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Technical Support Document (TSD): Preparation of
Emissions Inventories for the 2022v2 North American
Emissions Modeling Platform
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EPA-454/B-25-002
December 2025
Technical Support Document (TSD) Preparation of Emissions Inventories for the 2022v2 North American
Emissions Modeling Platform
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Authors:
Alison Eyth (EPA/OAR)
Karl Seltzer (EPA/OAR)
Jeff Vukovich (EPA/OAR)
Caroline Farkas (EPA/OAR)
Janice Godfrey (EPA/OAR)
Lindsay Dayton (EPA/OAR)
Yijia Dietrich (EPA/OAR)
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TABLE OF CONTENTS
LIST OF TABLES VII
LIST OF FIGURES IX
ACRONYMS X
1 INTRODUCTION 13
2 BASE YEAR EMISSIONS INVENTORIES AND APPROACHES 15
2.1 Point sources (ptegu, pt_oilgas, ptnonipm, ptnonipm_hr, airports) 21
2.1.1 EGU sector (ptegu) 23
2.1.2 Point source oil and gas sector (pt_oilgas) 25
2.1.3 Aircraft and ground support equipment (airports) 27
2.1.4 Non-IPM sector (ptnonipm) 28
2.2 NonpointSOURCES (afdust, fertilizer, livestock, np_oilgas, rwc, np_solvents, nonpt) 29
2.2.1 Area fugitive dust sector (afdust) 29
2.2.2 Agricultural Livestock (livestock) 35
2.2.3 Agricultural Fertilizer (fertilizer) 36
2.2.4 Nonpoint Oil and Gas Sector (np_oilgas) 38
2.2.5 Residential Wood Combustion (rwc) 43
2.2.6 Solvents (np_solvents) 44
2.2.7 Open burning (openburn) 45
2.2.8 Nonpoint (nonpt) 45
2.3 Onroad Mobile sources (onroad) 47
2.3.1 Inventory Development using SMOKE-MOVES 48
2.3.2 Onroad Activity Data Development 51
2.3.3 MOVES Emission Factor Table Development 55
2.3.4 Onroad California Inventory Development (onroad_ca_adj) 57
2.4 Nonroad Mobile sources (cmv, rail, nonroad) 58
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 58
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3) 64
2.4.3 Railway Locomotives (rail) 70
2.4.4 Nonroad Mobile Equipment (nonroad) 76
2.5 Fires (ptfire-rx, ptfire-wild, ptagfire) 79
2.5.1 Wild and Prescribed Fires (ptfire-rx, ptfire-wild) 79
2.5.2 Point source Agriculture Fires (ptagfire) 88
2.6 Biogenic Sources (beis) 90
2.7 Sources Outside ofthe UnitedStates 92
2.7.1 Point Sources in Canada and Mexico (canmex_point) 93
2.7.2 Fugitive Dust Sources in Canada (canada_afdust, canada_ptdust) 94
2.7.3 Agricultural Sources in Canada and Mexico (canmex_ag) 94
2.7.4 Surface-level Oil and Gas Sources in Canada (canada_og2D) 94
2.7.5 Nonpoint and Nonroad Sources in Canada and Mexico (canmex_area) 95
2.7.6 Onroad Sources in Canada and Mexico (canada_onroad, mexico_onroad) 95
2.7.7 Fires in Canada and Mexico (ptfire_othna) 97
2.7.8 Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury 97
3 EMISSIONS MODELING 98
3.1 Emissions Modeling Overview 98
3.2 Chemical Speciation 102
3.2.1 VOC speciation 105
3.2.2 PM speciation 109
3.2.2.1 Diesel PM 109
3.2.3 NOx speciation 109
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3.2.4 Sulfuric Acid Vapor (SULF) 110
3.2.5 Speciation of Metals and Mercury Ill
3.3 Temporal Allocation 113
3.3.1 Use of FF10 format for finer than annual emissions 114
3.3.2 Temporal allocation for non-EGU sources (ptnonipm, ptnonipm_hr) 115
3.3.3 Electric Generating Utility temporal allocation (ptegu) 115
3.3.4 Airport Temporal allocation (airports) 119
3.3.5 Residential Wood Combustion Temporal allocation (rwc) 122
3.3.6 Agricultural Ammonia Temporal Profiles (livestock) 126
3.3.7 Oil and gas temporal allocation (np_oilgas) 128
3.3.8 Onroad mobile temporal allocation (onroad) 128
3.3.9 Nonroad mobile temporal allocation (nonroad) 134
3.3.10 Fugitive dust temporal profiles (afdust) 135
3.3.11 Additional sector specific details (beis, cmv, rail, nonpt, np_solvents, ptfire-rx, ptfire-wild) 135
3.4 Spatial Allocation 138
3.4.1 Spatial Surrogates for U.S. emissions 138
3.4.2 Area-to-point spatial allocation (nonpt, nonroad) 153
3.4.3 Surrogates for Canada and Mexico emission inventories 153
4 EMISSION SUMMARIES 163
5 REFERENCES 166
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List of Tables
Table 2-1. Platform sectors used in the Emissions Modeling Process 16
Table 2-2. Point source oil and gas sector NAICS Codes 25
Table 2-3. Point source oil and gas sector emissions for 2022v2 26
Table 2-4. SCCs for the airports sector 28
Table 2-5. Afdust sector SCCs 30
Table 2-6. Total impact of 2022v2 fugitive dust adjustments to the unadjusted inventory 31
Table 2-7. SCCs for the livestock sector 35
Table 2-8. Source of input variables for EPIC 37
Table 2-9. Nonpoint oil and gas emissions for 2022v2 39
Table 2-10. State emissions totals for year 2022v2 for pipeline blowdowns and pigging sources 41
Table 2-11. State emissions totals for year 2022 for abandoned wells sources 42
Table 2-12. SCCs for the residential wood combustion sector 44
Table 2-13. SCCs in the openburn sector 45
Table 2-14. Datasets used to develop factors to adjust nonpoint emissions from 2020 to 2022 46
Table 2-15. MOVES vehicle (source) types 49
Table 2-16. 2023-to-2022 adjustment factors for EPA default vehicle population 52
Table 2-17. Fractions of short- and long-Haul VPOP by census region 52
Table 2-18. Outlier adjustments made for very young light duty vehicles 56
Table 2-19. SCCs for the cmv_clc2 sector 59
Table 2-20. Vessel groups in the cmv_clc2 sector 63
Table 2-21. SCCs for the cmv_c3 sector 65
Table 2-22. SCCs for the rail sector 70
Table 2-23. 2020 and 2022 R-l reported locomotive fuel use for Class I railroads 72
Table 2-24. 2020 Class ll/lll line haul fleet by tier level 73
Table 2-25. Rail freight values by year (quadrillion BTU) 74
Table 2-26. SCCs included in the ptfire sector for the 2022 platform 80
Table 2-27. Types of state-provided fire activity data 81
Table 2-28. Number of acres burned from HMS satellite only detected burns in 2022v2 87
Table 2-29. SCCs included in the ptagfire sector 89
Table 2-30. Meteorological variables required by BEIS4 90
Table 2-31. VOC adjustment factors applied to Mexico onroad emissions 96
Table 2-32. PS04 adjustment factors applied to Mexico onroad emissions in most municipios 96
Table 3-1. Key emissions modeling steps by sector 99
Table 3-2. Descriptions of the platform grids 101
Table 3-3. Emission model species produced for CB6R5_AE7 for CMAQ 102
Table 3-4. Additional HAP gaseous model species generated for toxics modeling 104
Table 3-5. Integration status for each platform sector 106
Table 3-6. Integrated species from MOVES sources 107
Table 3-7. NOx speciation profiles 110
Table 3-8. Mobile NOx and HONO fractions 110
Table 3-9. Sulfate split factor computation Ill
Table 3-10. SO2 speciation profiles Ill
Table 3-11. Particle size speciation of metals Ill
Table 3-12. Mercury speciation profiles 112
Table 3-13. Temporal settings used for the platform sectors in SMOKE 113
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Table 3-14. U.S. surrogates available for the 2022v2 modeling platform 141
Table 3-15. Shapefiles used to develop U.S. Surrogates 142
Table 3-16. Surrogates used to gapfill U.S. surrogates for CONUS grids 146
Table 3-17. Off-network mobile source surrogates 149
Table 3-18. Spatial surrogates used for oil and gas Sources 150
Table 3-19. Selected 2022v2 CAP emissions by sector for U.S. surrogates (short tons in 12US1) 151
Table 3-20. Canadian spatial surrogates 154
Table 3-21. Shapefiles and attributes used to compute Canadian spatial surrogates 156
Table 3-22. Shapefiles and attributes used to compute Mexican spatial surrogates 160
Table 3-23. 2022v2 CAP emissions allocated to Mexican and Canadian spatial surrogates for 12US1 (short
tons) 160
Table 4-1. National by-sector CAP emissions for the 2022v2 platform, year 2022,12US1 grid (tons/yr).... 164
Table 4-2. National by-sector VOC HAP emissions for the 2022v2 platform, year 2022,12US1 grid (tons/yr)
165
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List of Figures
Figure 2-1. Fugitive dust emissions and impact of adjustments due to transportable fraction, precipitation,
and cumulative 33
Figure 2-2. Map of 2022 Representative Counties 56
Figure 2-3. Commercial Marine Vessel Boundaries and Automatic Identification System Request Boxes 61
Figure 2-4. 2019 Class I Railroad Line Haul Activity 71
Figure 2-5. Class II and III Railroads in the United States 73
Figure 2-6. Amtrak National Rail Network 75
Figure 2-7. Amtrak Diesel Fuel Use 2020-2022 75
Figure 2-8. Processing flow for fire emission estimates in the 2022 inventory 83
Figure 2-9. Default fire type assignment by state and month where data are only from satellites 84
Figure 2-10. Default acres burned assumption map for HMS-only detected fires 84
Figure 2-11. Blue Sky Modeling Pipeline 85
Figure 2-12. Flint Hills Acreage Burned in 2022 86
Figure 2-13. 2022v2 annual county acres burned from satellite-only detected burns 88
Figure 2-14. Annual biogenic VOC BEIS4 emissions for the 12US1 domain 92
Figure 3-1. CMAQ Air quality modeling domains for this platform 101
Figure 3-2. Process of integrating HAPs and speciating VOC in a modeling platform 106
Figure 3-3. Eliminating unmeasured spikes in CEMS data 116
Figure 3-4. Regions used to Compute Temporal non-CEMS EGU Temporal Profiles 117
Figure 3-5. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type 118
Figure 3-6. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type 118
Figure 3-7. 2022 Airport Diurnal Profiles for PHX and state of Texas 120
Figure 3-8. 2022 Wisconsin and Atlanta annual-to-month profile for airport emissions 121
Figure 3-9. Alaska seaplane monthly profile 122
Figure 3-10. Example of RWC temporal allocation using a 50 versus 60 °F threshold 123
Figure 3-11. Example of Annual-to-day temporal pattern of recreational wood burning emissions 124
Figure 3-12. RWC diurnal temporal profile 124
Figure 3-13. Data used to produce a diurnal profile for hydronic heaters 125
Figure 3-14. Monthly temporal profile for hydronic heaters 126
Figure 3-15. Examples of 2022v2 livestock daily emissions profiles 127
Figure 3-16. Sample animal NH3 hourly temporal profiles 127
Figure 3-17. Example temporal variability of VMT compared to onroad NOx emissions 129
Figure 3-18 TMAS Data: VMT Fraction by Month for Montana by Vehicle Type 130
Figure 3-19. Example Day of Week Fractions for VMT on Urban Freeways and non-Freeways 131
Figure 3-20. National Average VMT Fraction by Hour of Day (weekday and weekend) 132
Figure 3-21. Example Vehicle Speeds for Weekdays by hour Urban Freeways and non-Freeways 133
Figure 3-22. Example Nonroad Day-of-week Temporal Profiles 134
Figure 3-23. Example Nonroad Diurnal Temporal Profiles 134
Figure 3-24. Agricultural burning diurnal temporal profile 137
Figure 3-25. Prescribed and Wildfire diurnal temporal profiles 137
Figure 3-26. 2022v2 Residential Wood Combustion Emissions using ACS-based Surrogate 140
Figure 3-27. 2022v2 Residential Wood Combustion Emissions using ACS and FEMA structure-based
Surrogate 140
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Acronyms
AADT Annual average daily traffic
AE6 CMAQ Aerosol Module, version 6, introduced in CMAQv5.0
AEO Annual Energy Outlook
AERMOD American Meteorological Society/Environmental Protection Agency Regulatory
Model
AIS Automated Identification System
APU Auxiliary power unit
BEIS Biogenic Emissions Inventory System
BELD Biogenic Emissions Land use Database
BenMAP Benefits Mapping and Analysis Program
BPS Bulk Plant Storage
BSP Blue Sky Pipeline
BTP BulkTerminal (Plant) to Pump
C1C2 Category 1 and 2 commercial marine vessels
C3 Category 3 (commercial marine vessels)
CAMD EPA's Clean Air Markets Division
CAMx Comprehensive Air Quality Model with Extensions
CAP Criteria Air Pollutant
CARB California Air Resources Board
CB05 Carbon Bond 2005 chemical mechanism
CB6 Version 6 of the Carbon Bond mechanism
CBM Coal-bed methane
CDB County database (input to MOVES model)
CEMS Continuous Emissions Monitoring System
CISWI Commercial and Industrial Solid Waste Incinerators
CMAQ Community Multiscale Air Quality
CMV Commercial Marine Vessel
CNG Compressed natural gas
CO Carbon monoxide
CONUS Continental United States
CoST Control Strategy Tool
CRC Coordinating Research Council
CSAPR Cross-State Air Pollution Rule
E0, E10, E85 0%, 10% and 85% Ethanol blend gasoline, respectively
ECA E missions Control Area
ECCC Environment and Climate Change Canada
EF Emission Factor
EGU Electric Generating Units
EIA Energy Information Administration
EIS Emissions Inventory System
EPA Environmental Protection Agency
EMFAC EMission FACtor (California's onroad mobile model)
EPIC Environmental Policy Integrated Climate modeling system
FAA Federal Aviation Administration
FCCS Fuel Characteristic Classification System
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FEST-C Fertilizer Emission Scenario Tool for CMAQ
FF10 Flat File 2010
FINN Fire Inventory from the National Center for Atmospheric Research
FIPS Federal Information Processing Standards
FHWA Federal Highway Administration
HAP Hazardous Air Pollutant
HMS Hazard Mapping System
HPMS Highway Performance Monitoring System
ICI Industrial/Commercial/lnstitutional (boilers and process heaters)
l/M Inspection and Maintenance
IMO International Marine Organization
IPM Integrated Planning Model
LADCO Lake Michigan Air Directors Consortium
LDV Light-Duty Vehicle
LPG Liquified Petroleum Gas
MACT Maximum Achievable Control Technology
MARAMA Mid-Atlantic Regional Air Management Association
MATS Mercury and Air Toxics Standards
MCIP Meteorology-Chemistry Interface Processor
MMS Minerals Management Service (now known as the Bureau of Energy
Management, Regulation and Enforcement (BOEMRE)
MOVES Motor Vehicle Emissions Simulator
MSA Metropolitan Statistical Area
MTBE Methyl tert-butyl ether
MWC Municipal waste combustor
MY Model year
NAAQS National Ambient Air Quality Standards
NAICS North American Industry Classification System
NBAFM Naphthalene, Benzene, Acetaldehyde, Formaldehyde and Methanol
NCAR National Center for Atmospheric Research
NEEDS National Electric Energy Database System
NEI National Emission Inventory
NESCAUM Northeast States for Coordinated Air Use Management
NH3 Ammonia
NLCD National Land Cover Database
NOAA National Oceanic and Atmospheric Administration
NONROAD OTAQ's model for estimation of nonroad mobile emissions
NOx Nitrogen oxides
NSPS New Source Performance Standards
OHH Outdoor Hydronic Heater
ONI Off network idling
OTAQ EPA's Office of Transportation and Air Quality
ORIS Office of Regulatory Information System
ORD EPA's Office of Research and Development
OSAT Ozone Source Apportionment Technology
pcSOA Potential combustion Secondary Organic Aerosol
PFC Portable Fuel Container
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PM2.5 Particulate matter less than or equal to 2.5 microns
PM 10 Particulate matter less than or equal to 10 microns
POA Primary Organic Aerosol
ppm Parts per million
ppmv Parts per million by volume
PSAT Particulate Matter Source Apportionment Technology
RACT Reasonably Available Control Technology
RBT Refinery to Bulk Terminal
RIA Regulatory Impact Analysis
RICE Reciprocating Internal Combustion Engine
RWC Residential Wood Combustion
RPD Rate-per-vehicle (emission mode used in SMOKE-MOVES)
RPH Rate-per-hour for hoteling (emission mode used in SMOKE-MOVES)
RPHO Rate-per-hour for off-network idling (emission mode used in SMOKE-MOVES)
RPP Rate-per-profile (emission mode used in SMOKE-MOVES)
RPS Rate-per-start (emission mode used in SMOKE-MOVES)
RPV Rate-per-vehicle (emission mode used in SMOKE-MOVES)
RVP Reid Vapor Pressure
SCC Source Classification Code
SMARTFIRE2 Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
version 2
SMOKE Sparse Matrix Operator Kernel Emissions
SO2 Sulfur dioxide
SOA Secondary Organic Aerosol
SIP State Implementation Plan
SPDPRO Hourly Speed Profiles for weekday versus weekend
S/L/T state, local, and tribal
TAF Terminal Area Forecast
TCEQ Texas Commission on Environmental Quality
TOG Total Organic Gas
TSD Technical support document
USDA United States Department of Agriculture
VIIRS Visible Infrared Imaging Radiometer Suite
VOC Volatile organic compounds
VMT Vehicle miles traveled
VPOP Vehicle Population
WRAP Western Regional Air Partnership
WRF Weather Research and Forecasting Model
2014NEIv2 2014 National Emissions Inventory (NEI), version 2
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1 Introduction
The U.S. Environmental Protection Agency (EPA), in conjunction with the National Emissions
Collaborative, developed an air quality modeling platform for criteria air pollutants that represents the
year 2022. The platform is based on the 2020 National Emissions Inventory (2020 NEI) published in April
2023 (EPA, 2023), with many sectors directly developed or adjusted to better reflect 2022 and/or use
data specific to the year 2022. The air quality modeling platform consists of all the emissions inventories
and ancillary data files used for emissions modeling, as well as the meteorological, initial condition, and
boundary condition files needed to run the air quality model. This document focuses on the emissions
modeling component of the 2022 air quality modeling platform, including the emission inventories, the
ancillary data files, and the approaches used to transform inventories for use in air quality modeling.
The emissions data in the modeling platform include criteria air pollutants and their precursors (CAPs),
two groups of hazardous air pollutants (HAPs), and diesel particulate matter. The first group of HAPs are
those explicitly used by the chemical mechanism in the Community Multiscale Air Quality (CMAQ) model
(Appel, 2018) for ozone/particulate matter (PM): chlorine (CI), hydrogen chloride (HCI), naphthalene,
benzene, acetaldehyde, formaldehyde, and methanol (the last five are abbreviated as NBAFM in
subsequent sections of the document). The second group of HAPs consists of over 50 HAPs or HAP
groups (such as polycyclic aromatic hydrocarbon groups) that are included in the emissions inventories
for the purposes of air quality modeling for a HAP+CAP platform.
Emissions were prepared for the Community Multiscale Air Quality (CMAQ) model version 5.41 which is
used to model ozone (O3) particulate matter (PM), and HAP concentrations. CMAQ requires hourly and
gridded emissions of the following inventory pollutants: carbon monoxide (CO), nitrogen oxides (NOx),
volatile organic compounds (VOC), sulfur dioxide (SO2), ammonia (NH3), primary particulate matter less
than or equal to 10 microns (PM10), and individual component species for primary particulate matter
less than or equal to 2.5 microns (PM2.5). In addition, the Carbon Bond mechanism version 6 (CB6) with
chlorine chemistry within CMAQ allows for explicit treatment of the VOC HAPs naphthalene, benzene,
acetaldehyde, formaldehyde and methanol (NBAFM), includes anthropogenic HAP emissions of HCI and
CI, and can model additional HAPs as described in Section 3. The short abbreviation for the final
modeling case name for this platform was "2022he", where 2022 is the year modeled, 'h' represents
that it was based on the 2020 NEI, and 'e' represents that it was the fifth version of a 2020 NEI-based
platform. A draft version of this case named "2022hd" was first developed and was used for a HAP+CAP
model run and for stakeholder review.
This TSD discusses the application of the emissions modeling platform for which CMAQ and the
Comprehensive Air Quality Model with Extensions (CAMx) were run. Emissions were also prepared for
an air dispersion modeling system: American Meteorological Society/Environmental Protection Agency
Regulatory Model (AERMOD) (EPA, 2018). AERMOD was run for 2022 for all NEI HAPs (about 130 more
than covered by CMAQ) across all 50 states in a similar way as was done for the 2018 version of
AirToxScreen (EPA, 2022a). This TSD does not address the data development for AERMOD. For this
platform, CMAQ was not run in Alaska, Hawaii, Puerto Rico and the Virgin Islands but AERMOD was run
1 CMAQ version 5.4: https://zenodo.org/record/7218076. CMAQ is also available from https://www.epa.gov/cmaq and the
Community Modeling and Analysis System (CMAS) Center at: https://www.cmascenter.org.
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in this areas. The effort to create the emissions inputs for this study included development of emission
inventories to represent emissions during the year of 2022, along with application of emissions modeling
tools to convert the inventories into the format and resolution needed by CMAQ and CAMx.
The emissions modeling platform includes point sources, nonpoint sources, onroad mobile sources,
nonroad mobile sources, biogenic emissions and fires for the U.S., Canada, and Mexico. Some platform
categories use more disaggregated data than are made available in the NEI. For example, in the
platform, onroad mobile source emissions are represented as hourly emissions by vehicle type, fuel
type, road type and process, while the NEI emissions are aggregated to vehicle type/fuel type totals and
annual temporal resolution. Emissions used in the CMAQ modeling from Canada are provided by
Environment and Climate Change Canada (ECCC) and Mexico are mostly provided by SEMARNAT and are
not part of the NEI. Year-specific emissions were used for fires, biogenic sources, fertilizer, point sources,
and onroad and nonroad mobile sources. Where available, hourly continuous emission monitoring
system (CEMS) data were used for electric generating unit (EGU) emissions.
The primary emissions modeling tool used to create the CMAQ model-ready emissions was the Sparse
Matrix Operator Kernel Emissions (SMOKE) modeling system. SMOKE version 5.2.1 was used to create
CMAQ-ready emissions files for a 12-km grid covering the continental U.S. Additional information about
SMOKE is available from http://www.cmascenter.org/smoke.
The gridded meteorological model used to provide input data for the emissions modeling was developed
using the Weather Research and Forecasting Model (WRF, https://github.com/wrf-
model/WRF/releases) version 4.4.2, Advanced Research WRF core (Skamarock, et al., 2008). The WRF
Model is a mesoscale numerical weather prediction system developed for both operational forecasting
and atmospheric research applications. The WRF model was run for 2022 over a domain covering the
continental U.S. (CONUS) at both 12km resolution and 36km resolution with 35 vertical layers, and also
for domains that cover Alaska, Hawaii, and Puerto Rico plus the Virgin Islands. The run for this platform
included high resolution sea surface temperature data from the Group for High Resolution Sea Surface
Temperature (GHRSST) (see https://www.ghrsst.org/) and is given the EPA meteorological case
abbreviation "22m." The full case abbreviation includes this suffix following the emissions portion of the
case name to fully specify the abbreviation of the draft case as "2022hd_cb6_22m" and the final case as
"2022he_cb6_22m."
Data files and summaries for this platform are available from this section of the air emissions modeling
website https://www.epa.gov/air-emissions-modeling/2022v2-emissions-modeling-platform. The
reports folder includes summaries that compare the 2022vl platform base year emissions (which has
the case name of "2022hc") with the draft (2022hd) and final (2022he) emissions for the 2022v2
platform, 2022v2 emissions by county and month, along with other information.
This document contains four additional sections. Section 2 describes the emission inventories input to
SMOKE. Section 3 describes the emissions modeling and the ancillary files used to process the emission
inventories into air quality model-ready inputs. Data summaries are provided in Section 4, and Section 5
provides references.
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2 Base Year Emissions Inventories and Approaches
This section describes the emissions inventories created for input to SMOKE, which are based on the
April 2023 version of the 2020 NEI with updates to reflect emissions in the year 2022. The NEI includes
four main data categories: a) nonpoint sources (including fires); b) point sources; c) nonroad mobile
sources; and d) onroad mobile sources. For CAPs, the NEI is largely compiled from data submitted by
state, local and tribal (S/L/T) agencies. HAP emissions are often generated through augmentation
(generated through speciation of relevant CAPs, e.g., VOC and PM2.5) by EPA when they are not
voluntarily submitted to the NEI by S/L/T agencies. The NEI was compiled using the Emissions Inventory
System (EIS). EIS collects and stores facility inventory and emissions data for the NEI and includes
hundreds of automated QA checks to improve data quality, and it also supports release point (stack)
coordinates separately from facility coordinates. EPA collaboration with S/L/T agencies helped prevent
duplication between point and nonpoint source categories such as industrial boilers. The 2020 NEI
Technical Support Document describes in detail the development of the 2020 emission inventories and
is available at https://www.epa.gov/air-emissions-inventories/2020-national-emissions-inventory-nei-
technical-support-document-tsd (EPA, 2023).
A complete set of emissions for all source categories is developed for the NEI every three years, with
2020 being the most recent year represented with a full "triennial" NEI. S/L/T agencies are required to
submit all applicable point sources to the NEI in triennial years, including the year 2020. Because only
select point source emissions were submitted by S/L/T agencies for 2022, emissions for any point
sources not submitted for 2022, and not marked as shutdown, were pulled forward from the 2020 NEI.
The SMARTFIRE2 system and the BlueSky Pipeline (https://github.com/pnwairfire/bluesky) emissions
modeling system were used to develop the fire emissions. SMARTFIRE2 categorizes all fires as either
prescribed burning or wildfire, and the BlueSky Pipeline system includes fuel loading, consumption and
emission factor estimates for both types of fires. Onroad and nonroad mobile source emissions were
developed for this project using MOVES5 (https://www.epa.gov/moves).
With the exception of fire emissions, Canadian emissions were provided by Environment Canada and
Climate Change (ECCC) for the years 2020 and 2023 and most 2022 emissions were developed by
interpolating between 2020 and 2023. For point EGUs, instead of interpolating from 2020 and 2023
(which unlike other point sources, has different sources in 2020 vs 2023), the provided 2023 emissions
were used as is to represent 2022. For Mexico, year 2016-based inventories from the 2019 emissions
modeling platform (EPA, 2022b) were used as the starting point with area, nonroad, and point data for
border states (i.e., Baja California, Chihuahua, Coahuila, Nuevo Leon, Sonora, and Tamaulipas)
supplemented with data for calendar year 2018, which is newer than the data used in the 2019
platform, developed by SEMARNAT in collaboration with U.S. EPA.
The emissions modeling process was performed using SMOKE v5.2.1. Through this process, the
emissions inventories were apportioned into the grid cells used by CMAQ and temporally allocated into
hourly values. In addition, the pollutants in the inventories (e.g., NOx, PM and VOC) were split into the
chemical species needed by CMAQ. For the purposes of preparing the CMAQ-ready emissions, the NEI
emissions were split into emissions modeling platform "sectors"; and emissions from sources other than
the NEI are added, such as the Canadian, Mexican, and offshore inventories. Emissions within the
emissions modeling platform were separated into sectors for groups of related emissions source
categories that were run through the appropriate SMOKE programs, except the final merge,
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independently from emissions categories in the other sectors. The final merge program called Mrggrid
combines low-level sector-specific gridded, speciated and temporalized emissions to create the final
CMAQ-ready emissions inputs. For biogenic and fertilizer emissions, the CMAQ model allows for these
emissions to be included in the CMAQ-ready emissions inputs, or to be computed within CMAQ itself
(the "inline" option). This study used the option to compute biogenic emissions within the model and
the CMAQ bidirectional ammonia process to compute the fertilizer emissions.
Following the compilation of the initial draft of the base year emission inventories within the 2022v2
Emissions Modeling Platform, the inventories were posted to the 2022v2 Emissions Modeling Platform
EPA website and to the 2022v2 platform Sharepoint site that facilitated the receipt of stakeholder
comments. Stakeholders were given the opportunity to comment on the inventory during an
approximate 30-day period. Following the comment period, EPA posted responses to the SharePoint site
and where possible, EPA incorporated the comments into the inventories prior to finalization. In total,
15 people from 13 individual organizations submitted 30 comments during the base-year emissions
review for the 2022v2 platform.
Table 2-1 presents the sectors in the emissions modeling platform used to develop the year 2022
emissions. The sector abbreviations are provided in italics and start with lower case letters; these
abbreviations are used in the SMOKE modeling scripts, the inventory file names, and throughout the
remainder of this section. Note that while the fires sectors are in nonpoint NEI data category, in the
modeling platform they are treated as day-specific point sources. The description column summarizes
whether the emissions were updated from the 2022vl (2022hc) platform. Further details on the changes
are provided later in the document.
Table 2-1. Platform sectors used in the Emissions Modeling Process
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
EGU units:
ptegu
Point
2022 NEI point source EGUs, replaced with hourly Continuous
Emissions Monitoring System (CEMS) values for NOx and S02, and
the remaining pollutants temporally allocated according to CEMS
heat input where the units are matched to the NEI. Emissions for all
sources not matched to CEMS data come from the 2022 NEI point
inventory. EGUs closed in 2022 are not part of the inventory. Annual
resolution for sources not matched to CEMS data, hourly for CEMS
sources. For 2022v2, CEMS data were updated based on data
downloaded on February 19, 2025. Some additional matches to
CEMS data were incorporated along with other minor changes.
Point source oil and gas:
pt_oilgas
Point
2022 NEI point sources that include oil and gas production
emissions processes for facilities with North American Industry
Classification System (NAICS) codes related to Oil and Gas
Extraction, Natural Gas Distribution, Drilling Oil and Gas Wells,
Support Activities for Oil and Gas Operations, Pipeline
Transportation of Crude Oil, and Pipeline Transportation of Natural
Gas. Includes U.S. offshore oil production. Annual resolution. For
2022v2, some sources were updated to correct double counts,
closures, outdated and incorrect data, and some other changes.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Aircraft and ground
support equipment:
airports
Point
EPA estimated 2022 emissions, including aircraft and airport ground
support for the top 51 airports. Smaller airports, including aircraft
and airport ground support were projected from 2020 NEI to 2022
based on the 2023 Terminal Area Forecast (TAF). Georgia provided
emissions for HJAIA. Annual resolution. For 2022v2, smaller airports
were reprojected from 2020 to 2022 using updated projection
factors based on the 2024 TAF, and other updates were
incorporated in Georgia and Washington.
Remaining non-EGU
point: ptnonipm
Point
All 2022 NEI point source records not matched to the airports,
ptegu, or pt_oilgas sectors. Includes 2020 NEI rail yard emissions
projected to 2022 using updated R-l reported yard fuel usage.
Annual resolution. For 2022v2, some changes were implemented
based on state comments.
Livestock:
livestock
Nonpoint
2022 nonpoint livestock emissions developed using a similar
method to 2020 NEI but with adjusted animal counts and using 2022
meteorology. Livestock includes ammonia and other pollutants
(except PM2.5). County and annual resolution. For2022v2, updates
were incorporated for Maricopa County.
Agricultural Fertilizer:
fertilizer
Nonpoint
2022 agricultural fertilizer ammonia emissions based on
bidirectional flux calculations computed inline within CMAQ. For
2022v2 there were no changes.
Area fugitive dust:
afdust_adj
Nonpoint
PM 10 and PM2.5 nonpoint fugitive dust sources including building
construction, road construction, agricultural dust from crops, and
mining and quarrying which were all held constant. Additional dust
sources not held constant include paved road dust and agricultural
dust from livestock, where paved road dust emissions were adjusted
to 2022 based on VMT and dust from livestock based on animal
count differences. The emissions modeling system applies a
transportable fraction reduction and zero-out adjustments based on
the year-specific gridded hourly meteorology (precipitation and
snow/ice cover). County and annual resolution. For 2022v2, changes
were incorporated for Oregon unpaved road dust.
Biogenic:
beis
Nonpoint
Year 2022 emissions from biogenic sources. These were left out of
the CMAQ-ready merged emissions, in favor of inline biogenic
emissions produced during the CMAQ model run itself. Version 4 of
the Biogenic Emissions Inventory System (BEIS) was used with
Version 6 of the Biogenic Emissions Landuse Database (BELD6). The
CMAQ-generated emissions are similar to the biogenic emissions
generated through running SMOKE, but they are not exactly the
same. Gridded and hourly resolution. For 2022v2, there were no
changes.
Category 1, 2 CMV:
cmv clc2
Nonpoint
2022 Category 1 (CI) and Category 2 (C2), commercial marine vessel
(CMV) emissions based on 2022 Automatic Identification System
(AIS) data categorized using SCCs specific to ship type. Point and
hourly resolution. For 2022v2, the factors used to compute HAP
emissions were updated but there were no changes to CAPs.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Category 3 CMV:
cmv_c3
Nonpoint
2022 Category 3 (C3) commercial marine vessel (CMV) emissions
based on 2022 AIS data categorized using SCCs specific to ship type.
Point and hourly resolution. For 2022v2, the factors used to
compute HAP emissions were updated but there were no changes
to CAPs.
Locomotives:
rail
Nonpoint
Class 1 line haul rail locomotives emissions from 2020 NEI projected
to 2022 using R-l reported fuel usage. County and annual
resolution. Class II and III locomotive emissions were projected from
2020 based on the 2021 U.S. Energy Information Administration's
Annual Energy Outlook. Commuter rail was projected from 2020
using fuel use per company from the Federal Transit
Administration's (FTA) 2022 National Transit Database. Amtrak
emissions were adjusted down based on 2020 fuel use reported in
Amtrak's FY22 AMTRAK Sustainability Report. County and annual
resolution. For 2022v2, there were no changes.
Nonpoint source oil and
gas:
np_oilgas
Nonpoint
2022 well activity data (production and exploration of oil, gas, etc.)
run through Oil and Gas tool. Abandoned wells based on 2022, plus
other state-specific inputs. County and annual resolution. For
2022v2, there were changes in multiple states.
Open Burning:
openburn
Nonpoint
This new sector for the 2022vl platform was split out from the prior
nonpt sector and includes emissions from yard waste, land clearing,
and residential household waste burning. These are SCCs starting
with 261. County and annual resolution. For 2022v2, some
emissions were removed in New Hampshire.
Residential Wood
Combustion:
rwc
Nonpoint
2020 NEI nonpoint sources with residential wood combustion (RWC)
processes, projected to 2022 with state-level adjustment factors
derived from the State Energy Data System (SEDS) plus specific
adjustments for California and Idaho. County and annual resolution.
For 2022v2, there were significant changes based on updated data
and methods that were developed for the 2023 NEI, along with
changes to speciation and spatial surrogates.
Solvents:
np_solvents
Nonpoint
Emissions of solvents based on methods used for the 2020 NEI.
2021 emissions are used to represent 2022. Includes household
cleaners, personal care products, adhesives, architectural and
aerosol coatings, printing inks, and pesticides. Annual and county
resolution. For 2022v2, cutback asphalt emissions were removed
from Maricopa County.
Remaining nonpoint:
nonpt
Nonpoint
Nonpoint sources not included in other platform sectors. Mostly
held constant at 2020 levels, but with some SCCs adjusted to 2022
based on population, energy consumption ratios and employment
data. County and annual resolution. For 2022v2, updates were
incorporated for Maricopa County, some LADCO states, Georgia,
and Delaware, along with some spatial surrogate changes.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Nonroad:
nonroad
Nonroad
2022 nonroad equipment emissions developed with MOVES5,
including the updates made to spatial apportionment that were
developed with the 2016vl platform. MOVES5 was used for all
states except California, which submitted their own emissions for
2020 and 2023 that were then interpolated to 2022. County and
monthly resolution. For 2022v2, MOVES5 was used and updates
were incorporated for Minnesota, Wisconsin, Utah, and Georgia
Onroad:
on road
Onroad
Onroad mobile source gasoline and diesel vehicles from parking lots
and moving vehicles for 2022 were developed using VMT from many
states, along with VMT data from 2020 NEI projected to 2022 using
factors based on FHWA VM-2 data. Includes the following emission
processes: exhaust, extended idle, auxiliary power units,
evaporative, permeation, refueling, vehicle starts, off network
idling, long-haul truck hoteling, and brake and tire wear. MOVES5
was run for 2022 to generate year-specific emission factors.
County/gridded and hourly resolution. For 2022v2, MOVES5 was
used along with significantly updated input data including spatial
surrogates, temporal profiles, age distributions, and activity data.
Onroad California:
onroad_ca_adj
Onroad
California-provided 2022 emissions for CAPs. VOC HAPs were
projected from California-provided 2020 NEI HAP emissions using
CAP trends. Onroad mobile source gasoline and diesel vehicles from
parking lots and moving vehicles based on Emission Factor (EMFAC),
gridded and temporalized based on outputs from MOVES5.
County/gridded and hourly resolution. For 2022v2, temporal and
spatial allocation were updated based on MOVES5.
Point source agricultural
fires:
ptagfire
Nonpoint
Agricultural fire sources for 2022 developed by EPA as point and
day-specific emissions.2 Includes 2022 satellite data and land use.
Florida, Georgia, Idaho, and North Carolina have separate datasets
and are removed from the national datasets. Washington has
supplemental datasets, to be used along with WA from the national
datasets. Agricultural fires are in the nonpoint data category of the
NEI, but in the modeling platform, they are treated as day-specific
point sources. Point and daily resolution. For 2022v2, updates were
incorporated in multiple states.
Point source prescribed
fires:
ptfire-rx
Nonpoint
Point source day-specific prescribed fires for 2022 computed using
SMARTFIRE 2 and BlueSky Pipeline. The ptfire emissions were run as
two separate sectors: ptfire-rx (prescribed, including Flint Hills /
grasslands) and ptfire-wild. Point and daily resolution. For 2022v2,
updates were incorporated in multiple states.
Point source wildfires:
ptfire-wild
Nonpoint
Point source day-specific wildfires for 2022 computed using
SMARTFIRE 2 and BlueSky Pipeline. Point and daily resolution. For
2022v2, updates were incorporated in multiple states.
2 Only EPA-developed agricultural fire data were included in this study; data submitted by states to the NEI were excluded.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Non-US. Fires:
ptfire_othna
N/A
Point source day-specific wildfires and agricultural fires outside of
the U.S. for 2022. Canadian fires were computed using SMARTFIRE 2
and BlueSky Pipeline. Mexico, Caribbean, Central American, and
other international fires, are from v2.5 of the Fire INventory (FINN)
from National Center for Atmospheric Research (Wiedinmyer, C.,
2023). Point and daily resolution. For 2022v2, there were no
updates.
Canada Area Fugitive
dust sources:
canada_afdust
N/A
Area fugitive dust sources from ECCCfor 2022 (interpolated
between provided 2020 and 2023 emissions) with transport fraction
and snow/ice adjustments based on 2022 meteorological data.
Annual and province resolution. For 2022v2, there were no
updates.
Canada Point Fugitive
dust sources:
canada_ptdust
N/A
Point source fugitive dust sources from ECCC for 2022 (interpolated
between provided 2020 and 2023 emissions) with transport fraction
and snow/ice adjustments based on 2022 meteorological data.
Monthly and province resolution. For 2022v2, there were no
updates.
Canada and Mexico
stationary point sources:
canmex_point
N/A
Canada and Mexico point source emissions not included in other
sectors. Canada point sources were provided by ECCCfor 2020 and
2023; Canadian EGUs from 2023 were used directly, while other
Canadian point sources were interpolated from 2020 and 2023 to
2022. Mexico point source emissions for six border states represent
2018 and were developed by SEMARNAT in collaboration with EPA,
while emissions for all other states were carried forward from
2019ge (EPA, 2022b). Annual and monthly point resolution. For
2022v2, new data were incorporated for some states in Mexico.
Canada and Mexico
agricultural sources:
canmexjag
N/A
Canada and Mexico agricultural emissions. Canada emissions were
provided by ECCCfor 2020 and 2023. Mexico agricultural emissions
were provided by SEMARNAT and include updated emissions for six
border states representing 2018 developed by SEMARNAT in
collaboration with EPA, while emissions for all other states were
carried forward from 2019ge. Annual municipio and province
resolution. For 2022v2, new data were incorporated for some
states in Mexico.
Canada low-level oil and
gas sources:
canada_og2D
N/A
Canada emissions from upstream oil and gas, provided by ECCCfor
2020 and 2023 and interpolated to 2022. This sector contains the
portion of oil and gas emissions which are not subject to plume rise.
The rest of the Canada oil and gas emissions are in the
canmex_point sector. Annual province resolution. For 2022v2, there
were no updates.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Canada and Mexico
nonpoint and nonroad
sources:
canmex_area
N/A
Canada and Mexico nonpoint source emissions not included in other
sectors. Canada: ECCC provided surrogates and 2020 and 2023
inventories, that were interpolated to 2022. Mexico: included
updated emissions for six border states representing 2018
developed by SEMARNAT in collaboration with EPA, while emissions
for all other states were carried forward from 2019ge. Annual and
monthly municipio and province resolution. For 2022v2, new data
were incorporated for some states in Mexico including some
spatial surrogate updates.
Canada onroad sources:
canada_onroad
N/A
Canada onroad emissions. 2020 and 2023 Canada inventories
provided by ECCC and interpolated to 2022; processed using
updated surrogates. Province monthly resolution. For2022v2, there
were no updates.
Mexico onroad sources:
mexico_onroad
N/A
Mexico onroad emissions. 2020 and 2023 emissions output from
MOVES-Mexico were interpolated to 2022. Municipio monthly
resolution. For 2022v2, a new version of MOVES_Mexico was used
to compute emissions
Ocean chlorine emissions were also merged in with the above sectors. The ocean chlorine gas emission
estimates are based on the build-up of molecular chlorine (Cb) concentrations in oceanic air masses
(Bullock and Brehme, 2002). Ocean chlorine data at 12 km resolution were available from earlier studies
and were not modified other than the name "CHLORINE" was changed to "CL2" because that is the
name required by the CMAQ model.
The emission inventories in SMOKE input formats for the platform are available from EPA's Air Emissions
Modeling website: https://www.epa.gov/air-emissions-modeling/2022v2-emissions-modeling-platform.
The platform informational text file indicates the zipped files associated with each platform sector. Some
emissions data summaries are available with the data files for this platform. The types of reports include
state summaries of inventory pollutants and model species by modeling platform sector and county
annual totals by modeling platform sector.
2.1 Point sources (ptegu, pt_oilgas, ptnonipm, ptnonipm_hr, airports)
Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude and
longitude) are specified, as in the case of an individual facility. A facility may have multiple emission
release points that may be characterized as units such as boilers, reactors, spray booths, kilns, waste
piles, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and
sometimes burns natural gas). With a couple of minor exceptions, this section describes only NEI point
sources within the contiguous U.S. The offshore oil platform (pt_oilgas sector) and CMV emissions
(cmv_clc2 and cmv_c3 sectors) are processed by SMOKE as point source inventories and are discussed
later in this section. A complete NEI is developed every three years. At the time of this writing, 2020 is
the most recently finished complete NEI. A comprehensive description about the development of the
2020 NEI is available in the 2020 NEI TSD (EPA, 2023). Point inventories are also available in EIS for non-
triennial NEI years such as 2019 and 2021. In the interim year point inventories, states are required to
update large sources with the emissions that occurred in that year, while sources not updated by states
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for the interim year were either carried forward from the most recent triennial NEI or marked as closed
and removed.
In preparation for modeling, the complete set of point sources in the NEI was exported from EIS for the
year 2022 into the Flat File 2010 (FF10) format that is compatible with SMOKE (see
https://cmascenter.Org/smoke/documentation/5.l/html/ch06s02s08.html) and was then split into
several sectors for modeling. Any sources without specific locations (i.e., the FIPS code ends in 777) were
dropped and inventories for the other point source sectors were created from the remaining point
sources. The point sectors are: EGUs (ptegu), point source oil and gas extraction-related sources
(pt_oilgas), airport emissions (airports), and the remaining non-EGUs (ptnonipm). The EGU emissions
were split out from the other sources to facilitate the use of distinct SMOKE temporal processing and
future-year projection techniques. The oil and gas sector emissions (pt_oilgas) and airport emissions
(airports) were processed separately for the purposes of developing emissions summaries and due to
distinct projection techniques from the remaining non-EGU emissions (ptnonipm).
In some cases, data about facility or unit closures are entered into EIS after the inventory modeling flat
files have been extracted. Prior to processing through SMOKE, submitted facility and unit closures were
reviewed and closed sources were removed from the inventory.
