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Technical Support Document (TSD): Preparation of
Emissions Inventories for the 2021 North American
Emissions Modeling Platform
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EPA-454/B-24-011
October 2024
Technical Support Document (TSD) Preparation of Emissions Inventories for the 2021 North American
Emissions Modeling Platform
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Authors:
Alison Eyth (EPA/OAR)
Jeff Vukovich (EPA/OAR)
Caroline Farkas (EPA/OAR)
Janice Godfrey (EPA/OAR)
Karl Seltzer (EPA/OAR)
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 EMISSIONS INVENTORIES AND APPROACHES 15
2.1 Point sources (ptegu, pt_oilgas, ptnonipm, airports) 20
2.1.1 EGUsector (ptegu) 22
2.1.2 Point source oil and gas sector (pt oilgas) 23
2.1.3 A ircraft and ground support equipment (airports) 25
2.1.4 Non-IPM sector (ptnonipm) 26
2.2 Nonpoint sources (afdust, fertilizer, livestock, np_oilgas, rwc, vcp, nonpt) 26
2.2.1 Area fugitive dust sector (afdust) 27
2.2.2 Agricultural Livestock (livestock) 32
2.2.3 Agricultural Fertilizer (fertilizer) 33
2.2.4 Nonpoint Oil and Gas Sector (np oilgas) 36
2.2.5 Residential Wood Combustion (rwc) 40
2.2.6 Solvents (np solvents) 41
2.2.7 Nonpoint (nonpt) 42
2.3 Onroad Mobile sources (onroad) 43
2.3.1 Inventory Development using SMOKE-MOVES 44
2.3.2 Onroad Activity Data Development 46
2.3.3 MOVES Emission Factor Table Development 48
2.3.4 Onroad California Inventory Development (onroad ca adj) 52
2.4 Nonroad Mobile sources (cmv, rail, nonroad) 53
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 53
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3) 60
2.4.3 Railway Locomotives (rail) 65
2.4.4 Nonroad Mobile Equipment (nonroad) 70
2.5 Fires (ptfire-rx, ptfire-wild, ptagfire) 73
2.5.1 Wild and Prescribed Fires (ptfire-rx, ptfire-wild) 73
2.5.2 Point source Agriculture Fires (ptagfire) 78
2.6 Biogenic Sources (beis) 79
2.7 Sources Outside of the United States 81
2.7.1 Point Sources in Canada and Mexico (canmex_point) 82
2.7.2 Fugitive Dust Sources in Canada (canadaafdust, Canada_ptdust) 83
2.7.3 Agricultural Sources in Canada and Mexico (canmex ag) 83
2.7.4 Surface-level Oil and Gas Sources in Canada (canada_og2D) 83
2.7.5 Nonpoint and Nonroad Sources in Canada and Mexico (canmex area) 83
2.7.6 Onroad Sources in Canada and Mexico (canadaonroad, mexico onroad) 84
2.7.7 Fires in Canada and Mexico (ptfire othna) 84
2.7.8 Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury 84
3 EMISSIONS MODELING 85
3.1 Emissions Modeling Overview 85
3.2 Chemical Speciation 89
3.2.1 VOC speciation 94
3.2.2 PM speciation 99
3.2.2.1 Diesel PM 99
3.2.3 NOx speciation 100
3.2.4 Sulfuric Acid Vapor (SULF) 100
3.2.5 Speciation of Metals and Mercury 101
3.3 Temporal Allocation 103
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3.3.1 Use of FF10 format for finer than annual emissions 105
3.3.2 Temporal allocation for non-EGU sources (ptnonipm) 105
3.3.3 Electric Generating Utility temporal allocation (ptegu) 105
3.3.4 Airport Temporal allocation (airports) 110
3.3.5 Residential Wood Combustion Temporal allocation (rwc) 113
3.3.6 Agricultural Ammonia Temporal Profiles (livestock) 117
3.3.7 Oil and gas temporal allocation (np oilgas) 120
3.3.8 Onroad mobile temporal allocation (onroad) 120
3.3.9 Nonroad mobile temporal allocation (nonroad) 125
3.3.10 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptfire-rx, ptfire-wild) 126
3.4 Spatial Allocation 128
3.4.1 Spatial Surrogates for U.S. emissions 128
3.4.2 Allocation method for airport-related sources in the U.S. 143
3.4.3 Surrogates for Canada and Mexico emission inventories 143
4 EMISSION SUMMARIES 154
5 REFERENCES 158
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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 23
Table 2-3. Point source oil and gas sector emissions for 2021 24
Table 2-4. SCCs for the airports sector 25
Table 2-5. Afdust sector SCCs 27
Table 2-6. Total impact of 2021 fugitive dust adjustments to unadjusted inventory 28
Table 2-7. SCCs for the livestock sector 33
Table 2-8. Source of input variables for EPIC 35
Table 2-9. Nonpoint oil and gas emissions for 2021 36
Table 2-10. State emissions totals for year 2021 for Pipeline Blowdowns and Pigging sources 38
Table 2-11. State emissions totals for year 2021 for Abandoned Wells sources 39
Table 2-12. SCCs for the residential wood combustion sector 41
Table 2-13. Non-VCPy SCCs in the np_solvents sector 42
Table 2-14. MOVES vehicle (source) types 44
Table 2-15. The fraction of IHS vehicle populations retained for 2020 NEI and 2021 emissions modeling
platform by model year 51
Table 2-16. SCCs for the cmv_clc2 sector 54
Table 2-17. Vessel groups in the cmv_clc2 sector 59
Table 2-18. SCCs for cmv_c3 sector 60
Table 2-19. SCCs for the Rail Sector 66
Table 2-20. 2020 R-l Reported Locomotive Fuel Use for Class I Railroads 67
Table 2-21. 2020 Class TT/TTT Line Haul Fleet by Tier Level 68
Table 2-22. SCCs included in the ptfire sector for the 2021 platform 73
Table 2-23. SCCs included in the ptagfire sector 78
Table 2-24. Meteorological variables required by BEIS4 80
Table 3-1. Key emissions modeling steps by sector 86
Table 3-2. Descriptions of the platform grids 88
Table 3-3. Emission model species produced for CB6R5 AE7 for CMAQ 89
Table 3-4. Additional HAP gaseous model species generated for toxics modeling 91
Table 3-5. Additional HAP particulate model species generated for toxics modeling 92
Table 3-6. PAH/POM pollutant groups 92
Table 3-7. Integration status for each platform sector 95
Table 3-8. Integrated species from MOVES sources 96
Table 3-9. Mobile Speciation Profile Updates 97
Table 3-10. Mobile NOx and HONO fractions 99
Table 3-11. NOx speciation profiles 100
Table 3-12. Sulfate Split Factor Computation 101
Table 3-13. SO2 speciation profiles 101
Table 3-14. Particle Size Speciation of Metals 102
Table 3-15. Mercury Speciation Profiles 103
Table 3-16. Temporal settings used for the platform sectors in SMOKE 104
Table 3-17. U.S. Surrogates available for the 2021 modeling platforms 131
Table 3-18. Shapefiles used to develop U.S. Surrogates 132
Table 3-19. Surrogates used to gapfill U.S. Surrogates 136
Table 3-20. Off-Network Mobile Source Surrogates 139
Table 3-21. Spatial Surrogates for Oil and Gas Sources 140
Table 3-22. Selected 2021 CAP emissions by sector for U.S. Surrogates (short tons in 12US1) 141
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Table 3-23. Canadian Spatial Surrogates 144
Table 3-24. Shapefiles and Attributes used to Compute Canadian Spatial Surrogates 145
Table 3-25. Shapefiles and Attributes used to Compute Mexican Spatial Surrogates 150
Table 3-26. 2021 CAP Emissions Allocated to Mexican and Canadian Spatial Surrogates for 12US1 (short
tons) 150
Table 4-1. National by-sector CAP emissions for the 2021 platform, 12US1 grid (tons/yr) 155
Table 4-2. National by-sector VOC HAP emissions for the 2021 platform, 12US1 grid (tons/yr) 156
Table 4-3. National by-sector Diesel PM and metal emissions for the 2021 platform, 12US1 grid (tons/yr)157
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List of Figures
Figure 2-1. Fugitive dust emissions and impact of adjustments due to transport fraction, precipitation, and
cumulative 30
Figure 2-2. "Bidi" modeling system used to compute emissions from fertilizer application 34
Figure 2-3. Map of Representative Counties 50
Figure 2-4. NEI Commercial Marine Vessel Boundaries and Automatic Identification System Request Boxes
for the 2021 Emissions Modeling Platform 57
Figure 2-5. 2019 Class I Railroad Line Haul Activity 67
Figure 2-6. Class II and III Railroads in the United States 69
Figure 2-7. Amtrak National Rail Network 70
Figure 2-8. Processing flow for fire emission estimates in the 2021 inventory 76
Figure 2-9. Default fire type assignment by state and month where data are only from satellites 76
Figure 2-10. Blue Sky Modeling Pipeline 77
Figure 2-11. Annual biogenic VOC BEIS4 emissions for the 12US1 domain 81
Figure 3-1. Air quality modeling domains 88
Figure 3-2. Process of integrating HAPs and speciating VOC in a modeling platform 95
Figure 3-3. Eliminating unmeasured spikes in CEMS data 106
Figure 3-4. Regions used to Compute Temporal non-CEMS EGU Temporal Profiles 108
Figure 3-5. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type 109
Figure 3-6. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type 109
Figure 3-7. 2021 Airport Diurnal Profiles for PHX and state of Texas Ill
Figure 3-8. 2021 Wisconsin and Atlanta annual-to-month profile for airport emissions 112
Figure 3-9. Alaska seaplane profile 113
Figure 3-10. Example of RWC temporal allocation using a 50 versus 60 °F threshold 114
Figure 3-11. Example of Annual-to-day temporal pattern of recreational wood burning emissions 115
Figure 3-12. RWC diurnal temporal profile 116
Figure 3-13. Data used to produce a diurnal profile for hydronic heaters 117
Figure 3-14. Monthly temporal profile for hydronic heaters 117
Figure 3-15. Examples of livestock temporal profiles in several parts of the country 119
Figure 3-16. Example of animal NFb emissions temporal allocation approach (daily total emissions) 119
Figure 3-17. TMAS Data: VMT Fraction by Hour of Day, Day of Week, and Month of Year 121
Figure 3-18. Example temporal variability of VMT compared to onroad NOx emissions 124
Figure 3-19. Example Nonroad Day-of-week Temporal Profiles 125
Figure 3-20. Example Nonroad Diurnal Temporal Profiles 126
Figure 3-21. Agricultural burning diurnal temporal profile 127
Figure 3-22. Prescribed and Wildfire diurnal temporal profiles 128
Figure 3-23. 2020 Residential Wood Combustion Emissions using NLCD Low Intensity Surrogate 130
Figure 3-24. 2020 Residential Wood Combustion Emissions using ACS-based Surrogate 130
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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
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FCCS Fuel Characteristic Classification System
FEST-C Fertilizer Emission Scenario Tool for CMAQ
FF10 Flat File 2010
FINN Fire Inventory from the National Center for Atmospheric Research
FIPS Federal Information Processing Standards
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
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pcSOA Potential combustion Secondary Organic Aerosol
PFC Portable Fuel Container
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) developed an air quality modeling platform for air
toxics and criteria air pollutants that represents the year 2021. The platform is based on the 2020
National Emissions Inventory (2020 NEI) published in April 2023 (EPA, 2023) along with other data
specific to the year 2021. 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 2021 modeling platform, including the emission inventories, the ancillary
data files, and the approaches used to transform inventories for use in air quality modeling.
The modeling platform includes all criteria air pollutants and 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 52 HAPs or HAP groups (such as
polycyclic aromatic hydrocarbon groups) that are included in CMAQ for the purposes of air quality
modeling for a HAP+CAP platform.
Emissions were prepared for the Community Multiscale Air Quality (CMAQ) model version 5.4,2 which
was used to model ozone (O3) particulate matter (PM), and HAPs. 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), particulate matter less than or equal to
10 microns (PM10), and individual component species for 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 modeling case name was
"2021hb", where 2021 is the year modeled, 'h' represents that it was based on the 2020 NEI, and 'b'
represents that it was the second version of a 2020 NEI-based platform.
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 2021 for all NEI HAPs (about 130 more than covered by CMAQ) over the continental U.S. in a similar
way as was done for the 2018 version of AirToxScreen (EPA, 2022a). This TSD focuses on the CMAQ
aspects of the 2021 emissions modeling platform from which ozone and PM data were also developed
for the Centers for Disease Control and Prevention. The effort to create the emission inputs for this
study included development of emission inventories to represent emissions during the year of 2021,
along with application of emissions modeling tools to convert the inventories into the format and
resolution needed by CMAQ and AERMOD.
2 CMAQ version 5.4: https://zenodo.org/record/7218076. CMAQ is also available from https://www.epa.gov/cmaa and the
Community Modeling and Analysis System (CMAS) Center at: https://www.cmascenter.org.
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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
process and road type 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 (ECC) 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 5.0 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://ral.ucar.edu/solutions/products/
weather-research-and-forecasting-model-wrf ) version 4.1.1, 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 2021 over
a domain covering the continental U.S. at a 12km resolution with 35 vertical layers. The run for this
platform included high resolution sea surface temperature data from the Group for High Resolution Sea
Surface Temperature (GHRSST) (see https://www.ghrsst.org/) and is given the EPA meteorological case
abbreviation "21k." The full case abbreviation includes this suffix following the emissions portion of the
case name to fully specify the abbreviation of the case as "2021hb_cb6_21k."
In support of AirToxScreen, CMAQ and AERMOD were run with the prepared emissions for each of the
four modeling domains. CMAQ outputs provide the overall mass, chemistry and formation for specific
hazardous air pollutants (HAPs) formed secondarily in the atmosphere (e.g., formaldehyde,
acetaldehyde, and acrolein), whereas AERMOD provides spatial granularity and more detailed source
attribution. CMAQ also provided the biogenic and fire concentrations, as these sources were not run in
AERMOD. Special steps were taken to estimate secondary HAPs, fire and biogenic emissions in these
areas. The outputs from CMAQ and AERMOD were combined to provide spatially refined concentration
estimates for HAPs, from which estimates of cancer and non-cancer risk were derived. 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/2021-emissions-modeling-platform.
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 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 2021. The NEI includes
five main data categories: a) nonpoint sources; b) point sources; c) nonroad mobile sources; d) onroad
mobile sources; and e) fires. For CAPs, the NEI data are largely compiled from data submitted by state,
local and tribal (S/L/T) agencies. HAP emissions data are often augmented 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
point source emissions were submitted by S/L/T agencies to develop the NEI for 2021, emissions for any
point sources not submitted for 2021, 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 MOVES4 (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 2021 emissions were developed by
interpolating between 2020 and 2023. For Mexico, inventories from the 2019 emissions modeling
platform (EPA, 2022b) were used as the starting point with data for border states supplemented with
data for 2018 developed by SEMARNAT in collaboration with U.S. EPA.
The emissions modeling process was performed using SMOKE v5.0. 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
inventories by data category 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, 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
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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.
Table 2-1 presents the sectors in the emissions modeling platform used to develop the year 2021
emissions for this project. The sector abbreviations are provided in italics; these abbreviations are used
in the SMOKE modeling scripts, the inventory file names, and throughout the remainder of this section.
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
2021 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 2021 NEI point inventory. Annual
resolution for sources not matched to CEMS data, hourly
for CEMS sources. EGUs closed in 2021 are not part of the
inventory.
Point source oil and gas:
pt_oilgas
Point
2021 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.
Aircraft and ground
support equipment:
airports
Point
2021 NEI point source emissions from airports, including
aircraft and airport ground support emissions projected to
2021 based on the 2022 Terminal Area Forecast (TAF).
Annual resolution.
Remaining non-EGU point:
Ptnonipm
Point
All 2021 NEI point source records not matched to the
airports, ptegu, or pt_oilgas sectors. Includes 2020 NEI rail
yard emissions projected to 2021. Annual resolution.
Livestock:
Livestock
Nonpoint
2021 nonpoint livestock emissions developed using a
similar method to 2020 NEI but with adjusted animal
counts and using 2021 meteorology. Livestock includes
ammonia and other pollutants (except PM2.5). County and
annual resolution.
Agricultural Fertilizer:
fertilizer
Nonpoint
2021 agricultural fertilizer ammonia emissions computed
inline within CMAQ.
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Platform Sector:
abbreviation
NEI Data Category
Description and resolution of the data input to SMOKE
PMio and PM2.5 fugitive dust sources from the 2020 NEI
nonpoint inventory; including building construction, road
construction, agricultural dust, and paved and unpaved
Area fugitive dust:
afdust_adj
Nonpoint
road dust where paved and unpaved road dust were
adjusted to 2021 based on VMT 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). Emissions are county and annual resolution.
Year 2021 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
Biogenic:
beis
Nonpoint
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 2021 biogenic emissions generated
through running SMOKE, but they are not exactly the same.
2021 Category 1 (CI) and Category 2 (C2), commercial
Category 1, 2 CMV:
Nonpoint
marine vessel (CMV) emissions based on 2021 Automatic
cmv_clc2
Identification System (AIS) data categorized using SCCs
specific to ship type. Point and hourly resolution.
Category 3 CMV:
cmv_c3
Nonpoint
2021 Category 3 (C3) commercial marine vessel (CMV)
emissions based on 2021 AIS data categorized using SCCs
specific to ship type. Point and hourly resolution.
Line haul rail locomotives emissions from 2020 NEI
Locomotives:
rail
Nonpoint
projected to 2021 using 5 percent growth based on Annual
Energy Outlook (AEO) changes from 2020 to 2021. County
and annual resolution.
Nonpoint source oil and
gas: np_oilgas
Nonpoint
Nonpoint emissions from oil and gas-related processes for
2021 computed using activity data for 2021. County and
annual resolution.
Residential Wood
Combustion:
rwc
2020 NEI nonpoint sources with residential wood
Nonpoint
combustion (RWC) processes, projected to 2021 with state-
level adjustment factors derived from the State Energy
Data System (SEDS). County and annual resolution.
Emissions of solvents for 2021 based on methods used for
the 2020 NEI (Seltzer, 2021). Includes household cleaners,
Solvents: np_solvents
Nonpoint
personal care products, adhesives, architectural and
aerosol coatings, printing inks, and pesticides. Annual and
county resolution.
Remaining nonpoint:
nonpt
Nonpoint
2020 NEI nonpoint sources not included in other platform
sectors. County and annual resolution.
17
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Platform Sector:
abbreviation
NEI Data Category
Description and resolution of the data input to SMOKE
Nonroad:
nonroad
Nonroad
2021 nonroad equipment emissions developed with
MOVES4, including the updates made to spatial
apportionment that were developed with the 2016vl
platform. MOVES4 was used for all states except California,
which submitted their own emissions for 2020 and 2023
from which an interpolation to 2021 was performed.
County and monthly resolution.
Onroad:
on road
Onroad
Onroad mobile source gasoline and diesel vehicles from
parking lots and moving vehicles for 2021 developed using
VMT data from 2020 NEI projected to 2021 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. MOVES4 was run for 2021 to generate year-specific
emission factors.
Onroad California:
onroad_ca_adj
Onroad
California-provided 2020 and 2023 CAPs that were
interpolated to 2021. HAPs speciated from CAPs. 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 MOVES4.
Point source agricultural
fires: ptagfire
Nonpoint
Agricultural fire sources for 2021 developed by EPA as point
and day-specific emissions.3 Only EPA-developed data were
used in this study, thus 2020 NEI state submissions are not
included. Agricultural fires are in the nonpoint data
category of the NEI, but in the modeling platform, they are
treated as day-specific point sources. Updated HAP-
augmentation factors were applied.
Point source prescribed
fires: ptfire-rx
Nonpoint
Point source day-specific prescribed fires for 2021
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 source wildfires:
ptfire-wild
Nonpoint
Point source day-specific wildfires for 2021 computed using
SMARTFIRE 2 and BlueSky Pipeline.
Non-US. Fires:
ptfire_othna
N/A
Point source day-specific wildfires and agricultural fires
outside of the U.S. for 2021. 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).
Canada Area Fugitive dust
sources:
canada_afdust
N/A
Area fugitive dust sources from ECCC for 2021 (interpolated
between provided 2020 and 2023 emisisons) with transport
fraction and snow/ice adjustments based on 2021
meteorological data. Annual and province resolution.
3 Only EPA-developed agricultural fire data were included in this study; data submitted by states to the NEI were excluded.
18
-------
Platform Sector:
abbreviation
NEI Data Category
Description and resolution of the data input to SMOKE
Canada Point Fugitive dust
sources:
canada_ptdust
N/A
Point source fugitive dust sources from ECCC for 2021
(interpolated between provided 2020 and 2023 emissions)
with transport fraction and snow/ice adjustments based on
2021 meteorological data. Monthly and province
resolution.
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 ECCC
for 2020 and 2023, and interpolated to 2021. Mexico point
source emissions for 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 resolution.
