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Technical Support Document (TSD): Preparation of
Emissions Inventories for the 2020 North American
Emissions Modeling Platform
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EPA-454/B-23-004
December 2023
Technical Support Document (TSD) Preparation of Emissions Inventories for the 2020 North American
Emissions Modeling Platform
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Authors:
Alison Eyth (EPA/OAR)
Jeff Vukovich (EPA/OAR)
Caroline Farkas (EPA/OAR)
Janice Godfrey (EPA/OAR)
Karl Seltzer (EPA/OAR)
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TABLE OF CONTENTS
LIST OF TABLES VII
LIST OF FIGURES IX
ACRONYMS X
1 INTRODUCTION 13
2 EMISSIONS INVENTORIES AND APPROACHES 15
2.1 Point sources (ptegu, pt_oilgas, ptnonipm, airports) 19
2.1.1 EGU sector (ptegu) 21
2.1.2 Point source oil and gas sector (pt oilgas) 22
2.1.3 Aircraft and ground support equipment (airports) 25
2.1.4 Non-IPM sector (ptnonipm) 25
2.2 Nonpoint sources (afdust, fertilizer, livestock, np_oilgas, rwc, vcp, nonpt) 26
2.2.1 Area fugitive dust sector (afdust) 26
2.2.2 Agricultural Livestock (livestock) 32
2.2.3 Agricultural Fertilizer (fertilizer) 32
2.2.4 Nonpoint Oil and Gas Sector (np oilgas) 35
2.2.5 Residential Wood Combustion (rwc) 38
2.2.6 Solvents (np solvents) 39
2.2.7 Nonpoint (nonpt) 40
2.3 Onroad Mobile sources (onroad) 40
2.3.1 Inventory Development using SMOKE-MOVES 41
2.3.2 Onroad Activity Data Development 44
2.3.3 MOVES Emission Factor Table Development 46
2.3.4 Onroad California Inventory Development (onroad ca adj) 49
2.4 Nonroad Mobile sources (cmv, rail, nonroad) 50
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 50
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3) 55
2.4.3 Railway Locomotives (rail) 58
2.4.4 Nonroad Mobile Equipment (nonroad) 62
2.5 Fires (ptfire-rx, ptfire-wild, ptagfire) 65
2.5.1 Wild and Prescribed Fires (ptfire-rx, ptfire-wild) 65
2.5.2 Point source Agriculture Fires (ptagfire) 69
2.6 Biogenic Sources (beis) 71
2.7 Sources Outside of the United States 73
2.7.1 Point Sources in Canada and Mexico (canmex_point) 74
2.7.2 Fugitive Dust Sources in Canada (canadaafdust, Canada_ptdust) 75
2.7.3 Agricultural Sources in Canada and Mexico (canmex ag) 75
2.7.4 Surface-level Oil and Gas Sources in Canada (canada_og2D) 75
2.7.5 Nonpoint and Nonroad Sources in Canada and Mexico (canmex area) 76
2.7.6 Onroad Sources in Canada and Mexico (canadaonroad, mexico onroad) 76
2.7.7 Fires in Canada and Mexico (ptfire othna) 76
2.7.8 Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury 77
3 EMISSIONS MODELING 78
3.1 Emissions Modeling Overview 78
3.2 Chemical Speciation 83
3.2.1 VOC speciation 88
3.2.2 PM speciation 92
3.2.2.1 Diesel PM 92
3.2.3 NOx speciation 92
3.2.4 Sulfuric Acid Vapor (SULF) 93
3.2.5 Speciation of Metals and Mercury 94
3.3 Temporal Allocation 95
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3.3.1 Use of FF10 format for finer than annual emissions 97
3.3.2 Temporal allocation for non-EGU sources (ptnonipm) 98
3.3.3 Electric Generating Utility temporal allocation (ptegu) 98
3.3.4 Airport Temporal allocation (airports) 102
3.3.5 Residential Wood Combustion Temporal allocation (rwc) 106
3.3.6 Agricultural Ammonia Temporal Profiles (livestock) 110
3.3.7 Oil and gas temporal allocation (np oilgas) 113
3.3.8 Onroad mobile temporal allocation (onroad) 113
3.3.9 Nonroad mobile temporal allocation (nonroad) 116
3.3.10 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptfire-rx, ptfire-wild) 118
3.4 Spatial Allocation 120
3.4.1 Spatial Surrogates for U.S. emissions 120
3.4.2 Allocation method for airport-related sources in the U.S. 132
3.4.3 Surrogates for Canada and Mexico emission inventories 132
4 EMISSION SUMMARIES 143
5 REFERENCES 150
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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 2020 23
Table 2-4. SCCs for the airports sector 25
Table 2-5. Afdust sector SCCs 26
Table 2-6. Total impact of 2020 fugitive dust adjustments to unadjusted inventory 28
Table 2-7. SCCs for the livestock sector 32
Table 2-8. Source of input variables for EPIC 34
Table 2-9. Nonpoint oil and gas emissions for 2020 35
Table 2-10. State emissions totals for year 2020 for Pipeline Blowdowns and Pigging sources 37
Table 2-11. SCCs for the residential wood combustion sector 38
Table 2-12. Non-VCPy SCCs in the np_solvents sector 39
Table 2-13. MOVES vehicle (source) types 42
Table 2-14. The fraction of IHS vehicle populations retained for 2020 NEI by model year 48
Table 2-15. SCCs for cmv_clc2 sector 51
Table 2-16. Vessel groups in the cmv_clc2 sector 53
Table 2-17. SCCs for cmv_c3 sector 56
Table 2-18. SCCs for the Rail Sector 58
Table 2-19. 2020 R-l Reported Locomotive Fuel Use for Class I Railroads 60
Table 2-20. 2020 Class TT/TTT Line Haul Fleet by Tier Level 60
Table 2-21. Selection hierarchy for the Nonroad Mobile data category 64
Table 2-22. SCCs included in the ptfire sector for the 2020 platform 65
Table 2-23. SCCs included in the ptagfire sector 70
Table 2-24. Meteorological variables required by BEIS4 71
Table 3-1. Key emissions modeling steps by sector 79
Table 3-2. Descriptions of the platform grids 81
Table 3-3. Emission model species produced for CB6R5 AE7 for CMAQ 83
Table 3-4. Additional HAP gaseous model species generated for toxics modeling 85
Table 3-5. Additional HAP particulate model species generated for toxics modeling 86
Table 3-6. PAH/POM pollutant groups 86
Table 3-7. Integration status for each platform sector 89
Table 3-8. Integrated species from MOVES sources 90
Table 3-9. NOx speciation profiles 93
Table 3-10. Sulfate Split Factor Computation 93
Table 3-11. SO2 speciation profiles 94
Table 3-12. Particle Size Speciation of Metals 94
Table 3-13. Mercury Speciation Profiles 95
Table 3-14. Temporal settings used for the platform sectors in SMOKE 96
Table 3-15. U.S. Surrogates available for the 2020 modeling platforms 121
Table 3-16. Shapefiles used to develop U.S. Surrogates 122
Table 3-17. Surrogates used to gapfill U.S. Surrogates 125
Table 3-18. Off-Network Mobile Source Surrogates 127
Table 3-19. Spatial Surrogates for Oil and Gas Sources 128
Table 3-20. Selected 2020 CAP emissions by sector for U.S. Surrogates (short tons in 12US1) 129
Table 3-21. Canadian Spatial Surrogates 132
Table 3-22. Shapefiles and Attributes used to Compute Canadian Spatial Surrogates 134
Table 3-23. Shapefiles and Attributes used to Compute Mexican Spatial Surrogates 139
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Table 3-24. 2020 CAP Emissions Allocated to Mexican and Canadian Spatial Surrogates for 12US1 (short
tons) 139
Table 4-1. National by-sector CAP emissions for the 2020 platform, 12US1 grid (tons/yr) 144
Table 4-2. National by-sector VOC HAP emissions for the 2020 platform, 12US1 grid (tons/yr) 145
Table 4-3. National by-sector Diesel PM and metal emissions for the 2020 platform, 12US1 grid (tons/yr)146
Table 4-4. Criteria Pollutant emissions in 2020 for Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands
(tons/yr) 146
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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 33
Figure 2-3. Map of Representative Counties 47
Figure 2-4. NEI Commercial Marine Vessel Boundaries and Automatic Identification System Request Boxes
for 2020 52
Figure 2-5. 2017 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT) 59
Figure 2-6. Class II and III Railroads in the United States 61
Figure 2-7. Amtrak National Rail Network 62
Figure 2-8. Processing flow for fire emission estimates in the 2020 inventory 68
Figure 2-9. Default fire type assignment by state and month where data are only from satellites 68
Figure 2-10. Blue Sky Modeling Pipeline 69
Figure 2-11. Annual biogenic VOC BEIS4 emissions for the 12US1 domain 73
Figure 3-1. Air quality modeling domains 81
Figure 3-2. Process of integrating HAPs and speciating VOC in a modeling platform 89
Figure 3-3. Eliminating unmeasured spikes in CEMS data 99
Figure 3-4. Regions used to Compute Temporal non-CEMS EGU Temporal Profiles 100
Figure 3-5. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type 101
Figure 3-6. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type 101
Figure 3-7. 2020 Airport Diurnal Profiles for PHX and state of Texas 103
Figure 3-8. 2020 Wisconsin month-to-day profile for airport emissions 104
Figure 3-9. Prepandemic weekly profile for airport emissions 104
Figure 3-10. Pre-pandemic monthly profile for airport emissions 105
Figure 3-11. 2020 Monthly airport profiles for ATL and state of Maryland 105
Figure 3-12. Alaska seaplane profile 106
Figure 3-13. Example of RWC temporal allocation using a 50 versus 60 °F threshold 107
Figure 3-14. Example of Annual-to-day temporal pattern of recreational wood burning emissions 108
Figure 3-15. RWC diurnal temporal profile 109
Figure 3-16. Data used to produce a diurnal profile for hydronic heaters 110
Figure 3-17. Monthly temporal profile for hydronic heaters 110
Figure 3-18. Examples of livestock temporal profiles in several parts of the country 112
Figure 3-19. Example of animal NH3 emissions temporal allocation approach (daily total emissions) 112
Figure 3-20. Example temporal variability of VMT compared to onroad NOx emissions 114
Figure 3-21. Sample onroad diurnal profiles for Fulton County, GA 115
Figure 3-22. Example Nonroad Day-of-week Temporal Profiles 117
Figure 3-23. Example Nonroad Diurnal Temporal Profiles 117
Figure 3-24. Agricultural burning diurnal temporal profile 119
Figure 3-25. Prescribed and Wildfire diurnal temporal profiles 119
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Acronyms
AADT Annual average daily traffic
AE6 CMAQ Aerosol Module, version 6, introduced inCMAQv5.0
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 Emissions Control Area
ECCC Environment and Climate Change Canada
EF Emission Factor
EGU Electric Generating Units
EIA Energy Information Administration
EIS Emissions Inventory System
EPA Environmental Protection Agency
EMFAC EMission FACtor (California's onroad mobile model)
EPIC Environmental Policy Integrated Climate modeling system
FAA Federal Aviation Administration
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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 2020. 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 2020. 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 2020 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
"2020ha2", where 2020 is the year modeled, 'h' represents that it was based on the 2020 NEI, and 'a'
represents that it was the first version of a 2020 NEI-based platform. The additional '2' after the 'ha' is
related to a second run of 2020ha performed with updated versions of some spatial surrogates.
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 2020 for all NEI HAPs (about 130 more than covered by CMAQ) across all 50 states, Puerto Rico and
the Virgin Islands in a similar way as was done for the 2018 version of AirToxScreen (EPA, 2022a). This
TSD focuses on the CMAQ aspects of the 2020 AirToxScreen 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 2020, 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 httos://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, continuous emission monitoring
system (CEMS) data were used for electric generating unit (EGLJ) 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 4.9 was used to create
CMAQ-ready emissions files for a 12-km grid covering the continental U.S. and also for grids covering
Alaska, Hawaii, and the area around Puerto Rico and the Virgin Islands. 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 was run for 2020 over a
domain covering the continental U.S. at a 12km resolution with 35 vertical layers, and also for domains
covering Alaska, Hawaii, and the area around Puerto Rico and the Virgin Islands. The run for this
platform included high resolution sea surface temperature data from the Group for High Resolution Sea
Surface Temperature (GHRSST) (see https://www.ghrsst.org/) and is given the EPA meteorological case
abbreviation "20k." The full case abbreviation includes this suffix following the emissions portion of the
case name to fully specify the abbreviation of the case as "2020ha2_cb6_20k."
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/2020-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. 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 full 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 all applicable
point sources were submitted for 2020, it was not necessary to pull forward unsubmitted sources from
another NEI year, as was done for interim years such as 2018 and 2019. The SMARTFIRE2 system and the
BlueSky Pipeline (https://github.com/pnwairfire/bluesky) emissions modeling system were used to
develop year 2020 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 for the year 2020 by running MOVES3 (https://www.epa.gov/moves).
With the exception of onroad and fire emissions, Canadian emissions were provided by Environment
Canada and Climate Change (ECCC) for the year 2020. For Mexico, inventories from the 2019 emissions
modeling platform (EPA, 2022b) were used as the starting point. Adjustments were made to the
Canadian and Mexican emissions to account for the impacts of the COVID pandemic.
The emissions modeling process was performed using SMOKE v4.9. 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 combined low-level sector-specific gridded, speciated and temporalized emissions to create the
final CMAQ-ready emissions inputs. For biogenic and fertilizer emissions, the CMAQ model allows for
these emissions to be included in the CMAQ-ready emissions inputs, or to be computed within CMAQ
itself (the "inline" option). This study used the option to compute biogenic emissions within the model
and the CMAQ bidirectional ammonia process to compute the fertilizer emissions.
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Table 2-1 presents the sectors in the emissions modeling platform used to develop the year 2020
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
2020 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 2020 NEI point inventory. Annual
resolution for sources not matched to CEMS data, hourly
for CEMS sources. EGUs closed in 2020 are not part of the
inventory.
Point source oil and gas:
ptjoiigas
Point
2020 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
2020 NEI point source emissions from airports, including
aircraft and airport ground support emissions. Annual
resolution.
Remaining non-EGU point:
Ptnonipm
Point
All 2020 NEI point source records not matched to the
airports, ptegu, or pt_oilgas sectors. Includes 2020 NEI rail
yard emissions. Annual resolution.
Livestock:
Livestock
Nonpoint
2020 NEI nonpoint livestock emissions. Livestock includes
ammonia and other pollutants (except PM2.5). County and
annual resolution.
Agricultural Fertilizer:
fertilizer
Nonpoint
2020 agricultural fertilizer ammonia emissions computed
inline within CMAQ.
Area fugitive dust:
afdustjadj
Nonpoint
PM10 and PM2.5 fugitive dust sources from the 2020 NEI
nonpoint inventory; including building construction, road
construction, agricultural dust, and paved and unpaved
road dust. The emissions modeling system applies a
transport fraction reduction and a zero-out based on 2020
gridded hourly meteorology (precipitation and snow/ice
cover). Emissions are county and annual resolution.
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Platform Sector:
abbreviation
NEI Data Category
Description and resolution of the data input to SMOKE
Biogenic:
Beis
Nonpoint
Year 2020 emissions from biogenic sources. These were left
out of the CMAQ-ready merged emissions, in favor of inline
biogenic emissions produced during the CMAQ model run
itself. Version 4 of the Biogenic Emissions Inventory System
(BEIS) was used with Version 6 of the Biogenic Emissions
Landuse Database (BELD6). Therefore, the biogenic
emissions used here are similar to the 2020 NEI biogenic
emissions, but not exactly the same.
Category 1, 2 CMV:
cmv_clc2
Nonpoint
2020 NEI Category 1 (CI) and Category 2 (C2), commercial
marine vessel (CMV) emissions based on Automatic
Identification System (AIS) data. Point and hourly
resolution.