For this platform, an analysis of point source stack parameters (e.g., stack height, diameter,
temperature, and velocity) was performed due to the presence of unrealistic and repeated stack
parameters. The defaulted values were noticed in data submissions for the states of Illinois, Louisiana,
Michigan, Pennsylvania, Texas, and Wisconsin. Where these defaults were detected and deemed to be
unreasonable for the specific process, the affected stack parameters were replaced by values from the
PSTK file that is input to SMOKE. PSTK contains default stack parameters by source classification code
(SCC).
The inventory pollutants processed through SMOKE for input to CMAQ for the ptegu, pt_oilgas,
ptnonipm, ptnonipm_hr, and airports sectors included: CO, NOx, VOC, SO2, NH3, PM10, and PM2.5 and
the following HAPs: HCI (pollutant code = 7647010), CI (code = 7782505), and several dozen other HAPs
listed in Section 3. NBAFM pollutants from the point sectors were utilized.
The ptnonipm, pt_oilgas, and airports sector emissions were provided to SMOKE as annual emissions.
The 'ptnonipm_hr' sector is new for the 2022v2 platform. It includes facilities and sources previously in
the ptnonipm sector, includes non-EGU sources with hourly emissions data. This includes hourly CEMs
data for select non-EGUs, and also hourly emissions data at Taconite mines in Minnesota. For sources in
the ptegu sector that could be matched to 2022 CEMS data, hourly CEMS NOx and SO2 emissions for
2022 from EPA's Acid Rain Program were used rather than annual inventory emissions. For all other
pollutants (e.g., VOC, PM2.5, HCI), annual emissions were used as-is from the annual inventory but were
allocated to hourly values using heat input from the CEMS data. For the unmatched units in the ptegu
sector, annual emissions were allocated to daily values using IPM region- and pollutant-specific profiles,
and similarly, region- and pollutant-specific diurnal profiles were applied to create hourly emissions.
The non-EGU, non-hourly, stationary point source (ptnonipm) emissions were used as inputs to SMOKE as
annual emissions. The full description of how the NEI emissions were developed is provided in the NEI
documentation - a brief summary of their development follows:
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a. CAP and HAP data were provided by States, locals and tribes under the Air Emissions Reporting
Rule (AERR) [the reporting size threshold is larger for inventory years between the triennial inventory
years of 2011, 2014, 2017, 2020,...].
b. EPA corrected known issues and filled PM data gaps.
c. EPA added HAP data from the Toxic Release Inventory (TRI) where corresponding data was not
already provided by states/locals.
d. EPA stored and applied matches of the point source units to units with CEMS data and for all EGU
units modeled by EPA's Integrated Planning Model (IPM).
e. Data for airports and rail yards were incorporated.
f. Off-shore platform data were added from the Bureau of Ocean Energy Management (BOEM).
The changes made to the NEI point sources prior to modeling with SMOKE are as follows:
• The tribal data, which do not use state/county Federal Information Processing Standards (FIPS)
codes in the NEI, but rather use the tribal code, were assigned a state/county FIPS code of 88XXX,
where XXX is the 3-digit tribal code in the NEI. This change was made because SMOKE requires all
sources to have a state/county FIPS code.
• Sources that did not have specific counties assigned (i.e., the county code ends in 777) were not
included in the modeling because it was only possible to know the state in which the sources
resided, but no more specific details related to the location of the sources were available.
Each of the point sectors is processed separately through SMOKE as described in the following
subsections.
2.1.1 EGU sector (ptegu)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• CEMS data were updated based on data downloaded on February 19, 2025.
• Some additional matches to CEMS data were incorporated along with other minor changes.
• CEMS data matching was improved for units where multiple CEMS units align with a single EIS
unit.
General Description
The ptegu sector contains emissions from EGUs in the 2022 point source inventory that could be
matched to units found in the National Electric Energy Database System (NEEDS) v6 that is used by the
Integrated Planning Model (IPM) to develop projected EGU emissions. It was necessary to put these
EGUs into a separate sector in the platform because EGUs use different temporal profiles than other
sources in the point sector and it is useful to segregate these emissions from the rest of the point
sources to facilitate summaries of the data. Sources not matched to units found in NEEDS were placed
into the pt_oilgas or ptnonipm sectors. For studies that include analytic years, the sources in the ptegu
sector are fully replaced with analytic year emissions computed by IPM or through engineering analysis.
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It is therefore important that the matching between the NEI and NEEDS database be as complete as
possible because there can be double counting of emissions in analytic year modeling scenarios if
emissions for units projected by IPM are not properly matched to the units in the base year point source
inventory. For details on the differences in the ptegu sector between the 2022vl, 2022v2 draft, and
2022v2 final cases, see the spreadsheets that compare emissions at facility and unit-level, stack
parameters, and locations available in the reports/point area of the 2022v2 FTP site.
The 2022 ptegu emissions inventory is a subset of the point source flat file exported from the Emissions
Inventory System (EIS). In the point source flat file, emission records for sources that have been matched
to the NEEDS database have a value filled into the IPM_YN column based on the matches stored within
EIS. Thus, unit-level emissions were split into a separate EGU flat file for units that have a populated
(non-null) ipm_yn field. A populated ipm_yn field indicates that a match was found for the EIS unit in the
NEEDS database. Updates were made to the flat file output from EIS as follows:
• ORIS facility and unit identifiers were updated based on additional matches in a cross-platform
spreadsheet, based on state comments, and using the EIS alternate identifiers table as described
later in this section.
Some units in the ptegu sector are matched to Continuous Emissions Monitoring System (CEMS) data via
Office of Regulatory Information System (ORIS) facility codes and boiler IDs. For the matched units, the
annual emissions of NOx and SO2 in the flat file were replaced with the hourly CEMS emissions in base
year modeling. For other pollutants at matched units, the hourly CEMS heat input data were used to
allocate the NEI annual emissions to hourly values. All stack parameters, stack locations, and Source
Classification Codes (SCCs) for these sources come from the flat file. If CEMS data exists for a unit, but
the unit is not matched to the NEI, the CEMS data for that unit were not used in the modeling platform.
However, if the source exists in the NEI and is not matched to a CEMS unit, the emissions from that
source are still modeled using the annual emission value in the NEI temporally allocated to hourly
values.
EIS stores many matches from NEI units to the ORIS facility codes and boiler IDs used to reference the
CEMS data. In the flat file, emission records for point sources matched to CEMS data have values filled
into the ORIS_FACILITY_CODE and ORIS_BOILER_ID columns. The CEMS data are available at
https://campd.epa.gov/data. Many smaller emitters in the CEMS program cannot be matched to the NEI
due to differences in the way a unit is defined between the NEI and CEMS datasets, or due to
uncertainties in source identification such as inconsistent plant names in the two data systems. In
addition, the NEEDS database of units modeled by IPM includes many smaller emitting EGUs that do not
have CEMS. Therefore, there will be more units in the ptegu sector than have CEMS data.
Matches from the NEI to ORIS codes and the NEEDS database were improved in the platform where
applicable. In some cases, NEI units in EIS match to many CAMD units. In these cases, a new entry was
made in the flat file with a "_M_" in the ipm_yn field of the flat file to indicate that there are "multiple"
ORIS IDs that match that unit. This helps facilitate appropriate temporal allocation of the emissions by
SMOKE. Temporal allocation for EGUs is discussed in more detail in the Ancillary Data section below.
The EGU flat file was split into two flat files: those that have unit-level matches to CEMS data using the
oris_facility_code and oris_boiler_id fields (egu_cems) and those that do not (egu_noncems) so that
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different temporal profiles could be applied. In addition, the hourly CEMS data were processed through
v2.1 of the CEMCorrect tool to mitigate the impact of unmeasured values in the data.
2.1.2 Point source oil and gas sector (pt_oilgas)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• Some sources were updated to correct double counts, closures, old data, and some other
changes.
• Overall emissions changes were minor. In total, nationwide NOx emissions increased by 98 tons
and nationwide VOC emissions decreased by 1,354 tons.
General Description
The pt_oilgas sector was separated from the ptnonipm sector by selecting sources with specific North
American Industry Classification System (NAICS) codes shown in Table 2-2. The emissions and other
source characteristics in the pt_oilgas sector are submitted by states, while EPA developed a dataset of
nonpoint oil and gas emissions for each county in the U.S. with oil and gas activity that was available for
states to use. Nonpoint oil and gas emissions can be found in the np_oilgas sector. The pt_oilgas sector
includes emissions from offshore oil platforms. Where available, the point source emissions submitted
as part of the 2022 NEI process with refinements based on the Collaborative data review process were
used. Sources without data submitted for 2022 were projected to 2022 from 2020 NEI emissions, or
where applicable, from 2021 NEI emissions. For details on the differences in the pt_oilgas sector
between the 2022vl, 2022v2 draft, and 2022v2 final cases, see the spreadsheets that compare
emissions at facility and unit-level, stack parameters, and locations available in the reports/point area of
the 2022v2 FTP site
Table 2-2. Point source oil and gas sector NAICS Codes
NAICS
NAICS description
2111
Oil and Gas Extraction
211112
Natural Gas Liquid Extraction (although no emissions for this
NAICS code exist in the 2022 inventory)
21112
Crude Petroleum Extraction
211120
Crude Petroleum Extraction
21113
Natural Gas Extraction
211130
Natural Gas Extraction
213111
Drilling Oil and Gas Wells
213112
Support Activities for Oil and Gas Operations
2212
Natural Gas Distribution
22121
Natural Gas Distribution
221210
Natural Gas Distribution
237120
Oil and Gas Pipeline and Related Structures Construction
4861
Pipeline Transportation of Crude Oil
48611
Pipeline Transportation of Crude Oil
486110
Pipeline Transportation of Crude Oil
4862
Pipeline Transportation of Natural Gas
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NAICS
NAICS description
48621
Pipeline Transportation of Natural Gas
486210
Pipeline Transportation of Natural Gas
Information on the development of the 2020 NEI oil and gas emissions can be found in Section 13 of the
2020 NEI TSD. The point oil and gas emissions for 2022 by state are shown in Table 2-3.
Table 2-3. Point source oil and gas sector emissions for 2022v2
State
2022 NOx
2022 VOC
Alabama
10,608
1,209
Alaska
38,698
1,730
Arizona
2,374
180
Arkansas
4,029
320
California
2,597
2,264
Colorado
13,359
10,470
Connecticut
59
35
Delaware
6
1
Florida
6,192
696
Georgia
3,114
526
Idaho
1,291
38
Illinois
4,571
1,039
Indiana
949
136
Iowa
3,962
223
Kansas
17,766
3,009
Kentucky
9,201
1,125
Louisiana
27,875
8,243
Maine
32
64
Maryland
186
123
Massachusetts
235
69
Michigan
9,134
990
Minnesota
2,377
172
Mississippi
22,452
1,930
Missouri
2,342
92
Montana
815
1,035
Nebraska
2,757
266
Nevada
236
22
New Jersey
95
94
New Mexico
34,980
63,796
New York
1,072
256
North Carolina
1,681
237
North Dakota
4,197
2,736
Ohio
8,828
1,584
Oklahoma
33,937
26,131
26
-------
State
2022 NOx
2022 VOC
Oregon
1,019
94
Pennsylvania
3,027
918
Puerto Rico
0
0
Rhode Island
39
25
South Carolina
315
121
South Dakota
358
10
Tennessee
6,452
532
Texas
46,844
20,622
Utah
2,453
652
Virginia
2,725
428
Washington
874
56
West Virginia
8,335
3,263
Wisconsin
429
204
Wyoming
13,165
50,572
Offshore
34,660
30,911
Tribal Data
7,859
2,220
2.1.3 Aircraft and ground support equipment (airports)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• Smaller airports were reprojected from 2020 to 2022 using updated projection factors based on
the 2024 TAF, an update compared to the 2023 TAF that was used in 2022vl.
• Locations were updated to match latitude and longitudes in EIS as of April 28, 2025.
• Other updates were incorporated in Georgia and Washington.
General Description
Emissions at airports were separated from other sources in the point inventory based on sources that
have the facility source type of 100 (airports). The airports sector includes all aircraft types used for
public, private, and military purposes and aircraft ground support equipment. The Federal Aviation
Administration's (FAA) Aviation Environmental Design Tool (AEDT) is used to estimate emissions for this
sector. Additional information about aircraft emission estimates can be found in section 3 of the 2020
NEI TSD (EPA, 2023). EPA ran AEDT for 2022 for the largest (51) airports in the United States. For more
information on the estimation of emissions from larger airports, please see, 2022 National Emissions
Inventory: Aviation Component (ERG, 2024a). Smaller airport emissions were projected from the 2020
NEI to 2022 using factors derived from the 2024 Terminal Area Forecast (TAF)3 data. EPA used airport-
specific factors where available. In 2022v2, emissions for Hartsfield-Jackson (ATL) airport were updated
based on data provided by Georgia EPD based on the AEDT version 3g. Airport emissions were spread
out into multiple 12km grid cells when the airport runways were determined to overlap multiple grid
cells. Otherwise, airport emissions for a specific airport are confined to one air quality model grid cell.
The SCCs included in the airport sector are shown in Table 2-4. Additional information about aircraft
3 See https://www.faa.gov/data research/aviation/taf for the 2024 TAF released January 2025
27
-------
emission estimates can be found in section 3 of the 2020 NEI TSD (EPA, 2023). Additional updates in
2022v2 were to remove military aircraft emissions (SCC=2275001000) from Kenmore Air Harbor
(12141411) in Washington and to update locations to match latitudes and longitudes in EIS as of April
28, 2025. For details on the differences in the airports sector between the 2022vl, 2022v2 draft, and
2022v2 final cases, see the spreadsheets that compare emissions at facility and unit-level, stack
parameters, and locations available in the reports/point area of the 2022v2 FTP site.
Table 2-4. SCCs for the airports sector
see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4
description
2265008005
Mobile Sources
Off-highway Vehicle
Gasoline, 4-Stroke
Airport Ground Support
Equipment
Total
2270008005
Mobile Sources
Off-highway Vehicle
Diesel
Airport Ground Support
Equipment
Total
2275001000
Mobile Sources
Aircraft
Military Aircraft
Total
2275020000
Mobile Sources
Aircraft
Commercial Aircraft
Total: All
Types
2275050011
Mobile Sources
Aircraft
General Aviation
Piston
2275050012
Mobile Sources
Aircraft
General Aviation
Turbine
2275060011
Mobile Sources
Aircraft
Air Taxi
Piston
2275060012
Mobile Sources
Aircraft
Air Taxi
Turbine
2275070000
Mobile Sources
Aircraft
Auxiliary Power Units
Total
2.1.4 Non-IPM sector (ptnonipm)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• Changes were made to one DC rail yard between 2022vl and v2 due to comments.
• Stack parameter and latitude/longitude location changes available prior to August 12, 2025 were
incorporated.
• Added a new sector 'ptnonipm_hr' which includes nonEGUs with hourly emissions data. This
includes hourly CEMs data for select nonEGUs, and also hourly emissions data at Taconite mines
in Minnesota. The units in the ptnonipm_hr sector had previously been part of the ptnonipm
sector.
• Utah provided emissions from trucks at Kennecott mine to be included in 2022v2 final.
General Description
With some exceptions, the ptnonipm sector contains the point sources that are not in the ptegu,
pt_oilgas, or airports sectors. For the most part, the ptnonipm sector reflects non-EGU emissions
sources and rail yards. However, it is possible that some low-emitting EGUs not matched to units in the
NEEDS database or to CEMS data are in the ptnonipm sector. For details on the differences in the
ptnonipm sector between the 2022vl (2022hc), 2022v2 draft (2022hd), and 2022v2 final (2022he) cases,
see the spreadsheets that compare emissions at facility and unit-level, stack parameters, and locations
available in the reports/point area of the 2022v2 FTP site.
28
-------
The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on
paved or unpaved roads at industrial facilities, coal handling at coal mines, and grain elevators. Sources
with state/county FIPS code ending with "111" are in the NEI but are not included in any modeling
sectors. These sources typically represent mobile (temporary) asphalt plants that are only reported for
some states and are generally in a fixed location for only a part of the year and are therefore difficult to
allocate to specific places and days as is needed for modeling. Therefore, these sources are dropped
from the point-based sectors in the modeling platform.
The ptnonipm sources (i.e., not EGUs and non -oil and gas sources) were used as-is from the 2022 NEI
point inventory following updates from the Collaborative review. Solvent emissions from point sources
were removed from the np_solvents sector to prevent double-counting, so that all point sources can be
retained in the modeling as point sources rather than as area sources. The modeling was based on the
point flat file exported from EIS on August 12, 2025, and included updates from the Collaborative review
process for the 2022 base year, and updates specific to ethylene oxide. The np_solvents sector is
described in more detail in Section 2.2.6.
Emissions from rail yards are included in the ptnonipm sector. Railyards are from the 2020 NEI railyard
inventory with a projection factor applied. Additional information about railyard estimates can be found
in section 3 of the 2020 NEI TSD.
2.2 Nonpoint sources (afdust, fertilizer, livestock, np_oilgas, rwc, np_solvents, nonpt)
This section describes the stationary nonpoint sources in the NEI nonpoint data category. Locomotives,
CI and C2 CMV, and C3 CMV are included in the NEI nonpoint data category but are mobile sources that
are described in Section 2.4. The 2020 NEI TSD includes documentation for the nonpoint data.
Nonpoint tribal emissions submitted to the NEI are dropped during spatial processing with SMOKE due
to the configuration of the spatial surrogates. Part of the reason for this is to prevent possible double-
counting with county-level emissions and 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)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• Changes were incorporated that impact Oregon's unpaved road dust emissions. Specifically, new
emissions were submitted by the State that reduced emissions by 46%.
General Description
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
29
-------
mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as
point sources. Table 2-5 is a listing of the Source Classification Codes (SCCs) in the afdust sector.
Table 2-5. Afdust sector SCCs
see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2294000000
Mobile Sources
Paved Roads
All Paved Roads
Total: Fugitives
2296000000
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
2311010000
Industrial Processes
Construction: SIC 15 -17
Residential
Total
2311020000
Industrial Processes
Construction: SIC 15 -17
Industrial/Commercial/
Institutional
Total
2311030000
Industrial Processes
Construction: SIC 15 -17
Road Construction
Total
2325000000
Industrial Processes
Mining and Quarrying: SIC
14
All Processes
Total
2325020000
Industrial Processes
Mining and Quarrying: SIC
14
Crushed and Broken
Stone
Total
2325030000
Industrial Processes
Mining and Quarrying: SIC
14
Sand and Gravel
Total
2325060000
Industrial Processes
Mining and Quarrying: SIC
10
Lead Ore Mining and
Milling
Total
2801000000
Miscellaneous Area
Sources
Ag. Production - Crops
Agriculture - Crops
Total
2801000003
Miscellaneous Area
Sources
Ag. Production - Crops
Agriculture - Crops
Tilling
2801000005
Miscellaneous Area
Sources
Ag. Production - Crops
Agriculture - Crops
Harvesting
2801000008
Miscellaneous Area
Sources
Ag. Production - Crops
Agriculture - Crops
Transport
2805100010
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Beef cattle -
finishing
operations on
feedlots (drylots)
2805100020
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Dairy Cattle
2805100030
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Broilers
2805100040
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Layers
2805100050
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Swine
2805100060
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Turkeys
Area Fugitive Dust Transportable Fraction Adjustments
The afdust sector was separated from other nonpoint sectors to allow for the application of a
"transportable fraction" and meteorological/precipitation reductions. These adjustments were applied
using a script that applies land use-based gridded transport fractions based on landscape roughness,
followed by another script that performs meteorological adjustments that zeroes out emissions for days
on which at least 0.01 inches of precipitation occurs or there is snow cover on the ground. The land use
data used to reduce the NEI emissions determines the amount of emissions that are subject to
transport. For example, less dust would be transported on a forest floor, than would be on an open
plain. This methodology is discussed in Pouliot, et al., 2010, and in "Fugitive Dust Modeling for the 2008
30
-------
Emissions Modeling Platform" (Adelman, 2012). Both the transportable fraction and meteorological
adjustments are based on the gridded resolution of the platform (i.e., 12km grid cells); therefore,
different emissions will result if the process were applied to different grid resolutions. A limitation of the
transportable fraction approach is the lack of monthly variability that would be expected with seasonal
changes in vegetative cover. While wind speed and direction were not accounted for in the emissions
processing, the hourly variability due to soil moisture, snow cover and precipitation were accounted for
in the subsequent meteorological adjustment. The factor is treated as a multiplicative factor for the
emissions. Thus, if the factor is 1 (i.e., water), the dust emissions are not reduced at all, and if the factor
is near 0, the emissions are substantially reduced.
Area Fugitive Dust 2020-2022 Projection Factors
Paved road dust emissions were from the 2020 NEI adjusted to 2022 levels based on changes between
2020 and 2022 VMT. Dust from livestock hooves were also adjusted based on ratios of 2022 to 2020
livestock counts but all other types of dust emissions were held constant from 2020 to 2022. For the
fugitive dust emissions compiled into the 2020 NEI, meteorological adjustments were applied to paved
and unpaved road SCCs but not transport-related adjustments. This is because the modeling platform
applies meteorological adjustments and transportable fraction adjustments based on unadjusted NEI
values. For the 2022 platform, the meteorological adjustments that were applied in the NEI for the
paved and unpaved road SCCs were backed out and reapplied in SMOKE at an hourly resolution for each
grid cell. The FF10 that is run through SMOKE consists of 100% unadjusted emissions, and after SMOKE
all afdust sources have both transportable and meteorological adjustments applied according to year
2022 meteorology. The total impacts of the transportable fraction and meteorological adjustments are
shown in Table 2-6.
Table 2-6. Total impact of 2022v2 fugitive dust adjustments to the unadjusted inventory
State
Unadjusted
PMio
Unadjusted
PM2.5
Change in
PM10
Change in
PM2.5
PM10
Reduction
PM2.5
Reduction
Alabama
274,336
35,494
-202,370
-25,973
73.8%
73.2%
Arizona
153,731
20,858
-56,313
-7,483
36.6%
35.9%
Arkansas
398,457
55,506
-276,219
-37,440
69.3%
67.5%
California
336,443
43,093
-141,194
-17,535
42.0%
40.7%
Colorado
276,997
39,377
-145,228
-19,465
52.4%
49.4%
Connecticut
21,526
3,333
-15,606
-2,409
72.5%
72.3%
Delaware
16,535
2,554
-9,629
-1,485
58.2%
58.2%
District of
Columbia
3,494
477
-2,325
-318
66.5%
66.6%
Florida
215,212
34,456
-117,215
-18,331
54.5%
53.2%
Georgia
296,225
41,844
-218,951
-30,621
73.9%
73.2%
Idaho
496,108
58,552
-288,360
-32,350
58.1%
55.3%
Illinois
702,578
90,846
-423,467
-53,836
60.3%
59.3%
Indiana
160,577
29,875
-98,424
-18,304
61.3%
61.3%
Iowa
370,922
54,793
-207,350
-29,994
55.9%
54.7%
Kansas
583,732
79,848
-238,579
-31,990
40.9%
40.1%
31
-------
State
Unadjusted
PMio
Unadjusted
PM2.5
Change in
PM10
Change in
PM2.5
PM10
Reduction
PM2.5
Reduction
Kentucky
179,629
29,151
-127,895
-20,583
71.2%
70.6%
Louisiana
196,181
29,769
-125,926
-18,865
64.2%
63.4%
Maine
41,717
5,878
-33,150
-4,674
79.5%
79.5%
Maryland
60,743
8,821
-39,057
-5,685
64.3%
64.4%
Massachusetts
63,722
8,640
-46,290
-6,146
72.6%
71.1%
Michigan
293,285
38,837
-199,932
-26,156
68.2%
67.3%
Minnesota
537,979
72,776
-331,440
-43,421
61.6%
59.7%
Mississippi
439,287
52,963
-320,366
-37,939
72.9%
71.6%
Missouri
1,439,199
165,014
-960,867
-108,935
66.8%
66.0%
Montana
498,406
66,114
-321,177
-40,534
64.4%
61.3%
Nebraska
507,702
69,197
-194,207
-25,958
38.3%
37.5%
Nevada
125,368
16,303
-43,345
-5,651
34.6%
34.7%
New Hampshire
16,102
3,307
-12,904
-2,645
80.1%
80.0%
New Jersey
36,477
7,100
-23,640
-4,526
64.8%
63.7%
New Mexico
176,997
22,719
-74,020
-9,334
41.8%
41.1%
New York
264,168
37,984
-196,585
-27,826
74.4%
73.3%
North Carolina
257,146
35,016
-183,489
-24,794
71.4%
70.8%
North Dakota
360,358
55,646
-197,012
-29,402
54.7%
52.8%
Ohio
276,882
43,091
-188,813
-29,160
68.2%
67.7%
Oklahoma
562,803
77,603
-279,109
-37,512
49.6%
48.3%
Oregon
440,531
52,819
-321,308
-36,850
72.9%
69.8%
Pennsylvania
149,280
26,152
-106,530
-18,937
71.4%
72.4%
Rhode Island
6,003
1,006
-4,058
-675
67.6%
67.1%
South Carolina
190,577
25,236
-137,313
-18,038
72.1%
71.5%
South Dakota
210,669
37,092
-95,151
-16,443
45.2%
44.3%
Tennessee
141,443
26,022
-98,464
-18,128
69.6%
69.7%
Texas
1,540,940
214,891
-691,053
-94,831
44.8%
44.1%
Utah
142,084
18,020
-81,037
-9,996
57.0%
55.5%
Vermont
58,010
6,495
-50,080
-5,574
86.3%
85.8%
Virginia
138,872
22,095
-106,660
-17,030
76.8%
77.1%
Washington
174,558
21,778
-101,154
-12,685
57.9%
58.2%
West Virginia
70,339
9,842
-62,594
-8,733
89.0%
88.7%
Wisconsin
202,901
34,398
-135,262
-22,891
66.7%
66.5%
Wyoming
588,124
62,948
-332,646
-35,217
56.6%
55.9%
Domain Total
(12km CONUS)
14,695,356
1,995,630
-8,663,762
-1,153,305
59.0%
57.8%
32
-------
For categories other than paved roads, where states submitted afdust data to the NEI, it was assumed
that the state-submitted data were not met-adjusted and therefore the meteorological adjustments
were applied.
Figure 2-1 illustrates the impact of each step of the adjustment. The reductions due to the transportable
fraction adjustments alone are shown at the top of the figure. The reductions due to the precipitation
adjustments alone are shown in the middle of the figure. The cumulative emission reductions after both
transport fraction and meteorological adjustments are shown at the bottom of the figure. The top plot
shows how the transport fraction has a larger reduction effect in the east, where forested areas are
more effective at reducing PM transport than in many western areas. The middle plot shows how the
meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust
emissions.
Figure 2-1. Fugitive dust emissions and impact of adjustments due to transportable fraction,
precipitation, and cumulative
2022v2 afdust annual : PM2 5, xportfrac + precip adjusted
33
-------
34
-------
2.2.2 Agricultural Livestock (livestock)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• Changes were incorporated for Maricopa County. Specifically, these updates slightly changed
VOC emissions for dairy and beef cattle.
General Description
The livestock SCCs are shown in Table 2-7. The livestock emissions are related to beef and dairy cattle,
poultry production and waste, swine production, waste from horses and ponies, and production and
waste for sheep, lambs, and goats. The sector does not include all the livestock NH3 emissions, as there
is a very small amount of NH3 emissions from livestock in the ptnonipm inventory (as point sources). In
addition to NH3, the sector includes livestock emissions for all pollutants other than PM2.5, since PM2.5
from dust kicked up from livestock hooves are included in the afdust sector.
Agricultural livestock emissions in the 2022 platform were developed using methods similar to those
used to develop the 2020 NEI, which is a mix of state-submitted data and EPA estimates. The 2020 NEI
approach for estimating livestock emissions utilizes daily emission factors by animal and county from a
model developed by Carnegie Mellon University (CMU) (Pinder, 2004, McQuilling, 2015) and 2020 U.S.
Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) survey. Details on the
approach used to develop livestock emissions for the 2020 NEI are provided in Section 10 of the 2020
NEI TSD. Animal populations used for estimating livestock emissions came from 2022 USDA survey data
(see QuickStats at https://quickstats.nass.usda.gov) for the available counties. The FEM model was run
for 2022 using the 2022 animal counts and meteorological data for 2022 to create the emission
inventories for the livestock sector.
Table 2-7. SCCs for the livestock sector
see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2805002000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Beef cattle production
composite
Not Elsewhere Classified
2805007100
Miscellaneous Area
Sources
Ag. Production -
Livestock
Poultry production - layers
with dry manure
management systems
Confinement
2805009100
Miscellaneous Area
Sources
Ag. Production -
Livestock
Poultry production - broilers
Confinement
2805010100
Miscellaneous Area
Sources
Ag. Production -
Livestock
Poultry production - turkeys
Confinement
2805018000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Dairy cattle composite
Not Elsewhere Classified
2805025000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Swine production composite
Not Elsewhere Classified
(see also 28-05-039, -047, -
053)
2805035000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Horses and Ponies Waste
Emissions
Not Elsewhere Classified
2805040000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Sheep and Lambs Waste
Emissions
Total
2805045000
Miscellaneous Area
Sources
Ag. Production -
Livestock
Goats Waste Emissions
Not Elsewhere Classified
35
-------
2.2.3 Agricultural Fertilizer (fertilizer)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• There were no changes in 2022v2 relative to earlier 2022 emissions modeling platforms.
General Description
As described in the 2020 NEI TSD, fertilizer emissions were based on the FEST-C model
(https://www.cmascenter.org/fest-c/). Unlike most of the other emissions input to the CMAQ model,
fertilizer emissions are computed during a run of CMAQ in bi-directional mode and are output during the
model run. The bidirectional version of CMAQ (v5.4) and the Fertilizer Emissions Scenario Tool for CMAQ
FEST-C (vl.3) were used to estimate ammonia (NH3) emissions from agricultural soils. The computed
emissions were saved during the CMAQ run so they can be included in emissions summaries and in
other model runs that do not use the bidirectional method.
FEST-C is the software program that processes land use and agricultural activity data to develop inputs
for the CMAQ model when run with bidirectional exchange. FEST-C reads land use data from the
Biogenic Emissions Landuse Dataset (BELD). meteorological variables from the Weather Research and
Forecasting (WRF) model for the year to be modeled, and nitrogen deposition data from a previous or
historical average CMAQ simulation. FEST-C, then uses the Environmental Policy Integrated Climate
(EPIC) modeling system (https://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. First, fertilizer application by crop
type was estimated using FEST-C modeled data. To develop the emissions for this platform, CMAQ v5.4
was run with the M3DRY option to develop the fertilizer emissions. Note that this was a different option
than was used for the 2020 NEI (see the 2020 NEI TSD for more details).
Fertilizer Activity Data
The following activity parameters were input into the EPIC model:
• Grid cell meteorological variables from WRF
• Initial soil profiles/soil selection
• Presence of 21 major crops: irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn,
silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans,
spring wheat, winter wheat, canola, and other crops (e.g., lettuce, tomatoes, etc.)
• Fertilizer sales to establish the type/composition of nutrients applied
• Management scenarios for the 10 USDA production regions. These include irrigation, tile
drainage, intervals between forage harvest, fertilizer application method (injected versus surface
applied), and equipment commonly used in these production regions.
36
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The WRF meteorological model was used to provide grid cell meteorological parameters for the year
2022 using a national 12-km rectangular grid covering the continental U.S. The meteorological
parameters in Table 2-8 were used as EPIC model inputs.
Table 2-8. Source of input variables for EPIC
EPIC input variable
Variable Source
Daily Total Radiation (MJ/m2)
WRF
Daily Maximum 2-m Temperature (C)
WRF
Daily minimum 2-m temperature (C)
WRF
Daily Total Precipitation (mm)
WRF
Daily Average Relative Humidity (unitless)
WRF
Daily Average 10-m Wind Speed (m s1)
WRF
Daily Total Wet Deposition Oxidized N (g/ha)
CMAQ
Daily Total Wet Deposition Reduced N (g/ha)
CMAQ
Daily Total Dry Deposition Oxidized N (g/ha)
CMAQ
Daily Total Dry Deposition Reduced N (g/ha)
CMAQ
Daily Total Wet Deposition Organic N (g/ha)
CMAQ
Initial soil nutrient and pH conditions in EPIC were based on the 1992 USDA Soil Conservation Service
(CSC) Soils-5 survey. The EPIC model then was run for 25 years using current fertilization and agricultural
cropping techniques to estimate soil nutrient content and pH for the 2017 EPIC/WRF/CMAQ simulation.
The presence of crops in each model grid cell was determined using USDA Census of Agriculture data
(2012) and USGS National Land Cover data (2011). These two data sources were used to compute the
fraction of agricultural land in a model grid cell and the mix of crops grown on that land.
Fertilizer sales data and the 6-month period in which they were sold were extracted from the 2014
Association of American Plant Food Control Officials (AAPFCO,
http://www.aapfco.org/publications.html). AAPFCO data were used to identify the composition (e.g.,
urea, nitrate, organic) of the fertilizer used, and the amount applied was estimated using the modeled
crop demand. These data were useful in making a reasonable assignment of what kind of fertilizer was
applied to which crops.
Management activity data refers to data used to estimate representative crop management schemes.
The USDA Agricultural Resource Management Survey (ARMS, ARMS Farm Financial and Crop Production
Practices I Economic Research Service) was used to provide management activity data. These data cover
10 USDA production regions and provide management schemes for irrigated and rain fed hay, alfalfa,
grass, barley, beans, grain corn, silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum,
silage sorghum, soybeans, spring wheat, winter wheat, canola, and other crops (e.g., lettuce, tomatoes,
etc.).
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2.2.4 Nonpoint Oil and Gas Sector (np_oilgas)
2022v2 updates relative to earlier 2022 emissions modeling platforms
The 2022v2 emissions changes for the non-point oil and gas sector included the following:
• Nonroad emissions factor correction which significantly reduced NOX emissions (about 74%)
from drill rigs and hydraulic fracturing engines
• Colorado emissions inventory update (submitted by Colorado)
o NOX up about 5% statewide; VOC emissions reduced about 60%
o The VOC reduction was mainly due to control devices on storage tanks and pneumatic
devices being recognized in 2022he but not in 2022hc
• Removing NH3 emissions from Texas compressor engines (minor change)
• North Dakota dehydrator VOC emissions (SCC 2310021400) were removed from non-point oil
and gas sector
• Minor activity updates and emissions impacts
o Updated with 2022 Kentucky county-level data
o Updated Arizona to actual oil and gas production for 2022 from state reports; EIA
adjustments in 2022vl no longer used
o Reassignment of wells within 3 nautical miles of Louisiana's coast
o Harmonization of oil, natural gas and coal-bed methane well types with the 2023 oil and
gas well datasets resulting in minor changes in five states (AL, AZ, CA, LA and Ml)
o The above minor activity changes resulted in minor changes to blowdown/pigging
emissions in 2022v2
o The above minor activity changes resulted in new gridding surrogates, monthly profiles
and chemical speciation cross-reference data being generated and used in 2022v2
o All of these minor activity changes in 2022he resulted in minor changes in emissions in
np_oilgas sector
General Description
The nonpoint oil and gas (np_oilgas) sector includes onshore and offshore oil and gas emissions. The EPA
estimated emissions for all counties with 2022 oil and gas activity data using the Oil and Gas Tool. The
types of sources covered include drill rigs, workover rigs, artificial lift, hydraulic fracturing engines,
pneumatic pumps and other devices, storage tanks, flares, truck loading, compressor engines, and
dehydrators. Because of the importance of emissions from this sector, special consideration was given
to the speciation, spatial allocation, and monthly temporalization of nonpoint oil and gas emissions,
instead of relying on older, more generalized profiles.
The 2020 NEI version of the Nonpoint Oil and Gas Emission Estimation Tool (i.e., the "NEI oil and gas
tool") populated with 2022-specific activity data and updated with Subpart W data was used to estimate
2022. Year 2022 oil and gas activity data were obtained from the Enverus activity database
(www.enverus.com) and supplied by some state air agencies. The NEI oil and gas tool is an Access
database that utilizes county-level activity data (e.g., oil production and well counts), operational
characteristics (types and sizes of equipment), and emission factors to estimate emissions. The tool was
used to create a CSV-formatted emissions dataset covering all national nonpoint oil and gas emissions.
This dataset was converted to the FF10 format for use in SMOKE modeling. More details on the inputs
for and running of the tool for 2020 are provided in the 2020 NEI TSD.
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While most states' np_oilgas emissions are from the NEI oil and gas tool, production-related emissions in
Oklahoma and Wyoming were projected from 2020NEI to 2022 at the request of those states. These
projected emissions were included in 2022vl and 2022v2 platforms in place of the production emissions
from the tool in those two states. Projection factors were provided by the corresponding state agencies;
Oklahoma incorporated separate projection factors for oil SCCs, gas SCCs, condensate SCCs, and SCCs
with a mix of oil and gas activity, each applied state-wide, while Wyoming used county-specific
projection factors for oil, gas, and mixed SCCs.
Table 2-9 shows the nonpoint oil and gas NOx and VOC emissions for 2022 by state. The Colorado
emissions in this table include updated emissions for the state developed from the Oil and Gas Tool and
state-submitted emissions, along with emissions submitted to the 2020 NEI within the Southern Ute
reservation that are still used in this 2022 platform. For spatial allocation purposes, the Southern Ute oil
and gas emissions - totaling 11,663 tons/yr of NOx and 879 tons/yr of VOC - were allocated to Colorado
counties, with 95% of the emissions in La Plata County (FIPS 08067) and 5% of the emissions in Archuleta
County (FIPS 08007).
Table 2-9. Nonpoint oil and gas emissions for 2022v2
State
2022 NOx
2022 VOC
Alabama
3,922
11,656
Alaska
2,777
9,664
Arizona
12
137
Arkansas
4,537
8,524
California
1,202
27,847
Colorado
31,161
22,174
Florida
19
1,123
Georgia
0
0
Idaho
10
99
Illinois
13,849
49,499
Indiana
2,676
13,338
Iowa
0
0
Kansas
22,858
62,635
Kentucky
16,186
42,782
Louisiana
14,001
52,998
Maryland
1
2
Michigan
10,419
13,240
Minnesota
0
0
Mississippi
1,784
17,382
Missouri
232
554
Montana
1,658
31,975
Nebraska
238
1,777
Nevada
3
160
New Mexico
61,889
280,801
New York
880
7,133
North Carolina
0
0
39
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State
2022 NOx
2022 VOC
North Dakota
31,937
221,171
Ohio
2,529
30,870
Oklahoma
36,830
170,061
Oregon
6
20
Pennsylvania
57,413
139,815
South Dakota
190
1,291
Tennessee
1,051
3,272
Texas
236,839
1,338,693
Utah
8,269
69,853
Virginia
3,820
7,883
Washington
0
3
West Virginia
24,863
77,702
Wyoming
841
8,529
A new source was added to the oil and gas sector starting with the 2020 NEI: Pipeline Blowdowns and
Pigging (SCC= 2310021801) emissions were estimated using US EPA Greenhouse Gas Reporting Program
(GHGRP) data. These Pipeline Blowdowns and Pigging emissions for the year 2022 included county-level
estimates of VOC, benzene, toluene, ethylbenzene, and xylene (BTEX). These emissions estimates were
calculated outside of the Oil and GasTool and submitted to EIS separately from the Oil and GasTool
emissions. These emissions were considered EPA default emissions and SLTs had the opportunity to
submit their own Pipeline Blowdowns and Pigging (e.g., Utah) emissions and/or accept/omit these
emissions using the Nonpoint Survey. Unfortunately, these EPA default Pipeline Blowdowns and Pigging
emissions did not get into the 2020 NEI release for the states that accepted these emissions due to EIS
tagging issues. These emissions were included in this 2022 Emissions Modeling Platform. Table 2-10
shows the emissions totals by state for Pipeline Blowdowns and Pigging sources. Note that Colorado
emissions are not included in this table because the inventory provided by Colorado includes these.
An additional new source of abandoned oil and gas well emissions in the USA was added to the oil and
gas sector starting with the 2021 modeling platform and also included in this 2022 platform although
unchanged from the 2022vl platform levels. . The term "abandoned wells" encompasses various types
of wells:
• Wells with no recent production, and not plugged. Common terms (such as those used in
state databases) might include inactive, temporarily abandoned, shut-in, dormant, and idle.
• Wells with no recent production and no responsible operator. Common terms might include
orphaned, deserted, long-term idle, and abandoned.
• Wells that have been plugged to prevent migration of gas or fluids.
As of year 2022, there were approximately 3.7 million abandoned wells in the U.S., with around 2.3
million abandoned oil wells, 0.6 million abandoned gas wells, and 0.8 million abandoned dry wells (may
be oil or gas wells). Abandoned wells may emit CH4, C02, VOC, and various HAP. These emissions are
unchanged from the 2022vl platform levels.
Estimates of greenhouse gas (GHG) emissions (CH4 and C02) from abandoned wells have been
estimated as part of the Inventory of U.S. Greenhouse Gas Emissions and Sinks since 2018. Currently, the
40
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inventory from 1990 - 2022 is available4. The GHG inventory (GHGI) methodology and estimates of
emissions from abandoned wells served as the starting point for development of the VOC and HAP
emissions inventory for abandoned wells used in this year 2022 modeling platform. Year 2022 estimates
of VOC and BTEX were estimated and used in this 2022 modeling platform.
Table 2-11 shows the emissions totals by state for Pipeline Blowdowns and Pigging sources. The
inventories for blowdowns and pigging and abandoned wells are separate from the emissions output
from the oil and gas tool.
Table 2-10. State emissions totals for year 2022v2 for pipeline blowdowns and pigging sources
State
VOC(tpy)
Benzene (tpy)
Ethylbenzene (tpy)
Toluene (tpy)
Xylene (tpy)
Alabama
350
1.38
0.07
1.18
0.36
Alaska
14
0.06
0.00
0.06
0.02
Arizona
97
0.44
0.02
0.39
0.11
Arkansas
22
0.01
0.00
0.00
California
146
0.67
0.04
0.59
0.17
Florida
2
0.00
0.00
0.00
0.00
Illinois
209
0.77
0.04
0.68
0.19
Indiana
29
0.05
0.00
0.04
0.01
Kansas
1,326
2.34
0.27
1.98
0.86
Kentucky
657
2.97
0.17
2.65
0.75
Louisiana
366
3.01
0.00
0.30
0.51
Maryland
0
0.00
0.00
0.00
0.00
Michigan
239
1.08
0.06
0.97
0.27
Mississippi
2,162
3.32
0.07
1.28
1.07
Missouri
4
0.00
0.00
0.00
0.00
Montana
147
0.67
0.04
0.59
0.17
Nebraska
57
0.14
0.01
0.17
0.05
New Mexico
1,044
New York
143
0.65
0.04
0.58
0.16
North Dakota
9
0.04
0.00
0.04
0.01
Ohio
388
1.76
0.10
1.57
0.45
Oklahoma
2,004
1.47
0.09
1.16
0.89
Oregon
8
0.04
0.00
0.03
0.01
Pennsylvania
66
0.30
0.02
0.27
0.08
South Dakota
2
0.01
0.00
0.01
0.00
Tennessee
13
0.06
0.00
0.05
0.02
Texas
9,599
9.05
0.24
3.82
3.23
Utah
18
0.09
0.01
0.08
0.04
Virginia
190
0.86
0.05
0.77
0.22
West Virginia
879
3.99
0.23
3.55
1.01
Wyoming
680
4.19
0.33
2.04
1.34
US total
20,869
39.44
1.90
24.89
12.00
4 Inventory of U.S. Greenhouse Gas Emissions and Sinks I US EPA
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Table 2-11. State emissions totals for year 2022 for abandoned wells sources
State
2022 VOC (tpy)
Alabama
198
Alaska
64
Arizona
10
Arkansas
794
California
5,357
Colorado
451
Florida
32
Georgia
0
Idaho
0
Illinois
6,738
Indiana
3,326
Iowa
0
Kansas
6,663
Kentucky
12,817
Louisiana
3,195
Maryland
1
Michigan
487
Minnesota
0
Mississippi
749
Missouri
118
Montana
740
Nebraska
141
Nevada
34
New Mexico
348
New York
596
North Carolina
0
North Dakota
401
Ohio
22,286
Oklahoma
8,944
Oregon
3
Pennsylvania
69,730
South Dakota
31
Tennessee
1,329
Texas
31,588
Utah
178
Virginia
69
Washington
3
West Virginia
2,723
Wyoming
552
US Total
180,694
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Lastly, EPA and the state of Oklahoma, New Mexico and Kansas worked together to exercise the point
source subtraction step in the Oil and Gas Tool during the 2022 platform development period. This point
source subtraction step is a process used to eliminate possible double counting of sources in the Oil and
Gas Tool that are already defined in the point source inventory.