Canada and Mexico
agricultural sources:
canmexjag
N/A
Canada and Mexico agricultural emissions. Canada
emissions were provided by ECCC for 2020 and 2023, and
interpolated to 2021. Mexico agricultural emissions were
provided by SEMARNAT and include updated emissions for
border states representing 2018 developed by SEMARNAT
in collaboration with EPAT, while emissions for all other
states were carried forward from 2019ge. Annual
resolution.
Canada low-level oil and
gas sources:
canada_og2D
N/A
Canada emissions from upstream oil and gas, provided by
ECCC for 2020 and 2023, and interpolated to 2021. 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
resolution.
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 2021. Mexico: include updated emissions
for 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 resolution.
Canada onroad sources:
canada_onroad
N/A
Canada onroad emissions. 2020 and 2023 Canada
inventories provided by ECCC and interpolated to 2021;
processed using updated surrogates. Province and monthly
resolution.
Mexico onroad sources:
mexico_onroad
N/A
Mexico onroad emissions. 2020 and 2023 emissions output
from MOVES-Mexico were interpolated to 2021. Municipio
and monthly resolution.
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
19
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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/2021-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 the 2021 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.
2.1Point sources (ptegu, pt_oilgas, ptnonipm, airports)
Point sources are sources of emissions for which specific geographic coordinates (e.g.,
latitude/longitude) are specified, as in the case of an individual facility. A facility may have multiple
emission release points that may be characterized as units such as boilers, reactors, spray booths, kilns,
etc. A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes
burns natural gas). 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 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 2021 into the Flat File 2010 (FF10) format that is compatible with SMOKE (see
https://cmascenter.Org/smoke/documentation/4.9/html/ch06s02s08.html) and was then split into
several sectors for modeling. For both flat files, 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), although this study does not include emissions projected to other years.
In some cases, data about facility or unit closures are entered into EIS after the inventory modeling
inventory flat files have been extracted. Prior to processing through SMOKE, submitted facility and unit
closures were reviewed and where closed sources were found in the inventory, those were removed.
For the 2021 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
20
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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). These updates impacted the ptnonipm and pt_oilgas inventories.
The inventory pollutants processed through SMOKE for input to CMAQ for the ptegu, pt_oilgas,
ptnonipm, 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. For AERMOD, additional HAPS were
included as described in the 2021 AirToxScreen TSD.
The ptnonipm, pt_oilgas, and airports sector emissions were provided to SMOKE as annual emissions.
For sources in the ptegu sector that could be matched to 2021 CEMS data, hourly CEMS NOx and SO2
emissions for 2021 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 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:
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 also for all
EGU units modeled by EPA's Integrated Planning Model (IPM).
e. Data for airports and rail yards were incorporated.
f. Off-shore platform data were added from the Bureau of Ocean Energy Management (BOEM).
The changes made to the NEI point sources prior to modeling with SMOKE are as follows:
• The tribal data, which do not use state/county Federal Information Processing Standards (FIPS)
codes in the NEI, but rather use the tribal code, were assigned a state/county FIPS code of 88XXX,
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.
21
-------
• 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)
The ptegu sector contains emissions from EGUs in the 2021 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 the emissions output from IPM. It is therefore important that the
matching between the NEI and NEEDS database be as complete as possible because there can be
double-counting of emissions in 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.
The 2021 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 v6 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 (SCC) 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
22
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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 and those that do not so that 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)
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 2021 NEI process were used. Sources without data submitted for 2021 were projected to
2021 from 2020 NEI emissions.
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 2021 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
23
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NAICS
NAICS description
486110
Pipeline Transportation of Crude Oil
4862
Pipeline Transportation of Natural Gas
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 2021 by state are shown in Table 2-3.
Table 2-3. Point source oil and gas sector emissions for 2021
State
2021 NOx
2021VOC
Alabama
9,694
1,054
Alaska
38,833
1,608
Arizona
2,460
183
Arkansas
3,235
262
California
2,623
2,529
Colorado
14,162
11,714
Connecticut
62
40
Delaware
6
1
Florida
5,409
623
Georgia
2,763
436
Idaho
1,057
30
Illinois
3,925
1,034
Indiana
1,601
185
Iowa
3,804
208
Kansas
15,641
2,982
Kentucky
10,000
1,398
Louisiana
28,478
8,149
Maine
30
58
Maryland
175
176
Massachusetts
183
64
Michigan
9,207
1,076
Minnesota
2,175
139
Mississippi
20,986
1,926
Missouri
1,847
78
Montana
714
972
Nebraska
2,597
218
Nevada
233
19
New Jersey
83
98
New Mexico
32,396
52,754
New York
905
264
North Carolina
1,776
220
24
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State
2021 NOx
2021VOC
North Dakota
4,556
2,799
Ohio
9,714
1,481
Oklahoma
34,400
25,119
Oregon
792
63
Pennsylvania
3,798
1,022
Puerto Rico
17
0
Rhode Island
50
22
South Carolina
279
104
South Dakota
358
10
Tennessee
6,251
514
Texas
44,710
19,906
Utah
2,359
488
Virginia
2,621
406
Washington
688
45
West Virginia
8,274
3,230
Wisconsin
376
223
Wyoming
13,327
49,362
Offshore
34,660
31,406
Tribal Data
7,909
2,254
2.1.3 Aircraft and ground support equipment (airports)
Emissions at airports were separated from other sources in the point inventory based on sources that
have the facility source type of 100 (airports). The airports sector includes all aircraft types used for
public, private, and military purposes and aircraft ground support equipment. The Federal Aviation
Administration's (FAA) Aviation Environmental Design Tool (AEDT) is used to estimate emissions for this
sector. Additional information about aircraft emission estimates can be found in section 3 of the 2020
NEI TSD. Airport emissions were projected from the 2020 NEI to 2021 using factors derived from the
2023 Terminal Area Forecast (TAF)4 data. EPA used airport-specific factors where available. 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.
Table 2-4. SCCs for the airports sector
see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
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
4 See https://www.faa.gov/data research/aviation/taf for 2023 TAF released in January 2024
25
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see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2275060012
Mobile Sources
Aircraft
Air Taxi
Turbine
2.1.4 Non-IPM sector (ptnonipm)
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 the
NEEDS database or to CEMS data are in the ptnonipm sector.
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 2021 NEI
point inventory. 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 the point flat file exported from EIS on January 28,
2023 with edits made through April 14, 2023 that included corrections to how the selection was
implemented in EIS, updates from the state/local review, 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, vcp, 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 also because spatial surrogates for tribal data are not currently
available. These omissions are not expected to have an impact on the results of the air quality modeling
at the 12-km resolution used for this platform.
The following subsections describe how the sources in the NEI nonpoint inventory were separated into
modeling platform sectors, along with any data that were updated (replaced) with non-NEI data.
26
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2.2.1 Area fugitive dust sector (afdust)
The area-source fugitive dust (afdust) sector contains PMio and PM2.5 emission estimates for nonpoint
SCCs identified by EPA as dust sources. Categories included in the afdust sector are paved roads,
unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production,
and mining and quarrying. It does not include fugitive dust from grain elevators, coal handling at coal
mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as
point sources so they are properly located. Table 2-5 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
27
-------
Area Fugitive Dust Transport Fraction
The afdust sector was separated from other nonpoint sectors to allow for the application of a "transport
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 zeroes out emissions for days on which at least 0.01 inches of precipitation occurs or
there is snow cover on the ground. The land use data used to reduce the NEI emissions determines the
amount of emissions that are subject to transport. This methodology is discussed in Pouliot, et al., 2010,
and in "Fugitive Dust Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). Both the
transport fraction and meteorological adjustments are based on the gridded resolution of the platform
(i.e., 12km grid cells); therefore, different emissions will result if the process were applied to different
grid resolutions. A limitation of the transport fraction approach is the lack of monthly variability that
would be expected with seasonal changes in vegetative cover. While wind speed and direction 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.
Paved and unpaved road dust emissions were from the 2020 NEI adjusted to 2021 levels based on
changes between 2020 and 2021 VMT. For the fugitive dust emissions compiled into the 2020 NEI,
meteorological adjustments were applied to paved and unpaved road SCCs but not transport
adjustments. This is because the modeling platform applies meteorological adjustments and transport
adjustments based on unadjusted NEI values. For the 2021 platform, the meteorological adjustments
that were applied in the NEI to 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 transport and meteorological
adjustments applied according to year 2021 meteorology. The total impacts of the transport fraction
and meteorological adjustments are shown in Table 2-6.
Table 2-6. Total impact of 2021 fugitive dust adjustments to unadjusted inventory
State
Unadjusted
PMio
Unadjusted
PM2.5
Change in
PM10
Change in
PM2.5
PM10
Reduction
PM2.5
Reduction
Alabama
286,292
36,777
-222,367
-28,460
78%
77%
Arizona
161,676
21,687
-60,138
-7,889
37%
36%
Arkansas
427,784
58,382
-319,511
-42,773
75%
73%
California
349,822
44,855
-149,476
-18,577
43%
41%
Colorado
292,813
41,223
-152,704
-20,416
52%
50%
Connecticut
22,679
3,535
-16,774
-2,595
74%
73%
Delaware
16,664
2,594
-9,530
-1,476
57%
57%
District of
Columbia
3,464
470
-2,210
-299
64%
64%
Florida
217,858
34,421
-118,661
-18,283
54%
53%
Georgia
304,157
42,745
-234,361
-32,658
77%
76%
Idaho
531,962
61,840
-299,851
-33,490
56%
54%
Illinois
742,277
94,786
-485,169
-61,670
65%
65%
Indiana
155,315
28,593
-111,213
-20,539
72%
72%
28
-------
State
Unadjusted
PMio
Unadjusted
PM2.5
Change in
PM10
Change in
PM2.5
PM10
Reduction
PM2.5
Reduction
Iowa
388,755
56,597
-232,197
-33,773
60%
60%
Kansas
635,604
85,521
-294,147
-39,002
46%
46%
Kentucky
181,268
29,313
-144,359
-23,309
80%
80%
Louisiana
206,623
30,708
-149,591
-22,157
72%
72%
Maine
44,767
6,183
-35,375
-4,897
79%
79%
Maryland
62,084
8,943
-39,187
-5,659
63%
63%
Massachusetts
65,930
8,755
-48,611
-6,315
74%
72%
Michigan
316,401
41,154
-223,319
-28,937
71%
70%
Minnesota
573,650
76,027
-365,089
-47,744
64%
63%
Mississippi
453,862
54,475
-353,342
-42,033
78%
77%
Missouri
1,566,937
177,571
-1,121,332
-126,505
72%
71%
Montana
539,661
70,598
-324,321
-40,283
60%
57%
Nebraska
546,482
74,164
-246,367
-32,652
45%
44%
Nevada
128,743
16,635
-44,929
-5,813
35%
35%
New Hampshire
16,230
3,319
-13,075
-2,662
81%
80%
New Jersey
140,214
17,371
-93,221
-11,439
66%
66%
New Mexico
192,717
24,285
-80,669
-10,015
42%
41%
New York
268,443
37,903
-210,393
-29,366
78%
77%
North Carolina
272,110
36,412
-201,347
-26,809
74%
74%
North Dakota
370,295
56,700
-188,627
-28,550
51%
50%
Ohio
285,183
44,047
-211,889
-32,724
74%
74%
Oklahoma
587,438
80,122
-315,498
-42,187
54%
53%
Oregon
842,361
93,017
-628,197
-67,489
75%
73%
Pennsylvania
149,717
26,243
-110,784
-19,704
74%
75%
Rhode Island
6,070
1,017
-4,144
-684
68%
67%
South Carolina
198,821
25,976
-147,357
-19,110
74%
74%
South Dakota
215,000
37,848
-109,221
-18,927
51%
50%
Tennessee
141,974
26,071
-106,932
-19,649
75%
75%
Texas
1,629,045
224,169
-827,471
-112,813
51%
50%
Utah
150,567
18,876
-87,685
-10,722
58%
57%
Vermont
63,537
7,034
-55,955
-6,180
88%
88%
Virginia
141,234
22,127
-109,282
-17,226
77%
78%
Washington
182,602
22,573
-99,565
-12,396
55%
55%
West Virginia
71,178
9,957
-64,115
-8,945
90%
90%
Wisconsin
207,935
34,793
-138,880
-23,227
67%
67%
Wyoming
674,782
71,920
-377,702
-39,839
56%
55%
Domain Total
(12km CONUS)
16,030,982
2,130,333
-9,986,143
-1,308,867
62%
61%
29
-------
For categories other than paved and unpaved roads, where states submitted afdust data it was assumed
that the state-submitted data were not met-adjusted and therefore the meteorological adjustments
were applied. Thus, if states submitted data that were met-adjusted for sources other than paved and
unpaved roads, these sources would have been adjusted for meteorology twice. Even with that
possibility, air quality modeling shows that, in general, dust is frequently overestimated in the air quality
modeling results.
Figure 2-1 illustrates the impact of each step of the adjustment. The reductions due to the transport
fraction adjustments alone are shown at the top of the figure. The reductions due to the precipitation
adjustments alone are shown in the middle of the figure. The cumulative emission reductions after both
transport fraction and meteorological adjustments are shown at the bottom of the figure. The top plot
shows how the transport fraction has a larger reduction effect in the east, where forested areas are
more effective at reducing PM transport than in many western areas. The middle plot shows how the
meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust
emissions.
Figure 2-1. Fugitive dust emissions and impact of adjustments due to transport fraction, precipitation,
and cumulative
2021hb afdust annual : PM2 5
Max: 975.7358 Min: 0.0
30
-------
-------
2021hb afdust annual : PM2 5
Max: 0^ Min:
>119
89
59
29
0
-29
-59
-89
<-119
c
01
u
CD
Q.
2.2.2 Agricultural Livestock (livestock)
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 quite all of the livestock NH j emissions,
as there is a very small amount of NHj emissions from livestock in the ptnonipm inventory (as point
sources). In addition to NH ?, the sector includes livestock emissions from all pollutants other than PM2.5.
PlVh.sfrom livestock are in the afdust sector.
Agricultural livestock emissions in the 2021 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 livestock utilizes daily emission factors by animal and county from a model developed by
Carnegie Mellon University (CMIJ) (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. VOC
livestock emissions, new for this sector, were estimated by multiplying a national VOC/NH3 emissions
ratio by the county NH s emissions. Animal populations used for estimating livestock emissions in the
2021 platform were adjusted from 2020 NEI levels to 2021 levels based on 2021 USDA survey data (see
QuickStats at https://quickstats.nass.usda.gov) for the available counties. The adjustment factors ranged
from 0.8 to 1.2. For other counties, the animal populations remained the same as 2020. The FEM model
was run for 2021 using meteorological data for 2021.
32
-------
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
2.2.3 Agricultural Fertilizer (fertilizer)
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 (v5.3) and the Fertilizer Emissions Scenario Tool for CMAQ
FEST-C (vl.3) were used to estimate ammonia (NH3) emissions from agricultural soils. The computed
emissions were saved during the CMAQ run 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, 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 NEI emissions, CMAQv5.4 was run with
the Surface Tiled Aerosol and Gaseous Exchange (STAGE) deposition option along with bidirectional
exchange to estimate fertilizer and biogenic NH3 emissions. However, for this study, the M3DRY option
was used to develop the fertilizer emissions.
33
-------
Figure 2-2. "Bidi" modeling system used to compute emissions from fertilizer application
The Fertilizer Emission Scenario Tool for CMAQ
(FEST-C)
Fertilizer Activity Data
The following activity parameters were input into the EPIC model;
• Grid cell meteorological variables from WRF
• Initial soil profiles/soil selection
• Presence of 21 major crops: irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn,
silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans,
spring wheat, winter wheat, canola, and other crops (e.g., lettuce, tomatoes, etc.)
• Fertilizer sales to establish the type/composition of nutrients applied
• Management scenarios for the 10 USDA production regions. These include irrigation, tile
drainage, intervals between forage harvest, fertilizer application method (injected versus surface
applied), and equipment commonly used in these production regions.
The WRF meteorological model was used to provide grid ceil meteorological parameters for the year
2021 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.
34
-------
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, https://www.nass.usda.gov/Surveys/
Guide to NASS Surveys/Ag Resource Management/) was used to provide management activity data.
These data cover 10 USDA production regions and provide 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.).
35
-------
2.2.4 Nonpoint Oil and Gas Sector (np_oilgas)
The nonpoint oil and gas (np_oilgas) sector includes onshore and offshore oil and gas emissions. The EPA
estimated emissions for all counties with 2021 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 "NEl oil and gas
tool") populated with 2021-specific activity data was used to estimate 2021. Year 2021 oil and gas
activity data were obtained from 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. Table 2-9 shows the nonpoint oil and gas NOx and VOC emissions for 2021 by state.
The Colorado emissions in this table include emissions submitted to the NEI within the Southern Ute
reservation. 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 2021
State
2021 NOx
2021 VOC
Alabama
3,830
9,596
Alaska
1,583
12,596
Arizona
8
113
Arkansas
4,540
8,686
California
1,438
16,827
Colorado
26,960
68,984
Florida
27
614
Georgia
-
0.04
Idaho
6
61
Illinois
13,558
56,158
Indiana
2,457
14,200
Iowa
-
0.01
Kansas
25,960
70,263
Kentucky
10,792
44,542
Louisiana
19,947
55,400
Maryland
1
2
Michigan
11,609
15,072
Minnesota
-
0.01
36
-------
State
2021 NOx
2021 VOC
Mississippi
3,323
8,018
Missouri
432
1,031
Montana
2,258
30,151
Nebraska
256
2,035
Nevada
4
180
New Mexico
120,851
295,051
New York
695
6,505
North Carolina
-
0.003
North Dakota
51,202
233,638
Ohio
2,450
34,243
Oklahoma
43,946
166,564
Oregon
6
31
Pennsylvania
65,308
197,669
South Dakota
175
1,213
Tennessee
874
3,529
Texas
273,305
1,289,662
Utah
13,940
67,149
Virginia
3,967
8,464
Washington
-
3
West Virginia
22,431
162,678
Wyoming
2,105
8,146
A new source was added to the oil and gas sector for 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 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 2021 Emissions Modeling Platform. Table 2-10 shows the
emissions totals by state for Pipeline Blowdowns and Pigging sources.
An additional new source was added to the oil and gas sector for this 2021 modeling platform. This new
source were Abandoned oil and gas wells in the USA. 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.
37
-------
As of year 2020, there are 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.
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, a
draft version of the inventory from 1990 - 2021 is available for public review. 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 2021
modeling platform. Year 2020 estimates of VOC and BTEX were estimated and used in this year 2021
modeling platform. Table 2-11 shows the emissions totals by state for Pipeline Blowdowns and Pigging
sources.
Table 2-10. State emissions totals for year 2021 for Pipeline Blowdowns and Pigging sources
State
VOC (tpy)
Benzene (tpy)
Ethylbenzene (tpy)
Toluene (tpy)
Xylene (tpy)
Alabama
363
1.57
0.088
1.38
0.40
Alaska
11
0.05
0.003
0.05
0.01
Arizona
77
0.35
0.020
0.31
0.09
Arkansas
24
0.01
0.000
0.00
0.00
California
236
1.08
0.061
0.96
0.27
Colorado
3,413
8.89
0.449
10.85
3.38
Florida
1
0.00
0.000
0.00
0.00
Illinois
239
0.88
0.050
0.79
0.22
Indiana
237
1.00
0.056
0.89
0.25
Kansas
4,046
6.86
0.789
6.53
2.79
Kentucky
661
2.99
0.169
2.67
0.76
Louisiana
453
3.26
0.003
0.36
0.57
Maryland
0
0.00
0.000
0.00
0.00
Michigan
241
1.10
0.062
0.98
0.28
Mississippi
1,002
1.55
0.034
0.61
0.50
Missouri
22
0.02
0.000
0.02
0.01
Montana
222
1.01
0.057
0.90
0.26
Nebraska
24
0.06
0.003
0.07
0.02
New Mexico
1,753
0.00
0.000
0.00
0.00
New York
192
0.87
0.049
0.78
0.22
North
Dakota
15
0.07
0.004
0.06
0.02
Ohio
476
2.16
0.122
1.93
0.55
Oklahoma
3,019
2.53
0.086
1.46
1.06
Oregon
14
0.06
0.003
0.05
0.02
Pennsylvania
1,366
6.20
0.351
5.53
1.57
South
Dakota
4
0.02
0.001
0.02
0.00
Tennessee
19
0.09
0.005
0.08
0.02
38
-------
State
VOC (tpy)
Benzene (tpy)
Ethylbenzene (tpy)
Toluene (tpy)
Xylene (tpy)
Texas
25,237
29.05
0.642
11.33
9.59
Utah
25
0.13
0.007
0.12
0.05
Virginia
294
1.33
0.076
1.19
0.34
West
Virginia
1,240
5.62
0.318
5.01
1.42
Wyoming
236
1.58
0.112
0.63
0.49
US Total
45,161
80.38
3.619
55.53
25.15
Table 2-11. State emissions totals for year 2021 for Abandoned Wells sources
State
2021 VOC
(tpy)
Alabama
185
Alaska
60
Arizona
10
Arkansas
909
California
5,239
Colorado
349
Florida
40
Georgia
0
Idaho
0
Illinois
6,726
Indiana
3,214
Iowa
0
Kansas
7,146
Kentucky
11,762
Louisiana
2,375
Maryland
1
Michigan
433
Minnesota
0
Mississippi
725
Missouri
118
Montana
598
Nebraska
137
Nevada
14
New Mexico
391
New York
658
North Carolina
0
North Dakota
418
Ohio
21,812
Oklahoma
7,426
Oregon
3
Pennsylvania
69,837
39
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State
2021VOC
(tpy)
South Dakota
27
Tennessee
1,279
Texas
31,921
Utah
173
Virginia
67
Washington
3
West Virginia
2,098
Wyoming
514
US Total
176,670
Lastly, EPA and the state of New Mexico worked together to exercise the point source subtraction step
in the Oil and Gas Tool during the 2020 NEI development period. This point source subtraction step was
used for New Mexico because additional oil and gas point sources submitted by New Mexico that were
the same processes estimated in the Oil and Gas Tool (non-point sources). This point source subtraction
step is a processed used to eliminate possible double counting of sources in the Oil and Gas Tool that are
already defined in the point source inventory. Unfortunately, the resulting non-point emissions from the
point source subtraction step for New Mexico did not get into the 2020 NEI release due to EIS tagging
issues. New Mexico non-point oil and gas emissions are overestimated in the 2020 NEI as a result. This
overestimation was corrected for this 2021 Emissions Modeling Platform.