Category 3 CMV:
cmv c3
Nonpoint
2020 NEI Category 3 (C3) commercial marine vessel (CMV)
emissions based on AIS data. Point and hourly resolution.
Locomotives :
Rail
Nonpoint
Line haul rail locomotives emissions from 2020 NEI. County
and annual resolution.
Nonpoint source oil and
gas: np_oilgas
Nonpoint
Nonpoint 2020 NEI sources from oil and gas-related
processes. County and annual resolution.
Residential Wood
Combustion:
Rwc
Nonpoint
2020 NEI nonpoint sources with residential wood
combustion (RWC) processes. County and annual
resolution.
Solvents: np_solvents
Nonpoint
Emissions of solvents from the 2020 NEI (Seltzer, 2021).
Includes household cleaners, 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.
Nonroad:
Nonroad
Nonroad
2020 NEI nonroad equipment emissions developed with
MOVES3, including the updates made to spatial
apportionment that were developed with the 2016vl
platform. MOVES3 was used for all states except California,
which submitted their own emissions for the 2020 NEI.
County and monthly resolution.
Onroad:
Onroad
Onroad
Onroad mobile source gasoline and diesel vehicles from
parking lots and moving vehicles from 2020 NEI. 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. MOVES3 was run for 2020 to
generate emission factors.
Onroad California:
onroad_ca_adj
Onroad
California-provided 2020 CAP and HAP (VOCs and metals)
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
MOVES3. Polycyclic aromatic hydrocarbon (PAH) emissions
are based on MOVES3.
17
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Platform Sector:
abbreviation
NEI Data Category
Description and resolution of the data input to SMOKE
Point source agricultural
fires: ptagfire
Nonpoint
Agricultural fire sources for 2020 developed by EPA as point
and day-specific emissions.3 Only EPA-developed ag. fire
data are 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 2020 NEI
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 2020 NEI 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 2020. Canadian fires for May through
December are provided by ECCC. All other fire emissions,
including Canadian emissions from January through April,
as well as 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 2020 with
transport fraction and snow/ice adjustments based on 2020
meteorological data. Annual and province resolution.
Canada Point Fugitive dust
sources:
canada_ptdust
N/A
2020 point source fugitive dust sources from ECCC with
transport fraction and snow/ice adjustments based on 2020
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 for 2020 were
provided by ECCC and Mexico point source emissions for
2016 were provided by SEMARNAT. Mexico sources were
projected from 2019ge (EPA, 2022b) with COVID
adjustments applied. Canada monthly temporalization
adjusted for COVID. Annual and monthly resolution.
Canada and Mexico
agricultural sources:
canmex_ag
Canada and Mexico agricultural emissions. Canada point
sources for 2020 were provided by ECCC and Mexico
emissions for 2016 were provided by SEMARNAT and
adjusted to 2019. COVID adjustments were not applied to
the ag sector. Annual resolution.
3 Only EPA-developed agricultural fire data were included in this study; data submitted by states to the NEI were excluded.
18
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Platform Sector:
abbreviation
NEI Data Category
Description and resolution of the data input to SMOKE
Canada low-level oil and
gas sources:
canada_og2D
2020 Canada emissions from upstream oil and gas. This
sector contains the portion of oil and gas emissions which
are not subject to plume rise. The rest of the 2020 Canada
oil and gas emissions are in the canmex_point sector.
Provided by ECCCwith COVID-adjusted monthly
temporalization. Monthly resolution.
Canada and Mexico
nonpoint and nonroad
sources:
canmex_area
N/A
2020 Canada and Mexico nonpoint source emissions not
included in other sectors. Canada: ECCC provided a 2020
inventory and surrogates. Mexico: applied COVID
adjustments to 2019ge. Monthly temporalization adjusted
for COVID.
Canada onroad sources:
canada_onroad
N/A
Canada onroad emissions. 2020 Canada inventory provided
by ECCC and processed using updated surrogates. COVID
impacts applied to monthly profiles (not to annual totals).
Province and monthly resolution.
Mexico onroad sources:
mexico_onroad
N/A
Mexico onroad emissions. 2020 MOVES-Mexico with COVID
adjustments applied. 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
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/2020-emissions-modeling-platform.
The platform informational text file indicates the particular zipped files associated with each platform
sector. Some emissions data summaries are available with the data files for the 2020 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
19
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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 2020 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 2020 platform, an analysis of point source stack parameters (e.g., stack height, diameter,
temperature, and velocity) was performed due to the presence of unrealistic and repeated stack
parameters. The defaulted values were noticed in data submissions for the states of Illinois, Louisiana,
Michigan, Pennsylvania, Texas, and Wisconsin. Where these defaults were detected and deemed to be
unreasonable for the specific process, the affected stack parameters were replaced by values from the
PSTK file that is input to SMOKE. PSTK contains default stack parameters by source classification code
(SCC). 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 2020 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 2020 CEMS data, hourly CEMS NOx and SO2
emissions for 2020 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.
20
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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.
• 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 2020 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.
21
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The 2020 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
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
22
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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 2020 NEI process were used. More information on the development of the 2020 NEI oil
and gas emissions can be found in Section 13 of the 2020 NEI TSD
Table 2-2. Point source oil and gas sector NAICS Codes
NAICS
NAICS description
2111
Oil and Gas Extraction
211112
Natural Gas Liquid Extraction
21112
Crude Petroleum Extraction
211120
Crude Petroleum Extraction
21113
Natural Gas Extraction
211130
Natural Gas Extraction
213111
Drilling Oil and Gas Wells
213112
Support Activities for Oil and Gas Operations
2212
Natural Gas Distribution
22121
Natural Gas Distribution
221210
Natural Gas Distribution
237120
Oil and Gas Pipeline and Related Structures Construction
4861
Pipeline Transportation of Crude Oil
48611
Pipeline Transportation of Crude Oil
486110
Pipeline Transportation of Crude Oil
4862
Pipeline Transportation of Natural Gas
48621
Pipeline Transportation of Natural Gas
486210
Pipeline Transportation of Natural Gas
More 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 2020 by state are shown in Table 2-3.
Table 2-3. Point source oil and gas sector emissions for 2020
State
2020 NOx
2020 VOC
Alabama
8,695
1,180
Alaska
38,507
1,669
Arizona
2,604
179
Arkansas
2,533
222
California
2,739
2,544
Colorado
14,819
12,519
Connecticut
52
40
Delaware
6
1
Florida
5,587
621
23
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State
2020 NOx
2020 VOC
Georgia
4,242
383
Idaho
968
27
Illinois
4,323
1,130
Indiana
1,384
140
Iowa
5,863
328
Kansas
19,734
2,966
Kentucky
9,940
1,363
Louisiana
28,813
8,091
Maine
25
51
Maryland
262
143
Massachusetts
176
51
Michigan
9,314
1,080
Minnesota
1,624
82
Mississippi
19,064
1,984
Missouri
2,418
129
Montana
674
972
Nebraska
3,623
286
Nevada
252
19
New Jersey
73
91
New Mexico
29,913
45,921
New York
1,059
180
North Carolina
1,704
203
North Dakota
5,135
2,435
Ohio
9,162
1,454
Oklahoma
38,383
28,508
Oregon
808
68
Pennsylvania
4,014
930
Rhode Island
41
19
South Carolina
281
96
South Dakota
358
10
Tennessee
6,092
502
Texas
47,687
21,894
Utah
2,379
481
Virginia
3,177
399
Washington
594
44
West Virginia
7,733
2,971
Wisconsin
280
63
Wyoming
13,865
51,000
Offshore
49,962
38,833
Tribal Data
8,047
2,301
24
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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. 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
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 2020 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.
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Emissions from rail yards are included in the ptnonipm sector. Railyards are from the 2020 NEI railyard
inventory. 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.
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
26
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see
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2801000005
Miscellaneous Area
Sources
Ag. Production - Crops
Agriculture - Crops
Harvesting
2801000008
Miscellaneous Area
Sources
Ag. Production - Crops
Agriculture - Crops
Transport
2805100010
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Beef cattle -
finishing
operations on
feedlots (drylots)
2805100020
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Dairy Cattle
2805100030
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Broilers
2805100040
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Layers
2805100050
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Swine
2805100060
Miscellaneous Area
Sources
Ag. Production - Livestock
Dust kicked up by
Livestock
Turkeys
Area Fugitive Dust 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 road dust emissions were from the 2020 NEI. 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 2020 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 2020 meteorology. The total impacts of the transport fraction
and meteorological adjustments are shown in Table 2-6.
27
-------
Table 2-6. Total impact of 2020 fugitive dust adjustments to unadjusted inventory
State
Unadjusted
PMio
Unadjusted
PM;.5
Change in
PMio
Change in
PM;.5
PMio
Reduction
PMi.5
Reduction
Alabama
362,881
45,813
-287,903
-36,130
79%
79%
Arizona
99,172
13,040
-34,935
-4,452
35%
34%
Arkansas
521,041
68,709
-407,602
-52,528
78%
76%
California
342,594
44,009
-142,121
-17,684
41%
40%
Colorado
262,423
37,688
-140,476
-18,929
54%
50%
Connecticut
21,108
3,216
-15,422
-2,341
73%
73%
Delaware
15,700
2,337
-9,549
-1,420
61%
61%
District of
Columbia
3,513
478
-2,373
-323
68%
68%
Florida
154,820
23,964
-85,789
-13,073
55%
55%
Georgia
395,234
54,200
-315,854
-42,878
80%
79%
Idaho
452,925
54,317
-268,967
-30,565
59%
56%
Illinois
789,153
99,767
-516,465
-64,721
65%
65%
Indiana
175,223
33,469
-122,277
-23,509
70%
70%
Iowa
409,593
59,124
-252,306
-36,286
62%
61%
Kansas
485,177
69,829
-236,542
-33,066
49%
47%
Kentucky
260,076
40,138
-214,040
-32,819
82%
82%
Louisiana
224,141
32,950
-160,893
-23,435
72%
71%
Maine
48,597
6,746
-39,442
-5,473
81%
81%
Maryland
60,891
8,753
-40,500
-5,853
67%
67%
Massachusetts
60,352
7,617
-45,271
-5,604
75%
74%
Michigan
308,099
40,282
-214,848
-27,879
70%
69%
Minnesota
615,344
80,620
-406,412
-52,360
66%
65%
Mississippi
609,999
71,137
-483,100
-55,750
79%
78%
Missouri
1,847,645
206,417
-1,388,275
-154,191
75%
75%
Montana
543,855
71,195
-362,947
-45,291
67%
64%
Nebraska
420,518
61,208
-193,841
-27,229
46%
44%
Nevada
99,267
13,105
-31,733
-4,189
32%
32%
New Hampshire
15,143
3,066
-12,058
-2,435
80%
79%
New Jersey
110,407
13,740
-75,819
-9,337
69%
68%
New Mexico
100,044
13,306
-42,493
-5,477
42%
41%
New York
334,861
45,794
-261,452
-35,380
78%
77%
North Carolina
404,151
52,651
-336,442
-43,475
83%
83%
North Dakota
336,874
53,269
-170,737
-26,691
51%
50%
Ohio
387,644
59,491
-293,833
-45,027
76%
76%
Oklahoma
490,614
69,516
-274,582
-37,649
56%
54%
Oregon
797,437
88,451
-620,821
-66,747
78%
75%
28
-------
State
Unadjusted
PMio
Unadjusted
PM;.5
Change in
PMio
Change in
PM2.5
PMio
Reduction
PM2.5
Reduction
Pennsylvania
159,273
28,469
-117,358
-21,431
74%
75%
Rhode Island
5,334
847
-3,647
-579
68%
68%
South Carolina
235,913
30,830
-185,965
-24,104
79%
78%
South Dakota
210,770
37,424
-107,785
-18,840
51%
50%
Tennessee
169,090
31,690
-131,860
-24,777
78%
78%
Texas
1,541,927
214,178
-747,016
-102,346
48%
48%
Utah
139,823
17,349
-77,705
-9,376
56%
54%
Vermont
86,423
9,435
-74,941
-8,150
87%
86%
Virginia
208,176
32,662
-175,118
-27,543
84%
84%
Washington
84,296
12,533
-45,961
-7,025
55%
56%
West Virginia
147,441
19,590
-136,064
-18,012
92%
92%
Wisconsin
218,105
36,509
-151,593
-25,372
70%
69%
Wyoming
500,445
54,291
-296,419
-31,572
59%
58%
Domain Total
(12km CONUS)
16,273,534
2,175,215
-10,759,553
-1,409,323
66%
65%
Alaska
37,618
4,009
-34,443
-3,659
92%
91%
Hawaii
16,492
2,126
-10,619
-1,385
64%
65%
Puerto Rico
9,140
1,472
-7,630
-1,249
83%
85%
Virgin Islands
484
65
-228
-31
47%
48%
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.
29
-------
Figure 2-1. Fugitive dust emissions arid impact of adjustments due to transport fraction, precipitation,
and cumulative
180
157
135
><
112 £
o
4_l
90
67
45
<22
>104
78
52
26
0
-26
-52
-78
<-104
>202
IMax: 0.0 Min: -1272.
2020ha
Max: 935.9601 Min; 0.0
2020ha2 afdust annual : PM2 5,
30
-------
31
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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 NH3 emissions,
as there is a very small amount of NH3 emissions from livestock in the ptnonipm inventory (as point
sources). In addition to NH3, the sector includes livestock emissions from all pollutants other than PM2.5.
PM2.5from livestock are in the afdust sector.
Agricultural livestock emissions in the 2020 platform were from the 2020 NEI, which is a mix of state-
submitted data and EPA estimates. Livestock emissions utilized improved animal population data. VOC
livestock emissions, new for this sector, were estimated by multiplying a national VOC/NH3 emissions
ratio by the county NH3 emissions. The 2020 NEI approach for livestock utilizes daily emission factors by
animal and county from a model developed by Carnegie Mellon University (CMU) (Pinder, 2004,
McQuilling, 2015) and 2020 U.S. Department of Agriculture (USDA) National Agricultural Statistics
Service (NASS) survey. Details on the approach are provided in Section 10 of the 2020 NEI TSD.
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 for 2020 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
32
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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, CMAQ v5.4 was run with
the Surface Tiled Aerosol and Gaseous Exchange (STAGE) deposition option along with bidirectional
exchange to estimate fertilizer and biogenic NHS emissions. However, for this study, the M3DRY option
was used to develop the fertilizer emissions.
Figure 2-2. "Bid!" 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:
33
-------
• Grid cell meteorological variables from WRF
• Initial soil profiles/soil selection
• Presence of 21 major crops: irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn,
silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans,
spring wheat, winter wheat, canola, and other crops (e.g., lettuce, tomatoes, etc.)
• Fertilizer sales to establish the type/composition of nutrients applied
• Management scenarios for the 10 USDA production regions. These include irrigation, tile
drainage, intervals between forage harvest, fertilizer application method (injected versus surface
applied), and equipment commonly used in these production regions.
The WRF meteorological model was used to provide grid cell meteorological parameters for year 2020
using a national 12-km rectangular grid covering the continental U.S. The meteorological parameters in
Table 2-8 were used as EPIC model inputs.
Table 2-8. Source of input variables for EPIC
EPIC input variable
Variable Source
Daily Total Radiation (MJ/m2)
WRF
Daily Maximum 2-m Temperature (C)
WRF
Daily minimum 2-m temperature (C)
WRF
Daily Total Precipitation (mm)
WRF
Daily Average Relative Humidity (unitless)
WRF
Daily Average 10-m Wind Speed (m s"1)
WRF
Daily Total Wet Deposition Oxidized N (g/ha)
CMAQ
Daily Total Wet Deposition Reduced N (g/ha)
CMAQ
Daily Total Dry Deposition Oxidized N (g/ha)
CMAQ
Daily Total Dry Deposition Reduced N (g/ha)
CMAQ
Daily Total Wet Deposition Organic N (g/ha)
CMAQ
Initial soil nutrient and pH conditions in EPIC were based on the 1992 USDA Soil Conservation Service
(CSC) Soils-5 survey. The EPIC model then was run for 25 years using current fertilization and agricultural
cropping techniques to estimate soil nutrient content and pH for the 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.,
34
-------
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.).