2.2.5 Residential Wood Combustion (rwc)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• For 2022v2, emissions were calculated and allocated based on new methods developed for the
2023 NEI which includes the use of many new SCCs.
General Description
The residential wood combustion (rwc) sector includes residential wood burning devices such as
fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor hydronic
heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepots and
chimeneas. Free standing woodstoves and inserts are further differentiated into three categories:
1) conventional (not EPA certified); 2) EPA certified, catalytic; and 3) EPA certified, noncatalytic.
Generally speaking, the conventional units were constructed prior to 1988. Units constructed after 1988
have to meet EPA emission standards and they are either catalytic or non-catalytic. As with the other
nonpoint categories, a mix of S/L and EPA estimates were used. The EPA's estimates use updated
methodologies for activity data and some changes to emission factors. The source classification codes
(SCCs) in the rwc sector are listed in Table 2-12 which shows SCCs new to the 2022v2 platform in italics.
For states and counties in which 2020NEI RWC emissions were based on S/L data, 2022v2 platform RWC
emissions are adjusted from 2020 NEI using SEDS data for 2021, and converted to 2023NEI-consistent
SCCs. This includes Alaska, Minnesota, Oregon, Texas, Vermont, Washington, Maricopa County in
Arizona, and Washoe County in Nevada. Additionally, Idaho provided updated RWC emissions data for
the final version of 2022v2 platform using the same new SCCs. In California, 2022v2 RWC emissions for
SCCs 2104008100 (all pollutants) and 2104008011/2104008021/2104008031 (all pollutants except NH3)
are based on data provided by CARB, while all other SCCs in California use EPA estimates.
Some improvements to RWC emissions estimates were developed as part of the 2020 NEI process. The
EPA, along with the Commission on Environmental Cooperation (CEC), the Northeast States for
Coordinated Air Use Management (NESCAUM), and Abt Associates, conducted a national survey of
wood-burning activity in 2018. The results of this survey were used to estimate county-level burning
activity data. The activity data for RWC processes is the amount of wood burned in each county, which is
based on data from the CEC survey on the fraction of homes in each county that use each wood-burning
appliance and the average amount of wood burned in each appliance. These assumptions were used
with the number of occupied homes in each county to estimate the total amount of wood burned in
each county, in cords for cordwood appliances and tons for pellet appliances. Cords of wood were
converted to tons using county-level density factors from the U.S. Forest Service. RWC emissions were
calculated by multiplying the tons of wood burned by emissions factors. For more information on the
development of the residential wood combustion emissions, see Section 27 of the 2020 NEI TSD.
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Table 2-12. SCCs for the residential wood combustion sector
see
Tier 1 Description
Tier 2
Description
Tier 3
Description
Tier 4 Description
2104008011
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: non-EPA
certified/exempt, <=4FT3
2104008021
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: EPA certified, non-
catalytic, single burn rate
2104008031
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: EPA certified,
catalytic or hybrid
2104008100
Stationary Source Fuel
Combustion
Residential
Wood
Fireplace: general
2104008300
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: freestanding, general
2104008400
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: pellet-fired, general
(freestanding or FP insert)
2104008500
Stationary Source Fuel
Combustion
Residential
Wood
Furnace: Indoor, cordwood-fired,
general
2104008530
Stationary Source Fuel
Combustion
Residential
Wood
Furnace: Indoor, pellet-fired,
general
2104008611
Stationary Source Fuel
Combustion
Residential
Wood
Fiydronic heater: outdoor, non-
EPA certified
2104008612
Stationary Source Fuel
Combustion
Residential
Wood
Fiydronic heater: outdoor, EPA
certified
2104008614
Stationary Source Fuel
Combustion
Residential
Wood
Fiydronic heater: indoor, non-EPA
certified
2104008615
Stationary Source Fuel
Combustion
Residential
Wood
Fiydronic heater: indoor, EPA
certified
2104008630
Stationary Source Fuel
Combustion
Residential
Wood
Hydronic heater: pellet-fired
2104008700
Stationary Source Fuel
Combustion
Residential
Wood
Outdoor wood burning device,
NEC (fire-pits, chimineas, etc)
2104009000
Stationary Source Fuel
Combustion
Residential
Fire log
Total: All Combustor Types
2.2.6 Solvents (np_solvents)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• For 2022v2, cutback asphalt emissions were removed from Maricopa County, per request from
the county.
General Description
The np_solvents sector is a diverse collection of emission sources for which emissions are driven by
evaporation. Included in this sector are everyday items, such as cleaners, personal care products,
adhesives, architectural and aerosol coatings, printing inks, and pesticides. These sources exclusively
emit organic gases and feature origins spanning residential, commercial, institutional, and industrial
settings. The organic gases that evaporate from these sources often fulfill other functions than acting as
44
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a traditional solvent (e.g., propellants, fragrances, emollients). For this reason, the solvents sector is
often referred to as "volatile chemical products." The base methodology used to estimate these
emissions are unchanged from the 2020 NEI, which is described in Section 32 of the 2020 NEI TSD.
including the SCCs that are included in the sector.
For 2022, all emissions in the np_solvents sector, except asphalt paving, are projected using the 2020
NEI as a base year. This includes State, Locality, and Tribal emissions submissions. Here, the model used
to estimate a majority of the nonpoint solvent emissions in the NEI (VCPy) was used to estimate 2021
emissions (2022 usage data were not available). From there a SCC-specific ratio (of 2021 / 2020) was
applied to the 2020 NEI. This method ensures that state-submitted emissions magnitudes are preserved.
In addition, some updates were made based on comments provided by New Jersey, and asphalt-related
SCCs featured temporal profile updates using ElA-based monthly profiles for "asphalt and road oil" by
PADD region.
2.2.7 Open burning (openburn)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• For 2022v2, emissions from burning of residential household waste were removed for New
Hampshire.
General Description
This new sector for the 2022vl platform was split out from the nonpt sector and includes emissions
from yard waste, land clearing, and residential household waste burning (SCCs starting with 261 as
shown in Table 2-13). For 2022vl and 2022v2, these emissions were held constant at 2020 NEI levels.
Table 2-13. SCCs in the openburn sector
see
Description
2610000100
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Yard Waste -
Leaf Species Unspecified
2610000400
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Yard Waste -
Brush Species Unspecified
2610000500
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Land Clearing
Debris
2610030000
Waste Disposal, Treatment, and Recovery; Open Burning; Residential; Household Waste
2610000300
Waste Disposal, Treatment, and Recovery; Open Burning; All Categories; Yard Waste -
Weed Species Unspecified (incl Grass)
2.2.8 Nonpoint (nonpt)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• For 2022v2, updates were incorporated for Maricopa County, Michigan, Minnesota, Ohio,
Wisconsin, Georgia, and Delaware.
• In Maricopa County, updates reflect a decrease in VOC emissions from commercial cooking
• In Delaware, updates reflect removal of managed burning of logging debris (2810005000).
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• In Michigan, Minnesota, Ohio, and Wisconsin, the county distributions of industrial and
commercial/institutional wood fuel combustion emissions within each state were updated.
• In Georgia, industrial and commercial/institutional wood fuel combustion emissions were wholly
removed, per the request of the State, as these emissions are reflected in the point inventory.
General Description
The 2022 platform nonpt sector inventory is based on the April 2023 version of the 2020 NEI but
adjusted to better reflect 2022 emissions levels as described below. Stationary nonpoint sources that
were not subdivided into the afdust, livestock, fertilizer, np_oilgas, rwc or np_solvents sectors were
assigned to the "nonpt" sector. Locomotives and CMV mobile sources from the 2020 NEI nonpoint
inventory are described with the mobile sources. The types of sources in the nonpt sector include:
• stationary source fuel combustion, including industrial, commercial, and residential and orchard
heaters;
• chemical manufacturing;
• industrial processes such as commercial cooking, metal production, mineral processes,
petroleum refining, wood products, fabricated metals, and refrigeration;
• storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline
service stations, aviation, and marine vessels;
• storage and transport of chemicals;
• waste disposal, treatment, and recovery via incineration, open burning, landfills, and
composting; and
• miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair
shops.
The nonpt sector includes emission estimates for Portable Fuel Containers (PFCs), also known as "gas
cans." The PFC inventory consists of three distinct sources of PFC emissions, further distinguished by
residential or commercial use. The three sources are: (1) displacement of the vapor within the can; (2)
emissions due to evaporation (i.e., diurnal emissions); and (3) emissions due to permeation. Note that
spillage and vapor displacement associated with using PFCs to refuel nonroad equipment are included in
the nonroad inventory.
The factors used to adjust the emissions were developed using the datasets as described in Table 2-14.
Emissions for SCC groups other than those listed in this table (e.g., waste disposal, treatment and
recovery) were held constant at 2020 NEI levels in the 2022 base year inventory.
Table 2-14. Datasets used to develop factors to adjust nonpoint emissions from 2020 to 2022
Source Category Group
2020-2022 Projection Method
All Other Nonpoint Source Fuel
Combustion
Apply EIA State Energy Data System energy consumption ratios.
Note that 2021 SEDS data are available for all fuels and 2022 data
are available for some fuels.
Stage 1 Gasoline Unloading at
Service Stations
Apply EIA State Energy Data System Transportation Sector/Motor
Gasoline consumption ratios
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Source Category Group
2020-2022 Projection Method
Stage 1 Gasoline Unloading at
Apply EIA State Energy Data System Total Motor Gasoline
Bulk Terminals/Plants
consumption ratios
Aviation Gasoline Stage 1 and II
Apply EIA State Energy Data System Aviation Gasoline consumption
ratios
Pipeline Gasoline
Apply EIA State Energy Data System Total Motor Gasoline
consumption ratios
Human Cremation
Estimate 2022 county-level number of cremations from 2022 actual
county-level deaths from CDC's Wonder Database and 2022 state-
level (projected) cremation rates from National Funeral Directors
Association's "Cremation and Burial Report" and apply 2022/2020
county-level cremation ratios to 2020 NEI cremation emissions to
compute 2022 cremation emissions
Commercial Cooking
Hold constant
Portable Fuel Containers
Hold constant
Asphalt Paving
Hold constant
Landfills/POTWs
Hold constant
Charcoal Grilling
Hold constant
2.3 Onroad Mobile sources (onroad)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• The 2022v2 draft and final emissions for onroad mobile sources were not changed, but there
were substantial differences between the 2022vl and 2022v2 onroad emissions.
• A new MOVES run was performed using MOVES5.0.0.
o MOVES5 includes the addition of age IDs 31 to 40 years old in the age distributions.
• VPOP and age distribution were updated using new registrations data obtained for the 2023 NEI
from S&P Global Mobility.
• Telematics data from StreetLight were incorporated into new speed distributions and new daily
and day-of-week temporal VMT distributions unique to each county. Factors were applied to
reduce emissions from gasoline refueling in New Jersey at the request of the state.
• The following changes were made to the 2022v2 CDBs from state-specific submitted data
o DC provided State II vapor recovery program fractions to populate the countyYear table
o Georgia provided updated IMCoverage, AVFT, age distribution, VMT, vehicle population,
and weekday starts activity for urban core counties.
General Description
Onroad mobile sources include emissions from motorized vehicles operating on public roadways. These
include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks, heavy-duty trucks,
and buses. The sources are further divided by the fuel they use, including diesel, gasoline, E-85,
compressed natural gas (CNG), or electric vehicles. The sector characterizes emissions from parked
vehicle processes (e.g., starts, hot soak, and extended idle) as well as from on-network processes (i.e.,
from vehicles as they move along the roads). For more details on the approach and for a summary of the
MOVES inputs submitted by states for the 2020 NEI, see Section 5 of the 2020 NEI TSD (EPA, 2023).
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Although the NEI TSD describes the general process, many updates were made to MOVES input data and
the MOVES version used to create emissions for the 2022v2 platform.
The 2022 emissions modeling platform activity data (i.e., vehicle miles traveled (VMT) and vehicle
population (VPOP)) were based on data submitted by state and local agencies for the 2020 NEI and for
the 2022 platform, as well as data from the Federal Highway Administration (FHWA). VMT were based
on county-level VM-2 data by road type from FHWA. For the 2022v2 platform, VPOP was updated to a
new dataset as described in the activity data development section below. A new MOVES run for the
2022v2 platform was done using MOVES5 to obtain year-specific emission factors. One of the data
updates incorporated into this run was the use of age distributions that go back 40 years for MOVES5, as
opposed to 30 years for earlier versions of MOVES. These updated age distributions were computed for
each representative county from data by county, model year, and source-type from S&P Global Mobility.
Because this data pull was from June of 2023, the age distributions were computed to represent the
year 2022. Additional attributes of these data were used to compute vehicle populations and to update
fuel splits used in the MOVES run. The updated data also included new splits for short vs long haul
vehicles.
Except for California, all onroad emissions were generated using the SMOKE-MOVES emissions modeling
framework that leverages MOVES-generated emission factors https://www.epa.gov/moves). county and
SCC-specific activity data, and hourly 2022 meteorological data. Specifically, EPA used vehicle miles
traveled (VMT) and other 2022-specific activity data, along with tools that interface between the MOVES
model and SMOKE. In this way, it was possible to take advantage of the gridded hourly temperature
data available from meteorological modeling that are also used for air quality modeling. The onroad
source classification codes (SCCs) in the emissions modeling platform are more finely resolved than
those in the National Emissions Inventory (NEI). The NEI SCCs distinguish vehicles and fuels, while the
SCCs used in the model platform also distinguish between emissions processes (i.e., off-network, on-
network, and extended idle), and road types.
A significant update for the 2022v2 platform with respect to the MOVES and SMOKE-MOVES runs was
the use of telematics data from StreetLight to inform temporal patterns and speed distributions for each
county. The day-of-week and diurnal distributions were updated to reflect 2022 levels based on
telematics data from Streetlight, as the corresponding data from 2022vl were based on data from
January of 2020. Inspection and maintenance program information was mostly based on the MOVES5
data except where Georgia and Colorado provided updates.
2.3.1 Inventory Development using SMOKE-MOVES
Except for California, onroad emissions were computed with SMOKE-MOVES by multiplying specific
types of vehicle activity data by the appropriate emission factors. This section includes discussions of the
activity data and the emission factor development. The vehicles (aka source types) for which MOVES
computes emissions are shown in Table 2-15 with the corresponding vehicle type from the highway
monitoring performance system (HPMS). SMOKE-MOVES was run for specific modeling grids. Emissions
for the contiguous U.S. states and Washington, D.C., were computed for a grid covering those areas.
Emissions for Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands were computed by running SMOKE-
MOVES for distinct grids covering each of those regions and are included in the onroad non-Conus
sector. In some summary reports these non-CONUS emissions are aggregated with emissions from the
onroad sector.
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Table 2-15. MOVES vehicle (source) types
MOVES vehicle type
Description
HPMS vehicle type
11
Motorcycle
10
21
Passenger Car
25
31
Passenger Truck
25
32
Light Commercial Truck
25
41
Other Bus
40
42
Transit Bus
40
43
School Bus
40
51
Refuse Truck
50
52
Single Unit Short-haul Truck
50
53
Single Unit Long-haul Truck
50
54
Motor Home
50
61
Combination Short-haul Truck
60
62
Combination Long-haul Truck
60
SMOKE-MOVES makes use of emission rate "lookup" tables generated by MOVES that differentiate
emissions by process (i.e., running, start, vapor venting, etc.), vehicle type, road type, temperature,
speed, hour of day, etc., to generate the MOVES emission rates that could be applied across the U.S. EPA
used an automated process to run MOVES to produce year 2022-specific emission factors by
temperature and speed for a series of "representative counties," to which every other county was
mapped. The representative counties for which emission factors were generated were selected
according to their state, elevation, fuels, age distribution, ramp fraction, and inspection and
maintenance programs. Each county was then mapped to a representative county based on its similarity
to the representative county with respect to those attributes. In both the 2022vl and 2022v2 platforms,
there are 259 representative counties in the continental U.S. and a total of 298 including the non-CONUS
areas. The only differences between the 2020 and 2022 platforms are a change in Alaska county
equivalents which removed one borough (county ID 2261, Valdez-Cordova Census Area) which in 2019
split into two areas (county ID 2063, Chugach Census Area; and county ID 2066, Copper River Census
Area), as well as some updates recommended by Texas.
Once representative counties were identified, emission factors were generated with MOVES for each
representative county and for two "fuel months" - January to represent winter months, and July to
represent summer months - due to the different types of fuels used. SMOKE selected the appropriate
MOVES emissions rates for each county, hourly temperature, SCC, and speed bin and then multiplied the
emission rate by appropriate activity data. For on-roadway emissions, vehicle miles traveled (VMT) is the
activity data; off-network processes use vehicle population (VPOP), vehicle starts, and hours of off-
network idling (ONI); and hoteling hours are used to develop emissions for extended idling of
combination long-haul trucks. These calculations were done for every county and grid cell in the
continental U.S. for each hour of the year.
The SMOKE-MOVES process for creating the model-ready emissions consists of the following steps:
1) Determine which counties will be used to represent other counties in the MOVES runs.
2) Determine which months will be used to represent other month's fuel characteristics.
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3) Create inputs needed only by MOVES. MOVES requires county-specific information on vehicle
populations, age distributions, and inspection-maintenance programs for each of the
representative counties.
4) Create inputs needed both by MOVES and by SMOKE, including temperatures and activity data.
5) Run MOVES to create emission factor tables for the temperatures found in each county.
6) Run SMOKE to apply the emission factors to activity data (VMT, VPOP, STARTS, off-network
idling, and HOTELING) to calculate emissions based on the gridded hourly temperatures in the
meteorological data.
7) Aggregate the results to the county-SCC level for summaries and quality assurance.
The onroad emissions were processed in six processing streams that were then merged together into
the onroad sector emissions after each of the six streams have been processed:
• rate-per-distance (RPD) uses VMT as the activity data plus speed and speed profile information
to compute on-network emissions from exhaust, evaporative, permeation, refueling, and brake
and tire wear processes;
• rate-per-vehicle (RPV) uses VPOP activity data to compute off-network emissions from exhaust,
evaporative, permeation, and refueling processes;
• rate-per-start (RPS) uses STARTS activity data to compute off-network emissions from vehicles
starts;
• rate-per-profile (RPP) uses VPOP activity data to compute off-network emissions from
evaporative fuel vapor venting, including hot soak (immediately after a trip) and diurnal (vehicle
parked for a long period) emissions;
• rate-per-hour (RPH) uses hoteling hours activity data to compute off-network emissions for idling
of long-haul trucks from extended idling and auxiliary power unit process; and
• rate-per-hour off-network idling (RPHO) uses off network idling hours activity data to compute
off-network idling emissions for all types of vehicles.
With some exceptions described in the following subsections, the onroad emissions inputs to MOVES for
the 2022v2 emissions modeling platform are based on the 2020 NEI, described in more detail in Section
5 of the 2020 NEI TSD. These inputs include:
• Key parameters in the MOVES County databases (CDBs) including Low Emission Vehicle (LEV)
table
• Fuel months
• Activity data (e.g., VMT, VPOP, speed, HOTELING)
Fuel months and some other inputs were consistent with those used to compute the 2020 NEI, although
many input data sets were updated for the 2022v2 platform. Activity data submitted by states and
development of the EPA default activity data sets for VMT, VPOP, hoteling hours, starts, and off-network
idling (ONI) hours follows a similar process to the 2020 NEI, but based on 2022-specific VMT and VPOP.
Any data carried over from the 2020 NEI are described in detail in the 2020 NEI TSD and supporting
documents. Factors were applied in SMOKE-MOVES to reduce emissions from gasoline refueling in New
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Jersey at the request of the state. Details regarding the data used to computed onroad mobile source
emissions for the 2022v2 platform are described below.
2.3.2 Onroad Activity Data Development
SMOKE-MOVES uses vehicle miles traveled (VMT), vehicle population (VPOP), vehicle starts, hours of off-
network idling (ONI), and hours of hoteling, to calculate emissions. These datasets are collectively
known as "activity data." For each of these activity datasets, first a national dataset was developed; this
national dataset is called the "EPA default" dataset. The default was supplemented with data submitted
by state and local agencies. In the EPA default dataset, VMT was derived from FHWA's county-level VM-
2 data for 2022. EPA default VPOP uses new data for 2022v2 platform, which is based on a draft version
of 2023 vehicle population for the upcoming 2023 NEI with MOVES5-compatible fuel splits, and with
backcast factors applied for the year 2022. State-submitted VPOP from the 2022vl platform was
retained for the 2022v2 platform, and other aspects of the activity data which are dependent on VPOP
(e.g. STARTS, VMT source type and fuel splits) were recomputed using the updated VPOP. As such,
except for new state-submitted VMT data in Georgia for 2022v2 platform, overall VMT was unchanged
from 2022vl to 2022v2, but the source type distributions and fuel splits within the VMT did change
based on the new VPOP dataset. ONI and HOTELING activity were also recomputed based on the
updated VMT. EPA default activity was used for California, but the emissions were scaled to California-
supplied values during the emissions processing.
Vehicle Miles Traveled (VMT) and Vehicle Population (VPOP)
Activity data submitted by states and development of the EPA default activity data sets for VMT, VPOP,
and hoteling hours are described in detail in the 2020 NEI TSD (EPA, 2023) and supporting documents.
The process for developing VMT for 2022 is similar to the 2020 NEI process, except starting with 2022-
specific VMT from the FHWA VM-2 (county-level and by road type) and VM-4 (distributions of VMT by
state and HPMS vehicle type). The year 2022 VM-2 and VM-4 data were combined to create a 2022 VMT
dataset by county, HPMS vehicle type, and road type. The new VPOP dataset for the 2022v2 platform
was then used to allocate VMT from HPMS vehicle type to MOVES vehicle type, and to different fuel
types, resulting in different MOVES vehicle type and fuel type distributions in the 2022v2 platform when
compared to the 2022vl platform. Monthly profiles for 2022 VMT from the 2022vl platform, based on
FHWA's Travel Monitoring and Analysis System (TMAS) data, were retained for the 2022v2 platform. See
Section 3.3.8 for more information on the use of TMAS data.
The following states submitted VMT for the 2022vl platform base year: AK, CO, CT, DE, GA, KS, MA, Ml,
MD, ME, NC, NH, NJ, NY, OR, PA, SC, TN, TX, UT, VA, WA, Wl, WV, and Jefferson Co. KY. For the 2022v2
platform, all state-submitted VMT from the 2022vl platform was retained from the 2022vl platform,
but with MOVES vehicle type and fuel splits recomputed as appropriate (i.e. where these splits were not
directly provided by the states). In addition, new VMT was provided by Georgia for the 2022v2 platform.
As in 2022vl platform, VMT for Colorado are based on EPA default data.
For the 2022v2 platform, vehicle population data were updated based on a draft version of the 2023 NEI
VPOP. Adjustment factors were applied to backcast the draft 2023 VPOP to 2022 estimates; those
factors are shown in Table 2-16. State-submitted vehicle population from the 2022vl platform in DE, NY,
and Wl data were retained in the 2022v2 platform, and new vehicle population was provided by Georgia
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for the 2022v2 platform. Updated fuel splits based on the EPA default VPOP were applied to the state-
submitted VPOP.
Table 2-16. 2023-to-2022 adjustment factors for EPA default vehicle population
Source Typel D
Source Type Description
Ratio 2022:2023
11
Motorcycle
0.9978
21
Passenger Car
1.0204
31
Passenger Truck
0.9803
32
Light Commercial Truck
0.9803
41
Intercity Bus
0.9780
42
Transit Bus
0.9780
43
School Bus
0.9780
51
Refuse Truck
0.9737
52
Single Unit Short-haulTruck
0.9737
53
Single Unit Long-haulTruck
0.9737
54
Motor Home
0.9737
61
Combination Short-haulTruck
0.9848
62
Combination Long-haulTruck
0.9848
Because vehicle registration data does not contain information on the usage patterns of heavy-duty
trucks to distinguish between short- versus long-haul trucks, surrogate information was applied to
estimate the population belonging to each category. New for the 2022v2 platform, the data used to split
Combination Unit Trucks into short/long haul came from the 2021 Vehicle Inventory and Use Survey
(VIUS2021) information at the state level. Combination Unit Truck populations were split into source
types 61 and 62 using the VIUS2021 field "CABDAY" where values of "Day Cab" were considered short-
haul (61s) and "Sleeper Cab" were considered long-haul (62s). Though the splits varied by state,
nationally they averaged 52% short-haul and 48% long-haul. Because the VIUS2021 did not have
information on single unit trucks, EPA continued using the prior method for source types 52 and 53
splitting, which was based on the Freight Analysis Framework (FAF) with separate values by four US
census regions: midwest, northeast, south, and west. The factors used are shown Table 2-17. The VMT
activity data were split into short and long-haul using the same splits as were used for VPOP.
Table 2-17. Fractions of short- and long-Haul VPOP by census region
Truck Type
Census Region
Fraction Short-haul
Fraction Long-haul
Single unit 52/53
Midwest
0.807
0.193
Single unit 52/53
Northeast
0.919
0.081
Single unit 52/53
South
0.860
0.140
Single unit 52/53
West
0.882
0.118
Combination (61/62)
Midwest
0.442
0.558
Combination (61/62)
Northeast
0.448
0.552
Combination (61/62)
South
0.535
0.465
Combination (61/62)
West
0.468
0.532
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Speed Activity (SPDIST)
Beginning with SMOKE 4.7, SMOKE-MOVES has used speed distributions similarly to how they are used
when running MOVES in inventory mode. The speed distribution file, called SPDIST, specifies the amount
of time spent in each MOVES speed bin for each county, vehicle (aka source) type, road type,
weekday/weekend, and hour of day. This file contains the same information at the same resolution as
the Speed Distribution table used by MOVES but is reformatted for SMOKE. Speed data from the year
2022 StreetLight dataset were used to generate hourly speed profiles by county, road type
weekday/weekend and three vehicle classes (i.e., light-duty, commercial medium-duty, and commercial
heavy-duty).
Hoteling Hours (HOTELING)
Hoteling hours were computed from the 2022v2 VMT, using a factor of 0.007248 hoteling hours per
VMT for combination long haul trucks on restricted highways. This is the same approach as in the 2020
NEI and the 2022vl platform, except the computation is based on values in the 2022v2 VMT. Hoteling
hours were capped by county at a theoretical maximum and any excess hours of the maximum were
reduced. For calculating reductions, a dataset of truck stop parking space availability was used, which
includes a total number of parking spaces per county. This same dataset is used to develop the spatial
surrogate for allocating county-total hoteling emissions to model grid cells. The parking space dataset
was last updated during the development of the 2016 platforms. There are 8,760 hours in the year 2022;
therefore, the maximum number of possible hoteling hours in a particular county is equal to 8,760 * the
number of parking spaces in that county. Hoteling hours were capped at that theoretical maximum
value for 2022 in all counties, with some exceptions. Also, Texas submitted hoteling activity for 2020
NEI, and their 2020 hoteling activity was projected to 2022 using ratios of 2022 VMT / 2020 VMT for
combination long haul trucks.
Because the truck stop parking space dataset may be incomplete in some areas, and trucks may
sometimes idle in areas other than designated spaces, it was assumed that every county has at least 12
parking spaces, even if fewer parking spaces are found in the parking space dataset. Therefore, hoteling
hours were never reduced below 105,120 hours for the year in any county. If the unreduced hoteling
hours were already below that maximum, the hours were left unchanged; in other words, hoteling
activity were never increased in this analysis. For recent NEIs, four states requested that no reductions
be applied to the hoteling activity based on parking space availability: CO, ME, NJ, and NY. For these
states, reductions based on parking space availability were not applied. Reductions were also not
applied in Texas, because the hoteling activity in that state are based on state-submitted data.
The final step related to hoteling activity is to split county totals into separate values for extended idling
(SCC 2202620153) and Auxiliary Power Units (APUs) (SCC 2202620191). For 2022 modeling with
MOVES4, an 9.8% APU split is used nationwide, meaning that during 9.8% of the hoteling hours auxiliary
power units are assumed to be running.
Starts
Onroad "start" emissions are the instantaneous exhaust emissions that occur at the engine start (e.g.,
due to the fuel rich conditions in the cylinder to initiate combustion) as well as the additional running
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exhaust emissions that occur because the engine and emission control systems have not yet stabilized at
the running operating temperature. Operationally, start emissions are defined as the difference in
emissions between an exhaust emissions test with an ambient temperature start and the same test with
the engine and emission control systems already at operating temperature. As such, the units for start
emission rates are instantaneous grams/start.
MOVES5 uses vehicle population information to sort the vehicle population into source bins defined by
vehicle source type, fuel type (gas, diesel, etc.), regulatory class, model year and age. The model uses
default data from instrumented vehicles (or user-provided values) to estimate the number of starts for
each source bin and to allocate them among eight operating mode bins defined by the amount of time
parked ("soak time") prior to the start. Thus, MOVES5 accounts for different amounts of cooling of the
engine and emission control systems. Each source bin and operating mode has an associated g/start
emission rate. Start emissions are also adjusted to account for fuel characteristics, light duty inspection
and maintenance (l/M) programs, and ambient temperatures.
Starts, which are computed as a function of vehicle population, were recomputed for the 2022v2
platform based on the new VPOP for 2022v2. Monthly profiles for starts were retained from 2022vl
platform. The weekday starts provided by Georgia were incorporated.
Off-network Idling Hours
After creating VMT inputs for SMOKE-MOVES, Off-network idle (ONI) activity data were also needed.
ONI is defined in MOVES as time during which a vehicle engine is running idle and the vehicle is
somewhere other than on the road, such as in a parking lot, a driveway, or at the side of the road. This
engine activity contributes to total mobile source emissions but does not take place on the road
network. Examples of ONI activity include:
• light duty passenger vehicles idling while waiting to pick up children at school or to pick up
passengers at the airport or train station,
• single unit and combination trucks idling while loading or unloading cargo or making
deliveries, and
• vehicles idling at drive-through restaurants.
Note that ONI does not include idling that occurs on the road, such as idling at traffic signals, stop signs,
and in traffic—these emissions are included as part of the running and crankcase running exhaust
processes on the other road types. ONI also does not include long-duration idling by long-haul
combination trucks (hoteling/extended idle), as that type of long duration idling is accounted for in other
MOVES processes.
ONI activity hours were calculated based on the 2022v2 VMT. For each representative county, the ratio
of ONI hours to onroad VMT (on all road types) was calculated using the MOVES ONI Tool by source
type, fuel type, and month. These ratios are then multiplied by each county's total VMT (aggregated by
source type, fuel type, and month) to get hours of ONI activity.
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2.3.3 MOVES Emission Factor Table Development
MOVES5 was run in emission rate mode to create emission factor tables for 2022, for all representative
counties and fuel months. The county databases used to run MOVES to develop the emission factor
tables included the state-specific control measures such as the California LEV program, and fuels
representing the year 2022. The range of temperatures run along with the average humidities used were
specific to the year 2022. The remaining settings for the CDBs are documented in the 2020 NEI TSD. To
create the emission factors, MOVES was run separately for each representative county and fuel month
for each temperature bin needed for the calendar year 2022. The MOVES results were post-processed
into CSV-formatted emission factor tables that can be read by SMOKE-MOVES. Additionally, MOVES was
run for all counties in Alaska, Hawaii, and Virgin Islands, and for a single representative county in Puerto
Rico.
The county databases (CDBs) used to run MOVES to develop the emission factor tables were based on
those used for the 2020 NEI. The 2022 emissions modeling platform development included an extensive
review of the various tables including speed distributions. Each county in the continental U.S. was
classified according to its state, altitude (high or low), fuel region, the presence of l/M programs, and the
mean light-duty age. A binning algorithm was executed to identify "like counties." The result was 259
representative counties for the CONUS shown in Figure 2-2 along with 39 for Alaska, Hawaii, Puerto
Rico, and the US Virgin Islands. The 2022v2 platform representative counties did not change from those
used in the 2022vl platform. The CONUS representation counties for 2022 are the same as those used
for 2020 NEI with the exception of Alaska, which, in 2019, removed one borough (county ID 2261,
Valdez-Cordova Census Area) and split that into two areas (county ID 2063, Chugach Census Area; and
county ID 2066, Copper River Census Area); as well as some updates recommended by Texas.
Age distributions are a key input to MOVES in determining emission rates. Age distributions were
updated in the 2022v2 platform based on the S&P Global Mobility data for 2023 using a process similar
to that used for the 2020 NEI, although a key difference between 2022v2 and 2020 was that the age
distributions go out to 40 years in 2022v2, as compared to 30 years in 2020 NEI. For more information
on the process of how age distributions were developed for the 2020 NEI, please see Section 5 of the
2020 NEI TSD. These additional MOVES tables have updated data for the 2022v2 platform: avft,
avgspeeddistribution, dayVMTFraction, hourVMTfraction, sourceTypeYear, and souceTypeVMT.
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Figure 2-2. Map of 2022 Representative Counties
In emissions modeling platforms for NEI years, adjustment factors to light duty vehicle populations by
age are typically computed based on submitted age distributions as compared to the national dataset of
vehicles by source type and age. For the 2022v2 platform no adjustment factors were applied because
there was no submitted data to compare with the national data. No antique plates were removed for
the 2022v2 platform as they have been for recent NEIs. Table 2-18 shows adjustments that were made
to age distributions for light-duty vehicles in a few counties that were discovered to have very young
fleets. The outlier review was limited to LDV source types 21, 31, and 32. Many rural counties have
outliers for low-population source types such as Transit Bus and Refuse Truck due to small sample sizes,
but these do not have much of an impact on the inventory overall and reflect sparse data in low-
population areas and therefore do not require correction.
Table 2-18. Outlier adjustments made for very young light duty vehicles
FIPS
code
County
State
Action
8035
Douglas
County
CO
Substitute ST 32 age distribution with those from county 8031 from
the same MSA (Denver-Aurora-Lakewood; CO)
40109
Oklahoma
County
OK
Substitute ST 21 age distribution with those from county 40027
from the same MSA (Oklahoma City; OK)
40143
Tulsa
County
OK
Substitute ST 21 and 32 age distributions with those from county
40131 from the same MSA (Tulsa; OK)
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To create the emission factors, MOVES was run separately for each representative county and fuel
month and for each temperature bin needed for calendar year 2022. The CDBs used to run MOVES
include the state-specific control measures such as the California low emission vehicle (LEV) program. In
addition, the range of temperatures and the average humidities used in the CDBs were specific to the
year 2022. The MOVES results were post-processed into CSV-formatted emission factor tables that can
be read by SMOKE-MOVES.
2.3.4 Onroad California Inventory Development (onroad_ca_adj)
California uses their own emission model, EMFAC, to develop onroad emissions inventories and provides
those inventories to EPA. EMFAC uses emission inventory codes (EICs) to characterize the emission
processes instead of SCCs. The EPA and California worked together to develop a code mapping to better
match EMFAC's EICs to EPA MOVES' detailed set of SCCs that distinguish between off-network and on-
network and brake and tire wear emissions. This detail is needed for modeling but not for the NEI.
California submitted onroad emissions for all 2022vl platform years, including 2022. Since California's
2022 inventory did not contain HAPs, VOC-based speciation factors were used to estimate VOC HAPs for
2022. Other HAPs such as PAHs and metals are not needed for this platform. The EPA added NH3 to the
CARB inventory by using the state total NH3 from MOVES and allocating it at the county level based on
CO. Refueling emissions were taken from MOVES for California.
California onroad mobile source emissions were created through a hybrid approach of combining state-
supplied annual emissions with EPA-developed SMOKE-MOVES runs. Through this approach, the
platform was able to reflect the California-developed emissions, while leveraging the more detailed SCCs
and the highly resolved spatial patterns, temporal patterns, and speciation from SMOKE-MOVES. The
basic steps involved in temporally allocating onroad emissions from California based on SMOKE-MOVES
results were:
1) Run CA using EPA inputs through SMOKE-MOVES to produce hourly emissions hereafter
known as "EPA estimates." These EPA estimates for CA were run in a separate sector called
"onroad_ca."
2) Calculate ratios between state-supplied emissions and EPA estimates. The ratios were
calculated for each county/SCC/pollutant combination based on the California onroad
emissions inventory. The 2020 California data did not separate off and on-network emissions
or extended idling and also did not include information for vehicles fueled by E-85, so these
differentiations were obtained using MOVES.
3) Create an adjustment factor file (CFPRO) that includes EPA-to-state estimate ratios.
4) Rerun CA through SMOKE-MOVES using EPA inputs and the new adjustment factor file.
Through this process, adjusted model-ready files were created that sum to annual totals from California,
but have the temporal and spatial patterns reflecting the highly resolved meteorology and SMOKE-
MOVES. After adjusting the emissions, this sector is called "onroad_ca_adj." Note that in emission
summaries, the emissions from the "onroad" and "onroad_ca_adj" sectors were summed and
designated as the emissions for the onroad sector.
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2.4 Nonroad Mobile sources (cmv, rail, nonroad)
The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions
(nonroad), locomotive (rail), and commercial marine CMV emissions (cmv_clc2 and cmv_c3).
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• There were no changes in CAP emissions in 2022v2 relative to earlier 2022 emissions modeling
platforms.
• Emission factors for some HAPs were updated for 2022v2 platform.
General Description
The cmv_clc2 sector contains Category 1 and 2 (C1C2) CMV emissions. Category 1 and 2 vessels use
diesel fuel. All emissions in this sector are annual and at county-SCC resolution; however, in the
emissions modeling platform they are provided at the sub-county level (e.g., port shapes) and by SCC
and emission type (e.g., hoteling, maneuvering). Starting with the 2021 emissions modeling platform,
EPA expanded the list of SCCs. SCCs are now further resolved based on ship type than they were for the
2020 NEI. A list of SCCs for the C1C2 sector can be seen in Table 2-19. For more information on the 2022
CMV C1C2 emissions development, see the supplemental documentation (ERG, 2024b). C1C2 emissions
that occur outside of state waters are not assigned to states. For this modeling platform, all CMV
emissions in the cmv_clc2 sector are treated as hourly gridded point sources with stack parameters that
should result in them being placed in layer 1.
Sulfur dioxide (S02) emissions reflect rules that reduced sulfur emissions for CMV that took effect in the
year 2015. The cmv_clc2 inventory sector contains small to medium-size engine CMV emissions.
Category 1 and Category 2 marine diesel engines typically range in size from about 700 to 11,000 hp.
These engines are used to provide propulsion power on many kinds of vessels including tugboats,
towboats, supply vessels, fishing vessels, and other commercial vessels in and around ports. They are
also used as stand-alone generators for auxiliary electrical power on many types of vessels. Category 1
represents engines up to 7 liters per cylinder displacement. Category 2 includes engines from 7 to 30
liters per cylinder.
The cmv_clc2 inventory sector contains sources that traverse state and federal waters along with
emissions from surrounding areas of Canada, Mexico, and international waters. The cmv_clc2 sources
are modeled as point sources but using plume rise parameters that cause the emissions to be released in
the ground layer of the air quality model.
The cmv_clc2 sources within state waters are identified in the inventory with the Federal Information
Processing Standard (FIPS) county code for the state and county in which the vessel is registered. The
cmv_clc2 sources that operate outside of state waters but within the Emissions Control Area (ECA) are
encoded with a state FIPS code of 85. The ECA areas include parts of the Gulf of Mexico, and parts of the
Atlantic and Pacific coasts. The cmv_clc2 sources are categorized as operating either in-port or
underway (i.e., not in-port or docked) and as main and auxiliary engines are encoded using the SCCs
58
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listed in Table 2-19. Level 1 and Level 2 descriptions for all of the entries are "Mobile Sources", and
"Marine Vessels, Commercial", respectively.
Table 2-19. SCCs for the cmv clc2 sector
see
Level 3 Description
Level 4 Description
2280201113
D
esel Barge
C1C2 Port Em
ssions
Main Engine
2280202113
D
esel Offshore support
C1C2 Port Em
ssions
Main Engine
2280203113
D
esel Bulk Carrier
C1C2 Port Em
ssions
Main Engine
2280204113
D
esel Commercial Fishing
C1C2 Port Em
ssions
Main Engine
2280205113
D
esel Container Ship
C1C2 Port Em
ssions
Main Engine
2280206113
D
esel Ferry
C1C2 Port Em
ssions
Main Engine
2280207113
D
esel General Cargo
C1C2 Port Em
ssions
Main Engine
2280208113
D
esel Government
C1C2 Port Em
ssions
Main Engine
2280209113
D
esel Miscellaneous
C1C2 Port Em
ssions
Main Engine
2280210113
D
esel RollOn RollOff
C1C2 Port Em
ssions
Main Engine
2280211113
D
esel Tanker
C1C2 Port Em
ssions
Main Engine
2280212113
D
esel Tour Boat
C1C2 Port Em
ssions
Main Engine
2280213113
D
esel Tug
C1C2 Port Em
ssions
Main Engine
2280214113
D
esel Refrigerated
C1C2 Port Em
ssions
Main Engine
2280215113
D
esel Cruise
C1C2 Port Em
ssions
Main Engine
2280216113
D
esel Passenger Other
C1C2 Port Em
ssions
Main Engine
2280201114
D
esel Barge
C1C2 Port Em
ssions
Auxil
ary Engine
2280202114
D
esel Offshore support
C1C2 Port Em
ssions
Auxil
ary Engine
2280203114
D
esel Bulk Carrier
C1C2 Port Em
ssions
Auxil
ary Engine
2280204114
D
esel Commercial Fishing
C1C2 Port Em
ssions
Auxil
ary Engine
2280205114
D
esel Container Ship
C1C2 Port Em
ssions
Auxil
ary Engine
2280206114
D
esel Ferry
C1C2 Port Em
ssions
Auxil
ary Engine
2280207114
D
esel General Cargo
C1C2 Port Em
ssions
Auxil
ary Engine
2280208114
D
esel Government
C1C2 Port Em
ssions
Auxil
ary Engine
2280209114
D
esel Miscellaneous
C1C2 Port Em
ssions
Auxil
ary Engine
2280210114
D
esel RollOn RollOff
C1C2 Port Em
ssions
Auxil
ary Engine
2280211114
D
esel Tanker
C1C2 Port Em
ssions
Auxil
ary Engine
2280212114
D
esel Tour Boat
C1C2 Port Em
ssions
Auxil
ary Engine
2280213114
D
esel Tug
C1C2 Port Em
ssions
Auxil
ary Engine
2280214114
D
esel Refrigerated
C1C2 Port Em
ssions
Auxil
ary Engine
2280215114
D
esel Cruise
C1C2 Port Em
ssions
Auxil
ary Engine
2280216114
D
esel Passenger Other
C1C2 Port Em
ssions
Auxil
ary Engine
2280201123
D
esel Barge
C1C2 Underway emissions
Main Engine
2280202123
D
esel Offshore support
C1C2 Underway emissions
Main Engine
2280203123
D
esel Bulk Carrier
C1C2 Underway emissions
Main Engine
2280204123
D
esel Commercial Fishing
C1C2 Underway emissions
Main Engine
2280205123
D
esel Container Ship
C1C2 Underway emissions
Main Engine
2280206123
D
esel Ferry
C1C2 Underway emissions
Main Engine
2280207123
D
esel General Cargo
C1C2 Underway emissions
Main Engine
59
-------
see
Level 3 Description
Level 4 Description
2280208123
D
esel Government
C1C2 Underway emissions
Main Engine
2280209123
D
esel Miscellaneous
C1C2 Underway emissions
Main Engine
2280210123
D
esel RollOn RollOff
C1C2 Underway emissions
Main Engine
2280211123
D
esel Tanker
C1C2 Underway emissions
Main Engine
2280212123
D
esel Tour Boat
C1C2 Underway emissions
Main Engine
2280213123
D
esel Tug
C1C2 Underway emissions
Main Engine
2280214123
D
esel Refrigerated
C1C2 Underway emissions
Main Engine
2280215123
D
esel Cruise
C1C2 Underway emissions
Main Engine
2280216123
D
esel Passenger Other
C1C2 Underway emissions
Main Engine
2280201124
D
esel Barge
C1C2 Underway emissions
Auxiliary Engine
2280202124
D
esel Offshore support
C1C2 Underway emissions
Auxiliary Engine
2280203124
D
esel Bulk Carrier
C1C2 Underway emissions
Auxiliary Engine
2280204124
D
esel Commercial Fishing
C1C2 Underway emissions
Auxiliary Engine
2280205124
D
esel Container Ship
C1C2 Underway emissions
Auxiliary Engine
2280206124
D
esel Ferry
C1C2 Underway emissions
Auxiliary Engine
2280207124
D
esel General Cargo
C1C2 Underway emissions
Auxiliary Engine
2280208124
D
esel Government
C1C2 Underway emissions
Auxiliary Engine
2280209124
D
esel Miscellaneous
C1C2 Underway emissions
Auxiliary Engine
2280210124
D
esel RollOn RollOff
C1C2 Underway emissions
Auxiliary Engine
2280211124
D
esel Tanker
C1C2 Underway emissions
Auxiliary Engine
2280212124
D
esel Tour Boat
C1C2 Underway emissions
Auxiliary Engine
2280213124
D
esel Tug
C1C2 Underway emissions
Auxiliary Engine
2280214124
D
esel Refrigerated
C1C2 Underway emissions
Auxiliary Engine
2280215124
D
esel Cruise
C1C2 Underway emissions
Auxiliary Engine
2280216124
D
esel Passenger Other
C1C2 Underway emissions
Auxiliary Engine
Category 1 and 2 CMV emissions were developed for the 2022 platform and were not based on the 2020
NEI although the methods used to develop the emissions were similar. The emissions were developed
based on signals from Automated Identification System (AIS) transmitters. AIS is a tracking system used
by vessels to enhance navigation and avoid collision with other AIS transmitting vessels. The USEPA
Office of Transportation and Air Quality received AIS data from the U.S. Coast Guard (USCG) to quantify
all ship activity which occurred between January 1 and December 31, 2022. During the acquisition of the
2022 AIS data from the U.S. Coast Guard, EPA was made aware of a data quality issue that started in late
March and continued through late June of 2022. To address this, emissions were substituted in from the
2021 CMV C1C2 inventory for this period. To ensure coverage for all of the areas needed by the NEI, the
requested and provided AIS data extend beyond 200 nautical miles from the U.S. coast. The area
covered by the AIS Area, 2022 Modeling Platform Geographical Extent, and U.S. ECA is shown in Figure
2-3 (a). This boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the
North American ECA, although some non-ECA activity are captured as well. Two types of AIS data were
received: satellite (S-AIS) and terrestrial (T-AIS). The distribution of terrestrial and satellite AIS data for
the 2022 emissions modeling platform are shown in Figure 2-3 (b). An additional enhancement for the
2022 C1C2 CMV inventory was the development and application of a mask that was applied to remove
any emissions over land due to stray AIS signals.
