cross-
reference. The PM2.5 profiles assigned to nonroad sources are listed in Table 3-15.
119
-------
Table 3-15. Nonroad PM2.5 profiles
SPECIATE4.5
Profile Code
SPECIATE4.5 Profile Name
Assigned to Nonroad
sources based on Fuel
Type
8996
Diesel Exhaust - Heavy-heavy duty truck - 2007
model year with NCOM
Diesel
91106
HDDV Exhaust - Composite
Diesel
91113
Nonroad Gasoline Exhaust - Composite
Gasoline
91156
Residential Natural Gas Combustion
CNG and LPG
(California only)
95219
CNG Transit Bus Exhaust
CNG and LPG
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 3-16 gives the split factor for these two profiles.
The onroad sector does not use the "HONO" profile to speciate NOx. MOVES2014 produces speciated
NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables
used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOx. The NO
fraction varies by heavy duty versus light duty, fuel type, and model year.
The NO2 fraction = 1 - NO - HONO. For more details on the NOx fractions within MOVES, see EPA
report "Use of data from 'Development of Emission Rates for the MOVES Model,'
Sierra Research, March 3, 2010" available at
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockev=P100F lA5.pdf.
Table 3-16. NOx speciation profiles
Profile
pollutant
species
split factor
HONO
NOX
N02
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
N02
0.1
NHONO
NOX
NO
0.9
3.2.4 Creation of Sulfuric Acid Vapor (SULF)
Since 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.
120
-------
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, AfWis the molecular weight of the compound. The molecular weights of H2SO4 and SO2
are 98 g/mol and 64 g/mol, respectively.
This method does not reduce SO2 emissions; it solely adds gaseous sulfate emissions as a function of S02
emissions. The derivation of the profiles is provided in Table 3-17; a summary of the profiles is provided
in Table 3-18.
Table 3-17. Sulfate split factor computation
fuel
SCCs
Profile
Code
Fraction
as S02
Fraction as
sulfate
Split factor (mass
fraction)
Bituminous
1-0X-002-YY, where X is 1,
2 or 3 and YY is 01 thru 19
and 21-ZZ-002-000 where
ZZ is 02,03 or 04
95014
0.95
0.014
.014/.95 * 98/64 =
0.0226
Subbituminous
1-0X-002-YY, where X is 1,
2 or 3 and YY is 21 thru 38
87514
.875
0.014
.014/.875 * 98/64 =
0.0245
Lignite
1-0X-003-YY, where X is 1,
2 or 3 and YY is 01 thru 18
and 21-ZZ-002-000 where
ZZ is 02,03 or 04
75014
0.75
0.014
.014/.75 * 98/64 =
0.0286
Residual oil
1-0X-004-YY, where X is 1,
2 or 3 and YY is 01 thru 06
and 21-ZZ-005-000 where
ZZ is 02,03 or 04
99010
0.99
0.01
.01/. 99 * 98/64 =
0.0155
Distillate oil
1-0X-005-YY, where X is 1,
2 or 3 and YY is 01 thru 06
and 21-ZZ-004-000 where
ZZ is 02,03 or 04
99010
0.99
0.01
Same as residual oil
Table 3-18. SO2 speciation profiles
Profile
pollutant
species
split factor
95014
S02
SULF
0.0226
95014
S02
S02
1
87514
S02
SULF
0.0245
87514
S02
S02
1
75014
S02
SULF
0.0286
121
-------
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 3-19 summarizes the temporal aspects of emissions modeling by
comparing the key approaches used for temporal processing across the sectors. In the table, "Daily
temporal approach" refers to the temporal approach for getting daily emissions from the inventory using
the SMOKE Temporal program. The values given are the values of the SMOKE L TYPE setting. The
"Merge processing approach" refers to the days used to represent other days in the month for the merge
step. If this is not "all," then the SMOKE merge step runs only for representative days, which could
include holidays as indicated by the right-most column. The values given are those used for the SMOKE
M TYPE setting (see below for more information).
Table 3-19. Temporal settings used for the platform sectors in SMOKE
Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process holidays
as separate days
afdust adj
Annual
Yes
week
All
Yes
afdust ak adj
Annual
Yes
week
All
Yes
ag
Monthly
No
all
All
No
airports
Annual
Yes
week
week
Yes
beis
Hourly
No
n/a
All
No
cmv clc2
Annual
Yes
aveday
aveday
No
cmv c3
Annual
Yes
aveday
aveday
No
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
No
mwdss
mwdss
Yes
np oilgas
Annual
Yes
aveday
aveday
No
onroad
Annual & monthly1
No
all
all
Yes
onroad ca adj
Annual & monthly1
No
all
all
Yes
onroad nonconus
Annual & monthly1
No
all
all
Yes
othafdust adj
Annual
Yes
week
all
No
othar
Annual & monthly
Yes
week
week
No
onroad can
Monthly
No
week
week
No
122
-------
Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process holidays
as separate days
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
Daily
No
all
all
No
ptfire othna
Daily
No
all
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No3
met-based3
all
No3
'Note the annual and monthly "inventory" actually refers to the activity data (VMT, hoteling, and VPOP) for onroad.
VMT and hoteling is monthly and VPOP is annual. The actual emissions are computed on an hourly basis.
2Only units that do not have matching hourly CEMS data use monthly temporal profiles.
3Except for 2 SCCs that do not use met-based speciation
The following values are used in the table. The value "all" means that hourly emissions are computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the
month. The value "mwdss" means hourly emissions for one representative Monday, representative
weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each month.
This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within
the month, but not week-to-week variation within the month. The value "aveday" means hourly
emissions computed for one representative day of each month, meaning emissions for all days within a
month are the same. Special situations with respect to temporal allocation are described in the following
subsections.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2016, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2015). For most sectors, emissions from December 2016
(representative days) were used to fill in emissions for the end of December 2015. For biogenic
emissions, December 2015 emissions were processed using 2015 meteorology.
3.3.1 Use of FF10 format for finer than annual emissions
The FF10 inventory format for SMOKE provides a consolidated format for monthly, daily, and hourly
emissions inventories. With the FF10 format, a single inventory file can contain emissions for all 12
months and the annual emissions in a single record. This helps simplify the management of numerous
inventories. Similarly, daily and hourly FF10 inventories contain individual records with data for all days
in a month and all hours in a day, respectively.
SMOKE prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual-to-month temporal allocation applied to it;
rather, it should only have month-to-day and diurnal temporal allocation. This becomes particularly
123
-------
important when specific sectors have a mix of annual, monthly, daily, and/or hourly inventories. The
flags that control temporal allocation for a mixed set of inventories are discussed in the SMOKE
documentation. The modeling platform sectors that make use of monthly values in the FF10 files are ag,
nonroad, onroad, onroad can, 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).
Figure 3-5. Eliminating unmeasured spikes in CEMS data
2016 January CEMs for 6068 3
Date
124
-------
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
platform, 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.
Annual Unit Power Output
Annual Unit Output (MW) =
y8760
Hourly HI (MW\
(BTU) ' 1000 (W) Equation 3-2
NEEDS Heat Rate (frrrr)
Unit Capacity Factor
Annual Unit Output (MW)
Capacity Factor
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).
125
-------
Figure 3-6. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification
Small EGU 2016 Temporal Profile Input Unit Counts
(peaknaftw
a»:0 /1 |
ml: fl / 0 j
CChei: 0 / D
1 Wqt" HofttfCentral ^
(iK^r^roTpciiiing):
'coal C,'41
.nai:4S,.',24
EM0IB
ulfitr. O'-'O
HMIE-VU .
;coK'3 '! { f \
1 J
ij I— <¦
-:L-rr:
¦West—1
(pesfcrijirw^jeaSiM^];
ant : 0/3
fasiW /137
"oil: 01 0
OOfer: 0 V <1
S—.
01: 74/S \ \
other: O / S3 \ /
South
(pBjkirtgi'narpeakifig):
0197 I
9WJ537-JJ7~L,
at-, 18/0
Other: fl / 4 k
WOCO
(pciitangi'rKnprsiun^}:
— 155
4L.U3&
EGU Regions
¦ LADCO
¦ MMIE-VU
I I Northwest
~ SESARM
i I South
I I Southwest
I 1 west
~ West North Central
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.
126
-------
Figure 3-7. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type
0.040 -
0.035 -
0.030 -
c 0.025 -
O
-M
u
£ 0.020 -
>.
Q 0.015 -
0.010 -
0.005 -
0.000 -
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2016
na\(
Figure 3-8. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type
0.10
0.08 -
0.06 -
h
0.04-
0.02 -
0.00
0 5 10 15 20
Hour
SMOKE uses a cross-reference file to select a monthly, daily, and diurnal profile for each source. For the
2016 beta and vl 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,
Daily Small EGU Profile for LADCO gas
Diurnal Small EGU Profile for MANE-VU coal
127
-------
PM2.5, and S02 for all SCCs in the unit. The SCC that contributed the highest total emissions to the unit
for selected pollutants was used to assign the unit fuel type. Peaking units were identified as any unit with
an oil, gas, or oil fuel type with a NAICS of 22111 or 221112. Some units may be assigned to a fuel type
within a region that does not have an available input unit with a matching fuel type in that region. These
units without an available profile for their group were assigned to use the regional composite profile.
MWC and cogen units were identified using the NEEDS primary fuel type and cogeneration flag,
respectively, from the NEEDS v6 database. The number of EGU units assigned each profile group are
shown by region in Figure 3-9.
Figure 3-9. Non-CEMS EGU Temporal Profile Application Counts
Small EGU 2016 Temporal Profile Application Counts
LADCO
KnrfMwKl1
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 shield (October through November and March
through April), and average summer (May through September) values were provided by the Integrated
Planning Model (IPM) for all units. The seasonal emissions for the 2023 and 2028 EGU 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
128
-------
applied to the seasonal emission projections for two seasons: summer (May through September) and
winter (October through April). The winter shield emissions are summed with the winter emissions for
consistency with previous platforms that did not have separate values for the winter shield season. The
Flat File used as the input to the temporal allocation process contains unit-level emissions and stack
parameters (i.e., stack location and other characteristics consistent with information found in the NEI).
When the flat file is produced from post-processed IPM outputs, a cross reference is used to map the units
in version 6 of the NEEDS database to the stack parameter and facility, unit, release point, and process
identifiers used in the NEI. This cross reference also maps sources to the hourly CEMS data used to
temporally allocate the emissions in the base year air quality modeling.
All units have seasonal information provided in the 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.
129
-------
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
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.
130
-------
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). Note that the future year IPM
output for 2030 also maps to the year 2028 and was therefore used for the 2028 modeling case.
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
May
2016
¦ 2016 CEMs
2030 CEMs
¦ 2030 Adjusted CEMs
Annual unit max
131
-------
Figure 3-11. Excess Emissions Apportioned to Hours Less than the Historic Maximum
12000
10000
8000
.c
§ 6000
rN
O
ui
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
rm
132
-------
Figure 3-12. Regional Profile Applied due to not being able to Adjust below Historic Maximum
2030 and 2016 Summer CEMs for 6095 2
2016 CEMs
2030 CEMs
2030 Adjusted CEMs
2030 Season Fuel
Annual unit max
May
2016
Figure 3-13. Regional Profile Applied, but Exceeds Historic Maximum in Some Hours
2030 and 2016 Summer CEMs for 2103 1
2016 CEMs
2030 CEMs
2030 Adjusted CEMs
2030 Season Fuel
Annual unit max
133
-------
3.3.3 Airport Temporal allocation (airports)
Airport temporal profiles were updated in 2014v7.0 and were kept the same for the 2016vl 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.
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.
Figure 3-14. Diurnal Profile for all Airport SCCs
Diurnal Airport Profile
Hour
134
-------
Figure 3-15. Weekly profile for all Airport SCCs
Weekly Airport Profile
0.18
Figure 3-16. Monthly Profile for all Airport SCCs
Monthly Airport Profile
0.05
0.04
0.03
0.02
0.01
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
135
-------
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
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.0rg/smoke/documentation/3.l/GenTPRO 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
136
-------
states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas. The algorithm is as follows:
IfTd >=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 degres 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.
Figure 3-18. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold
RWC temporal profile, Duval County, FL, Jan - Apr
0.04
0.035
0.03
\ 0.025
| 0.02
i
3 0.015
0.01
0.005
1
1
A.
i
i
J
i i
l v
/»
»
} I
I
h
i\i
:<
'\
\
1
K
1 , /A
lrv
\ 11
1 .1
\
/\ *
Ai A /
\ r\ \
\ a r\ f\ a
Mr v v
V\ \ A / A Ji V
> \/ — /; \\r: \ A
60F, alternate formula
) Oi O C O O O <
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.
137
-------
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, chimneas, etc.)" (i.e., "recreational RWC," SCC=21040087000) is
not based on temperature data, because the meteorologically-based temporal allocation used for the rest of
the rwc sector did not agree with observations for how these appliances are used.
For OHH, the annual-to-month, day-of-week and diurnal profiles were modified based on information in
the New York State Energy Research and Development Authority's (NYSERDA) "Environmental,
Energy Market, and Health Characterization of Wood-Fired Hydronic Heater Technologies, Final Report"
(NYSERDA, 2012), as well as a Northeast States for Coordinated Air Use Management (NESCAUM)
report "Assessment of Outdoor Wood-fired Boilers" (NESCAUM, 2006). A Minnesota 2008 Residential
Fuelwood Assessment Survey of individual household responses (MDNR, 2008) provided additional
annual-to-month, day-of-week, and diurnal activity information for OHH as well as recreational RWC
usage.
Data used to create the diurnal profile for OHH, shown in Figure 3-20, 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.
138
-------
Figure 3-20. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr)
Figure 3-21. Day-of-week temporal profiles for OHH and Recreational RWC
139
-------
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 NFb algorithm, the GenTPRO algorithm is based on an equation derived by
Jesse Bash of the EPA's ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation is
based on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to
estimate diurnal NFb emission variations from livestock as a function of ambient temperature,
aerodynamic resistance, and wind speed. The equations are:
Et.h = [161500/T, /, x eM380 x AR,,/, Equation 3-4
PE;,/; = Euh / Sum(E,,/,) Equation 3-5
where
• PE;,/; = Percentage of emissions in county i on hour h
• Eij, = Emission rate in county i on hour h
• Tin = Ambient temperature (Kelvin) in county i on hour h
• AR;,/; = Aerodynamic resistance in county i
GenTPRO was run using the "BASH NH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized
to the month. Figure 3-23 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.
140
-------
Figure 3-23. Example of animal NH3 emissions temporal allocation approach, summed to daily
emissions
2014fd Minnesota ag NH3 livestock daily temporal profiles
1600
1400
~ 1200
t? 1000
on
g 800
* 600
400
200
0
m
X
j
rl
1
,
Ailtr
,
ft
i iA
j
s
A rtjl
Hi"* r
AW
|
y
w V
1/1/2014 2/1/2014 3/4/2014 4/4/2014 5/5/2014 6/5/2014 7/6/2014 8/6/2014 9/6/2014 10/7/201411/7/201412/8/2014
-months
approach
¦ hourly
approach
For the 2016 platform, the GenTPRO approach is applied to all sources in the ag sector, NFb and non-
NFb, livestock and fertilizer. Monthly profiles are based on the daily-based EPA livestock emissions and
are the same as were used in 2014v7.0. Profiles are by state/SCC_category, where SCC_category is one
of the following: beef, broilers, layers, dairy, swine.
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.
3.3.7 Onroad mobile temporal allocation (onroad)
For the onroad sector, the temporal distribution of emissions is a combination of traditional temporal
profiles and the influence of meteorology. This section will discuss both the meteorological influences
and the development of the temporal profiles for this platform.
The "inventories" referred to in Table 3-19 consist of activity data for the onroad sector, not emissions.
For the off-network emissions from the rate-per-profile (RPP) and rate-per-vehicle (RPV) processes, the
VPOP activity data is annual and does not need temporal allocation. For rate-per-hour (RPH) processes
that result from hoteling of combination trucks, the HOTELING inventory is annual and was
temporalized to month, day of the week, and hour of the day through temporal profiles.
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
141
-------
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.
Figure 3-24. Example of temporal variability of NOx emissions
4
3.5
U
O
_c
3
l/l
oj
2.5
E
c
2
o
=
1.5
1-
1
>
>
0.5
0
7/8/140:00
VMT
NOX
2014v2 onroad RPD hourly NOX and VMT: Wake County, NC
7/9/140:00 7/10/140:00 7/11/140:00 7/12/140:00 7/13/140:00 7/14/140:00
Date and time (GMT)
0
7/15/140:00
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.
142
-------
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 coput regional average profiles.
Figure 3-25. Sample onroad diurnal profiles for Fulton County, GA
o.oi o.oi ^
0 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 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 road 2 road 3 road 4 road 5
Saturday Fulton Co passenger Sunday Fulton Co passenger
0.09 0.1
road 2 road 3 road 4 road 5 road 2 road 3 road 4 road 5
143
-------
Figure 3-26. Methods to Populate Onroad Speeds and Temporal Profiles by Road Type
Figure 3-27. Regions for computing Region Average Speeds and Temporal Profiles
144
-------
For hoteling, day-of-week profiles are the same as non-hoteling for combination trucks, while hour-of-day
non-hoteling profiles for combination trucks were inverted to create new hoteling profiles that peak
overnight instead of during the day. The combination truck profiles for Fulton County are shown in
Figure 3-28.
The CRC A-100 temporal profiles were used in the entire contiguous United States, except in California.
All California temporal profiles were carried over from 2014v7.0, although California hoteling uses CRC
A-100-based profiles just like the rest of the country, since CARB didn't have a hoteling-specific profile.
Monthly profiles in all states (national profiles by broad vehicle type) were also carried over from
2014v7.0 and applied directly to the VMT. For California, CARB supplied diurnal profiles that varied by
vehicle type, day of the week,29 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 13 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 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
29 California's diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and
Tuesday, Wednesday, Thursday had the same profile.
145
-------
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 use profile 9 and agricultural sources
continue to use profile 19.
Figure 3-29. Example Nonroad Day-of-week Temporal Profiles
Day of Week Profiles
0.24
0.22
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
mond^ tuesday Wednesday thursday friday Saturday Sunday
9 18 19
146
-------
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 upated 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
26a-New 27 25 a-New 26
3.3.9 Additional sector specific details (afdust, beis, cmv, rail, nonpt,
ptnonipm, ptfire)
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to
reduce the total emissions based on meteorological conditions. These adjustments are applied through
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and
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.
147
-------
For the cmv sectors, most areas use hourly emission inventories derived from the 5-minute AIS data. In
some areas where AIS data are not available, such as in Canada between the St. Lawrence Seaway and the
Great Lakes and in the southern Carribbean, the flat temporal profiles are used for hourly and day-of-
week values. Most regions without AIS data also use a flat monthly profile, with some offshore areas
using an average monthly profile derived from the 2008 ECA inventory monthly values. These areas
without AIS data also use flat day of week and hour of day profiles.
For the rail sector, new monthly profiles were developed for the 2016 platform. Monthly temporal
allocation for rail freight emissions is based on AAR Rail Traffic Data, Total Carloads and Intermodal, for
2016. For passenger trains, monthly temporal allocation is flat for all months. Rail passenger miles data
is available by month for 2016 but it is not known how closely rail emissions track with passenger activity
since passenger trains run on a fixed schedule regardless of how many passengers are aboard, and so a flat
profile is chosen for passenger trains. Rail emissions are allocated with flat day of week profiles, and
most emissions are allocated with flat hourly profiles.
For the ptagfire sector, the inventories are in the daily point fire format FF10 PTDAY. The diurnal
temporal profile for ag fires reflects the fact that burning occurs during the daylight hours - see Figure
3-31 (McCarty et al., 2009). This puts most of the emissions during the work day and suppresses the
emissions during the middle of the night.
Figure 3-31. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
0.18
0.16
0.14
I \ New McCarty Profile
0.12
j \ ..— OLD EPA
1 0.1
/ ^
I0"08
/
£
/ \
0.06
0.04
0.02
0
123456789 101112131415161718192021222324
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 2014v7.0 and 2014v7.1 modeling platforms. They are similar but not the same and vary according
148
-------
to the average meteorological conditions in each state. The 2016 alpha platform uses the ptfire diurnal
profiles form 2014v7.1 platform.
Figure 3-32. Prescribed and Wildfire diurnal temporal profiles
For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from MOVES. For California, CARB's annual inventory was temporalized to
monthly using monthly temporal profiles applied in SMOKE by SCC. This is an improvement over the
2011 platform, which applied monthly temporal allocation in California at the broader SCC7 level.
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 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 3-20 lists the
codes and descriptions of the surrogates. Surrogate names and codes listed in italics are not directly
149
-------
assigned to any sources for the 2016 alpha platform, but they are sometimes used to gapfill other
surrogates, or as an input for merging two surrogates to create a new surrogate that is used.
Many surrogates were updated or newly developed for use in the 2014v7.0 platform (Adelman, 2016).
They include the use of the 2011 National Land Cover Database (the previous platform used 2006) and
development of various development density levels such as open, low, medium high and various
combinations of these. These landuse surrogates largely replaced the FEMA category surrogates that
were used in the 2011 platform. Additionally, onroad surrogates were developed using average annual
daily traffic counts from the highway monitoring performance system (HPMS). Previously, the "activity"
for the onroad surrogates was length of road miles. This and other surrogates are described in a reference
(Adelman, 2016).
Several surrogates were updated or developed as new surrogates for the 2016v7.1 (aka alpha) platform:
Oil and gas surrogates were updated to 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 2016vl
platform;
Correction was made to the water surrogate to gap fill missing counties using the 2006 National
Land Cover Database (NLCD).
In addition, spatial surrogates 201 through 244, which concern road miles, annual average daily traffic
(AADT), and truck stops, were further updated for the 2016 beta and regional haze platforms. The
surrogates for the U.S. were mostly generated using the Surrogate Tool to drive the Spatial Allocator, but
a few surrogates were developed directly within ArcGIS or using scripts that manipulate spatial data in
PostgreSQL. The tool and documentation for the Surrogate Tool is available at
https://www.cmascenter.Org/sa-tools/documentation/4.2/SurrogateToolUserGuide 4 2.pdf.
Table 3-20. U.S. Surrogates available for the 2016vl modeling platforms
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2)
506
Education
100
Population
507
Heavy Light Construction Industrial Land
110
Housing
510
Commercial plus Industrial
131
urban Housing
515
Commercial plus Institutional Land
132
Suburban Housing
520
Commercial plus Industrial plus Institutional
134
Rural Housing
525
Golf Courses plus Institutional plus
Industrial plus Commercial
137
Housing Change
526
Residential - Non-Institutional
140
Housing Change and Population
527
Single Family Residential
150
Residential Heating - Natural Gas
535
Residential + Commercial + Industrial +
Institutional + Government
160
Residential Heating - Wood \
540
Retail Trade (COM1)
170
Residential Heating - Distillate Oil
545
Personal Repair (COM3)
180
Residential Heating - Coal
555
Professional/Technical (COM4) plus General
Government (GOV1)
190
Residential Heating - LP Gas
560
Hospital (COM6)
201
Urban Restricted Road Miles
575
Light and High Tech Industrial (1ND2 +
IND5)
202
Urban Restricted AADT
580
Food Drug Chemical Industrial (1ND3)
150
-------
Code
Surrogate Description
Code
Surrogate Description
205
Extended Idle Locations
585
Metals and Minerals Industrial (IND4)
211
Rural Restricted Road Miles
590
Heavy Industrial (IND1)
212
Rural Restricted AADT
595
Light Industrial (IND2)
221
Urban Unrestricted Road Miles ;
596
Industrial plus Institutional plus Hospitals
222
Urban Unrestricted AADT ]
650
Refineries and Tank Farms
231
Rural Unrestricted Road Miles
670
Spud Count - CBM Wells
232
Rural Unrestricted AADT \
671
Spud Count - Gas Wells
239
Total Road AADT
672
Gas Production at Oil Wells
240
Total Road Miles
673
Oil Production at CBM Wells
241
Total Restricted Road Miles =
674
Unconventional Well Completion Counts
242
All Restricted AADT
676
Well Count - All Producing
243
Total Unrestricted Road Miles
677
Well Count - All Exploratory
244
All Unrestricted AADT
678
Completions at Gas Wells
258
Intercity Bus Terminals
679
Completions at CBM Wells
259
Transit Bus Terminals
681
Spud Count - Oil Wells
260
Total Railroad Miles
683
Produced Water at All Wells
261
NT AD Total Railroad Density
685
Completions at Oil Wells
271
NT AD Class 12 3 Railroad Density
686
Completions at All Wells
272
NTAD Amtrak Railroad Density
687
Feet Drilled at All Wells
273
NTAD Commuter Railroad Density
691
Well Counts - CBM Wells
275
ERTACRail Yards
692
Spud Count-All Wells
280
Class 2 and 3 Railroad Miles i
693
Well Count - All Wells
300
NLCD Low Intensity Development
694
Oil Production at Oil Wells
301
NL CD Med Intensity Development
695
Well Count - Oil Wells
302
NLCD High Intensity Development \
696
Gas Production at Gas Wells
303
NLCD Open Space
697
Oil Production at Gas Wells
304
NLCD Open + Low
698
Well Count - Gas Wells
305
NLCD Low + Med
699
Gas Production at CBM Wells
306
NLCD Med + High
710
Airport Points
307
NLCD All Development
711
Airport Areas
308
NLCD Low + Med + High
801
Port Areas
309
NLCD Open + Low + Med
802
Shipping Lanes
310
NLCD Total Agriculture
805
Offshore Shipping Area
318
NLCD Pasture Land
806
Offshore Shipping NEI2014 Activity
319
NLCD Crop Land
807
Navigable Waterway Miles
320
NLCD Forest Land
808
2013 Shipping Density
321
NLCD Recreational Land
820
Ports NEI2014 Activity
340
NLCD Land
850
Golf Courses
350
NLCD Water
860
Mines
500
Commercial Land
890
Commercial Timber
505
Industrial Land
For the onroad sector, the on-network (RPD) emissions were allocated differently from the off-network
(RPP and RPV). On-network used AADT data and off network used land use surrogates as shown in
Table 3-21. Emissions from the extended (i.e., overnight) idling of trucks were assigned to surrogate 205,
which is based on locations of overnight truck parking spaces. This surrogate's underlying data were
updated for use in the 2016 platforms to include additional data sources and corrections based on
comments received.