2.2.5 Residential Wood Combustion (rwc)
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.
The 2021 platform RWC emissions are unchanged from the data in the 2020 NEI and include some
improvements to RWC emissions estimates 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
40
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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.
Table 2-12. SCCs for the residential wood combustion sector
see
Tier 1 Description
Tier 2
Description
Tier 3
Description
Tier 4 Description
2104008100
Stationary Source Fuel
Combustion
Residential
Wood
Fireplace: general
2104008210
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: fireplace inserts; non-
EPA certified
2104008220
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: fireplace inserts; EPA
certified; non-catalytic
2104008230
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: fireplace inserts; EPA
certified; catalytic
2104008300
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: freestanding, general
2104008310
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: freestanding, non-EPA
certified
2104008320
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: freestanding, EPA
certified, non-catalytic
2104008330
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: freestanding, EPA
certified, catalytic
2104008400
Stationary Source Fuel
Combustion
Residential
Wood
Woodstove: pellet-fired, general
(freestanding or FP insert)
2104008510
Stationary Source Fuel
Combustion
Residential
Wood
Furnace: Indoor, cordwood-fired,
non-EPA certified
2104008530
Stationary Source Fuel
Combustion
Residential
Wood
Furnace: Indoor, pellet-fired,
general
2104008610
Stationary Source Fuel
Combustion
Residential
Wood
Flydronic heater: outdoor
2104008620
Stationary Source Fuel
Combustion
Residential
Wood
Flydronic heater: indoor
2104008630
Stationary Source Fuel
Combustion
Residential
Wood
Flydronic heater: pellet-fired
2104008700
Stationary Source Fuel
Combustion
Residential
Wood
Outdoor wood burning device, NEC
(fire-pits, chimeneas, etc)
2104009000
Stationary Source Fuel
Combustion
Residential
Firelog
Total: All Combustor Types
2.2.6 Solvents (np_solvents)
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
41
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settings. The organic gases that evaporate from these sources often fulfill other functions than acting as
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.
For 2021, in many instances, emissions within the 2020 NEI were carried forward. This includes
pollutant/county/SCC emission submissions (including Hazardous Air Pollutants) from State, Locality,
and Tribal partners, and all asphalt related emissions. The np_solvents sector also includes emissions
from SCCs included in the 2020 NEI but not covered by VCPy, the model used to estimate most nonpoint
emissions in the solvent sector (Seltzer, et al., 2021). These emissions come from State, Locality, and
Tribal emission submissions for select SCCs, all of which are listed in Table 2-13. All other np_solvents
emissions were estimated using the same methods used to generate the 2020 NEI and are specific to
2021-year usage.
Table 2-13. Non-VCPy SCCs in the np_solvents sector
see
Description
2401050000
Solvent Utilization;Surface Coating;Miscellaneous Finished Metals: SIC 34 - (341 +
3498);Total: All Solvent Types
2440020000
Solvent Utilization;Miscellaneous lndustrial;Adhesive (Industrial) Application;Total: All
Solvent Types
2461021000
Solvent Utilization;Miscellaneous Non-industrial: Commercial;Cutback Asphalt;Total: All
Solvent Types
2461022000
Solvent Utilization;Miscellaneous Non-industrial: Commercial;Emulsified Asphalt;Total:
All Solvent Types
2461023000
Solvent Utilization;Miscellaneous Non-industrial: Commercial;Asphalt Roofing;Total: All
Solvent Types
2461025100
Solvent Utilization;Miscellaneous Non-industrial: Commercial; Asphalt Paving: Hot and
Warm Mix;Hot Mix Total: All Solvent Types
2461025200
Solvent Utilization;Miscellaneous Non-industrial: Commercial; Asphalt Paving: Hot and
Warm Mix;Warm Mix Total: All Solvent Types
2461800001
Solvent Utilization;Miscellaneous Non-industrial: Commercial;Pesticide Application: All
Processes;Surface Application
2.2.7 Nonpoint (nonpt)
The 2021 platform nonpt sector inventory is unchanged from the April 2023 version of the 2020 NEI.
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;
42
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• 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.
2.3 Onroad Mobile sources (onroad)
Onroad mobile source include emissions from motorized vehicles operating on public roadways. These
include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks, heavy-duty trucks,
and buses. The sources are further divided by the fuel they use, including diesel, gasoline, E-85,
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, see section 5 of the 2020 NEI TSD.
For the 2021 emissions modeling platform activity data (i.e., VMT, VPOP) were based on state submitted
CDBs for 2020, as well as data from Federal Highway Administration (FHWA) annual VMT at the county
level. VMT were projected from 2020 to 2021 using state VM-2 data from FHWA. VPOP was held
constant at 2020 levels. A new MOVES run for 2021 was done using MOVES4 to obtain year-specific
emission factors.
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 2021 meteorological data. Specifically, EPA used projected vehicle
miles traveled (VMT), and a factor of 2021 VMT/2020 VMT was applied to the 2020 hoteling hours;
along with tools that integrated the MOVES model with SMOKE. In this way, it was possible to take
advantage of the gridded hourly temperature data available from meteorological modeling that are also
used for air quality modeling. The onroad source classification codes (SCCs) in the emissions modeling
platform are more finely resolved than those in the National Emissions Inventory (NEI). The NEI SCCs
distinguish vehicles and fuels. The SCCs used in the model platform also distinguish between emissions
processes (i.e., off-network, on-network, and extended idle), and road types. EPA elected to keep 2020
NEI fuel splits (derived from MOVES3) and not upgrade to MOVES4 fuels.
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M0VES4 includes the following updates from MOVES3:
• Incorporates updates to vehicle populations, fuel supply, travel activity, and emission rates.
• Accounts for the emission impacts of the EPA heavy-duty low NOx rule for model years 2027 and
later and the light-duty greenhouse gas rule for model years 2023 and later.
• Adds the ability to model heavy-duty battery-electric and fuel-cell vehicles, as well as CNG long-
haul combination trucks.
• Improves the modeling of light-duty electric vehicles.
• Improves the user interface to make the model easier to use and updating the platform for
compatibility with newer software.
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 2-14. SMOKE-MOVES was run for specific modeling grids.
Emissions for the contiguous U.S. states and Washington, D.C., were computed for a grid covering those
areas. Emissions for Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands were computed by running
SMOKE-MOVES for distinct grids covering each of those regions and are included in the
onroad_nonconus sector. In some summary reports these non-CONUS emissions are aggregated with
emissions from the onroad sector.
Table 2-14. MOVES vehicle (source) types
MOVES vehicle type
Description
HPMS vehicle type
11
Motorcycle
10
21
Passenger Car
25
31
Passenger Truck
25
32
Light Commercial Truck
25
41
Other Bus
40
42
Transit Bus
40
43
School Bus
40
51
Refuse Truck
50
52
Single Unit Short-haul Truck
50
53
Single Unit Long-haul Truck
50
54
Motor Home
50
61
Combination Short-haul Truck
60
62
Combination Long-haul Truck
60
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 2021-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
44
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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. For this study, there are 254
representative counties in the continental U.S. and a total of 293 including the non-CONUS areas. The
only difference between 2020 and 2021 being 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).
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 travelled (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.
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-profile (RPS) uses STARTS activity data to compute off-network emissions from vehicles
starts;
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• 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.
The onroad emissions inputs to MOVES for the 2021 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, age distributions, and other inputs were consistent with those used to compute the 2020
NEI. 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 and supporting documents.
Hoteling hours activity were used to calculate emissions from extended idling and auxiliary power units
(APUs) by combination long-haul trucks. Hoteling hours for 2021 were calculated by applying a 2021
restricted road VMT/2020 restricted road VMT factor was applied to the 2020 hoteling hours.
2.3.2 Onroad Activity Data Development
SMOKE-MOVES uses vehicle miles traveled (VMT), vehicle population (VPOP), vehicle starts, hours of off-
network idling (ONI), and hours of hoteling, to calculate emissions. These datasets are collectively
known as "activity data". For each of these activity datasets, first a national dataset was developed; this
national dataset is called the "EPA default" dataset. The default dataset started with the 2020 NEI
activity data, which was supplemented with data submitted by state and local agencies. EPA grew the
2020 VMT to 2021 using factors derived from FHWA's VM-2 data. VPOP was held constant at 2020
levels, as were the starts and fuel splits. 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. A
growth factor, derived from FHWA data, was applied to the VMT to make it representative of 2021.
VPOP was held constant with the 2020 NEI VPOP.
Speed Activity (SPDIST)
In SMOKE 4.7, SMOKE-MOVES was updated to use speed distributions similarly to how they are used
when running MOVES in inventory mode. The speed distribution file, called SPDIST, specifies the amount
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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.
Due to pandemic effects on most of the 2020 data, for the 2021 emissions modeling platform, the best
estimate for year 2021 speeds available were the speeds from December 2020. Speed data from the
Streetlight dataset were used to generate hourly speed profiles by county, SCC, and month. The SPDIST
files for the 2021 emissions modeling platform are based on a combination of the Streetlight project
data and 2020 NEI MOVES CDBs. More information can be found in the 2020 NEI TSD (EPA, 2023) and
supporting documents.
Hoteling Hours (HOTELING)
Hoteling hours were based on the hoteling hours for the 2020 NEI. County-specific factors of 2021
restricted road VMT/2020 restricted road VMT for combination long haul trucks were applied to the
2020 hoteling hours to make them more representative of 2021.Hoteling hours were capped by county
at a theoretical maximum and any excess hours of the maximum were reduced. For calculating
reductions, a dataset of truck stop parking space availability was used, which includes a total number of
parking spaces per county. This same dataset is used to develop the spatial surrogate for allocating
county-total hoteling emissions to model grid cells. The parking space dataset includes several recent
updates based on new truck stops opening and other new information. There are 8,784 hours in the
year 2020; therefore, the maximum number of possible hoteling hours in a particular county is equal to
8,784 * the number of parking spaces in that county. Hoteling hours were capped at that theoretical
maximum value for 2020 in all counties, with some exceptions. This cap was only evaluated for the data
reflecting 2020 and not re-evaluated for 2021.
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 105,408 hours for the year in any county. If the unreduced hoteling
hours were already below that maximum, the hours were left unchanged; in other words, hoteling
activity were never increased in this analysis. For the 2020 NEI, four states requested that no reductions
be applied to the hoteling activity based on parking space availability: CO, ME, NJ, and NY. For these
states, reductions based on parking space availability were not applied.
The final step related to hoteling activity is to split county totals into separate values for extended idling
(SCC 2202620153) and Auxiliary Power Units (APUs) (SCC 2202620191). For 2021 modeling with
MOVES4, an 8.2% APU split is used nationwide, meaning that during 8.2% of the hoteling hours auxiliary
power units are assumed to be running.
Starts
Onroad "start" emissions are the instantaneous exhaust emissions that occur at the engine start (e.g.,
due to the fuel rich conditions in the cylinder to initiate combustion) as well as the additional running
exhaust emissions that occur because the engine and emission control systems have not yet stabilized at
47
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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.
MOVES4 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, MOVES4 accounts for different amounts of cooling of the
engine and emission control systems. Each source bin and operating mode has an associated g/start
emission rate. Start emissions are also adjusted to account for fuel characteristics, LD inspection and
maintenance (l/M) programs, and ambient temperatures. Starts were held constant from 2020 to 2021,
however, new monthly profiles were applied for 2021.
Off-network Idling Hours
After creating VMT inputs for SMOKE-MOVES, Off-network idle (ONI) activity data were also needed.
ONI is defined in MOVES as time during which a vehicle engine is running idle and the vehicle is
somewhere other than on the road, such as in a parking lot, a driveway, or at the side of the road. This
engine activity contributes to total mobile source emissions but does not take place on the road
network. Examples of ONI activity include:
• light duty passenger vehicles idling while waiting to pick up children at school or to pick up
passengers at the airport or train station,
• single unit and combination trucks idling while loading or unloading cargo or making
deliveries, and
• vehicles idling at drive-through restaurants.
Note that ONI does not include idling that occurs on the road, such as idling at traffic signals, stop signs,
and in traffic—these emissions are included as part of the running and crankcase running exhaust
processes on the other road types. ONI also does not include long-duration idling by long-haul
combination trucks (hoteling/extended idle), as that type of long duration idling is accounted for in other
MOVES processes.
ONI activity hours were calculated based on VMT. For each representative county, the ratio of ONI hours
to onroad VMT (on all road types) was calculated using the MOVES ONI Tool by source type, fuel type,
and month. These ratios are then multiplied by each county's total VMT (aggregated by source type, fuel
type, and month) to get hours of ONI activity.
2.3.3 MOVES Emission Factor Table Development
MOVES4 was run in emission rate mode to create emission factor tables for 2021, 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
48
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representing the year 2021. The range of temperatures run along with the average humidities used
were specific to the year 2021. 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 2021. 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 2021 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 254 representative counties for the CONUS shown in Figure 2-3 along with 39 for Alaska, Hawaii,
Puerto Rico, and the US Virgin Islands. The CONUS representation counties for 2021 are the same a
those used for 2020 NEI.
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Figure 2-3. Map of Representative Counties
Representative County Groups 2020NEI Final
Age distributions are a key input to MOVES in determining emission rates. Age distributions were held
constant from 2020 for the 2021 emissions modeling platform, The age distributions for 2020 were
updated based on vehicle registration data obtained from IHS Markit, subject to reductions for older
vehicles. One of the findings of CRC project A-115 is that IHS data contain higher vehicle populations
than state agency analyses of the same Department of Motor Vehicles data, and the discrepancies tend
to increase with increasing vehicle age (i.e., there are more older vehicles in the IHS data) and
appropriate decreases in older vehicles were applied when the age distributions were computed for
2020 as follows.
Although 33 S/L/T agencies participated in the data submittal process for 2020 NEI onroad mobile
sources, only 15 provided both LDV populations (MOVES "SourceTypeYear* table) and age distributions
(MOVES "SourceTypeAgeDistribution" table) based on 2020 registration data, which was a requirement
for comparison with the 2020 IHS data. Other agencies were excluded from the adjustment factor
analysis because they provided only one type of local data (e.g., population but no age distribution) or
data with outdated (e.g., year 2013) or unknown registration data draw dates. For the 15 areas that
50
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could be included in the analysis, EPA first combined the populations of passenger cars (source type 21)
and light-duty trucks (source types 31 and 32) at the county level to remove the uncertainty of VIN
decoding personal passenger vehicles as cars vs. light-duty trucks. EPA then allocated each county's LDV
total source type population to vehicle model years for comparison with IHS and found that the IHS
populations for 2020 were higher than the state data by 10.8 percent. Similar to prior years'
comparisons, EPA again found that the discrepancies in the 2020 data between IHS and states are larger
for older vehicles. Table 2-15 shows the adjustments EPA made to the 2020 IHS data prior to its use in
the NEI and in the 2021 emissions modeling platform.
EPA calculated the adjustment factors representing the fraction of population remaining in every model
year, with two exceptions. Model years from 2011 to 2020 received no adjustment and the model year
1990 received a capped adjustment that equals the adjustment for model year 1991. The adjustment
factors in Table 2-15 were applied to the 2020 IHS data to create the EPA Default set of population and
age distributions for the NEI.
Table 2-15. The fraction of IHS vehicle populations retained for 2020 NEI and 2021 emissions modeling
platform by model year
Model Year
LDV Adjustment Factor
pre-1991
0.722
1991
0.722
1992
0.728
1993
0.742
1994
0.754
1995
0.766
1996
0.774
1997
0.790
1998
0.787
1999
0.798
2000
0.796
2001
0.806
2002
0.808
2003
0.828
2004
0.844
2005
0.857
2006
0.874
2007
0.892
2008
0.905
2009
0.919
2010
0.929
2011-2020
1
EPA also removed the county-specific fractions of antique license plate vehicles present in the
registration data from IHS, based on the assumption that antique vehicles are operated significantly less
51
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than average. States without any CDB submittals received EPA Default populations and age distributions
based on the adjusted IHS data, and some states with submittals were overridden, decided on a case-by-
case basis.
In addition to removing the older and antique plate vehicles from the IHS data, 28 counties found to be
outliers because their fleet age was significantly younger than in typical counties. The outlier review was
limited to LDV source types 21, 31, and 32. Many rural counties have outliers for low-population source
types such as Transit Bus and Refuse Truck due to small sample sizes, but these do not have much of an
impact on the inventory overall and reflect sparse data in low-population areas and therefore do not
require correction.
The most extreme examples of LDV outliers were Light Commercial Truck age distributions where over
85 percent of the population in the entire county is 0 and 1 years old. These sorts of young fleets can
happen if the headquarters of a leasing or rental company is the owner/entity of a relatively large
number of vehicles relative to the county-wide population. While the business owner of thousands of
new vehicles may reside in a single county, the vehicles likely operate in broader areas without being
registered where they drive.
In areas where submitted vehicle population data were accepted for the 2020 NEI, the relative
populations of cars vs. light-duty trucks were reapportioned (while retaining the magnitude of the light-
duty vehicles from the submittals) using the county-specific percentages from the IHS data. In this way,
the categorization of cars versus light trucks is consistent from state to state. The county total light-duty
vehicle populations were preserved through this process.
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 2021. 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 2021. 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 the 2020 NEI, as well as the year 2023. An interpolation
between 2020 and 2023 was used for the 2021 modeling. Since California's 2023 inventory did not
contain HAPs, VOC-based speciation factors were used to estimate VOC HAPs for 2021, and PM2.5-
based speciation factors to estimate metal HAPs. Other HAPs (e.g., PAHs) were computed using MOVES.
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.
52
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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.
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)
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 (i.e., port shape ids) and by SCC
and emission type (e.g., hoteling, maneuvering). For 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 2-16. For more information on CMV emissions
development, see the supplemental documentation.5 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.
5 See https://gaftp.epa.gov/Air/emismod/2021/reports/ClC2 Documentation 2021 Final.pdf.
53
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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 and as main and auxiliary engines are encoded using the SCCs listed in Table 2-16. Level 1 and
Level 2 descriptions for all of the entries are "Mobile Sources", and "Marine Vessels, Commercial",
respectively.
Table 2-16. SCCs for the cmv clc2 sector
see
Level 3 Description
Level 4 Decription
2280201113
D
esel Barge
C1C2 Port Emissions
Main Engine
2280202113
D
esel Offshore support
C1C2 Port Emissions
Main Engine
2280203113
D
esel Bulk Carrier
C1C2 Port Emissions
Main Engine
2280204113
D
esel Commercial Fishing
C1C2 Port Emissions
Main Engine
2280205113
D
esel Container Ship
C1C2 Port Emissions
Main Engine
2280206113
D
esel Ferry
C1C2 Port Emissions
Main Engine
2280207113
D
esel General Cargo
C1C2 Port Emissions
Main Engine
2280208113
D
esel Government
C1C2 Port Emissions
Main Engine
2280209113
D
esel Miscellaneous
C1C2 Port Emissions
Main Engine
2280210113
D
esel RollOn RollOff
C1C2 Port Emissions
Main Engine
2280211113
D
esel Tanker
C1C2 Port Emissions
Main Engine
2280212113
D
esel Tour Boat
C1C2 Port Emissions
Main Engine
2280213113
D
esel Tug
C1C2 Port Emissions
Main Engine
2280214113
D
esel Refrigerated
C1C2 Port Emissions
Main Engine
2280215113
D
esel Cruise
C1C2 Port Emissions
Main Engine
2280216113
D
esel Passenger Other
C1C2 Port Emissions
Main Engine
2280201114
D
esel Barge
C1C2 Port Emissions
Auxiliary Engine
2280202114
D
esel Offshore support
C1C2 Port Emissions
Auxiliary Engine
2280203114
D
esel Bulk Carrier
C1C2 Port Emissions
Auxiliary Engine
2280204114
D
esel Commercial Fishing
C1C2 Port Emissions
Auxiliary Engine
2280205114
D
esel Container Ship
C1C2 Port Emissions
Auxiliary Engine
54
-------
see
Level 3 Description
Level 4 Decription
2280206114
D
esel Ferry
C1C2 Port Emissions: Auxil
ary Engine
2280207114
D
esel General Cargo
C1C2 Port Emissions: Auxil
ary Engine
2280208114
D
esel Government
C1C2 Port Emissions: Auxil
ary Engine
2280209114
D
esel Miscellaneous
C1C2 Port Emissions: Auxil
ary Engine
2280210114
D
esel RollOn RollOff
C1C2 Port Emissions: Auxil
ary Engine
2280211114
D
esel Tanker
C1C2 Port Emissions: Auxil
ary Engine
2280212114
D
esel Tour Boat
C1C2 Port Emissions: Auxil
ary Engine
2280213114
D
esel Tug
C1C2 Port Emissions: Auxil
ary Engine
2280214114
D
esel Refrigerated
C1C2 Port Emissions: Auxil
ary Engine
2280215114
D
esel Cruise
C1C2 Port Emissions: Auxil
ary Engine
2280216114
D
esel Passenger Other
C1C2 Port Emissions: 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
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
55
-------
see
Level 3 Description
Level 4 Decription
2280216124
Diesel Passenger Other
C1C2 Underway emissions: Auxiliary Engine
Category 1 and 2 CMV emissions were developed for the 2021 platform and were not based on 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, 2021. 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 NEI is shown in Figure 2-4 (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 counts of data received for S-AIS and T-AIS for the 2021 emissions modeling
platform are shown in Figure 2-4 (b).