60
-------
Figure 2-3. Commercial Marine Vessel Boundaries and Automatic Identification System Request Boxes
a) Entire AIS Area (Transparent Gray), 2022 Modeling Platform Geographical Extent (Black Outline),
and U.S. ECA (White Outline)
b) Distribution of Terrestrial and Satellite AIS Data
Num. Rows in S-AIS 2022
< 10,000,000
100,00,001 - 50,000,000
50,000,001 - 100,000,000
100,000,001 - 200,000,000
> 200,000,000
Num. Rows in T-AIS 2022
< 10,000,000
10,000,001 - 50,000,000
50,000,001 - 250,000,000
250,000,001 - 500,000,000
> 500,000,000
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
61
-------
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 with considerably less
uncertainty. These AIS data were used to define the locations of individual vessel movements, estimate
hours of operation, and quantify propulsion engine loads. The compiled AIS data also included the
vessel's International Marine Organization (IMO) number and Maritime Mobile Service Identifier
(MMSI); which allowed each vessel to be matched to their characteristics obtained from the Clarksons
ship registry (Clarksons, 2021).
The engine bore and stroke data were used to calculate cylinder volume. Any vessel that had a
calculated cylinder volume greater than 30 liters was incorporated into the USEPA's new Category 3
Commercial Marine Vessel (C3CMV) model. The remaining records were assumed to represent Category
1 and 2 (C1C2) or non-ship activity. The C1C2 AIS data were quality assured including the removal of
duplicate messages, signals from pleasure craft, and signals that were not from CMV vessels (e.g., buoys,
helicopters, and vessels that are not self-propelled).
The emissions were calculated for each time interval between consecutive AIS messages for each vessel
and allocated to the location of the message following to the interval. Emissions were calculated
according to Equation 2-1.
g
Emissionsinterval = Time (hr)interval x Power(kW) x x LLAF Equation 2-1
Power was calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval
and emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF
represents the low load adjustment factor, a unitless factor which reflects increasing propulsive
emissions during low load operations. Time indicates the activity duration time between consecutive
intervals.
Next, vessels were identified to determine their vessel type, and thus their vessel group, power rating,
and engine tier information which are required for the emissions calculations. See the 2022 CI C2 CMV
development documentation (ERG, 2024b) for more details on this process. Following the identification,
236 different vessel types were matched to the C1C2 vessels. Vessel attribute data were not available
for all these vessel types, so the vessel types were aggregated into 13 different vessel groups for which
surrogate data were available, as shown in Table 2-20.19,322 vessels were directly identified by their
ship and cargo number. The remaining group of miscellaneous ships represent 1.6 percent of the AIS
vessels (excluding recreational vessels) for which a specific vessel type could not be assigned.
62
-------
Table 2-20. Vessel groups in the cmv_clc2 sector
2017 Entire Area
2020 Entire Area
2021 Entire Area
2022 Entire Area
Vessel Group
Ship Count
Ship Count
Ship Count
Ship Count
Bulk Carrier
45
44
46
47
Commercial Fishing
1,686
4,262
5,826
5,859
Container Ship
8
16
11
15
Ferry Excursion
482
724
849
997
General Cargo
1,555
3,451
3,190
3,122
Government
1,368
1,192
1,179
1,216
Miscellaneous
1,810
269
291
300
Offshore support
1,203
1,337
1,416
1,377
Pilot
NA
17
15
15
Reefer
15
13
12
28
Ro Ro
27
218
219
212
Tanker
144
555
591
677
Tug
4,203
5,661
5,299
5,289
Work Boat
83
151
162
168
Total in Inventory:
12,629
17,910
19,106
19,322
As shown in Equation 2-1, power is an important component of the emissions computation. Vessel-
specific installed propulsive power ratings and service speeds were pulled from Clarksons ship registry
and adopted from the Global Fishing Watch (GFW) dataset when available. However, there is limited
vessel specific attribute data for most of the C1C2 fleet. This necessitated the use of surrogate engine
power and load factors, which were computed for each vessel group. In addition to the power required
by propulsive engines, power needs for auxiliary engines were also computed for each vessel group.
Emissions from main and auxiliary engines are inventoried with different SCCs as shown in Table 2-19.
The final components of the emissions computation equation are the emission factors and the low load
adjustment factor. The emission factors used in this inventory take into consideration the EPA's marine
vessel fuel regulations as well as exhaust standards that are based on the year that the vessel was
manufactured to determine the appropriate regulatory tier. Emission factors in g/kWhr by tier for NOx,
PMio, PM2.5, CO, CO2, SO2 and VOC were developed using Tables 3-7 through 3-10 in USEPA's (2008)
Regulatory Impact Analysis on engines less than 30 liters per cylinder. To compile these emissions
factors, population-weighted average emission factors were calculated per tier based on C1C2
population distributions grouped by engine displacement. Boiler emission factors were obtained from an
earlier Swedish Environmental Protection Agency study (Swedish EPA, 2004). If the year of manufacture
was unknown then it was assumed that the vessel was Tier 0, such that actual emissions may be less
than those estimated in this inventory. Without more specific data, the magnitude of this emissions
difference cannot be estimated.
Propulsive emissions from low-load operations were adjusted to account for elevated emission rates
associated with activities outside the engines' optimal operating range. The emission factor adjustments
were applied by load and pollutant, based on the data compiled for the Port Everglades 2015 Emission
63
-------
Inventory.5 Hazardous air pollutants and ammonia were added to the inventory according to
multiplicative factors applied either to VOC or PM2.5.
The stack parameters used for cmv_clc2 are a stack height of 1 ft, stack diameter of 1 ft, stack
temperature of 70°F, and a stack velocity of 0.1 ft/s. These parameters force emissions into layer 1. For
the 2022v2 platform, stack grouping was used for all CMV sources to improve CMAQ run-time and file
sizes.
For more information on the emission computations for 2022, see the supporting documentation for the
development of the 2022 C1C2 CMV emissions (ERG, 2024). The cmv_clc2 emissions were aggregated
to total hourly values in each grid cell and run through SMOKE as point sources. SMOKE requires an
annual inventory file to go along with the hourly data and this file was generated for 2022.
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• There were no changes in CAP emissions in 2022v2 relative to earlier 2022 emissions modeling
platforms.
• Emission factors for some HAPs were updated for 2022v2 platform.
General Description
The cmv_c3 sector contains large engine CMV emissions. Category 3 (C3) marine diesel engines at or
above 30 liters per cylinder. Typically, these are the largest CMV engines and are rated at 3,000 to
100,000 hp. C3 engines are typically used for propulsion on ocean-going vessels including container
ships, oil tankers, bulk carriers, and cruise ships. Emissions control technologies for C3 CMV sources are
limited due to the nature of the residual fuel used by these vessels.6 The cmv_c3 sector contains
sources that traverse state and federal waters; along with sources in waters not covered by the NEI in
surrounding areas of Canada, Mexico, and international waters.
The cmv_c3 sources that operate outside of state waters but within the federal Emissions Control Area
(ECA) are encoded with a FIPS state code of 85, with the "county code" digits representing broad regions
such as the Atlantic, Gulf of Mexico, and Pacific. The ECA areas include parts of the Gulf of Mexico, and
parts of the Atlantic and Pacific coasts. CMV C3 sources around Puerto Rico, Hawaii and Alaska, which
are outside the ECA areas, are included in the inventory but are in separate files from the emissions
around the continental United States (CONUS). The cmv_c3 sources in the inventory are categorized as
operating either in-port or underway and are encoded using the SCCs listed in Table 2-21 and distinguish
between diesel and residual fuel, in port areas versus underway, and main and auxiliary engines. The
Level 1 and Level 2 descriptions for each of the SCCs are "Mobile Sources" and "Marine Vessels,
Commercial", respectively.
5 USEPA. EPA and Port Everglades Partnership: Emission Inventories and Reduction Strategies. US Environmental Protection
Agency, Office of Transportation and Air Quality, June 2018. https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P100U KV8.pdf.
6 https://www.epa.gov/regulations-emissions-vehicles-and-engines/regulations-emissions-marine-vessels.
64
-------
Table 2-21. SCCs for the cmv c3 sector
see
Level 3 Description
Level 4 Description
2280201313
Diesel Barge
C3 Port Emissions: Main Engine
2280202313
Diesel Offshore support
C3 Port Emissions: Main Engine
2280203313
Diesel Bulk Carrier
C3 Port Emissions: Main Engine
2280204313
Diesel Commercial Fishing
C3 Port Emissions: Main Engine
2280205313
Diesel Container Ship
C3 Port Emissions: Main Engine
2280206313
Diesel Ferry
C3 Port Emissions: Main Engine
2280207313
Diesel General Cargo
C3 Port Emissions: Main Engine
2280208313
Diesel Government
C3 Port Emissions: Main Engine
2280209313
Diesel Miscellaneous
C3 Port Emissions: Main Engine
2280210313
Diesel RollOn RollOff
C3 Port Emissions: Main Engine
2280211313
Diesel Tanker
C3 Port Emissions: Main Engine
2280212313
Diesel Tour Boat
C3 Port Emissions: Main Engine
2280213313
Diesel Tug
C3 Port Emissions: Main Engine
2280214313
Diesel Refrigerated
C3 Port Emissions: Main Engine
2280215313
Diesel Cruise
C3 Port Emissions: Main Engine
2280216313
Diesel Passenger Other
C3 Port Emissions: Main Engine
2280201314
Diesel Barge
C3 Port Emissions: Auxiliary Engine
2280202314
Diesel Offshore support
C3 Port Emissions: Auxiliary Engine
2280203314
Diesel Bulk Carrier
C3 Port Emissions: Auxiliary Engine
2280204314
Diesel Commercial Fishing
C3 Port Emissions: Auxiliary Engine
2280205314
Diesel Container Ship
C3 Port Emissions: Auxiliary Engine
2280206314
Diesel Ferry
C3 Port Emissions: Auxiliary Engine
2280207314
Diesel General Cargo
C3 Port Emissions: Auxiliary Engine
2280208314
Diesel Government
C3 Port Emissions: Auxiliary Engine
2280209314
Diesel Miscellaneous
C3 Port Emissions: Auxiliary Engine
2280210314
Diesel RollOn RollOff
C3 Port Emissions: Auxiliary Engine
2280211314
Diesel Tanker
C3 Port Emissions: Auxiliary Engine
2280212314
Diesel Tour Boat
C3 Port Emissions: Auxiliary Engine
2280213314
Diesel Tug
C3 Port Emissions: Auxiliary Engine
2280214314
Diesel Refrigerated
C3 Port Emissions: Auxiliary Engine
2280215314
Diesel Cruise
C3 Port Emissions: Auxiliary Engine
2280216314
Diesel Passenger Other
C3 Port Emissions: Auxiliary Engine
2280201323
Diesel Barge
C3 Underway emissions: Main Engine
2280202323
Diesel Offshore support
C3 Underway emissions: Main Engine
2280203323
Diesel Bulk Carrier
C3 Underway emissions: Main Engine
2280204323
Diesel Commercial Fishing
C3 Underway emissions: Main Engine
2280205323
Diesel Container Ship
C3 Underway emissions: Main Engine
2280206323
Diesel Ferry
C3 Underway emissions: Main Engine
65
-------
see
Level 3 Description
Level 4 Description
2280207323
Diesel General Cargo
C3 Underway emissions: Main Engine
2280208323
Diesel Government
C3 Underway emissions: Main Engine
2280209323
Diesel Miscellaneous
C3 Underway emissions: Main Engine
2280210323
Diesel RollOn RollOff
C3 Underway emissions: Main Engine
2280211323
Diesel Tanker
C3 Underway emissions: Main Engine
2280212323
Diesel Tour Boat
C3 Underway emissions: Main Engine
2280213323
Diesel Tug
C3 Underway emissions: Main Engine
2280214323
Diesel Refrigerated
C3 Underway emissions: Main Engine
2280215323
Diesel Cruise
C3 Underway emissions: Main Engine
2280216323
Diesel Passenger Other
C3 Underway emissions: Main Engine
2280201324
Diesel Barge
C3 Underway emissions: Auxiliary Engine
2280202324
Diesel Offshore support
C3 Underway emissions: Auxiliary Engine
2280203324
Diesel Bulk Carrier
C3 Underway emissions: Auxiliary Engine
2280204324
Diesel Commercial Fishing
C3 Underway emissions: Auxiliary Engine
2280205324
Diesel Container Ship
C3 Underway emissions: Auxiliary Engine
2280206324
Diesel Ferry
C3 Underway emissions: Auxiliary Engine
2280207324
Diesel General Cargo
C3 Underway emissions: Auxiliary Engine
2280208324
Diesel Government
C3 Underway emissions: Auxiliary Engine
2280209324
Diesel Miscellaneous
C3 Underway emissions: Auxiliary Engine
2280210324
Diesel RollOn RollOff
C3 Underway emissions: Auxiliary Engine
2280211324
Diesel Tanker
C3 Underway emissions: Auxiliary Engine
2280212324
Diesel Tour Boat
C3 Underway emissions: Auxiliary Engine
2280213324
Diesel Tug
C3 Underway emissions: Auxiliary Engine
2280214324
Diesel Refrigerated
C3 Underway emissions: Auxiliary Engine
2280215324
Diesel Cruise
C3 Underway emissions: Auxiliary Engine
2280216324
Diesel Passenger Other
C3 Underway emissions: Auxiliary Engine
2280301313
Residual Barge
C3 Port Emissions: Main Engine
2280302313
Residual Offshore support
C3 Port Emissions: Main Engine
2280303313
Residual Bulk Carrier
C3 Port Emissions: Main Engine
2280304313
Residual Commercial Fishing
C3 Port Emissions: Main Engine
2280305313
Residual Container Ship
C3 Port Emissions: Main Engine
2280306313
Residual Ferry
C3 Port Emissions: Main Engine
2280307313
Residual General Cargo
C3 Port Emissions: Main Engine
2280308313
Residual Government
C3 Port Emissions: Main Engine
2280309313
Residual Miscellaneous
C3 Port Emissions: Main Engine
2280310313
Residual RollOn RollOff
C3 Port Emissions: Main Engine
2280311313
Residual Tanker
C3 Port Emissions: Main Engine
2280312313
Residual Tour Boat
C3 Port Emissions: Main Engine
2280313313
Residual Tug
C3 Port Emissions: Main Engine
66
-------
see
Level 3 Description
Level 4 Description
2280314313
Residual Refrigerated
C3 Port Emissions: Main Engine
2280315313
Residual Cruise
C3 Port Emissions: Main Engine
2280316313
Residual Passenger Other
C3 Port Emissions: Main Engine
2280301314
Residual Barge
C3 Port Emissions: Auxiliary Engine
2280302314
Residual Offshore support
C3 Port Emissions: Auxiliary Engine
2280303314
Residual Bulk Carrier
C3 Port Emissions: Auxiliary Engine
2280304314
Residual Commercial Fishing
C3 Port Emissions: Auxiliary Engine
2280305314
Residual Container Ship
C3 Port Emissions: Auxiliary Engine
2280306314
Residual Ferry
C3 Port Emissions: Auxiliary Engine
2280307314
Residual General Cargo
C3 Port Emissions: Auxiliary Engine
2280308314
Residual Government
C3 Port Emissions: Auxiliary Engine
2280309314
Residual Miscellaneous
C3 Port Emissions: Auxiliary Engine
2280310314
Residual RollOn RollOff
C3 Port Emissions: Auxiliary Engine
2280311314
Residual Tanker
C3 Port Emissions: Auxiliary Engine
2280312314
Residual Tour Boat
C3 Port Emissions: Auxiliary Engine
2280313314
Residual Tug
C3 Port Emissions: Auxiliary Engine
2280314314
Residual Refrigerated
C3 Port Emissions: Auxiliary Engine
2280315314
Residual Cruise
C3 Port Emissions: Auxiliary Engine
2280316314
Residual Passenger Other
C3 Port Emissions: Auxiliary Engine
2280301323
Residual Barge
C3 Underway emissions: Main Engine
2280302323
Residual Offshore support
C3 Underway emissions: Main Engine
2280303323
Residual Bulk Carrier
C3 Underway emissions: Main Engine
2280304323
Residual Commercial Fishing
C3 Underway emissions: Main Engine
2280305323
Residual Container Ship
C3 Underway emissions: Main Engine
2280306323
Residual Ferry
C3 Underway emissions: Main Engine
2280307323
Residual General Cargo
C3 Underway emissions: Main Engine
2280308323
Residual Government
C3 Underway emissions: Main Engine
2280309323
Residual Miscellaneous
C3 Underway emissions: Main Engine
2280310323
Residual RollOn RollOff
C3 Underway emissions: Main Engine
2280311323
Residual Tanker
C3 Underway emissions: Main Engine
2280312323
Residual Tour Boat
C3 Underway emissions: Main Engine
2280313323
Residual Tug
C3 Underway emissions: Main Engine
2280314323
Residual Refrigerated
C3 Underway emissions: Main Engine
2280315323
Residual Cruise
C3 Underway emissions: Main Engine
2280316323
Residual Passenger Other
C3 Underway emissions: Main Engine
2280301324
Residual Barge
C3 Underway emissions: Auxiliary Engine
2280302324
Residual Offshore support
C3 Underway emissions: Auxiliary Engine
2280303324
Residual Bulk Carrier
C3 Underway emissions: Auxiliary Engine
2280304324
Residual Commercial Fishing
C3 Underway emissions: Auxiliary Engine
67
-------
see
Level 3 Description
Level 4 Description
2280305324
Residual Container Ship
C3 Underway emissions: Auxiliary Engine
2280306324
Residual Ferry
C3 Underway emissions: Auxiliary Engine
2280307324
Residual General Cargo
C3 Underway emissions: Auxiliary Engine
2280308324
Residual Government
C3 Underway emissions: Auxiliary Engine
2280309324
Residual Miscellaneous
C3 Underway emissions: Auxiliary Engine
2280310324
Residual RollOn RollOff
C3 Underway emissions: Auxiliary Engine
2280311324
Residual Tanker
C3 Underway emissions: Auxiliary Engine
2280312324
Residual Tour Boat
C3 Underway emissions: Auxiliary Engine
2280313324
Residual Tug
C3 Underway emissions: Auxiliary Engine
2280314324
Residual Refrigerated
C3 Underway emissions: Auxiliary Engine
2280315324
Residual Cruise
C3 Underway emissions: Auxiliary Engine
2280316324
Residual Passenger Other
C3 Underway emissions: Auxiliary Engine
The EPA received Automated Identification System (AIS) data from United States Coast Guard (USCG) to
quantify all ship activity which occurred between January 1 and December 31, 2022. The International
Maritime Organization's (IMO's) International Convention for the Safety of Life at Sea (SOLAS) requires
AIS to be fitted aboard all international voyaging ships with gross tonnage of 300 or more, and all
passenger ships regardless of size.7 In addition, the USCG has mandated that all commercial marine
vessels continuously transmit AIS signals while transiting U.S. navigable waters. As the vast majority of
C3 vessels meet these requirements, any omitted from the inventory due to lack of AIS adoption are
deemed to have a negligible impact on national C3 emissions estimates. The activity data incorporated
into this inventory reflect ship operations within 200 nautical miles of the official U.S. baseline and
beyond. Activity data within the border of the U.S Exclusive Economic Zone and the North American ECA
are included as well as some activity data outside of the ECA.
The 2022 CMV emissions modeling platform data were computed based on the AIS data from the USGS
for the year of 2022. This process is described in more detail in the Category 3 Commercial Marine
Vessel 2022 Emissions Inventory (EPA, 2024a). During the acquisition of the 2022 AIS data from the U.S.
Coast Guard, EPA was made aware of a data quality issue that started in late March and continued
through late June of 2022. To address this, emissions were substituted in from the 2021 CMV C3
inventory for this period. The AIS data were coupled with ship registry data that contained engine
parameters, vessel power parameters, and other factors such as tonnage and year of manufacture
which helped to separate the C3 vessels from the C1C2 vessels. Where specific ship parameters were
not available, they were gap-filled. The types of vessels that remain in the C3 data set include bulk
carrier, chemical tanker, liquified gas tanker, oil tanker, other tanker, container ship, cruise, ferry,
general cargo, fishing, refrigerated vessel, roll-on/roll-off, tug, and yacht.
Prior to use, the AIS data were reviewed - data deemed to be erroneous were removed, and data found
to be at intervals greater than 5 minutes were interpolated to ensure that each ship had data every five
7 International Maritime Organization (IMO) Resolution MSC.99(73) adopted December 12th. 2000 and entered into force
July 1st, 2002; as amended by SOLAS Resolution CONF.5/32 adopted December 13th, 2002.
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minutes. The five-minute average data provide a reasonably refined assessment of a vessel's movement.
For example, using a five-minute average, a vessel traveling at 25 knots would be captured every two
nautical miles that the vessel travels. For slower moving vessels, the distance between transmissions
would be less. An additional enhancement for the 2022 C3 CMV inventory was the development and
application of a mask that was applied to remove any emissions over land due to stray AIS signals and
interpolated values.
The emissions were calculated for each C3 vessel in the dataset for each 5-minute time range and
allocated to the location of the message following to the interval. Emissions were calculated according
to Equation 2-2.
g
Emissionsinterval = Time (hr)interval x Power(kW) x x LLAF Equation 2-2
Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and
emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF
represents the low load adjustment factor, a unitless factor which reflects increasing propulsive
emissions during low load operations. Time indicates the activity duration time between consecutive
intervals.
Emissions were computed according to a computed power need (kW) multiplied by the time (hr) and by
an engine-specific emission factor (g/kWh) and finally by a low load adjustment factor that reflects
increasing propulsive emissions during low load operations.
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,8 but since the data were already in the form of point sources at the
center of each grid cell, and they were already hourly, no other processing was needed within SMOKE.
SMOKE requires an annual inventory file to go along with the hourly data, so this annual file was
generated for 2022.
On January 1st, 2015, the ECA initiated a fuel sulfur standard which regulated large marine vessels to use
fuel with 1,000 ppm sulfur or less. These standards are reflected in the cmv_c3 inventories.
The resulting point emissions centered on each grid cell were converted to an annual point 2010 flat file
format (FF10). A set of standard stack parameters were assigned to each release point in the cmv_c3
inventory. The assigned stack height was 65.62 ft, the stack diameter was 2.625 ft, the stack
temperature was 539.6 °F, and the velocity was 82.02 ft/s. Emissions were computed for each grid cell
needed for modeling. For the 2022v2 platform, stack grouping was used for all CMV sources to improve
CMAQ run-time and file sizes.
International Maritime Organization (IMO) Resolution MSC.99(73).
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2.4.3 Railway Locomotives (rail)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• There were no changes in 2022v2 relative to earlier 2022 emissions modeling platforms.
General Description
The rail sector includes all locomotives in the NEI nonpoint data category including line haul locomotives
on Class 1, 2, and 3 railroads along with emissions from commuter rail lines and Amtrak. The rail sector
excludes railway maintenance locomotives and point source yard locomotives. Railway maintenance
emissions are included in the nonroad sector. The point source yard locomotives are included in the
ptnonipm sector. The development of the 2022 rail inventory is summarized here but is described in
more detail in the 2022 National Emissions Inventory Locomotive Methodology documentation (ERG,
2024c).
The rail sector emissions for the 2022 emissions modeling platform are based on the 2020 NEI.
Projection factors were applied based on fuel use data for Class I locomotives and rail yards. For Class
ll/lll locomotives, activity data for the years 2012, 2017, 2020, and 2022 from the U.S. Energy
Information Administration's Annual Energy Outlook was examined. Based on these data, the fuel data
used in 2020 was increased across the rail system by 11.6% for the 2022 effort. The 2020 NEI is based on
methods developed during the development of the 2017 NEI rail inventory by the Lake Michigan Air
Directors Consortium (LADCO) and the State of Illinois with support from various other states. Class I
railroad emissions are based on confidential link-level line-haul activity GIS data layer maintained by the
Federal Railroad Administration (FRA). In addition, the Association of American Railroads (AAR) provided
preliminary 2023 national emission tier fleet mix information for Class I railroads. Class II and III railroad
emissions are based on a comprehensive nationwide GIS database of locations where short line and
regional railroads operate. Passenger rail (Amtrak) emissions follow a similar procedure as Class II and III,
except using a database of Amtrak rail lines. Yard locomotive emissions are based on a combination of
yard data provided by individual rail companies, and by using Google Earth and other tools to identify
rail yard locations for rail companies which did not provide yard data. Information on specific yards were
combined with fuel use data and emission factors to create an emissions inventory for rail yards.
Pollutant-specific factors were applied on top of the activity-based changes for the Class I rail. The
inventory SCCs are shown in Table 2-22. More detailed information on the development of the 2022
emission modeling platform rail inventory is available in the 2020 NEI TSD and in the 2020 National
Emissions Inventory Locomotive Methodology on the 2020 NEI supporting data FTP site.
Table 2-22. SCCs for the rail sector
see
Sector
Description: Mobile Sources prefix for all
2285002006
Rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class 1 Operations
2285002007
Rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
2285002008
Rail
Railroad Equipment; Diesel; Line Haul Locomotives: PassengerTrains (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; included in ptnonipm)
70
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Class I Line-haul Methodology
For the 2020 inventory, the Class i railroads granted EPA permission to use the confidential link-level line
haul activity geographic information system (GIS) data layer maintained and updated annually by the
Federal Railroad Administration (FRA). At the time of inventory development, 2019 million gross ton
(MGT) data was the most recent and complete data available. A map of the Class I railroad lines is shown
in Figure 2-4. The dataset contains three columns indicating railroad ownership and nine columns
indicating trackage rights for each rail segment. While most rail links have a single owner, some links
have up to six different Class 1 railroad companies operating on it. To prepare the FRA data for use in
the Class I line haul calculations, all segments associated with a railroad company were extracted to
identify the full network for each company. This involved iterating through each of those twelve columns
to identify all segments within each railroad company's network. This process was conducted seven
times, one for each Class I railroad company. This resulted in a complete inventory of rail links trafficked
by each Class I railroads with a record for each link/railroad company combination.
Figure 2-4. 2019 Class I Railroad Line Haul Activity
71
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EPA collected 2020 and 2022 Class I line haul fuel use data from the most recent R-l submittals from the
Surface Transportation Board.9 Consistent with previous inventory efforts, EPA summed line haul and
work train fuel usage, Table 2-23. Projection factors were developed based on the increased fuel use in
2022 and applied to the 2020 emissions.
Table 2-23. 2020 and 2022 R-l reported locomotive fuel use for Class I railroads
Class 1 Railroad
2020 Line Haul Fuel Use (gal)*
2022 Line Haul Fuel Use (gal)*
BNSF
1,137,598,007
1,175,184,806
Canadian National (CN)
96,337,392
107,012,486
Canadian Pacific (CPRS)
57,664,407
64,138,533
CSX Transportation (CSXT)
327,917,859
356,002,171
Kansas City Southern (KCS)
55,763,748
64,185,774
Norfolk Southern (NS)
342,470,779
354,139,306
Union Pacific (UP)
773,476,896
839,457,293
* Includes work train fuel usage
The Association of American Railroads (AAR) provided national Class I locomotive tier fleet mix
information that reflects engine turnover in the nation. Given the impact of the pandemic in 2020, AAR
provided a fleet mix that reflected active locomotives and excluded those that were held in storage. A
locomotive's Tier level determines its allowable emission rates based on the year when it was built
and/or re-manufactured. More accurate emission factors for each pollutant were calculated based on
the percentage of the operating Class I line haul locomotives for each USEPA Tier-level category.
Class II and III Methodology
There are approximately 630 Class II and III Railroads operating in the United States, most of which are
members of the American Short Line and Regional Railroad Association (ASLRRA). Data on Class II and III
locomotive operations is publicly available from Bureau of Transportation Statistics' National
Transportation Atlas Database (NTAD), along with related data including reporting mark, railroad name,
route miles owned or operated, and total route miles of links.
Class II and III railroads are widely dispersed across the country (see Figure 2-5), often utilizing older,
higher emitting locomotives than their Class I counterparts. AAR provided a national line-haul tier fleet
mix profile for 2020 which reflects the trend toward older engines in this sector as shown in Table 2-24.
These data continue to be used for the 2022 platform. The national fleet mix data was then used to
calculate weighted average in-use emissions factors for the locomotives operated by the Class II and III
railroads. Note that to be consistent with the 2020 inventory, the unweighted emission factors were the
same as the Class I line haul due to the conservative use of the EPA's large locomotive conversion factor
of 20.8 bhp-hr/gal. Emission factors for PM2.5, S02, NH3, VOC, and GHGs were calculated in the same
manner as those used for Class I line-haul inventory described above.
9 Surface Transportation Board. Available at https://www.stb.gov/reports-data/economic-data/annual-report-financial-data/
Retrieved 22 June 2021.
72
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Table 2-24. 2020 Class ll/lli line haul fleet by tier level
Tier
2020 Class ll/lli Locomotive Count
Percent of Total Fleet
0
1,664
48%
1
31
1%
2
169
5%
3
160
5%
4
64
2%
Not
Classified
1,359
39%
Total
3,447
100%
Figure 2-5. Class II arid III Railroads in the United States
Setftst Federal Ha tiwSAdmnsS-jhsn , J«mKH8
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For the 2022 inventory, EPA considered activity data for the years 2012, 2017, 2020, and 2022 from the
U.S. Energy Information Administration's Annual Energy Outlook, shown in Table 2-25 below.10 Based on
these data, the fuel data used in 2020 was increased across the rail system by 11.6% for the 2022 effort.
Table 2-25. Rail freight values by year (quadrillion BTU)
2012
2017
2020
2022
0.43
0.52
0.44
0.48
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 for 2020 and 2022 were based on data collected by the Federal
Transit Administration (FTA) for the National Transit Database and projection factors calculated. These
fuel use estimates were replaced with reported fuel use statistics for MBTA (Massachusetts) and Metra
(Illinois). The commuter railroads were separated from the Class II and III railroads so that the
appropriate SCC codes could be entered into the emissions calculation sheet.
Intercity Passenger Methodology (Amtrak)
The calculation methodology mimics that used for the Class II and III and commuter railroads with a few
modifications. Since link-level activity data for Amtrak was unavailable, the default assumption was
made to evenly distribute Amtrak's 2020 reported fuel use across all of it diesel-powered route-miles
shown in Figure 2-6.
10 USEIA, Annual Energy Outlook 2021. Accessed 3 Apr 2024. Available at
https://www.eia.gov/outlooks/aeo/data/browser/#/?id=7-AE02021&cases=ref2021&sourcekev=0
74
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Figure 2-6. Amtrak National Rail Network
For 2022 platform, the 2020 fuel use arid emissions were adjusted down based on the fuel use reported
in Amtrak's FY22 AMTRAK Sustainability Report as shown in Figure 2-7. The adjustment was applied
uniformly, so the spatial representation of the emissions did not change.
Figure 2-7. Amtrak Diesel Fuel Use 2020-2022
DIESEL FUEL USE
FY22 Gwl
lUduu <*«**< Tu«l gwqi by
5% to OW h wliftt
y**of FYt»
Progreii throughout FY22
ACHIEVED
FY20
FY21
mia
59.3 million gallons
FY22
75
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Upon receipt of state-provided comments, two adjustments were made to Amtrak emissions. First,
Delaware verified that all Amtrak passenger service in/through the state utilize electric locomotives only,
so fuel usage and emissions for Delaware SCC 2285002008 were removed. Second, Connecticut
confirmed that Amtrak trains operating on electrified lines do not have diesel emissions. The state
provided emissions estimates which were used to replace the previously calculated emissions.
Other Data Sources
The 2020 NEI locomotives sector includes data from SLT agency-provided emissions data, and an EPA
dataset of locomotive emissions. The following agencies also submitted emissions to locomotive SCCs:
Alaska Department of Environmental Conservation; California; Connecticut; District of Columbia;
Maricopa County, AZ; Minnesota; North Carolina; Texas; Virginia; Washington; and Washoe County, NV.
2.4.4 Nonroad Mobile Equipment (nonroad)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• For the 2022v2 draft, a new MOVES run was performed using MOVES5; these emissions were
retained unchanged in the 2022v2 final emissions in most areas.
• Updated recreational vehicle populations submitted by the Utah Department of Environmental
Quality were incorporated.
• At the request of Georgia Environmental Protection Division, the spatial distribution for
construction and agricultural equipment was replaced with the values developed during the 2016
Collaborative.
• Emissions from off-highway trucks at mines in Minnesota were incorporated at the request of
the Minnesota Pollution Control Agency.
• Improved state-to-county allocation factors for snowmobiles in Minnesota and Wisconsin
provided by Wisconsin Department of Natural Resources were applied.
General Description
The mobile nonroad equipment sector includes all mobile source emissions that do not operate on
roads, excluding commercial marine vehicles, railways, and aircraft. Types of nonroad equipment include
recreational vehicles, pleasure craft, and construction, agricultural, mining, and lawn and garden
equipment. Nonroad equipment emissions for 2022v2 were computed by running MOVES5, the most
current public version of MOVES available, whereas the 2022vl emissions were developed using
MOVES4. The only change to MOVES5 impacting the nonroad component of the model was updated
default fuel characteristics (primarily sulfur levels) for gasoline and marine diesel fuel, based on
nationwide retail fuel survey data. Additionally, MOVES5 was run using 2022 meteorological data.
MOVES provides a complete set of HAPs and incorporates updated nonroad emission factors for HAPs.
MOVES5 was used for all states other than California, which uses their own model. California nonroad
emissions were provided by the California Air Resources Board (CARB) for the 2020 NEI, as well as 2023.
For the 2022 emissions modeling platform CARB nonroad emissions were interpolated between 2020
and 2023. CARB emissions were used in California for all pollutants except PAHs and C02, which were
taken from MOVES.
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MOVES creates a monthly emissions inventory for criteria air pollutants (CAPs) and a full set of HAPs,
plus additional pollutants such as NONHAPTOG and ETHANOL, which are not included in the NEI but are
used for speciation. MOVES provides estimates of NONHAPTOG along with the speciation profile code
for the NONHAPTOG emission source. This was accomplished by using NHTOG#### as the pollutant code
in the flat file 2010 (FF10) inventory file that can be read into SMOKE, where #### is a speciation profile
code. For California, NHTOG####-VOC and HAP-VOC ratios from MOVES-based emissions were applied
to VOC emissions so that VOC emissions can be speciated consistently with other states.
MOVES also provides estimates of PM2.5 by speciation profile code for the PM2.5 emission source,
using PM25_#### as the pollutant code in the FF10 inventory file, where #### is a speciation profile
code. To facilitate calculation of PMC within SMOKE, and to help create emissions summaries, an
additional pollutant representing total PM2.5 called PM25TOTAL was added to the inventory. As with
VOC, PM25_####-PM25TOTAL ratios were calculated and applied to PM2.5 emissions in California so
that PM2.5 emissions in California can be speciated consistently with other states.
MOVES5 outputs emissions data in county-specific databases, and a post-processing script converts the
data into FF10 format. Additional post-processing steps were performed as follows:
• County-specific FFlOs were combined into a single FF10 file.
• Emissions were aggregated from the more detailed SCCs modeled in MOVES to the SCCs
modeled in SMOKE. A list of the aggregated SMOKE SCCs is in Appendix A of the 2016vl platform
nonroad specification sheet (NEIC, 2019).
• To reduce the size of the inventory, HAPs not needed for air quality modeling, such as dioxins
and furans, were removed from the inventory.
• To reduce the size of the inventory further, all emissions for sources (identified by county/SCC)
for which CAP emissions totaling less than 1*10 10 were removed from the inventory. The MOVES
model attributes a very tiny amount of emissions to sources that are actually zero, for example,
snowmobile emissions in Florida. Removing these sources from the inventory reduces the total
size of the inventory by about 7%.
• Gas and particulate components of HAPs that come out of MOVES separately, such as
naphthalene, were combined.
• VOC was renamed to VOCJNV so that SMOKE does not speciate both VOC and NONHAPTOG,
which would result in a double count.
• PM25TOTAL, referenced above, was also created at this stage of the process.
• Emissions for airport ground support vehicles (SCCs ending in -8005), and oil field equipment
(SCCs ending in -10010), were removed from the inventory at this stage, to prevent a double
count with the airports and np_oilgas sectors, respectively.
• California emissions from MOVES were deleted and replaced with the CARB-supplied emissions.
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National Updates: Agricultural and Construction Equipment Allocation
The modified MOVES default database for that included the refinements made to construction and
agricultural sectors in the 2016 platform process (movesdb20220105_nrupdates) and state-submitted
inputs in CDBs from the most recent NEI were used to run MOVES-Nonroad to produce emissions for all
states other than California. California-submitted emissions were used. Updated nrsurrogate,
nrstatesurrogate, and nrbaseyearequippopulation tables, along with instructions for utilizing these
tables in MOVES runs, are available for download from EPA's ftp site:
https://gaftp.epa.gov/air/emismod/2016/vl/reports/nonroad/). In the 2022v2 platform, these updates
to spatial allocation were also reflected in Georgia instead of their submitted values from the 2020 NEI.
Emissions Inside California
California nonroad emissions were provided by CARB for the 2020 NEI and 2023 NEI. The 2022 emissions
were interpolated between 2020 and 2023 where pollutants were available in both data sets. All
California nonroad inventories are annual, with monthly temporalization applied in SMOKE. Emissions
for oil field equipment (SCCs ending in -10010) were removed from the California inventory in order to
prevent a double count with the np_oilgas sector. VOC HAPs from California were incorporated into
speciation similarly to VOC HAPs from MOVES elsewhere, e.g., model species BENZ is equal to HAP
emissions for benzene as submitted by CARB. VOC and PM2.5 emissions were allocated to speciation
profiles. Ratios of VOC (PM2.5) by speciation profile to total VOC (PM2.5) were calculated by county and
SCC from the MOVES run in California, and then applied CARB-provided VOC (PM2.5) in the inventory so
that California nonroad emissions could be speciated consistently with the rest of the country.
State-Submitted Data
The CDBs used to run MOVES-Nonroad to produce emissions for all states other than California were
consistent with those used to develop the 2020 NEI. The following states submitted CDBs for the 2020
NEI: Arizona - Maricopa Co.; Connecticut; Georgia; Illinois; Indiana; Michigan; Minnesota; Ohio; Oregon;
Texas; Utah; Washington; and Wisconsin.
Following the completion of the MOVES runs, railway maintenance emissions were removed from
specific counties / census areas in Alaska because Alaska DEC specified that this type of activity does not
happen in those areas. Specifically, emissions from SCCs 2285002015, 2285004015, and 2285006015
were removed from the following counties / census areas: 02013, 02016, 02050, 02060, 02063, 02066,
02070, 02100, 02105, 02110, 02130, 02150, 02158, 02164, 02180, 02185, 02188, 02195, 02198, 02220,
02240, 02275, and 02282. Alaska DEC also specified some counties / census areas in which logging and
agricultural emissions do not happen, but the emissions for the specified SCCs were already zero in the
specified areas.
For more information on the development of the nonroad emissions inputs for the 2020 NEI see Section
4 of the 2020 NEI TSD.
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Nonroad emissions updates made outside of MOVES
After MOVES5 was run to generate nonroad emissions for the 2022v2 platform, two sets of
modifications were made to the resulting SMOKE-ready inventory:
- In Minnesota and Wisconsin, emissions from snowmobiles (SCC 2260001020) were recomputed with a
more realistic spatial (i.e., county) distribution in each state. Overall emissions totals by state did not
change, only the county distributions in each state changed.
- In St Louis County, Minnesota, emissions associated with offroad mining truck activity were provided
by Minnesota. Normally, this category of emissions is generated by MOVES under SCC 2270002051,
which is then combined with other diesel construction SCCs as part of aggregate SCC 2270002022. To
facilitate use of these updated emissions, MOVES-calculated emissions for SCC 2270002051 were
removed from MOVES post-processing and were not included in SCC 2270002022, and the Minnesota-
provided emissions were added to the SMOKE-ready inventory with SCC 2270002051. They were kept
separate rather than combined with 2270002022 to facilitate more detailed spatial allocation of these
sources as described in Section 3.4.2.
2.5 Fires (ptfire-rx, ptfire-wild, ptagfire)
Multiple types of fires are represented in the modeling platform. These include wild and prescribed fires
that are grouped into the ptfire-wild and ptfire-rx sectors, respectively, and agricultural fires that
comprise the ptagfire sector. All ptfire and ptagfire fires are in the United States. Fires outside of the
United States are described in the ptfire_othna sector later in this document.
2.5.1 Wild and Prescribed Fires (ptfire-rx, ptfire-wild)
2022v2 updates relative to earlier 2022 emissions modeling platforms
o Alabama, Mississippi and Texas state agencies provided prescribed burn activity
o These data were used to generate 2022v2 emissions estimates,
o Removed some satellite-only detected fires on DOI and USFS lands after further examination by
these agencies.
o Removed false detects that were over oil and gas operations (DOI lands in New Mexico),
o Improved characterization of several prescribed and wildfires after air quality modeling feedback.
o Prescribed burns in Minnesota and Texas; Wildfires in New Jersey and Oregon,
o Ditch burns on agricultural lands in the Midwest and in Idaho moved to an agricultural burn SCC
from a prescribed burn SCC (used same methodology as in 2023 NEI).
o Moved some satellite detected only fires from prescribed burn to agricultural burn based on
landuse analysis.
o Applied pile burn methodology to some prescribed burns in western states based on satellite
analysis.
o Duplicate wildland fires in Georgia removed.
o Washington: some prescribed burns moved from pile burns to broadcast burns on federal lands,
o Removed two prescribed burns in New Jersey,
o Minnesota prescribed burn was corrected
o Duplicate fires in North Carolina were removed,
o Pile burns in Georgia with bad coordinates were removed.
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General Description
Wildfire and prescribed burning emissions are contained in the ptfire-wild and ptfire-rx sectors, respectively. The
ptfire sector has emissions provided at geographic coordinates (point locations) and has daily emissions values.
The ptfire-rx sector excludes agricultural burning and other open burning sources that are included in the ptagfire,
openburn, and nonpt sectors. The ptfire-rx sector includes a new methodology for calculating pile burn emissions
with this year 2022 emissions modeling platform. Emissions are day-specific and include satellite-derived
latitude/longitude of the fire's origin and other parameters associated with the emissions such as acres burned
and fuel load, which allow estimation of plume rise. The SCCs used for the ptfire-rx and ptfire-wild sources
are shown in Table 2-26. The ptfire-rx and ptfire-wild inventories include separate SCCs for the flaming
and smoldering combustion phases for wildfire and prescribed burns. Note that the prescribed grassland
fires for Flint Hills, Kansas has their own SCC (2801500171) in the inventory. Any wild grassland fires
were assigned the standard wildfire SCCs shown in Table 2-26. A new source was added to wildland fires
for the 2022 platform. This new source was Pile Burns with a SCC = 2810005001. Pile burns have been a
burn method used for prescribed burns for many years, but no methodology for estimating the
emissions from these burns had been used in previous NEIs or Emissions Modeling Platforms.