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 2020 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") was used to estimate 2020. Year 2020 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 2020 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 2020
State
2020 NOx
2020 VOC
Alabama
4,010
10,438
Alaska
2,413
9,464
Arizona
8
93
Arkansas
4,203
7,838
California
1,927
9,090
Colorado
28,569
73,246
Florida
24
496
35
-------
State
2020 NOx
2020 VOC
Idaho
2
6
Illinois
13,394
53,666
Indiana
2,619
12,152
Kansas
22,168
56,729
Kentucky
12,086
36,733
Louisiana
19,205
58,385
Maryland
0
1
Michigan
9,017
11,429
Mississippi
1,748
6,865
Missouri
370
855
Montana
2,055
29,072
Nebraska
293
1,883
Nevada
3
122
New Mexico
49,623
222,555
New York
734
5,842
North Dakota
39,061
233,557
Ohio
1,572
17,561
Oklahoma
43,632
172,916
Oregon
10
26
Pennsylvania
45,284
121,465
South Dakota
181
1,212
Tennessee
774
2,185
Texas
231,514
1,205,544
Utah
12,940
63,406
Virginia
3,498
8,685
West Virginia
20,067
152,984
Wyoming
725
6,204
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 Gas Tool and submitted to EIS separately from the Oil and Gas Tool 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 2020 Emissions Modeling Platform. Table 2-10 shows the
emissions totals by state for Pipeline Blowdowns and Pigging sources.
36
-------
Table 2-10. State emissions totals for year 2020 for Pipeline Blowdowns and Pigging sources
State
VOC (tpy)
Benzene (tpy)
Ethylbenzene (tpy)
Toluene (tpy)
Xylene (tpy)
AL
713
1.66
0.07
1.07
0.48
AK
13
0.06
0.003
0.05
0.01
AZ
73
0.33
0.02
0.29
0.08
AR
34
0.01
-
0.001
0.001
CO
3,608
9.40
0.47
11.47
3.57
IL
380
1.49
0.08
1.32
0.38
IN
259
0.99
0.06
0.88
0.25
KS
942
1.69
0.20
1.43
0.64
KY
854
3.78
0.21
3.37
0.96
LA
549
3.70
0.00
0.42
0.66
MD
0.0
0.00021
0.00001
0.00018
0.00005
Ml
307
1.39
0.08
1.24
0.35
MS
484
0.74
0.02
0.28
0.24
MO
43
0.04
0.0005
0.03
0.01
MT
275
1.35
0.07
1.04
0.34
NE
89
0.21
0.01
0.27
0.09
NM
1,348
-
-
-
-
NY
202
0.92
0.05
0.82
0.23
ND
18
0.08
0.00
0.07
0.02
OH
476
2.16
0.12
1.92
0.55
OK
89
0.02
0.01
0.08
0.06
OR
9
0.04
0.002
0.04
0.01
PA
1,575
7.15
0.40
6.37
1.81
SD
5
0.02
0.001
0.02
0.01
TN
0.2
0.0010
0.0001
0.0009
0.0003
TX
6,285
7.91
0.19
3.17
2.68
UT
13
0.06
0.004
0.06
0.03
VA
1
0.00
0.0003
0.00
0.00
WV
1,300
5.89
0.33
5.25
1.49
Total:
19,941
51.09
2.42
41.00
14.96
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 2020 Emissions Modeling Platform.
37
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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 2-11.
The 2020 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
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 2-11. 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
38
-------
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
Hydronic heater: outdoor
2104008620
Stationary Source Fuel
Combustion
Residential
Wood
Hydronic heater: indoor
2104008630
Stationary Source Fuel
Combustion
Residential
Wood
Hydronic heater: pellet-fired
2104008700
Stationary Source Fuel
Combustion
Residential
Wood
Outdoor wood burning device, NEC
(fire-pits, 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
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." Emissions from this sector for the 2020 modeling
platform are unchanged from the 2020 NEI, and users should review Section 32 of the 2020 NEI TSD for
additional information on the construction of emissions estimates for solvents in the 2020 NEI.
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 2-12.
Table 2-12. 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
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see
Description
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 2020 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;
• 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 RFC inventory consists of three distinct sources of RFC 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, and
compressed natural gas (CNG) vehicles. The sector characterizes emissions from parked vehicle
processes (e.g., starts, hot soak, and extended idle) as well as from on-network processes (i.e., from
vehicles as they move along the roads). For more details on the approach and for a summary of the
MOVES inputs submitted by states, see section 5 of the 2020 NEI TSD.
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For the 2020 modeling platform activity data (i.e., VMT, VPOP, starts, on-network idling, and hoteling)
were based on state submitted CDBs, as well as data from Federal Highways administration (FHWA)
annual VMT at the county level. A new MOVES run for 2020 was done using MOVES3.
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 2020 meteorological data. Specifically, EPA used MOVES3 inputs
for representative counties, vehicle miles traveled (VMT), vehicle population (VPOP), and hoteling hours
data for all counties, along with tools that integrated the MOVES model with SMOKE. In this way, it was
possible to take advantage of the gridded hourly temperature data available from meteorological
modeling that are also used for air quality modeling. The onroad source classification codes (SCCs) in the
modeling platform are more finely resolved than those in the National Emissions Inventory (NEI). The
NEI SCCs distinguish vehicles and fuels. The SCCs used in the model platform also distinguish between
emissions processes (i.e., off-network, on-network, and extended idle), and road types.
MOVES3 includes the following updates from MOVES2014b:
• Updated emission rates:
o Updated heavy-duty (HD) diesel running emission rates based on manufacturer in-use
testing data from hundreds of HD trucks
o Updated HD gasoline and compressed natural gas (CNG) trucks
o Updated light-duty (LD) emission rates for hydrocarbons (HC), CO, NOx, and PM
• Includes updated fuel information
• Incorporates HD Phase 2 Greenhouse Gas (GHG) rule, allowing for finer distinctions among HD
vehicles
• Accounts for glider vehicles that incorporate older engines into new vehicle chassis
• Accounts for off-network idling - emissions beyond the idling that is already considered in the
MOVES drive cycle
• Includes revisions to inputs for hoteling
• Adds starts as a separate type of rate and activity data
2.3.1 Inventory Development using SMOKE-MOVES
Except for California, onroad emissions were computed with SMOKE-MOVES by multiplying specific
types of vehicle activity data by the appropriate emission factors. This section includes discussions of the
activity data and the emission factor development. The vehicles (aka source types) for which MOVES
computes emissions are shown in Table 2-13. SMOKE-MOVES was run for specific modeling grids.
Emissions for the contiguous U.S. states and Washington, D.C., were computed for a grid covering those
areas. 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.
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Table 2-13. 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 2020-specific emission factors by
temperature and speed for a series of "representative counties," to which every other county was
mapped. The representative counties for which emission factors were generated were selected
according to their state, elevation, fuels, age distribution, ramp fraction, and inspection and
maintenance programs. Each county was then mapped to a representative county based on its
similarity to the representative county with respect to those attributes. For this study, there are 254
representative counties in the continental U.S. and a total of 292 including the non-CONUS areas.
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.
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3) Create inputs needed only by MOVES. MOVES requires county-specific information on vehicle
populations, age distributions, and inspection-maintenance programs for each of the
representative counties.
4) Create inputs needed both by MOVES and by SMOKE, including temperatures and activity data.
5) Run MOVES to create emission factor tables for the temperatures found in each county.
6) Run SMOKE to apply the emission factors to activity data (VMT, VPOP, STARTS, off-network
idling, and HOTELING) to calculate emissions based on the gridded hourly temperatures in the
meteorological data.
7) Aggregate the results to the county-SCC level for summaries and quality assurance.
The onroad emissions were processed in six processing streams that were then merged together into
the onroad sector emissions after each of the six streams have been processed:
• rate-per-distance (RPD) uses VMT as the activity data plus speed and speed profile information
to compute on-network emissions from exhaust, evaporative, permeation, refueling, and brake
and tire wear processes;
• rate-per-vehicle (RPV) uses VPOP activity data to compute off-network emissions from exhaust,
evaporative, permeation, and refueling processes;
• rate-per-profile (RPS) uses STARTS activity data to compute off-network emissions from vehicles
starts;
• rate-per-profile (RPP) uses VPOP activity data to compute off-network emissions from
evaporative fuel vapor venting, including hot soak (immediately after a trip) and diurnal (vehicle
parked for a long period) emissions;
• rate-per-hour (RPH) uses hoteling hours activity data to compute off-network emissions for idling
of long-haul trucks from extended idling and auxiliary power unit process; and
• 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 2020 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.
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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 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.
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. This new speed distribution file, called SPDIST, specifies the
amount of time spent in each MOVES speed bin for each county, vehicle (aka source) type, road type,
weekday/weekend, and hour of day. This file contains the same information at the same resolution as
the Speed Distribution table used by MOVES but is reformatted for SMOKE. Using the SPDIST file results
in a SMOKE emissions calculation that is more consistent with MOVES than the old hourly speed profile
(SPDPRO) approach, because emission factors from all speed bins can be used, rather than interpolating
between the two bins surrounding the single average speed value for each hour as is done with the
SPDPRO approach.
For 2020 NEI, to more accurately reflect the variation of average speeds from month to month
throughout the year 2020, month-specific SPDIST files were generated. Speed data from the Streetlight
dataset were used to generate hourly speed profiles by county, SCC, and month. The SPDIST files for
2020 NEI 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 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.
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
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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 as a result of this analysis. Four states requested that no reductions be
applied to the hoteling activity based on parking space availability: CO, ME, NJ, and NY. For these states,
reductions based on parking space availability were not applied.
The final step related to hoteling activity is to split county totals into separate values for extended idling
(SCC 2202620153) and Auxiliary Power Units (APUs) (SCC 2202620191). For 2020 modeling with
MOVES3, a 7.2% APU split is used nationwide, meaning that during 7.2% 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
the running operating temperature. Operationally, start emissions are defined as the difference in
emissions between an exhaust emissions test with an ambient temperature start and the same test with
the engine and emission control systems already at operating temperature. As such, the units for start
emission rates are instantaneous grams/start.
MOVES3 uses vehicle population information to sort the vehicle population into source bins defined
by vehicle source type, fuel type (gas, diesel, etc.), regulatory class, model year and age. The model uses
default data from instrumented vehicles (or user-provided values) to estimate the number of starts for
each source bin and to allocate them among eight operating mode bins defined by the amount of time
parked ("soak time") prior to the start. Thus, MOVES3 accounts for different amounts of cooling of the
engine and emission control systems. Each source bin and operating mode has an associated g/start
emission rate. Start emissions are also adjusted to account for fuel characteristics, LD inspection and
maintenance programs, and ambient temperatures.
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.
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Note that ONI does not include idling that occurs on the road, such as idling at traffic signals, stop signs,
and in traffic—these emissions are included as part of the running and crankcase running exhaust
processes on the other road types. ONI also does not include long-duration idling by long-haul
combination trucks (hoteling/extended idle), as that type of long duration idling is accounted for in other
MOVES processes.
ONI activity hours were calculated based on VMT. For each representative county, the ratio of ONI hours
to onroad VMT (on all road types) was calculated using the MOVES ONI Tool by source type, fuel type,
and month. These ratios are then multiplied by each county's total VMT (aggregated by source type, fuel
type, and month) to get hours of ONI activity.
2.3.3 MOVES Emission Factor Table Development
MOVES3 was run in emission rate mode to create emission factor tables for 2020, for all representative
counties and fuel months. The county databases used to run MOVES to develop the emission factor
tables included the state-specific control measures such as the California LEV program, and fuels
represented the year 2020. The range of temperatures run along with the average humidities used were
specific to the year 2020. 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 2020. 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 those used
for the 2020 NEI and therefore included any updated data provided and accepted for the 2020 NEI
process. The 2020 NEI 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 inspection and maintenance programs, the mean light-duty age, and
the fraction of ramps. A binning algorithm was executed to identify "like counties. The result was 254
representative counties for CONUS shown in Figure 2-3 along with 38 for Alaska, Hawaii, Puerto Rico,
and the US Virgin Islands.
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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. 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 'SourceTypeAgeDistribution1 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
could be included in the analysis, EPA first combined the populations of passenger cars (source type 21)
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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 2-14 shows the adjustments EPA made to the 2020 IHS data prior to its use in
the NEI.
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 2-14 were applied to the 2020 IHS data to create the EPA Default set of population and
age distributions for the NEI.
Table 2-14. The fraction of IHS vehicle populations retained for 2020 NEI 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
than average. States without any CDB submittals received EPA Default populations and age distributions
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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 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 2020. 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 2020. 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, and these emissions were used for 2020
modeling. The California inventory had CAPs and some HAPs, but did not have NH3 or refueling
emissions. The EPA added NH3 to the CARB inventory by using the state total NH3 from MOVES and
allocating it at the county level based on CO. Refueling emissions were taken from MOVES in California.
HAP emissions for VOCs and metals as provided by California were used, while other HAPs (e.g., PAHs)
were from MOVES.
The California onroad mobile source emissions were created through a hybrid approach of combining
state-supplied annual emissions with EPA-developed SMOKE-MOVES runs. Through this approach, the
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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 CMV emissions.
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2)
The cmv_clc2 sector contains Category 1 and 2 CMV emissions. Category 1 and 2 vessels use diesel fuel.
All emissions in this sector are annual and at county-SCC resolution; however, in the NEI they are
provided at the sub-county level (i.e., port shape ids) and by SCC and emission type (e.g., hoteling,
maneuvering). For more information on CMV sources, see Section 11 of the 2020 NEI TSD and the
supplemental documentation.4 CI and C2 emissions that occur outside of state waters are not assigned
to states. For this modeling platform, all CMV emissions in the cmv_clc2 sector are treated as hourly
gridded point sources with stack parameters that should result in them being placed in layer 1.
Sulfur dioxide (S02) emissions reflect rules that reduced sulfur emissions for CMV that took effect in the
year 2015. The cmv_clc2 inventory sector contains small to medium-size engine CMV emissions.
Category 1 and Category 2 (C1C2) marine diesel engines typically range in size from about 700 to 11,000
hp. These engines are used to provide propulsion power on many kinds of vessels including tugboats,
towboats, supply vessels, fishing vessels, and other commercial vessels in and around ports. They are
also used as stand-alone generators for auxiliary electrical power on many types of vessels. Category 1
4 https://gaftp.epa.gov/Air/nei/2020/doc/supporting data/nonpoint/CMV
50
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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 2-15.
Table 2-15. SCCs for cmv clc2 sector
see
Tier 1 Description
Tier 2 Description
Tier 3 Description
Tier 4 Description
2280002101
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C1C2 Port emissions:
Main Engine
2280002102
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C1C2 Port emissions:
Auxiliary Engine
2280002201
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C1C2 Underway
emissions: Main
Engine
2280002202
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C1C2 Underway
emissions: Auxiliary
Engine
Category 1 and 2 CMV emissions were developed for the 2020 NEI. The emissions were developed
based signals from Automated Identification System (AIS) transmitters. AIS is a tracking system used by
vessels to enhance navigation and avoid collision with other AIS transmitting vessels. The USEPA Office
of Transportation and Air Quality received AIS data from the U.S. Coast Guard (USCG) to quantify all ship
activity which occurred between January 1 and December 31, 2020. 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 2020 NEI are shown in Figure
2-4 (b).
51
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S un' Raws in S-AIS 2023
¦¦ i> ¦>;.