56
-------
Figure 2-4. NEI Commercial Marine Vessel Boundaries and Automatic Identification System Request
Boxes for the 2021 Emissions Modeling Platform
a) NEI (solid) and ECA (dashed) geographical extent
Num. Rows in S-AIS 2021
< 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 2021
< 10,000,000
10,000,001 - 50,000,000
I 50,000,001 - 250,000,000
250,000,001 - 500,000,000
> 500,000,000
57
-------
The AIS data were compiled into five-minute intervals by the USCG, providing a reasonably refined
assessment of a vessel's movement. For example, using a five-minute average, a vessel traveling at 25
knots would be captured every two nautical miles that the vessel travels. For slower moving vessels, the
distance between transmissions would be less. The ability to track vessel movements through AIS data
and link them to attribute data, has allowed for the development of an inventory of very accurate
emission estimates. These AIS data were used to define the locations of individual vessel movements,
estimate hours of operation, and quantify propulsion engine loads. The compiled AIS data also included
the vessel's International Marine Organization (IMO) number and Maritime Mobile Service Identifier
(MMSI); which allowed each vessel to be matched to their characteristics obtained from the Clarksons
ship registry (Clarksons, 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 2021 CI C2 CMV
development documentation 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 2-17. 19,106 vessels were directly identified by their ship and
cargo number. The remaining group of miscellaneous ships represent 1.5 percent of the AIS vessels
(excluding recreational vessels) for which a specific vessel type could not be assigned.
58
-------
Table 2-17. Vessel groups in the cmv_clc2 sector
Vessel Group
2021 Area Ship Count
Bulk Carrier
46
Commercial Fishing
5,826
Container Ship
11
Ferry Excursion
849
General Cargo
3,190
Government
1,179
Miscellaneous
291
Offshore support
1,416
Pilot
15
Reefer
12
Ro Ro
219
Tanker
591
Tug
5,299
Work Boat
162
Total in Inventory:
19,106
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 2-16.
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
59
-------
Inventory.6 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 more information on the emission computations for 2021, see the supporting documentation for the
development of the 2021 C1C2 CMV emissions. 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 2021.
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3)
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.7 The cmv_c3 sector contains
sources that traverse state and federal waters; along with sources in waters not covered by the NEI in
surrounding areas of Canada, Mexico, and international waters. For more information on development
of C3 emissions refer to Section 11 of the 2020 NEI TSD for information on the C3 methodology.
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 2-18 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.
Table 2-18. SCCs for cmv c3 sector
see
Level 3 Description
Level 4 Decription
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
6 USEPA. EPA and Port Everglades Partnership: Emission Inventories and Reduction Strategies. US Environmental
Protection Agency, Office of Transportation and Air Quality, June 2018.
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100UKV8.pdf.
7 https://www.epa.gov/regulations-emissions-veliicles-and-engines/regulations-emissions-marine-vessels.
60
-------
see
Level 3 Description
Level 4 Decription
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
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
61
-------
see
Level 3 Description
Level 4 Decription
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
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
62
-------
see
Level 3 Description
Level 4 Decription
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
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
63
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see
Level 3 Description
Level 4 Decription
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, 2021. The International
Maritime Organization's (IMO's) International Convention for the Safety of Life at Sea (SOLAS) requires
AIS to be fitted aboard all international voyaging ships with gross tonnage of 300 or more, and all
passenger ships regardless of size.8 In addition, the USCG has mandated that all commercial marine
vessels continuously transmit AIS signals while transiting U.S. navigable waters. As the vast majority of
C3 vessels meet these requirements, any omitted from the inventory due to lack of AIS adoption are
deemed to have a negligible impact on national C3 emissions estimates. The activity 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 2021 CMV emissions modeling platform data were computed based on the AIS data from the USGS
for the year of 2021. The AIS data were coupled with ship registry data that contained engine
parameters, vessel power parameters, and other factors such as tonnage and year of manufacture
which helped to separate the C3 vessels from the C1C2 vessels. Where specific ship parameters were
not available, they were gap-filled. The types of vessels that remain in the C3 data set include bulk
carrier, chemical tanker, liquified gas tanker, oil tanker, other tanker, container ship, cruise, ferry,
general cargo, fishing, refrigerated vessel, roll-on/roll-off, tug, and yacht.
Prior to use, the AIS data were reviewed - data deemed to be erroneous were removed, and data found
to be at intervals greater than 5 minutes were interpolated to ensure that each ship had data every five
minutes. The five-minute average data provide a reasonably refined assessment of a vessel's movement.
For example, using a five-minute average, a vessel traveling at 25 knots would be captured every two
nautical miles that the vessel travels. For slower moving vessels, the distance between transmissions
would be less.
The emissions were calculated for each C3 vessel in the dataset for each 5-minute time range and
allocated to the location of the message following to the interval. Emissions were calculated according
to Equation 2-2.
8 International Maritime Organization (IMO) Resolution MSC.99(73) adopted December 12th. 2000 and entered into force July
1st, 2002; as amended by SOLAS Resolution CONF.5/32 adopted December 13th, 2002.
64
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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,9 but since the data were already in the form of point sources at the
center of each grid cell, and they were already hourly, no other processing was needed within SMOKE.
SMOKE requires an annual inventory file to go along with the hourly data, so this annual file was
generated for 2021.
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.
2.4.3 Railway Locomotives (rail)
The rail sector includes all locomotives in the NEI nonpoint data category including line haul locomotives
on Class 1, 2, and 3 railroads along with emissions from commuter rail lines and Amtrak. The rail sector
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 rail emissions for the 2021 emissions modeling platform are based on the 2020 NEI. A projection
factor of 1.05 was applied across all rail SCCs for the 2021 modeling platform (including railyards). This
was based on Annual Energy Outlook (AEO). The 2021 AEO shows a slight increase in fuel use from 2020
to 2021 (2021/2020=1.05). 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
International Maritime Organization (IMO) Resolution MSC.99(73).
65
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with support from various other states. Class I railroad emissions are based on confidential link-level
line-haul activity GIS data layer maintained by the Federal Railroad Administration (FRA). In addition, the
Association of American Railroads (AAR) provided national emission tier fleet mix information. Class II
and III railroad emissions are based on a comprehensive nationwide GIS database of locations where
short line and regional railroads operate. Passenger rail (Amtrak) emissions follow a similar procedure as
Class II and III, except using a database of Amtrak rail lines. Yard locomotive emissions are based on a
combination of yard data provided by individual rail companies, and by using Google Earth and other
tools to identify rail yard locations for rail companies which did not provide yard data. Information on
specific yards were combined with fuel use data and emission factors to create an emissions inventory
for rail yards. Pollutant-specific factors were applied on top of the activity-based changes for the Class I
rail. The inventory SCCs are shown in Table 2-19. More detailed information on the development of the
2021 emission modeling platform rail inventory is available in the 2020 NEI TSD and in the Rail 2020
National Emissions Inventory Supplementary Document on the 2020 NEI supporting data FTP site.
Table 2-19. 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)
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 2-5. 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.
66
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Figure 2-5. 2019 Class I Railroad Line Haul Activity
EPA collected 2020 Class I line haul fuel use data from the most recent R-l submittals from the Surface
Transportation Board,10 Consistent with previous inventory efforts, EPA summed line haul and work
train fuel usage, Table 2-20.
Table 2-20. 2020 R-l Reported Locomotive Fuel Use for Class I Railroads
Class 1 Railroad
Line Haul Fuel Use (galj*
BNSF
1,137,598,007
Canadian National (CN)
96,337,392
Canadian Pacific (CPRS)
57,664,407
CSX Transportation (CSXT)
327,917,859
Kansas City Southern (KCS)
55,763,748
Norfolk Southern (NS)
342,470,779
Union Pacific (UP)
773,476,896
* includes work train fuel usage
10 Surface Transportation Board. Available at https://www.stb.gov/reports-data/economic-data/annual-report-financial-data/
Retrieved 22 June 2021.
67
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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 2-6), 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 2-21.
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 2017 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.
Table 2-21. 2020 Class ll/lll Line Haul Fleet by Tier Level
Tier
2020 Class ll/lll
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%
68
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Figure 2-6. Class II arid III Railroads in the United States
Ssircst FederalHainwjAd'nruiiraton Jura ?SI8
Commuter Rail Methodology
Commuter rail emissions were calculated in the same way as the Class il and ill railroads. The primary
difference is that the fuel use estimates were based on data collected by the Federal Transit
Administration (FTA) for the National Transit Database. 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.
69
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Intercity Passenger Methodology (Amtrak)
The calculation methodology mimics that used for the Class il and Hi 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 2-7.
Figure 2-7. Amtrak National Rail Network
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)
The mobile nonroad equipment sector includes all mobile source emissions that do not operate on
roads, excluding commercial marine vehicles, railways, and aircraft. Types of nonroad equipment include
recreational vehicles, pleasure craft, and construction, agricultural, mining, and lawn and garden
equipment, Nonroad equipment emissions were computed by running MOVES4 which incorporates the
NONROAD model. MOVES3 incorporated updated nonroad engine population growth rates, nonroad
Tier 4 engine emission rates, and sulfur levels of nonroad diesel fuels. The only change made for
nonroad from MOVES3 to MOVES4 was a change to fuel properties. MOVES provides a complete set of
70
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HAPs and incorporates updated nonroad emission factors for HAPs. MOVES4 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 2021 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.
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.
MOVES4 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 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.
71
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• 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.
National Updates: Agricultural and Construction Equipment Allocation
The modified MOVES default database for MOVES3 containing refinements to construction and
agricultural sectors, (movesdb20220105_nrupdates) and state-submitted inputs in CDBs 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/).
Emissions Inside California
California nonroad emissions were provided by CARB for the 2020 NEI and 2023. The 2021 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.
72
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For more information on the development of the nonroad emissions in the 2020 NEI see Section 4 of the
2020 NEITSD.
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)
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
and nonpt sectors. Emissions are day-specific and include satellite-derived latitude/longitude of the fire's origin
and other parameters associated with the emissions such as acres burned and fuel load, which allow estimation
of plume rise.
The SCCs used for the ptfire-rx and ptfire-wild sources are shown in Table 2-22. The ptfire-rx and ptfire-
wild inventories include separate SCCs for the flaming and smoldering combustion phases for wildfire
and prescribed burns. Note that prescribed grassland fires or Flint Hills, Kansas has their own SCC
(2801500171) in the inventory. Any wild grassland fires were assigned the standard wildfire SCCs shown
in Table 2-22.
Table 2-22. SCCs included in the ptfire sector for the 2021 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)
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming
2811020002
Prescribed Rangeland Burning
Fire Information Data
Inputs to SMARTFIRE2 for 2021 include:
• The National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping System
(HMS) fire location information
• National Incident Feature Services (NIFS) (formerly GeoMAC) wildland fire perimeter
polygons
• The Incident Status Summary, also known as the "ICS-209", used for reporting specific
information on fire incidents of significance.
73
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• 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 Fish and Wildlife Service (USFWS) and
other 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
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 2021 inventory consisted of daily comma-delimited files containing fire
detect information including latitude-longitude, satellite used, time detected, and other information.
These detects were processed through Satellite Mapping Automated Reanalysis Tool for Fire Incident
Reconciliation version 2 SmartFire2/BlueSky Pipeline (SF2/BSP).
National Incident Feature Services (NIFS) 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 2021 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 2021 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 US Fish and Wildland Service (USFWS) also compiles wildfire and prescribed burn activity on their
federal lands every year. Year 2021 data were acquired from USFWS through direct communication with
USFWS staff and were used for 2021 platform development. The USFWS fire information provided fire
type, acres burned, latitude-longitude, and start and ending times. The Department of Interior also
provided National Fire Plan Operations and Reporting System (NFPORS) activity data that covers all
other DOI agencies.
Fire Emissions Estimation Methodology
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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 2021 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 2-22. The lofted smoldering
emissions were assigned to the flaming emissions SCCs in Table 2-22.
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 Blue Sky
Pipeline. SMARTFIRE2 is an algorithm and database system that s 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 2021 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.
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Figure 2-8. Processing flow for fire emission estimates in the 2021 inventory
Input Data Sets
(state/local/tribal and national data sets)
Data Preparation
Data Aggregation and Reconciliation
(SmartFire2)
I
Daily fire locations
with fire si2e and type
Emissions Post-Processing
Final Wildland Fire Emissions Inventory
Fuel Moisture and
Fuel Loading Data
USFS Bluesky Pipeline
Daily smoke emissions
for each fire
Figure 2-9. Default fire type assignment by state and month where data are only from satellites
2020 NEI HMS Default Wildfire Type Months
HMS WF Months
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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
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 2-10. The Smoke Emissions Reference
Application (SERA) in the BSP generates all the CAP emission factors for wildland fires used in the 2021
study. SERA factors can vary by phase, fire type, region, fuel type and more pollutants. SERA emissison
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 2-10. Blue Sky Modeling Pipeline
The FCCSv3 cross-reference was implemented along with the LANDFIREvl (at 200 meter resolution) to
provide better fuel bed information for the BlueSky Pipeline (BSP). The LANDFIREv2 was aggregated
from the native resolution and projection to 200 meter using a nearest-neighbor methodology.
Aggregation and reprojection were required for the proper function on BSP.
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).
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2.5.2 Point source Agriculture Fires (ptagfire)
In the NEI, agricultural fires are stored as county-annual emissions and are part of the nonpoint data
category. For this study agricultural fires are modeled as day specific fires derived from satellite data for
the year 2021 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 2021 USDA cropland
data layer (CDL). Satellite fire detects over agricultural lands are assumed to be agricultural burns and
assigned a crop type. Detects that are not over agricultural lands are output to a separate file for use in
the ptfire sector. Each detect is assigned an average size of between 40 and 120 acres varying by state.
Grassland/pasture fires were moved to the ptfire sectors for this 2021 modeling platform. Depending on
their origin, grassland fires are in both ptfire-rx and ptfire-wild sectors because both fire types do involve
grassy fuels.
The point source agricultural fire (ptagfire) inventory sector contains daily agricultural burning
emissions. Daily fire activity was derived from the NOAA Hazard Mapping System (HMS) fire activity
data. The agricultural fires sector includes SCCs starting with '28015'. The first three levels of
descriptions for these SCCs are: 1) Fires - Agricultural Field Burning; Miscellaneous Area Sources; 2)
Agriculture Production - Crops - as nonpoint; and 3) Agricultural Field Burning - whole field set on fire.
The SCC 2801500000 does not specify the crop type or burn method, while the more specific SCCs
specify field or orchard crops and, in some cases, the specific crop being grown. The SCCs for this sector
listed are in Table 2-23.
Table 2-23. SCCs included in the ptagfire sector
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
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
2811020002
Miscellaneous Area Sources;Other Combustion - as Event;Prescribed Rangeland Burning;Flaming
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Another feature of the ptagfire database is that the satellite detections for 2021 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 2021 meteorological Weather Research Forecast (WRF) model simulation.
The locations of fire detections were then compared with this daily snow cover file. For any day in which
a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the
inventory. Due to the inconsistent reporting of fire detections from the Visible Infrared Imaging
Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as VIIRS or
Suomi National Polar-orbiting Partnership satellite were excluded. In addition, certain crop types (corn
and soybeans) have been excluded from these specific midwestern states: Iowa, Kansas, Indiana, Illinois,
Michigan, Missouri, Minnesota, Wisconsin, and Ohio. The reason for these crop types being excluded is
because states have indicated that these crop types are not burned.
Heat flux for plume rise was calculated using the size and assumed fuel loading of each daily agricultural
fire. This information is needed for a plume rise calculation within a chemical transport modeling
system.
The daily agricultural 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 2021 agricultural fire inventories include emissions for HAPs, so HAP integration was
used for this study.
2.6 Biogenic Sources (beis)
Biogenic emissions were computed based on the 2021 meteorology data used for the 2021 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 2-24.
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Table 2-24. 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
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. BELD version 5 (BELD5) was used to
generate 2017 NEI 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).
Bug fixes included in BEIS4 included the following:
• Solar radiation attenuation in the shaded portion of the canopy was using the direct beam
photosynthetically active radiation (PAR) when the diffuse beam PAR attenuation coefficient
should have been used.
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o This update had little impact ori the total emissions but did result in slightly higher
emissions in the morning and evening transition periods for isoprene, methanol and
Methylbutenol (MBO).
• The fraction of solar radiation in the sunlit and shaded canopy layers, SOLSUN and SOLSHADE
respectively were estimated using a planar surface. These should have been estimated based on
the PAR intercepted by a hemispheric surface rather than a plane.
o This update can result in an earlier peak in leaf temperature, approximately up to an
hour.
• The quantum yield for isoprene emissions (ALPHA) was updated to the mean value in Niinemets
et al. 2010a and the integration coefficient (CL) was updated to yield 1 when PAR = 1000
following Niinemets et al 2010b.
o This updated resulted in a slight reduction in isoprene, methanol, and MBO emissions.
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 biogenics produced during the CMAQ, model run
itself using the same algorithm described above but with finer time steps within the air quality model.
Figure 2-11 provides an annual estimate of the biogenic VOC emissions in year 2021 from BEIS4.
Figure 2-11. Annual biogenic VOC BEIS4 emissions for the 12US1 domain
2021hb BEIS4 VOC BEIS annual
Max: 3452.9531 Min:
>1375
1222
1069
916
763
611
458
305
<152
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,
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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.
Emissions in these sectors were taken from 2020 and 2023 inventories provided by Environment and
Climate Change Canada (ECCC), interpolated to 2021. ECCC provided the following inventories; the
sectors in which they were incorporated are listed and the inventories are described in more detail
below:
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)
Nonroad and rail (canmex_area sector)
Airports (canmex_point sector)
Other area sources (canmex_area sector)
Other point sources (canmex_point sector)
The 2021 CMV data included coastal waters of Canada and Mexico with emissions derived from AIS data.
These NEI 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 of NBAFM 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 2021 modeling.
2.7.1 Point Sources in Canada and Mexico (canmex_point)
Canadian point source inventories provided by ECCC include emissions for airports and other point
sources. The Canadian point source inventory is pre-speciated for the CB6 chemical mechanism.
Point sources in Mexico were compiled in two parts. New 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. Mexico inventories
for other states were based on inventories projected from the Inventario Nacional de Emisiones de
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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.
2.7.2 Fugitive Dust Sources in Canada (canada_afdust, canada_ptdust)
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 2021 for this study. This inventory no longer contains agricultural dust. Different source
categories were provided as gridded point sources and area (nonpoint) source inventories. Gridded
point source emissions resulting from land tilling due to agricultural activities were provided as part of
the ECCC emission inventory. The provided wind erosion emissions were removed. 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)
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 2021). 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 were based on new
emissions inventories representing 2018 for the six Mexico border states (Baja California, Sonora,
Chihuahua, Coahuila, Nuevo Leon, and Tamaulipas), and emissions from the 2019 emissions platform
(SEMARNAT-provided 2016, projected to 2019) were carried forward for all other states.
2.7.4 Surface-level Oil and Gas Sources in Canada (canada_og2D)
Canadian point source inventories provided by ECCC included oil and gas emissions, and were
interpolated from 2020 and 2023 to 2021. 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
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)
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). 2021 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 were based on new emissions inventories representing 2018
for the six Mexico border states (Baja California, Sonora, Chihuahua, Coahuila, Nuevo Leon, and
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Tamaulipas), and emissions from the 2019 emissions platform (SEMARNAT-provided 2016, projected to
2019) were carried forward for all other states.