151
-------
Table 3-21. Off-Network Mobile Source Surrogates
Source type
Source Type name
Surrogate ID
Description
11
Motorcycle
307
NLCD All Development
21
Passenger Car
307
NLCD All Development
31
Passenger Truck
307
NLCD All Development
NLCD Low + Med +
32
Light Commercial Truck
308
High
41
Intercity Bus
258
Intercity Bus Terminals
42
Transit Bus
259
Transit Bus Terminals
43
School Bus
506
Education
51
Refuse Truck
306
NLCD Med + High
52
Single Unit Short-haul Truck
306
NLCD Med + High
53
Single Unit Long-haul Truck
306
NLCD Med + High
54
Motor Home
304
NLCD Open + Low
61
Combination Short-haul Truck
306
NLCD Med + High
62
Combination Long-haul Truck
306
NLCD Med + High
For the oil and gas sources in the np oilgas sector, the spatial surrogates were updated to those shown in
Table 3-22 using 2016 data consistent with what was used to develop the 2016 beta nonpoint oil and gas
emissions. The primary activity data source used for the development of the oil and gas spatial
surrogates was data from Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2017). This
database contains well-level location, production, and exploration statistics at the monthly level.
Due to a proprietary agreement with DI Desktop, individual well locations and ancillary
production cannot be made publicly available, but aggregated statistics are allowed. These data were
supplemented with data from state Oil and Gas Commission (OGC) websites (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 3-22. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
670
Spud Count - CBM Wells
671
Spud Count - Gas Wells
672
Gas Production at Oil Wells
673
Oil Production at CBM Wells
674
Unconventional Well Completion Counts
676
Well Count - All Producing
677
Well Count - All Exploratory
678
Completions at Gas Wells
679
Completions at CBM Wells
681
Spud Count - Oil Wells
683
Produced Water at All Wells
152
-------
Surrogate Code
Surrogate Description
685
Completions at Oil Wells
686
Completions at All Wells
687
Feet Drilled at All Wells
691
Well Counts - CBM Wells
692
Spud Count - All Wells
693
Well Count - All Wells
694
Oil Production at Oil Wells
695
Well Count - Oil Wells
696
Gas Production at Gas Wells
697
Oil Production at Gas Wells
698
Well Count - Gas Wells
699
Gas Production at CBM Wells
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-20 were not assigned to any SCCs, although many of the "unused"
surrogates are actually used to "gap fill" other surrogates that are used. When the source data for a
surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in
those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the
emission inventories. Table 3-23 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by
sector assigned to each spatial surrogate.
Table 3-23. Selected 2016 CAP emissions by sector for U.S. Surrogates (short tons in 12US1)
Sector
ID
Description
NH3
NOX
PM2 5
S02
VOC
afdust
240
Total Road Miles
294,379
afdust
304
NLCD Open + Low
1,053,145
afdust
306
NLCD Med + High
43,633
afdust
308
NLCD Low + Med + High
123,524
afdust
310
NLCD Total Agriculture
988,012
ag
310
NLCD Total Agriculture
3,409,761
194,779
nonpt
100
Population
0
0
0
0
1,240,692
nonpt
150
Residential Heating - Natural Gas
42,973
219,189
3,632
1,442
13,296
nonpt
170
Residential Heating - Distillate Oil
1,563
31,048
3,356
41,193
1,051
nonpt
180
Residential Heating - Coal
20
101
53
1,086
111
nonpt
190
Residential Heating - LP Gas
111
33,230
175
705
1,292
nonpt
239
Total Road AADT
0
25
551
0
274,266
nonpt
240
Total Road Miles
0
0
0
0
34,027
nonpt
242
All Restricted AADT
0
0
0
0
5,451
nonpt
244
All Unrestricted AADT
0
0
0
0
96,232
nonpt
271
NT AD Class 12 3 Railroad Density
0
0
0
0
2,252
nonpt
300
NLCD Low Intensity Development
5,198
27,727
104,108
3,722
71,770
nonpt
306
NLCD Med + High
27,518
180,692
207,536
62,698
950,022
nonpt
307
NLCD All Development
25
46,331
126,722
14,185
601,828
nonpt
308
NLCD Low + Med + High
1,027
171,603
16,096
13,527
65,123
153
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nonpt
310
NLCD Total Agriculture
0
0
37
0
204,819
nonpt
319
NLCD Crop Land
0
0
95
71
293
nonpt
320
NLCD Forest Land
69
378
1,289
9
474
nonpt
505
Industrial Land
0
0
0
0
174
nonpt
535
Residential + Commercial + Industrial +
Institutional + Government
5
2
130
0
39
nonpt
560
Hospital (COM6)
0
0
0
0
0
nonpt
650
Refineries and Tank Farms
0
22
0
0
99,564
nonpt
711
Airport Areas
0
0
0
0
271
nonpt
801
Port Areas
0
0
0
0
8,194
nonroad
261
NT AD Total Railroad Density
3
2,154
227
2
425
nonroad
304
NLCD Open + Low
4
1,824
159
5
2,727
nonroad
305
NLCD Low + Med
94
15,985
3,832
126
114,513
nonroad
306
NLCD Med + High
305
183,591
11,873
421
93,596
nonroad
307
NLCD All Development
99
31,526
15,340
125
169,943
nonroad
308
NLCD Low + Med + High
498
338,083
28,585
487
51,865
nonroad
309
NLCD Open + Low + Med
119
21,334
1,257
162
45,498
nonroad
310
NLCD Total Agriculture
422
378,388
28,387
425
40,707
nonroad
320
NLCD Forest Land
15
5,910
703
15
3,939
nonroad
321
NLCD Recreational Land
83
11,616
6,517
104
246,154
nonroad
350
NLCD Water
188
115,175
5,952
240
353,189
nonroad
850
Golf Courses
13
2,001
117
18
5,613
nonroad
860
Mines
2
2,691
281
3
521
np oilgas
670
Spud Count - CBM Wells
0
0
0
0
112
np oilgas
671
Spud Count - Gas Wells
0
0
0
0
6,284
np oilgas
674
Unconventional Well Completion Counts
12
18,802
720
9
1,264
np oilgas
678
Completions at Gas Wells
0
5,315
136
2,488
16,615
np oilgas
679
Completions at CBM Wells
0
3
0
80
395
np oilgas
681
Spud Count - Oil Wells
0
0
0
0
15,164
np oilgas
683
Produced Water at All Wells
0
11
0
0
47,271
np oilgas
685
Completions at Oil Wells
0
255
0
769
27,935
np oilgas
687
Feet Drilled at All Wells
0
36,162
1,309
22
2,664
np oilgas
691
Well Counts - CBM Wells
0
32,971
490
13
27,566
np oilgas
693
Well Count - All Wells
0
0
0
0
159
np oilgas
694
Oil Production at Oil Wells
0
4,165
0
15,385
1,062,178
np oilgas
695
Well Count - Oil Wells
0
134,921
2,953
32
566,235
np oilgas
696
Gas Production at Gas Wells
0
16,339
1,847
164
428,206
np oilgas
698
Well Count - Gas Wells
0
320,688
6,217
258
582,442
np oilgas
699
Gas Production at CBM Wells
0
2,413
312
25
7,602
onroad
205
Extended Idle Locations
230
78,126
794
36
13,711
onroad
239
Total Road AADT
5,755
onroad
242
All Restricted AADT
34,545
1,175,197
38,140
8,744
194,836
onroad
244
All Unrestricted AADT
65,543
1,773,993
67,525
17,788
477,839
onroad
258
Intercity Bus Terminals
147
2
0
34
154
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
onroad
259
Transit Bus Terminals
53
3
0
149
onroad
304
NLCD Open + Low
829
29
1
3,874
onroad
306
NLCD Med + High
15,209
333
17
19,917
onroad
307
NLCD All Development
546,312
10,195
910
1,073,380
onroad
308
NLCD Low + Med + High
40,054
722
62
62,127
onroad
506
Education
629
15
1
637
rail
261
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
15,439
31,282
316,943
7,703
340,941
For 36US3 modeling in the 2016 platforms, most U.S. emissions sectors were processed using 36-km
spatial surrogates, and if applicable, 36-km meteorology. Exceptions include:
- For the onroad and onroad ca adj sectors, 36US3 emissions were aggregated from 12US1 by
summing emissions from a 3x3 group of 12-km cells into a single 36-km cell. Differences in 12-
km and 36-km meteorology can introduce differences in onroad emissions, and so this approach
ensures that the 36-km and 12-km onroad emissions are consistent. However, this approach means
that 36US3 onroad does not include emissions in Southeast Alaska; therefore, Alaska onroad
emissions are included in a separate sector called 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
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 alpha platform. For the 2016v7.2 platform, a new set of
Canada shapefiles were provided by Environment Canada along with cross references spatially allocate
the year 2015 Canadian emissions. Gridded surrogates were generated using the Surrogate Tool
155
-------
(previously referenced); Table 3-24 provides a list. Due to computational reasons, total roads (1263) were
used instead of the unpaved rural road surrogate provided. The population surrogate was recently updated
for Mexico; surrogate code 11, which uses 2015 population data at 1 km resolution, replaces the previous
population surrogate code 10. The other surrogates for Mexico are circa 1999 and 2000 and were based
on data obtained from the Sistema Municipal de Bases de Datos (SIMBAD) de INEGI and the Bases de
datos del Censo Economico 1999. Most of the CAPs allocated to the Mexico and Canada surrogates are
shown in Table 3-25.
Table 3-24. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
TOTAL INSTITUTIONAL AND
100
Population
923
GOVERNEMNT
101
total dwelling
924
Primary Industry
104
capped total dwelling
925
Manufacturing and Assembly
106
ALL INDUST
926
Distribution and Retail (no petroleum)
113
Forestry and logging
927
Commercial Services
200
Urban Primary Road Miles
932
CANRAIL
210
Rural Primary Road Miles
940
PAVED ROADS NEW
211
Oil and Gas Extraction
945
Commercial Marine Vessels
212
Mining except oil and gas
946
Construction and mining
220
Urban Secondary Road Miles
948
Forest
221
Total Mining
951
Wood Consumption Percentage
222
Utilities
955
UNPAVED ROADS AND TRAILS
230
Rural Secondary Road Miles
960
TOTBEEF
233
Total Land Development
970
TOTPOUL
240
capped population
980
TOTS WIN
308
Food manufacturing
990
TOTFERT
321
Wood product manufacturing
996
urban area
323
Printing and related support activities
1251
OFFR TOTFERT
324
Petroleum and coal products manufacturing
1252
OFFR MINES
326
Plastics and rubber products manufacturing
1253
OFFR Other Construction not Urban
327
Non-metallic mineral product manufacturing
1254
OFFR Commercial Services
331
Primary Metal Manufacturing
1255
OFFR Oil Sands Mines
350
Water
1256
OFFR Wood industries CANVEC
412
Petroleum product wholesaler-distributors
1257
OFFR UNPAVED ROADS RURAL
448
clothing and clothing accessories stores
1258
OFFR Utilities
482
Rail transportation
1259
OFFR total dwelling
562
Waste management and remediation services
1260
OFFR water
901
AIRPORT
1261
OFFR ALL INDUST
902
Military LTO
1262
OFFR Oil and Gas Extraction
903
Commercial LTO
1263
OFFR ALLROADS
904
General Aviation LTO
1265
OFFR CANRAIL
921
Commercial Fuel Combustion
9450
Commercial Marine Vessel Ports
156
-------
Table 3-25. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3)
Sector
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
othafdust
106
CAN ALL INDUST
—
—
5,632
"
"
othafdust
212
CAN Mining except oil and gas
—
—
684
—
—
othafdust
221
CAN Total Mining
—
—
142,940
—
—
othafdust
222
CAN Utilities
—
—
23,640
—
—
othafdust
940
CAN Paved Roads New
—
—
210,336
—
—
othafdust
955
CANUNPAVED ROADS AND TRAILS
—
—
389,775
—
—
othafdust
960
CAN TOTBEEF
—
—
1,289
—
—
othafdust
970
CAN TOTPOUL
—
—
184
—
—
othafdust
980
CAN TOTS WIN
—
—
792
—
—
othafdust
990
CAN TOTFERT
—
—
321
—
—
othafdust
996
CAN urban area
—
—
617
—
—
othar
11
MEX 2015 Population
164,464
168,447
13,521
1,164
291,178
othar
14
MEX Residential Heating - Wood
0
23,842
305,597
3,658
2,101,03
3
othar
16
MEX Residential Heating - Distillate Oil
2
58
1
16
2
othar
20
MEX Residential Heating - LP Gas
0
26,526
838
0
505
othar
22
MEX Total Road Miles
1
1,046
2
7
2,308
othar
24
MEX Total Railroads Miles
0
63,136
1,407
551
2,494
othar
26
MEX Total Agriculture
713,253
399,070
80,458
18,650
33,742
othar
32
MEX Commercial Land
0
457
7,719
0
106,077
othar
34
MEX Industrial Land
8
3,383
4,833
1
563,953
othar
36
MEX Commercial plus Industrial Land
0
0
0
0
272,155
othar
38
MEX Commercial plus Institutional Land
3
6,740
235
3
148
othar
40
MEX Residential (RESl-4)+Commercial+
Industrial+Institutional+Government
0
16
39
0
331,216
othar
42
MEX Personal Repair (COM3)
0
0
0
0
26,261
othar
44
MEX Airports Area
0
13,429
306
1,561
3,766
othar
50
MEX Mobile sources - Border Crossing
5
161
1
3
293
othar
100
CAN Population
761
54
669
15
241
othar
101
CAN total dwelling
0
0
0
0
150,892
othar
104
CAN Capped Total Dwelling
421
37,205
2,766
206
1,952
othar
113
CAN Forestry and logging
185
2,210
11,310
45
6,246
othar
211
CAN Oil and Gas Extraction
0
31
60
22
925
othar
212
CAN Mining except oil and gas
0
0
3,079
0
0
othar
221
CAN Total Mining
0
0
43
0
0
othar
222
CAN Utilities
34
1,858
0
386
22
othar
308
CAN Food manufacturing
0
0
20,185
0
10,324
othar
321
CAN Wood product manufacturing
874
4,822
1,646
383
16,606
othar
323
CAN Printing and related support activities
0
0
0
0
11,770
othar
324
CAN Petroleum and coal products manufacturing
0
1,205
1,542
486
9,304
othar
326
CAN Plastics and rubber products manufacturing
0
0
0
0
23,283
othar
327
CAN Non-metallic mineral product manufacturing
0
0
6,695
0
0
othar
331
CAN Primary Metal Manufacturing
0
158
5,595
30
72
othar
350
CAN Water
0
120
2
0
4
othar
412
CAN Petroleum product wholesaler-distributors
0
0
0
0
45,257
157
-------
Sector
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
othar
448
CAN clothing and clothing accessories stores
0
0
0
0
149
othar
482
CAN Rail Transportation
2
4,980
106
12
310
othar
562
CAN Waste management and remediation services
271
1,977
2,710
2,528
13,138
othar
901
CAN Airport
0
109
11
0
11
othar
921
CAN Commercial Fuel Combustion
243
23,628
2,333
2,821
1,091
othar
923
CAN TOTAL INSTITUTIONAL AND
GOVERNEMNT
0
0
0
0
14,859
othar
924
CAN Primary Industry
0
0
0
0
40,376
othar
925
CAN Manufacturing and Assembly
0
0
0
0
71,198
othar
926
CAN Distribution and Retail (no petroleum)
0
0
0
0
7,461
othar
927
CAN Commercial Services
0
0
0
0
32,167
othar
932
CAN CANRAIL
61
132,985
3,107
485
6,567
othar
946
CAN Construction and Mining
0
0
0
0
4,359
othar
951
CAN Wood Consumption Percentage
1,950
21,662
179,087
3,095
253,523
othar
990
CAN TOTFERT
48
4,456
0
9,881
164
othar
1251
CAN OFFR TOTFERT
81
77,166
5,671
58
7,176
othar
1252
CAN OFFR MINES
1
1,004
70
1
138
othar
1253
CAN OFFR Other Construction not Urban
66
53,671
6,096
47
12,159
othar
1254
CAN OFFR Commercial Services
40
17,791
2,552
34
44,338
othar
1255
CAN OFFR Oil Sands Mines
18
9,491
311
10
1,025
othar
1256
CAN OFFR Wood industries CANVEC
9
5,856
476
7
1,318
othar
1257
CAN OFFR Unpaved Roads Rural
32
11,866
1,169
28
49,975
othar
1258
CAN OFFR Utilities
8
5,579
349
7
1,087
othar
1259
CAN OFFR total dwelling
16
5,768
773
14
15,653
othar
1260
CAN OFFR water
15
4,356
451
29
28,411
othar
1261
CAN OFFR ALL INDUST
4
5,770
253
3
1,049
othar
1262
CAN OFFR Oil and Gas Extraction
0
368
29
0
143
othar
1263
CAN OFFR ALLROADS
3
2,418
244
2
582
othar
1265
CAN OFFR CANRAIL
0
85
9
0
15
onroad_
can
200
CAN Urban Primary Road Miles
1,619
85,558
2,851
329
8,396
onroad_
can
210
CAN Rural Primary Road Miles
683
51,307
1,673
139
3,807
onroad_
can
220
CAN Urban Secondary Road Miles
3,021
136,582
5,708
690
22,374
onroad_
can
230
CAN Rural Secondary Road Miles
1,769
96,911
3,238
374
10,370
onroad_
can
240
CAN Total Road Miles
43
57,401
1,355
77
103,658
onroad_
mex
11
MEX 2015 Population
0
281,317
1,873
533
291,992
onroad_
mex
22
MEX Total Road Miles
10,321
1,208,461
54,823
25,855
251,931
onroad_
mex
36
MEX Commercial plus Industrial Land
0
7,975
142
29
9,192
158
-------
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 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 3-26. Each step of the CAMx-ready emissions conversion process includes conversion of CMAQ
species to CAMx species using a species mapping table which includes the mappings in Table 3-26.
159
-------
Table 3-26. Emission model species mappings for CMAQ and CAMx
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
nh,
NH3
NH3
NH3 FERT
n/a (not used in CAMx)
voc
ACET
ACET
ALD2
ALD2
ALDX
ALDX
BENZ
BENZ and BNZA (duplicate species)
CH4
CH4
ETH
ETH
ETHA
ETHA
ETHY
ETHY
ETOH
ETOH
FORM
FORM
IOLE
IOLE
ISOP
ISOP and ISP (duplicate species)
KET
KET
MEOH
MEOH
NAPH + XYLMN (sum)
XYL
NVOL
n/a (not used in CAMx)
OLE
OLE
PAR
PAR
PRPA
PRPA
SESQ
SQT
SOAALK
n/a (not used in CAMx)
TERP
TERP and TRP (duplicate species)
TOL
TOL and TOLA (duplicate species)
UNR + NR (sum)
NR
PM10
PMC
CPRM
PM2.5
PEC
PEC
PN03
PN03
POC
POC
PS04
PS04
PAL
PAL
PCA
PCA
PCL
PCL
PFE
PFE
PK
PK
PH20
PH20
PMG
PMG
PMN
PMN
PMOTHR
PMOTHR and FPRM (duplicate species)
PNA
NA
160
-------
Inventory Pollutant
CMAQ Model Species
CAMx Model Species
PNCOM
PNCOM
PNH4
PNH4
PSI
PSI
PTI
PTI
POC + PNCOM (sum)
POA1
PAL + PCA + PFE +
FCRS1
PMG + PK + PMN +
PSI + PTI (sum)
1 The POA species, which is the sum of POC and PNCOM, is passed to the CAMx model in addition to individual species POC
and PNCOM. The FCRS species, which is also a sum of multiple PM species, is passed to CAMx in addition to each of the
individual component species.
One feature which is part of CMAQ and is not part of CAMx involves plume rise for fires. For CMAQ
modeling, we process fire emissions through SMOKE as inline point sources, and plume rise for fires is
calculated within CMAQ using parameters from the inline emissions files (heat flux, etc). This is similar
to how non-fire point sources are handled, except that the fire parameters are used to calculate plume rise
instead of traditional stack parameters. The CAMx model supports inline plume rise calculations using
traditional stack parameters, but, does not support inline plume rise for fire sources. Therefore, for the
purposes of CAMx modeling, we must have SMOKE calculate plume rise for fires using the Laypoint
program. In this modeling platform, this must be done for the ptfire, ptfire othna, and ptagfire sectors. To
distinguish these layered fire emissions from inline fire emissions, layered fire emissions are processed
with the sector names "ptfire3D", "ptfire_othna3D", and "ptagfire3D". When converting layered fire
emissions files to CAMx format, stack parameters are added to the CAMx-ready fire emissions files to
force the correct amount of fire emissions into each layer for each fire location.
CMAQ modeling uses one gridded low-level emissions file, plus multiple inline point source emissions
files, per day. CAMx modeling also uses one gridded low-level emissions file per day - but instead of
reading multiple inline point source emissions files at once, CAMx can only read a single point source file
per day. Therefore, as part of the CAMx conversion process, all inline point source files are merged into a
single "mrgpt" file per day. The mrgpt file includes the layered fire emissions described in the previous
paragraph, in addition to all non-fire elevated point sources from the cmv_c3, othpt, ptegu, ptnonipm, and
pt oilgas sectors.
The remaining step in the CAMx emissions process is to generate sea salt aerosol emissions, which are
distinct from ocean chlorine emissions. Sea salt emissions do not need to be included in CMAQ-ready
emissions because they are calculated by the model, but, do need to be included in CAMx-ready
emissions. After the merged low-level emissions are converted to CAMx format, sea salt emissions are
generated using a program called "seasalt" and added to the low-level emissions. Sea salt emissions
depend on meteorology, vary on a daily and hourly basis, and exist for model species PCL, NA, PS04,
and SS (i.e., sea salt).
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
Technology (PSAT). These source apportionment techniques allow emissions from different types of
sources to be tracked through the CAMx model. For the Revised CSAPR Update study, OSAT modeling
was performed in CAMx for 2023 and 2028 using one-way nesting (i.e., the inner 12km grid takes
161
-------
boundary information from the outer 36km grid but the inner grid does not feed any concentration
information back to the outer grid). The emissions developed specifically for OSAT modeling used the
case names "2023fhl_ussa_16j" and "2028fhl_ussa_16j".
Source Apportionment modeling involves assigning tags to different categories of emissions. These tags
can be applied by region (e.g., state), by emissions type (e.g., SCC or sector), or a combination of the two.
For the Revised CSAPR Update study, emissions tagging was applied by state. All emissions from US
states, except for biogenics, fires, and fugitive dust (afdust), were assigned a state-specific tag. Emissions
from tribal lands were also assigned a separate tag, as well as offshore emissions. Other tags include a tag
for biogenics and afdust; a tag for all fires, both inside and outside the US; and a tag for all anthropogenic
emissions from Canada and Mexico. A full list of tags is provided in Table 3-27. State-level tags 2
through 51 exclude emissions from biogenics, fugitive dust, and fires, which are included in other tags.
Table 3-27. State tags for 2023fhl, 2028fhl USSA modeling
Tag
Emissions applied to tag
1
All biogenics (beis sector) and US fugitive dust (afdust sector)
2
Alabama
3
Arizona
4
Arkansas
5
California
6
Colorado
7
Connecticut
8
Delaware
9
District of Columbia
10
Florida
11
Georgia
12
Idaho
13
Illinois
14
Indiana
15
Iowa
16
Kansas
17
Kentucky
18
Louisiana
19
Maine
20
Maryland
21
Massachusetts
22
Michigan
23
Minnesota
24
Mississippi
25
Missouri
26
Montana
27
Nebraska
28
Nevada
29
New Hampshire
30
New Jersey
31
New Mexico
32
New York
162
-------
Tag
Emissions applied to tag
33
North Carolina
34
North Dakota
35
Ohio
36
Oklahoma
37
Oregon
38
Pennsylvania
39
Rhode Island
40
South Carolina
41
South Dakota
42
Tennessee
43
Texas
44
Utah
45
Vermont
46
Virginia
47
Washington
48
West Virginia
49
Wisconsin
50
Wyoming
51
Tribal Data
52
Canada and Mexico (except fires)
53
Offshore
54
All fires from US, Canada, and Mexico, including ag fires
For OSAT and PSAT modeling, all emissions must be input to CAMx in the form of a point source
(mrgpt) file, including low level sources that are found in gridded files for regular CAMx runs. In
addition, for two-way nested modeling, all emissions must be input in a single mrgpt file, rather than
separate mrgpt files for each of the two domains (36US3 and 12US2). Note that fire emissions require
special consideration in two-way nested model runs and for PSAT and OSAT modeling. That same
consideration must be given to any sector in which emissions are being gridded by SMOKE.