¦¦ IMXAKU -W.HCHXW3
¦I +WDM® - TtKEUKO
H oca, Ui U2>x« coo
i unworn ¦ 'jaxtacti
Figure 2-4. NEI Commercial Marine Vessel Boundaries and Automatic Identification System Request
Boxes for 2020
a) NEI (solid) and ECA (dashed) geographical extent
b) Areas of AIS request boxes and amount of data received
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
52
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(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-l
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 2020 NEI
documentation for more details on this process. Following the identification, 108 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 2-16. 11,302 vessels were directly identified by their ship and cargo number. The
remaining group of miscellaneous ships represent 13 percent of the AIS vessels (excluding recreational
vessels) for which a specific vessel type could not be assigned.
Table 2-16. Vessel groups in the cmv_clc2 sector
Vessel Group
NEI Area Ship Count
Bulk Carrier
37
Commercial Fishing
1,147
Container Ship
7
Ferry Excursion
441
General Cargo
1,498
Government
1,338
Miscellaneous
1,475
Offshore support
1,149
53
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Vessel Group
NEI Area Ship Count
Reefer
13
Ro
26
Tanker
100
Tug
3,994
Work Boat
77
Total in Inventory:
11,302
As shown in Equation 2-1, power is an important component of the emissions computation. Vessel-
specific installed propulsive power ratings and service speeds were pulled from Clarksons ship registry
and adopted from the Global Fishing Watch (GFW) dataset when available. However, there is limited
vessel specific attribute data for most of the C1C2 fleet. This necessitated the use of surrogate engine
power and load factors, which were computed for each vessel group. In addition to the power required
by propulsive engines, power needs for auxiliary engines were also computed for each vessel group.
Emissions from main and auxiliary engines are inventoried with different SCCs as shown in Table 2-15.
The final components of the emissions computation equation are the emission factors and the low load
adjustment factor. The emission factors used in this inventory take into consideration the EPA's marine
vessel fuel regulations as well as exhaust standards that are based on the year that the vessel was
manufactured to determine the appropriate regulatory tier. Emission factors in g/kWhr by tier for NOx,
PMio, PM2.5, CO, CO2, SO2 and VOC were developed using Tables 3-7 through 3-10 in USEPA's (2008)
Regulatory Impact Analysis on engines less than 30 liters per cylinder. To compile these emissions
factors, population-weighted average emission factors were calculated per tier based on C1C2
population distributions grouped by engine displacement. Boiler emission factors were obtained from an
earlier Swedish Environmental Protection Agency study (Swedish EPA, 2004). If the year of manufacture
was unknown then it was assumed that the vessel was Tier 0, such that actual emissions may be less
than those estimated in this inventory. Without more specific data, the magnitude of this emissions
difference cannot be estimated.
Propulsive emissions from low-load operations were adjusted to account for elevated emission rates
associated with activities outside the engines' optimal operating range. The emission factor adjustments
were applied by load and pollutant, based on the data compiled for the Port Everglades 2015 Emission
Inventory.5 Hazardous air pollutants and ammonia were added to the inventory according to
multiplicative factors applied either to VOC or PM2.5.
The stack parameters used for cmv_clc2 are a stack height of 1 ft, stack diameter of 1 ft, stack
temperature of 70°F, and a stack velocity of 0.1 ft/s. These parameters force emissions into layer 1.
5 USEPA. EPA and Port Everglades Partnership: Emission Inventories and Reduction Strategies. US Environmental
Protection Agency, Office of Transportation and Air Quality, June 2018.
https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P100UKV8.pdf.
54
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For more information on the emission computations for 2020, see the supporting documentation for the
2020 NEI 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 2020.
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.6 The cmv_c3 sector contains
sources that traverse state and federal waters; along with sources in waters not covered by the NEI in
surrounding areas of Canada, Mexico, and international waters. For more information on CMV sources
in the 2020 NEI, see Section 11 of the 2020 NEI TSD and the supplemental documentation for 2020 NEI
CMV.
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 2-17. and
distinguish between diesel and residual fuel, in port areas versus underway, and main and auxiliary
engines.
6 https://www.epa.gov/regulations-emissions-vehicles-and-engines/regulations-emissions-marine-vessels.
55
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Table 2-17. SCCs for cmv c3 sector
see
Tier 1
Description
Tier 2 Description
Tier 3
Description
Tier 4 Description
2280002103
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C3 Port emissions: Main Engine
2280002104
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C3 Port emissions: Auxiliary
Engine
2280002203
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C3 Underway emissions: Main
Engine
2280002204
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C3 Underway emissions: Auxiliary
Engine
2280003103
Mobile
Sources
Marine Vessels,
Commercial
Residual
C3 Port emissions: Main Engine
2280003104
Mobile
Sources
Marine Vessels,
Commercial
Residual
C3 Port emissions: Auxiliary
Engine
2280003203
Mobile
Sources
Marine Vessels,
Commercial
Residual
C3 Underway emissions: Main
Engine
2280003204
Mobile
Sources
Marine Vessels,
Commercial
Residual
C3 Underway emissions: Auxiliary
Engine
Prior to creation of the 2020 NEI, the EPA received Automated Identification System (AIS) data from
United States Coast Guard (USCG) to quantify all ship activity which occurred between January 1 and
December 31, 2020. The International Maritime Organization's (IMO's) International Convention for the
Safety of Life at Sea (SOLAS) requires AIS to be fitted aboard all international voyaging ships with gross
tonnage of 300 or more, and all passenger ships regardless of size.7 In addition, the USCG has mandated
that all commercial marine vessels continuously transmit AIS signals while transiting U.S. navigable
waters. As the vast majority of C3 vessels meet these requirements, any omitted from the inventory due
to lack of AIS adoption are deemed to have a negligible impact on national C3 emissions estimates. The
activity data incorporated into this inventory reflect ship operations within 200 nautical miles of the
official U.S. baseline and beyond. Activity data within the border of the U.S Exclusive Economic Zone and
the North American ECA are included as well as some activity data outside of the ECA.
The 2020 CMV C3 NEI data were computed based on the AIS data from the USGS for the year of 2020.
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.
7 International Maritime Organization (IMO) Resolution MSC.99(73) adopted December 12th. 2000 and entered into force July
1st, 2002; as amended by SOLAS Resolution CONF.5/32 adopted December 13th, 2002.
56
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Prior to use, the AIS data were reviewed - data deemed to be erroneous were removed, and data found
to be at intervals greater than 5 minutes were interpolated to ensure that each ship had data every five
minutes. The five-minute average data provide a reasonably refined assessment of a vessel's movement.
For example, using a five-minute average, a vessel traveling at 25 knots would be captured every two
nautical miles that the vessel travels. For slower moving vessels, the distance between transmissions
would be less.
The emissions were calculated for each C3 vessel in the dataset for each 5-minute time range and
allocated to the location of the message following to the interval. Emissions were calculated according
to Equation 2-2.
g
Emissionsinterval = Time (hr)interval x Power(kW) x x LLAF Equation 2-2
Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and
emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF
represents the low load adjustment factor, a unitless factor which reflects increasing propulsive
emissions during low load operations. Time indicates the activity duration time between consecutive
intervals.
Emissions were computed according to a computed power need (kW) multiplied by the time (hr) and by
an engine-specific emission factor (g/kWh) and finally by a low load adjustment factor that reflects
increasing propulsive emissions during low load operations.
The resulting emissions were available at 5-minute intervals. Code was developed to aggregate these
emissions to modeling grid cells and up to hourly levels so that the emissions data could be input to
SMOKE for emissions modeling with SMOKE. Within SMOKE, the data were speciated into the pollutants
needed by the air quality model,8 but since the data were already in the form of point sources at the
center of each grid cell, and they were already hourly, no other processing was needed within SMOKE.
SMOKE requires an annual inventory file to go along with the hourly data, so this file was also generated
for 2020.
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.
International Maritime Organization (IMO) Resolution MSC.99(73)
57
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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 2020 platform use the 2020 NEI. The 2020 NEI is based on methods developed
during the 2017 rail inventory developed for the 2017 NEI by the Lake Michigan Air Directors Consortium
(LADCO) and the State of Illinois with support from various other states. Class I railroad emissions are
based on confidential link-level line-haul activity GIS data layer maintained by the Federal Railroad
Administration (FRA). In addition, the Association of American Railroads (AAR) provided national
emission tier fleet mix information. Class II and III railroad emissions are based on a comprehensive
nationwide GIS database of locations where short line and regional railroads operate. Passenger rail
(Amtrak) emissions follow a similar procedure as Class II and III, except using a database of Amtrak rail
lines. Yard locomotive emissions are based on a combination of yard data provided by individual rail
companies, and by using Google Earth and other tools to identify rail yard locations for rail companies
which did not provide yard data. Information on specific yards were combined with fuel use data and
emission factors to create an emissions inventory for rail yards. Pollutant-specific factors were applied
on top of the activity-based changes for the Class I rail. The inventory SCCs are shown in Table 2-18.
More detailed information on the development of the 2020 NEI rail inventory for this study 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 2-18. 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: Passenger Trains (Amtrak)
2285002009
Rail
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
2285002010
Rail
Railroad Equipment; Diesel; Yard Locomotives (nonpoint)
28500201
Rail
Railroad Equipment; Diesel; Yard Locomotives (point)
Class I Line-haul Methodology
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 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
58
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associated with a railroad company were extracted to identify the full network for each company, This
involved iterating through each of those twelve columns to identify all segments within each railroad
company's network. This process was conducted seven times, one for each Class I railroad company.
This resulted in a complete inventory of rail links trafficked by each Class I railroads with a record for
each link/railroad company combination.
Figure 2-5. 2017 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT)
EPA collected 2020 Class I line haul fuel use data from the most recent R-l submittals from the Surface
Transportation Board.9 Consistent with previous inventory efforts, EPA summed line haul and work train
fuel usage, Table 2-19.
9 Surface Transportation Board. Available at https://www.stb.gov/reports-data/econornic-data/annual-report-financial-data/
Retrieved 22 June 2021.
59
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Table 2-19. 2020 R-l Reported Locomotive Fuel Use for Class I Railroads
Class 1 Railroad
Line Haul Fuel Use (gal)*
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
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 2-20.
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 2-20. 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%
60
-------
4
64
2%
Not Classified
1,359
39%
Total
3,447
100%
Figure 2-6. Class II and III Railroads in the United States
Class II and III Railroads
Commuter Railroads
Seme l edtfjl R«ifMdAdntn*»hyi> Juw SIS
Commuter Rail Methodology
Commuter rail emissions were calculated in the same way as the Class II and III railroads. The primary
difference is that the fuel use estimates were based on data collected by the Federal Transit
Administration (FTA) for the National Transit Database. 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.
61
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Intercity Passenger Methodology (Amtrak)
The calculation methodology mimics that used for the Class II and III and commuter railroads with a few
modifications. Since link-level activity data for Amtrak was unavailable, the default assumption was
made to evenly distribute Amtrak's 2020 reported fuel use across all of it diesel-powered route-miles
shown in Figure 2-7.
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 MOVES3 which incorporates the
NONROAD model. MOVES3 incorporated updated nonroad engine population growth rates, nonroad
Tier 4 engine emission rates, and sulfur levels of nonroad diesel fuels. MOVES provides a complete set of
HAPs and incorporates updated nonroad emission factors for HAPs. MOVES3 was used for all states
62
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other than California, which uses their own model. California nonroad emissions were provided by the
California Air Resources Board (CARB) for the 2020 NEI. CARB emissions were used in California for all
pollutants except PAHs, 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.
MOVES3 outputs emissions data in county-specific databases, and a post-processing script converts the
data into FF10 format. Additional post-processing steps were performed as follows:
• County-specific FFlOs were combined into a single FF10 file.
• Emissions were aggregated from the more detailed SCCs modeled in MOVES to the SCCs
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.
63
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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/). Note that these are not
included in MOVES3.
Emissions Inside California
California nonroad emissions were provided by CARB for the 2020 NEI. 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
CDBs were used to run MOVES-Nonroad to produce emissions for all states other than California. 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.
Table 2-21 shows the selection hierarchy for the nonroad data category.
Table 2-21. Selection hierarchy for the Nonroad Mobile data category
Priority
Dataset
Notes
1
S/L/T-supplied emissions
Three tribes submitted nonroad
emissions: Kootenai Tribe of Idaho, Nez
Perce Tribe, and Shoshone-Bannock Tribes
of the Fort Hall Reservation of Idaho.
California submitted emissions calculated
with their own model (EMFAC).
2
S/L/T-supplied input data through 2020 NEI process
3
2020EPA_NONROAD
All data from MOVES3
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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, 02070, 02100,
02105, 02110, 02130, 02150, 02158, 02164, 02180, 02185, 02188, 02195, 02198, 02220, 02240, 02261,
02275, and 02282. Alaska DEC also specified some counties / census areas in which logging and
agricultural emissions do not happen, but the emissions for the specified SCCs were already zero in the
specified areas.
For more information on the nonroad sector in the 2020 NEI see Section 4 of the 2020 NEI TSD.
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
sector. Emissions are day-specific and include satellite-derived latitude/longitude of the fire's origin and other
parameters associated with the emissions such as acres burned and fuel load, which allow estimation of plume
rise.
The SCCs used for the ptfire-rx and ptfire-wild sources are shown in Table 2-22. The ptfire-rx and ptfire-
wild inventories include separate SCCs for the flaming and smoldering combustion phases for wildfire
and prescribed burns. Note that prescribed grassland fires or Flint Hills, Kansas have their own SCC
(2811021000) in the inventory. These wild grassland fires were assigned the standard wildfire SCCs
shown in Table 2-22.
Table 2-22. SCCs included in the ptfire sector for the 2020 platform
SCC
Description
2810001001
Forest Wildfires; Smoldering; Residual smoldering only (includes grassland
wildfires)
2810001002
Forest Wildfires; Flaming (includes grassland wildfires)
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming
2811020002
Prescribed Rangeland Burning
2811021000
Prescribed Rangeland Burning - Tallgrass Prairie
Fire Information Data
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Inputs to SMARTFIRE2 for 2020 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
• 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
• Wildfire and prescribed date, location, and locations from S/L/T activity 2020 NEI submitters
(includes Alaska, Arizona, California, Delaware, Georgia, Florida, Iowa, Idaho, Kanas (Flint Hills
only), Louisiana, Maine, Massachusetts, Montana, New Jersey, North Carolina, Nevada
(Washoe Co.), Oklahoma, Oregon, Rhode Island, South Carolina, Texas, Utah, Virginia,
Washington, and Wyoming)
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 2020 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 2020 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.
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The US Forest Service (USFS) compiles a variety of fire information every year. Year 2020 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 2020 data were acquired from USFWS through direct communication with
USFWS staff and were used for 2020 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
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 2020 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 2020 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 2020 inventory
Input Data Sets
(state/local/tribal and national data sets)
Data Preparation
Data Aggregation and Reconciliation
(SmartFire2)
Daily fire locations
with fire sire 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. 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 Fire Emissions Production Simulator
(FEPS) in the BlueSky Pipeline generates all the CAP emission factors for wildland fires used in the 2020
study. 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).
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 2020 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 2020 USDA cropland
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data layer (CDL). Satellite fire detects over agricultural lands are assumed to be agricultural burns and
assigned a crop type. Detects that are not over agricultural lands are output to a separate file for use in
the ptfire sector. Each detect is assigned an average size of between 40 and 80 acres based on crop type.
Grassland/pasture fires were moved to the ptfire sectors for this 2020 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
2801500171
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Fallow
2801500220
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Field Crop is Rice: Burning Techniques Not Significant
2801500250
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Field Crop is Sugar Cane: Burning Techniques Not Significant
2801500262
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; Field Crop is Wheat: Backfire Burning
2801500264
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole
field set on fire; DoubleCrop Winter Wheat and Soybeans
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Another feature of the ptagfire database is that the satellite detections for 2020 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 2020 meteorological Weather Research Forecast (WRF) model simulation.