2.7.6 Onroad Sources in Canada and Mexico (canada_onroad, mexico_onroad)
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. 2021 was interpolated from the 2020 and 2023 emissions.
For Mexico onroad emissions, a version of the MOVES model for Mexico was run for 2020 and 2023.
2021 was then interpolated. This provided the same VOC HAPs and speciated VOCs as for the U.S.
MOVES model (ERG, 2016a). This includes NBAFM plus several other VOC HAPs such as toluene, xylene,
ethylbenzene and others. Except for VOC HAPs that are part of the speciation, no other HAPs are
included in the Mexico onroad inventory (such as particulate HAPs nor diesel particulate matter).
2.7.7 Fires in Canada and Mexico (ptfire_othna)
Annual 2021 wildland fire emissions for Mexico, Canada, Central America, and Caribbean nations are
included in the ptfire_othna sector. Annual 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 211. 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 2021
(Wiedenmyer, 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 study. 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. a I, 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 other natural mercury
emissions are included in the emissions merge step.
http:
ra/conference/2023/slides/2023-1
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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
resolution, pollutant resolution, and spatial resolution into the hourly, speciated, gridded and vertical
resolution required by the air quality model. Emissions modeling includes temporal allocation, spatial
allocation, and pollutant speciation. Emissions modeling sometimes includes the vertical allocation (i.e.,
plume rise) of point sources, but many air quality models also perform this task because it greatly
reduces the size of the input emissions files if the vertical layers of the sources are not included.
As seen in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary across
sectors and may be hourly, daily, monthly, or annual total emissions. The spatial resolution may be
individual point sources; totals by county (U.S.), province (Canada), or municipio (Mexico); or gridded
emissions. This section provides some basic information about the tools and data files used for
emissions modeling as part of the modeling platform.
3.1 Emissions Modeling Overview
SMOKE version 5.0 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) Center at
http://www.cmascenter.org. Additional information about SMOKE is available from http://www.smoke-
model.org. For sectors that have plume rise, the in-line plume rise capability allows for the use of
emissions files that are much smaller than full three-dimensional gridded emissions files. For quality
assurance of the emissions modeling steps, emissions totals by specie for the entire model domain are
output as reports that are then compared to reports generated by SMOKE on the input inventories to
ensure that mass is not lost or gained during the emissions modeling process.
When preparing emissions for the air quality model, emissions for each sector are processed separately
through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sector-
specific 2-D gridded emissions across sectors. The SMOKE settings in the run scripts and the data in the
SMOKE ancillary files control the approaches used by the individual SMOKE programs for each sector.
Table 3-1 summarizes the major processing steps of each platform sector with the columns as follows.
The "Spatial" column shows the spatial approach used: "point" indicates that SMOKE maps the source
from a point location (i.e., latitude and longitude) to a grid cell; "surrogates" indicates that some or all of
the sources use spatial surrogates to allocate county emissions to grid cells; and "area-to-point"
indicates that some of the sources use the SMOKE area-to-point feature to grid the emissions (further
described in Section 3.4.2). The "Speciation" column indicates that all sectors use the SMOKE speciation
step, though biogenics speciation is done within the Tmpbeis3 program and not as a separate SMOKE
step. The "Inventory resolution" column shows the inventory temporal resolution from which SMOKE
needs to calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no input
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inventory; instead, activity data and emission factors are used in combination with meteorological data
to compute hourly emissions.
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
Yes
monthly
np_oilgas
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 &
monthly
canmex_area
Surrogates
Yes
monthly
canmex_point
Point
Yes
monthly
in-line
canada_ptdust
Point
Yes
annual
None
canada_og2D
Point
Yes
monthly
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
rail
Surrogates
Yes
annual
rwc
Surrogates
Yes
annual
np_solvents
Surrogates
Yes
annual
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The "plume rise" column in indicates the sectors for which the "in-line" approach is used. These sectors
are the only ones with emissions in aloft layers based on plume rise. The term "in-line" means that the
plume rise calculations are done inside of the air quality model instead of being computed by SMOKE. In
all of the "in-line" sectors, all sources are output by SMOKE into point source files which are subject to
plume rise calculations in the air quality model. In other words, no emissions are output to layer 1
gridded emissions files from those sectors as has been done in past platforms. The air quality model
computes the plume rise using stack parameters, the Briggs algorithm, and the hourly emissions in the
SMOKE output files for each emissions sector. The height of the plume rise determines the model layers
into which the emissions are placed. The plume top and bottom are computed, along with the plumes'
distributions into the vertical layers that the plumes intersect. The pressure difference across each layer
divided by the pressure difference across the entire plume is used as a weighting factor to assign the
emissions to layers. This approach gives plume fractions by layer and source. Day-specific point fire
emissions are treated differently in CMAQ. After plume rise is applied, there are emissions in every layer
from the ground up to the top of the plume.
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 was run for grids
over Alaska, Hawaii, and Puerto Rico plus the Virgin Islands. 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_459X299
'LAM_40N97W'( -2556000, -1728000, 12.D3,
12.D3, 459, 299, 1
US 12 km or
"smaller"
CONUS-12
12 km
Smaller 12km CONUS plus
some of Mexico/Canada
12US2
'LAM_40N97W', -2412000,
-1620000, 12.D3, 12.D3, 396, 246, 1
Alaska 9km
9 km
Small 9 km Alaska 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. Air quality modeling domains
a) 12US1 and 12US2
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3.2Chemical 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. Table 3-4 and Table 3-5 list additional
model species that are generated when performing toxics modeling, and Table 3-6 lists the mapping
between individual polycyclic aromatic hydrocarbons (PAHs) to the PAH groups used in toxics modeling.
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
89
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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 biogenics only)
voc
SOAALK
Secondary Organic Aerosol (SOA) tracer
voc
TERP
Terpenes (from biogenics only)
voc
TOL
Toluene and other monoalkyl aromatics
voc
UNR
Unreactive
voc
XYLMN
Xylene and other polyalkyl aromatics, minus naphthalene
Naphthalene
NAPH
Naphthalene from inventory
Benzene
BENZ
Benzene from the inventory
Acetaldehyde
ALD2
Acetaldehyde from inventory
Formaldehyde
FORM
Formaldehyde from inventory
Methanol
MEOH
Methanol from inventory
PMio
PMC
Coarse PM > 2.5 microns and 0 10 microns
PM2.5
PEC
Particulate elemental carbon 0 2.5 microns
PM2.5
PN03
Particulate nitrate 0 2.5 microns
PM2.5
POC
Particulate organic carbon (carbon only) 0 2.5 microns
PM2.5
PS04
Particulate Sulfate 0 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
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Inventory Pollutant
Model Species
m-xylene, o-xylene, p-xylene, xylenes (mixed isomers)
XYLENES
Vinyl chloride
CL_ETHE
Table 3-5. Additional HAP particulate model species generated for toxics modeling
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
Mercury1
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
Mercury is multi-phase
Table 3-6. PAH/POM pollutant groups
PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(pg/m3)
PAH_000E0
120127
Anthracene
0
PAH_000E0
129000
Pyrene
0
PAH_000E0
85018
Phenanthrene
0
PAH_101E2
56495
3-Methylcholanthrene
0.01
PAH_114E1
57976
7,12-Dimethylbenz[a] Anthracene
0.114
PAH_176E2
189559
Dibenzo[a,i]Pyrene
9.6E-03
PAH_176E2
189640
Dibenzo[a,h] Pyrene
9.6E-03
PAH_176E2
191300
Dibenzo[a,l]Pyrene
9.6E-03
PAH_176E2
7496028
6-Nitrochrysene
9.6E-03
PAH_176E3
192654
Dibenzo[a,e] Pyrene
9.6E-04
PAH_176E3
194592
7H-Dibenzo[c,g]carbazole
9.6E-04
PAH_176E3
3697243
5-Methylchrysene
9.6E-04
PAH_176E3
41637905
Methylchrysene
9.6E-04
PAH_176E3
53703
Dibenzo[a,h] Anthracene
9.6E-04
PAH_176E4
193395
lndeno[l,2,3-c,d] Pyrene
9.6E-05
PAH_176E4
205823
Benzo[j]Fluoranthene
9.6E-05
PAH_176E4
205992
Benzo[b]Fluoranthene
9.6E-05
PAH_176E4
224420
Dibenzo[a,j]Acridine
9.6E-05
PAH_176E4
226368
Dibenz[a,h]acridine
9.6E-05
PAH_176E4
5522430
1-Nitropyrene
9.6E-05
PAH_176E4
56553
Benz[a] Anthracene
9.6E-05
PAH_176E5
207089
Benzo[k]Fluoranthene
9.6E-06
PAH_176E5
218019
Chrysene
9.6E-06
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PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(ng/m3)
PAH_176E5
86748
Carbazole
9.6E-06
PAH_192E3
8007452
Coal Tar
9.9E-04
PAH_880E5
130498292
PAH, total
4.8E-05
PAH_880E5
191242
Benzo[g,h,i,]Perylene
4.8E-05
PAH_880E5
192972
Benzo[e]Pyrene
4.8E-05
PAH_880E5
195197
Benzo(c)phenanthrene
4.8E-05
PAH_880E5
198550
Perylene
4.8E-05
PAH_880E5
203123
Benzo(g,h,i)Fluoranthene
4.8E-05
PAH_880E5
203338
Benzo(a)fluoranthene
4.8E-05
PAH_880E5
206440
Fluoranthene
4.8E-05
PAH_880E5
208968
Acenaphthylene
4.8E-05
PAH_880E5
2381217
1-Methylpyrene
4.8E-05
PAH_880E5
2422799
12-Methylbenz(a)Anthracene
4.8E-05
PAH_880E5
250
PAFI/POM - Unspecified
4.8E-05
PAH_880E5
2531842
2-Methylphenanthrene
4.8E-05
PAH_880E5
26914181
Methylanthracene
4.8E-05
PAH_880E5
284
Extractable Organic Matter (EOM)
4.8E-05
PAH_880E5
56832736
Benzofluoranthenes
4.8E-05
PAH_880E5
65357699
Methylbenzopyrene
4.8E-05
PAH_880E5
779022
9-Methyl Anthracene
4.8E-05
PAH_880E5
832699
1-Methylphenanthrene
4.8E-05
PAH_880E5
83329
Acenaphthene
4.8E-05
PAH_880E5
86737
Fluorene
4.8E-05
PAH_880E5
90120
1-Methylnaphthalene
4.8E-05
PAH_880E5
91576
2-Methylnaphthalene
4.8E-05
PAH_880E5
91587
2-Chloronaphthalene
4.8E-05
PAH_880E5
N590
Polycyclic aromatic compounds
(includes PAH/POM)
4.8E-05
The TOG and PM2.5 profiles used to speciate emissions are part of the SPECIATE v5.2 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. Emissions of VOC and PM2.5 emissions by county, sector, and profile
for all sectors other than onroad mobile can be found in the sector summaries. Total emissions for each
model species by state and sector can be found in the state-sector totals workbook.
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The following updates to profile assignments were made to this modeling platform and vary from prior
years:
• For PM2.5:
o All GSPRO files were generated by the S2S-Tool, dated 09-11-2023, and utilized SPECIATE
v5.3.
o Update of the CMV speciation cross-reference files to utilize the SCC updates for this
sector and use the new CROC profiles introduced in SPECIATE v5.3.
o Update onroad and nonroad mobile cross-reference files to utilize the CROC profiles
introduced in SPECIATE v5.3.
• ForVOC:
o All GSPRO and GSCNV files were generated by the S2S-Tool, dated 09-11-2023, and
utilized SPECIATE v5.3.
o All oil and gas well completion and abandoned wells emissions were updated (or added in
the case of abandoned wells) from 1101 and 8949, respectively, to 95404 and 95403,
respectively. However, this update was not performed for basin-specific profiles that
were output by the O&G Tool.
o Update of the CMV speciation cross-reference files to utilize the SCC updates for this
sector and use the new GROC profiles introduced in SPECIATE v5.3.
o Update usage of 95120a to 95120c.
o Update onroad and nonroad mobile cross-reference files to utilize the GROC profiles
introduced in SPECIATE v5.3.
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). In addition, HAPs can be introduced into a
modeling platform using speciation profiles. In this scenario, HAP-VOC emissions are "generated"
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 3-7.
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Figure 3-2. Process of integrating HAPs and speciating VOC in a modeling platform
Table 3-7. 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)
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)
ptegu
No integration, use NBAFM in inventory
ptfire-rx
Full integration (NBAFM)
ptf ire-wild
Partial integration (NBAFM)
ptfire_othna
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptnonipm
No integration, use NBAFM in inventory
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Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B), Acetaldehyde
(A), Formaldehyde (F) and Methanol (M)
rail
Full integration (NBAFM)
rwc
Full integration (NBAFM)
np_solvents
Partial 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 3-8) 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 3-8. 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
"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,
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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 advanges of making MOVES simpler, faster to run, and
makes it easier to change or update chemical mechanisms and speciation profiles used in the emissions
modeling process. Some speciation is still performed inside MOVES (i.e., for "integrated species"). 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 15 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 Code (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, or TOM (PNCOM plus PEC); and residual_PM, is RESID_PM (all other PM species). These
profile updates are included in the tables below.
Table 3-9. Mobile Speciation Profile Updates
Pollutant
Old profile
New profile
PM2.5
8992
100CROC
PM2.5
8993
101CROC
PM2.5
8994
103CROC (starts)
102CROC (other)
PM2.5
8995
103CROC
PM2.5
8996
104CROC
PM2.5
95219a
105CROC
PM2.5
95220a
106CROC
NONHAPTOG
1001
107GROC
NONHAPTOG
8757
101GROC (starts)
103GROC (other)
NONHAPTOG
8774
104GROC
NONHAPTOG
8775
105GROC
NONHAPTOG
8855
108GROC (starts)
109GROC (other)
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NONHAPTOG
8751a
100GROC (starts)
102GROC (other)
NONHAPTOG
95335a
106GROC
NONHAPTOG
95335a
106GROC
NONHAPTOG
95327
110GROC
NONHAPTOG
95328
111GROC
NONHAPTOG
95329
112GROC
NONHAPTOG
95330
113GROC
NONHAPTOG
95331
114GROC
NONHAPTOG
95332
115GROC
NONHAPTOG
95333
116GROC
NONHAPTOG
8775
105GROC
NONHAPTOG
1001
107GROC
NONHAPTOG
8860
117GROC
PM2_5
8996
109CROC
PM2_5
91106
108CROC
PM2_5
91113
107CROC
PM2_5
95219
105CROC
For this platform, MOVES runs were performed in inventory mode12 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. 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
by MOVES were speciated within SMOKE using the GSREF files and the NONHAPTOG GSPRO files
generated by the S2S-Tool. Further details on speciation methods involving MOVES can be found in
Table 3-10 and in the associated technical reports (EPA-420-R-22-017, EPA-420-R-23-006).
12 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.
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Table 3-10. Mobile NOx and HONO fractions
Fuel
Model Years
Process
NO
NOx
HONO
Gasoline
1960-1980
Running Exh.
0.975
0.017
0.008
Gasoline
1981-1990
Running Exh.
0.932
0.06
0.008
Gasoline
1991-1995
Running Exh.
0.954
0.038
0.008
Gasoline
1996-2050
Running Exh.
0.836
0.156
0.008
Gasoline
1960-1980
Start Exh.
0.975
0.017
0.008
Gasoline
1981-1990
Start Exh.
0.961
0.031
0.008
Gasoline
1991-1995
Start Exh.
0.987
0.005
0.008
Gasoline
1996-2050
Start Exh.
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
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[1]-0.966*NAPHTHALENE[1]
• PAR = PAR[1]-0.00001*NAPHTHALENE[1]
• SOAALK = 0.108*PAR[1]
3.2.2 PM speciation
Like VOC speciation, PM2.5 speciation does feature integrated species from the base inventory, 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
the moves2smk postprocessor using the SPECIATE profiles 95462 and 95460, respectively.
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
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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. Table 3-11 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 MOVES. For further details on NOx speciation within MOVES, please see the
associated technical report.
Table 3-11. 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
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 3-12 and Table 3-13 below.
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Table 3-12. 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 3-13. 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 3-14,
summarizes the particle size profiles used for each data category.
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Table 3-14. Particle Size Speciation of Metals
Source Type
Profile
Pollutant
Fine
Coarse
Onroad
OARS
Arsenic
0.95
0.05
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 3-15 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 pre-speciated in MOVES, nonroad emissions from California,
which use the appropriate profiles below, and onroad emissions from California, where MOVES-based
speciation is applied.
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Table 3-15. 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
3.3 Temporal Allocation
Temporal allocation is the process of distributing aggregated emissions to a finer temporal resolution,
thereby converting annual emissions to hourly emissions as is required by CMAQ. While the total
emissions are important, the timing of the occurrence of emissions is also essential for accurately
simulating ozone, PM, and other pollutant concentrations in the atmosphere. Many emissions
inventories are annual or monthly in nature. Temporal allocation takes these aggregated emissions and
distributes the emissions to the hours of each day. This process is typically done by applying temporal
profiles to the inventories in this order: monthly, day of the week, and diurnal, with monthly and day-of-
week profiles applied only if the inventory is not already at that level of detail.
The temporal factors applied to the inventory were selected using some combination of country, state,
county, SCC, and pollutant. Table 3-16 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).
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Table 3-16. 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
met-based
All
No
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
mwdss
mwdss
Yes
np_oilgas
Annual
Yes
aveday
aveday
No
onroad
Annual &
monthly1
all
all
Yes
onroad_ca_adj
Annual &
monthly1
all
all
Yes
canada_afdust
Annual & monthly
Yes
week
all
No
canmex_area
Monthly
week
week
No
canada_onroad
Monthly
week
week
No
mexico_onroad
Monthly
week
week
No
canmex_point
Monthly
Yes
mwdss
mwdss
No
canada_ptdust
Annual
Yes
week
all
No
canmex_ag
Annual
Yes
mwdss
mwdss
No
canada_og2D
Monthly
mwdss
mwdss
No
pt_oilgas
Annual
Yes
mwdss
mwdss
Yes
Ptegu
Annual & hourly
Yes2
all
All
No
ptnonipm
Annual
Yes
mwdss
mwdss
Yes
ptagfire
Daily
all
all
No
ptfire-rx
Daily
all
all
No
ptfire-wild
Daily
all
all
No
ptfire_othna
Daily
all
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No3
met-based3
all
No3
np_solvents
Annual
Yes
aveday
aveday
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 following values are used in the table. The value "all" means that hourly emissions were computed
for every day of the year and that emissions potentially have day-of-year variation. The value "week"
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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 to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2021, which is intended to mitigate the effects of initial condition concentrations. The ramp-
up period was 10 days (December 22-31, 2020). For all anthropogenic sectors, emissions from
December 2021 were used to fill in surrogate emissions for the end of December 2020. For biogenic
emissions, December 2020 emissions were computed using year 2020 meteorology.
3.3.1 Use of FF10 format for finer than annual emissions
The FF10 inventory format for SMOKE provides a consolidated format for monthly, daily, and hourly
emissions inventories. With the FF10 format, a single inventory file can contain emissions for all 12
months and the annual emissions in a single record. This helps simplify the management of numerous
inventories. Similarly, daily and hourly FF10 inventories contain individual records with data for all days
in a month and all hours in a day, respectively.
SMOKE prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual-to-month temporal allocation applied to it;
rather, it should only have month-to-day and diurnal temporal allocation. This becomes particularly
important when specific sectors have a mix of annual, monthly, daily, and/or hourly inventories. The
flags that control temporal allocation for a mixed set of inventories are discussed in the SMOKE
documentation. The modeling platform sectors that make use of monthly values in the FF10 files are
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.
3.3.2 Temporal allocation for non-EGU sources (ptnonipm)
Most temporal profiles in ptnonipm result in primarily constant emissions for each day of the year,
although some have lower emissions on Sundays. An update in the 2018 platform was an analysis of
monthly temporal profiles for non-EGU point sources in the ptnonipm sector. A number of profiles were
found to be not quite flat over the months but were so close to flat that the difference was not
meaningful. These profiles were replaced in the cross reference to point instead to the flat monthly
profile. The codes for the profiles that were replaced were: 202, 214, 220, 221, 222, 223, 227, 257, 263,
264, 265, 266, 267, 269, 271, 272, 279, 280, 295, 302, 303, 304, 305, 306, 309, 310, 327, 329, 332, and
333.
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
105
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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
for the seasons in which the CEMS are operating. If a CEMS-matched unit is determined to be a partial
year reporter, as can happen for sources that run CEMS only in the summer, emissions totaling the
difference between the annual emissions and the total CEMS emissions are allocated to the non-
summer months. Prior to use of the CEMS data in SMOKE it is processed through the CEMCorrect tool.