There are two main approaches for tagging emissions for CAMx modeling. One approach is to tag
emissions within SMOKE. Here, SMOKE will output tagged point source files (SGINLN files), which
can then be converted to CAMx point source format with the tags applied by SMOKE carried forward
into the CAMx inputs. The second approach is to, if necessary, depending on the nature of the tags, split
sectors into multiple components by tag so that each sector corresponds to a single tag. Then, the gridded
and/or point source format SMOKE outputs from those split sectors are converted to CAMx point source
format, and then merged into the full mrgpt file, with the tags applied at that last step. In some situtations,
a mix of the two approaches is appropriate.
For the Revised CSAPR Update study the first approach was used for most sectors, meaning tags were
applied in SMOKE. The exceptions were sectors where the entire sector receives only one tag: afdust,
beis, onroad ca adj, ptfire, ptagfire, ptfire othna, and all Canada and Mexico sectors. Afdust emissions
are not tagged by state because the current tagging methodology does not support applying transportable
fraction and meteorological adjustments to tagged emissions.
163
-------
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.
164
-------
4 Development of Future Year Emissions
The emission inventories for future years of 2023 and 2028 have been developed using projection
methods that are specific to the type of emissions source. Future emissions are projected from the 2016
base case either by running models to estimate future year emissions from specific types of emission
sources (e.g., EGUs, and onroad and nonroad mobile sources), or for other types of sources by adjusting
the base year emissions according to the best estimate of changes expected to occur in the intervening
years (e.g., non-EGU point and nonpoint sources). For some sectors, the same emissions are used in the
base and future years, such as biogenic and fire. For the remaining sectors, rules and specific legal
obligations that go into effect in the intervening years, along with changes in activity for the sector, are
considered when possible. These sectors have been projected to 2023 and 2028 as summarized in Table
4-1. The development of the 2021fi emissions for each sector is also discussed.
Table 4-1. Overview of projection methods for the 2023 and 2028 regional cases
Platform Sector:
abbreviation
Description of Projection Methods for 2023 and 2028
EGU units:
Ptegu
The Integrated Planning Model (IPM) was run to create the 2023 and 2028
emissions. IPM outputs from the January, 2020 version of the IPM platform were
used (httDs://www.eDa.gov/airmarkets/eDas-DOwer-sector-modeling-
platform-v6-using-ipm-ianuarv-2020-reference-case). For 2023. the 2023 IPM
output year was used and for 2028 the 2030 output year was used because the year
2028 maps to the 2030 output year. Emission inventory Flat Files for input to
SMOKE were generated using post-processed IPM output data. Temporal
allocation for future year emissions is discussed in the EGU-IPM specification
sheet for the 2016vl platform. For 2021fi, an engineering analysis-based inventory
was used. The inventory is available in Docket ID No. EPA-HQ-OAR-2020-0272
as "Final Rule State Emission Budgets Calculations and Engineering Analytics".
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 2017 using historic production data.
The production-related sources were then grown to 2023 and 2028 based on
growth factors derived from the Annual Energy Outlook (AEO) 2019 data for oil,
natural gas, or a combination thereof. The grown emissions were then controlled
to account for the impacts of relevant New Source Performance Standards (NSPS).
For 202 lfi, a set of projection and control factors for 2021 were developed
consistently with those used for 2023fh and applied to 2016fh inventories.
Remaining non-
EGU point:
Ptnonipm
First, known closures were applied to the 2016 ptnonipm sources. Closures were
obtained from the Emission Inventory System (EIS) and also submitted by the
states of Alabama, North Carolina, Ohio, Pennsylvania, and Virginia. Industrial
sources were grown using factors derived from the AEO 2019. Rail yard emissions
were grown using the same factors as line haul locomotives in the rail sector.
Controls were then applied to account for relevant NSPS for reciprocating internal
combustion engines (RICE), gas turbines, and process heaters. Reductions due to
consent decrees that had not been fully implemented by 2016 were also applied,
along with specific comments received by S/L/T agencies. For 20216, most
emissions were interpolated between 2016fi and 2023, additional closures were
implemented and new sources were added based on 2018NEI, and Pennsylvania
emissions were updated based on feedback from MARAMA. Rail yards were
interpolated between 2016 and 2023.
165
-------
Platform Sector:
abbreviation
Description of Projection Methods for 2023 and 2028
Airports
Starts with 2017 NEI. Airport emissions were grown using factors derived from
the Terminal Area Forecast (TAF) (see
https://www.faa.aov/data research/av iation/taf/). For 2021. a set of projection
factors consistent with 2023fhl were developed, and then applied to the corrected
2017 NEI emissions. Corrections to emissions for ATL from the state of Georgia
were also implemented.
Agricultural:
Ag
Livestock were projected based on factors created from USDA National livestock
inventory projections published in February 2018
(https://www.ers.usda.aov/webdocs/outlooks/87459/oce-2018-1.pdf?v=7587).
Fertilizer emissions were held constant at year 2016 levels. For 20216, the
emissions were interpolated between 2016 and 2023.
Area fugitive dust:
afdust, afdust ak
Paved road dust was grown to 2023 and 2028 levels based on the growth in VMT
from 2016 to 2023 and 2028. The remainder of the sector including building
construction, road construction, agricultural dust, and unpaved road dust was held
constant, except in the MARAMA region where some factors were provided for
categories other than paved roads. The projected emissions are reduced during
modeling according to a transport fraction (newly computed for the beta platform)
and a meteorology-based (precipitation and snow/ice cover) zero-out as they are
for the base year. For 20216, the emissions were interpolated between 2016 and
2023.
Category 1, 2 CMV:
cmv_clc2
Category 1 and category 2 (C1C2) CMV emissions sources outside of California
were projected to 2023 and 2028 based on factors from the Regulatory Impact
Analysis (RIA) Control of Emissions of Air Pollution from Locomotive Engines
and Marine Compression Ignition Engines Less than 30 Liters per Cylinder.
California emissions were projected based on factors provided by the state. For
20216, projection factors consistent with 2023fhl were developed and applied to
the 2016fh emissions. Canada emission were interpolated between 2015 and 2023.
Category 3 CMV:
cmv_c3
Category 3 (C3) CMV emissions were projected using a forthcoming EPA report
on projected bunker fiiel demand. The report projects bunker fiiel consumption by
region out to the year 2030. Bunker fiiel usage was used as a surrogate for marine
vessel activity. Factors based on the report were used for all pollutants except
NOx. Growth factors for NOx emissions were handled separately to account for
the phase in of Tier 3 vessel engines. The NOx growth rates from the EPA C3
Regulatory Impact Assessment (RIA) were refactored to use the new bunker fuel
usage growth rates. The assumptions of changes in fleet composition and
emissions rates from the C3 RIA were preserved and applied to the new bunker
fuel demand growth rates for 2023 and 2028 to arrive at the final growth rates. For
20216, projection factors consistent with 2023fhl were developed and applied to
the 2016fh emissions. Canada emission were interpolated between 2015 and 2023.
Locomotives:
rail
Passenger and freight were projected using separate factors. Freight emissions
were computed for future years based on future year fuel use values for 2020,
2023, and 2028. Specifically, they were based on AEO2018 freight rail energy use
growth rate projections and emission factors, which are based on historic
emissions trends that reflect the rate of market penetration of new locomotive
engines. For 20216, the emissions were interpolated between 2016 and 2023.
166
-------
Platform Sector:
abbreviation
Description of Projection Methods for 2023 and 2028
Remaining
nonpoint:
nonpt
Industrial emissions were grown according to factors derived from AEO2019.
Portions of the nonpt sector were grown using factors based on expected growth in
human population. Controls were applied to reflect relevant NSPS rules (i.e.,
reciprocating internal combustion engines (RICE), natural gas turbines, and
process heaters). Emissions were also reduced to account for fuel sulfur rules in
the mid-Atlantic and northeast. For 20216, most emissions were interpolated
between 2016 and 2023 and cellulosic emissions were removed after consultation
with the EPA Office of Transportation and Air Quality.
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 2017 using historical data
and then grown to 2023 and 2028 based on factors generated from AEO2019.
Based on the SCC, factors related to oil, gas, or combined growth were used.
Coalbed methane SCCs were projected independently. Controls were then applied
to account for NSPS for oil and gas and RICE. For 2021fi, a set of projection and
control factors for 2021 were developed consistently with those used for 2023fh
and applied to 2016fh inventories.
Residential Wood
Combustion:
rwc
RWC emissions were projected from 2016 to 2023 and 2028 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. RWC
emissions in California, Oregon, and Washington were held constant. For 202 lfi,
emissions were interpolated between 2016 and 2023.
Nonroad:
nonroad
Outside California, the MOVES2014b model was run to create nonroad emissions
for 2023 and 2028 without any state inputs. The fuels used are specific to the
future year, but the meteorological data represented the year 2016. For California,
datasets provided by the California Air Resources Board (CARB) circa 2017 were
used. For 20216, MOVES2014b was run for 2020 and the 2021 emissions were
interpolated between 2020 and 2023. Texas 2021 emissions were interpolated
between 2020 and 2023. California 2021 emissions were interpolated between
2016 and 2023.
Onroad:
onroad,
onroadnonconus
Activity data were projected from 2016 to 2023 and 2028 based on factors derived
from AEO2019. Where S/Ls provided activity data, those data were used. To
create the emission factors, MOVES2014b was run for the years 2023 and 2028,
with 2016 meteorological data and fuels, but with age distributions projected to
represent future years, and the remaining inputs consistent with those used in
2014NEIv2. The future year activity data and emission factors were then
combined using SMOKE-MOVES to produce the 2023 and 2028 emissions.
Section 4.3.2 describes the applicable rules that were considered when projecting
onroad emissions. For 20216, MOVES2014b was run for 2020 and 2020 activity
data were developed by interpolating between 2016 and 2023. Adjustment factors
from 2020 to 2021 were developed by SCC and pollutant from national runs of
MOVES2014b for those two years.
Onroad California:
onroadcaadj
CARB-provided emissions were used for California, but they were gridded and
temporalized using MOVES2014b-based data output from SMOKE-MOVES.
Volatile organic compound (VOC) HAP emissions derived from California-
provided VOC emissions and MOVES-based speciation. For 20216, emissions
were interpolated between 2016 and 2023.
167
-------
Platform Sector:
abbreviation
Description of Projection Methods for 2023 and 2028
Other Area Fugitive
dust sources not
from the NEI:
othafdust
Othafdust emissions for future years were provided by ECCC. The emissions were
extracted from a broader nonpoint source inventory. Adjustments to construction
dust were made to make those more consistent with the 2016 and ECCC 2010
inventories. Mexico emissions are not included in this sector. For 20216,
emissions were interpolated between 2016 and 2023
Other Point Fugitive
dust sources not
from the NEI:
othptdust
Wind erosion emissions were removed from the point fugitive dust inventory prior
to regional haze modeling. Base year 2015 inventories with the rotated grid pattern
removed were projected to 2023 and 2028 based on factors provided by ECCC. A
transport fraction adjustment is applied to the projected inventories along with a
meteorology-based (precipitation and snow/ice cover) zero-out. For 20216,
emissions were interpolated between 2016 and 2023.
Other point sources
not from the NEI:
othpt
For agricultural sources that were originally developed on the rotated 10-km grid,
the reallocated base year emissions were projected to 2023 and 2028 using
projection factors based on data provided by ECCC and applied by province,
pollutant, and ECCC sub-class code. Airports were also projected from 2016 using
ECCC-based factors. For the remaining sources in this sector, ECCC provided
future year inventories. For Mexico sources, inventories projected from Mexico's
2008 inventory to 2018, 2025, and 2030 were interpolated to the years 2023 and
2028. For 20216, emissions were interpolated between 2016 and 2023 except 2023
emissions were used for three inventories provided by ECCC that had unique
sources for each year.
Other non-NEI
nonpoint and
nonroad:
othar
Future year nonpoint inventories for many parts of this sector were provided by
ECCC and were split into sectors to match those in the base year inventory. For
Canadian nonroad sources, factors were provided from which the future year
inventories could be derived. For Mexico nonpoint and nonroad sources,
inventories projected to 2018, 2025, and 2030 from their 2008 inventory were
interpolated to 2023 and 2028. For 20216, emissions were interpolated between
2016 and 2023 except for one ECCC inventory for which 2023 emissions were
used directly because only 2023 emissons were available.
Other non-NEI
onroad sources:
onroadcan
For Canadian mobile onroad sources, fiiture year inventories were derived from
the base year 2015 inventory and data provided by ECCC. Projection factors were
applied by province, sub-class code, and pollutant. For 20216, emissions were
interpolated between 2016 and 2023.
Other non-NEI
onroad sources:
onroad mex
Monthly year Mexico (municipio resolution) onroad mobile inventories were
developed based runs of MOVES-Mexico for 2023 and 2028. For 20216,
emissions were interpolated between 2016 and 2023.
168
-------
4.1 EGU Point Source Projections (ptegu)
The original 2023fh and 2028fh EGU emissions inventories were developed from the output of the v6
platform using the May 2019 reference case run, while the 2023fhl and 2028fhl emissions are based on
the January 2020 reference case run of the Integrated Planning Model (IPM). IPM is a linear
programming model that accounts for variables and information such as energy demand, planned unit
retirements, and planned rules to forecast unit-level energy production and configurations. The following
specific rules and regulations are included in IPM v6 platform run from May 2019:
• The Cross-State Air Pollution Rule (CSAPR) Update, a federal regulatory measure to address
transport of ozone and its precursors under the 1997 and 2008 National Ambient Air Quality
Standards (NAAQS) for ozone.
• The Standards of Performance for Greenhouse Gas Emissions from New, Modified, and
Reconstructed Stationary Sources: Electric Utility Generating Units.
• The Mercury and Air Toxics Rule (MATS), which was initially finalized in 2011 and later revised
(https://www.epa.gov/mats/regulatorv-actions-final-mercury-and-air-toxics-standards-mats-power-
plants). MATS establishes National Emissions Standards for Hazardous Air Pollutants (NESHAP)
for the "electric utility steam generating unit" source category.
• Current and existing state regulations.
• The final actions EPA has taken to implement the Regional Haze Regulations and Guidelines for
Best Available Retrofit Technology (BART) Determinations Final Rule. This regulation requires
states to submit revised State Implementation Plans (SIPs) that include (1) goals for improving
visibility in Class I areas on the 20% worst days and allowing no degradation on the 20% best
days and (2) assessments and plans for achieving BART emission targets for sources placed in
operation between 1962 and 1977. Since 2010, EPA has approved SIPs or, in a very few cases, put
in place regional haze Federal Implementation Plans for several states. The BART limits approved
in these plans (as of summer 2017) that will be in place for EGUs are represented in the EPA
Platform v6.
• Three non-air federal rules affecting EGUs: National Pollutant Discharge Elimination System-
Final Regulations to Establish Requirements for Cooling Water Intake Structures at Existing
Facilities and Amend Requirements at Phase I Facilities, Hazardous and Solid Waste Management
System; Disposal of Coal Combustion Residuals From Electric Utilities; and the Effluent
Limitation Guidelines and Standards for the Steam Electric Power Generating Point Source
Category.
Some additional updates were made to IPM for the January 2020 case which includes rules that were in
effect by September 2019 along with other updates that are reflected in the 2023fhl and 2028fhl
emissions inventories:
• Updated NEEDS to the December 2019 version. This included more than 10 GW of
retirements, 4 GW of which were coal plants, along with some unit-level rate changes in Utah,
Nebraska, Kentucky, and New York.
• Updated (i.e., lowered) storage and renewal energy technology costs based on the National
Renewable Energy Laboratory (NREL) Annual Technology Baseline 2019 mid case.
• Implemented offshore wind power mandates in Maryland, New Jersey,
Connecticut, Massachusetts, and New York .
169
-------
• Incorporated clean energy standards in California, New Mexico, Nevada, New York, and
Washington.
• Implemented renewable portfolio standard updates in California, Washington D.C., Maryland,
Maine, New Mexico, Nevada, New York, Ohio, and Washington.
• Reflected the Affordable Clean Energy (ACE) rule (June 19, 2019).
• Incorporated the 26 U.S. Code § 45Q. Credit for carbon oxide sequestration
(https://www.energv.gov/sites/prod/files/2019/10/f67/Internal%20Revenue%20Code%20Tax
%20Fact%20Sheet.pdf).
IPM is run for a set of years, including the 2023 and 202830 future years used in the 2016vl platform.
Further documentation of the IPM model and the v6 platform can be found on the CAMD website
(https://www.epa.gov/airmarkets/documentation-epas-power-sector-modeling-platform-v6-ianuarv-202Q-
reference-case).
The EGU missions are calculated for the inventory using the output of the IPM model for the forecast
year. Units that are identified to have a primary fuel of landfill gas, fossil waste, non-fossil waste, residual
fuel oil, or distillate fuel oil may be missing emissions values for certain pollutants in the generated
inventory flat file. Units with missing emissions values are gapfilled using projected base year values. The
projections are calculated using the ratio of the future year seasonal generation in the IPM parsed file and
the base year seasonal generation at each unit for each fuel type in the unit as derived from the 2016 EIA-
923 tables. New controls identified at a unit in the IPM parsed file are accounted for with appropriate
emissions reductions in the gapfill projection values. When base year unit-level generation data cannot be
obtained no gapfill value is calculated for that unit. Additionally, some units, such as landfill gas, may not
be assigned a valid SCC in the initial flat file. The SCCs for these units are updated based on the base
year SCC for the unit-fuel type.
Combined cycle units produce some of their energy from process steam that turns a steam turbine. The
IPM model assigns a fraction of the total combined cycle production to the steam turbine. When the
emissions are calculated these steam units are assigned emissions values that come from the combustion
portion of the process. In the base year NEI steam turbines are usually implicit to the total combined cycle
unit. To achieve the proper plume rise for the total combined cycle emissions, the stack parameters for the
steam turbine units are updated with the parameters from the combustion release point.
Large EGUs in the IPM-derived flat file inventory are associated with hourly CEMS data for NOX and
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 2023 and 2028 regional cases. The
designation "fh" here refers to the May 2019 IPM case and "fhl" refers to the January 2020 IPM case.
30 2028 is not a specific output year for IPM, but 2028 maps to the 2030 output year. The IPM inputs were adjusted to make it
more suitable for modeling of 2028.
170
-------
Table 4-2. EGU sector NOx emissions by State for the 2023 and 2028 regional cases
State
2016fh
2023fh
2023fhl
2028fh
2028fhl
Alabama
28,596
9,545
9,954
11,812
12,376
Arizona
18,786
10,909
11,175
9,259
9,011
Arkansas
26,808
11,579
17,461
15,318
17,074
California
6,908
7,501
5,808
2,707
1,719
Colorado
30,152
17,965
16,561
18,616
15,448
Connecticut
4,088
4,359
4,365
4,249
4,202
Delaware
1,487
367
488
407
544
District of Columbia
NA
1
1
1
1
Florida
65,059
32,327
32,684
33,282
31,488
Georgia
29,384
14,292
13,760
15,950
15,666
Idaho
1,369
469
469
949
419
Illinois
30,250
31,189
21,321
32,474
21,668
Indiana
83,425
44,029
45,169
44,971
45,328
Iowa
22,971
23,069
24,264
22,976
23,379
Kansas
14,959
15,669
15,725
15,684
14,528
Kentucky
57,342
14,411
14,316
11,761
14,495
Louisiana
47,931
17,223
18,145
16,179
16,909
Maine
4,935
3,016
3,005
2,557
2,945
Maryland
10,448
5,387
5,436
5,115
5,599
Massachusetts
8,121
5,851
5,819
5,626
5,683
Michigan
37,149
30,141
28,344
31,948
32,895
Minnesota
21,737
15,565
17,497
15,364
12,665
Mississippi
16,414
5,749
5,604
6,248
6,135
Missouri
57,647
46,714
48,809
46,528
45,433
Montana
15,819
9,186
9,186
9,193
9,018
Nebraska
20,734
21,428
21,451
21,508
21,468
Nevada
3,949
2,215
2,368
1,458
1,531
New Hampshire
2,158
601
590
533
529
New Jersey
5,723
5,771
5,889
6,135
6,582
New Mexico
20,222
8,246
9,332
6,532
6,542
New York
13,770
14,740
14,552
13,699
13,707
North Carolina
27,892
30,088
29,482
21,685
24,320
North Dakota
38,400
25,458
25,772
25,314
24,151
Ohio
55,581
40,029
45,211
38,572
43,345
171
-------
State
2016fh
2023fh
2023fhl
2028fh
2028fhl
Oklahoma
25,084
17,877
17,396
17,342
16,375
Oregon
4,067
1,560
1,827
1,665
1,791
Pennsylvania
84,086
33,301
31,707
31,326
28,769
Rhode Island
261
769
764
739
737
South Carolina
13,734
13,460
13,474
13,053
13,048
South Dakota
1,095
692
756
832
776
Tennessee
18,752
4,285
5,896
4,753
5,958
Texas
111,612
81,051
82,699
80,579
77,506
Tribal Data
35,057
6,897
6,907
6,902
6,854
Utah
27,450
21,063
14,455
20,991
13,986
Vermont
302
21
21
20
20
Virginia
26,387
10,183
10,050
11,217
11,899
Washington
8,860
1,760
1,909
1,809
1,875
West Virginia
50,984
41,891
41,992
39,495
39,601
Wisconsin
16,148
10,238
10,467
10,048
9,293
Wyoming
36,095
15,216
17,463
13,300
13,371
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 2014v2 NEI (e.g., biodiesel and
cellulosic plants, yet-to-be constructed cement kilns). This subsection provides details on the data and
projection methods used for these sectors.
Because much of the projections and controls data are developed independently from how the EPA
defines its emissions modeling sectors, this section is organized primarily by the type of projections data,
with secondary consideration given to the emissions modeling sector (e.g., industrial source growth
factors are applicable to four emissions modeling sectors). The rest of this section is organized in the
order that the EPA uses the Control Strategy Tool (CoST) in combination with other methods to produce
future year inventories: 1) for point sources, apply plant (facility or sub-facility-level) closure information
via CoST; 2) apply all PROJECTION packets via CoST (multiplicative factors that could cause increases
or decreases); 3) apply all percent reduction-based CONTROL packets via CoST; and 4) append all other
future-year inventories not generated via CoST. This organization allows consolidation of the discussion
of the emissions categories that are contained in multiple sectors, because the data and approaches used
across the sectors are consistent and do not need to be repeated. Sector names associated with the CoST
packets are provided in parentheses. The projection and control factors applied by CoST to prepare the
2023fhl and 2028fhl emissions are provided on the 2016vl FTP site and in the docket for the final
Revised Cross-state Air Pollution Rule Update (RCU) (see https://regulations.gov EPA-HQ-OAR-2020-
0272).
172
-------
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, ag, cmv, rail, nonpt, np oilgas, ptnonipm, ptoilgas and rwc. Information
about CoST and related data sets is available from https://www.epa.gov/economic-and-cost-analvsis-air-
pollution-regulations/cost-analvsis-modelstools-air-pollution.
CoST allows the user to apply projection (growth) factors, controls and closures at various geographic
and inventory key field resolutions. Each of these CoST datasets, also called "packets" or "programs,"
provides the user with the ability to perform numerous quality assurance assessments as well as create
SMOKE-ready future year inventories. Future year inventories are created for each emissions modeling
sector via a CoST "strategy" and each strategy includes all base year 2016 inventories and applicable
CoST packets. For reasons discussed later, some emissions modeling sectors require multiple CoST
strategies to account for the compounding of control programs that impact the same type of sources.
There are also available linkages to existing and user-defined control measures databases and it is up to
the user to determine how control strategies are developed and applied. The EPA typically creates
individual CoST packets that represent specific intended purposes (e.g., aircraft projections for airports
are in a separate PROJECTION packet from residential wood combustion sales/appliance turnover-based
projections). CoST uses three packet types as described below:
1. CLOSURE: Applied first in CoST. This packet can be used to zero-out (close) point source
emissions at resolutions as broad as a facility to as specific as a stack. The EPA uses these types
of packets for known post-2016 controls as well as information on closures provided by states on
specific facilities, units or stacks. This packet type is only used in the ptnonipm and pt oilgas
sectors.
2. PROJECTION: This packet allows the user to increase or decrease emissions for virtually any
geographic and/or inventory source level. Projection factors are applied as multiplicative factors
to the 2011 emissions inventories prior to the application of any possible subsequent CONTROLS.
A PROJECTION packet is necessary whenever emissions increase from 2011 and is also desirable
when information is based more on activity assumptions rather than known control measures. The
EPA uses PROJECTION packet(s) in every non-EGU modeling sector.
3. CONTROL: These packets are applied after any/all CLOSURE and PROJECTION packet entries.
The user has similar level of control as PROJECTION packets regarding specificity of geographic
and/or inventory source level application. Control factors are expressed as a percent reduction (0
to 100) and can be applied in addition to any pre-existing inventory control, or as a replacement
control where inventory controls are first backed out prior to the application of a more-stringent
replacement control.
All of these packets are stored as data sets within the Emissions Modeling Framework and use comma-
delimited formats. As mentioned above, CoST first applies any/all CLOSURE information for point
sources, then applies PROJECTION packet information, followed by CONTROL packets. A hierarchy is
used by CoST to separately apply PROJECTION and CONTROL packets. In short, in a separate process
for PROJECTION and CONTROL packets, more specific information is applied in lieu of less-specific
information in ANY other packets. For example, a facility-level PROJECTION factor will be replaced by
a unit-level, or facility and pollutant-level PROJECTION factor. It is important to note that this hierarchy
does not apply between packet types (e.g., CONTROL packet entries are applied irrespective of
PROJECTION packet hierarchies). A more specific example: a state/SCC-level PROJECTION factor
will be applied before a stack/pollutant-level CONTROL factor that impacts the same inventory record.