The locations of fire detections were then compared with this daily snow cover file. For any day in which
a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the
inventory. Due to the inconsistent reporting of fire detections from the Visible Infrared Imaging
Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as VIIRS or
Suomi National Polar-orbiting Partnership satellite were excluded. In addition, certain crop types (corn
and soybeans) have been excluded from these specific midwestern states: Iowa, Kansas, Indiana, Illinois,
Michigan, Missouri, Minnesota, Wisconsin, and Ohio. The reason for these crop types being excluded is
because states have indicated that these crop types are not burned.
Heat flux for plume rise was calculated using the size and assumed fuel loading of each daily agricultural
fire. This information is needed for a plume rise calculation within a chemical transport modeling
system.
The daily agricultural and open burning emissions were converted from a tabular format into the
SMOKE-ready daily point flat file format. The daily emissions were also aggregated into annual values by
location and converted into the annual point flat file format.
For this modeling platform, a SMOKE update allows the use of HAP integration for speciation for PTDAY
inventories. The 2020 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 2020 meteorology data used for the 2020 NEI 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.
Table 2-24. Meteorological variables required by BEIS4
Variable
Description
LAI
leaf-area index
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Variable
Description
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 2020 NEI 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 on 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 2020 from BEIS4.
Figure 2-11. Annual biogenic VOC BEIS4 emissions for the 12US1 domain
>1750
1500
1250
1000 f"
g
750
500
<250
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,
Annual Emissions 2020 12US1 BEIS4 VOC BEIS
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canmex_ag, and canada_og2D. These sector names are new to 2020 platform, but the general
organization of these sectors is unchanged from the 2019 platform, except for agricultural emissions in
Canada and Mexico. 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 the 2020 inventories. Environment and Climate Change
Canada (ECCC) provided the following inventories for use in the 2020 modeling. 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 2020 NEI CMV 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.
2.7.1 Point Sources in Canada and Mexico (canmex_point)
Canadian point source inventories provided by ECCC for the 2020 NEI were adjusted for the impacts of
COVID. These inventories include emissions for airports and other point sources. The Canadian point
source inventory is pre-speciated for the CB6 chemical mechanism. Annual emissions provided by ECCC
already reflected pandemic effects, but the monthly distributions of emissions did not. To account for
pandemic effects, monthly emissions in Canada were redistributed using data from the CONFORM
dataset (https://permalink.aeris-data.fr/CONFORM), which provides country-specific adjustment factors
to account for pandemic effects for each month in 2020. Monthly temporal profiles were calculated
from the CONFORM dataset as ratios of monthly totals versus annual totals for several different
categories (aviation, energy, industry, public and commercial, residential, and transport) and applied to
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the annual emisions provided by ECCC, with each SCC mapped to a CONFORM category. Annual
emissions totals in Canada were not changed as part of this process, only the distribution to months.
Point sources in Mexico were compiled based on inventories projected from the Inventario Nacional de
Emisiones de Mexico, 2016 (Secretarfa de Medio Ambiente y Recursos Naturales (SEMARNAT)),
projected to 2019 as part of the 2019 emissions modeling platform, and then projected to 2020 to
include COVID pandemic effects. 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 and were additionally adjusted for COVID. Only CAPs are covered in the
Mexico point source inventory. The CONFORM dataset was used to apply pandemic adjustments to
emissions in Mexico, except that unlike in Canada, annual emissions as well as monthly temporal profiles
were adjusted. First, monthly emissions totals for the unadjusted 2019 inventory were calculated using
existing temporal profiles. Then, a 2019-to-2020 scaling factor was calculated for each month using data
from the CONFORM dataset, and for each emissions category in the CONFORM dataset (energy,
industry, public and commercial, residential, and transport). These scaling factors were applied to the
2019 monthly Mexico emissions, and a new annual total for 2020 was calculated from the adjusted
monthly totals.
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) as part of their 2020 emission
inventory. 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 2020 emission
inventory. The provided wind erosion emissions were removed. Both the canada_afdust and
canada_ptdust emissions have a COVID-adjusted monthly resolution based on the CONFORM dataset
categories of industry and transport, following a similar process as the canmex_point sector. 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 emission inventory. 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 are based on the 2019ge Mexico nonpoint inventory
at the municipio resolution. The 2019 inventory was based on a projection of 2016 inventories provided
by SEMARNAT. COVID pandemic adjustments were not applied to the agricultural sector.
2.7.4 Surface-level Oil and Gas Sources in Canada (canada_og2D)
Canadian point source inventories provided by ECCC for the 2020 NEI included oil and gas emissions. 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
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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. These emissions include COVID-adjusted
monthly data based on the CONFORM dataset industry sector.
2.7.5 Nonpoint and Nonroad Sources in Canada and Mexico (canmex_area)
ECCC provided year 2020 Canada province, and in some cases sub-province, resolution emissions from
for nonpoint and nonroad sources (canmex_area). The nonroad sources were monthly while the
nonpoint and rail emissions were annual. Annual emissions provided by ECCC already reflected
pandemic effects, but monthly distributions of emissions did not. Following a similar process as the
canmex_point sector, monthly emissions in Canada were redistributed using data from the CONFORM
dataset to reflect pandemic effects. The CONFORM categories used for the Canada monthly COVID
adjustments were energy, industry, public and commercial, residential, and transport.
For Mexico, 2019ge Mexico nonpoint and nonroad inventories at the municipio resolution (which were
based on a projection of 2016 inventories provided by SEMARNAT) were projected to 2020 to include
COVID pandemic effects using a process similar to the one described for the canmex_point sector. The
CONFORM categories used for the projection and monthly distribution included: industry, public and
commercial, residential, and transport.
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. Emissions for Canada are new for 2020. In Canada, COVID impacts were applied to the
monthly profiles (not to the annual totals) using the CONFORM dataset emissions from the transport
category.
For Mexico onroad emissions, a version of the MOVES model for Mexico was run that provided the same
VOC HAPs and speciated VOCs as for the U.S. MOVES model (ERG, 2016a). This includes NBAFM plus
several other VOC HAPs such as toluene, xylene, ethylbenzene and others. Except for VOC HAPs that are
part of the speciation, no other HAPs are included in the Mexico onroad inventory (such as particulate
HAPs nor diesel particulate matter). Emissions from MOVES-Mexico for the year 2020 did not include
any COVID pandemic effects, so monthly and annual emissions were adjusted using the monthly
CONFORM adjustment factors for Mexico transport.
2.7.7 Fires in Canada and Mexico (ptfire_othna)
Annual 2020 wildland fire emissions for Mexico, Canada, Central America, and Caribbean nations are
included in the ptfire_othna sector. Canadian fires from May-December were provided by ECCC and are
based on their Firework system (https://weather.gc.ca/firework/). Canadian fires for the non-summer
months along with fires in Mexico, Central America, and the Caribbean, were developed from the Fire
Inventory from NCAR (FINN) v2.5 daily fire emissions for 2020 (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.
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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.
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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 4.9 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
78
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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
Annual
cmv_clc2
Point
Yes
hourly
in-line
cmv_c3
Point
Yes
hourly
in-line
monpt
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
79
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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.
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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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b) 9AK1
c) 3HI1
HI3km domain
x,y origin: -391500. -346500
cols, rows: 225,201
d) 3PR1
e)
PR3km domain
x,y origin: -274500.-202500
cols, rows: 150,150
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3.2 Chemical Speciation
Chemical speciation involves the process of translating emissions from the inventory into the chemical
mechanism-specific "model species" needed by an air quality model. Using the CB6R5_AE7 chemical
mechanism as an example, which is the mechanism utilized by the 2020 NEI modeling platform, 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). This chemical mechanism is an updated version of
the CB6R3_AE7 chemical mechanism and features new reaction rates for some chemical reactions
(Yarwood et al., 2020). CMAQ's Aerosol Module version 7 (AE7) is an updated version of the AE6 aerosol
module, with alpha-pinene made an explicit emitted species. 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
S02
Sulfur dioxide
S02
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
nh3
NH3_FERT
Ammonia from fertilizer
VOC
AACD
Acetic acid
VOC
ACET
Acetone
VOC
ALD2
Acetaldehyde
VOC
ALDX
Propionaldehyde and higher aldehydes
VOC
APIN
Alpha pinene
VOC
BENZ
Benzene
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)
83
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Inventory Pollutant
Model Species
Model species description
voc
ISOP
Isoprene
voc
ISPD
Isoprene Product
voc
IVOC
Intermediate volatility organic compounds
voc
KET
Ketone Groups
voc
MEOH
Methanol
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 1110 microns
PM2.5
PEC
Particulate elemental carbon 11 2.5 microns
PM2.5
PN03
Particulate nitrate 0 2.5 microns
PM2.5
POC
Particulate organic carbon (carbon only) 11 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
84
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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 study 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 The profile for grass fires was updated to profile 95809.
o The profile for hydrogen boilers was updated to a gas combustion profile,
o Assignments for new PM2.5 SCCs in the 2020 point and nonpoint inventories were
included.
• For VOC:
o The profile for wildfires and prescribed fires was updated to profile 95861.
o Assignments for new VOC SCCs in the 2020 point and nonpoint inventories were included
(e.g., agricultural silage and asphalt paving),
o Several point and nonpoint SCCs which were previously assigned the overall average
profile were reassigned to more appropriate profiles,
o Speciation is now performed outside of MOVES
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)
ptfi re-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 if they are explicit in CB6R3_AE7. 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 functionality within SMOKE is
leveraged that allows profiles to be "blended" at the county/SCC-level using proportions included in the
input file. These variable VOC speciation methods are 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
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sources of emissions (e.g., varying proportions of emissions from associated gas, condensate tanks,
crude oil tanks, dehydrators, liquids unloading and well completions). The Nonpoint Oil and Gas
Emissions Estimation Tool generates an intermediate file that provides SCC and county-specific
emissions proportions, which are subsequently incorporated into the modeling platform.
For onroad and nonroad mobile sources, historically the speciation of total organic gas and particulate
matter emissions has been done by MOVES. However, this is now largely done outside of MOVES as a
post-processing step. This has the advanges of making MOVES simpler and faster to run, and making it
easier to change or update chemical mechanisms and speciation profiles used in the emissions modeling
process. Some speciation is still done inside MOVES for "integrated species" - species of gases and
particulate matter which are calculated directly by MOVES. 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. There are fewer PM integrated
species, such as elemental carbon (EC), sulfate, organic carbon, and non-carbon organic matter but the
concept is the same. The remaining unspeciated particulate mass is called Residual PM and can also
speciated outside MOVES, although this feature was not used in this 2020 platform.
In MOVES, speciation profiles for both gaseous and PM emissions 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.
For this platform, MOVES runs were performed in inventory mode10 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, so that speciation profiles can be accurately assigned. After this
step, emissions were normalized and aggregated to calculate the percentage of total NonHAPTOG and
Residual PM 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 then used to generate the needed data for preparing
speciation cross-references (GSREFs) for SMOKE from the outputs of the inventory mode runs. Although
they are similar in nature and outcome, the post-processing tools used for onroad and nonroad
emissions output from MOVES are different.
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 output from the MOVES postprocessing
10 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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and the NONHAPTOG GSPRO files generated by the S2S-Tool. Overall, this process allows speciation to
occur outside of MOVES, which better supports processing of onroad emissions for multiple chemical
mechanisms without having to rerun the MOVES model. For further details on speciation methods
involving MOVES can be found in the associated technical reports (EPA-420-R-22-017, EPA-420-R-23-
006).
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
Unlike VOC speciation, PM2.5 speciation does not integrate species from the base inventory. Except for
mobile sources, speciation is performed within SMOKE, using SPECIATE profiles that were post-
processed using the S2S-Tool. In this modeling platform, onroad PM2.5 speciation is performed within
MOVES, meaning that the model generates emissions factor tables that include total PM2.5 and each of
its components (e.g., POC, PEC, PFE, etc.). Nonroad PM2.5 speciation is also performed within MOVES,
but the output is not speciated emissions. Rather, MOVES outputs emissions of PM2.5 for each relevant
speciation profile. 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
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 3-9 provides the NOx speciation profiles used in EPA's modeling platforms. The only
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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 3-9. 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 3-10 and Table 3-11 below.
Table 3-10. 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
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Fuel
SCCs
Profile
Code
Fraction
as S02
Fraction
as Sulfate
Split Factor (Mass
Fraction)
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 3-11. 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 3-12,
summarizes the particle size profiles used for each data category.
Table 3-12. 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
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Source Type
Profile
Pollutant
Fine
Coarse
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 3-13 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.
Table 3-13. 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.
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The temporal factors applied to the inventory were selected using some combination of country, state,
county, SCC, and pollutant. Table 3-14 summarizes the temporal aspects of emissions modeling by
comparing the key approaches used for temporal processing across the sectors. In the table, "Daily
temporal approach" refers to the temporal approach for getting daily emissions from the inventory
using the SMOKE Temporal program. The values given are the values of the SMOKE L_TYPE setting. The
"Merge processing approach" refers to the days used to represent other days in the month for the
merge step. If this is not "all," then the SMOKE merge step runs only for representative days, which
could include holidays as indicated by the right-most column. The values given are those used for the
SMOKE M_TYPE setting (see below for more information).
Table 3-14. 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
week
week
Yes
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
Annual
Yes
met-based
All
Yes
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
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Platform sector short
name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process
holidays as
separate days
ptfire_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"
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, 2020, which is intended to mitigate the effects of initial condition concentrations. The ramp-
up period was 10 days (December 22-31, 2019). For all anthropogenic sectors, emissions from
December 2020 were used to fill in surrogate emissions for the end of December 2019. For biogenic
emissions, December 2019 emissions were computed using year 2019 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
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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
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).
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Figure 3-3. Eliminating unmeasured spikes in CEMS data
2017 January Unit 469_5
2000
1800
1600
^ 1400
2 1200
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400
200
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The region, fuel, and type (peaking or non-peaking) must be identified for each input EGU with CEMS
data so the data can be used to generate profiles. The identification of peaking units was done using
hourly heat input data from the 2020 base year and the two previous years (2018 and 2019). The heat
input was summed for each year. Equation 1 shows how the annual heat input value is converted from
heat units (BTU/year) to power units (MW) using the NEEDS v6 derived unit-level heat rate (BTU/kWh).
In equation 2 a capacity factor is calculated by dividing the annual unit MW value by the NEEDS v6 unit
capacity value (MW) multiplied by the hours in the year. A peaking unit was defined as any unit that had
a maximum capacity factor of less than 0.2 for every year (2018, 2019, and 2020) and a 3-year average
capacity factor of less than 0.1.
Equation 1. Annual unit power output
V8760 Hourly HI /mw\
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_ „ Annual Unit Output (MW)
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NEEDS
Unit Capacity ^^*8760 (h)
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
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unique profiles available based on 8 regions, 4 fuels, and 2 for peaking unit status (peaking and non-
peaking).
Figure 3-4. Regions used to Compute Temporal non-CEMS EGU Temporal Profiles
The daily and diurnal profiles were calculated for each region, fuel, and peaking type group from the
year 2020 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
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0.010 -
0.005 -
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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
2020 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 2020-specific temporal profiles for all airports other than
Alaska seaplanes (which are not in the CMAQ modeling domain). Hourly airport operations data were
obtained from the Aviation System Performance Metrics (ASPM) Airport Analysis website
(https://aspm.faa.gov/apm/svs/AnalysisAP.asp). A report of 2020 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
2020 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 2020 was generated. Day-of-month profiles were derived directly from the daily
airport operations report and examples are shown in Figure 3-8 while Figure 3-9 shows the pre-
pandemic day of week profile. The prepandemic annual-to-month profile is shown in Figure 3-10. The
2020 airport data were summed to crate the example annual-to-month temporal profiles shown in
Figure 3-11.
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 3-12. These
were assigned based on the facility ID.