The CEMCorrect tool identifies hours for which the data were not measured as indicated by the data
quality flags in the CEMS data files. Unmeasured data can be filled in with maximum values and thereby
cause erroneously high values in the CEMS data. When data were flagged as unmeasured and the
values were found to be more than three times the annual mean for that unit, the data for those hours
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
2017 January Unit 469_5
2000
1800
1600
1400
1200
1000
800
600
400
200
0
p>.rocr)Ln^HP>.rocr)Ln^HP>.rocr)Ln^HP>.rocr)Ln^HP>.rocr)Ln\-ihNrocr)
rMLnr^oroi-ncoorotDco^HrotDcn^H^rtDcnrM^r-vcnrMi-nr^ocN
HHHHiNfMfMfMfomfr)fo^^
-------
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 (2019, 2020, and 2021) and a 3-year average
capacity factor of less than 0.1.
Equation 1. Annual unit power output
8750 Hourly HI
(BTU) *100° IwJ
Annual Unit Output (MW) = ,B7.„ N
NEEDS Heat Rate (77^-
\kW hJ
Equation 2. Unit capacity factor
_ . „ Annual Unit Output (MW)
Capacity Factor = 77777777—7; :—tmp
NEEDS Unit Capacity ^—J*8760 Qi)
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).
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Figure 3-4. Regions used to Compute Temporal non-CEMS EGU Temporal Profiles
EGU Regions
~ LADCO
~ MANE-VU
| Northwest
~ SESARM
| | South
¦ West
| Southwest
U West North Central
The daily and diurnal profiles were calculated for each region, fuel, and peaking type group from the
year 2021 CEMS heat input values. The heat input values were summed for each input group to the
annual level at each level of temporal resolution: monthly, month-of-day, and diurnal. The sum by
temporal resolution value was then divided by the sum of annual heat input in that group to get a set of
temporalization factors. Diurnal factors were created for both the summer and winter seasons to
account for the variation in hourly load demands between the seasons. For example, the sum of all hour
1 heat input values in the group was divided by the sum of all heat inputs over all hours to get the hour 1
factor. Each grouping contained 12 monthly factors, up to 31 daily factors per month, and two sets of 24
hourly factors. The profiles were weighted by unit size where the units with more heat input have 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 peaking and non-peaking daily temporal profiles for the gas fuel type in the LADCO
region. Figure 3-6 shows the diurnal profiles for the coal fuel type in the Mid-Atlantic Northeast Visibility
Union (MANE-VU) region.
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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
0.040
0.035 -
0.005 •
0.000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2017
Figure 3-6. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type
Diurnal Small EGU Profile for MANE-VU coal
Summer Nonpeaking
Summer Peaking
Winter Nonpeaking
Winter Peaking
SMOKE uses a cross-reference file to select a monthly, daily, and diurnal profile for each source. For the
2021 platform, the temporal profiles were assigned in the cross-reference at the unit level to EGU
sources without hourly CEMS data. An inventory of all EGU sources without CEMS data was used to
identify the region, fuel type, and type (peaking/non-peaking) of each source. The region used to select
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the temporal profile is assigned based on the state from the unit FIPS. The fuel was assigned by SCC to
one of the four fuel types: coal, gas, oil, and other. A fuel type unit assignment is made by summing the
VOC, NOX, PM2.5, and S02 for all SCCs in the unit. The SCC that contributed the highest total emissions
to the unit for selected pollutants was used to assign the unit fuel type. Peaking units were identified as
any unit with an oil, gas, or oil fuel type with a NAICS of 22111 or 221112. Some units may be assigned
to a fuel type within a region that does not have an available input unit with a matching fuel type in that
region. These units without an available profile for their group were assigned to use the regional
composite profile. MWC and cogen units were identified using the NEEDS primary fuel type and
cogeneration flag, respectively, from the NEEDS v6 database. Assignments for each unit needed 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 2021-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
2021 hourly Departures and Arrivals for Metric Computation by airport was generated. An overview of
the ASPM metrics is at
http://aspmhelp.faa.gov/index.php/Aviation Performance Metrics %28APM%29. 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
2021 Aviation System Performance Metrics (ASPM) Airport Analysis
(https://aspm.faa.gov/apm/svs/AnalysisAP.asp). A report of all airport operations (takeoffs and
landings) by day for 2021 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 2020, 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.
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Figure 3-7. 2021 Airport Diurnal Profiles for PHX and state of Texas
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Figure 3-8. 2021 Wisconsin and Atlanta annual-to-month profile for airport emissions
2021 FAA State Monthly Profile: Wl default
2021 FAA State Monthly Profile: ATL
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Figure 3-9. Alaska seaplane profile
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3.3.5 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 are: (1)
a meteorological dataset consistent with that used by the AQ model is available (e.g., outputs from
WRF); (2) the meteorological model data are highly resolved in terms of spatial resolution; and (3) the
meteorological variables vary at hourly resolution and can, therefore, be translated into hour-specific
temporal allocation.
The SMOKE program Gentpro provides a method for developing meteorology-based temporal
allocation. Currently, the program can utilize three types of temporal algorithms: annual-to-day
temporal allocation for residential wood combustion (RWC); month-to-hour temporal allocation for
agricultural livestock NH3; and a generic meteorology-based algorithm for other situations.
Meteorological-based temporal allocation was used for portions of the rwc sector and for the entire ag
sector.
Gentpro reads in gridded meteorological data (output from MCIP) along with spatial surrogates and uses
the specified algorithm to produce a new temporal profile that can be input into SMOKE. The
meteorological variables and the resolution of the generated temporal profile (hourly, daily, etc.)
depend on the selected algorithm and the run parameters. For more details on the development of
these algorithms and running Gentpro, see the Gentpro documentation and the SMOKE documentation
at
http://www.cmascenter.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 day-of-year 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
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sector are zero. Conversely, the program temporally allocates the largest percentage of emissions to the
coldest days. Similar to other temporal allocation profiles, the total annual emissions do not change,
only the distribution of the emissions within the year is affected. The temperature threshold for RWC
emissions was 50 °F for most of the country, and 60 °F for the following states: Alabama, Arizona,
California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and Texas. The algorithm is as
follows:
If Td >= Tt: no emissions that day
If Td < Tt: daily factor = 0.79*(Tt -Td)
where (Td = minimum daily temperature; Tt = threshold temperature, which is 60 degrees F in southern states and
50 degrees F elsewhere).
Once computed, the factors 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
For the 2021 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, only days in which the
temperature falls within a range of 50°F and 80°F at some point during the day receive emissions. 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
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summer months while southern states show a flatter pattern with emissions distributed more evenly
throughout the months.
Figure 3-11. Example of Annual-to-day temporal pattern of recreational wood burning emissions
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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 based temporal profiles (https://s3.amazonaws.com/marama.org/wp-
content/uploads/2019/11/04184303/Qpen Burning Residential Areas Emissions Report-2004.pdf).
This profile was created by averaging three indoor and three RWC outdoor temporal profiles from
counties in Delaware and aggregating them into a single RWC diurnal profile. This new profile was
compared to a concentration-based analysis of aethalometer measurements in Rochester, New York
(Wang et al. 2011) for various seasons and days of the week and was found that the new RWC profile
generally tracked the concentration based temporal patterns.
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Figure 3-12. RWC diurnal temporal profile
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 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.
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Figure 3-13. Data used to produce a diurnal profile for hydronic heaters
Figure 3-14. Monthly temporal profile for hydronic heaters
3.3.6 Agricultural Ammonia Temporal Profiles (livestock)
For the ag sector, agricultural GenTPRO temporal allocation was applied to livestock emissions and to all
pollutants within the sector, not just NH3. The GenTPRO algorithm is based on an equation derived by
Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2014) empirical equation. This equation is based
on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to estimate
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diurnal NH3 emission variations from livestock as a function of ambient temperature, aerodynamic
resistance, and wind speed. The equations are:
Equation 3-1
Ei,h = [161500/T,^ x e<-138°/V] x ARhh
PEi,h = Ei,h / Sum(E/^) Equation 3-2
where
• PEi,h = Percentage of emissions in county /' on hour h
• Ei,h = Emission rate in county /' on hour h
• Ti,h = Ambient temperature (Kelvin) in county /' on hour h
• ARi,h = Aerodynamic resistance in county /'
Some examples plots of the profiles by animal type in different parts of the country are shown in Figure
3-15.
To develop month-to-hour temporal profiles of livestock emissions, GenTPRO was run using the
"BASH_NH3" profile method to create for these sources. Because these profiles distribute to the hour
based on monthly emissions, the monthly emissions were obtained from a monthly inventory, or from
an annual inventory that has been temporalized to the month. Figure 3-16 compares the daily emissions
for Minnesota from the "old" approach (uniform monthly profile) with the "new" approach (GenTPRO
generated month-to-hour profiles) for 2014. Although the GenTPRO profiles show daily (and hourly)
variability, the monthly total emissions are the same between the two approaches.
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Figure 3-15. Examples of livestock temporal profiles in several parts of the country
Tulare County, CA Duplin County, NC
0.25
0.2
0-15
0.1
0.05
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
0 Beef 9 Broiler » Dairy * layer m Swine
Sioux County, IA
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
¦ Beef m Broiler > Dairy » Layer m Swine
0.25
0.2
0.15
0.1
0.05
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
¦^-Beef —~—Broiler » Dairy ¦ Layer m Swine
Lancaster County, PA
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
~—Beef m Broiler » Dairy » Layer > Swine
Figure 3-16. Example of animal NH3 emissions temporal allocation approach (daily total emissions)
12.0
10.0
8.0
- 6.0
0.0
MN ag NH3 livestock temporal profiles
. 1
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-old
-new
1/1/2008 2/1/2008 3/1/2008 4/1/2008 5/1/2008 6/1/2008 7/1/2008 8/1/2008 9/1/2008 10/1/2008 11/1/2008 12/1/2008
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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). Factors were specific to each county and SCC. For use in SMOKE, each
unique set of factors was assigned a label (OG20M_0001 through OG20M_6306), 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 ERG 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 2021 platform EPA utilized the FHWA's Travel
Monitoring and Analysis System (TMAS). This system measures monthly traffic volume, by class and
weight. The primary purpose for using TMAS in 2021 platform was to replace the month VMT
distribution from 2020, which were marked by the COVID pandemic shutdowns in mid-March/April. The
2021 TMAS month VMT distribution looks more like a typical nonpandemic year. We also used day/hour
distributions from the same dataset because they were available and for the correct year (2021). TMAS
data was processed for each state, for each month, and vehicle class. Figure 3-17 shows TMAS data. The
first plot shows hour of the day for the state of Maryland. Note that you can see the the rush hour in the
morning and the evening. The second plot shows the state of Minnesota for the month of June. Notice
that motorcycles come out in the spring (winter months show less VMT for motorcycles) and are driven
more on Saturday. The third plot shows an annual, by month plot of 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.
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Figure 3-17. TiYIAS Data: VMT Fraction by Hour of Day, Day of Week, and Month of Year
TMAS Data: VMT Fraction v. Hour by State
state=Maryland dayType=Weekday
t I
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t
Hour
Total Vehicles
30s - Light-duty Trucks
61 - Combination Short-haul True
11 - Motorcycles
40s - Buses
62 - Combination Long-haul Truck
21 - Passenger Cars
50s - Single Unit Trucks
/proj 1 /EP A_2020NEl/FHWA_TMAS_Qass Data_2021 /piot TMA S hour.sas 13SEP23 13:59|
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TMAS Data: VMT Fraction v. Day of Week by State
state=Minnesota monthlD=6
Total Vehides
30s - Light-duty Trucks
61 - Combination Short-haul True
Day of Week
11 - Motorcycles
~ 40s - Buses
62 - Combination Long-haul Truck
21 - Passenger Cars
50s - Single Unit Trucks
/proj 1/EPA_2020_NEI/FHWA_TMAS_Class_Data_2021/plot_TMAS_day.sas 22SEP23 13:48
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TMAS Data: VMT Fraction v. Month by State
state=Montana
> 0.12
Month
Total Vehides
30s - Light-duty Trucks
61 - Combination Short-haul True
11 - Motorcycles
40s - Buses
62 - Combination Long-haul Truck
21 - Passenger Cars
50s- Single Unit Trucks
/proj 1/EPA_2020_NEI/FHWA_TMAS_Class_Data_2021/plot_TMAS.sas 15SEP23 15:17
The "inventories" referred to in Table 3-16 consist of activity data for the onroad sector, not emissions.
VMT is the activity data used for on-network rate-per-distance (RPD) processes. For the off-network
emissions from the rate-per-profile (RPP) and rate-per-vehicle (RPV) processes, the VPOP activity data
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
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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 3-18 (from 2020) illustrates the temporal allocation of the onroad activity data
(i.e., VMT) and the pattern of the emissions that result after running SMOKE-MOVES. In this figure, it
can be seen that the meteorologically varying emission factors add variation on top of the temporal
allocation of the activity data.
Figure 3-18. 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.
Day-of-week, hour-of-day, and month-of-year temporal profiles for VMT were developed fromTMAS
data. Data were provided for motorcycles (11), passenger vehicles (21), lightduty 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.
The StreetLight temporal profiles were used in areas of the contiguous United States that did not submit
temporal profiles of sufficient detail for the 2020 NEI. For this platform, the data selection hierarchy
favored local input data over EPA-developed information, with the exception of the three MOVES tables
"hourVMTFraction", dayVMTFraction", and "avgSpeedDistribution" where county-level, telematics-based
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EPA Defaults were adopted for the NEI universally. For 2021, data from December 2020 were used for
speed distributions. 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.
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.
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.
Figure 3-19 shows two previously existing temporal profiles (9 and 18) 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 3-19. 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
mondsy tuesday Wednesday thursday friday Saturday sundae
9 18 19
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Figure 3-20 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 3-20. Example Nonroad Diurnal Temporal Profiles
Hour of Day Profiles
26a-New 27 25a-New 26
For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from MOVES. For California, CARB's annual inventory was temporalized to
monthly using monthly temporal profiles applied in SMOKE by SCC.
3.3.10 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptfire-rx,
ptfire-wild)
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used
to reduce the total emissions based on meteorological conditions. These adjustments are applied
through sector-specific scripts, beginning with the application of land use-based gridded transport
fractions and then subsequent zero-outs for hours during which precipitation occurs or there is snow
cover on the ground. The land use data used to reduce the NEI emissions 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, somewhat different emissions will result from
different grid resolutions. Application of the transport fraction and meteorological adjustments
prevents the overestimation of fugitive dust impacts in the grid modeling as compared to ambient
samples.
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Biogenic emissions from the BEIS model vary each day of the year because they are developed using
meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions
are computed using appropriate emission factors according to the vegetation in each model grid cell,
while taking the meteorological data into account.
For the cmv sectors, most areas use hourly emission inventories derived from the 5-minute AIS data. In
some areas where AIS data are not available, such as in Canada between the St. Lawrence Seaway and
the Great Lakes and in the southern Caribbean, the flat temporal profiles are used for hourly and day-of-
week values. Most regions without AIS data also use a flat monthly profile, with some offshore areas
using an average monthly profile derived from the 2008 ECA inventory monthly values. These areas
without AIS data also use flat day of week and hour of day profiles.
For the rail sector, 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
3-21 (McCarty et al., 2009). This puts most of the emissions during the work-day and suppresses the
emissions during the middle of the night.
Figure 3-21. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
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Industrial processes that are not likely to shut down on Sundays, such as those at cement plants, use
profiles that include emissions on Sundays, while those that would shut down on Sundays use profiles
that reflect Sunday shutdowns.
For the ptfire sectors, the inventories are in the daily point fire format FF10 PTDAY, 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 3-22 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 reflected 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 weekforall
agricultural burning emissions in all states.
Figure 3-22. Prescribed and Wildfire diurnal temporal profiles
3.4 Spatial Allocation
The methods used to perform spatial allocation are summarized in this section. For the modeling
platform, spatial factors are typically applied by county and SCC. Spatial allocation was performed for
each of the modeling grids 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 EPA updated surrogates to use circa
2020 data. The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain 12US1
shown in Figure 3-1. While highlights of information are provided below, the file
Surrogate_specifications_2021_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 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-to-point
approach overrides the use of surrogates for airport refueling sources.
The surrogates for the platform are based on a variety of geospatial data sources, including the
American Community Survey (ACS) for census-related data, the National Land Cover Database (NLCD)
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Onroad surrogates are based on average annual daily traffic counts (AADT) from the highway monitoring
performance system (HPMS).
U.S. Surrogate datasets used for this platform include:
County boundaries used for all surrogates were use the 2020 TIGER boundaries.
Oil and gas surrogates represent activity from the year 2021.
ACS-based surrogates use the 2020 ACS.
NLCD-based surrogates use NLCD 2019.
Animal specific livestock waste surrogates were derived from National Pollutant Discharge
Elimination System (NPDES) animal operation water permits and Food and Agriculture
Organization (FAO) gridded livestock count data.
Surrogates for fuel stations, asphalt surfaces, and unpaved roads are based on data from the
OpenStreetMap database.
Gravel and lead mines use separate surrogates based on the more general United States
Geological Survey (USGS) mining surrogate.
Residential wood combustion surrogates are based on ACS data.
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. In the 2020 platform, NLCD-based surrogates were
updated from using the 2011 National Land Cover Database (NLCD) to use the 2019 National Land Cover
Database. During these updates, EPA also examined the Residential Wood Combustion (RWC)
surrogates that were based on the NLCD. This was done to see if there are other sources of spatial data
that could improve the geographic representation of the RWC sector when disaggregating the county-
level emissions provided by the emissions inventory to grid cells. For the RWC sector prior to the 2020
platform, the spatial surrogate used was #300 computed from "NLCD Low Intensity development" (i.e.,
land areas with 20-49% impervious surface). This surrogate was initially selected for RWC to capture
geographic areas where there may be houses but generally in less developed spaces. However, this
surrogate does not differentiate by development or structure type. The result is that RWC emissions can
end up concentrated around roads, commercial, and other low to moderately developed grid cells.
In the 2020 platform, housing data provided by the American Community Survey (ACS) were used. The
particular attributes used are: single family detached, single family attached, dual family and mobile
home and combinations of these, depending on the particular RWC specific source category. Using
types of housing seemed more reflective of where RWC emissions would be located. However, a
downside of using the ACS housing data is that the census shapes are broad (particularly in rural areas),
so the emissions can appear more spread out in some areas than when using the NLCD-based
surrogates. When comparing the two approaches for RWC surrogates (NLCD vs ACS), the ACS-based
surrogates looked reasonable, and in fact better than the NLCD Low intensity development surrogate. A
comparison of the PM2.5 emissions gridded with each of these approaches is shown in Figure 3-23 and
Figure 3-24. In the future, a goal is to further improve the resolution surrogates, as such, the use of
building structure data weighted by the ACS will be examined for future platform updates.
129
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Figure 3-23. 2020 Residential Wood Combustion Emissions using NLCD Low Intensity Surrogate
2020ha RWC PM2 5 - old surrogates, total emissions
>14.4
12.8
11.2
9.6
8.0 Ł
o
-4-f
6.4
4.8
3.2
<1.6
Max: 315.8044 Min:
Figure 3-24. 2020 Residential Wood Combustion Emissions using ACS-based Surrogate
>14.4
12.8
11.2
9.6
k_
8.0 "j?
o
6.4
4.8
3.2
<1.6
Surrogates for the U.S. were generated using the Surrogate Tools DB with the Java-based Surrogate tools
used to perform gapfilling and normalization where needed. The tool and documentation for the
original Surrogate Tool are available at https://www.cmascenter.org/sa-
130
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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 3-17 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 gapfill other surrogates. When
the source data for a surrogate have no values for a particular county, gap filling 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 3-18.