Table 2-26. SCCs included in the ptfire sector for the 2022 platform
SCC
Description
2801500171
Agricultural Field Burning - whole field set on fire; Fallow
2810001001
Forest Wildfires; Smoldering; Residual smoldering only (includes grassland
wildfires)
2810001002
Forest Wildfires; Flaming (includes grassland wildfires)
2810005001
Prescribed burning; pile burns
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming
2811020002
Prescribed Rangeland Burning
Fire Information Data
Inputs to SMARTFIRE2 for 2022 include:
• The National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping System
(HMS) fire location information
• Wildland Fire Interagency Geospatial Services (WFIGS) wildland fire perimeter polygons
• The Incident Status Summary, also known as the "ICS-209", used for reporting specific
information on fire incidents of significance
• Hazardous fuel treatment reduction polygons for prescribed burns from the Forest Service
Activity Tracking System (FACTS)
• Fire activity on federal lands from the United States Department of Interior agencies
The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and
Atmospheric Administration's (NOAA) National Environmental Satellite and Data Information
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Service (NESDIS) as a tool to identify fires over North America in an operational environment. The
system utilizes geostationary and polar orbiting environmental satellites. Automated fire detection
algorithms are employed for each of the sensors. When possible, HMS data analysts apply quality
control procedures for the automated fire detections by eliminating those that are deemed to be false
and adding hotspots that the algorithms have not detected via a thorough examination of the satellite
imagery.
The HMS product used for the 2022 inventory consisted of daily comma-delimited files containing fire
detect information including latitude-longitude, satellite used, time detected, and other information.
These detects were processed through a modified, python-based, Satellite Mapping Automated
Reanalysis Tool for Fire Incident Reconciliation version 2 SmartFire2/BlueSky Pipeline (SF2/BSP).
Wildland Fire Interagency Geospatial Services (WFIGS) is an online wildfire mapping application designed
for fire managers to access maps of current U.S. fire locations and perimeters. The wildfire perimeter
data are based upon input data from incident intelligence sources from multiple agencies, GPS data, and
infrared (IR) imagery from fixed wing and satellite platforms.
The Incident Status Summary, also known as the "ICS-209" is used for reporting specific information on
significant fire incidents. The ICS-209 report is a critical interagency incident reporting tool giving daily
'snapshots' of the wildland fire management situation and individual incident information which include
fire behavior, size, location, cost, and other information. Data from two tables in the ICS-209 database
were merged and used for the 2022 ptfire inventory: the SIT209_HISTORY_INCIDENT_209_REPORTS
table contained daily 209 data records for large fires, and the SIT209_HISTORY_INCIDENTS table
contained summary data for additional smaller fires.
The US Forest Service (USFS) compiles a variety of fire information every year. Year 2022 data from the
USFS Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and used
for emissions inventory development. This database includes information about activities related to
fire/fuels, silviculture, and invasive species. The FACTS database consists of shapefiles for prescribed
burns that provide acres burned and start and ending time information.
The Department of Interior (DOI) also compiles wildfire and prescribed burn activity on their federal
lands every year. Year 2022 DOI data were acquired from National Fire Plan Operations and Reporting
System (NFPORS) and through direct communication with DOI staff and were used for 2022 platform
development. The DOI fire information provided fire type, acres burned, latitude-longitude, and start
and ending times.
About 30 different states provided fire activity that was used in developing the wildland fire inventory.
Table 2-27 below gives a listing of the type of fire activity data provided by each state that participated.
Table 2-27. Types of state-provided fire activity data
SLT
Wildfire
Prescribed
burns
RX includes pile
burns
Ag burns
Alabama
No
Yes
Yes
Yes
Arizona
No
Yes
Yes
No
Arkansas
Yes
Yes
Yes
Yes
California
Yes
Yes
Yes
No
Colorado
No
Yes
Yes
No
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SLT
Wildfire
Prescribed
burns
RX includes pile
burns
Ag burns
Connecticut
Yes
Yes
No
No
Delaware
No
Yes
No
Few
Florida
Yes
Yes
Yes
Yes
Georgia
Yes
Yes
No
Yes
Idaho
No
No
No
Yes
Iowa
Yes
Yes
Yes
No
Kansas
No
Yes
No
No
Maine
Yes
Few
No
No
Maryland
Yes
Yes
Yes
No
Minnesota
No
Yes
No
No
Mississippi
No
Yes
No
Yes
Missouri
No
Yes
No
Yes
Montana
No
Yes
Yes
No
Nevada
No
Yes
Yes
No
New Jersey
Yes
Yes
No
No
New Mexico
Yes
Yes
No
No
Nez Perce Tribe
No
Yes
Yes
Yes
North Carolina
Yes
Yes
No
No
North Dakota
No
Yes
No
No
Oklahoma
No
Yes
No
No
Oregon
Yes
Yes
Yes
No
Pennsylvania
Yes
Yes
No
No
South Carolina
Yes
Yes
Yes
Yes
Texas
Yes
Yes
Yes
No
Utah
No
Yes
Yes
No
Virginia
Yes
Yes
No
No
Washington
No
Yes
Yes
Yes
Wyoming
Yes
Yes
Yes
No
Fire Emissions Estimation Methodology
The national and S/L/T data mentioned earlier were used to estimate daily wildfire and prescribed burn
emissions from flaming combustion and smoldering combustion phases for the 2022 inventory. Flaming
combustion is more complete combustion than smoldering and is more prevalent with fuels that have a
high surface-to-volume ratio, a low bulk density, and low moisture content. Smoldering combustion
occurs without a flame, is a less complete burn, and produces some pollutants, such as PM2.5, VOCs,
and CO, at higher rates than flaming combustion. Smoldering combustion is more prevalent with fuels
that have low surface-to-volume ratios, high bulk density, and high moisture content. Models
sometimes differentiate between smoldering emissions that are lofted with a smoke plume and those
that remain near the ground (residual emissions), but for the purposes of the inventory the residual
smoldering emissions were allocated to the smoldering SCCs listed in Table 2-26. The lofted smoldering
emissions were assigned to the flaming emissions SCCs in Table 2-26.
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Figure 2-8 is a schematic of the data processing stream for the inventory of wildfire and prescribed burn
sources. The ptfire-rx and ptfire-wild inventory sources were estimated using Satellite Mapping
Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Pipeline.
SMARTFIRE2 is an algorithm and database system that is within a geographic information system (GIS).
SMARTFIRE2 combines multiple sources of fire information and reconciles them into a unified GIS
database. It reconciles fire data from space-borne sensors and ground-based reports, thus drawing on
the strengths of both data types while avoiding double-counting of fire events. At its core, SMARTFIRE2
is an association engine that links reports covering the same fire in any number of multiple databases. In
this process, 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 2022 platform, the national and S/L/T fire information was input into SMARTFIRE2 and then
merged and associated based on user-defined weights for each fire information dataset. The output
from SMARTFIRE2 was daily acres burned by fire type, and latitude-longitude coordinates for each fire.
The fire type assignments were made using the fire information datasets. if the only information for a
fire was a satellite detect for fire activity, then the flow described in Figure 2-8 was used to make fire
type assignment by state and by month in conjunction with the default fire type assignments shown in
Figure 2-9. The default fire type assignments are the same for both the 2022vl and 2022v2 platforms.
Figure 2-8. Processing flow for fire emission estimates in the 2022 inventory
Input Data Sets
(state/local/tribal and national data sets)
* 4 #
Data Preparation
4 *
Data Aggregation and Reconciliation 1
(SmartFire2) 1
Daily fire locations
with fire size and type
Fuel Moisture and
Fuel Loading Data
USFS Bluesky Pipeline
Daily smoke emissions
for each fire
Emissions Post-Processing
Final Wildland Fire Emissions Inventory
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Jun - Aug
[jun-Sep
Jun - Oct
Figure 2-9. Default fire type assignment by state and month where data are only from satellites
2022vl platform HMS default wildfire type months
Apr-Jul
May - Sep
May - Oct
in SMARTFIRE2, there are cases where an HMS satellite detect does not reconcile with any national or
SLT agency activity dataset. These HMS-only detected burns are assigned to a fire type according to the
land cover, each with a different size assumption. The land cover assumption for each of these detects
is shown in Figure 2-10,
Figure 2-10. Default acres burned assumption map for HMS-only detected fires
Acres per pixel
18 - Aspen
Q 22 - Boreal
12 - Closed Conifer Forest
I I 39 - Eastern Deciduous Forest
I I 39 - Other not listed
I I 62 - Grassland
I I 27 - Juniper
I I 15 - Open Conifer Forests
I I 33 - Pacific broadleaved Forest
I I 57 - Riparian
59 - Savanna
41 - Shrubland
The second system used to estimate emissions is the BlueSky Modeling Pipeline (BSP). The framework
supports the calculation of fuel loading and consumption, and emissions using various models
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depending on the available inputs as well as the desired results. The contiguous United States and
Alaska, where Fuel Characteristic Classification System (FCCS) fuel loading data are available, were
processed using the modeling chain described in Figure 2-11. The Smoke Emissions Reference
Application (SERA) in the BSP generates all the CAP emission factors for wildland fires used in the 2022
study. SERA factors can vary by phase, fire type, region, fuel type and pollutants. SERA emission factors
are available here: https://depts.washington.edu/nwfire/sera/index.php. SERA consists of existing peer-
reviewed emission factors (EFs) of 276 known air pollutants. The SERA database enables the analysis and
summaries of existing EFs, and creation of average EFs to be used in decision support tools for smoke
management, including BSP. HAP emission factors were obtained from Urbanski's (2014) work and
applied by region and by fire type.
Figure 2-11. Blue Sky Modeling Pipeline
The FCCSv4 cross-reference was implemented along with the LANDFIREv2 (at 120-meter resolution) to
provide better fuel bed information for the BlueSky Pipeline (BSP). The LANDFIREv2 was aggregated
from the native resolution and projection to 120 meter using a nearest-neighbor methodology.
Aggregation and reprojection were required for the proper function on BSP.
The Flint Hills grasslands typically have 1 to 2 million acres of prescribed burns each year usually
between late February to early May. Kansas Department of Health Environment provided county acres
burned information for these prescribed burns for 2022 (Figure 2-12) that cover most of eastern Kansas
and 4 additional counties in eastern Oklahoma. Between February 15-April 30 about 2.1M acres were
burned in the Flint Hills. The HMS detects for this time period and for these counties (about 21000
detects) were used to temporally and spatially allocate these prescribed burns and the associated
estimated emissions. The emissions estimation process is done outside of BSP using SERA emissions
factors, except for PM2.5 where a factor of 12.68 g/kg was used for these Flint Hill burns. The Flint Hills
emissions are assigned the SCC 2801500171.
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Figure 2-12. Flint Hills Acreage Burned in 2022
Flint Hills Acreage Burned (February 14 - April 30, 2022)
Geary _ - J-
-.. r"
wmm*
**»*>»
'•• I
i 1 «*' - — • l ¦ W |
¦ & ^" 2 ~ 4* •>,
. s4. ^
SA,:' - V-
Yao Tang
Bureau Of Alt KDHE
County
Acres Burned
Butler
163,895
Chase
237.442
Chautauqua
57,901
Coffey
85,902
Cowley
88,095
Elk
109,933
Geary
17,035
Greenwood
315,605
Lyon
180,190
Marion
37,483
Morris
96,126
Osage (KS)
83,894
Pottawatomie
59,106
Riley
53,700
Wabaunsee
182,259
Wilson
33,592
Woodson
69,422
Nowata (OK)
43,507
Osage (OK)
156,297
Washington (OK)
30,842
Kay (OK)
10,533
Total
2,112,759
In 2022vl arid in the 2020 NEI, HMS detects on or near corn and soybean fields in the Midwest were
assumed to be nearby irrigation ditch or other type of ditch burns. These emissions were also estimated
outside of BSP using the assumption of fuels being similar to grasses. In 2Q22vl, these ditch burns were
put into the prescribed burn sector (ptfire-rx) and assigned a Rangeland burning SCC 2811020002. In
2022v2, these ditch burns were moved to the agricultural burn sector and leveraged the SCC
2801540000.
The final products from this process were annual and daily FFlO-formatted emissions inventories. These
SMOKE-ready inventory files contain both CAPs and HAPs. The BAFM HAP emissions from the inventory
were used directly in modeling and were not overwritten with VOC speciation profiles (i.e., an "integrate
HAP" use case).
For the 2022 platform, pile burn (PB) emissions were estimated using a combination of federal, state,
local, and tribal activity data. This activity data was supplied in the form of daily estimates of area
treated, pile volume, pile dimensions, or mass piled by location, varying by data source. As with the RX
and WF S/L/T data, the pile burn data was imported into SF2 so that it could be reconciled with other
data sources to avoid duplication of activity and emissions. HMS satellite detects that reconciled only
with the location of the PB activity were removed from the BSP workflow as pile burns. The PB activity
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data was then directly imported into a calculator script that estimates the amount of biomass consumed
at each location and the resulting emissions. The consumption calculations made are consistent with
those used in the University of Washington pile burn calculator
(https://depts.washington.edu/nwfire/piles/). For activity data where only a treated area is provided a
default fuel loading of 4.5 tons per acre is used based on an analysis of California and Washington
historical pile burn permits. A consumption efficiency of 90% is assumed unless otherwise specified in
the activity data. Emissions factors averaged over pile burn studies in the SERA database were applied to
estimate CAPs from the consumed piled biomass.
The 2022v2 EMP includes emissions from the 4.5M acres of wildfires plus an estimated 12.7M acres in
prescribed burns. Activity data from federal and state agencies yielded about 8.2M acres of prescribed
burn land. The remaining 4.5M prescribed burn acres were estimated using a default acre burn
assumption were not reconciled with any federal or state agency fire activity data (see Table 2-28). The
default acre burn assumption was applied to any HMS detects that did not reconcile with any federal or
state agency activity data.
Table 2-28. Number of acres burned from HMS satellite only detected burns in 2022v2
Fire Type
Total Acres
Burned
HMS
satellite
detected
only
Reconciled
with activity
data
% not reconciled
with activity in
v2
Prescribed burns
12,733,214
4,512,461
8,220,753
35.4%
Wildfires
4,503,624
70,921
4,432,703
1.6%
Most of the 4.5M prescribed burn acres that are estimated using a default acres burn and fire type
assumption are located in South Central USA (Texas, Louisiana, Missouri, Oklahoma and Arkansas) as
shown in Figure 2-13. Approximately 417,500 tons of primary PM2.5 emissions are estimated in 2022v2
from satellite detections labeled as prescribed burns using the fire type assumption method. White
areas indicate no HMS-only prescribed burns.
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Figure 2-13. 2022v2 annual county acres burned from satellite-only detected burns
2022v2 Prescribed burns HMS-only acres burned by county
¦ 17500
- 15000
¦ 12500 g
2.5.2 Point source Agriculture Fires (ptagfire)
• Ditch burns were moved from the prescribed burn sector to the agricultural burn sector
o Ditch burns assigned to SCC 2801540000
o Emissions from ditch burning were added in 2022hd and 2022he for Idaho, Arkansas,
Illinois, Indiana, Iowa, Kansas, Kentucky, Michigan, Minnesota, Nebraska, Ohio,
Oklahoma, Tennessee, Wisconsin, and Missouri
• Emissions from prescribed rangeland burning (flaming) were added in 2022hd and 2022he for
Arizona, Louisiana, Minnesota, New Mexico, Oklahoma, South Dakota, and Wyoming.
• Emissions from prescribed rangeland burning (flaming) increased (~15%) in 2022hd and 2022he
for Idaho.
• Emissions from two agricultural field burning SCCs (2801500150, 2801500171) decreased (~13%)
in 2022hd and 2022he for Arizona.
• All other state-level changes were very small (< 5%), restricted to agricultural field burning, and
only included Arizona, Idaho, Louisiana, Mississippi, Oklahoma, South Dakota, and Wyoming.
General Description
In the NEI, agricultural fires are stored as county-annual emissions and are part of the nonpoint data
category. For this 2022 platform, agricultural fires are modeled as day specific fires derived from satellite
data for the year 2022 in a similar way to the emissions in ptfire.
Daily year-specific agricultural burning emissions are derived from HMS fire activity data, which contains
the date and location of remote-sensed anomalies. The activity is filtered using the 2022 USDA cropland
data layer (CDL). Satellite fire detects over agricultural lands are assumed to be agricultural burns and
assigned a crop type based on the CDL crop type. Detects that are not over agricultural lands are output
to a separate file for use in the ptfire sector. Each detect is assigned an average size of between 40 and
120 acres varying by state. Grassland/pasture fires were moved to the ptfire sectors for this 2022
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modeling platform. Depending on their origin, grassland fires are in both ptfire-rx and ptfire-wild sectors
because both fire types do involve grassy fuels.
The point source agricultural fire (ptagfire) inventory sector contains daily agricultural burning
emissions. Daily fire activity was derived from the NOAA Hazard Mapping System (HMS) fire activity
data. The agricultural fires sector includes SCCs starting with '28015'. The first three levels of
descriptions for these SCCs are: 1) Fires - Agricultural Field Burning; Miscellaneous Area Sources; 2)
Agriculture Production - Crops - as nonpoint; and 3) Agricultural Field Burning - whole field set on fire.
The SCC 2801500000 does not specify the crop type or burn method, while the more specific SCCs
specify field or orchard crops and, in some cases, the specific crop being grown. The SCCs for this sector
listed are in Table 2-29.
Table 2-29. SCCs included in the ptagfire sector
see
Description
2801500000
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Unspecified crop type and Burn Method
2801500141
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Field Crop is Bean (red): Headfire Burning
2801500150
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Field Crop is Corn: Burning Techniques Not Important
2801500160
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Field Crop is Cotton: Burning Techniques Not Important
2801500171
Miscellaneous Area Sources;Agriculture Production - Crops;Agricultural Field Burning - whole field set on
fire;Fallow
2801500220
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Field Crop is Rice: Burning Techniques Not Significant
2801500250
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Field Crop is Sugar Cane: Burning Techniques Not Significant
2801500262
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Field Crop is Wheat: Backfire Burning
2801500264
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; DoubleCrop Winter Wheat and Soybeans
2801540000
Miscellaneous Area Sources;Other Combustion;Agricultural Field Burning - Ditch Burning;Unspecified
2811020002
Miscellaneous Area Sources; Other Combustion - as Event;
Prescribed Rangeland Burning; Flaming
Another feature of the ptagfire database is that the satellite detections for 2022 were filtered out to
exclude areas covered by snow during the winter months. To do this, the daily snow cover fraction per
grid cell was extracted from a 2022 meteorological Weather Research Forecast (WRF) model simulation.
The locations of fire detections were then compared with this daily snow cover file. For any day in which
a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the
inventory. Due to the inconsistent reporting of fire detections from the Visible Infrared Imaging
Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as VIIRS or
Suomi National Polar-orbiting Partnership satellite were excluded.
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Heat flux for plume rise was calculated using the size and assumed fuel loading of each daily agricultural
fire. This information is needed for a plume rise calculation within a chemical transport modeling
system.
The daily agricultural emissions were converted from a tabular format into the SMOKE-ready daily point
flat file format. The daily emissions were also aggregated into annual values by location and converted
into the annual point flat file format.
For this modeling platform, a SMOKE update allows the use of HAP integration for speciation for PTDAY
inventories. The 2022 agricultural fire inventories include emissions for HAPs, so HAP integration was
used for this study.
2.6 Biogenic Sources (beis)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• There were no changes in 2022v2 relative to earlier 2022 emissions modeling platforms.
General Description
Biogenic emissions were computed based on the 2022 meteorology data used for the 2022 platform and
were developed using the Biogenic Emission Inventory System version 4 (BEIS4) within CMAQ. BEIS4
creates gridded, hourly, model-species emissions from vegetation and soils. It estimates CO, VOC (most
notably isoprene, terpene, and sesquiterpene), and NO emissions for the contiguous U.S. and for
portions of Mexico and Canada. In the BEIS4 two-layer canopy model, the layer structure varies with
light intensity and solar zenith angle (Pouliot and Bash, 2015). Both layers include estimates of sunlit and
shaded leaf area based on solar zenith angle and light intensity, direct and diffuse solar radiation, and
leaf temperature (Bash et al., 2015). BEIS4 computes the seasonality of emissions using the 1-meter soil
temperature (SOIT2) instead of the BIOSEASON file, and canopy temperature and radiation
environments are now modeled using the driving meteorological model's (WRF) representation of leaf-
area index (LAI) rather than the estimated LAI values from BELD data alone. See these CMAQ Release
Notes for technical information on BEIS4: https://github.com/USEPA/CMAQ/wiki/CMAQ-Release-
Notes:-Emissions-Updates:-BEIS-Biogenic-Emissions. The variables output from the Meteorology-
Chemistry Interface Processor (MCIP) that are used to convert WRF outputs to CMAQ inputs are shown
in Table 2-30.
Table 2-30. Meteorological variables required by BEIS4
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2 m
RC
convective precipitation per met TSTEP
RGRND
solar rad reaching surface
RN
nonconvective precipitation per met TSTEP
RSTOMI
inverse of bulk stomatal resistance
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Variable
Description
SLYTP
soil texture type by USDA category
SOIM1
volumetric soil moisture in top cm
SOIT1
soil temperature in top cm
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 m
WSAT_PX
soil saturation from (Pleim-Xiu Land Surface Model) PX-LSM
The Biogenic Emissions Landcover Database version 6 (BELD6) was used as the input gridded land use
information in generating the biogenic emissions estimates. There are now two different BELD6 datasets
that are input into BEIS4. The gridded landuse and the other is the gridded dry leaf biomass (grams/m2)
values for various vegetation types. The BELD6 includes the following datasets:
• High resolution tree species and biomass data from Wilson et al. 2013a, and Wilson et al.
2013b for which species names were changed from non-specific common names to scientific
names
• Tree species biogenic volatile organic carbon (BVOC) emission factors for tree species were
taken from the NCAR Enclosure database (Wiedinmyer, 2001)
o https://www.sciencedirect.com/science/article/pii/S135223100100429Q
• Agricultural land use from US Department of Agriculture (USDA) crop data layer
• Global Moderate Resolution Imaging Spectroradiometer (MODIS) 20 category data with
enhanced lakes and Fraction of Photosynthetically Active Radiation (FPAR) for vegetation
coverage from National Center for Atmospheric Research (NCAR)
• Canadian BELD land use, updates to Version 4 of the Biogenic Emissions Landuse Database
(BELD4) for Canada and Impacts on Biogenic VOC Emissions
(https://www.epa.gov/sites/default/files/2019-08/documents/800am zhang 2 O.pdf).
o
Biogenic emissions computed with BEIS were used to review and prepare summaries but were left out of
the CMAQ-ready merged emissions in favor of inline biogenic emissions produced during the CMAQ
model run itself using the same algorithm described above but with finer time steps within the air
quality model. Figure 2-14 provides an annual estimate of the biogenic VOC emissions in year 2022 from
BEIS4.
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Figure 2-14. Annual biogenic VOC BEIS4 emissions for the 12US1 domain
2022vl beis4 12US1 annual : VOC BEIS
Max: 3304.1482 _Min:
>1795
1596
1396
1197
>
997 £
o
4-1
798
598
399
<199
2.7 Sources Outside of the United States
The emissions from Canada and Mexico are included as part of the emissions modeling sectors:
canmex_point, canmex_area, canada_afdust, canada_ptdust, canada_onroad, mexico_onroad,
canmex_ag, and canada_og2D. The canmex_ag sector is processed as a separate sector for reporting
and tracking purposes, and unlike in other recent emissions platforms, the Canada ag sources are area
sources in this platform rather than pre-gridded point sources. As in prior platforms, fugitive dust
emissions in Canada are represented as both area sources (canada_afdust sector, formerly "othafdust")
and point sources (canada_ptdust sector, formerly "othptdust"). Due to the large number of individual
points, low-level oil and gas emissions in Canada are processed separately from the canmex_point sector
to reduce the number of individual points to track within CMAQ, and also to reduce the size of the
model-ready emissions files.
Canadian emissions in these sectors were generally taken from 2020 and 2023 inventories provided by
Environment and Climate Change Canada (ECCC), interpolated to 2022. ECCC provided the inventories
listed below. The sectors in which they were incorporated are listed and the inventories are also
described.
Agricultural livestock and fertilizer, area source format (canmex_ag sector)
Surface-level oil and gas emissions in Canada (canada_og2D sector)
Agricultural fugitive dust, point source format (canada_ptdust sector)
Other area source dust (canada_afdust sector)
Onroad (canada_onroad sector)
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Nonroad and rail (canmex_area sector)
Airports (canmex_point sector)
Other area sources (canmex_area sector)
Other point sources (canmex_point sector)
The 2022 CMV data included coastal waters of Canada and Mexico with emissions derived from AIS data.
These emissions were used for all areas of Canada and Mexico and are included in the cmv_clc2 and
cmv_c3 sectors. Both the C1C2 and C3 emissions were developed in a point source format with point
locations at the center of the 12km grid cells.
Other than the CB6 species present in the speciated point source data, there are no explicit HAP
emissions in these Canadian inventories. In addition to emissions inventories, the ECCC 2020 dataset
also included shapefiles for creating spatial surrogates. These surrogates were used for this study.
While emissions in the 2020 platform were adjusted at the monthly level to reflect COVID pandemic
effects, no such adjustments were made for 2022 modeling.
2.7.1 Point Sources in Canada and Mexico (canmex_point)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• New data were incorporated for five states in Mexico: Baja California Sur, Durango, San Luis
Potosi, Sinaloa, and Zacatecas.
General Description
Canadian point source inventories provided by ECCC include emissions for airports and other point
sources. The Canadian industrial point source inventory is pre-speciated for the CB6 chemical
mechanism. All Canada point source emissions were interpolated from 2020 and 2023 inventories to
2022 except for the point EGU inventory, for which the 2023 inventory was used directly. This is because
for point EGUs, the ECCC inventories contain different facilities in different years, making an
interpolation difficult.
Point sources in Mexico were compiled in two parts. In the 2022vl platform, emissions inventories
representing 2018 developed through a collaboration between EPA and SEMARNAT were used for the
six Mexico border states: Baja California, Sonora, Chihuahua, Coahuila, Nuevo Leon, and Tamaulipas. In
the 2022v2 platform, year 2018 emissions for point sources were used for the additional states of Baja
California Sur, Durango, San Luis Potosi, Sinaloa, and Zacatecas. Mexico inventories for all other states
were based on inventories projected from the Inventario Nacional de Emisiones de Mexico, 2016
(Secretarfa de Medio Ambiente y Recursos Naturales (SEMARNAT)), projected to 2019 as part of the
2019 emissions modeling platform. For the emissions carried forward from the 2019 platform, the point
source emissions were converted to English units and into the FF10 format that could be read by
SMOKE, missing stack parameters were gapfilled using SCC-based defaults, latitude and longitude
coordinates were verified and adjusted if they were not consistent with the reported municipality. Only
CAPs are covered in the Mexico point source inventory.
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2.7.2 Fugitive Dust Sources in Canada (canada_afdust, canada_ptdust)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• There were no changes in 2022v2 relative to earlier 2022 emissions modeling platforms.
General Description
Fugitive dust sources of particulate matter emissions excluding land tilling from agricultural activities,
were provided by Environment and Climate Change Canada (ECCC) for 2020 and 2023 and were
interpolated to 2022 for this study. Dust emissions resulting from land tilling due to agricultural activities
and livestock were provided as part of the ECCC area source dust inventory. The provided wind erosion
emissions were removed. The ECCC point source dust inventory includes emissions from road dust. A
transport fraction adjustment that reduces dust emissions based on land cover types was applied to
both point and nonpoint dust emissions, along with a meteorology-based (precipitation and snow/ice
cover) zero-out of emissions when the ground is snow covered or wet.
2.7.3 Agricultural Sources in Canada and Mexico (canmex_ag)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• New data were incorporated for some states in Mexico: Baja California Sur, Durango, San Luis
Potosi, Sinaloa, and Zacatecas.
General Description
Agricultural emissions from Canada and Mexico, excluding fugitive dust, are included in the canmex_ag
sector. Canadian agricultural emissions were provided by Environment and Climate Change Canada
(ECCC) as part of their 2020 and 2023 emission inventories (interpolated to 2022). Unlike in recent
platforms, Canadian agricultural were not represented as point sources, instead they were represented
as area sources and gridded using spatial surrogates. In Mexico, agricultural sources in the 2022vl
platform were based on new emissions inventories representing 2018 for the six Mexico border states
(Baja California, Sonora, Chihuahua, Coahuila, Nuevo Leon, and Tamaulipas), and for the 2022v2
platform 2018 emissions were used for these additional states: Baja California Sur, Durango, San Luis
Potosi, Sinaloa, and Zacatecas. Emissions for all other states are from the 2019 emissions platform
(SEMARNAT-provided 2016, projected to 2019).
2.7.4 Surface-level Oil and Gas Sources in Canada (canada_og2D)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• There were no changes in 2022v2 relative to earlier 2022 emissions modeling platforms.
General Description
Canadian point source inventories provided by ECCC included oil and gas emissions and were
interpolated between 2020 and 2023 for 2022. A very large number of these oil and gas point sources
are surface level emissions, appropriate to be modeled in layer 1. Reducing the size of the canmex_point
sector improves air quality model run time because plume rise calculations are needed for fewer
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sources, so these surface level oil and gas sources were placed into the canada_og2D sector for layer 1
modeling.
2.7.5 Nonpoint and Nonroad Sources in Canada and Mexico (canmex_area)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• New data were incorporated for some states in Mexico: Baja California Sur, Durango, San Luis
Potosi, Sinaloa, and Zacatecas.
General Description
ECCC provided year 2020 and 2023 at the Canada province, and in some cases sub-province, resolution
emissions from for nonpoint and nonroad sources (canmex_area). 2022 was interpolated from the 2020
and 2023 emissions. The nonroad sources were monthly while the nonpoint and rail emissions were
annual.
In Mexico, nonroad and nonpoint sources the 2022vl platform emissions were based on new emissions
inventories representing 2018 for the six Mexico border states (Baja California, Sonora, Chihuahua,
Coahuila, Nuevo Leon, and Tamaulipas) and in 2022v2 2018 data were used for these additional states:
Baja California Sur, Durango, San Luis Potosi, Sinaloa, and Zacatecas. Emissions for all remaining states
are from the 2019 emissions platform (SEMARNAT-provided 2016, projected to 2019).
2.7.6 Onroad Sources in Canada and Mexico (canada_onroad, mexico_onroad)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• There were no changes in 2022v2 relative to earlier 2022 emissions modeling platforms for
canada_onroad.
• For mexico_onroad, a new version of MOVES_Mexico was used to compute emissions.
General Description
The onroad emissions for Canada and Mexico are in the canada_onroad and mexico_onroad sectors,
respectively. ECCC provided year 2020 and 2023 at the Canada province, and in some cases sub-province
resolution. 2022 was interpolated from the 2020 and 2023 emissions.
For Mexico onroad emissions, an updated version of the MOVES model for Mexico was run for the
2022v2 platform. This new version provided the same VOC HAPs and speciated VOCs as the previous
version of the MOVES-Mexico model. . This includes NBAFM plus several other VOC HAPs such as
toluene, xylene, and ethylbenzene. Except for VOC HAPs that are part of the speciation, no other HAPs
are included in the Mexico onroad inventory (such as particulate HAPs nor diesel particulate matter).
The new version of MOVES-Mexico includes three new source types unique to Mexico: 22 (Taxis), 44
(Microbus Colectivos), and 45 (Metrobus).
In addition to the new emissions based on the MOVES-Mexico model, the Mexico onroad emissions for
2022v2 platform also incorporate three sets of adjustments which were applied after the MOVES model
was run:
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• A correction factor of 0.84964 was applied to all on-network emissions from light duty cars and
trucks (MOVES source types 21, 22, 31, and 32), to account for a VMT correction;
• The correction factors listed in Table 2-31 were applied to all VOC-related emissions (total VOC,
speciated VOC, and all VOC HAPs); and
• The correction factors listed in Table 2-32 were applied to particulate sulfate emissions (PS04) in
all Mexico municipios except for those in and around Mexico City, Monterrey, and Guadalajara.
Emissions for the 2022v2 draft platform (2022hd case) include only the first two sets of adjustment
factors listed above, while emissions for the 2022v2 final platform (2022he case) includes all three sets
of adjustments.
Table 2-31. VOC adjustment factors applied to Mexico onroad emissions
Source Type
Source Type Description
VOC Correction Factor
11
Motorcycle
1.0000
21
Passenger Car
0.9999
22
Taxis
1.0000
31
Passenger Truck
0.9874
32
Light Commercial Truck
0.9937
41
Intercity Bus
0.7478
42
Transit Bus
0.6863
43
School Bus
0.7706
44
Microbuses Colectivos
1.0000
45
Metrobus
1.0000
51
Refuse Truck
1.0000
52
Single Unit Short-haul Truck
0.6094
53
Single Unit Long-haul Truck
0.6385
61
Combination Short-haul Truck
0.4676
62
Combination Long-haul Truck
0.4962
Table 2-32. PS04 adjustment factors applied to Mexico onroad emissions in most municipios
Source Type
Source Type Description
PS04 Correction Factor
11
Motorcycle
1.0000
21
Passenger Car
0.9999
22
Taxis
1.0000
31
Passenger Truck
0.9776
32
Light Commercial Truck
0.9888
41
Intercity Bus
0.5528
42
Transit Bus
0.4437
43
School Bus
0.5932
44
Microbuses Colectivos
1.0000
45
Metrobus
1.0000
51
Refuse Truck
1.0000
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Source Type
Source Type Description
PS04 Correction Factor
52
Single Unit Short-haul Truck
0.3075
53
Single Unit Long-haul Truck
0.3591
61
Combination Short-haul Truck
0.0560
62
Combination Long-haul Truck
0.1068
2.7.7 Fires in Canada and Mexico (ptfire_othna)
2022v2 updates relative to earlier 2022 emissions modeling platforms
• There were no changes in 2022v2 relative to earlier 2022 emissions modeling platforms.
General Description
Annual 2022 wildland fire emissions for Mexico, Canada, Central America, and Caribbean nations are
included in the ptfire_othna sector. Canadian fire activity was developed by processing the Canadian
Wildland Fire Information System's National Burned Area Composite (NBAC) and NOAA's Hazard
Mapping System (HMS) through SMARTFIRE 2.11 Emissions were estimated from the wildland fire
activity using BlueSky pipeline with Canadian Fire Behavior Prediction (FBP) fuel beds mapped to Fuel
Characteristic Classification System (FCCS) fuel beds. Fires in Mexico, Central America, and the
Caribbean, were developed from the Fire Inventory from NCAR (FINN) v2.5 daily fire emissions for 2022
(Wiedinmyer, 2023). For FINN fires, listed vegetation type codes of 1 and 9 are defined as agricultural
burning, all other fire detections and assumed to be wildfires. All wildland fires that are not defined as
agricultural are assumed to be wildfires rather than prescribed. FINN fire detects of less than 50 square
meters (0.012 acres) are removed from the inventory. The locations of FINN fires are geocoded from
latitude and longitude to FIPS code.
2.7.8 Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb)
concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution
were available and were not modified other than the model-species name "CHLORINE" was changed to
"CL2" to support CMAQ modeling.
For mercury, the volcanic mercury emissions that were used in the recent modeling platforms were not
included in this 2022vl platform because no HAP+CAP modeling was performed. The emissions were
originally developed for a 2002 multipollutant modeling platform with coordination and data from
Christian Seigneur and Jerry Lin for 2001 (Seigneur et. a I, 2004 and Seigneur et. al, 2001). The volcanic
emissions from the most recent eruption were not included in the because they have diminished by the
year 2019. Thus, no volcanic emissions were included.
Because of mercury bidirectional flux within the latest version of CMAQ, no natural mercury emissions
are included in the emissions merge step for HAP+CAP platforms.
http:
ra/conference/2023/slides/2023-1
97
Updates-CMAS.pptx.
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3 Emissions Modeling
The CMAQ and CAMx air quality models require hourly emissions of specific gas and particle species for
the horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain). To
provide emissions in the form and format required by the model, it is necessary to "pre-process" the
"raw" emissions (i.e., emissions input to SMOKE) for the sectors described above in Section 2. In brief,
the process of emissions modeling transforms the emissions inventories from their original temporal,
pollutant, and spatial resolution into the hourly, speciated, gridded and vertical resolution required by
the air quality model. Emissions modeling includes temporal allocation, spatial allocation, and pollutant
speciation. Emissions modeling sometimes includes the vertical allocation (i.e., plume rise) of point
sources, but many air quality models also perform this task because it greatly reduces the size of the
input emissions files if the vertical layers of the sources are not included.
As seen in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary across
sectors and may be hourly, daily, monthly, or annual total emissions. The spatial resolution may be
individual point sources; totals by county (U.S.), province (Canada), or municipio (Mexico); or gridded
emissions. This section provides some basic information about the tools and data files used for
emissions modeling as part of the modeling platform.
3.1 Emissions Modeling Overview
SMOKE was used to process the raw emissions inventories into emissions inputs for each modeling
sector into a format compatible with CMAQ. SMOKE executables and source code are available from the
Community Multiscale Analysis System (CMAS) (cmascenter.org/SMOKE). 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.
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 speciation of biogenic emissions 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; instead, activity data and emission factors are used in combination with
meteorological data to compute hourly emissions.
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Table 3-1. Key emissions modeling steps by sector
Platform sector
Spatial
Speciation
Inventory
resolution
Plume
rise
afdust_adj
Surrogates
Yes
annual
airports
Point
Yes
annual
None
beis
Pre-gridded
land use
in BEIS4
computed hourly in
CMAQ
fertilizer
EPIC
No
computed hourly in
CMAQ
Livestock
Surrogates
Yes
daily
cmv_clc2
Point
Yes
hourly
in-line
cmv_c3
Point
Yes
hourly
in-line
nonpt
Surrogates &
area-to-point
Yes
annual
nonroad
Surrogates &
area-to-point
Yes
monthly
np_oilgas
Surrogates
Yes
annual
np_solvents
Surrogates
Yes
annual
openburn
Surrogates
Yes
annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
onroad_ca_adj
Surrogates
Yes
monthly activity,
computed hourly
canada_onroad
Surrogates
Yes
monthly
mexico_onroad
Surrogates
Yes
monthly
canada_afdust
Surrogates
Yes
annual
canmex_area
Surrogates
Yes
annual & monthly
canmex_point
Point
Yes
annual & monthly
in-line
canada_ptdust
Point
Yes
monthly
None
canada_og2D
Point
Yes
annual
None
canmex_ag
Surrogates
Yes
annual
ptagfire
Point
Yes
daily
in-line
pt_oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire-rx
Point
Yes
daily
in-line
ptfire-wild
Point
Yes
daily
in-line
ptfire_othna
Point
Yes
daily
in-line
ptnonipm
Point
Yes
annual
in-line
ptnonipm_hr
Point
Yes
hourly
in-line
rail
Surrogates
Yes
annual
rwc
Surrogates
Yes
annual
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
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plume rise calculations are done inside of the air quality model instead of being computed by SMOKE. In
all of the "in-line" sectors, all sources are output by SMOKE into point source files which are subject to
plume rise calculations in the air quality model. In other words, no emissions are output to layer 1
gridded emissions files from those sectors. The air quality model computes the plume rise using stack
parameters, the Briggs algorithm, and the hourly emissions in the SMOKE output files for each emissions
sector. The height of the plume rise determines the model layers into which the emissions are placed.
The plume top and bottom are computed, along with the plumes' distributions into the vertical layers
that the plumes intersect. The pressure difference across each layer divided by the pressure difference
across the entire plume is used as a weighting factor to assign the emissions to layers. This approach
gives plume fractions by layer and source. Day-specific point fire emissions are treated differently in
CMAQ. After plume rise is applied, there are emissions in every layer from the ground up to the top of
the plume.
Note that SMOKE has the option of grouping sources so that they are treated as a single stack when
computing plume rise. For the modeling cases discussed in this document, no grouping was performed
because grouping combined with "in-line" processing will not give identical results as "offline"
processing (i.e., when SMOKE creates 3-dimensional files). This occurs when stacks with different stack
parameters or latitude and longitudes are grouped, thereby changing the parameters of one or more
sources. The most straightforward way to get the same results between in-line and offline is to avoid the
use of stack grouping.
Biogenic emissions can be modeled two different ways in the CMAQ model. The BEIS model in SMOKE
can produce gridded biogenic emissions that are then included in the gridded CMAQ-ready emissions
inputs, or alternatively, CMAQ can be configured to create "in-line" biogenic emissions within CMAQ
itself. For this study, the in-line biogenic emissions option was used, and so biogenic emissions from BEIS
were not included in the gridded CMAQ-ready emissions.
For this study, SMOKE was run for the larger 12-km CONtinental United States "CONUS" modeling
domain (12US1) shown in Figure 3-1, but the air quality model was run on the smaller 12-km domain
(12US2). More specifically, SMOKE was run on the 12US1 domain and emissions were extracted from
12US1 data files to create 12US2 emissions. The grids used a Lambert-Conformal projection, with Alpha =
33, Beta = 45 and Gamma = -97, with a center of X = -97 and Y = 40. In addition, SMOKE-MOVES was run
for grids over Alaska, Hawaii, and Puerto Rico plus the Virgin Islands and CMV emissions were developed
for those grids. Later sections provide details on the spatial surrogates and area-to-point data used to
accomplish spatial allocation with SMOKE. Table 3-2 describes the grids. WRF, SMOKE, and CMAQ all
presume the Earth is a sphere with a radius of 6370000 m.
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Table 3-2. Descriptions of the platform grids
Common Name
Grid
Cell
Size
Description
Grid
name
Parameters listed in SMOKE grid description
(GRIDDESC) file: projection name, xorig, yorig,
xcell, ycell, ncols, nrows, nthik
Continental 12km
grid
12 km
Entire conterminous US plus
some of Mexico/Canada
12US1
'LAM_40N97W', -2556000, -1728000, 12.D3,
12.D3, 459, 299, 1
US 12 km or
"smaller" CONUS-
12
12 km
Smaller 12km CONUS plus
some of Mexico/Canada
12US2
'LAM_40N97W', -2412000,
-1620000, 12.D3, 12.D3, 396, 246, 1
Alaska 9km
9 km
9 km Alaska grid with
parts of Canada
9AK1
LAM_36N_155W', -1107000, -1134000, 9000,
9000, 312, 252, 1
Hawaii 3km
3 km
Small 3 km Hawaii
3HI1
LAM_21N_157W', -391500, -346500, 3000,
3000, 225, 201, 1
Puerto Rico &
Virgin Islands
3km
3 km
Small 3 km covering
Puerto Rico and the
Virgin Islands
3PR1
LAM_18N_66W', -274500, -202500, 3000,
3000, 150, 150, 1
Figure 3-1. CMAQ Air quality modeling domains for this platform
12US1 and 12US2
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3.2 Chemical Speciation
Chemical speciation involves the process of translating emissions from the inventory into the chemical
mechanism-specific "model species" needed by an air quality model. Using the CB6R5_AE7 chemical
mechanism as an example, these model species either represent explicit chemical compounds (e.g.,
acetone, benzene, ethanol) or groups of species (i.e., "lumped species;" e.g., PAR, OLE, KET). Table 3-3
lists the model species generated by SMOKE for this mechanism and Table 3-4 lists additional model
species that are generated when performing toxics modeling. Note that Table 3-3 through Table 3-4 are
unchanged between the 2022vl and 2022v2 platforms.