173
-------
However, an inventory source that is subject to a CLOSURE packet record is removed from consideration
of subsequent PROJECTION and CONTROL packets.
The implication for this hierarchy and intra-packet independence is important to understand and quality
assure when creating future year strategies. For example, with consent decrees, settlements and state
comments, the goal is typically to achieve a targeted reduction (from the 2011NEI) or a targeted future-
year emissions value. Therefore, as encountered with this future year base case, consent decrees and state
comments for specific cement kilns (expressed as CONTROL packet entries) needed to be applied instead
of (not in addition to) the more general approach of the PROJECTION packet entries for cement
manufacturing. By processing CoST control strategies with PROJECTION and CONTROL packets
separated by the type of broad measure/program, it is possible to show actual changes from the base year
inventory to the future year inventory as a result of applying each packet.
Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a
hierarchal matching approach to assign PROJECTION factors to the inventory. For example, a packet
entry with Ranking=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 2011 inventory, but rather to the
intermediate inventory after application of any/all PROJECTION packet matches (and CLOSURES). A
subset of the more than 70 hierarchy options is shown in Table 4-3, although the fields in the table are not
necessarily named the same in CoST, but rather are similar to those in the SMOKE FF10 inventories. For
example, "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
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
174
-------
Rank
Matching Hierarchy
Inventory Type
24
NAICS
point, nonpoint
25
REGION CD, SCC, POLL
point, nonpoint
26
STATE, SCC, POLL
point, nonpoint
27
SCC, POLL
point, nonpoint
28
REGION CD, SCC
point, nonpoint
29
STATE, SCC
point, nonpoint
30
SCC
point, nonpoint
31
REGION CD, POLL
point, nonpoint
32
REGION CD
point, nonpoint
33
STATE, POLL
point, nonpoint
34
STATE
point, nonpoint
35
POLL
point, nonpoint
The contents of the controls, local adjustments and closures for the future year base case are described in
the following subsections. Year-specific projection factors (PROJECTION packets) for the future year
were used to create the future year base case, unless noted otherwise in the specific subsections. The
contents of a few of these projection packets (and control reductions) are provided in the following
subsections where feasible. However, most sectors used growth or control factors that varied
geographically and their contents could not be provided in the following sections (e.g., facilities and units
subject to the Boiler MACT reconsideration has thousands of records). The remainder of Section 4.2 is
divided into several subsections that are summarized in Table 4-4. Note that future year inventories were
used rather than projection or control packets for some sources.
Table 4-4. Summary of non-EGU stationary projections subsections
Subsection
Title
Sector(s)
Brief Description
4.2.2
CoST Plant CLOSURE
packet
ptnonipm,
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
ag
PROJECTION packet: national, by-animal type
resolution, based on animal population
projections.
4.2.3.3
Category 1, 2, and 3
commercial marine
vessels
cmv
PROJECTION packet: Category 1 & 2: CMV uses
SCC/poll for all states except Calif.
4.2.3.4
Category 3 commercial
marine vessels
cmv
PROJECTION packet: Category 3: region-level
by-pollutant, based on cumulative growth and
control impacts from rulemaking.
4.2.3.5
Oil and gas and industrial
source growth
nonpt,
npoilgas,
ptnonipm,
ptoilgas
Several PROJECTION packets: varying
geographic resolutions from state, county, to
oil/gas play-level and by-process/fuel-type
applications. Data derived from AEO2019 with
several modifications.
175
-------
Subsection
Title
Sector(s)
Brief Description
4.2.3.6
Non-IPM Point Sources
ptnonipm
Several PROJECTION packets: specific
projections from MARAMA region and states,
EIA-based projection factors for industrial sources
for non-MARAMA states.
4.2.3.7
Nonpoint sources
nonpt
Several PROJECTION packets: MARAMA states
projection for Portable Fuel Containers and for all
other nonpt sources. Non-MARAMA states
projected with EIA-based factors for industrial
sources. Evaporative Emissions from Finished
Fuels projected using EIA-based factors. Human
population used as growth for applicable sources.
4.2.3.8
Airport Sources
ptnonipm
PROJECTION packet: by-airport for all direct
matches to FAA Terminal Area Forecast data,
with state-level factors for non-matching NEI
airports.
4.2.3.9
Residential wood
combustion
rwc
PROJECTION packet: national with exceptions,
based on appliance type sales growth estimates
and retirement assumptions and impacts of recent
NSPS.
4.2.4
CoST CONTROL packets
ptnonipm,
nonpt,
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
npoil
gas,
pt oilgas
4.2.4.2
RICE NSPS
ptnonipm,
nonpt,
npoilgas,
pt oilgas
CONTROL packet: applies reductions for lean
burn, rich burn, and combined engines for
identified SCCs.
4.2.4.3
Fuel Sulfur Rules
ptnonipm,
nonpt
CONTROL packet: updated by MARAMA,
applies reductions to specific units in ten states.
4.2.4.4
Natural Gas Turbines
NOx NSPS
ptnonipm
CONTROL packet: applies NOx emission
reductions established by the NSPS.
4.2.4.5
Process Heaters NOx
NSPS
ptnonipm
CONTROL packet: applies NOx emission limits
established by the NSPS.
4.2.4.6
CISWI
ptnonipm
CONTROL packet: applies controls to specific
CISWI units in 11 states.
4.2.4.7
Petroleum Refineries
NSPS Subpart JA
ptnonipm
CONTROL packet: control efficiencies are
applied to identified delayed coking and storage
tank units.
4.2.4.8
State-Specific Controls
ptnonipm
CONTROL packets and comments submitted by
individual states for rules that may only impact
their state or corrections noted from previous
review.
176
-------
4.2.2 CoST Plant CLOSURE Packet (ptnonipm, pt_oilgas)
Packets:
CLOSURES2016_beta_platform_04oct2019_v 1 (for2023fhl and2028fhl)
CLOSURES2016_beta_platform_l9aug2020_nf_v2 (for 202lfi)
The CLOSURES packet contains facility, unit and stack-level closure information derived from an
Emissions Inventory System (EIS) unit-level report from March 5, 2019, with closure status equal to "PS"
(permanent shutdown; i.e., post-2016 permanent facility/unit shutdowns known in EIS as of the date of
the report). In addition, comments on past modeling platforms received by states and other agencies
specified additional closures, as well as some previously specified closures which should remain open, in
the following states: Alabama, North Carolina, Ohio, Pennsylvania, and Virginia. The list of closures for
2021fi also includes two Pennsylvania facilities that were only partially closed in prior runs, but in 2021fi
are 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 are
shown in Table 4-5.
Table 4-5. Reductions from all facility/unit/stack-level closures in 2016vl
Pollutant
ptnonipm
ptoilgas
CO
1,010
187
NH3
59
0
NOX
1,373
284
PM10
447
9
PM2.5
358
9
S02
727
178
VOC
2,211
106
4.2.3 CoST PROJECTION Packets (afdust, ag, cmv, rail, nonpt,
np_°ilgas, ptnonipm, pt_oilgas, rwc)
As previously discussed, for point inventories, after application of any/all CLOSURE packet information,
the next step in running a CoST control strategy is the application of all CoST PROJECTION packets.
Regardless of inventory type (point or nonpoint), the PROJECTION packets applied prior to the CoST
packets. For several emissions modeling sectors (i.e., afdust, ag, cmv, rail and rwc), there is only one
CoST PROJECTION packet. For all other sectors, there are several different sources of PROJECTIONS
data and, therefore, there are multiple PROJECTION packets that are concatenated and quality-assured
for duplicates and applicability to the inventories in the CoST strategy. The PROJECTION (and
CONTROL) packets were separated into a few "key" control program types to allow for quick summaries
of these distinct control programs. The remainder of this section is broken out by CoST packet, with the
exception of discussion of the various packets used for oil and gas and industrial source projections; these
packets are a mix of different sources of data that target similar sources.
MARAMA provided PROJECTION and CONTROL packets for years 2023 and 2028 for states
including: Connecticut, Delaware, Maryland, Massachusetts, New Hampshire, New York, New Jersey,
North Carolina, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia, Maine, and the District of
177
-------
Columbia. MARAMA only provided pt oilgas and np oilgas packets for Rhode Island, Maryland and
Massachusetts. For the 202lfi case, new projection factors for sources affected by the Pennsylvania
Reasonably Available Control Technology (RACT) II were included in the projections. Also for 202lfi,
MARAMA provided 2023 emissions directly 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_version l_platform_MARAMA_04oct2019_v 1
Proj ection_2016_2023_afdust_version l_platform_NJ_l 3 sep2019_v0
Proj ection_2016_2023_afdust_version l_platform_national_04oct2019_v 1
Proj ection_2016_2023_all_nonpoint_version l_platform_NC_04oct2019_v2
Proj ection_2016_2028_afdust_version l_platform_MARAMA_04oct2019_v 1
Proj ection_2016_2028_afdust_version l_platform_NJ_l 3 sep2019_v0
Proj ection_2016_2028_afdust_version l_platform_national_04oct2019_v 1
Proj ection_2016_2028_all_nonpoint_version l_platform_NC_04oct2019_v2
MARAMA States
MARAMA submitted projection factors for their states to project 2016 afdust emissions to future years
2023 and 2028. These county-specific projection factors impacted paved roads (SCC 2294000000),
residential construction dust (SCC 2311010000), industrial/commercial/institutional construction dust
(SCC 2311020000), road construction dust (SCC 2311030000), dust from mining and quarrying (SCC
2325000000), agricultural crop tilling dust (SCC 2801000003), and agricultural dust kick-up from beef
cattle hooves (SCC 2805001000). Other afdust emissions, including unpaved road dust emissions, were
held constant in future year projections. Note that North Carolina and New Jersey provided their own
packets for this sector.
Non-MARAMA States
For paved roads (SCC 2294000000), the 2016 afdust emissions were projected to future years 2023 and
2028 based on differences in county total VMT:
Future year afdust paved roads = 2016 afdust paved roads * (Future year county total VMT) / (2016
county total VMT)
The VMT projections are described in the onroad section.
All emissions other than paved roads are held constant in future year projections. The impacts of the
projections are shown in Table 4-6.
Table 4-6. Increase in total afdust PM2.5 emissions from projections in 2016vl
2016 Emissions
2023 Emissions
percent Increase
2023
2028 Emissions
percent Increase
2028
2,530,625
2,557,970
1.09%
2,570,714
1.60%
178
-------
4.2.3.2 Livestock population growth (ag)
Packets:
Proj ection_2016_2023_all_nonpoint_version l_platform_NC_04oct2019_v2
Proj ection_2016_2028_all_nonpoint_version l_platform_NC_04oct2019_v2
Proj ection_2017_2023_ag_version l_platform_l 1 sep2019_v0
Proj ection_2017_2023_ag_version l_platform_NJ_l 1 sep2019_v0
Proj ection_2017_2028_ag_version l_platform_l 1 sep2019_v0
Proj ection_2017_2028_ag_version l_platform_NJ_l 1 sep2019_v0
The 2017NEI livestock emissions were projected to year 2023 and 2028 using projection factors created
from USDA National livestock inventory projections published in March 2019
(https://www.ers.usda.gov/publications/pub-details/?pubid=92599) and are shown in Table 4-7. For
emission projections to 2023, a ratio was created between animal inventory counts for 2023 and 2017 to
create a projection factor. This process was completed for the animal categories of beef, dairy, broilers,
layers, turkeys, and swine. The projection factor was then applied to the 2017NEI base emissions for the
specific animal type to estimate 2023 NH3 and VOC emissions. For emission projections to 2028, the
same projection method was used. New Jersey (NJ) provided NJ-specific projection factors that were used
to grow livestock waste emissions from 2017 to 2023 and 2028. North Carolina (NC) provided NC-
specific projection factors that used a 2016-based projection, therefore, NC's livestock waste emissions
are projected from the 2016 back-casted base year emissions to 2023 and 2028.
Table 4-7. National projection factors for livestock: 2016 to 2023 and 2028
Animal
2023
2028
beef
-0.02%
-2.87%
swine
+7.47%
+10.36%
broilers
+8.60%
+12.50%
turkeys
-0.03%
+1.57%
layers
+9.28%
+15.93%
dairy
+0.92%
+1.24%
4.2.3.3 Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
Packets:
Proj ection_2016_2023_cmv_c 1 c2_version l_platform_04oct2019_v 1
Proj ection_2016_2023_cmv_Canada_versionl_platform_24sep2019_v0
Proj ection_2016_2028_cmv_c 1 c2_version l_platform_04oct2019_v 1
Proj ection_2016_2028_cmv_Canada_versionl_platform_24sep2019_v0
The cmv_clc2 emissions outside of California were projected from 2016 to 2023 and 2028 using factors
derived from the Regulatory Impact Analysis (RIA) Control of Emissions of Air Pollution from
Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters per Cylinder
(https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-emissions-air-
pollution-locomotive). Table 4-8 lists the pollutant-specific projection factors to 2023, and 2028 that were
used for cmv_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.
179
-------
Table 4-8. National projection factors for cmv_clc2
Pollutant
20l6-to-2023 (%)
20I6-1O-2028 (%)
20I6-1O-2023
20I6-1O-2028
CO
-1.3%
0.3%
0.987
1.003
NOX
-29.3%
-44.6%
0.707
0.554
PM10
-28.3%
-43.4%
0.717
0.566
PM2.5
-28.3%
-43.4%
0.717
0.566
S02
-65.3%
-65.9%
0.347
0.341
VOC
-31.5%
-47.2%
0.685
0.528
Table 4-9. California projection factors for cmv_clc2
Pollutant
20l6-to-2023 (%)
20I6-1O-2028 (%)
20I6-1O-2023
20I6-1O-2028
CO
20.1%
25.3%
1.201
1.253
NOX
-15.0%
-17.7%
0.850
0.823
PM10
-29.9%
-33.5%
0.701
0.665
PM2.5
-29.9%
-33.5%
0.701
0.665
S02
24.1%
48.7%
1.241
1.487
VOC
1.5%
1.9%
1.015
1.019
4.2.3.4 Category 3 Commercial Marine Vessels (cmv_c3)
Packets:
Proj ection_2016_2023_cmv_c3_version l_platform_04oct2019_v2_Mexico
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_2028_cmv_c3_version l_platform_04oct2019_v2_Mexico
Proj ection_2016_2028_cmv_c3_version l_platform_24sep2019_v 1
Proj ection_2016_2028_cmv_Canada_versionl_platform_24sep2019_v0
Growth rates for cmv_c3 emissions from 2016 to 2023 and 2028 were developed using a forthcoming
EPA report on projected bunker fuel demand. The report projects bunker fuel consumption by region out
to the year 2030. Bunker fuel usage was used as a surrogate for marine vessel activity. To estimate future
year emissions of CO, C02, hydrocarbons, PM10, and PM2.5, the bunker fuel growth rate from 2016 to
2023, and 2028 were directly applied to the estimated 2016 emissions.
Growth factors for NOx emissions were handled separately to account for the phase in of Tier 3 vessel
engines. To estimate these emissions, the NOx growth rates from the EPA C3 Regulatory Impact
Assessment (RIA)31 were refactored to use the new bunker fuel usage growth rates. The assumptions of
changes in fleet composition and emissions rates from the C3 RIA were preserved and applied to the new
bunker fuel demand growth rates for 2023, and 2028 to arrive at the final growth rates. The Category 3
marine diesel engines Clean Air Act and International Maritime Organization standards from April, 2010
(https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-emissions-new-
marine-compression-O) were also considered for emission estimates.
31 https://nepis.epa.gov/Exe/ZvPURL.cgi?Dockev=P1005ZGH.TXT
180
-------
The 2023 and 2028 projection factors are shown in Table 4-10. Some regions for which 2016 projection
factors were available did not have 2023 or 2028 projection factors specific to that region, so factors from
another region were used as follows:
• Alaska was projected using North Pacific factors.
• Hawaii was projected using South Pacific factors.
• Puerto Rico and Virgin Islands were projected using Gulf Coast factors.
• Emissions outside Federal Waters (FIPS 98) were projected using the factors given in
Table 4-10 for the region "Other".
• California was projected using a separate set of state-wide projection factors based on
CMV emissions data provided by the California Air Resources Board (CARB). These
factors are shown in Table 4-11
Table 4-10. 2016-to-2023 and 2016-2028 CMV C3 projection factors outside of California
Region
2016-io-2023
2016-lo-2023
2016-lo-2028
2016-lo-2028
\()\
other polliiliinls
\()\
oilier polliiliinls
US East Coast
-6.05%
27.71%
-7.54%
49.71%
US South Pacific
(ex. California)
-24.79%
20.89%
-33.97%
45.86%
US North Pacific
-3.37%
22.57%
-4.07%
41.31%
US Gulf
-6.88%
20.82%
-12.40%
36.41%
US Great Lakes
8.71%
14.55%
19.80%
28.29%
Other
23.09%
23.09%
42.58%
42.58%
Non-IVilernl Waters
2016-lo-2023
2016-lo-2028
S02
-77.21%
-73.60%
PM (main engines)
-36.06%
-25.93%
PM (aux. engines)
-39.69%
-30.14%
Other pollutants
+23.09%
+42.58%
Table 4-11. 2016-to-2023 and 2016-2028 CMV C3 projection factors for California
I'olllltillll
20l6-lo-2023
20I6-IO-2028
CO
1.180
1.340
Nox
1.156
1.327
PMio / PM2.5
1.205
1.381
S02
1.183
1.332
VOC
1.242
1.461
181
-------
4.2.3.5 Oil and Gas Sources (pt_oilgas, np_oilgas)
Packets:
Proj ection_2016_202X_pt_oilgas_P A_NGtrans_fromMARAMA_09sep2019_v0
Proj ection_2016_2023_oilgas_version l_platform_09sep2019_v0
Proj ection_2016_2023_pt_oilgas_version l_platform_VA_NGtrans_l 6sep2019_v0
Proj ection_2016_2028_oilgas_version l_platform_09sep2019_v0
Proj ection_2016_2028_pt_oilgas_version l_platform_VA_NGtrans_l 6sep2019_v0
Proj ection_2016_2023_oilgas_version l_platform_09sep2019_v0
Proj ection_2016_2028_oilgas_version l_platform_09sep2019_v0
Future year projections for the 2016vl platform were generated for point oil and gas sources for years
2023 and 2028. These projections consisted of three components: (1) applying facility closures to the
ptoilgas sector using the CoST CLOSURE packet; (2) using historical and/or forecast activity data to
generate future-year emissions before applicable control technologies are applied using the CoST
PROJECTION packet; and (3) estimating impacts of applicable control technologies on future-year
emissions using the CoST CONTROL packet. Applying the CLOSURE packet to the pt oilgas sector
resulted in small emissions changes to the national summary shown inTable 4-5. Note the closures for
years 2023 and 2028 are the same.
For pt oilgas growth to 2023 and 2028, the oil and gas sources were separated into production-related and
exploration-related sources by SCC. These sources were further subdivided by fuel-type by SCC into
either OIL, natural gas (NGAS), BOTH oil-natural gas fuels possible, or coal-bed methane (CBM). The
next two subsections describe the growth component process.
For npoilgas growth to 2023 and 2028, oil and gas sources were separated into production-related,
transmission-related, and all other point sources by NAICS. These sources are further subdivided by fuel-
type by SCC into either OIL, natural gas (NGAS), or BOTH oil-natural gas fuels possible.
Production-related Sources (pt oilgas, np oilgas)
The growth factors for the production-related NAICS-SCC combinations were generated in a two-step
process. The first step used historical production data at the state-level to get state-level short-term trends
or factors from 2016 to year 2017. In some cases, historical data for year 2018 were available for a state,
in these cases a 2016 to 2018 factor was calculated. These historical data were acquired from EIA from
the following links:
• Historical Natural Gas: http://www.eia.gov/dnav/ng/ng sum lsum a 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) 2019 reference case for the Lower 48
forecast production tables to project from year 2017 to the years of 2023 and 2028. Specifically, AEO
2019 Table 60 "Lower 48 Crude Oil Production and Wellhead Prices by Supply Region " and AEO 2019
Table 61 "Lower 48 Natural Gas Production and Supply Prices by Supply Region " were used in this
projection process. The AEO2019 forecast production is supplied for each EIA Oil and Gas Supply
region shown in Figure 4-1.
182
-------
Figure 4-1. EI A Oil and Gas Supply Regions as of AEO2019
Pacific
The result of this second step is a growth factor for each Supply Region from 2017 (or 2018) to 2023 and
from 2017 (or 2018) to 2028. A Supply Region mapping to FIPS cross-walk was developed so the
regional growth factors could be applied for each FIPS (for pt_oilgas) or to the county-level np_oilgas
inventories. Note that portions of Texas are in three different Supply Regions and portions of New
Mexico are in two different supply regions. The state-level historical factor (2016 to 2017 or 2018) was
then multiplied by the Supply Region factor (2017 or 2018 to future years) to produce a state-level or
FlPS-level factor to grow from 2016 to 2023 and from 2016 to 2028. This process was done using crude
production forecast information to generate a factor to apply to oil-production related SCCs or NAICS-
SCC combinations and it was also done using natural gas production forecast information to generate a
factor to apply to natural gas-production related NAICS-SCC combinations. For the N AICS-SCC
combinations that are designated "BOTH" the average of the oil-production and natural-gas production
factors was calculated and applied to these specific combinations.
The state of Texas provided specific technical direction for growth of production-related point sources.
Texas provided updated basin specific production for 2016 and 2017 to allow for a better calculation of
the estimated growth for this one-year period. The AEO2019 was used as described above for the three
AEO Oil and Gas Supply Regions that include Texas counties to grow from 2017 to 2023 and 2028 years.
However, Texas only wanted these growth factors applied to sources in the Permian and Eagle Ford
basins. The oil and gas production point sources in the other basins in Texas were not grown (i.e.,
2016vl=2023=2028 emissions).
Transmission-related Sources (pt oilgas)
Projection factors were generated using the same AEO2019 tables used for production sources. The
growth factors for transmission sources were developed solely using AEO 2019 data by Oil and Gas
Supply Regions shown in Figure 4-1. Additionally, limits were put on these regional factors where the
minimum factor was set to l.Oand the maximum factor was set to 1.5. The states of Virginia and
183
-------
Pennsylvania provided source specific growth factors for natural gas transmission sources to be used in
place of the AEO regional factors.
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 activity data were averaged and the average activity
input into EPA's Oil and Gas Tool to produce "averaged" emissions for exploration sources (Table 4-12).
This four-year average (2014-2017) activity data were used because they were readily available for use
with the 2016vl platform. These averaged emissions were used for both the 2023 and 2028 future years in
the 2016vl emissions modeling platform. Colorado, Pennsylvania, California, and Oklahoma submitted
inventories for use. Note CoST was not used for this step for exploration sources.
Table 4-12. Year 2014-2017 high-level summary of national oil and gas exploration activity
Parameter (all US states)
Year2014
Year2015
Year2016
Year2017
4-year
average
Total Well Completions
40,306
22,754
15,605
21,850
25,129
Unconventional Well
Completions
20,896
11,673
7,610
11,617
12,949
Total Oil Spuds
36,104
17,240
7,014
14,322
18,670
Total Natural Gas Spuds
4,750
3,168
4,244
4,025
4,047
Total Coalbed Methane Spuds
239
130
141
222
183
Total Spuds
41,093
20,538
11,399
18,569
22,900
Total Feet Drilled
327,832,580
178,297,779
106,468,774
181,164,800
198,440,983
4.2.3.6 Non-EGU point sources (ptnonipm)
Packets:
Proj ection_2016_202X_ptnonipm_version l_platform_WI_supplement_25 sep2019_v0
Proj ection_2016_2023_corn_ethanol_E0B0_Volpe_27 sep2019_v0
Proj ection_2016_2023_finished_fuels_volpe_04oct2019_v2
Proj ection_2016_2023_industrial_byNAIC S_SCC_version l_platform_l 3 sep2019_v0
Proj ection_2016_2023_industrial_by SCC_version l_platform_20sep2019_v 1
Projection_2016_2023_ptnonipm_airports_railyards_versionl_platform_NC_nopoll_26sep2019_v0
Proj ection_2016_2023_ptnonipm_version l_platform_MARAMA_l 1 sep2019_nf_v 1
Proj ection_2016_2023_ptnonipm_version l_platform_NJ_l 0sep2019_v0
Proj ection_2016_2023_ptnonipm_version l_platform_VA_04oct2019_v 1
proj ection_2016_2028_corn_ethanol_E0B0_Volpe_l 1 sep2019_v0
Proj ection_2016_2028_finished_fuels_volpe_04oct2019_vl
Proj ection_2016_2028_industrial_byNAIC S_SCC_version l_platform_l 3 sep2019_v0
Proj ection_2016_2028_industrial_by SCC_version l_platform_20sep2019_v 1
Projection_2016_2028_ptnonipm_airports_railyards_versionl_platform_NC_nopoll_26sep2019_v0
Proj ection_2016_2028_ptnonipm_version l_platform_MARAMA_l 1 sep2019_nf_v 1
Proj ection_2016_2028_ptnonipm_version l_platform_NJ_l 0sep2019_v0
Proj ection_2016_2028_ptnonipm_version l_platform_VA_04oct2019_v 1
184
-------
The 2023 and 2028 ptnonipm projections involved several growth and projection methods described here.
The projection of all oil and gas sources is explained in the oil and gas specification sheet and will not be
discussed in these methods.