Table 3-17. U.S. Surrogates available for the 2021 modeling platforms
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2)
650
Refineries and Tank Farms
100
Population
669
All Abandoned Wells
110
Housing
6691
All Abandoned Oil Wells
135
Detached Housing
6692
All Abandoned Gas Wells
136
Single and Dual Unit Housing
6693
All Abandoned CBM Wells
137
Single + Dual Unit + Manufactured Housing
6694
All Abandoned Oil Wells - Plugged
150
Residential Heating - Natural Gas
6695
All Abandoned Gas Wells - Plugged
170
Residential Heating - Distillate Oil
6696
All Abandoned CBM Wells - Plugged
180
Residential Heating - Coal
6697
All Abandoned Oil Wells - Unplugged
190
Residential Heating - LP Gas
6698
All Abandoned Gas Wells - Unplugged
205
Extended Idle Locations
670
Spud Count - CBM Wells
239
Total Road AADT
671
Spud Count - Gas Wells
240
Total Road Miles
672
Gas Production at Oil Wells
242
All Restricted AADT
673
Oil Production at CBM Wells
244
All Unrestricted AADT
674
Unconventional Well Completion Counts
258
Intercity Bus Terminals
676
Well Count - All Producing
259
Transit Bus Terminals
677
Well Count - All Exploratory
260
Total Railroad Miles
678
Completions at Gas Wells
261
NT AD Total Railroad Density
679
Completions at CBM Wells
271
NT AD Class 12 3 Railroad Density
681
Spud Count - Oil Wells
300
NLCD Low Intensity Development
683
Produced Water at All Wells
304
NLCD Open + Low
6831
Produced Water at CBM Wells
305
NLCD Low + Med
6832
Produced Water at Gas Wells
306
NLCD Med + High
6833
Produced Water at Oil Wells
307
NLCD All Development
685
Completions at Oil Wells
308
NLCD Low + Med + High
686
Completions at All Wells
309
NLCD Open + Low + Med
687
Feet Drilled at All Wells
310
NLCD Total Agriculture
689
Gas Produced - Total
318
NLCD Pasture Land
691
Well Counts - CBM Wells
319
NLCD Crop Land
692
Spud Count - All Wells
131
-------
320
NLCD Forest Land
693
Well Count - All Wells
321
NLCD Recreational Land
694
Oil Production at Oil Wells
340
NLCD Land
695
Well Count - Oil Wells
350
NLCD Water
696
Gas Production at Gas Wells
401
FAO 2010 Cattle
697
Oil Production at Gas Wells
4011
FAO 2010 Large Cattle Operations
698
Well Count - Gas Wells
4012
NPDES 2020 Beef Cattle
699
Gas Production at CBM Wells
4013
NPDES 2020 Dairy Cattle
711
Airport Areas
402
FAO 2010 Pig
801
Port Areas
4021
NPDES 2020 Swine
850
Golf Courses
403
FAO 2010 Chicken
860
Mines
4031
NPDES 2020 Chicken
861
Sand and Gravel Mines
404
FAO 2010 Goat
862
Lead Mines
4041
NPDES 2020 Goat
863
Crushed Stone Mines
405
FAO 2010 Horse
900
OSMFuel
406
FAO 2010 Sheep
901
OSM Asphalt Surfaces
4071
NPDES 2020 Turkey
902
OSM Unpaved Roads
508
Public Schools
Table 3-18. 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
135
Detached Housing
ACS_2020_5YR_BG_pop_hu
detachedh
136
Single and Dual Unit Housing
ACS_2020_5YR_BG_pop_hu
Ittriunit
137
Single + Dual Unit +
Manufactured Housing
ACS 2020 5YR BG pop hu mobile
sngdlmobl
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
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
hpms2017_v3_04052020
aadt
moves2014 IN
('02703704705')
240
Total Road Miles
hpms2017_v3_04052020
NONE
moves2014 IN
('02703704705')
242
All Restricted AADT
hpms2017_v3_04052020
aadt
moves2014 IN
('02704')
132
-------
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
244
All Unrestricted AADT
hpms2017_v3_04052020
aadt
moves2014 IN
('03705')
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
00m II
NONE
GRIDCODE != 11
350
NLCD 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'
133
-------
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
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
ABAN DO N E D_WE LLS_ALL_COU NTS_669_202
1
ACTIVITY
6696
All Abandoned CBM Wells -
Plugged
ABAN DON ED_WELLS_CBM_PLUGGED_6696_
2021
ACTIVITY
6693
All Abandoned CBM Wells
ABAN DONE D_WELLS_CBM_PLUGGE D_UNPL
UGGED 6693 2021
ACTIVITY
6695
All Abandoned Gas Wells -
Plugged
ABAN DON ED_WELLS_GAS_PLUGGED_6695_
2021
ACTIVITY
6692
All Abandoned Gas Wells
ABAN DON ED_WELLS_GAS_PLUGGED_UNPLU
GGED 6692 2021
ACTIVITY
6698
All Abandoned Gas Wells -
Unplugged
ABAN DON ED_WELLS_GAS_UNPLUGGED_669
8 2021
ACTIVITY
6694
All Abandoned Oil Wells -
Plugged
ABAN DON ED_WELLS_OIL_PLUGGED_6694_2
021
ACTIVITY
6691
All Abandoned Oil Wells
ABAN DON ED_WELLS_OIL_PLUGGED_UNPLU
GGED 6691 2021
ACTIVITY
6697
All Abandoned Oil Wells -
Unplugged
ABAN DON ED_WELLS_OIL_UNPLUGGED_6697
2021
ACTIVITY
670
Spud Count - CBM Wells
SPUD CBM 670 2021
ACTIVITY
671
Spud Count - Gas Wells
SPUD GAS 671 2021
ACTIVITY
672
Gas Production at Oil Wells
ASSOCIATED_GAS_PRODUCTION_672_20
21
ACTIVITY
673
Oil Production at CBM Wells
CONDENSATE_CBM_PRODUCTION_673_
2021
ACTIVITY
674
Unconventional Well
Completion Counts
COMPLETIONS_UNCONVENTIONAL_674_
2021
ACTIVITY
676
Well Count - All Producing
TOTAL PROD WELL 676 2021
ACTIVITY
677
Well Count - All Exploratory
TOTAL EXPL WELL 677 2021
ACTIVITY
678
Completions at Gas Wells
COMPLETIONS GAS 678 2021
ACTIVITY
679
Completions at CBM Wells
COMPLETIONS CBM 679 2021
ACTIVITY
681
Spud Count - Oil Wells
SPUD OIL 681 2021
ACTIVITY
683
Produced Water at All Wells
PRODUCED_WATER_ALL_683_2021
ACTIVITY
134
-------
Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
6831
Produced Water at CBM Wells
PRODUCED WATER CBM 6831 2021
ACTIVITY
6832
Produced Water at Gas Wells
PRODUCED WATER GAS 6832 2021
ACTIVITY
6833
Produced Water at Oil Wells
PRODUCED WATER OIL 6833 2021
ACTIVITY
685
Completions at Oil Wells
COMPLETIONS OIL 685 2021
ACTIVITY
686
Completions at All Wells
COMPLETIONS ALL 686 2021
ACTIVITY
687
Feet Drilled at All Wells
FEET DRILLED 687 2021
ACTIVITY
689
Gas Produced - Total
TOTAL GAS PRODUCTION 689 2021
ACTIVITY
691
Well Counts - CBM Wells
CBM WELLS 691 2021
ACTIVITY
692
Spud Count - All Wells
SPUD ALL 692 2021
ACTIVITY
693
Well Count - All Wells
TOTAL WELL 693 2021
ACTIVITY
694
Oil Production at Oil Wells
OIL PRODUCTION 694 2021
ACTIVITY
695
Well Count - Oil Wells
OIL WELLS 695 2021
ACTIVITY
696
Gas Production at Gas Wells
GAS PRODUCTION 696 2021
ACTIVITY
697
Oil Production at Gas Wells
CONDENSATE_GAS_PRODUCTION_697_2
021
ACTIVITY
698
Well Count - Gas Wells
GAS WELLS 698 2021
ACTIVITY
699
Gas Production at CBM Wells
CBM PRODUCTION 699 2021
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 hpms2017_v3_04052020. Similarly,
most surrogates use the GEOID as the Data attribute while the HPMS surrogates use FIPS. The gapfilling
configuration for the surrogates is shown in Table 3-19. 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 gapfilling is performed, SMOKE does not know that emissions for that
135
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county were from a secondary, tertiary or quarternary surrogate and any reports will assign the
emissions in gapfilled counties to the primary surrogate.
Table 3-19. Surrogates used to gapfill U.S. Surrogates
SURROGATE
CODE
SURROGATE
SECONDARY
SURROGATE
TERTIARY
SURROGATE
QUARTERNARY
SURROGATE
100
Population
110
Housing
Population
135
Detached Housing
NLCD Low Intensity
Development
136
Single and Dual Unit Housing
NLCD Low Intensity
Development
137
Single + Dual Unit +
Manufactured Housing
NLCD Low Intensity
Development
NLCD Land
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
340
NLCD Land
350
NLCD Water
401
FAO 2010 Cattle
NLCD Total Agriculture
NLCD Open + Low
136
-------
SURROGATE
CODE
SURROGATE
SECONDARY
SURROGATE
TERTIARY
SURROGATE
QUARTERNARY
SURROGATE
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
6696
All Abandoned CBM Wells -
Plugged
All Abandoned CBM
Wells
Well Count - All
Wells
NLCD Open + Low
6693
All Abandoned CBM 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
137
-------
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
138
-------
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
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 3-20. Emissions from the extended (i.e., overnight) idling of
trucks were assigned to surrogate 205, which is based on locations of overnight truck parking spaces.
The underlying data for this surrogate were updated during the development of the 2016 platforms to
include additional data sources and corrections based on comments received and these updates were
carried into this platform.
Table 3-20. 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 3-21 using 2021 data consistent with what was used to develop the nonpoint oil and gas
emissions. The exploration and production of oil and gas have increased in terms of quantities and
locations over the last seven years, primarily through 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 2021.
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
completed during 2020. The spatial surrogates were gapfilled using fallback surrogates as shown in Table
3-19. All gapfilling was performed with the Surrogate Tool.
139
-------
Table 3-21. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
669
All Abandoned Wells
6691
All Abandoned Oil Wells
6692
All Abandoned Gas Wells
6693
All Abandoned CBM Wells
6694
All Abandoned Oil Wells - Plugged
6695
All Abandoned Gas Wells - Plugged
6696
All Abandoned CBM 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
140
-------
Table 3-22 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by sector assigned to each
spatial surrogate.
Table 3-22. Selected 2021 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
302,957
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
945,886
0
0
afdust
4012
NPDES 2020 Beef Cattle
0
0
191,883
0
0
afdust
4013
NPDES 2020 Dairy Cattle
0
0
15,033
0
0
afdust
4021
NPDES 2020 Swine
0
0
658
0
0
afdust
4031
NPDES 2020 Chicken
0
0
5,071
0
0
afdust
4071
NPDES 2020 Turkey
0
0
1,959
0
0
fertilizer
310
NLCD Total Agriculture
1,275,333
0
0
0
0
livestock
405
FAO 2010 Horse
31,969
0
0
0
2,558
livestock
406
FAO 2010 Sheep
18,776
0
0
0
1,502
livestock
4012
NPDES 2020 Beef Cattle
731,315
0
0
0
58,505
livestock
4013
NPDES 2020 Dairy Cattle
583,465
0
0
0
46,677
livestock
4021
NPDES 2020 Swine
875,338
0
0
0
70,027
livestock
4031
NPDES 2020 Chicken
483,224
0
0
0
38,658
livestock
4041
NPDES 2020 Goat
19,231
0
0
0
1,538
livestock
4071
NPDES 2020 Turkey
81,326
0
0
0
6,506
nonpt
100
Population
454
0
0
0
36
nonpt
135
Detatched Housing
0
16,359
81,108
2,724
18,946
nonpt
150
Residential Heating - Natural Gas
44,524
214,626
2,669
1,436
12,680
nonpt
170
Residential Heating - Distillate Oil
1,499
25,521
3,165
624
1,086
nonpt
180
Residential Heating - Coal
0
2
1
7
2
nonpt
190
Residential Heating - LP Gas
127
36,460
150
164
1,435
nonpt
239
Total Road AADT
0
0
0
0
6,536
nonpt
244
All Unrestricted AADT
0
0
0
0
90,591
nonpt
271
NTAD Class 12 3 Railroad Density
0
0
0
0
2,074
nonpt
300
NLCD Low Intensity Development
2,860
3,417
17,009
400
26,432
nonpt
306
NLCD Med + High
17,672
242,209
372,991
84,968
131,292
nonpt
307
NLCD All Development
76,463
28,172
126,918
10,917
81,342
nonpt
308
NLCD Low + Med + High
958
156,265
18,261
4,907
9,688
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
11
31
0
44
nonpt
650
Refineries and Tank Farms
0
0
0
0
90,120
141
-------
i/oc
367
,351
,354
,993
,912
326
,729
,807
,471
,048
,333
,334
,572
,472
,845
,941
,946
439
19
,835
,473
,964
124
,357
719
,700
,197
,845
,036
68
2
,684
,183
,444
,163
,243
,101
148
71
,402
,988
900
ID
Description
NH3
NOX
PM2 5
711
Airport Areas
0
801
Port Areas
900
OSM Fuel
4011
FAO 2010 Large Cattle Operations
0
0
136
Single and Dual Unit Housing
99
14,624
2,927
261
NTAD Total Railroad Density
1,572
157
304
NLCD Open + Low
1,635
147
305
NLCD Low + Med
852
1,020
306
NLCD Med + High
376
157,391
9,005
307
NLCD All Development
112
29,271
16,129
308
NLCD Low + Med + High
595
221,128
18,228
309
NLCD Open + Low + Med
133
21,811
1,303
310
NLCD Total Agriculture
357
235,108
16,563
320
NLCD Forest Land
15
1,986
407
321
NLCD Recreational Land
79
13,265
4,898
350
NLCD Water
202
113,666
4,161
850
Golf Courses
13
2,119
122
860
Mines
2,382
221
670
Spud Count - CBM Wells
0
671
Spud Count - Gas Wells
674
Unconventional Well Completion
Counts
43
31,853
683
678
Completions at Gas Wells
6,337
133
679
Completions at CBM Wells
681
Spud Count - Oil Wells
683
Produced Water at All Wells
38
685
Completions at Oil Wells
249
0
687
Feet Drilled at All Wells
161,321
3,701
689
Gas Produced - Total
457
25
691
Well Counts - CBM Wells
18,985
298
692
Spud Count - All Wells
540
31
693
Well Count - All Wells
0
694
Oil Production at Oil Wells
3,428
695
Well Count - Oil Wells
160,298
4,293
107,599
696
Gas Production at Gas Wells
43,089
233
697
Oil Production at Gas Wells
247
0
698
Well Count - Gas Wells
301,795
4,476
699
Gas Production at CBM Wells
24
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
142
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nP_oilgas
6832
Produced water at gas wells
0
0
0
0
15,339
nP_oilgas
6833
Produced water at oil wells
0
0
0
0
68,474
np_solvents
100
Population
0
0
0
0
1,422,267
np_solvents
240
Total Road Miles
0
0
0
0
45,681
np_solvents
306
NLCD Med + High
0
0
0
0
468,793
np_solvents
307
NLCDAII Development
0
0
0
0
234,418
np_solvents
308
NLCD Low + Med + High
0
0
0
0
34,900
np_solvents
310
NLCD Total Agriculture
0
0
0
0
171,047
np_solvents
901
OSM Asphalt Surface
0
0
0
0
339,778
onroad
205
Extended Idle Locations
0
33,216
311
15
2,940
onroad
242
All Restricted AADT
58,206
781,682
20,026
2,892
118,369
onroad
244
All Unrestricted AADT
118,185
1,156,599
43,773
5,273
323,939
onroad
259
Transit Bus Terminals
36
1,474
32
1
459
onroad
304
NLCD Open + Low
0
476
12
0
2,571
onroad
306
NLCD Med + High
849
94,910
2,481
68
23,317
onroad
307
NLCDAII Development
6,409
171,002
7,199
472
539,749
onroad
308
NLCD Low + Med + High
259
17,306
479
26
27,836
onroad
508
Public Schools
10
1,514
63
1
391
rail
261
NTAD Total Railroad Density
13
23,285
629
16
1,066
rail
271
NTAD Class 12 3 Railroad Density
283
420,839
10,354
352
17,301
rwc
135
Detatched Housing
6,666
8,591
128,302
3,216
120,590
rwc
137
Single + Dual Unit + Manufactured
Housing
15,951
36,199
318,692
8,678
332,453
3.4.2 Allocation method for airport-related sources in the U.S.
There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground support
equipment, and jet refueling. The modeling platform includes the aircraft and airport ground support
equipment emissions as point sources. For the modeling platform, the EPA used the SMOKE "area-to-
point" approach for only jet refueling in the nonpt sector. The following SCCs use this approach:
2501080050 and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/rocket engine
firing and testing). The ARTOPNT file that lists the nonpoint sources to locate using point data was
unchanged from the 2005-based platform.
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 3-23. The
Shapefiles used to compute these surrogates and some configuration information are shown in Table
3-24. Note that the name of most Data Shapefiles have been abbreviated to shorten the table. The
complete names and additional details on surrogate computation for Canada and Mexico are available in
the file Surrogate_specifications_2021_platform_US_Can_Mex.xlsx that is posted in the reports folder
for this platform. Mexico surrogates were updated for the 2021 EMP.
143
-------
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 3-25. 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 3-26.