Table 3-3. Emission model species produced for CB6R5_AE7 for CMAQ
Inventory Pollutant
Model Species
Model species description
Cl2
CL2
Atomic gas-phase chlorine
HCI
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOx
NO
Nitrogen oxide
NOx
N02
Nitrogen dioxide
NOx
HONO
Nitrous acid
S02
SO 2
Sulfur dioxide
S02
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
nh3
NH3_FERT
Ammonia from fertilizer
VOC
AACD
Acetic acid
VOC
ACET
Acetone
VOC
ALD2
Acetaldehyde
VOC
ALDX
Propionaldehyde and higher aldehydes
VOC
APIN
Alpha pinene
VOC
BENZ
Benzene
VOC
CAT1
Methyl-catechols
VOC
CH4
Methane
VOC
CRES
Cresols
VOC
CRON
Nitro-cresols
VOC
ETH
Ethene
VOC
ETHA
Ethane
VOC
ETHY
Ethyne
VOC
ETOH
Ethanol
VOC
FACD
Formic acid
VOC
FORM
Formaldehyde
VOC
GLY
Glyoxal
VOC
GLYD
Glycolaldehyde
VOC
IOLE
Internal olefin carbon bond (R-C=C-R)
VOC
ISOP
Isoprene
VOC
ISPD
Isoprene Product
VOC
IVOC
Intermediate volatility organic compounds
VOC
KET
Ketone Groups
VOC
MEOH
Methanol
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Inventory Pollutant
Model Species
Model species description
voc
MGLY
Methylglyoxal
voc
NAPH
Naphthalene
voc
NVOL
Non-volatile compounds
voc
OLE
Terminal olefin carbon bond (R-C=C)
voc
PACD
Peroxyacetic and higher peroxycarboxylic acids
voc
PAR
Paraffin carbon bond
voc
PRPA
Propane
voc
SESQ
Sesquiterpenes (from biogenic emissions only)
voc
SOAALK
Secondary Organic Aerosol (SOA) tracer
voc
TERP
Terpenes (from biogenic emissions only)
voc
TOL
Toluene and other monoalkyl aromatics
voc
UNR
Unreactive
voc
XYLMN
Xylene and other polyalkyl aromatics, minus naphthalene
Naphthalene
NAPH
Naphthalene from inventory
Benzene
BENZ
Benzene from the inventory
Acetaldehyde
ALD2
Acetaldehyde from inventory
Formaldehyde
FORM
Formaldehyde from inventory
Methanol
MEOH
Methanol from inventory
PMio
PMC
Coarse PM > 2.5 microns and < 10 microns
PM2.5
PEC
Particulate elemental carbon 0 2.5 microns
PM2.5
PN03
Particulate nitrate < 2.5 microns
PM2.5
POC
Particulate organic carbon (carbon only) < 2.5 microns
PM2.5
PS04
Particulate Sulfate < 2.5 microns
PM2.5
PAL
Aluminum
PM2.5
PCA
Calcium
PM2.5
PCL
Chloride
PM2.5
PFE
Iron
PM2.5
PK
Potassium
PM2.5
PH20
Water
PM2.5
PMG
Magnesium
PM2.5
PMN
Manganese
PM2.5
PMOTHR
PM2.5 not in other AE6 species
PM2.5
PNA
Sodium
PM2.5
PNCOM
Non-carbon organic matter
PM2.5
PNH4
Ammonium
PM2.5
PSI
Silica
PM2.5
PTI
Titanium
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Table 3-4. Additional HAP gaseous model species generated for toxics modeling
Inventory Pollutant
Model Species
Acetaldehyde
ALD2_PRIMARY
Formaldehyde
FORM_PRIMARY
Acetonitrile
ACETONITRILE
Acrolein
ACROLEIN
Acrylic acid
ACRYLICACID
Acrylonitrile
ACRYLONITRILE
Benzo[a]Pyrene
BENZOAPYRNE
1,3-Butadiene
BUTADIENE13
Carbon tetrachloride
CARBONTET
Carbonyl Sulfide
CARBSULFIDE
Chloroform
CHCL3
Chloroprene
CHLOROPRENE
l,4-Dichlorobenzene(p)
DICHLOROBENZENE
1,3-Dichloropropene
DICHLOROPROPENE
Ethylbenzene
ETHYLBENZ
Ethylene dibromide (Dibromoethane)
BR2_C2_12
Ethylene dichloride (1,2-Dichloroethane)
CL2_C2_12
Ethylene oxide
ETOX
Hexamethylene-l,6-diisocyanate
HEXAMETH_DIIS
Hexane
HEXANE
Hydrazine
HYDRAZINE
Maleic Anyhydride
MAL_ANYHYDRIDE
Methyl Chloride
METHCLORIDE
Methylene chloride (Dichloromethane)
CL2_ME
Specific PAHs assigned w
th URE =0
PAH_000E0
Specific PAHs assigned w
th URE =9.6E-06 (previously 1.76E-5)
PAH_176E5
Specific PAHs assigned w
th URE =4.8E-05 (previously 8.8E-5)
PAH_880E5
Specific PAHs assigned w
th URE =9.6E-05 (previously 1.76E-4)
PAH_176E4
Specific PAHs assigned w
th URE =9.6E-04 (previously 1.76E-3)
PAH_176E3
Specific PAHs assigned w
th URE =9.6E-03 (previously 1.76E-2)
PAH_176E2
Specific PAHs assigned w
th URE =0.01 (previously 1.01E-2)
PAH_101E2
Specific PAHs assigned w
th URE =1.14E-1
PAH_114E1
Specific PAHs assigned w
th URE =9.9E-04 (previously 1.92E-3)
PAH_192E3
Propylene dichloride (1,2-Dichloropropane)
PROPDICHLORIDE
Quinoline
QUINOLINE
Styrene
STYRENE
1,1,2,2-Tetrachloroethane
CL4 ETHANE1122
Tetrachloroethylene (Perchloroethylene)
CL4 ETHE
Toluene
TOLU
2,4-Toluene diisocyanate
TOL DIIS
Trichloroethylene
CL3 ETHE
Triethylamine
TRIETHYLAMINE
m-xylene, o-xylene, p-xylene, xylenes (mixed isomers)
XYLENES
Vinyl chloride
CL_ETHE
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Inventory Pollutant
Model Species
Arsenic
ARSENIC_C, ARSENIC_F
Beryllium
BERYLLIUM_C, BERYLLIUM_F
Cadmium
CADMIUM_C, CADMIUM_F
Chromium VI, Chromic Acid (VI), Chromium Trioxide
CHROMHEX_C,
CHROMHEX F
Chromium III
CHROMTRI_C, CHROMTRI_F
Lead
LEAD_C, LEAD_F
Manganese
MANGANESE_C,
MANGANESE F
Mercury (note that mercury is multi-phase)
HGIIGAS, HGNRVA, PHGI
Nickel, Nickel Oxide, Nickel Refinery Dust
NICKEL_C, NICKEL_F
Diesel-PMIO, Diesel-PM25
DIESEL_PMC,
DIESEL_PMFINE,
DIESEL_PMEC,
DIESEL_PMOC,
DIESEL_PMN03,
DIESEL_PMS04
The TOG and PM2.5 profiles used to speciate emissions are part of the SPECIATE v5.3 database
(https://www.epa.gov/air-emissions-modeling/speciate). The SPECIATE database is developed and
maintained by the EPA's Office of Research and Development (ORD), Office of Transportation and Air
Quality (OTAQ), and the Office of Air Quality Planning and Standards (OAQPS), in cooperation with
Environment Canada (EPA, 2016). These profiles are processed using the EPA's S2S-Tool
(https://github.com/USEPA/S2S-Tool) to generate the GSPRO and GSCNV files needed by SMOKE. As
with previous platforms, some Canadian point source inventories are provided from Environment
Canada as pre-speciated emissions. Speciation profiles (GSPRO files) and cross-references (GSREF files)
for this platform are available in the SMOKE input files for the platform.
3.2.1 VOC speciation
The base emissions inventory for this modeling platform includes total VOC and individual HAP
emissions. Often, individual HAPs are components of VOC (HAP-VOC), and these HAP-VOCs are included
("integrated") in the speciation process. This HAP integration is performed in a way to ensure double
counting of emitted mass does not occur and requires specific data processing by the S2S-Tool and user
input in SMOKE.
To incorporate HAP emissions from the base inventory into the modeling platform, one of two methods
are performed. (1) Integrate, HAP-use is a method where the mass of integrated HAP-VOCs is summed
and subtracted from VOC, and the residual mass (NONHAPVOC) is speciated using a renormalized
speciation profile that does not include the integrated HAP-VOCs (they are subtracted from the profile
and then the profile is renormalized to 100%). (2) No-Integrate, HAP-use is a method where the mass of
VOC is speciated using a speciation profile that does not include the integrated HAP-VOCs (they are
subtracted from the profile and the profile is not renormalized to 100%). In this scenario, the HAP-VOC
and VOC portions of the inventory are difficult to harmonize, and it is assumed that the proportions of
HAPs from these sources are adequately captured in the speciation profile used to speciate the VOC
emissions (which is why there is no renormalization). Separately, HAPs can be introduced into a
modeling platform using speciation profiles. In this scenario, HAP-VOC emissions are "generated"
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through VOC speciation and are not incorporated from the base inventory. This method is called
"Criteria" speciation. An illustration of these methods is shown in Figure 3-2 and the integration
methods used for this platform for each sector are shown in Table 3-5.
Figure 3-2. Process of integrating HAPs and speciating VOC in a modeling platform
Table 3-5. Integration status for each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B), Acetaldehyde
(A), Formaldehyde (F) and Methanol (M)
afdust
N/A - sector contains no VOC
airports
No integration, use NBAFM in inventory
beis
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
cmv clc2
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
cmv c3
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
fertilizer
N/A - sector contains no VOC
livestock
Full integration (NBAFM)
nonpt
Partial integration (NBAFM)
nonroad
Full integration (internal to MOVES)
np_oilgas
Partial integration (NBAFM)
np_solvents
Partial integration (NBAFM)
openburn
Partial integration (NBAFM)
onroad
Full integration (internal to MOVES)
Canada onroad
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
mexico_onroad
Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation was
older CB6, so post-SMOKE emissions were converted to CB6R3AE6
canada afdust
N/A - sector contains no VOC
canmex area
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
canmex_point
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
canada_ptdust
N/A - sector contains no VOC
canada_og2D
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
canmex_ag
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
pt_oilgas
No integration, use NBAFM in inventory
ptagfire
Full integration (NBAFM)
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Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B), Acetaldehyde
(A), Formaldehyde (F) and Methanol (M)
ptegu
No integration, use NBAFM in inventory
ptfire-rx
Partial integration (NBAFM)
ptf ire-wild
Partial integration (NBAFM)
ptfire_othna
Full integration for Canada wildfires (NBAFM), no integration for rest of sector (create
NBAFM from VOC speciation)
ptnonipm
No integration, use NBAFM in inventory
ptnonipm_hr
No integration, use NBAFM in inventory
rail
Full integration (NBAFM)
rwc
Full integration (NBAFM)
The HAPs integrated from the base inventory into the modeling platform are sector and chemical
mechanism specific. In recent years, CB6R3_AE7 has been the primary chemical mechanism used at the
EPA. Within that mechanism, naphthalene (NAPH), benzene (BENZ), acetaldehyde (ALD2), formaldehyde
(FORM), and methanol (MEOH) are explicit HAP-VOCs, and these compounds are collectively referred to
as NBAFM. Since NBAFM are explicitly modeled in CB6R3_AE7, these species have become the default
collection of integrated HAP species at the EPA. MOVES, the EPA's mobile emissions model, features
additional species that are explicitly modeled (e.g., ethanol). These species (Table 3-6) are also
incorporated directly into modeling platforms. To incorporate these species, additional files from the
S2S-Tool are required. For California, speciation of NONHAPTOG is performed on CARB's VOC
submissions using the county-specific speciation profile assignments generated by MOVES in California.
Table 3-6. Integrated species from MOVES sources
MOVES ID
Pollutant Name
5
Methane (CFI4)
20
Benzene
21
Ethanol
22
MTBE
24
1,3-Butadiene
25
Formaldehyde
26
Acetaldehyde
27
Acrolein
40
2,2,4-Trimethylpentane
41
Ethyl Benzene
42
Flexane
43
Propionaldehyde
44
Styrene
45
Toluene
46
Xylene
185
Naphthalene gas
Several sectors require VOC speciation to occur at the county-level and consistent speciation profiles
cannot be applied across the nation. To accomplish this, the GSREF is setup to provide profiles that are
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"blended" at the county/SCC-level using proportions included in the input file. These variable VOC
speciation methods are year-specific and applied in the oil and gas sector and for various mobile
emissions sources. In both the np_oilgas and pt_oilgas sector, VOC speciation profiles are weighted to
reflect region-specific application of controls, differences in gas composition, and variable sources of
emissions (e.g., varying proportions of emissions from associated gas, condensate tanks, crude oil tanks,
dehydrators, liquids unloading and well completions). The Nonpoint Oil and Gas Emissions Estimation
Tool generates an intermediate file that provides SCC and county-specific emissions proportions, which
are subsequently incorporated into the modeling platform.
For onroad and nonroad mobile sources, the speciation of total organic gas and particulate matter
emissions has historically been performed within MOVES. However, this is now performed outside of
MOVES as a post-processing step. This has the advantages of making MOVES simpler, faster to run, and
making it easier to change or update chemical mechanisms and speciation profiles used in the emissions
modeling process. In should be noted that MOVES still generates emissions for some explicit,
"integrated species," as noted above. In many cases, these integrated species have effects like
temperature or fuel effects which are not always well captured by external speciation profiles. For total
organic gases, MOVES calculates 16 integrated species, such as methane and benzene, and the
remainder is called NONHAPTOG and speciated outside MOVES.
In MOVES, speciation profiles are assigned by emission process, fuel subtype, regulatory class, and
model year. Each of these dimensions are available in MOVES output except for fuel subtype, which is
aggregated as part of each fuel type. To apply speciation outside of MOVES and make it compatible with
the needs of SMOKE, we need to determine the speciation profile mapping by SMOKE process
(aggregation of MOVES emission processes) and SMOKE Source Classification Codes (SCC), which are
defined by fuel type, source type, and road type. To support use of new ROC-based speciation profiles
for mobile sources, during nonroad inventory post-MOVES processing, speciation profile assignments
were updated to both NONHAPTOG and PM2.5 in a one-to-one manner. As well, to support use of these
new profiles, PM2.5 was split into four parts: PEC and PS04 (based on the new speciation profiles); total
organic matter, orTOM (PNCOM plus PEC); and residual PM is RESID_PM (all other PM species).
For this platform, MOVES runs were performed in inventory mode for each representative county and
season (i.e., winter and summer) to compute NONHAPTOG output by emission process, fuel type,
regulatory class, and model year. Inventory mode was run rather than rates mode because: 1) MOVES
inventory mode is faster than rates mode, 2) there are several dimensions of rates mode output which
are not relevant to the assigning of speciation profiles, such as speed bin and temperature profile and 3)
weighting speciation profiles by their emissions inventory is both easier and more accurate than by
MOVES output activity or emission rates. Emissions were then disaggregated by fuel subtype using the
market share of each fuel blend in each county. Then, emissions were normalized and aggregated to
calculate the percentage of total NONHAPTOG emissions that should be speciated by each profile for
each SMOKE SCC and process. Finally, these percentages were applied in SMOKE-MOVES to all counties
based on their representative county. A MOVES post-processing tool was used to generate the
speciation cross-references (GSREFs) for SMOKE from the outputs of the inventory mode runs.
To generate onroad emissions and to perform the subsequent speciation, SMOKE-MOVES was first run
to estimate emissions and both the MEPROC and INVTABLE files were used to control which pollutants
are processed and eventually integrated. From there, the NONHAPTOG emission factor tables produced
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by MOVES were speciated within SMOKE using the GSREF files and the NONHAPTOG GSPRO files
generated by the S2S-Tool.
In Canada, a GSPRO_COMBO file is used to generate speciated gasoline emissions that account for
various ethanol mixes. In Mexico, onroad emissions are pre-speciated from the MOVES-Mexico model,
thus eliminating the need for a GSPRO_COMBO file. For both Canada and Mexico, nonroad VOC
emissions are not defined by mode (e.g., exhaust versus evaporative), which necessitates the need for a
GSPRO_COMBO file that splits total VOC into exhaust and evaporative components. In addition, MOVES-
Mexico uses an older version of MOVES that is hardcoded for an older version of the CB6 chemical
mechanism ("CB6-CAMx"). This version does not generate the model species XYLMN or SOAALK, so
additional post-processing is performed to generate those emissions:
• XYLMN = XYL-0.966 * NAPHTHALENE[1]
• PAR = PAR -0.00001 * NAPHTHALENE
• SOAALK = 0.108 * PAR
3.2.2 PM speciation
Like VOC speciation, PM2.5 speciation does feature integrated species from the base inventory for
nonroad and onroad emissions, though there are far fewer (only BC and SO4). The remaining mass is
either TOM (total organic matter) or RESID_PM (residual PM = PM2.5 - BC - SO4 - TOM), which is
speciated using SPECIATE profiles that were post-processed using the S2S-Tool. Small adjustments to the
methods were needed to accommodate the reporting by California. Since California does not provide
speciated PM2.5 emissions, total PM2.5 emissions for onroad and nonroad sources in California were
speciated using the profile proportions estimated by MOVES in California. Finally, onroad brake and tire
wear PM2.5 emissions were speciated in SMOKE-MOVES using the SPECIATE profiles 95462 and 95460,
respectively. In the 2022v2 platform, new profiles were applied to specific SCCs in the RWC sector:
2104008031 now uses profile 95873 and 2104008611 and 2104008614 now use profile 8898a.
3.2.2.1 Diesel PM
Diesel PM emissions are explicitly included in the NEI using the pollutant names DIESEL-PM10 and
DIESEL-PM25 for select mobile sources whose engines burn diesel or residual oil fuels. This includes
sources in onroad, nonroad, point airport ground support equipment, point locomotives, nonpoint
locomotives, and all PM from diesel or residual oil fueled nonpoint CMV. These emissions are equal to
their primary PM10-PRI and PM25-PRI counterparts, are exclusively from exhaust (i.e., do not include
brake/tire wear), and are exclusively used in toxics modeling. Diesel PM is then speciated in SMOKE
using the same speciation profiles and methods as primary PM, except that diesel PM is mapped to
model species that feature "DIESEL_PM" in their species name.
3.2.3 NOx speciation
In the NEI, NOx emissions are inventoried on a NO2 weighted basis, but must be speciated into NO, NO2,
and HONO for modeling platforms to provide the species used by air quality models. Table 3-7 provides
the NOx speciation profiles used in EPA's modeling platforms. The only difference between the two
profiles is the allocation of some NO2 mass to HONO in the "HONO" profile. HONO emissions from
mobile sources have been identified in tunnel studies and its inclusion in emissions inventories is
important for urban chemistry. Here, a HONO to NOx ratio of 0.008 was selected (Sarwar, 2008). In this
modeling platform, all non-mobile sources use the "NHONO" profile, all non-onroad mobile sources
(including nonroad, cmv, and rail) use the "HONO" profile, and all onroad NOx speciation occurs within
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MOVES. For further details on NOx speciation within MOVES, please see Table 3-8 and in the associated
technical reports (EPA-420-R-22-017, EPA-420-R-23-006).
Table 3-7. NOx speciation profiles
Profile
Pollutant
Species
Mass Split Factor
HONO
NOX
N02
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
N02
0.1
NHONO
NOX
NO
0.9
Table 3-8. Mobile NOx and HONO fractions
Fuel
Model Years
Process
NO
Nox
HONO
Gasoline
1960-1980
Running Exhaust
0.975
0.017
0.008
Gasoline
1981-1990
Running Exhaust
0.932
0.06
0.008
Gasoline
1991-1995
Running Exhaust
0.954
0.038
0.008
Gasoline
1996-2050
Running Exhaust
0.836
0.156
0.008
Gasoline
1960-1980
Start Exhaust
0.975
0.017
0.008
Gasoline
1981-1990
Start Exhaust
0.961
0.031
0.008
Gasoline
1991-1995
Start Exhaust
0.987
0.005
0.008
Gasoline
1996-2050
Start Exhaust
0.951
0.041
0.008
Diesel
1960-2003
Exhaust
0.9622
0.0298
0.008
Diesel
2004-2006
Exhaust
0.9325
0.0595
0.008
Diesel
2007-2009
Exhaust
0.7529
0.2381
0.008
Diesel
2010-2060
Exhaust
0.8035
0.1885
0.008
3.2.4 Sulfuric Acid Vapor (SULF)
Sulfuric acid vapor (SULF) is added for coal and distillate oil fuel combustion sources to the emissions
files using SO2 emissions from the base inventory. This process utilizes profiles assignments in the GSREF
file and the profiles were derived using data from AP-42 (EPA, 1998). The weight fraction of added
sulfuric acid vapor is fuel specific, assumes that gaseous sulfate is primarily H2SO4, and is calculated as
follows:
fraction of S emitted as sulfate MW H2S04
SULF emissions = S02 emissions x — : —— : - —— x ¦
fraction of S emitted as S02 MW S02
In the above, the molecular weight {MW) of sulfate and sulfur dioxide are 98 g/mol and 64 g/mol,
respectively. The fractions of sulfur emissions emitted as sulfate and sulfur dioxide, as well as the
resulting sulfuric acid vapor split factors, by fuel, are summarized in Table 3-9 and Table 3-10 below.
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Table 3-9. Sulfate split factor computation
Fuel
SCCs
Profile
Code
Fraction
as S02
Fraction
as Sulfate
Split Factor (Mass
Fraction)
Bituminous
1-0X-002-YY
X is 1, 2, or 3
YY is 01-19
21-0Z-002-000
Z is 2, 3, or 4
95014
0.95
0.014
.014/.95 * 98/64 =
0.0226
Subbituminous
1-0X-002-YY
X is 1, 2, or 3
YY is 21-38
87514
0.875
0.014
.014/.875 * 98/64
= 0.0245
Lignite
1-0X-003-YY
X is 1, 2, or 3
YY is 01-18
75014
0.75
0.014
.014/.75 * 98/64 =
0.0286
Residual oil
1-0X-004-YY
X is 1, 2, or 3
YY is 01-06
21-0Z-005-000
Z is 2, 3, or 4
99010
0.99
0.01
.01/.99 * 98/64 =
0.0155
Distillate oil
1-0X-005-YY
X is 1, 2, or 3
YY is 01-06
21-0Z-004-000
Z is 2, 3, or 4
99010
0.99
0.01
Same as residual
oil
Table 3-10. SO2 speciation profiles
Profile
pollutant
species
split factor
95014
S02
SULF
0.0226
95014
S02
S02
1
87514
S02
SULF
0.0245
87514
S02
S02
1
75014
S02
SULF
0.0286
75014
S02
S02
1
99010
S02
SULF
0.0155
99010
S02
S02
1
3.2.5 Speciation of Metals and Mercury
Metals and mercury emissions from the base inventory require speciation for use in modeling. Non-
mercury metals must be speciated into coarse and fine size ranges for use in CMAQ, and Table 3-11,
summarizes the particle size profiles used for each data category.
Table 3-11. Particle size speciation of metals
Source Type
Profile
Pollutant
Fine
Coarse
Onroad
OARS
Arsenic
0.95
0.05
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Source Type
Profile
Pollutant
Fine
Coarse
Onroad
ONMN
Manganese
0.4375
0.5625
Onroad
ONNI
Nickel
0.83
0.17
Onroad
CRON
Chromhex
0.86
0.14
Nonroad
NOARS
Arsenic
0.83
0.17
Nonroad
NONMN
Manganese
0.67
0.33
Nonroad
NONNI
Nickel
0.49
0.51
Nonroad
CRNR
Chromhex
0.80
0.20
Stationary
STANI
Nickel
0.59
0.41
Stationary
STACD
Cadmium
0.76
0.24
Stationary
STAMN
Manganese
0.67
0.33
Stationary
STAPB
Lead
0.74
0.26
Stationary
STABE
Beryllium
0.68
0.32
Stationary
CRSTA
Chromhex
0.71
0.29
Stationary
STARS
Arsenic
0.59
0.41
Mercury is speciated into one of the three forms used by CMAQ; elemental, divalent gaseous, and
divalent particulate. Table 3-12 provides the mercury speciation profiles used in the modeling platform.
All relevant SCCs were mapped to these profiles within the GSREF. A caveat is the onroad and nonroad
sectors, where mercury emissions are estimated in MOVES, nonroad emissions from California, which
use the appropriate profiles below, and onroad emissions from California, where MOVES-based
speciation is applied.
Table 3-12. Mercury speciation profiles
Profile Code
Description
Elemental
Divalent Gas
Particulate
HGCEM
Cement kiln exhaust
0.66
0.34
0
HGCLI
Cement clinker cooler
0
0
1
HBCMB
Fuel combustion
0.5
0.4
0.1
HGCRE
Human cremation
0.8
0.15
0.05
HGELE
Elemental only (used?)
1
0
0
HGGEO
Geothermal power plants
0.87
0.13
0
HGGLD
Gold mining
0.8
0.15
0.05
HGHCL
Chlor-Alkali plants
0.972
0.028
0
HGINC
Waste incineration
0.2
0.6
0.2
HGIND
Industrial average
0.73
0.22
0.05
HGMD
Mobile diesel
0.56
0.29
0.15
HGMG
Mobile gas
0.915
0.082
0.003
HGMET
Metal production
0.8
0.15
0.005
HGMWI
Medical waste incineration
0.2
0.6
0.2
HGPETCOKE
Petroleum coke
0.6
0.3
0.1
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3.3 Temporal Allocation
Temporal allocation is the process of distributing aggregated emissions for a specific time period to a
finer temporal resolution, such as converting annual emissions to hourly emissions as is required by
CMAQ. While the total of the emissions are important, the timing of the occurrence of emissions is also
essential for accurately simulating ozone, PM, and other pollutant concentrations in the atmosphere.
Many emissions inventories are annual or monthly in nature. Temporal allocation takes these
aggregated emissions and distributes the emissions to the hours of each day. This process is typically
done by applying temporal profiles to the inventories in this order: monthly, day of the week, and
diurnal, with monthly and day-of-week profiles applied only if the inventory is not already at that level of
detail.
The temporal factors applied to the inventory were selected using some combination of country, state,
county, SCC, and pollutant, as appropriate for the specific sector. Table 3-13 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 3-13. Temporal settings used for the platform sectors in SMOKE
Platform sector
short name
Inventory resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process
holidays as
separate days
afdust_adj
Annual
Yes
week
all
Yes
airports
Annual
Yes
all
all
No
beis
Hourly
n/a
all
No
cmv_clc2
Annual & hourly
all
all
No
cmv_c3
Annual & hourly
all
all
No
fertilizer
Monthly
met-based
all
Yes
livestock
Daily
No
all
all
No
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
mwdss
mwdss
Yes
np_oilgas
Annual
Yes
aveday
aveday
No
np_solvents
Annual
Yes
aveday
aveday
No
openburn
Annual
Yes
aveday
aveday
No
onroad
Annual & monthly1
all
all
Yes
onroad_ca_adj
Annual & monthly1
all
all
Yes
pt_oilgas
Annual
Yes
mwdss
mwdss
Yes
ptegu
Annual & hourly
Yes2
all
all
No
ptnonipm
Annual
Yes
mwdss
mwdss
Yes
ptnonipm_hr
Hourly
all
all
No
ptagfire
Daily
all
all
No
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Platform sector
short name
Inventory resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process
holidays as
separate days
ptfire-rx
Daily
all
all
No
ptfire-wild
Daily
all
all
No
ptfire_othna
Daily
all
all
No
canada_afdust
Annual
Yes
week
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No3
met-based3
all
No3
canmex_area
Annual & monthly
Yes
week
week
No
canada_onroad
Monthly
week
week
No
mexico_onroad
Monthly
week
week
No
canmex_point
Annual & monthly
Yes
mwdss
mwdss
No
canada_ptdust
Monthly
week
all
No
canmex_ag
Annual
Yes
mwdss
mwdss
No
canada_og2D
Annual
Yes
mwdss
mwdss
No
1Note the annual and monthly "inventory" actually refers to the activity data (VMT, hoteling, and VPOP) for onroad.
VMT and hoteling is monthly and VPOP is annual. The actual emissions are computed on an hourly basis.
2Only units that do not have matching hourly CEMS data use monthly temporal profiles.
3Except for 3 SCCs that do not use met-based speciation.
The value "all" in the "Merge processing approach" column means that hourly emissions were
computed for every day of the year and that emissions potentially have day-of-year variation. The value
"week" means that hourly emissions were computed for all days in one "representative" week,
representing all weeks for each month. This means emissions have day-of-week variation, but not week-
to-week variation within the month. The value "mwdss" means hourly emissions for one representative
Monday, representative weekday (Tuesday through Friday), representative Saturday, and representative
Sunday for each month. This means emissions have variation between Mondays, other weekdays,
Saturdays and Sundays within the month, but not week-to-week variation within the month. The value
"aveday" means hourly emissions computed for one representative day of each month, meaning
emissions for all days within a month are the same. Special situations with respect to temporal
allocation are described in the following subsections.
In addition, temporal processing includes a "spin-up" period of several days prior to January 1, 2022,
which is intended to mitigate the effects of initial conditions on simulated air quality pollutant
concentrations. Here, the spin-up period was 10 days (December 22-31, 2021). For all anthropogenic
sectors, emissions from December 2022 were used to fill in surrogate emissions for the end of
December 2021. For biogenic emissions, December 2021 emissions were computed using year
meteorology.
3.3.1 Use of FF10 format for finer than annual emissions
The FF10 inventory format for SMOKE provides a consolidated format for monthly, daily, and hourly
emissions inventories. With the FF10 format, a single inventory file can contain emissions for all 12
months and the annual emissions in a single record. This helps simplify the management of numerous
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inventories. Similarly, daily and hourly FF10 inventories contain individual records with data for all days
in a month and all hours in a day, respectively.
SMOKE prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual-to-month temporal allocation applied to it;
rather, it should only have month-to-day and diurnal temporal allocation. This becomes particularly
important when specific sectors have a mix of annual, monthly, daily, and/or hourly inventories. The
flags that control temporal allocation for a mixed set of inventories are discussed in the SMOKE
documentation. The modeling platform sectors that make use of monthly values in the FF10 files are
nonroad, onroad (for activity data), and all Canada and Mexico inventories except for agriculture.
Commercial marine vessels in cmv_c3 and cmv_clc2 use hourly data in the FF10 files, as well as units in
the ptegu sector which are matched to CEMS data.
3.3.2 Temporal allocation for non-EGU sources (ptnonipm, ptnonipm hr)
New for the 2022v2 platform, select units from the ptnonipm sector were split into a new sector called
"ptnonipm_hr", consisting of units for which hourly emissions are available. The ptnonipm_hr sector
includes two types of units:
• Taconite facilities in Minnesota, for which hourly emissions were provided by the Minnesota
Pollution Control Agency (MPCA); and
• Units from nonEGUs which could be matched to hourly CEMS data.
The ptnonipm_hr sector is processed separately from the remainder of ptnonipm though SMOKE, using
hourly emissions inventories to process emissions for each day of the year. The methodology is similar
to the ptegu sector as described in Section 3.3.3, except that all sources in the ptnonipm_hr sector are
present in the hourly inventory, and no temporal profiles are needed. NOx and SO2 emissions are based
on MPCA data for Taconite facilities, and CEMS data for other units in the sector. Hourly temporalization
for pollutants other than NOx and SO2 is scaled to hourly NOx for Taconite facilities and is based on
hourly heat input for units with CEMS data. As in the ptegu sector, CEMS data for nonEGUs in the
ptnonipm_hr sector is processed through the CEMCorrect tool.
Most temporal profiles for sources remaining in the ptnonipm sector result in constant emissions for
each day of the year, although some have lower emissions on Sundays. For the 2022vl platform,
temporal profiles for SCC 40202501 emissions for which are related to surface coating for metals were
changed to use hourly profiles number 11 that reflects operations from 7AM to 5PM local time.
3.3.3 Electric Generating Utility temporal allocation (ptegu)
Electric generating unit (EGU) sources matched to ORIS units were temporally allocated to hourly
emissions needed for modeling using the hourly CEMS data for units that could be matched to the CEMS
emissions. Those hourly data were processed through v2.1 of the CEMCorrect tool to mitigate the
impact of unmeasured values in the data.
The temporal allocation procedure for EGUs in the base year is differentiated by whether or not the unit
could be directly matched to a unit with CEMS data via its ORIS facility code and boiler ID. Note that for
units matched to CEMS data, annual totals of their emissions input to CMAQ may be different than the
values in the annual inventory because the CEMS data replace the NOx and SO2 annual inventory data
115
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for the seasons during which the CEMS are operating. If a CEMS-matched unit is determined to be a
partial year reporter, as can happen for sources that run CEMS only in the summer, emissions totaling
the difference between the annual emissions and the total CEMS emissions are allocated to the non-
summer months. Prior to use of the CEMS data in SMOKE it is processed through the CEMCorrect tool.
The CEMCorrect tool identifies hours for which the data were not measured as indicated by the data
quality flags in the CEMS data files. Unmeasured data can be filled in with maximum values and thereby
cause erroneously high values in the CEMS data. When data were flagged as unmeasured and the values
were found to be more than three times the annual mean for that unit, the data for those hours were
replaced with annual mean values (Adelman et al., 2012). These adjusted CEMS data were then used for
the remainder of the temporal allocation process described below (see Figure 3-3 for an example).
Figure 3-3. Eliminating unmeasured spikes in CEMS data
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The region, fuel, and type (peaking or non-peaking) must be identified for each EGU with CEMS data so
the data can be used to generate profiles. The identification of peaking units was done using summed
hourly heat input data from 2022 and the two previous years (2020 and 2021). Equation 1 shows how
the annual heat input value is converted from heat units (BTU/year) to power units (MW) using the
NEEDS v6 derived unit-level heat rate (BTU/kWh). In equation 2 a capacity factor is calculated by dividing
the annual unit MW value by the NEEDS v6 unit capacity value (MW) multiplied by the hours in the year.
A peaking unit was defined as any unit that had a maximum capacity factor of less than 0.2 for every
year (2020, 2021, and 2022) and a 3-year average capacity factor of less than 0.1.
Equation 1. Annual unit power output
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_ _ Annual Unit Output (MW)
Capacity Factor =
NEEDS Unit Capacity (^-)*8760 (ft)
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Input regions were determined from one of the eight EGU modeling regions based on MJO and climate
regions. Regions were used to group units with similar climate-based load demands. Region assignment
is made on a state level, where all units within a state were assigned to the appropriate region. Unit fuel
assignments were made using the primary NEEDS v6 fuel. Units fueled by bituminous, subbituminous, or
lignite were assigned to the coal fuel type. Natural gas units were assigned to the gas fuel type. Distillate
and residual fuel oil were assigned to the oil fuel type. Units with any other primary fuel were assigned
the "other" fuel type. Figure 3-4 shows the regions used to generate the profiles. Currently there are 64
unique profiles available based on 8 regions, 4 fuels, and 2 for peaking unit status (peaking and non-
peaking).
Figure 3-4. Regions used to Compute Temporal non-CEMS EGU Temporal Profiles
EGtl Regions
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The daily and diurnal profiles were calculated for each region, fuel, and peaking type group from the
year 2022 CEMS heat input values. The heat input values were summed for each input group to the
annual level at each level of temporal resolution; monthly, month-of-day, and diurnal. The sum by
temporal resolution value was then divided by the sum of annual heat input in that group to get a set of
temporalization factors. Separate diurnal factors were created for four seasons: summer (May through
September), fall (October and November), winter (December through February), and spring (March and
April), 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 input over all hours to get
the hour 1 factor. Each grouping contained 12 monthly factors, up to 31 daily factors per month, and
four sets of 24 hourly factors. The profiles were weighted by unit size where the units with more heat
input have more influence on the shape of the profile. Composite profiles were created for each region
and type across all fuels as a way to provide profiles for a fuel type that does not have hourly CEMS data
in that region. Figure 3-5 shows example peaking and non-peaking daily temporal profiles for the gas
fuel type in the LADCO region. Figure 3-6 shows example diurnal profiles for the coal fuel type in the
Mid-Atlantic Northeast Visibility Union (MANE-VU) region.
117
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Figure 3-5. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type
Daily Small EGU Profile for LADCO gas
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-------
one of the four fuel types: coal, gas, oil, and other. A fuel type unit assignment is made by summing the
VOC, NOX, PM2.5, and S02 for all SCCs in the unit. The SCC that contributed the highest total emissions
to the unit for selected pollutants was used to assign the unit fuel type. Peaking units were identified as
any unit with an oil, gas, or oil fuel type with a NAICS of 22111 or 221112. Some units may be assigned
to a fuel type within a region that does not have an available input unit with a matching fuel type in that
region. These units without an available profile for their group were assigned to use the regional
composite profile. Municipal waste combustor and cogeneration units were identified using the NEEDS
primary fuel type and cogeneration flag, respectively, from the NEEDS database. Assignments for each
unit needing a profile were made using the regions shown in Figure 3-4.
3.3.4 Airport Temporal allocation (airports)
Airport temporal profiles were updated to 2022-specific temporal profiles for all airports other than
Alaska seaplanes. Hourly airport operations data were obtained from the Aviation System Performance
Metrics (ASPM) Airport Analysis website (https://aspm.faa.gov/apm/svs/AnalysisAP.asp). A report of
2022 hourly Departures and Arrivals for Metric Computation by airport was generated. An overview of
the ASPM metrics is at
https://aspmhelp.faa.gov/index/Aviation System Performance Metrics (ASPM).html. Figure 3-7 shows
examples of diurnal airport profiles for the Phoenix airport (PHX) and the default profile for Texas.
Month-to-day and Annual-to month temporal profiles were developed based on a separate query of the
2022 ASPM. A report of all airport operations (takeoffs and landings) by day for 2022 was generated.
Annual-to-month profiles were derived directly from the daily airport operations report and examples
are shown for Wisconsin and Atlanta in Figure 3-8.
For 2022, all airport SCCs (i.e., 2275*, 2265008005, 2267008005, 2268008005 and 2270008005) were
assigned to individual commercial airports where a match could be made between the inventory facility
and the FAA identifier in the ASPM derived data. State average profiles were calculated as the average
of the temporal fractions for all airports within a state. The state average profiles were assigned by state
to all airports in the inventory that did not have an airport specific match in the ASPM data. Package
processing hubs at the Memphis (MEM), Indianapolis (IND), Louisville (SDF), and Chicago Rockford (RFD)
airports produced peaks in the average state profiles at times not typical for activity in smaller
commercial airports. These packaging hubs were removed from the state averages. Airports that
required state-defaults in states lacking ASPM data use national average profiles calculated from the
average of the state temporal profiles.
Alaska seaplanes, which are outside the CONUS domain use the monthly profile in Figure 3-9. These
were assigned based on the facility ID.
119
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Figure 3-7. 2022 Airport Diurnal Profiles for PHX and state of Texas
2022 FAA Airport Diurnal Profile: PHX
120
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Figure 3-8. 2022 Wisconsin and Atlanta annual-to-month profile for airport emissions
2022 FAA State Monthly Profile: Wl default
2022 FAA Airport Monthly Profile: ATL
121
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Figure 3-9. Alaska seaplane monthly profile
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3.3.5 Residential Wood Combustion Temporal allocation (rwc)
There are many factors that impact the timing of when emissions occur, and for some sectors this
includes meteorology. The benefits of utilizing meteorology as a method for temporal allocation can be
realized when: (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 livestock
and fertilizer setors.
Gentpro reads in gridded meteorological data (output from MCIP) along with spatial surrogates and uses
the specified algorithm to produce a new temporal profile that can be input into SMOKE. The
meteorological variables and the resolution of the generated temporal profile (hourly, daily, etc.)
depend on the selected algorithm and the run parameters. For more details on the development of
these algorithms and running Gentpro, see the Gentpro documentation and the SMOKE documentation
at
http://www.cmascenter.Org/smoke/documentation/3.l/GenTPRQ TechnicalSummary Aug2012 Final.p
df and https://www.cmascenter.Org/smoke/documentation/4.5/html/ch05s03s05.html. respectively.
For the rwc sector, two different algorithms for calculating temporal allocation are used. For most SCCs
in the sector, in which wood burning is more prominent on colder days, Gentpro was used to compute
annual-to-daily temporal profiles based on the daily minimum temperature. These profiles distribute
annual RWC emissions to the coldest days of the year. On days where the minimum temperature does
not drop below a user-defined threshold, RWC emissions for most sources in the sector are zero.
Conversely, the program temporally allocates the largest percentage of emissions to the coldest days.
122
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Similar to other temporal allocation profiles, the total annual emissions do not change, only the
distribution of the emissions within the year is affected. The temperature threshold for RWC emissions
was 50 °F for most of the country, and 60 °F for the following states: Alabama, Arizona, California,
Florida, Georgia, Louisiana, Mississippi, South Carolina, and Texas. The algorithm is as follows:
If Td >= Tt: no emissions that day
If Td < Tt: daily factor = 0.79*(Tt -Td)
where (Td = minimum daily temperature; Tt = threshold temperature, which is 60 degrees F in southern
states and 50 degrees F elsewhere).
Once computed, the factors were normalized to sum to 1 to ensure that the total annual emissions are
unchanged (or minimally changed) during the temporal allocation process.
Figure 3-10 illustrates the impact of changing the temperature threshold for a warm climate county. The
plot shows the temporal fraction by day for Duval County, Florida, for the first four months of 2007. The
default 50 °F threshold creates large spikes on a few days, while the 60 °F threshold dampens these
spikes and distributes a small amount of emissions to the days that have a minimum temperature
between 50 and 60 °F.
Figure 3-10. Example of RWC temporal allocation using a 50 versus 60 °F threshold
RWC temporal profile, Duval County, FL, Jan - Apr
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Starting with the 2022 emissions modeling platform, a separate algorithm is used to determine temporal
allocation of recreational wood burning, e.g., fire pits (SCC 2104008700) and is applied by Gentpro.
Recreational wood burning depends on both minimum and maximum daily temperatures by county and
also uses a day-of-week temporal profile (61500) in which emissions are much higher on weekends than
on weekdays. According to the recreational wood burning algorithm, the only days with receive
emissions are those in which the temperature falls within a range of 50°F and 80°F at some point during
the day. On days when the maximum temperature is less than 50°F or the minimum temperature is
above 80°F, the daily temporal factor is zero. For all other days, the day-of-week profile 61500 is applied,
which has 33% of the emissions on each weekend day and lower emissions on weekdays. An example is
shown in Figure 3-11. As a result of applying this algorithm, northern states have more recreational
wood burning in summer months while southern states show a flatter pattern with emissions distributed
more evenly throughout the year.
123
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Figure 3-11. Example of Annual-to-day temporal pattern of recreational wood burning emissions
The diurnal profile used for most RWC sources (see Figure 3-12) places more of the RWC emissions in
the morning and the evening when people are typically using these sources. This profile is based on a
2004 MANE-VU survey (MANE-VU, 2004). This profile was created by averaging three indoor and three
RWC outdoor temporal profiles from counties in Delaware and aggregating them into a single RWC
diurnal profile. This new profile was compared to a concentration-based analysis of aethalometer
measurements in Rochester, New York (Wang et al. 2011) for various seasons and days of the week and
was found that the new RWC profile generally tracked the concentration based temporal patterns.
Figure 3-12. RWC diurnal temporal profile
Comparison of RWC diurnal profile
124
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The temporal profiles for hydronic heaters" (i.e., SCCs=2104008610 [outdoor], 2104008620 [indoor],
and 2104008620 [pellet-fired]) are not based on temperature data, because the meteorologically based
temporal allocation used for the rest of the rwc sector did not agree with observations for how these
appliances are used.
For hydronic heaters, the annual-to-month, day-of-week and diurnal profiles were modified based on
information in the New York State Energy Research and Development Authority's (NYSERDA)
"Environmental, Energy Market, and Health Characterization of Wood-Fired Hydronic Heater
Technologies, Final Report" (NYSERDA, 2012), as well as a Northeast States for Coordinated Air Use
Management (NESCAUM) report "Assessment of Outdoor Wood-fired Boilers" (NESCAUM, 2006). A
Minnesota 2008 Residential Fuelwood Assessment Survey of individual household responses (MDNR,
2008) provided additional annual-to-month, day-of-week, and diurnal activity information for outdoor
hydronic heaters (OHH) as well as recreational RWC usage.
Data used to create the diurnal profile for hydronic heaters, shown in Figure 3-13, are based on a
conventional single-stage heat load unit burning red oak in Syracuse, New York.
Annual-to-month temporal allocation for OHH was computed from the MDNR 2008 survey and is
illustrated in Figure 3-14. The hydronic heater emissions still exhibit strong seasonal variability, but do
not drop to zero because many units operate year-round for water and pool heating.
Figure 3-13. Data used to produce a diurnal profile for hydronic heaters
125
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Figure 3-14. Monthly temporal profile for hydronic heaters
3.3.6 Agricultural Ammonia Temporal Profiles (livestock)
For the livestock sector, day-specific emissions are based on the output from the livestock waste
emissions model (FEM). Agricultural GenTPRO temporal allocation was applied to livestock emissions
and to all pollutants within the sector and was used to temporally allocate the FEM-computed daily
emissions to the hourly level. FEM day specific emissions were used directly for broilers, layers, beef
cows, dairy cows, and swine. For temporal allocation purposes, turkeys follow the FEM daily temporal
distributions for broilers, while horses, sheep, and goats follow the FEM daily temporal distributions for
dairy cows. Figure 3-15 shows some example plots of daily emissions by animal type in different parts of
the country, as computed by the FEM model.
To develop day-to-hour temporal profiles of livestock emissions, GenTPRO was run using the
"BASH_NH3" profile method to create for these sources The GenTPRO algorithm is based on an
equation derived based on the Zhu, Henze, et al. (2014) empirical equation. Figure 3-16 shows hourly
distributions of emissions for a typical summer day, as computed by Gentpro, for the same counties and
animal types as Figure 3-15. A general non-Gentpro hourly profile (profile 26) is also included in Figure
3-16 for comparison.