2023 and 2028 Point Inventory - inside MARAMA region
2016-to-2023 and 2016-to-2028 projection packets for point sources were provided by MARAMA for the
following states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV.
The MARAMA projection packets were used throughout the MARAMA region, except in North
Carolina, New Jersey, and Virginia. Those three states provided their own projection packets for the
ptnonipm sector, and those projection packets were used instead of the MARAMA packets in those states.
The Virginia growth factors for one facility were edited to incorporate emissions limits provided by
MARAMA for that facility.
2023 and 2028 Point Inventory - outside MARAMA region
The Energy Information Administration's (EIA) AEO for year 2019 was used as a starting point for
projecting industrial sources in this sector. SCC's were mapped to AEO categories and projection factors
were created using a ratio between the base year and projection year estimates from each specific AEO
category. Table 4-13 below details the 2019 AEO tables used to map SCCs to AEO categories for the
projections of industrial sources. Depending on the category, a projection factor may be national or
regional. The maximum projection factor was capped at 1.25 and the minimum projection factor was
capped at 0.5. MARAMA states were not projected using this method, nor were aircraft and rail sources.
An SCC-NAICS projection was also developed using AEO2019. SCC/NAICS combinations with
emissions >100tons/year for any CAP were mapped to AEO sector and fuel. Projection factors for this
method were capped at a maximum of 2.5 and a minimum of 0.5.
Table 4-13. EIA's 2019 Annual Energy Outlook (AEO) tables used to project industrial sources
Table #
Table name
2
Energy Consumption by Sector and Source
25
Refining Industry Energy Consumption
26
Food Industry Energy Consumption
27
Paper Industry Energy Consumption
28
Bulk Chemical Industry Energy Consumption
29
Glass Industry Energy Consumption
30
Cement Industry Energy Consumption
31
Iron and Steel Industries Energy Consumption
32
Aluminum Industry Energy Consumption
33
Metal Based Durables Energy Consumption
34
Other Manufacturing Sector Energy Consumption
35
Nonmanufacturing Sector Energy Consumption
185
-------
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.
4.2.3.7 Nonpoint Sources (nonpt)
Packets:
Proj ection_2016_2023_all_nonpoint_version l_platform_NC_04oct2019_v2
Proj ection_2016_2023_finished_fuels_volpe_04oct2019_v2
Proj ection_2016_2023_industrial_by SCC_version l_platform_20sep2019_v 1
Proj ection_2016_2023_nonpt_other_version l_platform_MARAMA_20sep2019_v 1
Proj ection_2016_2023_nonpt_PFC_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_2028_all_nonpoint_version l_platform_NC_04oct2019_v2
Proj ection_2016_2028_finished_fuels_volpe_04oct2019_vl
Proj ection_2016_2028_industrial_by SCC_version l_platform_20sep2019_v 1
Proj ection_2016_2028_nonpt_other_version l_platform_MARAMA_20sep2019_v 1
Proj ection_2016_2028_nonpt_PFC_version l_platform_MARAMA_20sep2019_v 1
Proj ection_2016_2028_nonpt_population_beta_platform_ext_20sep2019_v 1
Proj ection_2016_2028_nonpt_version l_platform_NJ_04oct2019_v 1
Inside MARAMA region
2016-to-2023 and 2016-to-2028 projection packets for all nonpoint sources were provided by MARAMA
for the following states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV.
MARAMA provided one projection packet per year for portable fuel containers (PFCs), and a second
projection packet per year for all other nonpt sources.
The MARAMA projection packets were used throughout the MARAMA region, except in North Carolina
and New Jersey. Both NC and NJ provided separate projection packets for the nonpt sector, and those
projection packets were used instead of the MARAMA packets in those two states. New Jersey did not
provide projection factors for PFCs, and so NJ PFCs were projected using the MARAMA PFC growth
packet.
Industrial Sources outside MARAMA region
The EIA's AEO for year 2019 was used as a starting point for projecting industrial sources in this sector.
SCC's were mapped to AEO categories and projection factors were created using a ratio between the base
year and projection year estimates from each specific AEO category. For the nonpoint sector, only 2018
AEO Table 2 was used to map SCCs to AEO categories for the projections of industrial sources.
Depending on the category, a projection factor may be national or regional. The maximum projection
factor was capped at a factor of 1.25 and the minimum projection factor was capped at 0.5. Aircraft and
rail sources were not projected using this method. Sources within the MARAMA region were not
projected with these factors, but with the MARAMA-provided growth factors.
Evaporative Emissions from Transport of Finished Fuels outside MARAMA region
Estimates on growth of evaporative emissions from transporting finished fuels are partially covered in the
nonpoint and point oil and gas projection packets. However, there are some processes with evaporative
186
-------
emissions from storing and transporting finished fuels which are not included in the nonpoint and point
oil and gas projection packets, e.g., withdrawing fuel from tanks at bulk plants, filling tanks at service
stations, etc., and those processes are included in nonpoint other. The EIA's AEO for year 2018 was used
as a starting point for projecting volumes of finished fuel that would be transported in future years, i.e.,
2023 and 2028. Then these volumes were used to calculate inventories associated with evaporative
emissions in 2016, 2023, and 2028 using the upstream modules. Those emission inventories were
mapped to the appropriate SCCs and projection packets were generated from 2016 to 2023 and 2016 to
2028 using the upstream modules. Sources within the MARAMA region were not projected with these
factors, but with the MARAMA-provided growth factors.
Human Population Growth outside MARAMA region
For SCCs that are projected based on human population growth, population projection data were available
from the Benefits Mapping and Analysis Program (BenMAP) model by county for several years,
including 2017, 2023, and 2028. These human population data were used to create modified county-
specific projection factors. Note that 2017 is being used as the base year since 2016 human population is
not available in this dataset. A newer human population dataset was assessed but it did not have
trustworthy near-term (e.g., 2023/2028) projections, and was not used; for example, rural areas of NC
were projected to have more growth than urban areas, which is the opposite of what one would expect.
Growth factors were limited to a range of 0.9-1.35 for 2023 and 0.85-1.6 for 2028, but none of the factors
fell outside that range. (The 1.35 and 1.6 caps are based on 5% annual growth.) Sources within the
MARAMA region were not projected with these factors, but with the MARAMA-provided growth
factors.
4.2.3.8 Airport sources (airports)
Packets:
airport_proj ections_itn_2017_2023_09sep2019_v0
airport_proj ections_itn_2017_2028_09sep2019_v0
Airport emissions were projected from the 2017 NEI April 2020 release, the original source of the airport
inventory, to 2023 and 2028 mostly using 2018 Terminal Area Forecast (TAF) data available from the
Federal Aviation Administration (https://www.faa.gov/data research/aviation/taf/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. Note: the 2016fh, 2023fhl and 2028fhl cases as modeled for the RCU had commercial
aircraft emissions that were up to twice as high as they should have been due to an error in the 2017 NEI
(April 2020 version) airport emissions.
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.
187
-------
Qn = Qo { [ (1 + Pf) t- 1 ] Fn + (1 - Ri) t Fe + [ 1 - (1 - Ri) t] Fn ] } Equation 4-1
where:
Qn = emissions in projection year
Qo = emissions in base year
Pf = growth rate expressed as ratio (e.g., 1.5=50 percent cumulative growth)
t = number of years between base and 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 and 2028 projections:
r. , r-rr- ¦ /n/A -,nn (a \(Pf202x-i)xFn+(i-Ri)12+(i-(i-Ri)12)xFn]\ Equation 4-2
Control Efficiency202*(%) = 100 x 1 - L —-1—*— '—L L)
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 4-14
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 (https://www.epa.gov/sites/production/files/2017-
ll/documents/2011v6.3 2023en update emismod tsd oct2017.pdf).
Table 4-14. Assumed retirement rates and new source emission factor ratios for NSPS rules
NSPS Rule
Sector(s)
Retirement
Rate years
(%/year)
Pollutant
Impacted
Applied where?
New Source
Emission Factor
(Fn)
Oil and
Gas
np_oilgas,
pt_oilgas
No
assumption
VOC
Storage Tanks: 70.3% reduction in
growth-only (>1.0)
0.297
Gas Well Completions: 95% control
(regardless)
0.05
Pneumatic controllers, not high-bleed
>6scfm or low-bleed: 77% reduction in
growth-only (>1.0)
0.23
Pneumatic controllers, high-bleed
>6scfm or low-bleed: 100% reduction in
growth-only (>1.0)
0.00
Compressor Seals: 79.9% reduction in
growth-only (>1.0)
0.201
188
-------
NSPS Rule
Sector(s)
Retirement
Rate years
(%/year)
Pollutant
Impacted
Applied where?
New Source
Emission Factor
(Fn)
Fugitive Emissions: 60% Valves, flanges,
connections, pumps, open-ended lines,
and other
0.40
Pneumatic Pumps: 71.3%; Oil and Gas
0.287
RICE
np_oilgas,
pt_oilgas,
nonpt,
ptnonipm
40, (2.5%)
NOx
Lean burn: PA, all other states
0.25, 0.606
Rich Burn: PA, all other states
0.1, 0.069
Combined (average) LB/RB: PA, other
states
0.175, 0.338
CO
Lean burn: PA, all other states
1.0 (n/a), 0.889
Rich Burn: PA, all other states
0.15, 0.25
Combined (average) LB/RB: PA, other
states
0.575, 0.569
VOC
Lean burn: PA, all other states
0.125, n/a
Rich Burn: PA, all other states
0.1, n/a
Combined (average) LB/RB: PA, other
states
0.1125,n/a
Gas
Turbines
pt_oilgas,
ptnonipm
45 (2.2%)
NOx
California and NOx SIP Call states
0.595
All other states
0.238
Process
Heaters
pt_oilgas,
ptnonipm
30 (3.3%)
NOx
Nationally to Process Heater SCCs
0.41
4.2.4.1 Residential Wood Combustion (rwc)
Packets:
Proj ection_2016_2023_all_nonpoint_version l_platform_NC_04oct2019_v2
Proj ection_2016_2023_rwc_version l_platform_fromMARAMA_20aug2019_v0
Proj ection_2016_2028_all_nonpoint_version l_platform_NC_04oct2019_v2
Proj ection_2016_2028_rwc_version l_platform_fromMARAMA_20aug2019_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 2028 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 2028 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)
189
-------
and residential fire logs) are estimated based on the average growth in the number of houses between
2002 and 2012, about 1% (U.S. Census, 2012).
In addition to new appliance sales and forecasts extrapolating beyond 2012, assumptions on the
replacement of older, existing appliances are needed. Based on long lifetimes, no replacement of
fireplaces, outdoor wood burning devices (not elsewhere classified) or residential fire logs is assumed. It
is assumed that 95% of new woodstoves will replace older non-EPA certified freestanding stoves (pre-
1988 NSPS) and 5% will replace existing EPA-certified catalytic and non-catalytic stoves that currently
meet the 1988 NSPS (Houck, 2011).
Equation 4-1 was applied with RWC-specific factors from the rule. The EPA RWC NSPS experts assume
that 10% of new pellet stoves and OHH replace older units and that because of their short lifespan, that
10%) of indoor furnaces are replaced each year; these are the same assumptions used since the 2007
emissions modeling platform (EPA, 2012d). The resulting growth factors for these appliance types varies
by appliance type and also by pollutant because the emission rates, from EPA RWC tool (EPA, 2013rwc),
vary by appliance type and pollutant. For EPA certified units, the projection factors for PM are lower
than those for all other pollutants. The projection factors also vary because the total number of existing
units in 2016 varies greatly between appliance types.
Table 4-15 contains the factors to adjust the emissions from 2016 to 2023 and 2028. California, Oregon,
and Washington RWC were held constant at NEI2014v2 levels for 2016, 2023, and 2028 due to the
unique control programs those states have in place.
Table 4-15. Projection factors for RWC
S( (
SC'C description
I'olliililiH"
2016-1 o-
2023
2016-to-
202S
2104008100
Fireplace: general
7.19%
12.36%
2104008210
Woodstove: fireplace inserts; non-EPA certified
-13.92%
-17.97%
2104008220
Woodstove: fireplace inserts; EPA certified; non-
catalytic
PM10-PRI
4.09%
5.08%
2104008220
Woodstove: fireplace inserts; EPA certified; non-
catalytic
PM25-PRI
4.09%
5.08%
2104008220
Woodstove: fireplace inserts; EPA certified; non-
catalytic
8.34%
10.28%
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
PM10-PRI
6.06%
7.68%
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
PM25-PRI
6.06%
7.68%
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
12.08%
15.27%
2104008310
Woodstove
freestanding, non-EPA certified
CO
-12.09%
-15.72%
2104008310
Woodstove
freestanding, non-EPA certified
PM10-PRI
-12.67%
-16.52%
2104008310
Woodstove
freestanding, non-EPA certified
PM25-PRI
-12.67%
-16.52%
2104008310
Woodstove
freestanding, non-EPA certified
VOC
-11.40%
-14.84%
2104008310
Woodstove
freestanding, non-EPA certified
-12.09%
-15.72%
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
PM10-PRI
4.09%
5.08%
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
PM25-PRI
4.09%
5.08%
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
8.34%
10.28%
2104008330
Woodstove
freestanding, EPA certified, catalytic
PM10-PRI
6.07%
7.69%
2104008330
Woodstove
freestanding, EPA certified, catalytic
PM25-PRI
6.07%
7.69%
2104008330
Woodstove
freestanding, EPA certified, catalytic
12.08%
15.27%
2104008400
Woodstove
insert)
pellet-fired, general (freestanding or FP
PM10-PRI
30.09%
38.02%
190
-------
S( (
SC'C description
Polliiiiinr
2016-1 o-
2023
2016-to-
202S
2104008400
Woodstove: pellet-fired, general (freestanding or FP
insert)
PM25-PRI
30.09%
38.02%
2104008400
Woodstove: pellet-fired, general (freestanding or FP
insert)
26.96%
33.85%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
CO
-64.93%
-84.78%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM10-PRI
-62.99%
-82.89%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM25-PRI
-62.99%
-82.89%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
VOC
-65.02%
-84.89%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
-64.93%
-84.78%
2104008610
Hydronic heater: outdoor
PM10-PRI
0.06%
-0.40%
2104008610
Hydronic heater: outdoor
PM25-PRI
0.06%
-0.40%
2104008610
Hydronic heater: outdoor
-0.73%
-1.30%
2104008700
Outdoor wood burning device, NEC (fire-pits,
chimineas, etc)
7.19%
9.25%
2104009000
Fire log total
7.19%
9.25%
* If no pollutant is specified, facture is used for any pollutants that do not have a pollutant-specific factor
4.2.4.2 Oil and Gas NSPS (np_oilgas, pt_oilgas)
Packets:
Control_2016_2023_OilGas_N SPS_pt_oilgas_v l_platform_l 7 sep2019_v0
Control_2016_2028_OilGas_N SPS_pt_oilgas_v l_platform_l 7 sep2019_v0
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 4-14, the 70.3 percent VOC NSPS control to
this new growth will result in a 23.4 percent control: 100 *(70.3 * (1.5 -1) / 1.5); this yields an "effective"
growth rate (combined PROJECTION and CONTROL) of 1.1485, or a 70.3 percent reduction from 1.5 to
1.0. The impacts of all non-drilling completion VOC NSPS controls are therefore greater where growth
in oil and gas production is assumed highest. Conversely, for oil and gas basins with assumed negative
growth in activity/production, VOC NSPS controls will be limited to well completions only. These
reductions are year-specific because projection factors for these sources are year-specific. Table 4-16
(npoilgas) and Table 4-18 (ptoilgas) list the SCCs where Oil and Gas NSPS controls were applied; note
controls are applied to production and exploration-related SCCs. Table 4-17 (np oilgas) and Table 4-19
(pt oilgas) shows the reduction in VOC emissions after the application of the Oil and Gas NSPS
CONTROL packet for both future years 2023 and 2028.
Table 4-16. Non-point (np oilgas) SCCs in 2016vl 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
191
-------
OILGAS NSPS
TOOL OR
STATE
see
SRC TYPE
CATEGORY
see
SRC CAT TYPE
SCCDESC
2310011500
OIL
5. Fugitives
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Production; Fugitives: All
Processes
2310011501
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Production; Fugitives:
Connectors
2310011502
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Production; Fugitives:
Flanges
2310011503
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Production; Fugitives: Open
Ended Lines
2310011505
OIL
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Production; Fugitives:
Valves
2310021010
NGAS
1. Storage Tanks
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Storage Tanks:
Condensate
2310021300
NGAS
3. Pnuematic
controllers: not high
or low bleed
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Gas Well
Pneumatic Devices
2310021310
NGAS
6. Pneumatic Pumps
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Gas Well
Pneumatic Pumps
2310021501
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives:
Connectors
2310021502
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives:
Flanges
2310021503
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives: Open
Ended Lines
2310021505
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives:
Valves
2310021506
NGAS
5. Fugitives
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives:
Other
2310021509
NGAS
5. Fugitives
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Fugitives: All
Processes
2310021601
NGAS
2. Well Completions
STATE
EXPLORATION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production; Gas Well
Venting - Initial Completions
2310030300
NGAS
1. Storage Tanks
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; Natural Gas Liquids; Gas Well Water Tank
Losses
2310111401
OIL
6. Pneumatic Pumps
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Exploration; Oil Well
Pneumatic Pumps
2310111700
OIL
2. Well Completions
TOOL
EXPLORATION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Oil Exploration; Oil Well
Completion: All Processes
2310121401
NGAS
6. Pneumatic Pumps
TOOL
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Exploration; Gas Well
Pneumatic Pumps
192
-------
see
SRC TYPE
OILGAS NSPS
CATEGORY
TOOL OR
STATE
see
SRC CAT TYPE
SCCDESC
2310121700
NGAS
2. Well Completions
TOOL
EXPLORATION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Exploration; Gas Well
Completion: All Processes
2310421010
NGAS
1. Storage Tanks
STATE
PRODUCTION
Industrial Processes; Oil and Gas Exploration and
Production; On-Shore Gas Production -
Unconventional; Storage Tanks: Condensate
2310421700
NGAS
2. Well Completions
STATE
EXPLORATION
Gas Well Completion: All Processes Unconventional
Table 4-17. Emissions reductions for npoilgas sector due to application of Oil and Gas NSPS
year
poll
2016vl
2016
pre-CoST
emissions
emissions
change from
2016
%
change
2023
VOC
2817303
2881217
-863524
-30.0%
2028
VOC
2817303
2881217
-1077514
-37.4%
Table 4-18. Point source SCCs in pt oilgas sector where Oil and Gas NSPS controls were applied.
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
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
193
-------
Table 4-19. VOC reductions (tons/year) for the ptoilgas sector after application of the Oil and Gas
NSPS CONTROL packet for both future years 2023 and 2028.
Year
Pollutant
2016vl
Emissions Reductions
% change
2023
VOC
129,253
-2,523
-2.0%
2028
VOC
129,253
-2,808
-2.2%
4.2.4.3 RICE NSPS (nonpt, ptnonipm, np_oilgas, pt_oilgas)
Packets:
CONTROL_2016_2023_RICE_NSPS_nonpt_ptnonipm_beta_platform_extended_04oct2019_vl
CONTROL2016_2023_RICE_NSPS_ptnonipm_v l_platform_MARAMA_l 0sep2019_v0
CONTROL_2016_2028_RICE_NSPS_nonpt_ptnonipm_beta_platform_extended_04oct2019_vl
CONTROL2016_2028_RICE_NSPS_ptnonipm_v l_platform_MARAMA_l 0sep2019_v0
For RICE NSPS controls, the EPA emission requirements for stationary engines differ according to
whether the engine is new or existing, whether the engine is located at an area source or major source, and
whether the engine is a compression ignition or a spark ignition engine. Spark ignition engines are further
subdivided by power cycle, two-stroke versus four-stroke, and whether the engine is rich burn or lean
burn. We applied NSPS reduction for lean burn, rich burn and "combined" engines using Equation 4-2
and information listed in Table 4-14. Table 4-20, Table 4-21 and Table 4-25 list the SCCs where RICE
NSPS controls were applied for the 2016vl platform. Table 4-22, Table 4-23, Table 4-24 and Table 4-26
show the reductions in emissions in the nonpoint, ptnonipm, and nonpoint oil and gas sectors after the
application of the RICE NSPS CONTROL packet for both future years 2023 and 2028. Note that for
nonpoint oil and gas, VOC reductions were only appropriate in the state of Pennsylvania.
Table 4-20. SCCs and Engine Type in 2016vl modeling platform where RICE NSPS controls
applied for nonpt and ptnonipm sectors.
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
194
-------
Table 4-21. Non-point Oil and Gas SCCs in 2016vl 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 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
195
-------
see
Lean, Rich,
or Combined
category
SRC_TYPE
TOOL OR
STATE
see
SRC CAT TYPE
SCCDESC
Processes - Unconventional; Drill
Rigs
Table 4-22. Nonpoint Emissions reductions after the application of the RICE NSPS
year
poll
2016vl
(tons)
emissions
reductions
(tons)
%
change
2023
CO
2,688,250
-16,982
-0.6%
2023
NOX
718,766
-23,704
-3.3%
2028
CO
2,688,250
-23,145
-0.9%
2028
NOX
718,766
-33,621
-4.7%
Table 4-23. Ptnonipm Emissions reductions after the application of the RICE NSPS
year
poll
2016vl
(tons)
emissions
reductions
(tons)
%
change
2023
CO
1,446,353
-2,756
-0.2%
2023
NOX
952,181
-3,400
-0.4%
2023
VOC
774,289
-2
0.0%
2028
CO
1,446,353
-3,295
-0.2%
2028
NOX
952,181
-4,232
-0.4%
2028
VOC
774,289
-3
0.0%
Table 4-24. Oil and Gas Emissions reductions for np oilgas sector due to application of RICE NSPS
year
poll
2016vl
2016
pre-CoST
emissions
emissions
reduction
%
change
2023
CO
762706
767414
-106005
-13.8%
2023
NOX
574133
598738
-93806
-15.7%
2023
VOC
2817303
2881217
-525
-0.02%
2028
CO
762706
767414
-145622
-19.0%
2028
NOX
574133
598738
-134144
-22.4%
2028
VOC
2817303
2881217
-785
-0.03%
Table 4-25. Point source SCCs in pt oilgas sector where RICE NSPS controls applied.
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
196
-------
see
Lean, Rich, or
Combined
SCCDESC
20200254
Lean
Internal Combustion Engines; Industrial; Natural Gas;4-cycle Lean Burn
20200256
Combined
Internal Combustion Engines; Industrial; Natural Gas;4-cycle Clean Burn
20300201
Combined
Internal Combustion Engines; Commercial/Institutional; Natural Gas; Reciprocating
31000203
Combined
Industrial Processes; Oil and Gas Production; Natural Gas Production; Compressors
(See also 310003-12 and -13)
Table 4-26. Emissions reductions (tons/year) in ptoilgas sector after the application of the RICE
NSPS CONTROL packet for future years 2023 and 2028.
Year
Pollutant
2016vl
Emissions Reductions
% change
2023
CO
177,690
-20,258
-11.4%
2023
NOX
379,866
-53,694
-14.1%
2023
VOC
129,253
-436
-0.3%
2028
CO
177,690
-26,095
-14.7%
2028
NOX
379,866
-70,659
-18.6%
2028
VOC
129,253
-512
-0.4%
4.2.4.4 Fuel Sulfur Rules (nonpt, ptnonipm)
Packets:
Control_2016_202X_MANEVU_Sulfur_fromMARAMA_v l_platform_23 sep2019_v0
Fuel sulfur rules, based on web searching and the 2011 emissions modeling notice of data availability
(NODA) comments, are currently limited to the following states: Connecticut, Delaware, Maine,
Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. The
fuel limits for these states are incremental starting after year 2012, but are fully implemented by July 1,
2018, in all of these states. The control packet representing these controls was updated by MARAMA for
version 1 platform.
Summaries of the sulfur rules by state, with emissions reductions are provided in Table 4-27 and Table
4-28. These tables reflect the impacts of the MARAMA packet only, as these reductions are not estimated
in non-MARAMA states. Most of these reductions occur in the nonpt sector; a small amount of reductions
occurs in the ptnonipm sector, and a negligible amount of reductions occur in the pt oilgas sector.
Table 4-27. Summary of fuel sulfur rule impacts on nonpoint S02 emissions for 2023 and 2028
year
poll
2016vl
(tons)
emissions
reductions
(tons)
%
change
2023
S02
140,469
-28,137
-20.0%
2028
S02
140,469
-24,200
-17.2%
197
-------
Table 4-28. Summary of fuel sulfur rule impacts on ptnonipm S02 emissions for 2023 and 2028
year
poll
2016vl
(tons)
emissions
reductions
(tons)
%
change
2023
S02
658,204
-1,183
-0.2%
2028
S02
658,204
-1,241
-0.2%
4.2.4.5 Natural Gas Turbines NOx 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_2028_Natural_Gas_Turbines_NSPS_ptnonipm_beta_platform_extended_04oct2019_v 1
CONTROL_2016_2028_NG_Turbines_NSPS_ptnonipm_v l_platform_MARAMA_l 0sep2019_v0
Natural Gas Turbines NSPS controls were generated based on examination of emission limits for
stationary combustion turbines that are not in the power sector. In 2006, the EPA promulgated standards
of performance for new stationary combustion turbines in 40 CFR part 60, subpart KKKK. The standards
reflect changes in NOx emission control technologies and turbine design since standards for these units
were originally promulgated in 40 CFR part 60, subpart GG. The 2006 NSPSs affecting NOx and SO2
were established at levels that bring the emission limits up-to-date with the performance of current
combustion turbines. Stationary combustion turbines were also regulated by the NOx State
Implementation Plan (SIP) Call, which required affected gas turbines to reduce their NOx emissions by
60 percent. Table 4-29 compares the 2006 NSPS emission limits with the NOx Reasonably Available
Control Technology (RACT) regulations in selected states within the NOx SIP Call region. The map
showing the states and partial-states in the NOx SIP Call Program can be found at:
http://www3.epa.gov/airmarkets/progress/reports/program basics.html. The state NOx RACT regulations
summary (Pechan, 2001) is from a year 2001 analysis, so some states may have updated their rules since
that time.