Table 3-23. 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
Petroleum and coal products
324
manufacturing
968
80117_Turkeys
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
144
-------
Code
Canadian Surrogate Description
Code
Description
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
Table 3-24. Shapefiles and Attributes used to Compute Canadian Spatial Surrogates
Cod
e
Surrogate
Data Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
100
Population
gPr_gda
pruid
da_popdwell_100m_nolakes
_lnovl7
Pop
145
-------
Cod
e
Surrogate
Data Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
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_dwellJ
ul2014
Cap_Dwell
106
ALL INDUST
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
ALL INDUST
111
Farms
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
FARMS
113
Forestry and logging
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
FORLOG
116
Total Resources
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m 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 100m 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 100m noLake
UTILITIES
1259
OFFR total dwelling
gcd
CDID
da2006_pop_labour_SimP_
MaxOff 100m noLake
DATDWELL20
1260
OFFR water
gcd
CDID
lulOO valid
1261
OFFR ALL INDUST
gcd
CDID
da2006_pop_labour_SimP_
MaxOff 100m noLake
ALL INDUST
1262
OFFR Oil and Gas Extraction
gcd
CDID
da2006_pop_labour_SimP_
MaxOff 100m noLake
OILGASEXTR
1263
OFFR ALLROADS
gcd
CDID
allroads
1264
OFFR AIRPORT
gcd
CDID
offroad_osm_airport_locs_s
pring2017
Movements
1265
OFFR RAILWAY
gcd
CDID
sh p_ra i lway_ca n vec Ju 117_v
2
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 100m noLake
OILGASEXTR
212
Mining except oil and gas
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff_100m_noLake
MINING2
146
-------
Cod
e
Surrogate
Data Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
215
Oil Sands Mines
prov2006
pruid
OS MinePit D v2
216
Oil Sands Tailing Ponds
prov2006
pruid
OS_WetTailing_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
221
Total Mining
prov2006
Pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
TOTALMI3
222
Utilities
prov2006
Pruid
da2006_pop_labour_SimP_
MaxOff 100m 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 100m noLake
TOTLND
240
capped population
gcd_ON4
CDID
da_popdwell_100m_nolakes
lnovl7
CAPURPOP
308
Food manufacturing
prov2006
Pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
FOODMANU
321
Wood product manufacturing
prov2006
Pruid
da2006_SimplifyP_250m_sp
here_treesa_Clip
WOODMANU
323
Printing and related support
activities
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
PRINTSUPRT
324
Petroleum and coal products
manufacturing
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
PETCOLMANU
326
Plastics and rubber products
manufacturing
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
PLASTCMANU
327
Non-metallic mineral product
manufacturing
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
MINERLMANU
331
Primary Metal Manufacturing
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
METALMANU
340
Construction - Oil and Gas
gPr_gda
pruid
loc_land_UOG2015_CO_v3_
Que_NB_NS
350
Water
coast
pruid
CONT42_pop_water_Clip_b
pop
412
Petroleum product wholesaler-
distributors
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
PETPRWSL
416
Building material and supplies
wholesaler-distributors
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
BUILDPRWSL
447
Gasoline stations
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
GASSTOR
448
clothing and clothing
accessories stores
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
CLOTHSTOR
482
Rail transportation
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
RAILTRANS
562
Waste management and
remediation services
prov2006
pruid
da2006_pop_labour_SimP_
MaxOff 100m noLake
WASTEMGMT
901
AIRPORT
gcd
CDID
offroad_osm_airport_locs_s
pring2017
Movements
902
Military LTO
surg_2017
FAKEFIPS
aviation_runways_spring201
7
Military
903
Commercial LTO
surg_2017
FAKEFIPS
aviation_runways_spring201
7
Commercial
147
-------
Cod
Data
Weight
e
Surrogate
Data Shapefile
Attribute
Weight Shapefile
Attribute
aviation_runways_spring201
904
General Aviation LTO
surg_2017
FAKEFIPS
7
General Av
Airport_movements_2006_
905
Air Taxi LTO
prov2006
pruid
MultiRingBuffer
SCC2275060
da2006_pop_labour_SimP_
921
Commercial Fuel Combustion
prov2006
pruid
MaxOff 100m noLake
COMFUEL
TOTAL INSTITUTIONAL AND
da2006_pop_labour_SimP_
923
GOVERNEMNT
prov2006
pruid
MaxOff 100m noLake
TOTINSTGOV
da2006_pop_labour_SimP_
924
Primary Industry
prov2006
pruid
MaxOff 100m noLake
PRIM1
da2006_pop_labour_SimP_
925
Manufacturing and Assembly
prov2006
pruid
MaxOff 100m noLake
MAN ASS EM
Distribtution and Retail (no
da2006_pop_labour_SimP_
926
petroleum)
prov2006
pruid
MaxOff 100m noLake
DISRET
da2006_pop_labour_SimP_
927
Commercial Services
prov2006
pruid
MaxOff 100m noLake
COMSER
sh p_ra i lway_ca n vec Ju 117_v
933
Rail-Passenger
gPr_gda
pruid
2
Passenger
sh p_ra i lway_ca n vec Ju 117_v
934
Rail-Freight
gPr_gda
pruid
2
Fret
sh p_ra i lway_ca n vec Ju 117_v
935
Rail-Yard
gPr_gda
pruid
2
Yard
NRN_CA_Simp2_16Apr2016_
940
PAVED ROADS NEW
gpr
fips
sphere
PAVEDRD
942
UNPAVED ROADS
prov2006
pruid
unpaved4
945
Commercial Marine Vessels
lowmedjetjl
CLASS
marine
S02
MERGE: 0.5*Mining except
oil and gas+0.5*Total Land
946
Construction and mining
Development
MERGE 0.34*Total Resources
Agriculture Construction and
+ 0.66 * Construction and
947
mining
mining
948
Forest
prov2006
pruid
treesa valid
MERGE: 0.20*urban
dwelling+0.80* rural
949
Combination of Dwelling
dwelling
da2006 SimP 100m WoodC
951
Wood Consumption Percentage
gpr
fips
on_lAugl4
WoodComp
955
UNPAVED ROADS AND TRAILS
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
animal nh3 to agri sic 801
961
80110 Broilers
gPr_gda
pruid
10 valid
QUANTITY
animal nh3 to agri sic 801
962
80111_Cattle_dairy_and_Heifer
gPr_gda
pruid
ll_valid
QUANTITY
148
-------
Cod
e
Surrogate
Data Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
963
80112_Cattle_non-Dairy
gPr_gda
pruid
animal nh3 to agri sic 801
12 valid
QUANTITY
964
80113_Laying_hens_and_Pullets
gPr_gda
pruid
animal nh3 to agri sic 801
13 valid
QUANTITY
965
80114 Horses
gPr_gda
pruid
animal nh3 to agri sic 801
14 valid
QUANTITY
966
80115_Sheep_and_Lamb
gPr_gda
pruid
animal nh3 to agri sic 801
15 valid
QUANTITY
967
80116 Swine
gPr_gda
pruid
animal nh3 to agri sic 801
16 valid
QUANTITY
968
80117_Turkeys
gPr_gda
pruid
animal nh3 to agri sic 801
17 valid
QUANTITY
969
80118 Goat
gPr_gda
pruid
animal nh3 to agri sic 801
18 valid
QUANTITY
971
80119 Buffalo
gPr_gda
pruid
animal nh3 to agri sic 801
19 valid
QUANTITY
972
80120_Uama_and_Alpacas
gPr_gda
pruid
animal nh3 to agri sic 801
20 valid
QUANTITY
973
80121 Deer
gPr_gda
pruid
animal nh3 to agri sic 801
21 valid
QUANTITY
974
80122 Elk
gPr_gda
pruid
animal nh3 to agri sic 801
22 valid
QUANTITY
975
80123 Wild boars
gPr_gda
pruid
animal nh3 to agri sic 801
23 valid
QUANTITY
976
80124 Rabbit
gPr_gda
pruid
animal nh3 to agri sic 801
24 valid
QUANTITY
977
80125 Mink
gPr_gda
pruid
animal nh3 to agri sic 801
25 valid
QUANTITY
978
80126 Fox
gPr_gda
pruid
animal nh3 to agri sic 801
26 valid
QUANTITY
979
80127 Mules and Asses
gPr_gda
pruid
animal nh3 to agri sic 801
27 valid
QUANTITY
981
Harvest Annual
gPr_gda
pruid
h a rvest_p m 10_An n u a l_to_a
gri_slc_valid
QUANTITY
982
Harvest Perennial
gPr_gda
pruid
harvest_pmlO_Perennial_to
_agri_slc_valid
QUANTITY
983
Synthfert_Annual
gPr_gda
pruid
synth_fert_nh3_Annual_to_a
gri_slc_valid
QUANTITY
984
Synthfert_Perennial
gPr_gda
pruid
synth_fert_nh3_Perennial_t
°_agri_slc_valid
QUANTITY
985
Tillage_Annual
gPr_gda
pruid
tillage_pmlO_Annual_to_agr
i sic valid
QUANTITY
601
SCL:12003 Petroleum Liquids
Transportation (PIRD)
gPr_gda
pruid
scl 12003 valid
602
SCL:12007 Oil Sands In-Situ
Extraction and Processing (PIRD)
gPr_gda
pruid
scl 12007 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
scll2011 valid
NONE
605
SCL:12012 Well Servicing (PIRD)
gPr_gda
pruid
scll2012_valid
NONE
149
-------
Cod
e
Surrogate
Data Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
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
scll2020
NONE
952
Residential Fuel Wood
Combustion (PIRD)
gPr_gda
pruid
scl20401 valid
NONE
651
MEITC1C2 Anchored
lowmedjet_ll
CLASS
MEIT 2280002101 2018
fuel
652
MEITC1C2 Underway
lowmedjetjl
CLASS
MEIT 2280002202 2018
fuel
653
MEITC1C2 Berthed
lowmedjet_ll
CLASS
MEIT 2280002301 2018
fuel
661
MEIT C3 Anchored
lowmedjet_ll
CLASS
MEIT 2280003101 2018
fuel
662
MEIT C3 Underway
lowmedjet_ll
CLASS
MEIT 2280003200 2018
fuel
663
MEIT C3 Berthed
lowmedjet_ll
CLASS
MEIT_2280003301_2018
fuel
Table 3-25. Shapefiles and Attributes used to Compute Mexican Spatial Surrogates
Code
SURROGATE
WEIGHT SHAPEFILE
WEIGHT
ATTRIBUTE
10
MEX Population
mex_population_2020
gridcode_Y
22
MEX Total Road Miles
mex roads
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 3-26. 2021 CAP Emissions Allocated to Mexican and Canadian Spatial Surrogates for 12US1
(short tons)
Code
Mexican or Canadian Surrogate
Description
NH3
NO*
PM2.5
SO2
voc
11
MEX Population
25,943
81,217
1,905
7,606
148,785
14
MEX Residential Heating - Wood
206
1,737
6,171
182
20,519
16
MEX Residential Heating - Distillate
Oil
0
5
0
0
0
150
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NOx
PM2.5
SO2
voc
20
MEX Residential Heating - LP Gas
0
2,967
206
25
104
22
MEXTotal Road Miles
2,508
279,841
12,582
5,616
86,744
24
MEX Total Railroads Miles
0
22,452
497
198
900
26
MEX Total Agriculture
137,780
11,683
13,655
13,571
2,375
32
MEX Commercial Land
0
73
1,591
0
5,448
34
MEX Industrial Land
44
1,809
652
9
28,743
36
MEX Commercial plus Industrial Land
0
2,630
61
5
111,541
38
MEX Commercial plus Institutional
Land
0
714
50
6
43
40
MEX Residential (RES1-
4)+Comercial+lndustrial+lnstitutional
+Government
0
44
236
7
139,071
42
MEX Personal Repair (COM3)
0
0
0
0
6,729
44
MEX Airports Area
0
2,894
60
307
1,740
48
MEX Brick Kilns
3
237
3,892
169
187
50
MEX Mobile sources - Border Crossing
4
86
3
0
65
100
CAN Population
704
57
223
17
3,912
101
CAN total dwelling
0
0
0
0
107,219
104
CAN Capped Total Dwelling
313
32,274
2,434
1,979
1,654
106
CAN ALL INDUST
0
0
570
0
0
113
CAN Forestry and logging
83
627
2,934
15
2,717
200
CAN Urban Primary Road Miles
1,559
75,445
2,678
192
7,265
210
CAN Rural Primary Road Miles
596
40,590
1,414
82
2,937
212
CAN Mining except oil and gas
0
0
1,702
0
0
220
CAN Urban Secondary Road Miles
2,926
119,891
5,416
382
19,355
221
CAN Total Mining
0
0
12,915
0
0
222
CAN Utilities
0
2,280
2,628
32
99
230
CAN Rural Secondary Road Miles
1,579
74,961
2,705
199
7,837
240
CAN Total Road Miles
338
45,469
1,178
39
80,841
308
CAN Food manufacturing
0
0
17,395
0
5,168
321
CAN Wood product manufacturing
515
1,689
585
210
8,419
323
CAN Printing and related support
activities
0
0
0
0
19,532
324
CAN Petroleum and coal products
manufacturing
0
988
1,383
411
6,286
326
CAN Plastics and rubber products
manufacturing
0
0
0
0
21,856
327
CAN Non-metallic mineral product
manufacturing
0
0
6,946
0
0
331
CAN Primary Metal Manufacturing
0
130
4,563
25
54
412
CAN Petroleum product wholesaler-
distributors
0
0
0
0
37,272
151
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NO*
PM2.5
SO2
voc
448
CAN clothing and clothing accessories
stores
0
0
0
0
178
562
CAN Waste management and
remediation services
2,681
1,245
2,351
2,139
16,053
601
CAN SCL12003 Petroleum Liquids
Transportation (PIRD)
0
0
12
158
6,092
602
CAN SCL12007 Oil Sands In-Situ
Extraction and Processing (PIRD)
0
0
0
0
109
603
CAN SCL12010 Light Medium Crude
Oil Production (PIRD)
0
0
0
0
2
604
CAN SCL
12011 Well Drilling (PIRD)
0
0
0
585
626
605
CAN SCL
12012 Well Servicing (PIRD)
0
0
0
65
69
606
CAN SCL
12013 Well Testing (PIRD)
0
0
0
0
0
607
CAN SCL
Producti
12014 Natural Gas
on (PIRD)
0
30
1
0
203
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
95,082
612
CAN SCL:12020 Natural Gas
Transmission and Storage (PIRD)
1
735
55
11
402
901
CAN Airport
0
98
9
0
11
921
CAN Commercial Fuel Combustion
193
21,981
2,413
442
954
923
CAN TOTAL INSTITUTIONAL AND
GOVERNEMNT
0
0
0
0
14,399
924
CAN Primary Industry
0
0
0
0
32,546
925
CAN Manufacturing and Assembly
0
0
0
0
67,574
926
CAN Distribtution and Retail (no
petroleum)
0
0
0
0
6,649
927
CAN Commercial Services
0
0
0
0
30,535
933
CAN Rail-Passenger
1
3,064
62
1
118
934
CAN Rail-Freight
48
77,088
1,533
43
3,410
935
CAN Rail-Yard
1
4,562
95
1
278
940
CAN Paved Roads New
0
0
25,020
0
0
946
CAN Construction and Mining
43
2,758
156
269
40
951
CAN Wood Consumption Percentage
1,090
12,112
73,726
1,730
102,859
955
CAN UNPAVED ROADS AND TRAILS
0
0
418,718
0
0
961
CAN 80110 Broilers
13,041
0
115
0
12,784
962
CAN 80111_Cattle_dairy_and_Heifer
59,965
0
276
0
40,508
963
CAN 80112_Cattle_non-Dairy
171,295
0
884
0
42,868
964
CAN 80113_Laying_hens_and_Pullets
9,768
0
40
0
10,594
965
CAN 80114 Horses
3,046
0
19
0
1,321
966
CAN 80115_Sheep_and_Lamb
2,200
0
6
0
170
152
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NOx
PM2.5
SO2
voc
967
CAN 80116 Swine
61,897
0
824
0
9,947
968
CAN 80117_Turkeys
5,046
0
41
0
4,508
969
CAN 80118 Goat
1,743
0
2
0
135
971
CAN 80119 Buffalo
2,175
0
6
0
517
972
CAN 80120_Llama_and_Alpacas
114
0
0
0
0
973
CAN 80121 Deer
19
0
0
0
0
974
CAN 80122 Elk
19
0
0
0
0
975
CAN 80123 Wild boars
35
0
0
0
0
976
CAN 80124 Rabbit
75
0
0
0
1
977
CAN 80125 Mink
285
0
0
0
951
978
CAN 80126 Fox
4
0
0
0
3
981
CAN Harvest Annual
0
0
24,815
0
0
983
CAN Synthfert_Annual
170,809
3,564
2,114
5,870
129
985
CAN Tillage_Annual
0
0
106,769
0
0
996
CAN urban area
0
0
3,570
0
0
1251
CAN OFFR TOTFERT
83
61,875
4,283
57
6,205
1252
CAN OFFR MINES
1
579
41
1
81
1253
CAN OFFR Other Construction not
Urban
67
38,266
4,513
45
10,335
1254
CAN OFFR Commercial Services
46
16,605
2,489
39
38,169
1255
CAN OFFR Oil Sands Mines
0
0
0
0
0
1256
CAN OFFR Wood industries CANVEC
9
3,294
265
7
933
1257
CAN OFFR Unpaved Roads Rural
24
10,153
634
21
27,111
1258
CAN OFFR Utilities
8
4,164
214
6
863
1259
CAN OFFR total dwelling
17
6,245
600
15
12,435
1260
CAN OFFR water
17
4,725
363
25
24,577
1261
CAN OFFR ALL INDUST
4
5,000
176
2
880
1262
CAN OFFR Oil and Gas Extraction
1
389
31
0
122
1263
CAN OFFR ALLROADS
3
1,782
175
2
463
1265
CAN OFFR_CANRAIL
0
65
6
0
12
153
-------
4 Emission Summaries
Tables 4-1 through 4-3 summarize emissions by sector for the 2021 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; 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." Table 4-4 shows the emissions for key criteria pollutants by sector
for Alaska, Hawaii, Puerto Rico, and the Virgin Islands.
State totals and other summaries are available in the reports area on the FTP site for the 2021 platform
(https://gaftp.epa.gov/Air/emismod/2021/).
154
-------
Table 4-1. National by-sector CAP emissions for the 2021 platform, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2_5
S02
VOC
afdust_adj
6,027,656
821,738
airports
333,660
0
83,674
8,521
7,533
9,126
50,041
cmv_clc2
19,892
68
134,167
3,662
3,548
615
5,116
cmv_c3
10,252
44
81,846
2,507
2,307
5,767
4,687
fertilizer
1,275,333
livestock
2,824,644
225,971
nonpt
2,173,885
145,073
723,480
711,625
622,905
106,258
1,005,040
nonroad
11,037,304
1,998
816,810
80,205
75,312
917
945,175
nP_oilgas
654,275
43
728,663
14,048
13,880
139,514
2,876,480
np_solvents
0
0
0
0
0
0
2,716,884
onroad
14,391,846
183,954
2,258,178
188,833
74,375
8,748
1,039,569
ptegu
467,560
21,482
879,533
125,564
109,306
968,652
26,731
ptagfire
773,523
172,492
33,830
114,547
74,469
13,729
125,668
ptfire-rx
7,825,125
68,537
125,890
1,267,230
1,131,103
80,356
1,586,259
ptfire-wild
17,682,184
178,672
163,750
3,826,054
2,386,263
166,480
4,865,824
ptnonipm
1,226,638
61,712
793,938
350,108
228,948
456,300
726,236
pt_oilgas
174,223
9,095
318,687
12,460
11,916
31,186
195,937
rail
96,705
296
444,124
11,360
10,982
369
18,367
rwc
2,940,341
22,616
44,790
448,615
446,995
11,894
453,043
beis
3,314,764
989,492
28,539,802
CON US + beis
63,122,176
4,966,059
8,620,851
13,192,993
6,021,580
1,999,910
45,406,832
Canada ag
500,395
6,562
1,875
124,257
Canada oil and gas 2D
8
306,206
Canada afdust
1,028,722
194,713
Canada ptdust
3,588
443
Canada area
2,040,850
5,983
317,182
184,382
134,440
14,175
711,153
Canada onroad
1,669,722
6,994
356,236
24,858
13,378
893
118,094
Canada point
1,021,439
18,569
538,357
112,670
42,409
483,703
148,235
Canada fires
18,068,782
259,108
302,681
3,543,123
3,141,541
173,644
5,070,468
Canada cmv_clc2
3,179
10
20,497
541
525
64
720
Canada cmv_c3
7,750
27
60,418
1,498
1,378
3,331
3,773
Mexico ag
137,778
53,862
11,638
Mexico area
98,400
26,201
57,960
42,108
20,576
21,937
425,809
Mexico onroad
1,418,503
2,509
350,527
13,377
9,349
5,778
127,181
Mexico point
158,097
979
199,367
90,822
53,973
341,038
32,822
Mexico fires
415,564
6,820
24,903
54,701
45,743
4,240
204,334
Mexico cmv_clc2
157
0
1,016
27
26
4
42
Mexico cmv_c3
9,601
87
82,079
4,907
4,514
12,970
4,596
Offshore cmv_clc2
4,445
14
28,377
743
721
88
1,065
Offshore cmv_c3
51,349
309
414,286
17,467
16,069
43,957
25,126
Offshore pt_oilgas
28,548
5
34,658
422
416
321
31,400
Can/Mex/offshore total
24,996,385
965,797
2,788,544
5,184,380
3,693,725
1,106,143
7,335,281
155
-------
Table 4-2. National by-sector VOC HAP emissions for the 2021 platform, 12US1 grid (tons/yr)
Sector
Acetaldehyd
e
Benzene
Formaldehyde
Methanol
Naphthalen
e
Acrolein
1,3-
Butadiene
airports
1,469
660
4,252
615
728
836
596
cmv_clc2
22
10
94
9
9
5
cmv_c3
20
10
86
9
9
5
livestock
1,786
486
20,352
0
nonpt
11,536
6,952
7,434
14,561
500
169
1,036
nonroad
8,524
25,793
21,093
1,191
1,462
1,286
4,369
nP_oilgas
4,146
33,081
40,914
2,691
124
2,567
579
np_solvents
72
351
7
15,551
7,804
onroad
8,979
18,353
11,404
1,451
1,416
817
2,441
ptegu
273
288
2,195
94
27
210
4
ptagfire
10,283
2,064
8,480
841
ptfire-rx
64,740
20,900
126,337
94,386
19,090
25,806
16,037
ptfire-wild
131,650
36,614
234,404
241,636
45,902
40,205
20,944
ptnonipm
5,307
2,756
5,888
49,370
674
827
742
pt_oilgas
2,772
2,098
11,858
1,636
78
1,813
249
rail
1,437
413
4,092
50
294
34
rwc
51,488
13,358
36,066
6,978
1,958
3,632
beis
367,982
504,811
1,959,801
CON US + beis
672,484
164,188
1,019,415
2,403,335
84,852
76,804
51,514
Canada ag
1,398
159
0
32,663
0
Canada oil and
gas 2D
0
922
0
0
0
Canada area
15,577
12,804
13,095
4,031
2,601
Canada onroad
2,138
5,153
2,955
0
39
Canada point
1,506
2,006
5,056
10,313
23
Canada fires
193,822
51,813
385,491
432,821
63,192
58,857
31,274
Canada
cmv_clc2
3
1
13
0
1
1
1
Canada cmv_c3
16
8
69
0
7
7
4
Mexico area
3,099
1,802
2,538
2,663
470
Mexico onroad
530
3,026
1,283
573
180
91
446
Mexico point
65
1,208
2,587
519
11
Mexico fires
10,924
2,947
12,434
4,760
576
0
0
Mexico
cmv clc2
0
0
1
0
0
0
0
Mexico cmv_c3
15
7
64
0
22
8
5
Offshore
cmv clc2
4
2
20
0
2
2
1
Offshore cmv_c3
94
46
412
0
79
46
25
156
-------
Offshore
pt_oilgas
248
41
595
0
0
0
0
Non-U.S. Total
229,438
81,946
426,612
488,342
67,203
59,013
31,755
Table 4-3. National by-sector Diesel PM and metal emissions for the 2021 platform, 12US1 grid
(tons/yr)
Sector
Diesel
PMio
Diesel
PM2.5
Chromium
Hex
Arsenic
Cadmium
Nickel
Manganese
Ethylene
Oxide
airports
26
26
0.00
0.00
0.00
0.00
0.00
0.00
cmv_clc2
3,662
3,548
0.00
0.09
0.84
2.44
0.01
0.00
cmv_c3
2,507
2,307
0.00
0.06
0.54
1.58
0.01
0.00
nonpt
0
0
0.37
7.78
5.58
37.19
11.59
0.98
nonroad
40,060
38,709
0.01
0.75
0.00
4.49
1.27
0.00
nP_oilgas
0
0
0.00
0.06
0.19
0.20
0.12
0.00
onroad
41,285
37,986
0.07
7.23
0.00
15.24
36.30
0.00
ptegu
0
0
3.63
14.58
5.06
47.26
81.97
0.00
ptnonipm
145
140
21.45
22.08
10.26
136.70
485.01
84.29
pt_oilgas
0
0
0.02
0.02
0.28
5.24
2.46
0.00
rail
11,360
10,982
0.00
0.06
0.00
0.21
0.12
0.00
rwc
0
0
0.00
0.00
0.11
0.10
0.83
0.00
Con. U.S. Total
99,045
93,699
25.55
52.70
22.85
250.65
619.68
85.27
Canada
cmv_clc2
541
525
0.00
0.01
0.12
0.36
0.00
0.00
Canada cmv_c3
1,498
1,378
0.00
0.04
0.33
0.95
0.00
0.00
Mexico
cmv_clc2
27
26
0.00
0.00
0.01
0.02
0.00
0.00
Mexico cmv_c3
4,907
4,514
0.00
0.12
1.07
3.10
0.01
0.00
Offshore
cmv_clc2
743
721
0.00
0.02
0.17
0.49
0.00
0.00
Offshore cmv_c3
17,467
16,069
0.00
0.42
3.79
11.04
0.05
0.00
Offshore
pt_oilgas
0
0
0.02
0.00
0.26
0.00
0.00
0.00
157
-------
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-24-011
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