126
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Figure 3-15. Examples of 2022v2 livestock daily emissions profiles
2022v2 Tulare County, CA (06107) dairy NH3
2022v2 Deaf Smith Cou nty, TX (48117) beef NH3
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Sample hourly temporal profiles for 2022v2 livestock, 7/15/2022
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hour-of-day (local time)
•06107 daiiy 37163 swliie -^—48117 beef lOOOSbroilers —i>rofile26
127
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3.3.7 Oil and gas temporal allocation (np_oilgas)
Monthly temporalization of np_oilgas emissions is based primarily on year-specific monthly factors from
the Oil and Gas Tool (OGT) for 2022v2. Factors were specific to each county and SCC. For use in SMOKE,
each unique set of factors was assigned a label (starting from OG22M_0001 and counting upward), and
then a SMOKE-formatted ATPRO_MONTHLY and an ATREF were developed. This dataset of monthly
temporal factors included profiles for all counties and SCCs in the Oil and Gas Tool inventory. Because
we are using non-tool datasets in some states, this monthly temporalization dataset did not cover all
counties and SCCs in the entire inventory used for this study. To fill in the gaps in those states, state
average monthly profiles for oil, natural gas, and combination sources were calculated from Energy
Information Administration (EIA) data and assigned to each county/SCC combination not already
covered by the OGT monthly temporal profile dataset. Coal bed methane (CBM) and natural gas liquid
sources were assigned flat monthly profiles where there was not already a profile assignment in the
dataset.
3.3.8 Onroad mobile temporal allocation (onroad)
For the onroad sector, the temporal distribution of emissions is a combination of traditional temporal
profiles and the influence of meteorology. For the 2022v2 platform, EPA utilized both the FHWA's Travel
Monitoring and Analysis System (TMAS) and telematics vehicle activity data for all months of 2022 and
months January through May of 2023 from StreetLight. Information from these datasets was converted
into MOVES and SMOKE model inputs. Data from the years 2022 and 2023 were used to generate daily
and hourly temporal profiles, as well as average speed distributions by county, weekday/weekend, hour
of day, and SCC.
The "inventories" referred to in Table 3-13 consist of activity data for the onroad sector, not emissions.
VMT is the activity data used for on-network rate-per-distance (RPD) processes. The off-network
emissions from the rate-per-profile (RPP) and rate-per-vehicle (RPV) processes use the VPOP activity
data, which are annual and do not need temporal allocation. For rate-per-hour (RPH) processes that
result from hoteling of combination trucks, the HOTELING inventory is annual and was temporalized to
month, day of the week, and hour of the day through temporal profiles. Day-of-week and hour-of-day
temporal profiles are also used to temporalize the starts activity used for rate-per-start (RPS) processes,
and the off-network idling (ONI) hours activity used for rate-per-hour-ONI (RPHO) processes. The
inventories for starts and ONI activity contain monthly activity so that monthly temporal profiles are not
needed.
For on-roadway RPD processes, the VMT activity data are annual for some sources and monthly for
other sources, depending on the source of the data. Sources without monthly VMT were temporalized
from annual to month through temporal profiles. VMT was also temporalized from month-to-day of the
week, and then to hourly through temporal profiles. The RPD processes also use hourly speed
distributions (SPDIST) as discussed in Section 2.3.
For onroad, the temporal profiles and SPDIST will impact not only the distribution of emissions through
time but also the total emissions. SMOKE-MOVES calculates emissions for RPD processed based on the
VMT, speed and meteorology. Thus, if the VMT or speed data were shifted to different hours, it would
align with different temperatures and hence different emission factors. In other words, two SMOKE-
MOVES runs with identical annual VMT, meteorology, and MOVES emission factors, will have different
total emissions if the temporal allocation of VMT changes. Figure 3-17 (an example taken from 2021)
128
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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, the meteorologically varying emission
factors add variation on top of the temporal allocation of the activity data.
Figure 3-17. Example temporal variability of VMT compared to onroad NOx emissions
Wake County, NC 2021 VMT and Onroad NOx emissions
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Meteorology is not used in the development of the temporal profiles, but rather it impacts the
calculation of the hourly emissions through the program Movesmrg. The result is that the emissions vary
at the hourly level by grid cell. More specifically, the on-network (RPD) and the off-network parked and
stationary vehicle (RPV, RPH, RPHO, RPS, and RPP) processes use the gridded meteorology (MCIP) either
directly or indirectly. For RPD, RPV, RPH, RPHO, and RPS, Movesmrg determines the temperature for
each hour and grid cell and uses that information to select the appropriate emission factor for the
specified SCC/pollutant/mode combination. For RPP, instead of reading gridded hourly meteorology,
Movesmrg reads gridded daily minimum and maximum temperatures. The total of the emissions from
the combination of these six processes (RPD, RPV, RPH, RPHO, RPS, and RPP) comprise the onroad sector
emissions. In summary, the temporal patterns of emissions in the onroad sector are influenced by
meteorology.
Month-of-year temporal profiles for VMT were developed from FHWA's Travel Monitoring and Analysis
System (TMAS) and are unchanged from 2022vl platform. This system measures monthly traffic volume,
by class and weight. TMAS data was processed for each state, month, and vehicle class. Data were
provided for motorcycles (11), passenger vehicles (21), light duty trucks (30s), buses (40s), single unit
trucks (50s), and combination short-haul trucks (61), and combination long-haul trucks (62). The dataset
includes temporal profiles for individual states, utilized the Figure 3-18 shows an annual, by month plot
of TMAS data for Montana. Note that there is an increase in passenger cars and light duty trucks during
the month of July. This may be due to an increase in tourism during the warmer months.
129
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Figure 3-18 TMAS Data: VMT Fraction by Month for Montana by Vehicle Type
TMAS Data: VMT Fraction v. Month by State
state=Montana
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Day-of-week and hour-of-day temporal profiles and speed data for the 2022v2 platform are derived
from StreetLight data. Day-of-week profiles vary by county, road type, month, and for three vehicle
classes (i.e., light-duty, commercial medium-duty, and commercial heavy-duty), while hour-of-day
profiles vary by county, road type, day of week, and the saOme three vehicle classes. No submitted
temporal profiles were carried forward from the 2020 NEI. 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.
Figure 3-19 shows example day-of-week fractions for VMT on urban freeways and non-freeways. The
non-freeways show a steeper drop off on weekends than the freeways for all vehicle classes. Figure 3-20
shows national average StreetLight VMT fraction by vehicle class for urban non-freeways. The first plot
shows hour of the day for a weekday in local time. Note that you can see the rush hour in the morning
and the evening. The second plot shows hour of day for a weekend day. Figure 3-21 shows example
vehicle speeds on urban freeways and non-freeways in local time. The freeways show higher speeds
and a more pronounced dip in speeds during rush hours than the non-freeways.
130
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Figure 3-19. Example Day of Week Fractions for VMT on Urban Freeways and non-Freeways
Urban Freeways
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0
Mon Tue Wed Thu Fri Sat Sun
Urban non-freeways
PERSONAL
-COMM-MD
COMM-HD
0.25
c
o
u
ro
03
Q
0.2
0.15
0.1
>• 0.05
0
Mon Tue Wed Thu Fri Sat Sun
PERSONAL
COMM-MD
COMM-HD
131
-------
Figure 3-20. National Average VMT Fraction by Hour of Day (weekday and weekend)
Weekday
0.1
u
CO
3
o
X
0.08
2 0.06
0.04
0.02
0
1 3 5 7 9 1113151719 2123
Weekend
0.1
PERSONAL
•COMM-MD
•COMM-HD
c
o
"•f-1
u
CO
zs
o
X
0.08
0.06
0.04
0.02
0
PERSONAL
¦COMM-MD
-COMM-HD
1 3 5 7 9 1113151719 2123
132
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Figure 3-21, Example Vehicle Speeds for Weekdays by hour Urban Freeways and non-Freeways
Urban Freeways
E
"O
CD
OJ
CL
CO
Urban non-freeways
Q.
E,
T3
QJ
CU
Q.
GO
Temporal profiles for RPHO are based on the same temporal profiles as the on-network processes in
RPD, but since the on-network profiles are road-type-specific and ONI is not road-type-specific, the
RPHO profiles were assigned to use rural unrestricted profiles for counties considered "rural" and urban
unrestricted profiles for counties considered "urban." RPS uses the same day-of-week profiles as on-
network processes in RPD, but uses a separate set of diurnal temporal profiles specifically for starts
activity. For starts, there are two hour-of-day temporal profiles for each source type, one for weekdays
and one for weekends. The starts diurnal temporal profiles are applied nationally and are based on the
default starts-hour-fraction tables from MOVES.
70
60
50
40
30
20
10
0
PERSONAL
1 LI \Uvl NHL
COMM-MD
V/ V-/ 1 II 1 II L/
COMM-HD
1
3
5
7
9
11 13 15 17 19 21 23
70
60
50
40
30
20
10
0
—PFRSONAI
1 l_ 1 \W 1 N n 1_
COMM-MD
COMM-HD
V-/ v—/ 1 1 1 1 II L/
1
3
5
7
9
11 13 15 17 19 21 23
133
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3.3.9 Nonroad mobile temporal allocation (nonroad)
For nonroad mobile sources, temporal allocation is performed differently for different SCCs, Beginning
with the final 2011 platform, improvements to temporal allocation of nonroad mobile sources were
made to make the temporal profiles more realistically reflect real-world practices. The specific updates
were made for agricultural sources (e.g., tractors), construction, and commercial residential lawn and
garden sources, in the 2022vl platform, temporal profiles for residential and commercial snowblowers
were changed to be flat for each day of the week since snowfall is not influenced by the day of the week.
Figure 3-22 shows two previously existing temporal profiles (9 and IS) and a newer temporal profile (19)
which has lower emissions on weekends. In this platform, construction and commercial lawn and garden
sources use the new profile 19 which has lower emissions on weekends. Residental lawn and garden
sources continue to use profile 9 and agricultural sources continue to use profile 19.
Figure 3-22. 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
mood a/ tuesday Wednesday thursday friday Saturday Sunday
Figure 3-23 shows the previously existing temporal profiles 26 and 27 along with newer temporal
profiles (25a and 26a) which have lower emissions overnight. In this platform, construction sources use
profile 26a. Commercial lawn and garden and agriculture sources use the profiles 26a and 25a,
respectively. Residental lawn and garden sources use profile 27.
Figure 3-23. Example Nonroad Diurnal Temporal Profiles
Hour of Day Profiles
0.11
26 a-New .^—27 25a-New 26
134
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For the nonroad sector, the modeling platform uses monthly inventories from output from MOVES. For
California, CARB's annual inventory was temporalized to monthly using monthly temporal profiles
applied in SMOKE by SCC.
3.3.10 Fugitive dust temporal profiles (afdust)
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used
to reduce the total emissions based on meteorological conditions. These adjustments are applied
through sector-specific scripts, beginning with the application of land use-based gridded transport
fractions and then subsequent zero-outs for hours during which precipitation occurs or there is snow
cover on the ground ("met adjusted"). The land use data used to reduce the NEI emissions explain the
amount of emissions that are subject to transport. This methodology is discussed in (Pouliot et al.,
2010), and in "Fugitive Dust Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). The
precipitation adjustment is applied to remove all emissions for hours where measurable rain occurs, or
where there is snow cover. Therefore, the afdust emissions vary day-to-day based on the precipitation
and/or snow cover for each grid cell and hour. Both the transport fraction and meteorological
adjustments are based on the gridded resolution of the platform; therefore, slightly different emissions
will result from different grid resolutions.
Starting with the 2022vl platform, some changes were made to temporal profiles in the afdust sector:
• New temporal profiles (monthly, weekly, hourly) were created for paved and unpaved road dust.
The monthly profile is based on monthly emissions from the 2022hc onroad PM2.5 brake and tire
wear, since that has less temperature dependence than other pollutants and process. Weekly
and hourly profiles are based on averages of the TMAS profiles used in SMOKE-MOVES. Unpaved
road dust profiles use averages of passenger trucks only; paved road dust profiles use weighted
averages of 3/4 light-duty vehicle emissions excluding motorcycles, and 1/4 heavy duty emissions
excluding buses. There are separate hourly profiles for weekdays vs weekends.
• For agricultural tilling, flat day-of-week profiles are now being used along with new monthly
profiles mostly based on nonroad ag emissions. The monthly nonroad ag profiles are based on
LADCO-provided MOVES data and more accurately reflect tilling activities, peaking in spring and
fall.
• For dust from livestock, the monthly profiles for 2805100010 and 2805100050 (beef cattle and
swine) were updated to the 2022 data from https://u.osu.edu/beef/2023/10/25/more-heifers-
supporting-feedlot-inventory/. Profiles for other livestock dust are not changed from the 2020
platform.
3.3.11 Additional sector specific details (beis, cmv, rail, nonpt, np solvents,
ptfire-rx, ptfire-wild)
Starting with the 2022vl platform, some changes were made to temporal profiles in the nonpt sector:
• Evaporative SCCs starting with 250105 and 250106 were updated to use monthly temporal
profiles based on monthly total VOC emissions computed from the 2022hc onroad evaporative
off-network processes in the RPP and RPV subsectors contained in the final 2022 onroad
emissions.
135
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• Residential natural gas (SCC 2104006000) monthly temporal profiles were derived for each state
based on Energy Information Administration (EIA) data for 2022.
Starting with the 2022vl platform, some changes were made to temporal profiles in the np_solvents
sector:
• All asphalt SCCs (paving and roofing) are using new ElA-based monthly profiles for "asphalt and
road oil" by PADD region. The data source is
https://www.eia.gov/dnav/pet/PET CONS PSUP A EPPA VPP MBBL A.htm.
• Architectural coating (2401001000): created a new monthly profile PAINT22 based on 2022 data
from https://fred.stlouisfed.org/series/MRTSSM44412USN/.
• Pesticides (SCCs 2461850000, 2461800001, and 2460800000): monthly profiles were changed as
follows: AZ/CA/FL/HI/TX (the warmest states) are flat annual. Other moderately warm southeast
states from North Carolina south and west to Oklahoma are flat from March through October,
and zero in other months. All other states are flat from April through September and zero in
other months.
Biogenic emissions from the BEIS model vary by hourly because they are developed using meteorological
data including temperature, surface pressure, and radiation/cloud data. The emissions are computed
using appropriate emission factors according to the vegetation in each model grid cell, while taking the
meteorological data into account.
For the cmv sectors, most areas use hourly emission inventories derived from the 5-minute AIS data.
For the rail sector, monthly profiles from the 2016 platform were used. Monthly temporal allocation for
rail freight emissions is based on AAR Rail Traffic Data, Total Carloads and Intermodal, for 2016. For
passenger trains, monthly temporal allocation is flat for all months. Rail passenger miles data is available
by month but it is not known how closely rail emissions track with passenger activity since passenger
trains run on a fixed schedule regardless of how many passengers are aboard, and so a flat profile is
chosen for passenger trains. Rail emissions are allocated with flat day of week profiles, and most
emissions are allocated with flat hourly profiles.
For the ptagfire sector, the inventories are in the daily point fire format FF10 PTDAY. The diurnal
temporal profile for ag fires reflects the fact that burning occurs during the daylight hours - see Figure
3-24 (McCarty et al., 2009). This profile puts most of the emissions during the work-day and suppresses
the emissions during the middle of the night.
136
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Figure 3-24. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
Industrial processes that are not likely to shut down on Sundays, such as those at cement plants, use
profiles that include emissions on Sundays, while those that would typically shut down on Sundays use
profiles that reflect Sunday shutdowns.
For the ptfire sectors, the inventories are in the daily point fire format FF10 PTDAY, so temporal profiles
are only used to go from day-specific to hourly emissions. Separate hourly profiles for prescribed and
wildfires were used. For ptfire, state-specific hourly profiles were used, with distinct profiles for
prescribed fires and wildfires. Figure 3-25 below shows the profiles used for each state for the platform.
The wildfire diurnal profiles are similar but vary according to the average meteorological conditions in
each state. For all agricultural burning, the diurnal temporal profile used reflects the fact that burning
occurs during the daylight. This puts most of the emissions during the workday and suppresses the
emissions during the middle of the night. This diurnal profile was used for each day of the week for all
agricultural burning emissions in all states.
Figure 3-25. Prescribed and Wildfire diurnal temporal profiles
US Prescribed Fire diurnal profile: Flaming vs residual
smoldering example
0.18
0.16
/ V
0.14
~ \
0.12
y rK \
0.1
r / \\
0.08
/ / \ \
0.06
/ / Vt:
0.04
0.02
/y \
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
FLAME7_10LST RESID7_10LST
Wildfire diurnal profiles: State
0.01
o.oo
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
ID — IL IN IA KS — KY IA — ME MD«^— MA
— Ml MN MS MO MT NE NV NH NJ NM
NY NC ND OH OK OR PA Rl SC — SD
—¦TN —TX UT VT — VA WA WV Wl - WY
137
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3.4 Spatial Allocation
For the modeling platform, spatial factors are typically applied by county and SCC. Spatial allocation was
performed for the 12US1 grid shown in Section 3.1. To accomplish this, SMOKE used national 12-km
spatial surrogates and a SMOKE area-to-point data file. For the U.S., the surrogates mostly utilize circa
2020 geographic data. The U.S., Mexican, and Canadian spatial surrogates cover the entire CONUS
domain 12US1 shown in Figure 3-1. While highlights of information are provided below, the file
Surrogate specifications 2022\j2 platform US Can Mex.xlsx documents the complete configuration
for generating the surrogates and can be referenced for more details.
3.4.1 Spatial Surrogates for U.S. emissions
There are more than 90 spatial surrogates available for spatially allocating U.S. county-level emissions to
the grid cells used by the air quality model. As described in Section 3.4.2, an area-to-point approach
overrides the use of surrogates for airport refueling sources and off-road diesel trucks at mines in
St. Louis County, Minnesota.
The surrogates for the platform are based on a variety of geospatial data sources, including the
American Community Survey (ACS) for census-related data and the National Land Cover Database
(NLCD). Onroad surrogates are based on average annual daily traffic counts (AADT) from the highway
monitoring performance system (HPMS). New for the 2022v2 platform, Federal Emergency
Management Agency (FEMA) structure data, were used to spatially allocate emissions from residential
wood combustion.
When developing modeling platforms, EPA routinely updates surrogates to utilize updated versions of
the underlying surrogate databases or to use a different source of data when it is deemed more
representative for a particular source category. Spatial surrogates for the U.S. for this platform were
developed as follows:
County boundaries for all surrogates are from the 2020 Topological^ Integrated Geographic
Encoding and Referencing (TIGER) boundaries.
Oil and gas activity data for other than abandoned wells were from the year 2022 and were
slightly updated from those used for the 2022vl platform by using the latest available data for
2022v2.
The 5-year 2020 ACS data were used to derive surrogates for population, housing, and residential
heating.
The NLCD 2019 data were used to derive surrogates for land, water, agriculture, and various
levels of development.
Animal specific livestock waste surrogates were derived from National Pollutant Discharge
Elimination System (NPDES) 2020 animal operation water permits and Food and Agriculture
Organization (FAO) 2010 gridded livestock count data.
Surrogates for fuel stations, asphalt surfaces, and unpaved roads are based on data extracted
from the OpenStreetMap database in 2024.
Gravel and lead mines use separate surrogates based on the more general United States
Geological Survey (USGS) mining surrogate circa 2024.
138
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Residential wood combustion surrogates are based on a combination of 2020 ACS population
and FEMA residential structure data circa 2025.
Roadway locations, miles, and AADT for 2022 from the Federal Highway Administration (FHWA)
HPMS. This was an update from the 2022vl platform, which used HPMS data for 2017.
The 2022v2 platform uses the following spatial surrogates for RWC emissions sources:
"All Residential Structures" (111), "Single Family + Manufactured + small Multi-family Residential
Structures" (112), and "Single Family Residential Structures" (113). Surrogate 111 is used for fireplaces
(SCC 2104008100) and fire logs (SCC 2104009000), surrogate 113 is used for hydronic heaters (SCCs
21040086*), and 112 is used for all other RWC SCCs. For the 2022v2 platform EPA examined surrogates
for RWC emissions based on housing data from the ACS. The particular attributes previously used were:
single family detached, single family attached, dual family and mobile home and combinations of these,
depending on the particular RWC specific source category. However, evaluation of the ACS block group-
level RWC surrogates against satellite basemaps showed that emissions were allocated to locations
without residential structures, such as forests and industrial areas.
To further constrain emissions to locations with residential wood combustion activity the RWC spatial
surrogates in the 2022v2 platform incorporated FEMA structure data (https://gis-
fema.hub.arcgis.com/pages/usa-structures). For each census block group, FEMA polygons were subset
to residential polygons. If structures identified as residential were not available in the Census block
group other structure types were used to gap-fill the dataset. The ACS housing data are considered more
complete and accurate than the FEMA structure data in terms of unit count and structure classification,
therefore each FEMA structure was assigned a weighting factor based on the total ACS units by type
(e.g. detached, attached, multi-family, etc.) divided by total FEMA structures by type in each block
group. An improvement resulting from including structures is that emissions are allocated only to
residential areas with structures instead of being uniformly distributed over the entire area of the
censes block group and therefore the approach including structure data was used. A comparison of the
2022v2 RWC emissions of PM2.5 gridded with each of these approaches is shown in Figure 3-26 and
Figure 3-27.
139
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Figure 3-26. 2022v2 Residential Wood Combustion Emissions using ACS-based Surrogate
2022v2 RWC PM2.5 - ACS Only Based Surrogates, Total Emissions
Figure 3-27. 2022v2 Residential Wood Combustion Emissions using ACS and FEMA structure-based
Surrogate
2022v2 RWC PM2.5 - ACS with FEMA Structure Based Surrogates, Total Emissions
140
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Surrogates for the U.S. were generated using the Surrogate Tools DB with the Java-based Surrogate tools
used to perform gap filling and normalization where needed. The tool and documentation for the
original Surrogate Tool are available at https://www.cmascenter.org/sa-
tools/documentation/4.2/SurrogateToolUserGuide 4 2.pdf. and the tool and documentation for the
Surrogate Tools DB is available from https://www.cmascenter.org/surrogate tools db/. Table 3-14 lists
the codes and descriptions of the surrogates. Surrogate names and codes listed in italics are not directly
assigned to any sources in the platform, but they are sometimes used to gap fill other surrogates. When
the source data for a surrogate have no values for a particular county, gapfilling is used to provide values
for the spatial surrogate in those counties to ensure that no emissions are dropped when the spatial
surrogates are applied to the emission inventories. The Shapefiles used to develop the US surrogates
along with the attributes and filters used are shown in Table 3-15.
Table 3-14. U.S. surrogates available for the 2022v2 modeling platform
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2)
508
Public Schools
100
Population
650
Refineries and Tank Farms
110
Housing
669
All Abandoned Wells
111
All Residential Structures
6691
All Abandoned Oil Wells
112
Single Family + Manufactured + small Multi-
family Residential Structures
6692
All Abandoned Gas Wells
113
Single Family Residential Structures
6694
All Abandoned Oil Wells - Plugged
135
Detached Housing
6695
All Abandoned Gas Wells - Plugged
136
Single and Dual Unit Housing
6697
All Abandoned Oil Wells - Unplugged
150
Residential Heating - Natural Gas
6698
All Abandoned Gas Wells - Unplugged
170
Residential Heating - Distillate Oil
670
Spud Count - CBM Wells
180
Residential Heating-Coal
671
Spud Count - Gas Wells
190
Residential Heating - LP Gas
672
Gas Production at Oil Wells
205
Extended Idle Locations
674
Unconventional Well Completion Counts
239
Total Road AADT
676
Well Count - All Producing
240
Total Road Miles
677
Well Count - All Exploratory
242
All Restricted AADT
678
Completions at Gas Wells
244
All Unrestricted AADT
679
Completions at CBM Wells
258
Intercity Bus Terminals
681
Spud Count - Oil Wells
259
Transit Bus Terminals
683
Produced Water at All Wells
261
NTAD Total Railroad Density
6831
Produced Water at CBM Wells
271
NTAD Class 12 3 Railroad Density
6832
Produced Water at Gas Wells
300
NLCD Low Intensity Development
6833
Produced Water at Oil Wells
304
NLCD Open + Low
685
Completions at Oil Wells
305
NLCD Low + Med
686
Completions at All Wells
306
NLCD Med + High
687
Feet Drilled at All Wells
307
NLCD All Development
689
Gas Produced - Total
308
NLCD Low + Med + High
691
Well Counts-CBM Wells
309
NLCD Open + Low + Med
692
Spud Count - All Wells
141
-------
Code
Surrogate Description
Code
Surrogate Description
310
NLCD Total Agriculture
693
Well Count - All Wells
319
NLCD Crop Land
694
Oil Production at Oil Wells
320
NLCD Forest Land
695
Well Count - Oil Wells
321
NLCD Recreational Land
696
Gas Production at Gas Wells
340
NLCD Land
697
Oil Production at Gas Wells
350
NLCD Water
698
Well Count - Gas Wells
401
FAO 2010 Cattle
699
Gas Production at CBM Wells
4011
FAO 2010 Large Cattle Operations
711
Airport Areas
4012
NPDES 2020 Beef Cattle
801
Port Areas
4013
NPDES 2020 Dairy Cattle
850
Golf Courses
402
FAO 2010 Pig
860
Mines
4021
NPDES 2020 Swine
861
Sand and Gravel Mines
403
FAO 2010 Chicken
862
Lead Mines
4031
NPDES 2020 Chicken
863
Crushed Stone Mines
404
FAO 2010 Goat
900
OSM Fuel
4041
NPDES 2020 Goat
901
OSM Asphalt Surfaces
405
FAO 2010 Horse
902
OSM Unpaved Roads
406
FAO 2010 Sheep
4071
NPDES2020 Turkey
Table 3-15. Shapefiles used to develop U.S. Surrogates
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
100
Population
ACS_2020_5YR_BG_pop_hu
POP2020
110
Housing
ACS_2020_5YR_BG_pop_hu
HU2020
111
All Residential Structures
structure_population_rwc_2022
occjabel IN
('single_familyVm
anufactured'/mul
ti_family_2_4'/m
ulti_family_5+')
112
Single Family + Manufactured
+ small Multi-family
Residential Structures
structure_population_rwc_2022
occjabel IN
('single_familyVm
anufactured'/mul
ti_family_2_4')
113
Single Family Residential
Structures
structure_population_rwc_2022
occjabel =
'single_family'
135
Detached Housing
ACS_2020_5YR_BG_pop_hu
detachedh
136
Single and Dual Unit Housing
ACS_2020_5YR_BG_pop_hu
Ittriunit
150
Residential Heating - Natural
Gas
ACS_2020_5YR_BG_pop_hu
UTIL GAS
170
Residential Heating - Distillate
Oil
ACS_2020_5YR_BG_pop_hu
FUEL_OIL
142
-------
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
180
Residential Heating - Coal
ACS_2020_5YR_BG_pop_hu
COAL
190
Residential Heating - LP Gas
ACS_2020_5YR_BG_pop_hu
LP GAS
205
Extended Idle Locations
pil_2019_06_24
rev truck
rev truck>0
239
Total Road AADT
hpms_roadways_2022_us_moves5
aadt
moves5type IN (
'2', '3', '4', '5')
240
Total Road Miles
hpms_roadways_2022_us_moves5
NONE
moves5type IN (
'2', '3', '4', '5')
242
All Restricted AADT
hpms_roadways_2022_us_moves5
aadt
moves5type IN (
'2', '4')
244
All Unrestricted AADT
hpms_roadways_2022_us_moves5
aadt
moves5type IN (
'3', '5')
259
Transit Bus Terminals
ntad_2016_ipcd
NONE
bus t=l
260
Total Railroad Miles
tiger_2014_rail
NONE
261
NTAD Total Railroad Density
ntad 2014 rail fixed
dens
RAILTYPE IN
(1,2,3)
271
NTAD Class 12 3 Railroad
Density
ntad 2014 rail fixed
dens
RAILTYPE=1
300
NLCD Low Intensity
Development
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE=22
304
NLCD Open + Low
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(21,22)
305
NLCD Low + Med
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(22,23)
306
NLCD Med + High
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(23,24)
307
NLCD All Development
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(21,22,23,24)
308
NLCD Low + Med + High
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(22,23,24)
309
NLCD Open + Low + Med
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(21,22,23)
310
NLCD Total Agriculture
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(81,82)
318
NLCD Pasture Land
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE=81
319
NLCD Crop Land
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE=82
320
NLCD Forest Land
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(41,42,43)
321
NLCD Recreational Land
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE IN
(21,31,41,42,43,5
2,71)
340
NLCD Land
nlcd_2019_land_cover_l48_20210604_5
OOmJI
NONE
GRIDCODE != 11
143
-------
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
350
N LCD Water
nlcd_2019_land_cover_l48_20210604_5
00m II
NONE
GRIDCODE=ll
401
FAO 2010 Cattle
fao_Cattle_2010_Da_nlcdproj_masked
DN
4011
FAO 2010 Large Cattle
Operations
fao_LargeCattle_2010_Da_nlcdproj_mas
ked
DN
4012
NPDES 2020 Beef Cattle
livestock_npdes_state_permits_subset
Population
Animal = 'Beef'
4013
NPDES 2020 Dairy Cattle
livestock_npdes_state_permits_subset
Population
Animal = 'Dairy'
402
FAO 2010 Pig
fao_Pig_2010_Da_nlcdproj_masked
DN
4021
NPDES 2020 Swine
livestock_npdes_state_permits_subset
Population
Animal = 'Swine'
403
FAO 2010 Chicken
fao_Chicken_2010_Da_nlcdproj_masked
DN
4031
NPDES 2020 Chicken
livestock_npdes_state_permits_subset
Population
Animal =
'Chicken'
404
FAO 2010 Goat
fao_Goat_2010_Da_nlcdproj_masked
DN
4041
NPDES 2020 Goat
livestock_npdes_state_permits_subset
Population
Animal = 'Goat'
405
FAO 2010 Horse
fao_Horse_2010_Da_nlcdproj_masked
DN
406
FAO 2010 Sheep
fao_Sheep_2010_Da_nlcdproj_masked
DN
4071
NPDES 2020 Turkey
livestock_npdes_state_permits_subset
Population
Animal = 'Turkey'
508
Public Schools
public_schools_2018_2019
TOTAL
650
Refineries and Tank Farms
eia 2015 us oil
NONE
669
All Abandoned Wells
AW ALL COUNTS 669 2022
ACTIVITY
6695
All Abandoned Gas Wells -
Plugged
AW GAS PLUGGED 6695 2022
ACTIVITY
6692
All Abandoned Gas Wells
AW_GAS_PLUGGED_UNPLUGGED_6692_
2022
ACTIVITY
6698
All Abandoned Gas Wells -
Unplugged
AW GAS UNPLUGGED 6698 2022
ACTIVITY
6694
All Abandoned Oil Wells -
Plugged
AW OIL PLUGGED 6694 2022
ACTIVITY
6691
All Abandoned Oil Wells
AW_OIL_PLUGGED_UNPLUGGED_6691_
2022
ACTIVITY
6697
All Abandoned Oil Wells -
Unplugged
AW OIL UNPLUGGED 6697 2022
ACTIVITY
670
Spud Count - CBM Wells
SPUD CBM 670 2022v2
ACTIVITY
671
Spud Count - Gas Wells
SPUD GAS 671 2022v2
ACTIVITY
672
Gas Production at Oil Wells
ASSOCIATED_GAS_PRODUCTION_672_20
22v2
ACTIVITY
673
Oil Production at CBM Wells
CONDENSATE_CBM_PRODUCTION_673_
2022v2
ACTIVITY
674
Unconventional Well
Completion Counts
COMPLETIONS_UNCONVENTIONAL_674_
2022v2
ACTIVITY
676
Well Count - All Producing
TOTAL PROD WELL 676 2022v2
ACTIVITY
677
Well Count - All Exploratory
TOTAL EXPL WELL 677 2022v2
ACTIVITY
678
Completions at Gas Wells
COMPLETIONS_GAS_678_2022v2
ACTIVITY
144
-------
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
679
Completions at CBM Wells
COMPLETIONS CBM 679 2022v2
ACTIVITY
681
Spud Count - Oil Wells
SPUD OIL 681 2022v2
ACTIVITY
683
Produced Water at All Wells
PRODUCED WATER ALL 683 2022v2
ACTIVITY
6831
Produced Water at CBM Wells
PRODUCED WATER CBM 6831 2022v2
ACTIVITY
6832
Produced Water at Gas Wells
PRODUCED WATER GAS 6832 2022v2
ACTIVITY
6833
Produced Water at Oil Wells
PRODUCED WATER OIL 6833 2022v2
ACTIVITY
685
Completions at Oil Wells
COMPLETIONS OIL 685 2022v2
ACTIVITY
686
Completions at All Wells
COMPLETIONS ALL 686 2022v2
ACTIVITY
687
Feet Drilled at All Wells
FEET DRILLED 687 2022v2
ACTIVITY
689
Gas Produced - Total
TOTAL GAS PRODUCTION 689 2022v2
ACTIVITY
691
Well Counts - CBM Wells
CBM WELLS 691 2022v2
ACTIVITY
692
Spud Count - All Wells
SPUD ALL 692 2022v2
ACTIVITY
693
Well Count - All Wells
TOTAL WELL 693 2022v2
ACTIVITY
694
Oil Production at Oil Wells
OIL PRODUCTION 694 2022v2
ACTIVITY
695
Well Count - Oil Wells
OIL WELLS 695 2022v2
ACTIVITY
696
Gas Production at Gas Wells
GAS PRODUCTION 696 2022v2
ACTIVITY
697
Oil Production at Gas Wells
CONDENSATE_GAS_PRODUCTION_697_2
022v2
ACTIVITY
698
Well Count - Gas Wells
GAS WELLS 698 2022v2
ACTIVITY
699
Gas Production at CBM Wells
CBM PRODUCTION 699 2022v2
ACTIVITY
711
Airport Areas
airport_area
area
801
Port Areas
Ports 2014NEI
area_sqmi
850
Golf Courses
usa_golf_courses_2019_10
NONE
860
Mines
usgs_mrds_active_mines
NONE
861
Sand and Gravel Mines
usgs_mrds_active_mines
NONE
CAT='Gravel'
862
Lead Mines
usgs_mrds_active_mines
NONE
CAT='Lead'
863
Crushed Stone Mines
usgs_mrds_active_mines
NONE
CAT='Stone'
900
OSM Fuel
osm_fuel_points_us_mar2023
NONE
901
OSM Asphalt Surfaces
osm_asphalt_surfaces_us_mar2023
NONE
902
OSM Unpaved Roads
osm_unpaved_roads_us_mar2023
NONE
The 'Data Shapefile' used for all of the U.S. surrogates except for those based on HPMS data is
cb_2020_us_county_500k, while the HPMS-based surrogates use hpms_roadways_2022_us_moves5.
Similarly, all surrogates use the GEOID. The gapfilling configuration for the surrogates is shown in Table
3-16. If there are no entries for a county for the primary surrogate, the values for the county from the
secondary surrogate are used. If there are also no entries for the secondary surrogate, the values for the
tertiary surrogate are used, with the quarternary surrogate being the final fallback. Typically, only
surrogates that should have values for all counties are selected as the quarternary surrogate. This
process is used to limit any emissions that could be dropped if there are emissions in the inventory in a
county for which the primary surrogate does not have values. It is important to note that once ga pf il ling
is performed, SMOKE does not know that emissions for that county were from a secondary, tertiary or
145
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quarternary surrogate and any reports will assign the emissions in gapfilled counties to the primary
surrogate.
Table 3-16. Surrogates used to gapfill U.S. surrogates for CONUS grids
SURROGATE
CODE
SURROGATE
SECONDARY
SURROGATE
TERTIARY
SURROGATE
QUARTERNARY
SURROGATE
100
Population
110
Housing
Population
111
All Residential Structures
112
Single Family + Manufactured
+ small Multi-family
Residential Structures
113
Single Family Residential
Structures
135
Detached Housing
NLCD Low Intensity
Development
136
Single and Dual Unit Housing
NLCD Low Intensity
Development
150
Residential Heating - Natural
Gas
Population
170
Residential Heating -
Distillate Oil
Housing
180
Residential Heating-Coal
Housing
190
Residential Heating - LP Gas
Housing
205
Extended Idle Locations
Total Road Miles
239
Total Road AADT
Total Road Miles
240
Total Road Miles
242
All Restricted AADT
Total Road Miles
244
All Unrestricted AADT
Total Road Miles
259
Transit Bus Terminals
Population
NLCD Land
260
Total Railroad Miles
Total Road Miles
Population
261
NTAD Total Railroad Density
Total Railroad Miles
Total Road Miles
Population
271
NTAD Class 12 3 Railroad
Density
NTAD Total Railroad
Density
Total Railroad Miles
Total Road Miles
300
NLCD Low Intensity
Development
Housing
Population
NLCD Land
304
NLCD Open + Low
Housing
Population
NLCD Land
305
NLCD Low + Med
Housing
Population
NLCD Land
306
NLCD Med + High
Housing
Population
NLCD Land
307
NLCD All Development
Housing
Population
NLCD Land
308
NLCD Low + Med + High
Housing
Population
NLCD Land
309
NLCD Open + Low + Med
Housing
Population
NLCD Land
310
NLCD Total Agriculture
NLCD Open + Low
NLCD Land
318
NLCD Pasture Land
Housing
NLCD Land
319
NLCD Crop Land
Housing
NLCD Land
320
NLCD Forest Land
Housing
NLCD Land
321
NLCD Recreational Land
Housing
NLCD Land
146
-------
SURROGATE
CODE
SURROGATE
SECONDARY
SURROGATE
TERTIARY
SURROGATE
QUARTERNARY
SURROGATE
340
NLCD Land
350
NLCD Water
401
FAO 2010 Cattle
NLCD Total Agriculture
NLCD Open + Low
4011
FAO 2010 Large Cattle
Operations
FAO 2010 Cattle
NLCD Total
Agriculture
NLCD Open + Low
4012
NPDES 2020 Beef Cattle
FAO 2010 Cattle
NLCD Total
Agriculture
NLCD Open + Low
4013
NPDES 2020 Dairy Cattle
FAO 2010 Large Cattle
Operations
NLCD Total
Agriculture
NLCD Open + Low
402
FAO 2010 Pig
NLCD Total Agriculture
NLCD Open + Low
4021
NPDES 2020 Swine
FAO 2010 Pig
NLCD Total
Agriculture
NLCD Open + Low
403
FAO 2010 Chicken
NLCD Total Agriculture
NLCD Open + Low
4031
NPDES 2020 Chicken
FAO 2010 Chicken
NLCD Total
Agriculture
NLCD Open + Low
404
FAO 2010 Goat
NLCD Total Agriculture
NLCD Open + Low
4041
NPDES 2020 Goat
FAO 2010 Goat
NLCD Total
Agriculture
NLCD Open + Low
405
FAO 2010 Horse
NLCD Total Agriculture
NLCD Open + Low
406
FAO 2010 Sheep
NLCD Total Agriculture
NLCD Open + Low
4071
NPDES 2020 Turkey
NLCD Total Agriculture
NLCD Open + Low
508
Public Schools
Population
NLCD Land
650
Refineries and Tank Farms
NLCD Low + Med
Population
NLCD Land
669
All Abandoned Wells
Well Count - All Wells
NLCD Open + Low
6695
All Abandoned Gas Wells -
Plugged
All Abandoned Gas
Wells
Well Count - All
Wells
NLCD Open + Low
6692
All Abandoned Gas Wells
Well Count - All Wells
NLCD Open + Low
6698
All Abandoned Gas Wells -
Unplugged
All Abandoned Gas
Wells
Well Count - All
Wells
NLCD Open + Low
6694
All Abandoned Oil Wells -
Plugged
All Abandoned Oil
Wells
Well Count - All
Wells
NLCD Open + Low
6691
All Abandoned Oil Wells
Well Count - All Wells
NLCD Open + Low
6697
All Abandoned Oil Wells -
Unplugged
All Abandoned Oil
Wells
Well Count - All
Wells
NLCD Open + Low
670
Spud Count - CBM Wells
Spud Count - All Wells
Well Count - All
Wells
671
Spud Count - Gas Wells
Well Count - Gas Wells
Well Count - All
Wells
672
Gas Production at Oil Wells
NLCD Open + Low
Well Count - Oil
Wells
Well Count - All
Wells
673
Oil Production at CBM Wells
Well Count-CBM
Wells
Well Count - All
Wells
NLCD Open + Low
674
Unconventional Well
Completion Counts
Completions at All
Wells
Well Count - All
Wells
NLCD Open + Low
676
Well Count - All Producing
Well Count - All Wells
NLCD Open + Low
677
Well Count - All Exploratory
Well Count - All Wells
NLCD Open + Low
678
Completions at Gas Wells
Spud Count - All Wells
Well Count - All
Wells
NLCD Open + Low
147
-------
SURROGATE
CODE
SURROGATE
SECONDARY
SURROGATE
TERTIARY
SURROGATE
QUARTERNARY
SURROGATE
679
Completions at CBM Wells
Spud Count - All Wells
Well Count - All
Wells
NLCD Open + Low
681
Spud Count - Oil Wells
Well Count - Oil Wells
Well Count - All
Wells
NLCD Open + Low
683
Produced Water at All Wells
Completions at All
Wells
Well Count - All
Wells
NLCD Open + Low
6831
Produced Water at CBM
Wells
Well Counts - CBM
Wells
Well Count - All
Wells
NLCD Open + Low
6832
Produced Water at Gas Wells
Well Count - Gas Wells
Well Count - All
Wells
NLCD Open + Low
6833
Produced Water at Oil Wells
Well Count - Oil Wells
Well Count - All
Wells
NLCD Open + Low
685
Completions at Oil Wells
Spud Count - All Wells
Well Count - All
Wells
NLCD Open + Low
686
Completions at All Wells
Well Count - All
Exploratory
Well Count - All
Wells
NLCD Open + Low
687
Feet Drilled at All Wells
Well Count - All
Exploratory
Well Count - All
Wells
NLCD Open + Low
689
Gas Produced - Total
Well Count - All Wells
NLCD Open + Low
691
Well Counts - CBM Wells
Completions at CBM
Wells
Well Count - All
Wells
NLCD Open + Low
692
Spud Count - All Wells
Completions at All
Wells
Well Count - All
Wells
NLCD Open + Low
693
Well Count - All Wells
NLCD Open + Low
694
Oil Production at Oil Wells
Completions at Oil
Wells
Well Count - All
Wells
NLCD Open + Low
695
Well Count - Oil Wells
Completions at Oil
Wells
Well Count - All
Wells
NLCD Open + Low
696
Gas Production at Gas Wells
Completions at Gas
Wells
Well Count - All
Wells
NLCD Open + Low
697
Oil Production at Gas Wells
Well Count - Gas Wells
Well Count - All
Wells
NLCD Open + Low
698
Well Count - Gas Wells
Completions at Gas
Wells
Well Count - All
Wells
NLCD Open + Low
699
Gas Production at CBM Wells
Well Counts - CBM
Wells
Well Count - All
Wells
NLCD Open + Low
711
Airport Areas
Population
NLCD Land
801
Port Areas
NLCD Water
850
Golf Courses
Housing
Population
NLCD Land
860
Mines
NLCD Open + Low
NLCD Land
861
Sand and Gravel Mines
Mines
NLCD Open + Low
NLCD Land
862
Lead Mines
Mines
NLCD Open + Low
NLCD Land
863
Crushed Stone Mines
Mines
NLCD Open + Low
NLCD Land
900
OSM Fuel
Total Road AADT
Total Road Miles
901
OSM Asphalt Surfaces
NLCD All Development
902
OSM Unpaved Roads
NLCD Open + Low
For the onroad sector, the on-network (RPD) emissions were spatially allocated differently from other
off-network processes (i.e., RPV, RPP, RPHO, RPS, RPH). Surrogates for on-network processes are based
148
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on AADT data and off network processes (including the off-network idling included in RPHO) are based
on land use surrogates as shown in Table 3-17. Emissions from the extended (i.e., overnight) idling of
trucks were assigned to surrogate 205, which is based on locations of overnight truck parking spaces.
The underlying data for roadway surrogates were updated from 2017 based Highway Performance
Monitoring System data to 2022 based data to better account for road network changes.