Table 4-29. Stationary gas turbines NSPS analysis and resulting emission rates used to compute
controls
NOx Emission Limits for New Stationary Combustion Turbines
Firing Natural Gas
<50 MMBTU/hr
50-850
MMBTU/hr
>850
MMBTU/hr
Federal NSPS
100
25
15
ppm
State RACT Regulations
5-100
MMBTU/hr
100-250
MMBTU/hr
>250
MMBTU/hr
Connecticut
225
75
75
ppm
Delaware
42
42
42
ppm
Massachusetts
65*
65
65
ppm
New Jersey
50*
50
50
ppm
New York
50
50
50
ppm
New Hampshire
55
55
55
ppm
198
-------
NOx Emission Limits for New Stationary Combustion Turbines
* 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.
Table 4-30 and Table 4-32 list the point source SCCs where Natural Gas Turbines NSPS controls were
applied for the 2016vl platform. Table 4-31 and Table 4-33 show the reduction in NOx emissions after
the application of the Natural Gas Turbines NSPS CONTROL packet for both future years 2023 and
2028. The values in Table 4-31 and Table 4-33 include emissions both inside and outside the MARAMA
region.
Table 4-30. Ptnonipm SCCs in 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:
Table 4-31. Ptnonipm emissions reductions after the application of the Natural Gas Turbines NSPS
year
poll
2016vl (tons)
emissions
reduction (tons)
0/
/O
change
2023
NOX
952,181
-2,531
-0.3%
199
-------
2028
NOX
952,181
-3,346
-0.4%
Table 4-32. 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 4-33. Emissions reductions (tons/year) for pt oilgas after the application of the Natural Gas
Turbines NSPS CONTROL packet for future years 2023 and 2028.
Year
Pollutant
2016vl
Emissions
Reduction
%
change
2023
NOX
379,866
-8,079
-2.1%
2028
NOX
379,866
-11,282
-3.0%
4.2.4.6 Process Heaters NOx NSPS (ptnonipm, pt_oilgas)
Packets:
Control_2016_2023_Process_Heaters_NSPS_ptnonipm_beta_platform_ext_25sep2019_v0
Control_2016_2028_Process_Heaters_NSPS_ptnonipm_beta_platform_ext_25sep2019_v0
Process heaters are used throughout refineries and chemical plants to raise the temperature of feed
materials to meet reaction or distillation requirements. Fuels are typically residual oil, distillate oil,
refinery gas, or natural gas. In some sense, process heaters can be considered as emission control devices
because they can be used to control process streams by recovering the fuel value while destroying the
VOC. The criteria pollutants of most concern for process heaters are NOx and SO2.
In 2016, it is assumed that process heaters have not been subject to regional control programs like the
NOx SIP Call, so most of the emission controls put in-place at refineries and chemical plants have
resulted from RACT regulations that were implemented as part of SIPs to achieve ozone NAAQS in
specific areas, and refinery consent decrees. The boiler/process heater NSPS established NOx emission
limits for new and modified process heaters. These emission limits are displayed in Table 4-41.
200
-------
Table 4-34. 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
4-35 and Table 4-37 list the point source SCCs where Process Heaters NSPS controls were applied for the
2016vl platform. Table 4-36 and Table 4-38 show the reduction in NOx emissions after the application
of the Process Heaters NSPS CONTROL packet for both future years 2023 and 2028.
Table 4-35. Ptnonipm SCCs in 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
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
201
-------
see
sccdesc
30600107
Industrial Processes; Petroleum Industry; Process Heaters; Liquified Petroleum Gas (LPG)
30600199
Industrial Processes; Petroleum Industry; Process Heaters; Other Not Classified
30990003
Industrial Processes; Fabricated Metal Products; Fuel Fired Equipment; Natural Gas:
Process Heaters
31000401
Industrial Processes; Oil and Gas Production; Process Heaters; Distillate Oil (No. 2)
31000402
Industrial Processes; Oil and Gas Production; Process Heaters; Residual Oil
31000403
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil
31000404
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas
31000405
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas
31000406
Industrial Processes; Oil and Gas Production; Process Heaters; Propane/Butane
31000413
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil: Steam
Generators
31000414
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas: Steam
Generators
31000415
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas: Steam
Generators
39900501
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Distillate Oil
39900601
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Natural Gas
39990003
Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous
Manufacturing Industries; Natural Gas: Process Heaters
Table 4-36. Ptnonipm emissions reductions after the application of the Process Heaters NSPS
year
poll
2016vl
(tons)
emissions
reductions
(tons)
%
change
2023
NOX
952,181
-9,511
-1.0%
2028
NOX
952,181
-12,692
-1.3%
Table 4-37. Point source SCCs in pt oilgas sector where Process Heaters NSPS controls were
applied
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
202
-------
see
SCC Description
31000404
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas
31000405
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas
31000413
Industrial Processes; Oil and Gas Production; Process Heaters; Crude Oil: Steam
Generators
31000414
Industrial Processes; Oil and Gas Production; Process Heaters; Natural Gas: Steam
Generators
31000415
Industrial Processes; Oil and Gas Production; Process Heaters; Process Gas: Steam
Generators
39900501
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Distillate Oil
39900601
Industrial Processes; Miscellaneous Manufacturing Industries; Process Heater/Furnace;
Natural Gas
Table 4-38. NOx emissions reductions (tons/year) in ptoilgas sector after the application of the
Process Heaters NSPS CONTROL packet for futures years 2023 and 2028.
Year
Poll
2016vl
Emissions Reductions
%
change
2023
NOX
379,866
-1,698
-0.4%
2028
NOX
379,866
-2,376
-0.6%
4.2.4.7 CISWI (ptnonipm)
Packets:
Control_2016_202X_CISWI_ptnonipm_beta_platform_ext_25 sep2019_v0
On March 21, 2011, the EPA promulgated the revised NSPS and emission guidelines for Commercial and
Industrial Solid Waste Incineration (CISWI) units. This was a response to the voluntary remand that was
granted in 2001 and the vacatur and remand of the CISWI definition rule in 2007. In addition, the
standards redevelopment included the 5-year technology review of the new source performance standards
and emission guidelines required under Section 129 of the Clean Air Act. The history of the CISWI
implementation is documented here: https://www.epa.gov/stationary-sources-air-pollution/commercial-
and-industrial-solid-waste-incineration-units-ciswi-new. Baseline and CISWI rule impacts associated with
the CISWI rule are documented here: https://www.regulations.gov/document?D=EPA-HQ-OAR-2003-
0119-2559. The EPA mapped the units from the CISWI baseline and controlled dataset to the 2014 NEI
inventory and computed percent reductions such that our future year emissions matched the CISWI
controlled dataset values. Table 4-39 summarizes the total impact of CISWI controls for 2023 and 2028.
Note that this rule applies to specific units in 11 states: Alaska, Arkansas, Illinois, Iowa, Louisiana,
Maine, Oklahoma, Oregon, Pennsylvania, Tennessee, and Texas for CO, S02, and NOX.
Table 4-39. Summary of CISWI rule impacts on ptnonipm emissions for 2023 and 2028
year
poll
2016vl
(tons)
emissions
reductions
(tons)
%
change
2023
CO
1,446,353
-2,745
-0.2%
2023
NOX
952,181
-1,711
-0.2%
203
-------
2023
S02
658,204
-1,807
-0.3%
2028
CO
1,446,353
-2,937
-0.2%
2028
NOX
952,181
-1,722
-0.2%
2028
S02
658,204
-1,933
-0.3%
4.2.4.8 Petroleum Refineries NSPS Subpart JA (ptnonipm)
Packets:
Control_2016_202X_NSPS_Subpart_Ja_ptnonipm_beta_platform_ext_25sep2019_v0
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 4-40 below reflects the impacts of these NSPS controls on
the ptnonipm sector. This control is applied to all pollutants; Table 4-40 summarizes reductions for the
years 2023 and 2028 for NOX, S02, and VOC.
Table 4-40. Summary of NSPS Subpart JA rule impacts on ptnonipm emissions for 2023 and 2028
2016vl
emissions
year
poll
(tons)
reductions (tons)
% change
2023
NOX
952,181
-1
0.0%
2023
S02
658,204
-3
0.0%
2023
VOC
774,289
-5,269
-0.7%
2028
NOX
952,181
-1
0.0%
2028
S02
658,204
-3
0.0%
2028
VOC
774,289
-5,233
-0.7%
4.2.4.9 Ozone Transport Commission Rules (nonpt)
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
204
-------
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.
Not all states adopted all rules.
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.
MARAMA provided control packets to apply the solvent and PFC rule controls.
4.2.4.10 State-Specific Controls (ptnonipm)
Packets:
Control_2016_202X_ptnonipm_NC_BoilerMACT_beta_platform_ext_25sep2019_v0
Control_2016_202X_AZ_Regional_Haze_ptnonipm_beta_platform_ext_25sep2019_v0
CONTROL_2016_202X_Consent_Decrees_other_state_comments_beta_platform_extended_04oct2019_v 1
CONTROL_2016_202X_Consent_Decrees_ptnonipm_v l_platform_MARAMA_l 0sep2019_v0
CONTROL_2016_202X_DC_supplemental_ptnonipm_v l_platform_04oct2019_v 1
ICI Boilers - North Carolina
The Industrial/Commercial/Institutional Boilers and Process Heaters MACT Rule, hereafter simply
referred to as the "Boiler MACT," was promulgated on January 31, 2013, based on reconsideration.
Background information on the Boiler MACT can be found at: https://www.epa.gov/stationary-sources-
air-pollution/industrial-commercial-and-institutional-boilers-and-process-heaters. The Boiler MACT
promulgates national emission standards for the control of HAPs (NESHAP) for new and existing
industrial, commercial, and institutional (ICI) boilers and process heaters at major sources of HAPs. The
expected cobenefit for CAPs at these facilities is significant and greatest for 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.
205
-------
Consent Decrees
MARAMA provided a list of controls relating to consent decrees to be applied to specific units within the
MARAMA region. This list includes sources in North Carolina that were subject to controls in the beta
version of this emission modeling platform. Outside of the MARAMA region, controls related to consent
decrees were applied to several sources, including the LaFarge facility in Michigan (8127411), for which
NOX emissions must be reduced by 18.633% to meet the decree; and the Cabot facilities in Louisiana and
Texas, which had been subject to consent decree controls in the 2011 platforms, and 2016 emissions
values suggest controls have not yet taken effect. Other facilities subject to a consent decree were
determined to already be in compliance based on 2016 emissions values.
State Comments
A comment from the State of Illinois that was included in the 2011 platform was carried over for the
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.
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 MOVES2014b model was run separately for each future year, including
2023 and 2028, resulting in a separate inventory for each year. The fuels used are specific to each future
year, but the meteorological data represented the year 2016. The 2023 and 2028 nonroad emission factors
account for regulations such the Emissions Standards for New Nonroad Spark-Ignition Engines,
Equipment, and Vessels (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-
control-emissions-nonroad-spark-ignition). Locomotives and Marine Compression-Ignition Engines Less
than 30 Liters per Cylinder (https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-
206
-------
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 each future year. Because the CARB and TCEQ inventories already reflect
future year emissions, no additional work related to projections was required except to include them as
SMOKE input files.
4.3.2 Onroad Mobile Sources (onroad)
The MOVES2014b model was run separately for each future year, including 2023 and 2028, resulting in
separate emission factors for each year. The 2023 and 2028 onroad emission factors account for changes
in activity data and the impact of on-the-books rules that are implemented into MOVES2014b. These
include regulations such as the Light Duty Vehicle GHG Rule for Model-Year 2017-2025, and the Tier 3
Motor Vehicle Emission and Fuel Standards Rule (https://www.epa.gov/regulations-emissions-vehicles-
and-engines/final-rule-control-air-pollution-motor-vehicles-tier-3). Local inspection and maintenance
(I/M) and other onroad mobile programs are included such as California LEVIII, the National Low
Emissions Vehicle (LEV) and Ozone Transport Commission (OTC) LEV regulations
(https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-air-pollution-new-
motor-vehicles-and-2). local fuel programs, and Stage II refueling control programs. Regulations finalized
after the year 2014 are not included, such as the Safer Affordable Fuel Efficient (SAFE) Vehicles Final
Rule for Model Years 2021-2026 and the Final Rule for Phase 2 Greenhouse Gas Emissions Standards
and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles (HD GHG P2).
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.
Where state and local agencies did not provide future year activity data, future year VMT were computed
based on annual VMT data from the AEO2019 reference case for VMT by fuel and vehicle type.
Specifically, the following two AEO2019 tables were used:
• Light Duty (LD): Light-Duty VMT by Technology Type (table #51:
https://www.eia. gov/outlooks/aeo/data/browser/#/?id=51-
AEQ2019&cases=ref2019&sourcekev=0)
• Heavy Duty (HD): Freight Transportation Energy Use (table #58:
https://www.eia. gov/outlooks/aeo/data/browser/#/?id=58-
AEQ2019&cases=ref2019&sourcekev=0)
Total VMT for each MOVES fuel and vehicle grouping was calculated for the years 2016, 2020, 2023,
and 2028 based on the AEO-to-MOVES mappings above. From these totals, 2016-2023 and 2016-2028
VMT trends were calculated for each fuel and vehicle grouping. Those trends became the national VMT
projection factors. The AEO2019 tables include data starting from the year 2017. Since we were
207
-------
projecting from 2016, 2016-to-2017 projection factors were calculated from AEO2018, and then
multiplied by 2017-to-future projection factors from AEO2019. MOVES fuel and vehicle types were
mapped to AEO fuel and vehicle classes. The resulting 2016-to-future year national VMT projection
factors used for the 2016vl platform are provided in Table 4-41. These factors were adjusted to prepare
county-specific projection factors for light duty vehicles based on human population data available from
the BenMAP model by county for the years 2017, 2023, and 2028 (https://www.woodsandpoole.com/
circa 2015). The purpose of this adjustment based on population changes helps account for areas of the
country that are growing more than others. Where agencies provided future year VMT data, those data
were used.
Table 4-41. Factors used to Project 2016 VMT to 2023 and 2028
SCC6
description
2023
factor
2028 factor
220111
LD gas
5.99%
6.99%
220121
LD gas
5.99%
6.99%
220131
LD gas
5.99%
6.99%
220132
LD gas
5.99%
6.99%
220142
Buses gas
8.43%
19.86%
220143
Buses gas
8.43%
19.86%
220151
MHDgas
8.43%
19.86%
220152
MHDgas
8.43%
19.86%
220153
MHDgas
8.43%
19.86%
220154
MHDgas
8.43%
19.86%
220161
HHDgas
-51.15%
-64.99%
220221
LD diesel
86.79%
177.3%
220231
LD diesel
86.79%
177.3%
220232
LD diesel
86.79%
177.3%
220241
Buses diesel
14.30%
21.23%
220242
Buses diesel
14.30%
21.23%
220243
Buses diesel
14.30%
21.23%
220251
MHD diesel
14.30%
21.23%
220252
MHD diesel
14.30%
21.23%
220253
MHD diesel
14.30%
21.23%
220254
MHD diesel
14.30%
21.23%
220261
HHD diesel
12.91%
17.85%
220262
HHD diesel
12.91%
17.85%
220342
Buses CNG
65.57%
88.00%
220521
LD E-85
-0.70%
-10.03%
220531
LD E-85
-0.70%
-10.03%
220532
LD E-85
-0.70%
-10.03%
220921
LD Electric
1258%
2695%
220931
LD Electric
1258%
2695%
220932
LD Electric
1258%
2695%
Future year VPOP data were projected using calculations of VMT/VPOP ratios for each county, fuel, and
vehicle type from the 2016 VMT and VPOP data. Those ratios were then applied to the future year
208
-------
projected VMT to estimate future year VPOP. Future year VPOP data submitted by state and local
agencies were then incorporated into the VPOP projections. Future year VPOP data were provided by
state and local agencies in NH, NJ, NC, WI, Pima County, AZ, and Clark County, NV. All of these
submissions were the same as for the 2016beta platform except for New Jersey, which provided a new
submission for the 2016vl platform. For Pima County, just like with the VMT, future year VPOP was
only provided for 2022 (used directly for 2023) and not for 2028. Where necessary, VPOP was split to
SCC (full FF10) using SCC distributions from the EPA projection. Both VMT and VPOP were
redistributed between the LD car and truck vehicle types (21/31/32) based on splits from the EPA
projection, and used the EPA projection for buses in North Carolina and state-provided VPOP for all
other vehicles in North Carolina.
Hoteling hours were projected to the future years by calculating 2016 inventory HOTELING/VMT ratios
for each county for combination long-haul trucks on restricted roads only. Those ratios were then applied
to the future year projected VMT for combination long-haul trucks on restricted roads to calculate future
year hoteling. Some counties had hoteling activity but did not have combination long-haul truck restricted
road VMT in 2016; in those counties, the national AEO2018-based projection factor for diesel
combination trucks was used to project 2016 hoteling to the future years. This procedure gives county-
total hoteling for the future years. Each future year also has a distinct APU percentage based on MOVES
input data that was used to split county total hoteling to each SCC: 22.6% APU for 2023, and 25.9% APU
for 2028.
4.3.3 Locomotives (rail)
Rail emissions were computed for future years based on future year fuel use values for 2020, 2023, and
2028 were based on the Energy Information Administration's 2018 Annual Energy Outlook (AEO)
freight rail energy use growth rate projections for 2016 thru 2028 (see Table 4-42) and emission factors
based on historic emissions trends that reflect the rate of market penetration of new locomotive engines.
A correction factor was added to adjust the AEO projected fuel use for 2017 to match the actual 2017 R-l
fuel use data. The additive effect of this correction factor was carried forward for each subsequent year
from 2018 thru 2028. The modified AEO growth rates were used to calculate future year Class I line-haul
fuel use totals for 2020, 2023, and 2028. As shown in Table 4-42 the future year fuel use values ranged
between 3.2 and 3.4 billion gallons, which matched up well with the long-term line-haul fuel use trend
between 2005 and 2018. The emission factors for NOx, PM10 and VOC were derived from trend lines
based on historic line-haul emission factors from the period of 2007 through 2017.
Table 4-42. Class I Line-haul Fuel Projections based on 2018 AEO Data
Year
AEO Kreighl
Eador
Projection
Kaclor
Corrected AEO Kuel
Uaw AEO l uel
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
209
-------
Year
AKO Kreight
Kaclor
Projection
Kador
Corrected AKO Kuel
Uaw AEO l uel
2023
0.9969
1.0103
3,236,531,624
3,193,540,268
2024
1.0221
1.0355
3,317,383,183
3,274,391,827
2025
1.0355
1.0489
3,360,367,382
3,317,376,026
2026
1.0410
1.0544
3,377,946,201
3,334,954,845
2027
1.0419
1.0553
3,380,697,189
3,337,705,833
2028
1.0356
1.0490
3,360,491,175
3,317,499,820
The projected fuel use data was combined with the emission factor estimates to create future year link-
level emission inventories based on the MGT traffic density values contained in the FRA's 2016
shapefile. The link-level data created for 2020, 2023, and 2028 was aggregated to create county, state, and
national emissions estimates (see Table 4-43) which were then converted into FF10 format for use in the
2016vl emissions platform.
Table 4-43. Class I Line-haul Historic and Future Year Projected Emissions
Inventory
CO
IK
Ml 13
NOx
I'M 10
IWI2.5
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 vl
94,020
21,727
294
489,562
14,538
14,102
332
2017 NEI
97,272
21,560
304
492,385
14,411
13,979
343
2020 Projected
96,482
19,133
302
448,924
12,800
12,415
340
2023 Projected
94,987
16,550
297
404,329
11,059
10,728
335
2028 Projected
98,625
13,847
309
361,914
9,236
8,959
348
2016 vs 2028
4.90%
-36.27%
4.90%
-26.07%
-36.47%
-36.47%
4.90%
Other rail emissions were projected based on AEO growth rates as shown in Table 4-44. See the 2016vl
rail specification sheet for additional information on rail projections.
Table 4-44. AEO growth rates for rail sub-groups
Sector
2016
2020
2023
2028
Rail Yards
1.0
0.97513
0.947802
0.952483
Class II/III Railroads
1.0
0.97513
0.947802
0.952483
C ommuter/Pas senger
1.0
1.033858
1.071348
1.136023
4.3.1 Sources Added in the 2021fi Case
New units were identified in the 2018 NEI point source inventory which were not in the 2016fi inventory.
These four units were included in the ptnonipm sector of the 2021fi case with emissions values from
2018. The sources added in the 2021fi case are listed in Table 4-45.
210
-------
Table 4-45. Sources Added in the 2021fi Case
II PS
( ounlv/Slale
1'acililv id
1'acilitv name
\()\
I'M 2.5
YOC
08081
Moffat Co, CO
1839411
COLOWYO COAL CO -
COLOWYO & COLLOM
MINES
725
20
0
27137
St Louis Co, MN
13598411
US Steel Corp - Keetac
5,005
443
49
28141
Tishomingo Co, MS
17942211
MISSISSIPPI SILICON
LLC
837
79
3
30031
Gallatin Co, MT
7766011
TRIDENT
1,081
29
0
4.3.2 Sources Outside of the United States (onroad_can, onroad_mex,
othpt, ptfire_othna, othar, othafdust, othptdust)
This section discusses the projection of emissions from Canada and Mexico and other areas outside of the
U.S. Information about the base inventory used for these projections or the the naming conventions can be
found in Section 2.7. Emissions for Mexico are based on the Inventario Nacional de Emisiones de
Mexico, 2008 projected to years 2023 and 2028 (ERG, 2014a). Additional details for these sectors can be
found in the 2016vl platform specification sheets.
4.3.2.1 Canadian fugitive dust sources (othafdust, othptdust)
Canadian area source dust (othafdust)
ECCC provided area stationary source inventories for the years 2023 and 2028. Unlike in their 2015
inventories in which area dust emissions were grouped into a separate file, these sources were not
provided as separate inventories for the future years, and so othafdust sector emissions were extracted
from that single area source inventory. As with 2015, the future year dust emissions are pre-adjusted, so
future year othafdust follows the same emissions processing methodology as the base year. To make the
future year emissions consistent with the base year, the same 2015->2010 adjustment factors for
construction dust that were applied to the base year inventory were also applied to the future year
projected inventories.
Canadian point source dust (othptdust)
ECCC had provided their own future year projections of the harvest and tillage point ag dust inventories,
but those inventories exhibited the same waffle pattern as 2015, so we instead decided to project the
improved 2015 inventories. ECCC separately provided data from which future year projections could be
derived in a file called "Projected_CAN2015_2023_2028.xlsx", which includes emissions data for 2015,
2023, and 2028 by pollutant, province, ECCC sub-class code, and other source categories. This data was
used to calculate 2015-to-2023 and 2015-to2028 projection factors, which were then applied to the
improved 2015 Canada point ag dust inventories to create projections for 2023 and 2028. Emissions
values from these in-house projections were found to be close in magnitude to ECCC's own projections.
Projection factors were applied by province, sub-class code, and pollutant. The ECCC projection
workbook included additional source information which provides more detail than do the subclass codes,
but that more detailed information could not be easily mapped to the inventory, and the level of detail
offered by the sub-class codes was considered sufficient for projection purposes. For the othptdust sector,
there are separate sub-class codes for each of the two inventories (harvest and tillage).
211
-------
4.3.2.2 Point Sources in Canada and Mexico (othpt)
Canada point airport and agriculture emissions
Future year airport and agriculture emission inventories from ECCC were not available in time for
inclusion in the platform. Instead, ECCC provided data from which future year projections of these
inventories could be derived. This data, provided by ECCC in a file called
"Projected_CAN2015_2023_2028.xlsx", includes emissions data for 2015, 2023, and 2028 by pollutant,
province, ECCC sub-class code, and other source categories. This data was used to calculate 2015-to-
2023 and 2015-to-2028 projection factors, which were then applied to the improved 2015 point airport
and ag inventories to create projections of Canadian emissions for 2023 and 2028. Projection factors were
applied by province, sub-class code, and pollutant. The ECCC projection workbook included additional
source information which provides more detail than do the subclass codes, but that more detailed
information could not be easily mapped to the inventory, and the level of detail offered by the sub-class
codes was considered sufficient for projection purposes. For the ag inventories, the sub-class codes are
similar in detail to SCCs: fertilizer has a single sub-class code, and animal emissions categories (broilers,
dairy, horses, sheep, etc) each have a separate sub-class code. Sub-class codes for airport emissions are
similar in detail to SCCs, with separate codes for piston and turbine emissions from military aircraft,
commercial aircraft, and general aviation.