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Figure 3-7. 2020 Airport Diurnal Profiles for PHX and state of Texas
hour
v
2020 FAA State Diurnal Profile: TX default
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Figure 3-8. 2020 Wisconsin month-to-day profile for airport emissions
March 2020 FAA State Daily Profile: Wl default
Figure 3-9. Prepandemic weekly profile for airport emissions
Weekly Airport Profile
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0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
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4?
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Figure 3-10. Pre-pandemic monthly profile for airport emissions
Pre-2020 Monthly Airport Profile
0.06
0.04
0.02
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 3-11. 2020 Monthly airport profiles for ATL and state of Maryland
2020 FAA Airport Monthly Profile: ATL
2020 FAA State Monthly Profile: MD default
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Figure 3-12. 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 3-13 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 3-13. Example of RWC temporal allocation using a 50 versus 60 °F threshold
For the 2020 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
3-14. 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 3-14. Example of Annual-to-day temporal pattern of recreational wood burning emissions
The diurnal profile used for most RWC sources (see Figure 3-15) 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 3-15. RWC diurnal temporal profile
Comparison of RWC diurnal 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 3-16, 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 3-17. 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 3-16. Data used to produce a diurnal profile for hydronic heaters
Figure 3-17. 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<-1380V] * 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
3-18.
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 3-19 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 3-18. Examples of livestock temporal profiles in several parts of the country
Tulare County, CA Duplin County, NC
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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Figure 3-19. Example of animal NH3 emissions temporal allocation approach (daily total emissions)
12.0
10.0
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-old
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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 2020 NEI EPA purchased county-level telematics data
from StreetLight for characterization of vehicle speed profiles and VMT temporal distributions for 2020.
Temporal profiles for speeds by road type were obtained by month, day of week, and hour. Vehicle
types included personal, commercial medium-duty, and commercial heavy-duty. This section will discuss
both the meteorological influence and the development of the temporal profiles for this platform.
The "inventories" referred to in Table 3-14 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
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 3-20 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.
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Figure 3-20. Example temporal variability of VMT compared to onroad NOx emissions
40
35
30
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VMT NOX (tons)
25
20
15
10
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 and hour-of-day temporal profiles for VMT were developed for use in the 2020 NEI using
data acquired from StreetLight. Data were provided for three vehicle categories: passenger vehicles
(11/21/31), commercial trucks (32/52), and combination trucks (53/61/62). StreetLight data did not
cover buses, refuse trucks, or motor homes, so those vehicle types were mapped to other vehicle types
as follows: 1) other/transit buses were mapped to commercial trucks; 2) Motor homes were mapped to
passenger vehicles for day-of-week and commercial trucks for hour-of-day; 3) School buses and refuse
trucks were mapped to commercial trucks. In addition to temporal profiles, StreetLight data were also
used to develop the hourly speed distributions (SPDIST) used by SMOKE-MOVES.
The StreetLight dataset includes temporal profiles for individual counties. Temporal profiles also vary by
each of the MOVES road types, and there are distinct hour-of-day profiles for each day of the week.
Plots of hour-of-day profiles for all vehicles and road types in Fulton County, GA, are shown in Figure
3-21. Separate plots are shown for Monday, Saturday, and Sunday in January 2020, and each line
corresponds to a particular MOVES road type (i.e., road type 2 = rural restricted, 3 = rural unrestricted, 4
= urban restricted, and 5 = urban unrestricted) and vehicle type (as described in the previous
paragraph). In the pre-pandemic profiles shown in this figure, there are bimodal peaks for light-duty
vehicles on Monday, but there is only a single peak on the weekend days.
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Figure 3-21. Sample ortroad diurnal profiles for Fulton County, GA
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€
13121 SU ml 11 2 13121_SU_m1 11 3 13121 SU ml 11 4
13121 SU m1~31 2 13121 SU m1~31 3 - 13121~SU mf31~4
13121 SU ml 52 2 13121 SU ml 52 3 13121 SU ml 52 4
13121_SU_m1_61_2 13121_SU_m1_61_3 13121_SU_m1_61_4
label
13121_SU_m1_11_5 13121_SU_m1_21_2 13121 SU m1_21 3 13121_SU_m1_21_4 13121_SU_m1_21_5
13121 SU~m1 31 5 13121~SU~m1 32 2 13121~SU~m1 32 3 13121 SU ml 32~4 13121~SU~m1 32~5
13121 SU ml 52 5 13121 SU ml 53 2 13121 SU ml 53 3 13121 SU ml 53 4 13121 SU ml 53 5
13121 SU_m1_61_5 13121_SU_m1_62_2 13121_SU m1_62_3 13121_SU_m1_62_4 13121_SU_m1_62_5
115
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State/local-provided data for the 2020 NEI were accepted for use in the 2020 NEI if they were deemed
to be at least as credible as the StreetLight data (i.e., reflected the effects of COVID). The 2020 NEI TSD
includes more details on which data were used for which counties. In areas of the contiguous United
States where state/local-provided data were not provided or deemed unacceptable, the StreetLight
temporal profiles were used, including in California. 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 EPA Defaults were adopted for the NEI
universally due to unique activity patterns by month during 2020.
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 3-22 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.
116
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Figure 3-22. Example Nonroad Day-of-week Temporal Profiles
Day of Week Profiles
0.24
0.22
0.2
0.1S
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
monda/ Tuesday Wednesday Thursday fnday Saturday Sunday
9 18 "19
Figure 3-23 shows the previously existing temporal profiles 26 and 27 along with newer temporal
profiles (25a and 26a) which have lower emissions overnight. In this platform, construction sources use
profile 26a. Commercial lawn and garden and agriculture sources use the profiles 26a and 25a,
respectively. Residental lawn and garden sources use profile 27.
Figure 3-23. Example Nonroad Diurnal Temporal Profiles
Hour of Day Profiles
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,
117
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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.
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
3-24 (McCarty et al., 2009). This puts most of the emissions during the work-day and suppresses the
emissions during the middle of the night.
118
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Figure 3-24. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
Industrial processes that are not likely to shut down on Sundays, such as those at cement plants, use
profiles that include emissions on Sundays, while those that would shut down on Sundays use profiles
that reflect Sunday shutdowns,
For the ptfire sectors, the inventories are in the daily point fire format FF10 PTDAY, so temporal profiles
are only used to go from day-specific to hourly emissions. Separate hourly profiles for prescribed and
wildfires were used. For ptfire, state-specific hourly profiles were used, with distinct profiles for
prescribed fires and wildfires. Figure 3-25 below shows the profiles used for each state for the platform.
The wildfire diurnal profiles are similar but vary according to the average meteorological conditions in
each state. For all agricultural burning, the diurnal temporal profile used 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 week for all
agricultural burning emissions in all states.
Figure 3-25. Prescribed and Wildfire diurnal temporal profiles
119
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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_2020_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 100 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.
Table 3-15 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 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)
Onroad surrogates are based on average annual daily traffic counts (AADT) from the highway monitoring
performance system (HPMS).
U.S. Surrogate updates for this platform include:
County boundaries used for all surrogates were updated to use the 2020 TIGER boundaries.
Oil and gas surrogates were updated to represent 2020.
ACS-based surrogates were updated to use the 2020 ACS.
Updated surrogates for residential wood combustion were developed based on ACS data.
NLCD-based surrogates were updated to 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.
New surrogates for fuel stations, asphalt surfaces, and unpaved roads were created using data
from the OpenStreetMap database.
Gravel and lead mines were split out to their own surrogates from the more general United
States Geological Survey mining surrogate.
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
120
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original Surrogate Tool are available at https://www.cmascenter.org/sa-
tools/documentation/4.2/SurrogateToolUserGuide 4 2.pdf. and the tool and documentation for the
Surrogate Tools DB is available from https://www.cmascenter.org/surrogate tools db/. The Shapefiles
used to develop the US surrogates along with the attributes and filters used are shown in Table 3-16.
Table 3-15. U.S. Surrogates available for the 2020 modeling platforms
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2)
672
Gas production - oil wells
100
Population
674
Unconventional Well Completion Counts
110
Housing
676
Well count - all producing
135
Detatched Housing
677
Well count - all exploratory
136
Single and Dual Unit Housing
678
Completions at Gas Wells
150
Residential Heating - Natural Gas
679
Completions at CBM Wells
170
Residential Heating - Distillate Oil
681
Spud Count - Oil Wells
180
Residential Heating-Coal
683
Produced Water at All Wells
190
Residential Heating - LP Gas
6831
Produced water at CBM wells
205
Extended Idle Locations
6832
Produced water at gas wells
239
Total Road AADT
6833
Produced water at oil wells
240
Total Road Miles
685
Completions at Oil Wells
242
All Restricted AADT
686
Completions - all wells
244
All Unrestricted AADT
687
Feet Drilled at All Wells
258
Intercity Bus Terminals
689
Gas Produced - Total
259
Transit Bus Terminals
691
Well Counts - CBM Wells
261
NTAD Total Railroad Density
692
Spud Count - All Wells
271
NTAD Class 12 3 Railroad Density
693
Well Count - All Wells
300
NLCD Low Intensity Development
694
Oil Production at Oil Wells
304
NLCD Open + Low
695
Well Count - Oil Wells
305
NLCD Low + Med
696
Gas Production at Gas Wells
306
NLCD Med + High
697
Oil production - gas wells
307
NLCD All Development
698
Well Count - Gas Wells
308
NLCD Low + Med + High
699
Gas Production at CBM Wells
309
NLCD Open + Low + Med
711
Airport Areas
310
NLCD Total Agriculture
801
Port Areas
319
NLCD Crop Land
850
Golf Courses
320
NLCD Forest Land
860
Mines
321
NLCD Recreational Land
861
Sand and Gravel Mines
340
NLCD Land
862
Lead Mines
350
NLCD Water
863
Crushed Stone Mines
401
FAO 2010 Cattle
900
OSM Fuel
402
FAO 2010 Pig
901
OSM Asphalt Surfaces
403
FAO 2010 Chicken
902
OSM Unpaved Roads
404
FAO 2010 Goat
4011
FAO 2010 Large Cattle Operations
405
FAO 2010 Horse
4012
NPDES 2020 Beef Cattle
406
FAO 2010 Sheep
4013
NPDES 2020 Dairy Cattle
508
Public Schools
4021
NPDES 2020 Swine
650
Refineries and Tank Farms
4031
NPDES 2020 Chicken
670
Spud Count - CBM Wells
4041
NPDES 2020 Goat
671
Spud Count - Gas Wells
4071
NPDES2020 Turkey
121
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Table 3-16. 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
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
('02','03','04','05')
240
Total Road Miles
hpms2017_v3_04052020
NONE
moves2014 IN
('02','03','04','05')
242
All Restricted AADT
hpms2017_v3_04052020
aadt
moves2014 IN
('02704')
244
All Unrestricted AADT
hpms2017_v3_04052020
aadt
moves2014 IN
('03','05')
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 148 20210604 500m II
NONE
GRIDCODE=22
304
NLCD Open + Low
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE IN
(21,22)
305
NLCD Low + Med
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE IN
(22,23)
306
NLCD Med + High
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE IN
(23,24)
307
NLCD All Development
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE IN
(21,22,23,24)
308
NLCD Low + Med + High
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE IN
(22,23,24)
309
NLCD Open + Low + Med
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE IN
(21,22,23)
310
NLCD Total Agriculture
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE IN
(81,82)
318
NLCD Pasture Land
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE=81
319
NLCD Crop Land
nlcd_2019_land_cover_l48_20210604_500m_ll
NONE
GRIDCODE=82
122
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Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
320
NLCD Forest Land
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE IN
(41,42,43)
321
NLCD Recreational Land
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE IN
(21,31,41,42,43,52,
71)
340
NLCD Land
nlcd 2019 land cover 148 20210604 500m II
NONE
GRIDCODE != 11
350
NLCD Water
nlcd 2019 land cover 148 20210604 500m 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_masked
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
fa o_P ig_2010_Da_n 1 cd p roj_m asked
DN
4021
NPDES 2020 Swine
livestock_npdes_state_permits_subset
Population
Animal = 'Swine'
403
FAO 2010 Chicken
fao_Chicken_2010_Da_nlcdproj_masked
DN
4031
NPDES 2020 Chicken
livestock_npdes_state_permits_subset
Population
Animal = 'Chicken'
404
FAO 2010 Goat
fao_Goat_2010_Da_nlcdproj_masked
DN
4041
NPDES 2020 Goat
livestock_npdes_state_permits_subset
Population
Animal = 'Goat'
405
FAO 2010 Horse
fao_Horse_2010_Da_nlcdproj_masked
DN
406
FAO 2010 Sheep
fao_Sheep_2010_Da_nlcdproj_masked
DN
4071
NPDES2020 Turkey
livestock_npdes_state_permits_subset
Population
Animal = 'Turkey'
650
Refineries and Tank
Farms
eia 2015 us oil
NONE
670
Spud Count - CBM Wells
SPUD CBM CONUS 2020
ACTIVITY
671
Spud Count - Gas Wells
SPUD GAS CONUS 2020
ACTIVITY
672
Gas Production at Oil
Wells
ASSOCIATED GAS PRODUCTION CONUS 2020
ACTIVITY
673
Oil Production at CBM
Wells
CONDENSATE_CBM_PRODUCTION_CONUS_202
0
ACTIVITY
674
Unconventional Well
Completion Counts
COMPLETIONS_UNCONVENTIONAL_CONUS_20
20
ACTIVITY
676
Well Count - All
Producing
TOTAL PROD WELL CONUS 2020
ACTIVITY
677
Well Count - All
Exploratory
TOTAL EXPL WELL CONUS 2020
ACTIVITY
678
Completions at Gas
Wells
COMPLETIONS GAS CONUS 2020
ACTIVITY
679
Completions at CBM
Wells
COMPLETIONS CBM CONUS 2020
ACTIVITY
681
Spud Count - Oil Wells
SPUD OIL CONUS 2020
ACTIVITY
683
Produced Water at All
Wells
PRODUCED WATER ALL CONUS 2020
ACTIVITY
6831
Produced Water at CBM
Wells
PRODUCED WATER CBM CONUS 2020
ACTIVITY
6832
Produced Water at Gas
Wells
PRODUCED_WATER_GAS_CONUS_2020
ACTIVITY
123
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Code
Surrogate
Weight Shapefile
Weight
Attribute
Filter Function
6833
Produced Water at Oil
Wells
PRODUCED WATER OIL CONUS 2020
ACTIVITY
685
Completions at Oil Wells
COMPLETIONS OIL CONUS 2020
ACTIVITY
686
Completions at All Wells
COMPLETIONS ALL CONUS 2020
ACTIVITY
687
Feet Drilled at All Wells
FEET DRILLED CONUS 2020
ACTIVITY
689
Gas Produced - Total
TOTAL GAS PRODUCTION CONUS 2020
ACTIVITY
691
Well Counts - CBM Wells
CBM WELLS CONUS 2020
ACTIVITY
692
Spud Count - All Wells
SPUD ALL CONUS 2020
ACTIVITY
693
Well Count - All Wells
TOTAL WELL CONUS 2020
ACTIVITY
694
Oil Production at Oil
Wells
OIL PRODUCTION CONUS 2020
ACTIVITY
695
Well Count - Oil Wells
OIL WELLS CONUS 2020
ACTIVITY
696
Gas Production at Gas
Wells
GAS PRODUCTION CONUS 2020
ACTIVITY
697
Oil Production at Gas
Wells
CONDENSATE_GAS_PRODUCTION_CONUS_202
0
ACTIVITY
698
Well Count - Gas Wells
GAS WELLS CONUS 2020
ACTIVITY
699
Gas Production at CBM
Wells
CBM PRODUCTION CONUS 2020
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-Gravel1
862
Lead Mines
usgs_mrds_active_mines
NONE
CAT-Lead'
863
Crushed Stone Mines
usgs_mrds_active_mines
NONE
CAT-Stone1
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 3-17. 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
county were from a secondary, tertiary or quarternary surrogate and any reports will assign the
emissions in gapfilled counties to the primary surrogate.