Table 3-17. Off-network mobile source surrogates
Source type
Source Type name
Surrogate ID
Description
11
Motorcycle
307
NLCD All Development
21
Passenger Car
307
NLCD All Development
31
Passenger Truck
307
NLCD All Development
32
Light Commercial Truck
308
NLCD Low + Med + High
41
Other Bus
306
NLCD Med + High
42
Transit Bus
259
Transit Bus Terminals
43
School Bus
508
Public Schools
51
Refuse Truck
306
NLCD Med + High
52
Single Unit Short-haul Truck
306
NLCD Med + High
53
Single Unit Long-haul Truck
306
NLCD Med + High
54
Motor Home
304
NLCD Open + Low
61
Combination Short-haul Truck
306
NLCD Med + High
62
Combination Long-haul Truck
306
NLCD Med + High
For the oil and gas sources in the np_oilgas sector, the spatial surrogates were updated to those shown
in Table 3-18 using 2022 data consistent with what was used to develop the nonpoint oil and gas
emissions. The exploration and production of oil and gas have generally increased in terms of quantities
and locations over recent years, primarily due to the use of new technologies, such as hydraulic
fracturing. Census-tract, 2-km, and 4-km sub-county Shapefiles were developed, from which the 2020 oil
and gas surrogates were generated. All spatial surrogates for np_oilgas are developed based on known
locations of oil and gas activity for year 2022. The oil and gas-related spatial surrogates in the 2022v2
platform were updated based on Shapefiles consistent with the 2022v2 oil and gas emissions, other than
the coal-bed methane abandoned well spatial surrogates that are based on data from the 2021 platform
and the remaining abandoned well surrogates that are based on data from the 2022vl platform. All
surrogate changes for oil and gas in 2022v2 were minor changes.
The primary activity data source used for the development of the oil and gas spatial surrogates was data
from ENVERUS [formerly Drilling Info (Dl) Desktop's HPDI] database (ENVERUS, 2023). This database
contains well-level location, production, and exploration statistics at the monthly level. Due to a
proprietary agreement with ENVERUS, individual well locations and ancillary production cannot be made
publicly available, but aggregated statistics are allowed. These data were supplemented with data from
state Oil and Gas Commission (OGC) websites (Alaska, Arizona, Idaho, Illinois, Indiana, Kentucky,
Louisiana, Michigan, Mississippi, Missouri, Nevada, Oregon, Pennsylvania, and Tennessee). In cases
when the desired surrogate parameter was not available (e.g., feet drilled), data for an alternative
surrogate parameter (e.g., number of spudded wells) were downloaded and used. Under that
methodology, both completion date and date of first production from HPDI were used to identify wells
149
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completed during 2022. The spatial surrogates were gapfilled using fallback surrogates as shown in Table
3-16. All gapfilling was performed with the Surrogate Tool.
Table 3-18. Spatial surrogates used for oil and gas Sources
Surrogate Code
Surrogate Description
669
All Abandoned Wells
6691
All Abandoned Oil Wells
6692
All Abandoned Gas Wells
6694
All Abandoned Oil Wells - Plugged
6695
All Abandoned Gas Wells - Plugged
6697
All Abandoned Oil Wells - Unplugged
6698
All Abandoned Gas Wells - Unplugged
670
Spud Count - CBM Wells
671
Spud Count - Gas Wells
672
Gas Production at Oil Wells
673
Oil Production at CBM Wells
674
Unconventional Well Completion Counts
676
Well Count - All Producing
677
Well Count - All Exploratory
678
Completions at Gas Wells
679
Completions at CBM Wells
681
Spud Count - Oil Wells
683
Produced Water at All Wells
685
Completions at Oil Wells
686
Completions at All Wells
687
Feet Drilled at All Wells
689
Gas Produced - Total
691
Well Counts-CBM Wells
692
Spud Count - All Wells
693
Well Count - All Wells
694
Oil Production at Oil Wells
695
Well Count - Oil Wells
696
Gas Production at Gas Wells
697
Oil Production at Gas Wells
698
Well Count - Gas Wells
699
Gas Production at CBM Wells
6831
Produced water at CBM wells
6832
Produced water at gas wells
6833
Produced water at oil wells
Table 3-19 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by sector assigned to each
spatial surrogate.
150
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Table 3-19. Selected 2022v2 CAP emissions by sector for U.S. surrogates (short tons in 12US1)
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
afdust
240
Total Road Miles
0
0
305,537
0
0
afdust
306
NLCD Med + High
0
0
41,167
0
0
afdust
308
NLCD Low + Med + High
0
0
122,726
0
0
afdust
310
NLCD Total Agriculture
0
0
502,702
0
0
afdust
861
Sand and Gravel Mines
0
0
271
0
0
afdust
863
Crushed Stone Mines
0
0
291
0
0
afdust
902
OSM Unpaved Roads
0
0
823,325
0
0
afdust
4012
NPDES2020 Beef Cattle
0
0
185,956
0
0
afdust
4013
NPDES 2020 Dairy Cattle
0
0
12,408
0
0
afdust
4021
NPDES 2020 Swine
0
0
630
0
0
afdust
4031
NPDES 2020 Chicken
0
0
4,948
0
0
afdust
4071
NPDES 2020 Turkey
0
0
1,948
0
0
fertilizer
310
NLCD Total Agriculture
1,671,402
0
0
0
0
livestock
405
FAO 2010 Horse
31,973
0
0
0
2,560
livestock
406
FAO 2010 Sheep
18,425
0
0
0
1,474
livestock
4012
NPDES 2020 Beef Cattle
775,290
0
0
0
61,885
livestock
4013
NPDES 2020 Dairy Cattle
350,829
0
0
0
28,168
livestock
4021
NPDES 2020 Swine
839,869
0
0
0
67,190
livestock
4031
NPDES 2020 Chicken
473,844
0
0
0
37,908
livestock
4041
NPDES 2020 Goat
17,609
0
0
0
1,409
livestock
4071
NPDES 2020 Turkey
82,538
0
0
0
6,603
nonpt
0
Area-to-point spatial allocation
0
126
4
12
26,843
nonpt
100
Population
454
0
0
0
36
nonpt
150
Residential Heating - Natural Gas
47,317
228,596
2,638
1,522
13,491
nonpt
170
Residential Heating - Distillate Oil
1,718
29,360
3,626
738
1,246
nonpt
180
Residential Heating - Coal
0
2
1
7
2
nonpt
190
Residential Heating - LP Gas
136
39,187
156
175
1,539
nonpt
239
Total Road AADT
0
0
0
0
6,536
nonpt
244
All Unrestricted AADT
0
0
0
0
98,151
nonpt
271
NTAD Class 12 3 Railroad Density
0
0
0
0
2,074
nonpt
300
NLCD Low Intensity Development
155
2,315
12,856
180
21,920
nonpt
306
NLCD Med + High
17,326
232,474
346,115
65,183
130,101
nonpt
307
NLCD All Development
0
0
0
0
19
nonpt
308
NLCD Low + Med + High
1,065
176,160
18,615
5,173
10,906
nonpt
310
NLCD Total Agriculture
517
311
504
31
440
nonpt
319
NLCD Crop Land
0
0
95
70
292
nonpt
320
NLCD Forest Land
0
0
3
0
3
nonpt
650
Refineries and Tank Farms
0
0
0
0
98,366
nonpt
711
Airport Areas
0
0
0
0
414
nonpt
801
Port Areas
0
0
0
0
2,351
nonpt
900
OSM Fuel
0
0
0
0
221,575
nonpt
4011
FAO 2010 Large Cattle Operations
0
0
0
0
295,993
151
-------
t/OC
32
,727
315
,597
,964
,475
,682
,447
,247
,336
,427
,608
,945
,091
423
43
,273
875
,312
694
,932
,585
,258
,014
,069
,442
1
,359
,732
,590
,387
,829
115
64
,197
,255
,025
,257
,687
,197
,466
,245
,011
ID
Description
NH3
NOX
PM2 5
Area-to-point spatial allocation
1,383
23
136
Single and Dual Unit Housing
100
14,573
2,944
261
NTAD Total Railroad Density
1,484
146
304
NLCD Open + Low
1,583
140
305
NLCD Low+ Med
867
1,027
306
NLCD Med + High
387
155,576
8,687
307
NLCD All Development
113
28,655
16,188
308
NLCD Low + Med + High
597
202,042
16,433
309
NLCD Open + Low + Med
134
21,884
1,309
310
NLCD Total Agriculture
355
214,915
14,943
320
NLCD Forest Land
15
1,614
378
321
NLCD Recreational Land
81
13,823
5,085
350
NLCD Water
203
111,414
3,862
850
Golf Courses
14
2,144
124
860
Mines
2,316
210
670
Spud Count - CBM Wells
0
671
Spud Count - Gas Wells
674
Unconventional Well Completion
Counts
13
14,496
190
678
Completions at Gas Wells
6,076
130
679
Completions at CBM Wells
681
Spud Count - Oil Wells
683
Produced Water at All Wells
434
685
Completions at Oil Wells
368
687
Feet Drilled at All Wells
24,786
656
689
Gas Produced - Total
276
26
691
Well Counts - CBM Wells
19,717
321
692
Spud Count - All Wells
15
694
Oil Production at Oil Wells
3,428
0
695
Well Count-Oil Wells
170,235
4,244
696
Gas Production at Gas Wells
2,689
698
Well Count - Gas Wells
349,563
5,007
699
Gas Production at CBM Wells
33
6694
All Abandoned Oil Wells - Plugged
6695
All Abandoned Gas Wells - Plugged
6697
All Abandoned Oil Wells - Unplugged
6698
All Abandoned Gas Wells - Unplugged
6831
Produced water at CBM wells
6832
Produced water at gas wells
6833
Produced water at oil wells
100
Population
240
Total Road Miles
306
NLCD Med + High
307
NLCD All Development
152
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
np_solvents
308
NLCD Low + Med + High
0
0
0
0
31,056
np_solvents
310
NLCD Total Agriculture
0
0
0
0
173,739
np_solvents
901
OSM Asphalt Surfaces
0
0
0
0
339,732
openburn
135
Detached Housing
0
16,203
80,344
2,698
18,784
openburn
300
NLCD Low Intensity Development
2,704
1,113
4,159
226
4,514
openburn
307
NLCD All Development
76,463
28,172
126,918
10,917
81,324
onroad
205
Extended Idle Locations
7
28,995
202
15
2,453
onroad
242
All Restricted AADT
71,490
874,793
28,295
4,371
197,694
onroad
244
All Unrestricted AADT
109,981
1,056,368
46,549
6,439
303,918
onroad
259
Transit BusTerminals
7
2,263
37
1
231
onroad
304
NLCD Open + Low
467
13
0
2,466
onroad
306
NLCD Med + High
1,255
106,083
2,248
83
24,305
onroad
307
NLCD All Development
6,042
163,308
7,255
636
587,182
onroad
308
NLCD Low + Med + High
271
15,888
486
37
27,628
onroad
508
Public Schools
16
1,624
48
1
323
rail
261
NTAD Total Railroad Density
16
26,427
763
18
1,249
rail
271
NTAD Class 12 3 Railroad Density
287
430,178
10,685
324
17,539
rwc
111
All Residential Structures
3,321
7,677
55,925
847
49,969
rwc
112
Single Family + Manufactured + small
Multi-family Residential Structures
15,603
26,108
273,568
7,478
397,651
rwc
113
Single Family Residential Structures
2,999
2,442
109,712
3,579
116,538
3.4.2 Area-to-point spatial allocation (nonpt, nonroad)
The NEI contains numerous airport-related emission sources, such as aircraft, airport ground support
equipment, and jet refueling. The modeling platform includes the aircraft and airport ground support
equipment emissions in the airports sector as point sources. Some airport-related emissions are
included in the nonpt sector as area sources, specifically, SCCs 2501080050 and 2501080100 (petroleum
storage at airports), and 2810040000 (aircraft/rocket engine firing and testing). For the modeling
platform, the EPA used the SMOKE "area-to-point" approach for these nonpt sector SCCs. The ARTOPNT
file that lists the nonpoint sources to locate using point data was unchanged from the 2005-based
platform.
In the nonroad sector, 2022v2 platform contains emissions representing emissions from offroad diesel
trucks at mines in St. Louis County, Minnesota, as SCC 2270002051. These emissions were spatially
allocated using an ARTOPNT file containing the locations of mines throughout St. Louis County.
3.4.3 Surrogates for Canada and Mexico emission inventories
The surrogates for Canada to spatially allocate the Canadian emissions are based on the 2020 Canadian
inventories and associated data. The spatial surrogate data came from ECCC, along with cross
references. The shapefiles they provided were used in the Surrogate Tool (previously referenced) to
create spatial surrogates. The Canadian surrogates used for this platform are listed in Table 3-20. The
Canadian surrogates were the same for the 2022vl and 2022v2 platforms. The Shapefiles used to
compute these surrogates and some configuration information are shown in Table 3-21. Note that the
name of most Data Shapefiles have been abbreviated to shorten the table. The complete names and
153
-------
additional details on surrogate computation for Canada and Mexico are available in the file
Surrogate_specifications_2022v2_platform_US_Can_Mex.xlsx that is posted in the reports folder for this
platform.
Mexico surrogates were updated for the 2021 EMP. The data source for the Mexico population
surrogate is the INEGI National Geostatistical Framework's Censo de Poblacion y Vivienda 2020 based on
the 2020 GPW v4 (see https://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=889463807469 ).
Other data sources used are Sistema Nacional de Informacion Estadistic y Geografica (SNIEG), US
Department of Transportation's (DOT) North American Rail Network Lines, and US DOT's Bureau of
Transportation Statistics Border Crossing Data. The Shapefiles and some configuration information used
to develop the Mexico surrogates are shown in Table 3-22. The Data Shapefile for all Mexico surrogates
is areas_geoestadisticas_municipales_ll and the Data Attribute is FIPS. Most of the CAP emissions
allocated to the Mexico and Canada surrogates are shown in Table 3-23.
During the development of 2022v2 EMP it was discovered that there was a surrogate data duplication
issue that affected 2022vl EMP. The error resulted in Mexico surrogate "MEX Commercial plus Industrial
Land" using the same dataset as "MEX Total Agriculture" resulting in identical surrogates. The
duplication issue was resolved by updating the commercial land surrogate with the appropriate dataset.
Table 3-20. Canadian spatial surrogates
Code
Canadian Surrogate Description
Code
Description
100
Population
925
Manufacturing and Assembly
101
total dwelling
926
Distribution and Retail (no petroleum)
102
urban dwelling
927
Commercial Services
103
rural dwelling
933
Rail-Passenger
104
capped total dwelling
934
Rail-Freight
105
capped meat cooking dwelling
935
Rail-Yard
106
ALL INDUST
940
PAVED ROADS NEW
113
Forestry and logging
945
Commercial Marine Vessels
116
Total Resources
946
Construction and mining
200
Urban Primary Road Miles
948
Forest
210
Rural Primary Road Miles
949
Combination of Dwelling
211
Oil and Gas Extraction
951
Wood Consumption Percentage
212
Mining except oil and gas
952
Residential Fuel Wood Combustion (PIRD)
220
Urban Secondary Road Miles
955
UNPAVED ROADS AND TRAILS
221
Total Mining
960
TOTBEEF
222
Utilities
961
80110 Broilers
230
Rural Secondary Road Miles
962
80111_Cattle_dairy_and_Fleifer
233
Total Land Development
963
80112_Cattle_non-Dairy
240
capped population
964
80113_Laying_hens_and_Pullets
308
Food manufacturing
965
80114 Florses
321
Wood product manufacturing
966
80115_Sheep_and_Lamb
323
Printing and related support activities
967
80116 Swine
324
Petroleum and coal products
manufacturing
968
80117_Turkeys
154
-------
Code
Canadian Surrogate Description
Code
Description
Plastics and rubber products
326
manufacturing
969
80118 Goat
Non-metallic mineral product
327
manufacturing
970
TOTPOUL
331
Primary Metal Manufacturing
971
80119 Buffalo
340
Construction - Oil and Gas
972
80120_Llama_and_Alpacas
350
Water
973
80121 Deer
Petroleum product wholesaler-
412
distributors
974
80122 Elk
448
clothing and clothing accessories stores
975
80123 Wild boars
Waste management and remediation
562
services
976
80124 Rabbit
SCL12003 Petroleum Liquids
601
Transportation (PIRD)
977
80125 Mink
SCL12007 Oil Sands In-Situ Extraction
602
and Processing (PIRD)
978
80126 Fox
SCL12010 Light Medium Crude Oil
603
Production (PIRD)
980
TOTSWIN
604
SCL:12011 Well Drilling (PIRD)
981
Harvest Annual
605
SCL:12012 Well Servicing (PIRD)
982
Harvest Perennial
606
SCL:12013 Well Testing (PIRD)
983
Synthfert_Annual
607
SCL:12014 Natural Gas Production (PIRD)
984
Syn thfert_ Perennial
608
SCL:12015 Natural Gas Processing (PIRD)
985
Tillage_Annual
SCL12016 Heavy Crude Oil Cold
609
Production (PIRD)
990
TOTFERT
SCL12018 Disposal and Waste Treatment
610
(PIRD)
996
urban area
SCL:12019 Accidents and Equipment
611
Failures (PIRD)
1251
OFFR TOTFERT
SCL:12020 Natural Gas Transmission and
612
Storage(PIRD)
1252
OFFR MINES
651
MEIT C1C2 Anchored
1253
OFFR Other Construction not Urban
652
MEIT C1C2 Underway
1254
OFFR Commercial Services
653
MEIT C1C2 Berthed
1255
OFFR Oil Sands Mines
661
MEIT C3 Anchored
1256
OFFR Wood industries CANVEC
662
MEIT C3 Underway
1257
OFFR UNPAVED ROADS RURAL
663
MEIT C3 Berthed
1258
OFFR Utilities
901
AIRPORT
1259
OFFR total dwelling
902
Military LTO
1260
OFFR water
903
Commercial LTO
1261
OFFR ALL INDUST
904
General Aviation LTO
1262
OFFR Oil and Gas Extraction
905
Air Taxi LTO
1263
OFFR ALLROADS
921
Commercial Fuel Combustion
1264
OFFR AIRPORT
TOTAL INSTITUTIONAL AND
923
GOVERNEMNT
1265
OFFR RAILWAY
924
Primary Industry
155
-------
Table 3-21. Shapefiles and attributes used to compute Canadian spatial surrogates
Code
Surrogate
Data
Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
100
Population
gPr_gda
pruid
da_popdwell_100m_nolakes_lnovl7
Pop
101
total dwelling
gPr_gda
pruid
da_popdwell_100m_nolakes_lnovl7
Urdwell
102
urban dwelling
gPr_gda
pruid
da_popdwell_100m_nolakes_lnovl7
Uadwell
103
rural dwelling
gPr_gda
pruid
da_popdwell_100m_nolakes_lnovl7
Radwell
104
capped total dwelling
gPr_gda
pruid
da_popdwell_100m_nolakes_lnovl7
CAP URDWEL
105
capped meat cooking dwelling
gpr
pruid
da_SimP_100m_pop_dwell_jul2014
Cap_Dwell
106
ALL INDUST
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
ALL INDUST
111
Farms
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
FARMS
113
Forestry and logging
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
FORLOG
116
Total Resources
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
TOTRESOURC
1251
OFFR TOTFERT
gcd
CDID
naesi fert
TOTFERT
1252
OFFR MINES
gcd
CDID
mine
MINES
1253
OFFR Other Construction not
Urban
gcd
CDID
construction other
TOTAL
1254
OFFR Commercial Services
gcd
CDID
da2006_pop_labour_SimP_MaxOff_100
m noLake
COMSER
1255
OFFR Oil Sands Mines
gcd
CDID
OS MinePit D v2
1256
OFFR Wood industries CANVEC
gcd
CDID
wood industries
WOOD
1257
OFFR UNPAVED ROADS RURAL
gcd
CDID
unpaved_ur
1258
OFFR Utilities
gcd
CDID
da2006_pop_labour_SimP_MaxOff_100
m noLake
UTILITIES
1259
OFFR total dwelling
gcd
CDID
da2006_pop_labour_SimP_MaxOff_100
m noLake
DATDWELL20
1260
OFFR water
gcd
CDID
lulOO valid
1261
OFFR ALL INDUST
gcd
CDID
da2006_pop_labour_SimP_MaxOff_100
m noLake
ALL INDUST
1262
OFFR Oil and Gas Extraction
gcd
CDID
da2006_pop_labour_SimP_MaxOff_100
m noLake
OILGASEXTR
1263
OFFR ALLROADS
gcd
CDID
allroads
1264
OFFR AIRPORT
gcd
CDID
offroad_osm_airport_locs_spring2017
Movements
1265
OFFR RAILWAY
gcd
CDID
shp_railway_canvec_jull7_v2
LENGTH
200
Urban Primary Road Miles
gcd_ON4
CDID
NRN_CA_Simp2_16Apr2016_sphere
Classl
210
Rural Primary Road Miles
gcd_ON4
CDID
NRN_CA_Simp2_16Apr2016_sphere
Class2
211
Oil and Gas Extraction
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
OILGASEXTR
212
Mining except oil and gas
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
MINING2
215
Oil Sands Mines
prov2006
pruid
OS MinePit D v2
216
Oil Sands Tailing Ponds
prov2006
pruid
OS_W etT a i 1 i ng_D_2015
217
Oil Sands Plants
prov2006
Pruid
OS PlantSite D 2015
220
Urban Secondary Road Miles
gcd_ON4
CDID
NRN_CA_Simp2_16Apr2016_sphere
Class3
156
-------
Code
Surrogate
Data
Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
221
Total Mining
prov2006
Pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
TOTALMI3
222
Utilities
prov2006
Pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
UTILITIES
230
Rural Secondary Road Miles
gcd_ON4
CDID
NRN_CA_Simp2_16Apr2016_sphere
Class4
233
Total Land Development
prov2006
Pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
TOTLND
240
capped population
gcd_ON4
CDID
da_popdwell_100m_nolakes_lnovl7
CAPURPOP
308
Food manufacturing
prov2006
Pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
FOODMANU
321
Wood product manufacturing
prov2006
Pruid
da2006_SimplifyP_250m_sphere_treesa
_Clip
WOODMANU
323
Printing and related support
activities
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
PRINTSUPRT
324
Petroleum and coal products
manufacturing
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
PETCOLMANU
326
Plastics and rubber products
manufacturing
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
PLASTCMANU
327
Non-metallic mineral product
manufacturing
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
MINERLMANU
331
Primary Metal Manufacturing
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
METALMANU
340
Construction - Oil and Gas
gPr_gda
pruid
loc_land_UOG2015_CO_v3_Que_NB_N
S
350
Water
coast
pruid
CONT42_pop_water_Clip_b
Pop
412
Petroleum product wholesaler-
distributors
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
PETPRWSL
416
Building material and supplies
wholesaler-distributors
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
BUILDPRWSL
447
Gasoline stations
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
GASSTOR
448
clothing and clothing
accessories stores
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
CLOTHSTOR
482
Rail transportation
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
RAILTRANS
562
Waste management and
remediation services
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
WASTEMGMT
901
AIRPORT
gcd
CDID
offroad_osm_airport_locs_spring2017
Movements
902
Military LTO
surg_2017
FAKEFIPS
aviation_runways_spring2017
Military
903
Commercial LTO
surg_2017
FAKEFIPS
aviation_runways_spring2017
Commercial
904
General Aviation LTO
surg_2017
FAKEFIPS
aviation_runways_spring2017
General Av
905
Air Taxi LTO
prov2006
pruid
Airport_movements_2006_MultiRingBu
ffer
SCC2275060
921
Commercial Fuel Combustion
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
COMFUEL
923
TOTAL INSTITUTIONAL AND
GOVERNEMNT
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
TOTINSTGOV
924
Primary Industry
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m_noLake
PRIM1
157
-------
Code
Surrogate
Data
Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
925
Manufacturing and Assembly
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
MAN ASS EM
926
Distribution and Retail (no
petroleum)
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
DISRET
927
Commercial Services
prov2006
pruid
da2006_pop_labour_SimP_MaxOff_100
m noLake
COMSER
933
Rail-Passenger
gPr_gda
pruid
shp_railway_canvec_jull7_v2
Passenger
934
Rail-Freight
gPr_gda
pruid
shp_railway_canvec_jull7_v2
Fret
935
Rail-Yard
gPr_gda
pruid
shp_railway_canvec_jull7_v2
Yard
940
PAVED ROADS NEW
gpr
fips
NRN_CA_Simp2_16Apr2016_sphere
PAVEDRD
942
UNPAVED ROADS
prov2006
pruid
unpaved4
945
Commercial Marine Vessels
lowmedje
t II
CLASS
marine
S02
946
Construction and mining
MERGE: 0.5*Mining except oil and
gas+0.5*Total Land Development
947
Agriculture Construction and
mining
MERGE 0.34*Total Resources + 0.66 *
Construction and mining
948
Forest
prov2006
pruid
treesa valid
949
Combination of Dwelling
MERGE: 0.20*urban dwelling+0.80*
rural dwelling
951
Wood Consumption
Percentage
gpr
fips
da2006_SimP_100m_WoodCon_lAugl
4
WoodComp
955
U N PAVE D_ROADS_AN D_TRAI L
S
prov2006
pruid
unpaved5
960
TOTBEEF
prov2006
pruid
naesi livestk
TOTBEEF
970
TOTPOUL
prov2006
pruid
naesi livestk
TOTPOULT
980
TOTSWIN
prov2006
pruid
naesi livestk
TOTSWINE
990
TOTFERT
prov2006
pruid
naesi fert
TOTFERT
996
urban area
prov2006
pruid
ua2001
961
80110 Broilers
gPr_gda
pruid
animal nh3 to agri sic 80110 valid
QUANTITY
962
80111_Cattle_dairy_and_Heife
r
gPr_gda
pruid
animal nh3 to agri sic 80111 valid
QUANTITY
963
80112_Cattle_non-Dairy
gPr_gda
pruid
animal nh3 to agri sic 80112 valid
QUANTITY
964
80113_Laying_hens_and_Pulle
ts
gPr_gda
pruid
animal nh3 to agri sic 80113 valid
QUANTITY
965
80114 Horses
gPr_gda
pruid
animal nh3 to agri sic 80114 valid
QUANTITY
966
80115_Sheep_and_Lamb
gPr_gda
pruid
animal nh3 to agri sic 80115 valid
QUANTITY
967
80116 Swine
gPr_gda
pruid
animal nh3 to agri sic 80116 valid
QUANTITY
968
80117_Turkeys
gPr_gda
pruid
animal nh3 to agri sic 80117 valid
QUANTITY
969
80118 Goat
gPr_gda
pruid
animal nh3 to agri sic 80118 valid
QUANTITY
971
80119 Buffalo
gPr_gda
pruid
animal nh3 to agri sic 80119 valid
QUANTITY
972
80120_Uama_and_Alpacas
gPr_gda
pruid
animal nh3 to agri sic 80120 valid
QUANTITY
973
80121 Deer
gPr_gda
pruid
animal nh3 to agri sic 80121 valid
QUANTITY
974
80122 Elk
gPr_gda
pruid
animal nh3 to agri sic 80122 valid
QUANTITY
975
80123 Wild boars
gPr_gda
pruid
animal nh3 to agri sic 80123 valid
QUANTITY
976
80124_Rabbit
gPr_gda
pruid
animal nh3 to agri sic 80124 valid
QUANTITY
158
-------
Code
Surrogate
Data
Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
977
80125 Mink
gPr_gda
pruid
animal nh3 to agri sic 80125 valid
QUANTITY
978
80126 Fox
gPr_gda
pruid
animal nh3 to agri sic 80126 valid
QUANTITY
979
80127 Mules and Asses
gPr_gda
pruid
animal nh3 to agri sic 80127 valid
QUANTITY
981
Harvest Annual
gPr_gda
pruid
h a rvest_p m 10_An n u a l_to_ag ri_s lc_va 1 i
d
QUANTITY
982
Harvest Perennial
gPr_gda
pruid
harvest_pmlO_Perennial_to_agri_slc_v
alid
QUANTITY
983
Synthfert_Annual
gPr_gda
pruid
synth_fert_nh3_Annual_to_agri_slc_vali
d
QUANTITY
984
Synthfert_Perennial
gPr_gda
pruid
synth_fert_nh3_Perennial_to_agri_slc_
valid
QUANTITY
985
Tillage_Annual
gPr_gda
pruid
tillage_pmlO_Annual_to_agri_slc_valid
QUANTITY
601
SCL:12003 Petroleum Liquids
Transportation (PIRD)
gPr_gda
pruid
scll2003 valid
602
SCL:12007 Oil Sands In-Situ
Extraction and Processing
(PIRD)
gPr_gda
pruid
scll2007 valid
NONE
603
SCL:12010 Light Medium Crude
Oil Production (PIRD)
gPr_gda
pruid
scll2010 valid
NONE
604
SCL:12011 Well Drilling (PIRD)
gPr_gda
pruid
scl 12011 valid
NONE
605
SCL:12012 Well Servicing
(PIRD)
gPr_gda
pruid
scll2012 valid
NONE
606
SCL:12013 Well Testing (PIRD)
gPr_gda
pruid
scll2013 valid
NONE
607
SCL:12014 Natural Gas
Production (PIRD)
gPr_gda
pruid
scll2014 valid
NONE
608
SCL:12015 Natural Gas
Processing (PIRD)
gPr_gda
pruid
scll2015 valid
NONE
609
SCL:12016 Heavy Crude Oil
Cold Production (PIRD)
gPr_gda
pruid
scll2016 valid
NONE
610
SCL:12018 Disposal and Waste
Treatment (PIRD)
gPr_gda
pruid
scll2018 valid
NONE
611
SCL:12019 Accidents and
Equipment Failures (PIRD)
gPr_gda
pruid
scll2019 valid
NONE
612
SCL:12020 Natural Gas
Transmission and Storage
(PIRD)
gPr_gda
pruid
scl 12020
NONE
952
Residential Fuel Wood
Combustion (PIRD)
gPr_gda
pruid
scl20401 valid
NONE
651
MEITC1C2 Anchored
lowmedje
t II
CLASS
MEIT 2280002101 2018
Fuel
652
MEITC1C2 Underway
lowmedje
t II
CLASS
MEIT 2280002202 2018
Fuel
653
MEITC1C2 Berthed
lowmedje
t II
CLASS
MEIT 2280002301 2018
Fuel
661
MEIT C3 Anchored
lowmedje
t II
CLASS
MEIT 2280003101 2018
Fuel
662
MEIT C3 Underway
lowmedje
t II
CLASS
MEIT 2280003200 2018
Fuel
663
MEIT C3 Berthed
lowmedje
tjl
CLASS
MEIT_2280003301_2018
Fuel
159
-------
Table 3-22. Shapefiles and attributes used to compute Mexican spatial surrogates
Code
SURROGATE
WEIGHT SHAPEFILE
WEIGHT
ATTRIBUTE
11
MEX Population
mex_population_2020
gridcode_Y
22
MEX Total Road Miles
mex_roadways
NONE
24
MEX Total Railroads Miles
mex railroads
NONE
26
MEX Total Agriculture
mex_agriculture
NONE
36
MEX Commercial plus Industrial Land
mex com ind land
NONE
44
MEX Airports Area
mex_airports_area
NONE
45
MEX Airports Point
mex_airports_point
NONE
48
MEX Brick Kilns
mex brick kilns
NONE
50
MEX Border Crossings
mex_border_crossings
SUM_Value
Table 3-23. 2022v2 CAP emissions allocated to Mexican and Canadian spatial surrogates for 12US1
(short tons)
Code
Mexican or Canadian Surrogate
Description
NH3
NOx
PM2.5
SO2
voc
11
MEX Population
31,796
64,550
8,670
14,350
141,272
22
MEXTotal Road Miles
3,154
519,098
26,957
3,640
234,111
24
MEXTotal Railroads Miles
0
22,953
513
200
929
26
MEXTotal Agriculture
154,058
10,264
13,732
31,306
2,234
36
MEX Commercial plus Industrial Land
37
24,223
2,577
37
302,503
44
MEX Airports Area
0
2,751
58
318
1,729
48
MEX Brick Kilns
0
215
3,115
26
206
50
MEX Border Crossings
4
86
3
0
65
100
CAN Population
710
57
225
17
4,025
101
CAN total dwelling
0
0
0
0
109,016
104
CAN capped total dwelling
305
31,578
2,383
1,928
1,620
106
CAN ALLJNDUST
0
0
596
0
0
113
CAN Forestry and logging
83
627
2,934
15
2,717
200
CAN Urban Primary Road Miles
1,590
75,668
2,697
209
7,406
210
CAN Rural Primary Road Miles
608
40,578
1,422
89
2,995
212
CAN Mining except oil and gas
0
0
1,785
0
0
220
CAN Urban Secondary Road Miles
2,985
120,376
5,476
406
19,742
221
CAN Total Mining
0
0
13,564
0
0
222
CAN Utilities
0
1,998
2,751
32
89
230
CAN Rural Secondary Road Miles
1,613
75,161
2,728
211
7,997
240
CAN capped population
345
45,969
1,175
41
82,324
308
CAN Food manufacturing
0
0
17,199
0
5,233
321
CAN Wood product manufacturing
513
1,677
591
213
8,464
323
CAN Printing and related support activities
0
0
0
0
20,852
324
CAN Petroleum and coal products
manufacturing
0
1,056
1,481
439
6,751
160
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NOx
PM2.5
SO2
voc
326
CAN Plastics and rubber products
manufacturing
0
0
0
0
21,858
327
CAN Non-metallic mineral product
manufacturing
0
0
7,206
0
0
331
CAN Primary Metal Manufacturing
0
148
5,247
28
62
412
CAN Petroleum product wholesaler-
distributors
0
0
0
0
37,775
448
CAN clothing and clothing accessories
stores
0
0
0
0
178
562
CAN Waste management and remediation
services
2,707
1,230
2,300
2,159
16,100
601
CAN SCL12003 Petroleum Liquids
Transportation (PIRD)
0
0
12
154
6,042
602
CAN SCL: 12007 Oil Sands In-Situ
Extraction and Processing (PIRD)
0
0
0
0
110
603
CAN SCL12010 Light Medium Crude Oil
Production (PIRD)
0
0
0
0
2
604
CAN SCL12011 Well Drilling (PIRD)
0
0
0
607
658
605
CAN SCL12012 Well Servicing (PIRD)
0
0
0
68
73
606
CAN SCL12013 Well Testing (PIRD)
0
0
0
0
0
607
CAN SCL:12014 Natural Gas Production
(PIRD)
0
28
1
0
191
608
CAN SCL:12015 Natural Gas Processing
(PIRD)
0
0
0
0
0
611
CAN SCL:12019 Accidents and Equipment
Failures (PIRD)
0
0
0
0
90,229
612
CAN SCL:12020 Natural Gas Transmission
and Storage (PIRD)
1
671
54
11
396
901
CAN AIRPORT
0
98
9
0
11
921
CAN Commercial Fuel Combustion
190
21,587
2,373
435
940
923
CAN TOTAL INSTITUTIONAL AND
GOVERNEMNT
0
0
0
0
14,522
924
CAN Primary Industry
0
0
0
0
33,308
925
CAN Manufacturing and Assembly
0
0
0
0
70,606
926
CAN Distribution and Retail (no petroleum)
0
0
0
0
6,666
927
CAN Commercial Services
0
0
0
0
30,828
933
CAN Rail-Passenger
1
3,089
63
1
115
934
CAN Rail-Freight
48
76,567
1,530
43
3,389
935
CAN Rail-Yard
1
4,536
95
1
276
940
CAN PAVED ROADS NEW
0
0
26,017
0
0
946
CAN Construction and mining
44
2,842
163
281
41
951
CAN Wood Consumption Percentage
1,061
11,794
71,798
1,685
100,154
955
CAN U N PAVED_ROADS_AN D_TRAI LS
0
0
433,847
0
0
961
CAN 80110_Broilers
13,453
0
115
0
12,782
962
CAN 80111_Cattle_dairy_and_Heifer
61,989
0
276
0
40,501
963
CAN 80112_Cattle_non-Dairy
177,740
0
884
0
42,860
161
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NOx
PM2.5
SO2
voc
964
CAN 80113_Laying_hens_and_Pullets
10,085
0
40
0
10,592
965
CAN 80114_Horses
3,155
0
19
0
1,320
966
CAN 80115_Sheep_and_Lamb
2,278
0
6
0
170
967
CAN 80116_Swine
64,225
0
824
0
9,945
968
CAN 80117_Turkeys
5,215
0
41
0
4,507
969
CAN 80118_Goat
1,806
0
2
0
135
971
CAN 80119_Buffalo
2,258
0
6
0
517
972
CAN 80120_Llama_and_Alpacas
118
0
0
0
0
973
CAN 80121_Deer
20
0
0
0
0
974
CAN 80122_Elk
19
0
0
0
0
975
CAN 80123_Wild boars
37
0
0
0
0
976
CAN 80124_Rabbit
78
0
0
0
1
977
CAN 80125_Mink
287
0
0
0
951
978
CAN 80126_Fox
4
0
0
0
3
981
CAN Harvest_Annual
0
0
24,824
0
0
983
CAN Synthfert_Annual
164,425
3,513
2,111
5,807
127
985
CAN Tillage_Annual
0
0
106,806
0
0
996
CAN urban_area
0
0
3,716
0
0
1251
CAN OFFR_TOTFERT
84
59,946
4,056
57
6,120
1252
CAN OFFR_MINES
1
573
40
1
81
1253
CAN OFFR Other Construction not Urban
68
37,617
4,378
46
10,431
1254
CAN OFFR Commercial Services
47
16,663
2,499
40
38,507
1255
CAN OFFR Oil Sands Mines
0
0
0
0
0
1256
CAN OFFR Wood industries CANVEC
9
3,245
257
7
944
1257
CAN OFFR UNPAVED ROADS RURAL
24
10,275
642
21
27,343
1258
CAN OFFRJJtilities
8
4,339
223
6
897
1259
CAN OFFR total dwelling
18
6,288
601
15
12,539
1260
CAN OFFR_water
17
4,785
371
26
24,782
1261
CAN OFFR_ALL_INDUST
4
5,218
183
2
918
1262
CAN OFFR Oil and Gas Extraction
1
378
31
0
123
1263
CAN OFFR_ALLROADS
3
1,753
169
2
463
1265
CAN OFFR_RAILWAY
0
64
6
0
12
162
-------
4 Emission Summaries
Table 4-1 and Table 4-2 summarize base year emissions by sector for CAPs and key HAPs for the year
2022 in this platform. These summaries are provided at the national level by sector for the contiguous
U.S. and for the portions of Canada and Mexico inside the larger 12km domain (12US1) discussed in
Section 3.1. Note that totals for the 12US2 domain are not available here, but the sum of the U.S.
sectors would be essentially the same and only the Canadian and Mexican emissions would change
according to the extent of the grids to the north and south of the continental United States. The afdust
sector emissions here represent the emissions after application of both the land use (transport fraction)
and meteorological adjustments; therefore, this sector is called "afdust_adj" in these summaries. The
onroad sector totals are post-SMOKE-MOVES totals, representing air quality model-ready emission
totals, and include CARB emissions for California. The cmv sectors include U.S. emissions within state
waters only within the 12US1 grid; these extend to roughly 3-5 miles offshore and include CMV
emissions at U.S. ports. "Offshore" represents CMV emissions that are outside of U.S. state waters.
Canadian CMV emissions are included in the other sector. The total of all US sectors is listed as "Con U.S.
Total."
State totals and other summaries are available in the reports area on the FTP site for the 2022 platform
(https://gaftp.epa.gov/Air/emismod/2022/vl).
163
-------
Table 4-1. National by-sector CAP emissions for the 2022v2 platform, year 2022, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2_5
S02
voc
afdust_adj
6,081,020
848,604
airports
370,488
0
116,833
9,085
8,065
11,982
43,814
cmv_clc2
20,296
70
137,145
3,748
3,632
626
5,265
cmv_c3
10,207
32
84,352
1,833
1,686
4,141
4,676
fertilizer
1,671,402
livestock
2,590,376
207,196
nonpt
766,248
68,690
708,532
447,173
384,613
73,091
932,300
nonroad
11,159,560
2,018
774,273
76,278
71,523
1,035
938,317
nP_oilgas
692,990
13
592,126
10,643
10,580
279,331
2,714,995
np_solvents
0
0
0
0
0
0
2,590,447
onroad
15,274,967
189,098
2,250,203
174,154
85,148
11,583
1,146,496
openburn
1,392,576
79,167
45,489
231,096
211,421
13,841
104,621
ptegu
466,000
17,974
858,786
107,049
91,872
879,962
26,333
ptagfire
908,682
10,438
39,154
127,019
81,900
12,710
140,086
ptfire-rx
7,435,936
64,195
124,213
1,216,451
1,083,296
74,592
1,501,455
ptfire-wild
6,424,718
66,228
65,762
1,349,584
878,415
63,745
1,757,219
ptnonipm
1,200,410
60,747
746,130
342,134
221,433
419,249
730,056
ptnonipm_hr
4,998
259
26,180
2,260
2,086
15,291
366
pt_oilgas
180,631
9,324
327,205
13,434
12,765
32,087
208,830
rail
96,147
303
456,604
11,803
11,448
341
18,789
rwc
2,673,623
21,924
36,227
440,558
439,205
11,904
564,158
beis
3,376,155
964,950
30,694,065
CONUSw/beis
52,454,632
4,852,258
8,354,165
10,645,323
4,447,692
1,905,513
44,329,483
Canada ag
506,067
6,564
1,875
124,234
Canada oil and gas 2D
8
293,600
Canada afdust
975,005
183,021
Canada ptdust
3,980
510
Canada area
2,061,247
5,978
312,938
184,538
133,031
14,092
712,989
Canada onroad
1,715,237
7,135
357,211
25,404
13,469
955
120,229
Canada point
1,034,599
19,020
521,418
113,269
43,293
440,207
150,300
Canada fires
2,629,627
24,664
30,565
586,235
329,838
13,818
629,981
Canada cmv_clc2
3,193
10
20,631
545
529
66
726
Canada cmv_c3
8,394
22
66,152
1,255
1,155
2,625
4,082
Mexico ag
154,055
49,130
10,481
0
Mexico area
90,001
31,840
58,056
49,183
23,626
46,201
389,436
Mexico onroad
2,492,380
3,154
585,916
32,570
21,503
3,671
293,471
Mexico point
160,373
948
214,134
92,310
55,240
365,114
32,702
Mexico fires
295,838
4,842
13,179
43,405
34,413
2,562
62,292
Mexico cmv_clc2
199
1
1,296
35
34
7
50
Mexico cmv_c3
9,626
95
95,412
5,362
4,933
14,099
4,777
Offshore cmv_clc2
4,864
15
31,122
822
797
123
1,148
Offshore cmv_c3
52,623
313
470,598
17,673
16,259
44,675
25,782
Offshore pt_oilgas
28,548
4
34,658
422
416
299
30,905
164
-------
Table 4-2. National by-sector VOC HAP emissions for the 2022v2 platform, year 2022, 12US1 grid
(tons/yr)
Sector
Acetaldehyde
Benzene
Formaldehyde
Methanol
Naphthalene
Acrolein
1,3-
Butadiene
airports
1,629
726
4,714
682
724
927
660
cmv_clc2
28
14
123
0
90
5
3
cmv_c3
25
12
109
0
80
5
3
livestock
1,479
473
13,699
0
nonpt
9,218
2,406
5,116
14,564
427
31
326
nonroad
8,102
25,860
19,928
1,170
1,434
1,185
4,374
nP_oilgas
3,001
32,853
53,390
2,504
94
2,440
520
np_solvents
73
336
7
13,782
7,807
openburn
2,124
4,583
2,199
0
56
132
695
onroad
9,798
19,021
11,981
1,517
1,500
843
2,493
ptegu
268
294
2,151
96
29
204
2
ptagfire
11,522
2,125
8,668
0
0
1,050
ptfire-rx
60,166
19,851
119,616
87,162
17,696
24,998
15,540
ptfire-wild
50,045
14,589
90,570
92,239
17,284
15,292
7,773
ptnonipm
5,186
2,801
5,843
46,421
768
889
653
ptnonipm_hr
0
9
1
0
0
8
pt_oilgas
2,919
2,117
12,407
1,910
82
1,911
261
rail
1,471
423
4,190
0
51
301
35
rwc
65,071
16,553
45,413
0
8,792
2,463
4,573
beis
374,228
513,183
2,110,685
CONUSw/beis
606,352
145,045
899,609
2,386,431
56,913
51,634
38,960
Can. ag
1,398
159
0
32,657
0
Can. oil & gas 2D
0
877
0
0
0
Can. Area
15,252
12,725
12,871
4,082
2,589
Can. Onroad
2,170
5,247
2,997
0
40
Can. Point
1,543
1,986
5,262
10,627
26
Can. Fires
22,007
5,953
44,136
49,594
7,291
6,703
3,548
Can. cmv_clc2
4
2
17
0
12
1
0
Can. cmv_c3
22
11
95
0
70
4
2
Mex. Area
3,228
1,061
2,675
2,772
489
Mex. Onroad
7,463
7,239
21,941
895
1,869
1,132
549
Mex. Point
63
1,214
2,460
476
10
Mex. Fires
3,401
889
3,761
1,378
167
0
0
Mex. cmv_clc2
0
0
1
0
1
0
0
Mex. cmv_c3
26
12
111
0
82
5
3
Off. cmv_clc2
6
3
27
0
20
1
1
Off. cmv_c3
138
67
602
0
441
26
14
Off. pt_oilgas
248
41
595
0
0
0
0
165
-------
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