Other Canada point sources
Future year projections for stationary point sources (excluding ag) were provided by ECCC for 2023 and
2028. ECCC provided emissions inventories for upstream oil and gas sources (UOG) and for all other
stationary point sources, including electric power generation. These inventories were generally used as-is,
with the following exceptions. The 2015 non-UOG stationary point source inventories included monthly
emissions as well as annual emissions. In the future year projected inventories provided by ECCC,
monthly emissions were included not included for EPG (electric power generation) sources, but were for
the rest of the non-UOG sources. For consistency with the base year, monthly emissions were added to
the EPG sources in the inventory, using facility-specific monthly temporal profiles derived from the 2015
inventory. For new facilities that were not in 2015, monthly emissions were left blank in the inventory,
and monthly temporalization is applied SMOKE using profiles assigned by SCC. For 2015, ECCC
provided a pre-speciated point source inventory including species for the CB6 mechanism. For the future
years, ECCC did not provide a pre-speciated inventory, but advised that speciation for the future years is
unchanged from the base year. Because the baseline VOC emissions are different in the future year
projections, it was necessary to develop a prespeciated CB6 inventory for the future years which is
consistent with the 2015 inventory but is based on future year projections of VOC. For this, speciation
profiles for each facility-SCC in 2015 were calculated using the 2015 CB6 inventory, and these profiles
were applied to future year VOC to create a CB6 future year inventory. Speciation profiles were also
developed by SCC from 2015, for application to future year facility-SCC combinations which could not
be matched to 2015. The future year inventories also include SCCs which were not in the 2015 inventory
all; for those sources, we apply standard speciation profiles in SMOKE. To prevent double counting of
VOC speciated within SMOKE with pre-speciated VOC, the point source inventory has VOC emissions
represented as VOCINV for sources that are in the pre-speciated CB6 inventory, and as VOC for sources
that are not pre-speciated. Only the VOC and not the VOC INV is speciated within SMOKE. Changes to
point source IDs in the stationary source inventory were necessary for the PMC calculation, which is
based on inventory PM10 and PM2.5. This SMOKE calculation requires that PM10 and PM2.5 emissions
are assigned to the same point source IDs, but that was not always the case with respect to the
rel_point_id and process id fields for each unit. This was also an issue with the 2015 inventory, but the
procedure that was used to fix 2015 did not help resolve this issue in the future year inventories, and so a
more robust fix was implemented for 2023 and 2028. All rel_point_id and processed values in the 2023
212
-------
and 2028 Canada stationary point inventories were redefined, such that all records with the same FIPS
code, latitude, longitude, and stack parameters (implying emissions from the same stack) were assigned
the same rel_point_id and process id for all pollutants. This fixed all instances in which PM10 and PM2.5
from the same source were assigned different point source IDs, but there are still sources in the future
year inventories in which PM10 emissions are less than the PM2.5 emissions from the same source.
Mexico
The othpt sector includes a general point source inventory in Mexico. This inventory is based on
projections of a 2008 inventory. The inventory was originally projected to years 2018, 2025, and 2030 by
ERG1 . For the beta and vl platform future year projections, emissions values from 2018 and 2025 were
interpolated to 2023, and values from 2025 and 2030 were interpolated to 2028. These inventories are
unchanged from the 2011 platform.
4.3.2.3 Nonpoint sources in Canada and Mexico (othar)
Canadian stationary sources
ECCC provided area stationary source inventories for the years 2023 and 2028. Unlike in their 2015
inventories in which dust and agricultural emissions were grouped into separate files, these sources were
not provided as separate inventories for the future years. Therefore, dust emissions from the othafdust and
othptdust sectors, and ag emissions from the othpt sector, needed to be removed from the future year area
source inventory to prevent a double count. PM emissions for all SCCs in the othafdust inventory (see
othafdust sector document) were moved to a separate inventory. Then, most emissions from agricultural
SCCs (2801- and 2805-) were removed, since the NH3 and VOC emissions overlap the point format ag
inventories which are part of the othpt sector, and the PM emissions were either already moved to the
othafdust sector, overlap the othptdust sector, or were not present in 2015 (see note about fertilizer
below). One ag SCC was partially retained in the area source inventory according to both the SCC and
ECCC's 5-digit "sub-class codes". SCC 2805000000 for sub-class code 80104, which represents
agricultural fuel combustion, was not removed from the area source inventory, since these emissions were
part of the othar sector in 2016ff and are not included in any of the other inventories. PM emissions from
fertilizer were not present in any 2015 ECCC inventory, but did appear in the future year area source
inventory. According to ECCC, this was an error in 2015, and the 2015 inventories should have included
approximately 7,000 tons per year of PM emissions from fertilizer. Fertilizer PM emissions were also
excluded from in future year modeling to preserve consistency between modeling years. ECCC provided
an additional stationary area source inventory for 2023 and 2028 representing electric power generation
(EPG). According to ECCC, this inventory's emissions were covered by the point source EPG inventory
in 2015 and does not double count the 2023 and 2028 point source inventories, and it is appropriate to
include this new area source EPG inventory in the othar sector.
Canadian mobile sources
For mobile nonroad sources, including rail and CMV, future year inventories from ECCC were not
available in time for inclusion in beta platform. Instead, ECCC provided data from which future year
projections of these inventories could be derived. This data, provided by ECCC in a file called
"Projected_CAN2015_2023_2028.xlsx", includes emissions data for 2015, 2023, and 2028 by pollutant,
province, ECCC sub-class code, and other source categories. This data was used to calculate 2015-to-
2023 and 2015-to-2028 projection factors, which were then applied to the 2015 mobile source inventories
to create projections of Canadian mobile source emissions for 2023 and 2028. Projection factors were
applied by province, sub-class code, and pollutant. The ECCC projection workbook included additional
source information which provides more detail than do the subclass codes, but that more detailed
information could not be easily mapped to the inventory, and the level of detail offered by the sub-class
213
-------
codes was considered sufficient for projection purposes. For the nonroad inventory, the sub-class code is
analogous to the SCC7 level in U.S. inventories. For example, there are separate sub-class codes for fuels
(e.g. 2-stroke gasoline, diesel, LPG) and category (e.g. construction, lawn and garden) but not for
individual vehicle types within each category (e.g. snowmobiles, tractors). For CMV and rail, the sub-
class code is closer to full SCC, because there are separate codes for port and underway emissions, and for
freight and passenger rail emissions.
Mexico
The othar sector includes two Mexico inventories, an area inventory and a nonroad inventory. Similar to
2016, the future year Mexico inventories are based on projections of a 2008 inventory, but are based on
different interpolations. In addition to the 2014 and 2018 projections that were the basis for 2016, these
inventories were also originally projected to years 2025 and 2030. For future year projections, emissions
values from 2018 and 2025 were interpolated to 2023, and emissions values from 2025 and 2030 were
interpolated to 2028. These emissions are unchanged from the 2011 platform, except that CMV emissions
were removed from the nonroad inventory to prevent a double count with the Mexico CMV inventory,
which was not part of the 2011 platform.
4.3.2.1 Onroad sources in Canada and Mexico (onroad_can,
onroad_mex)
For Canadian mobile onroad sources, future year inventories from ECCC were not available in time for
inclusion in the vl platform. Instead, ECCC provided data from which future year projections of these
inventories could be derived. This data, provided by ECCC in a file called
"Projected_CAN2015_2023_2028.xlsx", includes emissions data for 2015, 2023, and 2028 by pollutant,
province, ECCC sub-class code, and other source categories. This data was used to calculate 2015-to-
2023 and 2015-to-2028 projection factors, which were then applied to the 2015 mobile source inventories
to create projections of Canadian mobile source emissions for 2023 and 2028. Projection factors were
applied by province, sub-class code, and pollutant. The ECCC projection workbook included additional
source information which provides more detail than do the subclass codes, but that more detailed
information could not be easily mapped to the inventory, and the level of detail offered by the sub-class
codes was considered sufficient for projection purposes. For the onroad inventory, the sub-class code is
analogous to the SCC6+process level in U.S. inventories, in that it specifies fuel type, vehicle type, and
process (e.g. brake, tire, exhaust, refueling), but not road type.
For Mexican mobile onroad sources, MOVES-Mexico was run to create emissions inventories for years
2023 and 2028. Results from those runs are used in future year emissions processing for the vl platform.
These emissions are unchanged from the 2011 platform.
214
-------
5 Emission Summaries
Tables 5-1 through 5-6 summarize emissions by sector for the 2016fh, 2023fhl, and 2028fhl cases. These
summaries are provided at the national level by sector for the contiguous U.S. and for the portions of
Canada and Mexico inside the larger 12km domain (12US1) discussed in Section 3.1 and for the 36-km
domain (36US3). Note that totals for the 12US2 domain are not available here, but the sum of the U.S.
sectors would be essentially the same and only the Canadian and Mexican emissions would change
according to how far north/south the grids go. Tables 5-7 and 5-8 summarize emissions for the 2016fi and
2021fi cases. 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."
Tables 5-9 and 5-10 show national total ozone season NOx and VOC emissions, respectively. A
spreadsheet of these emissions that includes state totals is included in the docket EPA-HQ-OAR-2020-0272
on on https://regulations.gov as "State totals of ozone season NOx emissions across years" (i.e.,
state_totals_2016-2021 -2023-2028_maysep_calc202 l_updated_airports_v3 .xlsx).
State totals and other summaries are available in the reports area on the web and FTP sites for the 2016vl
platform (https://www.epa.gov/air-emissions-modeling/2016vl-platform.
ftp://newftp.epa.gov/air/emismod/2016/vl/). If you cannot access the FTP site through the provided link,
this link points to the same data: https://gaftp.epa.gov/Air/emismod/2016/v 1 /.
215
-------
Table 5-1. National by-sector CAP emissions summaries for the 2016fh case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdustadj
7,203,692
1,006,446
ag
3,409,761
194,779
airports
674,176
0
185,454
11,068
9,805
25,412
85,768
cmv_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
nonpt
2,629,755
78,509
710,918
570,314
463,807
138,650
3,695,093
nonroad
10,593,274
1,845
1,110,277
109,196
103,230
2,133
1,128,691
npoilgas
759,771
12
572,043
14,050
13,984
19,243
2,792,092
onroad
19,889,617
100,318
3,630,693
239,997
117,758
27,559
1,852,260
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
658,346
23,976
1,290,190
163,981
133,517
1,540,589
33,739
ptfire
13,717,466
239,605
227,337
1,461,693
1,234,062
111,291
3,109,465
ptnonipm
1,439,081
63,731
940,031
396,884
254,386
654,527
770,204
ptoilgas
167,531
4,338
339,280
11,301
10,784
33,227
127,565
rail
104,551
326
559,381
16,344
15,819
457
26,082
rwc
2,119,402
15,439
31,282
317,469
316,943
7,703
340,941
Con. U.S. Total
53,053,119
3,989,258
9,880,090
10,561,336
3,713,836
2,569,647
14,188,893
beis
7,167,921
965,761
42,133,700
CONUS + beis
60,221,040
3,989,258
10,845,852
10,561,336
3,713,836
2,569,647
56,322,592
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada othafdust
1,060,979
187,228
Canada othar
2,727,917
4,842
397,394
313,494
248,467
19,939
832,491
Canada onroadcan
1,665,792
6,877
404,856
25,204
14,076
1,556
143,213
Canada othpt
1,081,673
503,214
657,348
115,280
46,765
993,944
797,611
Canada othptdust
150,832
55,539
Canada ptfireothna
761,402
13,032
16,359
84,476
71,745
6,731
185,476
Canada CMV
10,741
37
93,456
1,682
1,563
2,984
5,184
Mexico othar
241,571
201,994
220,491
115,460
54,294
7,717
522,236
Mexico onroad mex
1,828,101
2,789
442,410
15,151
10,836
6,247
158,812
Mexico othpt
171,065
5,049
371,671
67,173
51,791
436,802
67,343
Mexico ptfire othna
383,162
7,436
16,604
44,992
38,176
2,785
131,499
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
33,224
128
293,102
7,188
6,658
28,060
16,209
Offshore cmv outside
Federal waters
23,338
440
257,615
24,828
22,848
181,941
11,083
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
8,978,039
745,854
3,219,997
2,027,409
810,652
1,689,208
2,919,366
216
-------
Table 5-2. National by-sector CAP emissions summaries for the 2023fhl case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdustadj
7,255,011
1,016,777
ag
3,543,157
205,451
airports
738,835
0
219,766
11,358
10,127
30,208
92,473
cmv_clc2
23,570
59
116,344
3,191
3,093
242
4,527
cmv_c3
16,709
48
104,555
2,623
2,413
5,380
10,397
nonpt
2,644,789
79,342
709,268
579,169
472,935
106,355
3,756,888
nonroad
10,581,376
2,032
737,625
71,457
66,940
1,527
856,474
npoilgas
788,072
20
585,230
16,221
16,102
31,269
3,203,738
onroad
13,773,993
89,285
1,751,007
199,979
72,468
12,484
1,098,966
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
659,538
36,544
996
144,758
124,433
18,820
35,922
ptfire
13,717,466
239,605
227,337
1,461,693
1,234,062
111,291
3,109,465
ptnonipm
1,448,566
63,739
928,896
400,192
257,145
572,494
771,838
ptoilgas
186,242
4,377
361,166
13,602
12,973
38,125
156,725
rail
105,988
330
469,157
12,778
12,376
460
20,436
rwc
2,046,853
14,793
31,902
304,464
303,920
7,010
329,017
Con. U.S. Total
46,994,644
4,124,607
6,253,489
10,515,185
3,632,716
939,358
13,669,497
beis
7,167,921
965,761
42,133,700
CONUS + beis
54,162,565
4,124,607
7,219,250
10,515,185
3,632,716
939,358
55,803,196
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada othafdust
1,178,439
207,111
Canada othar
2,689,047
4,702
310,393
303,854
228,992
19,477
823,199
Canada onroadcan
1,418,143
6,043
234,813
25,849
10,996
752
87,466
Canada othpt
1,094,900
610,668
541,448
87,726
46,205
868,739
684,095
Canada othptdust
150,854
55,547
Canada ptfireothna
760,345
13,015
16,337
84,366
71,652
6,721
185,224
Canada CMV
11,597
40
67,837
1,819
1,690
3,158
5,525
Mexico othar
263,826
198,635
240,372
118,422
56,685
7,993
583,403
Mexico onroad mex
1,772,026
3,266
427,900
17,023
11,764
7,556
161,115
Mexico othpt
200,105
6,273
380,429
75,143
57,034
365,518
84,277
Mexico ptfire othna
384,764
7,466
16,665
45,198
38,354
2,798
131,980
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
39,846
150
257,244
8,460
7,815
34,951
19,345
Offshore cmv outside
Federal waters
28,551
277
314,614
15,644
14,397
41,490
13,542
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
8,713,201
850,550
2,856,743
2,113,463
808,909
1,359,655
2,827,380
217
-------
Table 5-3. National by-sector CAP emissions summaries for the 2028fhl case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdustadj
7,279,406
1,021,715
ag
3,564,066
207,123
airports
803,407
0
245,192
11,871
10,622
33,866
100,258
cmv_clc2
24,002
47.404946
92,763
2,549
2,471
243.87567
3,574
cmv_c3
19,175
53.299262
104,503
3,010
2,770
6,160
11,990
nonpt
2,665,492
79,603
708,891
593,878
485,092
106,954
3,800,741
nonroad
10,892,398
2,104
611,510
58,356
54,323
1,545
801,819
npoilgas
774,404
20.377326
560,267
16,462
16,343
33,574
3,331,524
onroad
10,308,234
87,913
1,246,069
189,838
58,925
11,703
836,112
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
648,829
35,883
748,663
140,100
120,420
781,397
33,831
ptfire
13,717,466
239,605
227,337
1,461,693
1,234,062
111,291
3,109,465
ptnonipm
1,460,891
63,990
933,843
402,471
258,983
575,210
772,997
ptoilgas
186,008
4,383
355,109
14,119
13,477
40,437
160,295
rail
110,026
342.97954
423,103
10,953
10,611
472.9168
17,558
rwc
2,023,977
14,612
32,049
300,378
299,829
6,788
325,390
Con. U.S. Total
43,896,953
4,143,899
6,299,537
10,523,775
3,616,594
1,713,335
13,529,856
beis
7,167,921
965,761
42,133,700
CONUS + beis
51,064,874
4,143,899
7,265,298
10,523,775
3,616,594
1,713,335
55,663,555
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada othafdust
1,267,025
222,026
Canada othar
2,687,318
4,670
282,912
301,578
221,810
19,502
849,301
Canada onroadcan
1,303,551
5,492
168,631
26,129
9,498
698
60,932
Canada othpt
1,133,173
695,896
443,884
93,439
49,576
855,167
752,057
Canada othptdust
151,228
55,685
Canada ptfireothna
760,345
13,015
16,337
84,366
71,652
6,721
185,224
Canada CMV
12,247
42
73,084
1,921
1,785
3,361
5,832
Mexico othar
277,263
200,038
252,523
120,590
58,294
8,206
628,715
Mexico onroad mex
1,615,412
3,732
393,339
18,728
12,667
8,530
164,793
Mexico othpt
215,237
7,273
423,250
85,626
64,575
394,409
98,420
Mexico ptfire othna
384,764
7,466
16,665
45,198
38,354
2,798
131,980
Mexico CMV
0
0
0
0
0
0
0
Offshore cmv in Federal
waters
45,623
171
240,686
9,623
8,879
40,870
22,153
Offshore cmv outside
Federal waters
32,972
320
363,173
18,088
16,645
48,061
15,638
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
8,517,957
938,131
2,723,176
2,224,208
832,112
1,388,825
2,963,253
218
-------
Table 5-4. National by-sector CAP emissions summaries for the 2016fh case, 36US3 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdustadj
7,205,579
1,006,637
ag
3,409,762
194,779
airports
675,321
0
185,708
11,097
9,832
25,452
85,912
cmv_clc2
23,786
84
164,075
4,498
4,360
636
6,489
cmv_c3
14,296
40
113,795
2,260
2,080
4,666
8,743
nonpt
2,631,492
78,565
711,375
570,526
463,960
138,883
3,695,797
nonroad
10,596,610
1,846
1,110,476
109,228
103,260
2,134
1,129,520
npoilgas
759,771
12
572,043
14,050
13,984
19,243
2,792,092
onroad
19,894,976
100,332
3,631,843
240,071
117,803
27,562
1,853,073
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
658,346
23,976
1,290,190
163,981
133,517
1,540,589
33,739
ptfire
13,717,466
239,605
227,337
1,461,693
1,234,062
111,291
3,109,465
ptnonipm
1,439,095
63,731
940,048
396,913
254,394
654,527
770,205
ptoilgas
167,531
4,338
339,280
11,301
10,784
33,227
127,565
rail
104,551
326
559,381
16,344
15,819
457
26,082
rwc
2,119,890
15,442
31,291
317,537
317,011
7,704
341,020
36US3 U.S. Total
53,065,776
3,989,335
9,887,082
10,563,766
3,714,454
2,570,065
14,191,662
beis
7,232,588
968,624
42,374,150
36US3 U.S. Total + beis
60,298,364
3,989,335
10,855,706
10,563,766
3,714,454
2,570,065
56,565,812
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada othafdust
1,101,762
194,352
Canada othar
2,933,979
5,152
437,979
327,343
260,341
20,590
885,639
Canada onroadcan
1,730,052
7,125
425,462
26,286
14,757
1,606
148,376
Canada othpt
1,312,748
521,088
826,476
149,520
56,407
1,116,771
979,359
Canada othptdust
150,320
54,747
Canada ptfireothna
6,282,821
104,683
134,301
685,135
580,928
60,914
1,501,988
Canada CMV
13,802
49
121,859
2,292
2,126
5,172
6,760
Mexico othar
2,684,115
878,370
707,975
585,933
415,474
25,671
3,739,965
Mexico onroad mex
6,273,194
10,319
1,497,028
74,169
56,782
26,400
552,952
Mexico othpt
743,265
36,318
698,064
256,840
179,384
2,110,426
340,352
Mexico ptfire othna
7,133,496
120,584
346,990
1,155,522
745,819
45,208
2,259,747
Mexico CMV
64,730
0
204,997
16,286
15,087
109,778
8,817
Offshore cmv in Federal
waters
36,317
163
322,293
9,143
8,466
40,888
17,404
Offshore cmv outside
Federal waters
88,556
1,178
1,008,678
92,681
85,293
685,101
40,344
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
29,347,127
1,685,043
6,780,791
4,633,898
2,670,630
4,249,027
10,529,914
219
-------
Table 5-5. National by-sector CAP emissions summaries for the 2023fhl case, 36US3 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdustadj
7,256,900
1,016,968
ag
3,543,158
205,451
airports
740,248
0
220,047
11,394
10,161
30,253
92,649
cmv_clc2
23,806
60
117,456
3,220
3,122
243
4,563
cmv_c3
17,126
49
107,776
2,696
2,480
5,549
10,572
nonpt
2,646,550
79,408
709,732
579,371
473,087
106,585
3,757,585
nonroad
10,584,399
2,033
737,782
71,479
66,960
1,527
857,041
npoilgas
788,072
20
585,230
16,221
16,102
31,269
3,203,738
onroad
13,777,542
89,297
1,751,649
200,035
72,495
12,486
1,099,467
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
659,538
36,544
996
144,758
124,433
18,820
35,922
ptfire
13,717,466
239,605
227,337
1,461,693
1,234,062
111,291
3,109,465
ptnonipm
1,448,583
63,739
928,917
400,219
257,153
572,494
771,839
ptoilgas
186,242
4,377
361,166
13,602
12,973
38,125
156,725
rail
105,988
330
469,157
12,778
12,376
460
20,436
rwc
2,047,318
14,796
31,911
304,528
303,984
7,011
329,092
36US3 U.S. Total
47,005,523
4,124,692
6,259,396
10,517,582
3,633,307
939,807
13,671,726
beis
7,232,588
968,624
42,374,150
36US3 U.S. Total + beis
54,238,111
4,124,692
7,228,020
10,517,582
3,633,307
939,807
56,045,876
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada othafdust
1,222,521
214,760
Canada othar
2,896,925
5,004
351,959
316,554
239,499
20,395
875,086
Canada onroadcan
1,471,769
6,260
247,154
26,948
11,536
778
90,813
Canada othpt
1,306,333
631,845
682,142
99,818
53,521
977,647
851,263
Canada othptdust
150,273
54,730
Canada ptfireothna
6,282,821
104,683
134,301
685,165
580,958
60,914
1,501,988
Canada CMV
14,789
52
88,545
2,463
2,285
5,507
7,134
Mexico othar
2,873,134
864,397
767,216
610,423
438,710
26,588
4,050,948
Mexico onroad mex
6,053,503
12,083
1,447,199
94,407
72,468
31,838
560,284
Mexico othpt
930,547
44,909
777,407
303,309
210,038
2,111,906
427,407
Mexico ptfire othna
7,136,168
120,627
347,132
1,155,991
746,107
45,222
2,260,695
Mexico CMV
79,677
0
252,331
20,046
18,571
19,304
10,853
Offshore cmv in Federal
waters
43,338
191
280,425
10,740
9,920
50,540
20,650
Offshore cmv outside
Federal waters
108,334
741
1,234,211
58,177
53,538
155,668
49,468
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
29,247,390
1,790,809
6,658,712
4,757,504
2,707,306
3,506,810
10,754,799
220
-------
Table 5-6. National by-sector CAP emissions summaries for the 2028fhl case, 36US3 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdustadj
7,281,296
1,021,906
ag
3,564,067
207,123
airports
804,754
0
245,466
11,900
10,649
33,910
100,417
cmv_clc2
24,241
47
93,634
2,572
2,494
245
3,602
cmv_c3
19,655
54
107,701
3,094
2,847
6,354
12,192
nonpt
2,667,254
79,670
709,358
594,080
485,244
107,185
3,801,426
nonroad
10,895,363
2,105
611,654
58,375
54,340
1,545
802,328
npoilgas
774,404
20
560,267
16,462
16,343
33,574
3,331,524
onroad
10,310,777
87,925
1,246,494
189,887
58,944
11,705
836,476
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
648,829
35,883
748,663
140,100
120,420
781,397
33,831
ptfire
13,717,466
239,605
227,337
1,461,693
1,234,062
111,291
3,109,465
ptnonipm
1,460,908
63,990
933,863
402,498
258,991
575,210
772,998
ptoilgas
186,008
4,383
355,109
14,119
13,477
40,437
160,295
rail
110,026
343
423,103
10,953
10,611
473
17,558
rwc
2,024,434
14,615
32,058
300,440
299,891
6,789
325,463
36US3 U.S. Total
43,906,764
4,143,984
6,304,947
10,526,157
3,617,170
1,713,809
13,531,879
beis
7,232,588
968,624
42,374,150
36US3 U.S. Total + beis
51,139,352
4,143,984
7,273,571
10,526,157
3,617,170
1,713,809
55,906,029
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada othafdust
1,314,491
230,228
Canada othar
2,896,712
4,968
319,942
313,751
231,705
20,393
902,227
Canada onroadcan
1,353,512
5,692
177,653
27,234
9,960
723
63,284
Canada othpt
1,344,360
719,520
564,509
106,041
57,167
965,763
928,552
Canada othptdust
150,646
54,865
Canada ptfireothna
6,282,821
104,683
134,301
685,165
580,958
60,914
1,501,988
Canada CMV
15,570
55
95,172
2,598
2,409
5,866
7,502
Mexico othar
2,995,073
871,163
800,519
627,824
454,427
27,308
4,263,367
Mexico onroad mex
5,496,594
13,807
1,336,088
108,810
83,255
36,064
574,688
Mexico othpt
1,007,430
51,510
870,465
346,653
239,665
2,188,067
495,677
Mexico ptfire othna
7,136,168
120,627
347,132
1,155,991
746,107
45,222
2,260,695
Mexico CMV
92,295
0
292,291
23,221
21,512
22,361
12,572
Offshore cmv in Federal
waters
49,577
218
261,208
12,259
11,309
59,247
23,628
Offshore cmv outside
Federal waters
125,652
858
1,424,152
67,233
61,846
180,627
57,032
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
28,845,814
1,893,116
6,672,122
4,942,583
2,786,081
3,613,056
11,139,423
221
-------
Table 5-7. National by-sector CAP emissions summaries for the 2016fi case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
7,203,692
1,006,446
ag
3,409,761
194,779
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
nonpt
2,629,755
78,509
710,918
570,314
463,807
138,650
3,695,093
nonroad
10,593,274
1,845
1,110,277
109,196
103,230
2,133
1,128,691
np oilgas
759,771
12
572,043
14,050
13,984
19,243
2,792,092
onroad
19,889,617
100,318
3,630,693
239,997
117,758
27,559
1,852,260
pt oilgas
167,531
4,338
339,280
11,301
10,784
33,227
127,565
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
658,346
23,976
1,319,553
163,981
133,517
1,565,446
33,739
ptfire
13,717,466
239,605
227,337
1,461,693
1,234,062
111,291
3,109,465
ptnonipm
1,436,952
63,731
938,248
396,857
254,364
654,417
770,177
rail
104,551
326
559,381
16,344
15,819
457
26,082
rwc
2,119,402
15,439
31,282
317,469
316,943
7,703
340,941
Grand Total
52,863,051
3,989,258
9,848,929
10,560,252
3,712,741
2,584,228
14,157,289
* Only the emissions for airports, ptegu, and ptnonipm are different from 2016fh
222
-------
Table 5-8. National by-sector CAP emissions summaries for the 2021fi case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
afdust ad)
7,240,348
1,013,825
ag
3,505,044
202,401
airports
505,328
0
140,174
9,950
8,712
17,004
56,616
cmv clc2
23,438
65
128,204
3,482
3,375
239
5,017
cmv c3
16,709
45
104,555
2,623
2,413
5,380
10,397
nonpt
2,638,873
79,104
707,398
576,267
470,174
115,476
3,739,021
nonroad
10,518,831
1,997
829,445
80,691
75,820
1,527
909,600
np oilgas
801,948
20
597,124
16,115
15,997
31,299
3,203,182
onroad
14,816,054
87,838
2,020,269
205,721
80,499
12,675
1,202,768
pt oilgas
187,415
4,377
364,905
13,523
12,896
37,859
156,053
ptagfire
262,645
51,276
10,240
38,688
26,951
3,694
17,181
ptegu
534,284
28,546
928,956
175,815
135,329
985,418
30,198
ptfire
13,717,466
239,605
227,337
1,461,693
1,234,062
111,291
3,109,465
ptnonipm
1,444,231
63,698
923,229
398,559
255,393
583,384
769,284
rail
105,578
329
494,935
13,797
13,360
459
22,049
rwc
2,067,581
14,978
31,725
308,180
307,641
7,208
332,424
Con. U.S. Total
47,640,381
4,076,923
7,508,497
10,545,452
3,656,446
1,912,913
13,765,657
beis
7,167,921
965,761
42,133,700
CONUS + beis
54,808,302
4,076,923
8,474,258
10,545,452
3,656,446
1,912,913
55,899,357
Canada/Mexico/offshore (12US1)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada othafdust
1,149,074
202,140
Canada othar
2,700,443
4,739
333,690
306,299
233,888
19,792
825,525
Canada onroad can
1,480,052
6,252
277,315
25,688
11,766
953
101,399
Canada othpt
1,108,562
584,643
563,863
87,892
46,372
869,684
714,401
Canada othptdust
150,926
55,585
Canada ptfire othna
760,345
13,015
16,337
84,366
71,652
6,721
185,224
Canada CMV
11,383
39
74,242
1,784
1,658
3,114
5,440
Mexico othar
257,467
199,595
234,691
117,575
56,001
7,914
565,928
Mexico onroad mex
1,787,920
3,130
432,042
16,488
11,499
7,182
160,451
Mexico othpt
191,807
5,924
377,918
72,865
55,535
385,884
79,438
Mexico ptfire othna
384,764
7,466
16,665
45,198
38,354
2,798
131,980
Mexico CMV
0
0
0
0
0
0
0
CMV - Offshore ECA
37,466
142
264,343
7,978
7,375
32,504
18,196
CMV - outside ECA
26,696
259
294,251
14,617
13,452
38,728
12,664
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non. U.S. Total
8,796,957
825,218
2,934,048
2,081,419
805,946
1,375,777
2,848,856
223
-------
Table 5-9. National by-sector Ozone Season NOx emissions summaries 12US1 grid (tons/o.s.)