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Table 3-17. 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
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
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
125
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SURROGATE
CODE
SURROGATE
SECONDARY
SURROGATE
TERTIARY
SURROGATE
QUARTERNARY
SURROGATE
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
NPDES2020 Turkey
NLCD Total Agriculture
NLCD Open + Low
650
Refineries and Tank Farms
NLCD Low + Med
Population
NLCD Land
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
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
126
-------
SURROGATE
CODE
SURROGATE
SECONDARY
SURROGATE
TERTIARY
SURROGATE
QUARTERNARY
SURROGATE
691
Well Counts - CBM Wells
Completions at CBM
Wells
Well Count - All
Wells
NLCD Open + Low
692
Spud Count - All Wells
Completions at All
Wells
Well Count - All
Wells
NLCD Open + Low
693
Well Count - All Wells
NLCD Open + Low
694
Oil Production at Oil Wells
Completions at Oil
Wells
Well Count - All
Wells
NLCD Open + Low
695
Well Count - Oil Wells
Completions at Oil
Wells
Well Count - All
Wells
NLCD Open + Low
696
Gas Production at Gas Wells
Completions at Gas
Wells
Well Count - All
Wells
NLCD Open + Low
697
Oil Production at Gas Wells
Well Count - Gas Wells
Well Count - All
Wells
NLCD Open + Low
698
Well Count - Gas Wells
Completions at Gas
Wells
Well Count - All
Wells
NLCD Open + Low
699
Gas Production at CBM Wells
Well Counts - CBM
Wells
Well Count - All
Wells
NLCD Open + Low
711
Airport Areas
Population
NLCD Land
801
Port Areas
NLCD Water
850
Golf Courses
Housing
Population
NLCD Land
860
Mines
NLCD Open + Low
NLCD Land
861
Sand and Gravel Mines
Mines
NLCD Open + Low
NLCD Land
862
Lead Mines
Mines
NLCD Open + Low
NLCD Land
863
Crushed Stone Mines
Mines
NLCD Open + Low
NLCD Land
900
OSM Fuel
Total Road AADT
Total Road Miles
901
OSM Asphalt Surfaces
NLCD All Development
902
OSM Unpaved Roads
NLCD Open + Low
For the onroad sector, the on-network (RPD) emissions were spatially allocated differently from other
off-network processes (i.e. RPV, RPP, RPHO, RPS, RPH). Surrogates for on-network processes are based
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 3-18. 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 3-18. 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
127
-------
Source type
Source Type name
Surrogate ID
Description
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 3-19 using 2020 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 2020.
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, 2021). 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, numbered 670 through 699 and also 6831, 6832, and 6833, were gapfilled using
fallback surrogates. For each surrogate, the last two fallbacks were surrogate 693 (Well Count - All
Wells) and 304 (NLCD Open + Low). Where appropriate, other surrogates were also parts of the
gapfilling procedure. For example, surrogate 670 (Spud Count - CBM Wells) was first gapfilled with 692
(Spud Count - All Wells), and then 693 and finally 304. All gapfilling was performed with the Surrogate
Tool.
Table 3-19. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
670
Spud Count - CBM Wells
671
Spud Count - Gas Wells
672
Gas Production at Oil Wells
673
Oil Production at CBM Wells
128
-------
Surrogate Code
Surrogate Description
674
Unconventional Well Completion Counts
676
Well Count - All Producing
677
Well Count - All Exploratory
678
Completions at Gas Wells
679
Completions at CBM Wells
681
Spud Count - Oil Wells
683
Produced Water at All Wells
685
Completions at Oil Wells
686
Completions at All Wells
687
Feet Drilled at All Wells
689
Gas Produced - Total
691
Well Counts - CBM Wells
692
Spud Count - All Wells
693
Well Count - All Wells
694
Oil Production at Oil Wells
695
Well Count - Oil Wells
696
Gas Production at Gas Wells
697
Oil Production at Gas Wells
698
Well Count - Gas Wells
699
Gas Production at CBM Wells
6831
Produced water at CBM wells
6832
Produced water at gas wells
6833
Produced water at oil wells
Table 3-20 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by sector assigned to each
spatial surrogate.
Table 3-20. Selected 2020 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
333,425
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
960,028
0
0
afdust
4012
NPDES 2020 Beef Cattle
0
0
191,878
0
0
afdust
4013
NPDES 2020 Dairy Cattle
0
0
15,033
0
0
afdust
4021
NPDES 2020 Swine
0
0
658
0
0
129
-------
i/oc
0
0
0
,558
,539
,170
,786
,096
,157
,538
,640
36
,946
,680
,086
2
,435
,536
,591
,074
,432
,283
,342
,691
440
292
44
,120
367
,351
,354
,993
,098
341
,964
,724
,364
,321
,408
,114
,069
,532
ID
Description
NH3
NOX
PM2 5
S02
4031
NPDES 2020 Chicken
5,069
4071
NPDES2020 Turkey
0
1,959
310
NLCD Total Agriculture
1,832,594
0
405
FAO 2010 Horse
31,969
406
FAO 2010 Sheep
19,235
4012
NPDES 2020 Beef Cattle
702,119
4013
NPDES 2020 Dairy Cattle
572,321
4021
NPDES 2020 Swine
838,696
4031
NPDES 2020 Chicken
426,996
4041
NPDES 2020 Goat
19,231
4071
NPDES2020 Turkey
83,001
100
Population
454
0
0
0
135
Detached Housing
0
16,359
81,108
2,724
150
Residential Heating - Natural Gas
44,524
214,626
2,669
1,436
170
Residential Heating - Distillate Oil
1,499
25,521
3,165
624
180
Residential Heating-Coal
0
190
Residential Heating - LP Gas
127
36,460
150
164
239
Total Road AADT
0
244
All Unrestricted AADT
271
NTAD Class 12 3 Railroad Density
0
0
0
300
NLCD Low Intensity Development
2,860
3,417
17,009
400
306
NLCD Med + High
17,840
251,201
383,854
85,559
307
NLCD All Development
76,463
28,172
126,918
10,917
308
NLCD Low + Med + High
961
162,993
18,656
5,676
310
NLCD Total Agriculture
517
311
504
31
319
NLCD Crop Land
95
70
320
NLCD Forest Land
11
31
650
Refineries and Tank Farms
711
Airport Areas
801
Port Areas
900
OSM Fuel
4011
FAO 2010 Large Cattle Operations
0
0
136
Single and Dual Unit Housing
99
14,706
2,913
47
261
NTAD Total Railroad Density
1,664
168
304
NLCD Open + Low
1,695
155
305
NLCD Low + Med
837
1,014
306
NLCD Med + High
366
160,863
9,452
257
307
NLCD All Development
112
29,888
16,088
52
308
NLCD Low + Med + High
585
242,493
20,187
235
309
NLCD Open + Low + Med
133
21,682
1,301
64
310
NLCD Total Agriculture
358
257,080
18,310
166
320
NLCD Forest Land
15
2,439
438
130
-------
i/oc
,202
,398
,875
452
35
,544
,222
,821
489
,225
807
,055
,426
,237
,464
74
2
,474
,896
,686
,524
,727
,334
875
,908
,418
,567
,753
,876
,955
,003
,587
,778
,717
,641
,973
476
,811
,456
,726
,126
440
ID
Description
NH3
NOX
PM2 5
321
NLCD Recreational Land
80
12,898
5,082
350
NLCD Water
203
115,290
4,502
850
Golf Courses
13
2,108
122
860
Mines
2,439
231
670
Spud Count - CBM Wells
0
671
Spud Count - Gas Wells
674
Unconventional Well Completion
Counts
16
23,908
540
678
Completions at Gas Wells
5,343
121
679
Completions at CBM Wells
681
Spud Count - Oil Wells
683
Produced Water at All Wells
41
685
Completions at Oil Wells
217
687
Feet Drilled at All Wells
35,527
733
689
Gas Produced - Total
485
29
691
Well Counts-CBM Wells
19,267
307
692
Spud Count - All Wells
589
34
693
Well Count - All Wells
0
694
Oil Production at Oil Wells
3,060
695
Well Count - Oil Wells
159,345
4,270
696
Gas Production at Gas Wells
42,067
228
697
Oil Production at Gas Wells
261
0
698
Well Count - Gas Wells
281,181
4,185
699
Gas Production at CBM Wells
22
6831
Produced water at CBM wells
6832
Produced water at gas wells
6833
Produced water at oil wells
100
Population
240
Total Road Miles/
306
NLCD Med + High
307
NLCD All Development
308
NLCD Low + Med + High
310
NLCD Total Agriculture
901
OSM Asphalt Surfaces
0
205
Extended Idle Locations
290
33,058
750
242
All Restricted AADT
29,464
783,301
20,867
244
All Unrestricted AADT
54,906
1,215,064
45,715
259
Transit Bus Terminals
42
1,539
37
304
NLCD Open + Low
510
13
306
NLCD Med + High
914
91,100
2,823
307
NLCD All Development
3,519
182,771
7,802
308
NLCD Low + Med + High
179
18,151
535
508
Public Schools
13
1,589
72
131
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
rail
261
NTAD Total Railroad Density
13
22,177
599
16
1,015
rail
271
NTAD Class 12 3 Railroad Density
269
400,799
9,861
336
16,478
rwc
135
Detached Housing
7,054
13,004
132,683
3,635
124,847
rwc
136
Single and Dual Unit Housing
15,681
31,864
315,389
8,383
330,813
3.4.2 Allocation method for airport-related sources in the U.S.
There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground support
equipment, and jet refueling. The modeling platform includes the aircraft and airport ground support
equipment emissions as point sources. For the modeling platform, the EPA used the SMOKE "area-to-
point" approach for only jet refueling in the nonpt sector. The following SCCs use this approach:
2501080050 and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/rocket engine
firing and testing). The ARTOPNT approach is described in detail in the 2002 platform documentation:
http://www3.epa.gov/scram001/reports/Emissions%20TSD%20Voll 02-28-08.pdf. The ARTOPNT file
that lists the nonpoint sources to locate using point data 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 3-21. The
Shapefiles used to compute these surrogates and some configuration information are shown in Table
3-22. 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_2020_platform_US_Can_Mex.xlsx that is posted in the reports folder
for this platform. Most of the surrogates for Mexico are circa 1999 and 2000 and were based on data
obtained from the Sistema Municpal de Bases de Datos (SIMBAD) de INEGI and the Bases de datos del
Censo Economico 1999. The population surrogate for Mexico is based on the 2015 GPW v4 (see
https://sedac.ciesin.columbia.edu/data/collection/gpw-v4/sets/browse). The Shapefiles and some
configuration information used to develop the Mexico surrogates are shown in Table 3-23. The Data
Shapefile for all Mexico surrogates is REP_CRUCES and the Data Attribute is ID_MUN. Most of the CAP
emissions allocated to the Mexico and Canada surrogates are shown in Table 3-24.
Table 3-21. 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
132
-------
Code
Canadian Surrogate Description
Code
Description
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
UN PAVED ROADS AND TRAILS
221
Total Mining
960
TOTBEEF
222
Utilities
961
80110 Broilers
230
Rural Secondary Road Miles
962
80111_Cattle_dairy_and_Heifer
233
Total Land Development
963
80112_Cattle_non-Dairy
240
capped population
964
80113_Laying_hens_and_Pullets
308
Food manufacturing
965
80114 Horses
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
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
SCL12011 Well Drilling (PIRD)
981
Harvest Annual
605
SCL12012 Well Servicing (PIRD)
982
Harvest Perennial
606
SCL12013 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
133
-------
Code
Canadian Surrogate Description
Code
Description
612
SCL12020 Natural Gas Transmission and
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
923
TOTAL INSTITUTIONAL AND
GOVERNEMNT
1265
OFFR RAILWAY
924
Primary Industry
Table 3-22. 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
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
134
-------
Cod
e
Surrogate
Data Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
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
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
135
-------
Cod
Data
Weight
e
Surrogate
Data Shapefile
Attribute
Weight Shapefile
Attribute
Petroleum and coal products
da2006_pop_labour_SimP_
324
manufacturing
prov2006
pruid
MaxOff 100m noLake
PETCOLMANU
Plastics and rubber products
da2006_pop_labour_SimP_
326
manufacturing
prov2006
pruid
MaxOff 100m noLake
PLASTCMANU
Non-metallic mineral product
da2006_pop_labour_SimP_
327
manufacturing
prov2006
pruid
MaxOff 100m noLake
MINERLMANU
da2006_pop_labour_SimP_
331
Primary Metal Manufacturing
prov2006
pruid
MaxOff 100m noLake
METALMANU
loc land UOG2015 CO v3
340
Construction - Oil and Gas
gPr_gda
pruid
Que_NB_NS
350
Water
coast
pruid
CONT42_pop_water_Clip_b
pop
Petroleum product wholesaler-
da2006_pop_labour_SimP_
412
distributors
prov2006
pruid
MaxOff 100m noLake
PETPRWSL
Building material and supplies
da2006_pop_labour_SimP_
416
wholesaler-distributors
prov2006
pruid
MaxOff 100m noLake
BUILDPRWSL
da2006_pop_labour_SimP_
447
Gasoline stations
prov2006
pruid
MaxOff 100m noLake
GASSTOR
clothing and clothing
da2006_pop_labour_SimP_
448
accessories stores
prov2006
pruid
MaxOff 100m noLake
CLOTHSTOR
da2006_pop_labour_SimP_
482
Rail transportation
prov2006
pruid
MaxOff 100m noLake
RAILTRANS
Waste management and
da2006_pop_labour_SimP_
562
remediation services
prov2006
pruid
MaxOff 100m noLake
WASTEMGMT
offroad_osm_airport_locs_s
901
AIRPORT
gcd
CDID
pring2017
Movements