Socio r
2(11 (.Hi
20l(.li
202 in
20231111
2!!2Xlli 1
20231111_
l'i\;iir
202X1111_
l'i\;iir
airports
82,400
56,300
62,281
97,645
108,942
64,685
68.-T
cmv clc2 12
90,624
90,624
71,370
64,719
51,424
64,719
51,424
cmv c3 12
264,816
264,816
270,721
277,635
294,186
277,635
294,186
nonpt
204,293
204,293
203,506
204,554
205,760
204,554
205,760
nonroad
566,218
566,218
424,735
377,911
312,399
377,911
312,399
np oilgas
237,354
237,354
248,007
243,092
232,869
243,092
232,869
onroad
1,436,216
1,436,216
790,537
689,145
481,066
689,145
481,066
onroad ca adj
100,197
100,197
62,845
47,973
42,323
47,973
42,323
pt oilgas
162,562
162,562
173,295
171,730
169,199
171,730
169,199
ptagfire
3,193
3,193
3,193
3,193
3,193
3,193
3,193
ptegu
590,601
605,064
409,870
366,285
358,597
366,285
358,597
ptnonipm
393,846
393,102
386,810
389,030
390,948
389,030
390,948
rail
236,771
236,771
209,477
198,559
179,051
198,559
179,051
i'\vc
2,705
2,705
2,796
2,833
2,868
2,833
2,868
loliil I .S. Amlim
4.35'UI5
3.31 y.443
3.134.303
2.X32.X2"7
3.101.343
2."792.(.XI
bds
581,479
581,479
581,479
581,479
581,479
581,479
581,479
pi fne
"5.S5I
"5.S5 1
"5.S5 1
"5.S5 1
"5.S5 1
"5.S5I
"5.S5 1
(ii'iind loliil
5.02'U 25
5.111 (.."'45
3.«J"7f»."7"'3
3/"JI. (.33
3.4')O.I5"7
3."75X.(>"73
3.450.012
* The 2023fhl_fixair and 2028fhl_fixair cases include airport emissions consistent with the corrected 2017NEI for those
years.
224
-------
Table 5-10. National by-sector Ozone Season VOC emissions summaries 12US1 grid (tons/o.s.)
Seelor
201 (.Hi
20l(.li
202 in
20231111
2028lli 1
20231111_
l'i\air
2028lli l_
I'ixair
ag
137,555
137,555
142,962
145,124
146,354
145,124
146,354
airports
38,108
24,078
25,155
41,087
44,546
25,614
26,751
cmv clc2 12
3,538
3,538
2,749
2,476
1,946
2,476
1,946
cmv c3 12
14,553
14,553
16,776
17,966
20,834
17,966
20,834
nonpt
1,550,432
1,550,432
1,568,595
1,575,983
1,594,820
1,575,983
1,594,820
nonroad
570,765
570,765
448,509
418,518
386,522
418,518
386,522
np oilgas
1,127,829
1,127,829
1,287,481
1,288,459
1,336,473
1,288,459
1,336,473
onroad
753,557
753,557
486,080
446,342
331,068
446,342
331,068
onroad ca ad)
45,633
45,633
32,977
27,926
23,048
27,926
23,048
pt oilgas
73,625
73,625
85,556
85,837
87,331
85,837
87,331
ptagfire
6,314
6,314
6,314
6,314
6,314
6,314
6,314
ptegu
16,215
16,212
14,133
16,746
16,070
16,746
16,070
ptfire
1,277,287
1,277,287
1,277,287
1,277,287
1,277,287
1,277,287
1,277,287
ptnonipm
322,200
322,189
321,771
322,833
323,270
322,833
323,270
rail
11,039
11,039
9,331
8,648
7,429
8,648
7,429
rwc
25,674
25,674
26,040
26,186
26,315
26,186
26,315
Total U.S. Anthro
5,974,324
5,960,279
5,751,716
5,707,731
5,629,630
5,692,258
5,611,834
beis
32,291,364
32,291,364
32,291,364
32,291,364
32,291,364
32,291,364
32,291,364
ptfire
1,277,287
1,277,287
1,277,287
1,277,287
1,277,287
1,277,287
1,277,287
Grand Total
39,542,975
39,528,930
39,320,367
39,276,382
39,198,280
39,260,908
39,180,485
* The 2023fhl_fixair and 2028fhl_fixair cases include airport emissions consistent with the corrected 2017NEI for those
years.
225
-------
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
ftp://newftp.epa.gov/Air/emismod/2014/v 1/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 al outl 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.
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.
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
athttps://www.tandfonline.com/doi/abs/10.1080/10473289.2007.10465291.
EPA, 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 .
226
-------
EPA, 2012d. Preparation of Emission Inventories for the Version 5.0, 2007 Emissions Modeling Platform
Technical Support Document. Available from:
http://epa.gov/ttn/chief/emch/2007v5/20Q7v5 2020base EmisMod TSD 13dec2012.pdf.
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=P 100NQJG.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, 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://www3.epa.gov/ttn/scram/models/aermod/aermod mfed.pdf.
ERG, 2014a. Develop Mexico Future Year Emissions Final Report. Available at
ftp://ftp.epa.gov/EmisInventory/201 Iv6/v2platform/2011 emissions/Mexico Emissions WA%204
-09 final report 121814.pdf.
ERG, 2016b. "Technical Memorandum: Modeling Allocation Factors for the 2014 Oil and Gas Nonpoint
Tool." Available at ftp://newftp.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 ftp://newftp.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
ftp://newftp.epa.gov/air/emismod/2016/vl/reports/2016%20Nonpoint%20Qil%20and%20Gas%20
Emission%20Estimation%20Tool%20Vl 0%20December 2018.pdf.
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.
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.
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://doi.Org/10.1016/i.scitotenv.2009.07.009.
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.
227
-------
NCAR, 2016. FIRE EMISSION FACTORS AND EMISSION INVENTORIES, FINN Data, downloaded
2014 SAPRC99 version from http://bai.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/20Q6-1031-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.
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://getblueskv.org/smartfire/docs.cfm.
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. Apel, 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.
228
-------
Available at https://www.healtheffects.org/publication/characterization-metals-emitted-motor-
vehicles.
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: http://www2.mmm.ucar.edu/wrf/users/docs/arw v3 bw.pdf.
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. Available at:
http ://www. epa. gov/ttn/chief/conferences, html.
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.
Wiedinmyer, C., S.K. Akagi, R.J. Yokelson, L.K. Emmons, J.A. Al-Saadi3, J. J. Orlando1, and A. J. Soja.
(2011) "The Fire INventory from NCAR (FINN): a high resolution global model to estimate the
emissions from open burning", Geosci. Model Dev., 4, 625-641. http://www.geosci-model-
dev.net/4/625/2011/ doi: 10.5194/gmd-4-625-2011.
Yarwood, G., J. Jung,, G. Whitten, G. Heo, J. Mellberg, and M. Estes,2010: Updates to the Carbon Bond
Chemical Mechanism for Version 6 (CB6). Presented at the 9th Annual CMAS Conference,
Chapel Hill, NC. Available at
https://www.cmascenter.org/conference/2010/abstracts/emery updates carbon 2010.pdf.
Zhu, Henze, et al, 2013. "Constraining U.S. Ammonia Emissions using TES Remote Sensing
Observations and the GEOS-Chem adjoint model", Journal of Geophysical Research:
Atmospheres, 118: 1-14. Available at https://doi.org/10.1002/igrd.50166.
229
-------
Appendix A: CB6 Assignment for New Species
September 27, 2016
MEMORANDUM
To: Alison Eyth and Madeleine Strum, QftQPS, EPA
From: Ross Beardsiey and Greg Yarwood, Rambofl Environ
Species r^sp^ngs fo» Csb ana CaGa fa,- use v».th SFfOATE 4.5
Summarf
Ramholl Environ fREf reviewed version 4,5 of the SPEClfeTE database,, and created C6Q5 an; C5i
mechanism species mappings fat newly added GDir.pc_r.ds. In addition, the map: mi guids Sines for
Carbon Bond {CB} mechanisms were expanded to promote consistency in euroanc fu: - 'e • v ori.
Background
The :n.- r: rarer.: a Pr:tec:.o • Age—y 5 s?ECU~£ gas r: ; 3 tk.'cte matter
•: = :i;:;,on woffles of air pollution sources, which are used in the generation of emissions data for air
quality nodels{AQMjisucr == cvaq, ;*tt?://www.amascenter.arg/cinaq/]| and GAMx
! f«Kp://'A"Aw.cam*.co!n:|. However, the condensed dterrical ¦nachenisms aiitf wthi" these
phctDchs "ileal models utilize fewer species than SPfClATE tc rep'esen* gas ;h=== chert =:ry, a-d^ ami CBS
.'t:::.- ¦ =q'c.:= =r ^axai edv: -o.=:t ~f:-~riZ_1Z- 'I I Oil. il-ai2%20FinafK2DR£port.pdf).
Methods
CB Mode Species
Organic g£s== are "lapisr :: ?-= C5 rrechan -r eiT" =r as explicit v •ep.-E = E"ted individual
compounds ;=.g. ,m_D2 for scera-dehyie;, or as a comb reticr cf r ode' ipecias that represent
:ommo" structural graucs (E.g. flLDX *"cr other aldehydes, par for a kyl groupsi "able- I lists all of
the exploit ard structura mode spec as in CEC5 ard CB6 nachanisms, each cf '.v^ ch -ep^se-ts 3
defiiec r.^ce- ~r csr:;n s-.z-m si :-.vi *% r:r :=-bc - :: be :onse~ved n 3 csie:. Cif cctahs f:-u"
rooneeKp' :i: r?ds ipse =it~-b-> CEC5 =«-d a~ =r Di- D*a str. grru; tc -ep-a-s'-t ¦-:e:c"-n. T- =
€B05 rec-esentafon of tr,e five add tional CB6 spsries "s provided in t-a *."nc.'vifstf in C6C5 column of
Table 1.
%r*£«ti Ewi,ir«\ 773 San f/li-rin Oriix, 5afte IMS, fiowtlro, CA34996
VTl«l3,SSS>B7lia F+l 415.2890707
230
-------
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:
NVOL- Very low volatility SPEC!ATE 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 melecula' weight (e.g. decsbromodiphenyl oxide is mapped as 9E-9.2
hJVOL|, v/ftich allov.sforthe total mass of all NVOL to be determined.
UNK-Compounds that are unable tc be mapped to CB using the ava labia model species. This
approach should be avoided unless absolutely necessary, and will lead to a warning message
in the speciation tool.
Table l. Model species in the CBQ5 and CBS chemical mechanisms.
Included in
Vncel
Plumber
CB05
S pee es
~1
(structural
Included
llsire
Desertion
Cartons
mapping)
in CBS
Esplict model species
ACET
f> cetane (proparone |
1
Mo (a PAP|
res
ald:
Acet: Idehyde |ettrsnalj
2_
rej
res
BENZ
Serpens
6
Ho(l PAR.3
UNR|
res
CH4
Methane
1
res
res
ETH
Etl-.ene (ethylene]
2
res
res
ET HA
Ethane
2
res
res
ETHV
Efl-,»ne |acetylene)
2
Mo (1 PAR... 1
JHR|
res
E70H
Ettisnei
2
res
res
=ORM
=ormalctehvde imetrianalj'
1
res
res
SOP
.saprene [l-rnettiyl-i^a-tiutBdene)
3
res
res
MEOH
Methanol
1
res
res
PRFA
'rapane
3
Wo (1.3 PA 3,
1.5 UNRi
res
Commofi Structural groups
ALDX
- igtier: dehyde ^raup [-C-tHO'
2
res
res
OLE
ntenal cletin grcup |3;R..>C=C<3 RHJ
4
res
res
C=C|
2
res
res
aAR
^araffinic group (R <
-------
ENVIRON
Mapping guidelines for nan-explicit organicgases using CB model species
5PECIATE compounds that are not treated explicitly ore mapped to CB mcdel species that represent
common structural groups. Table 2 lists the cart>on number and general mapping guidelines for each
of the structure model species.
Table2. General Guidelines for mapping using CB6 structural model species.
CB4
Species
Name
dumber ~!
Cirtofis
Represents
ALOX
2
Aldehyde group ALOX represents 2 carbcns and additicnal carbons are represented as
alkyl groups fnncsfiy PAR), e.g. prapionaldehv:ie is AL3X + PAR
OLE
4
-itemal ciefin grcup. IOLE represents 4 cartons and additicnal mrtons are represented as
alkyl groups [mastsy PAR|, e.g. 2-pentene isomers are I0LE+ PAR.
trcepCwni:
• OLE with 2 carton branches cr. both sides of the double bend are downgraded to
OLE
SET
1
-------
Tac-lr 2. Mapp nf gu :'e: n==fo'sc-iE r!ffi:l it :o ma: cc"" pound :i-3=£s= = _d =ti.c:.o grcups
:c "i •'
I!s: £
: :
¦:2 ¦ : Jic ?; rt: -t:s -i?i : -
lilDTobenienes and
other halogensteif
Herat-f:
Stiifcline:
« 3 or less hslogsns - J =>A^ 5 'IS
* - :-r -1:-; nalog:-.: -6 5
Star-:!?:
* . i S-lf i-rnfeen::te -
* ~:t~::l-l:-Dber\:er:s - 6 J'.®
Suiitefine:
• 1 '.-OLE with adriitiomri cartons represented as aflcyl |rn«ps (generally
Shf::;:
¦ Mettiytcytfopentsdisie- . : .= : 3AS
* 1 0-E 3 °«
fumafPfireHB
Suirt:
¦ 2 OLE with additional carteru represertfc k s »,l 5r31.p1 (generally
:=4J)
Btamplei:
* 1-oj*
• i-ftsfityaiirin —: :-.e : :ar
¦ : :
¦ l'-fi1eth-|fp|TlBlE - 2 OLE, 1 PAR
-tte'3c,cl: areTiitie
:ciF:jr3:
:c -its lie 2 r:n-
:»rt:n atari:
Siiiaains:
¦ i OLE with mnir> i; zartmris, repress ned ei iltfl pgup (generally
~ ^yrezine —1 OLE - :--
¦ l-meHiylpyrazoie-1 ;.E : r-R
« C5-3*iiethfIoiniQls - i C.£ S *«
SiiitJeiine:
"• : ?::-3:s ;7*vi; ); "... ™!:m
fu«tlion»lgrayp. f 1 campe-uni contiira mors tlsir cie tr all fccnrt
¦nd no alterreactive fund-ions! groups, ihencne C fie tp Pie tenrts
ia treated as OLE with additional carbons trials a a: s'lrvl 5*: up s.
£*artples:
¦ "i - i -I .1 I
¦ . :1 par
¦ i-S—ec:BB if! - „ O.E. 5
These guidelines weee used to mao tne new species from SPEICATC4.5, and also to revise same
crs-.ic.s ,¦ '•aiD'sd ::nrpc.rd; ever5 , 3ts:s c- :~E rev.* ssross*x*i 5=-=«::iTEv^ 5 •. •„-«-= rsoped
ami 7 previously mapped species were revised based on the new guidelines.
Ilii-fceli Eii'kircci US Capaattm, 7/S a»n -Marin Ilfwe, S«te 2il;3. Hnvoto, IA1MB8B
V -4 415,1155,0700 »+l4SESSU?W
233
-------
Recommiendation
Complete a systematic review of the mapping of all species to ensure conformity with current
-¦¦srrra ^ " = = ¦ ~~= =:= | ts i* e- =TPj -:rrpc_*"ds f=t ar= s-rri ; •:: new =redes '-ere
••ev ev>ed 3*: -=viisc tc r-rc'ite :?nsi=t=rcv i* '"•szprg azsrca-:*-*-. ru* the o*
existing species mappings- were net: reviewed as ft was outside the scope of this work.
De -:c :p e. 'lef.octicgv T3' ziassryhi e :¦=:)• '•= l3f.ge.' ? \zii : cc-z-o.- -esed :n trei ¦
vc =ti t-. i-sf' intsfned sti. :r o-..= -:c a:;! tv. tc nrpT-.-E jwpzrrrf** •siots-*. organ : eer-sol
;5CA. irccs ;Fig f«= t"= .-c =:i tr'b=sis =et i.\ 55; SOA nc:=:. v.-h'rK i avs^a? = r bcr-- C'.'AQ
ami crntx.. A preliminary* investigation of the possibility of doing-so has been performed, and is
discussed in a separate memorandum.
ills,
234
-------
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE4.5 that were used
in the 2016 alpha platform
Profile
SPECIATE
comment
Sector
Pollutant
code
Profile description
version
5.0 (not
Replacement for v4.5
yet
profile 95223; Used 70%
released)
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 (not
Replacement for v4.5
yet
profile 95240. Used 70%
released)
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 (not
Replacement for v4.5
yet
profile 95241. Used 70%
released)
methane, 20% ethane;
the 10% remaining VOC
nonpt
voc
G95241TOG
Swine Farm and Animal Waste
is from profile 95241
nonpt,
5.0 (not
Composite of AE6-ready
ptnonipm,
yet
versions of SPECIATE4.5
pt_oilgas,
Composite -Refinery Fuel Gas and Natural
released)
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
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
235
-------
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
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
236
-------
pt_oilgas,
Oil and Gas -Permian Basin Produced Gas
4.5
ptnoniom
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
237
-------
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
40400111
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
238
-------
see
Typ
e
Description
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 RfTnk
40400117
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (250000 Bbl Cap.) - Float RfTnk
40400118
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400119
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400120
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400130
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
40400131
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400132
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400133
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
40400140
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Ext. Float Roof Tank w/ Secondy Seal
40400141
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400142
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400143
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400148
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
40400149
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
40400150
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Miscellaneous Losses/Leaks: Loading Racks
40400151
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Valves, Flanges, and Pumps
40400152
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Collection Losses
40400153
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Control Unit Losses
40400160
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
40400161
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
40400162
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
40400163
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
239
-------
see
Typ
e
Description
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 RfTnk
40400211
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400212
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400213
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400230
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
40400231
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
240
-------
see
Typ
e
Description
40400232
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400233
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
40400240
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400241
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400248
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10/13/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
40400249
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
40400250
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Loading Racks
40400251
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Valves, Flanges, and Pumps
40400252
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Collection Losses
40400253
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Control Unit Losses
40400260
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
40400261
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
40400262
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
40400263
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
40400270
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400271
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400272
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400273
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400278
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10/13/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal)
241
-------
see
Typ
e
Description
40400279
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)
40400401
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 13: Breathing Loss
40400402
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 13: Working Loss
40400403
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 10: Breathing Loss
40400404
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 10: Working Loss
40400405
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 7: Breathing Loss
40400406
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 7: Working Loss
40600101
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading
40600126
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading
40600131
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Normal Service)
40600136
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading (Normal Service)
40600141
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Balanced Service)
40600144
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading (Balanced Service)
40600147
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Clean Tanks)
40600162
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Loaded with Fuel (Transit Losses)
40600163
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Return with Vapor (Transit Losses)
40600199
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Not Classified
40600231
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Cleaned and Vapor Free Tanks
40600232
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers
242
-------
see
Typ
e
Description
40600233
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Cleaned and Vapor Free Tanks
40600234
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Ballasted Tank
40600235
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Ocean Barges Loading - Ballasted Tank
40600236
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Uncleaned Tanks
40600237
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Ocean Barges Loading - Uncleaned Tanks
40600238
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Uncleaned Tanks
40600239
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tankers: Ballasted Tank
40600240
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Average Tank Condition
40600241
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tanker Ballasting
40600299
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Not Classified
40600301
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Splash Filling
40600302
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Submerged Filling w/o Controls
40600305
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Unloading
40600306
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Balanced Submerged Filling
40600307
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Underground Tank Breathing and Emptying
40600399
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Not Classified **
40600401
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling
Vehicle Gas Tanks - Stage II; Vapor Loss w/o Controls
40600501
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Leaks
40600502
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Venting
40600503
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station
40600504
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station Leaks
40600602
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage II; Liquid Spill Loss w/o Controls
243
-------
see
Typ
e
Description
40600701
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Splash Filling
40600702
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Submerged Filling w/o Controls
40600706
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Balanced Submerged Filling
40600707
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Underground Tank Breathing and Emptying
40688801
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Fugitive
Emissions; Specify in Comments Field
2501050120
RBT
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Terminals: All Evaporative
Losses; Gasoline
2501055120
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Plants: All Evaporative
Losses; Gasoline
2501060050
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Total
2501060051
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Submerged Filling
2501060052
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Splash Filling
2501060053
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Balanced Submerged Filling
2501060200
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Total
2501060201
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Breathing and Emptying
2501995000
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; All Storage Types: Working
Loss; Total: All Products
2505000120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; All Transport Types; Gasoline
2505020120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline
2505020121
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline -
Barge
2505030120
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Transport; Truck; Gasoline
2505040120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Pipeline; Gasoline
2660000000
BTP
/BPS
Waste Disposal, Treatment, and Recovery; Leaking Underground Storage Tanks; Leaking
Underground Storage Tanks; Total: All Storage Types
244
-------
245
------- |