aviation_runways_spring201
902
Military LTO
surg_2017
FAKEFIPS
7
Military
aviation_runways_spring201
903
Commercial LTO
surg_2017
FAKEFIPS
7
Commercial
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
MANASSEM
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
136
-------
Cod
Data
Weight
e
Surrogate
Data Shapefile
Attribute
Weight Shapefile
Attribute
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
TOTSWIN E
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
11 valid
QUANTITY
animal nh3 to agri sic 801
963
80112_Cattle_non-Dairy
gPr_gda
pruid
12 valid
QUANTITY
animal nh3 to agri sic 801
964
80113_Laying_hens_and_Pullets
gPr_gda
pruid
13 valid
QUANTITY
animal nh3 to agri sic 801
965
80114 Horses
gPr_gda
pruid
14 valid
QUANTITY
animal nh3 to agri sic 801
966
80115_S h ee p_a n d_La m b
gPr_gda
pruid
15 valid
QUANTITY
animal nh3 to agri sic 801
967
80116 Swine
gPr_gda
pruid
16 valid
QUANTITY
animal nh3 to agri sic 801
968
80117_Turkeys
gPr_gda
pruid
17 valid
QUANTITY
animal nh3 to agri sic 801
969
80118 Goat
gPr_gda
pruid
18 valid
QUANTITY
animal nh3 to agri sic 801
971
80119 Buffalo
gPr_gda
pruid
19 valid
QUANTITY
animal nh3 to agri sic 801
972
80120_Uama_and_Alpacas
gPr_gda
pruid
20 valid
QUANTITY
animal nh3 to agri sic 801
973
80121 Deer
gPr_gda
pruid
21 valid
QUANTITY
animal nh3 to agri sic 801
974
80122_Elk
gPr_gda
pruid
22_valid
QUANTITY
137
-------
Cod
e
Surrogate
Data Shapefile
Data
Attribute
Weight Shapefile
Weight
Attribute
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
h a rvest_p m 10_Pe re n n i a l_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
606
SCL
12013 Well Testing (PIRD)
gPr_gda
pruid
scll2013 valid
NONE
607
SCL
Pro
12014 Natural Gas
duction (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
lowmedjet_ll
CLASS
MEIT 2280002202 2018
fuel
653
MEITC1C2 Berthed
lowmedjet_ll
CLASS
MEIT 2280002301 2018
fuel
661
MEITC3 Anchored
lowmedjet_ll
CLASS
MEIT 2280003101 2018
fuel
662
MEIT C3 Underway
lowmedjet_ll
CLASS
MEIT 2280003200 2018
fuel
663
MEITC3 Berthed
lowmedjetjl
CLASS
MEIT_2280003301_2018
fuel
138
-------
Table 3-23. Shapefiles and Attributes used to Compute Mexican Spatial Surrogates
Code
SURROGATE
WEIGHT SHAPEFILE
WEIGHT
ATTRIBUTE
10
MEX Population
REPMEX ES HE ATI
P001
11
MEX 2015 Population
gpw-v4-popcount2015-
mexico
GRIDCODE
12
MEX Housing
com ind viv
110 2000
14
MEX Residential Heating - Wood
REPMEX ES HE ATI
HOG LENA
16
MEX Residential Heating - Distillate Oil
REPMEX ES HE ATI
HOGAR PET
18
MEX Residential Heating - Coal
REPMEX ES HE ATI
HOGAR CARB
20
MEX Residential Heating - LP Gas
REPMEX ES HE ATI
HOG GAS
22
MEX Total Road Miles
carretera ESPHE
SHAPE len
24
MEXTotal Railroads Miles
mexico rr
LENGTH
26
MEX Total Agriculture
A_Agricola
HA
28
MEX Forest Land
BOSQUE LAD
320 2000
30
MEX Land Area
REPMEX ES HE ATI
P001
32
MEX Commercial Land
com ind viv
500 2000
34
MEX Industrial Land
com ind viv
505 2000
36
MEX Commercial plus Industrial Land
com ind viv
510 2000
38
MEX Commercial plus Institutional Land
com ind viv
515 2000
40
Residential (RES1-
4)+Comercial+lndustrial+lnstitutional+Government
com ind viv
535 2000
42
MEX Personal Repair (COM3)
REP CRUCES
545 1999
44
Airports Area
mexico air
VALUE
46
MEX Marine Ports
mexico_ports
VALUE
48
Brick Kilns - Mexico
BOSQUE LAD
LAD 2000
50
MEX Border Crossings
hwybdrx
NONE
Table 3-24. 2020 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 2015 Population
0
60,516
330
133
167,796
14
MEX Residential Heating - Wood
0
2,468
6,890
201
18,559
16
MEX Residential Heating - Distillate
Oil
1
31
0
0
1
22
MEXTotal Road Miles
2,130
249,454
8,629
4,749
48,885
24
MEXTotal Railroads Miles
0
21,516
450
204
806
26
MEX Total Agriculture
115,677
20,235
16,414
527
3,658
32
MEX Commercial Land
0
59
1,287
0
21,908
34
MEX Industrial Land
72
1,598
927
5
24,672
36
MEX Commercial plus Industrial Land
5
6,830
324
14
79,869
139
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NOx
PM2.5
SO2
voc
40
MEX Residential (RES1-
4)+Comercial+lndustrial+lnstitutional
+Government
0
13
48
1
16,400
42
MEX Personal Repair (COM3)
0
0
0
0
4,049
44
MEX Airports Area
0
3,805
53
268
1,440
48
MEX Brick Kilns
0
210
4,180
371
102
50
MEX Mobile sources - Border Crossing
3
64
2
0
50
100
CAN Population
698
56
221
16
3,798
101
CAN total dwelling
0
0
0
0
105,422
104
CAN Capped Total Dwelling
321
32,970
2,486
2,030
1,688
106
CAN ALLJNDUST
0
0
543
0
0
113
CAN Forestry and logging
83
627
2,934
15
2,717
200
CAN Urban Primary Road Miles
1,527
75,221
2,659
176
7,124
210
CAN Rural Primary Road Miles
584
40,602
1,405
74
2,880
212
CAN Mining except oil and gas
0
0
1,618
0
0
220
CAN Urban Secondary Road Miles
2,866
119,406
5,355
357
18,967
221
CAN Total Mining
0
0
12,266
0
0
222
CAN Utilities
0
2,562
2,504
32
110
230
CAN Rural Secondary Road Miles
1,545
74,760
2,682
187
7,677
240
CAN Total Road Miles
330
44,970
1,181
38
79,357
308
CAN Food manufacturing
0
0
17,591
0
5,104
321
CAN Wood product manufacturing
517
1,700
578
207
8,374
323
CAN Printing and related support
activities
0
0
0
0
18,212
324
CAN Petroleum and coal products
manufacturing
0
920
1,285
384
5,820
326
CAN Plastics and rubber products
manufacturing
0
0
0
0
21,854
327
CAN Non-metallic mineral product
manufacturing
0
0
6,686
0
0
331
CAN Primary Metal Manufacturing
0
112
3,880
21
45
412
CAN Petroleum product wholesaler-
distributors
0
0
0
0
36,768
448
CAN clothing and clothing accessories
stores
0
0
0
0
177
562
CAN Waste management and
remediation services
2,656
1,259
2,401
2,119
16,006
601
CAN SCL12003 Petroleum Liquids
Transportation (PIRD)
0
0
12
163
6,141
602
CAN SCL12007 Oil Sands In-Situ
Extraction and Processing (PIRD)
0
0
0
0
108
603
CAN SCL12010 Light Medium Crude
Oil Production (PIRD)
0
0
0
0
2
140
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NO*
PM2.5
SO2
voc
604
CAN SCL12011 Well Drilling (PIRD)
0
0
0
563
594
605
CAN SCL12012 Well Servicing (PIRD)
0
0
0
62
65
606
CAN SCL12013 Well Testing (PIRD)
0
0
0
0
0
607
CAN SCL12014 Natural Gas
Production (PIRD)
0
31
1
0
215
608
CAN SCL12015 Natural Gas
Processing (PIRD)
0
0
0
0
0
611
CAN SCL12019 Accidents and
Equipment Failures (PIRD)
0
0
0
0
99,936
612
CAN SCL12020 Natural Gas
Transmission and Storage (PIRD)
1
800
55
11
408
901
CAN Airport
0
99
9
0
10
921
CAN Commercial Fuel Combustion
195
22,375
2,452
449
969
923
CAN TOTAL INSTITUTIONAL AND
GOVERNEMNT
0
0
0
0
14,276
924
CAN Primary Industry
0
0
0
0
31,784
925
CAN Manufacturing and Assembly
0
0
0
0
64,541
926
CAN Distribtution and Retail (no
petroleum)
0
0
0
0
6,633
927
CAN Commercial Services
0
0
0
0
30,243
933
CAN Rail-Passenger
1
3,038
60
1
121
934
CAN Rail-Freight
49
77,610
1,537
43
3,430
935
CAN Rail-Yard
1
4,587
95
1
279
940
CAN Paved Roads New
24,023
946
CAN Construction and Mining
42
2,675
149
257
38
951
CAN Wood Consumption Percentage
1,119
12,431
75,655
1,776
105,563
955
CAN U NPAVED_ROADS_AND_TRAILS
0
0
403,589
0
00
961
CAN 80110_Broilers
12,630
0
115
0
12,787
962
CAN 80111_Cattle_dairy_and_Heifer
57,942
0
276
0
40,516
963
CAN 80112_Cattle_non-Dairy
164,849
0
884
0
42,876
964
CAN 80113_Laying_hens_and_Pullets
9,451
0
40
0
10,596
965
CAN 80114_Horses
2,937
0
19
0
1,321
966
CAN 80115_Sheep_and_Lamb
2,122
0
6
0
170
967
CAN 80116_Swine
59,569
0
824
0
9,949
968
CAN 80117_Turkeys
4,877
0
41
0
4,509
969
CAN 80118_Goat
1,680
0
2
0
135
971
CAN 80119_Buffalo
2,092
0
6
0
517
972
CAN 80120_Llama_and_Alpacas
110
0
0
0
0
973
CAN 80121_Deer
18
0
0
0
0
974
CAN 80122_Elk
18
0
0
0
0
975
CAN 80123_Wild boars
34
0
0
0
0
976
CAN 80124_Rabbit
73
0
0
0
1
141
-------
Code
Mexican or Canadian Surrogate
Description
NH3
NOx
PM2.5
SO2
voc
977
CAN 80125_Mink
284
0
0
0
951
978
CAN 80126_Fox
4
0
0
0
3
981
CAN Harvest_Annual
0
0
24,807
0
0
983
CAN Synthfert_Annual
177,194
3,616
2,117
5,933
132
985
CAN Tillage_Annual
0
0
106,732
0
0
996
CAN urban_area
0
0
3,423
0
0
1251
CAN OFFR_TOTFERT
83
63,804
4,510
57
6,290
1252
CAN OFFR_MINES
1
585
42
1
81
1253
CAN OFFR Other Construction not
Urban
66
38,916
4,649
44
10,239
1254
CAN OFFR Commercial Services
44
16,547
2,478
38
37,831
1255
CAN OFFR Oil Sands Mines
0
0
0
0
0
1256
CAN OFFR Wood industries CANVEC
9
3,343
272
6
922
1257
CAN OFFR Unpaved Roads Rural
23
10,032
626
20
26,879
1258
CAN OFFRJJtilities
7
3,988
205
6
829
1259
CAN OFFR total dwelling
17
6,202
598
14
12,332
1260
CAN OFFR_water
16
4,665
355
24
24,371
1261
CAN OFFR_ALL_INDUST
3
4,781
168
2
842
1262
CAN OFFR Oil and Gas Extraction
1
400
32
0
120
1263
CAN OFFR_ALLROADS
3
1,811
182
2
463
1265
CAN OFFR_CANRAIL
0
65
6
0
12
142
-------
4 Emission Summaries
Tables 4-1 through 4-3 summarize emissions by sector for the 2020 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 2020 platform
(https://gaftp.epa.gov/Air/emismod/2020/).
143
-------
Table 4-1. National by-sector CAP emissions for the 2020 platform, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2_5
S02
VOC
afdust_adj
5,513,981
765,892
airports
324,335
0
81,729
8,295
7,334
8,889
48,680
cmv_clc2
17,242
57
113,213
3,051
2,956
571
3,973
cmv_c3
9,216
29
91,850
1,640
1,508
3,690
4,233
fertilizer
1,401,045
livestock
2,693,568
215,483
nonpt
2,199,000
145,244
739,200
724,647
634,164
107,619
1,007,035
nonroad
11,005,619
1,980
866,081
85,040
79,961
990
977,863
nP_oilgas
621,795
16
571,317
10,541
10,453
135,998
2,583,242
np_solvents
2,586,519
onroad
14,063,910
89,328
2,327,115
188,720
78,626
9,785
1,030,292
ptegu
400,900
21,491
847,682
101,118
86,781
820,839
25,466
ptagfire
664,858
140,954
28,037
102,245
66,604
11,025
107,166
ptfire-rx
7,181,506
114,977
140,674
794,163
681,777
64,751
1,654,719
ptfire-wild
18,664,856
306,009
239,530
1,885,536
1,597,986
135,617
4,399,094
ptnonipm
1,157,963
63,289
769,850
343,959
222,800
443,029
705,590
pt_oilgas
171,082
8,264
330,517
12,668
12,168
35,130
196,102
rail
92,100
282
422,975
10,819
10,459
351
17,492
rwc
2,955,189
22,735
44,869
450,864
448,073
12,019
455,660
beis
3,265,206
980,749
28,254,267
CONUS + beis
62,794,777
5,009,270
8,595,386
10,237,288
4,707,543
1,790,303
44,272,876
Canada ag
495,216
6,567
1,876
124,394
Canada oil and gas 2D
8
318,720
Canada afdust
799,628
154,654
Canada ptdust
2,791
361
Canada area
2,020,228
5,987
321,437
184,241
135,848
14,263
709,347
Canada onroad
1,622,797
6,848
354,849
24,288
13,272
830
115,863
Canada point
1,011,453
18,160
549,975
111,671
41,376
499,692
146,194
Canada fires
654,404
8,746
10,058
118,455
102,005
5,444
215,854
Canada cmv_clc2
2,596
8
16,691
441
428
60
580
Canada cmv_c3
7,160
19
71,623
1,051
967
2,167
3,497
Mexico ag
115,994
66,380
14,465
0
Mexico area
115,014
81
55,083
29,228
16,992
1,586
278,327
Mexico onroad
1,241,148
2,130
311,807
11,557
8,144
4,888
110,159
Mexico point
124,965
949
144,798
39,649
27,670
293,438
29,882
Mexico fires
211,379
3,612
13,079
24,985
21,413
2,000
109,543
Mexico cmv_clc2
118
0
766
20
19
2
32
Mexico cmv_c3
7,375
72
79,149
4,088
3,761
10,888
3,442
Offshore cmv_clc2
3,647
11
23,290
610
591
64
885
Offshore cmv_c3
43,133
254
434,674
14,334
13,187
36,361
20,624
Offshore pt_oilgas
52,008
8
50,096
638
637
463
38,910
Can/Mex/offshore total
7,117,423
658,106
2,437,376
1,440,620
557,665
872,147
2,226,254
144
-------
Table 4-2. National by-sector VOC HAP emissions for the 2020 platform, 12US1 grid (tons/yr)
Sector
Acetaldehyde
Benzene
Formaldehyde
Methanol
Naphthalene
Acrolein
1,3-
Butadiene
airports
1,444
648
4,180
605
766
822
586
cmv_clc2
39
19
170
0
11
7
4
cmv_c3
41
20
181
0
12
8
4
livestock
1,735
454
0
19,917
0
0
0
nonpt
11,615
6,987
7,496
14,587
508
172
1,040
nonroad
9,055
26,370
22,503
1,269
1,523
1,406
4,425
nP_oilgas
4,185
31,140
39,324
2,836
115
2,635
598
np_solvents
61
349
3
15,498
7,820
0
0
onroad
9,481
19,700
12,107
1,671
1,506
870
2,660
ptegu
273
268
2,408
105
21
176
4
ptagfire
6,226
1,455
5,488
0
0
0
651
ptfire-rx
57,005
17,576
105,740
79,170
16,258
22,899
13,230
ptfire-wild
137,604
37,390
249,446
263,230
46,704
42,039
22,146
ptnonipm
4,985
2,740
6,140
49,097
824
825
582
pt_oilgas
2,574
2,156
11,852
1,583
81
1,861
262
rail
1,373
395
3,911
0
48
281
33
rwc
52,229
13,677
36,636
0
7,082
1,989
3,692
beis
362,170
496,628
1,930,590
CONUS + beis
662,096
161,343
1,004,213
2,380,158
83,277
75,990
49,916
Canada ag
1,398
160
0
32,651
0
0
0
Canada oil and gas 2D
0
966
0
0
0
0
0
Canada area
15,975
13,054
13,356
3,976
2,607
0
0
Canada onroad
2,115
5,108
2,923
0
39
0
0
Canada point
1,482
2,032
5,000
10,002
23
0
0
Canada fires
7,257
3,682
14,918
9,417
1,144
0
0
Canada cmv_clc2
6
3
25
0
2
1
1
Canada cmv_c3
34
17
149
0
10
6
4
Mexico area
3,080
4,766
2,271
1,294
402
0
0
Mexico onroad
468
2,674
1,128
480
160
80
397
Mexico point
60
682
2,527
374
11
0
0
Mexico fires
3,686
1,873
7,584
4,782
579
0
0
Mexico cmv_clc2
0
0
1
0
0
0
0
Mexico cmv_c3
34
16
147
0
9
6
3
Offshore cmv_clc2
9
4
38
0
2
2
1
Offshore cmv_c3
202
98
881
0
56
38
21
Offshore pt_oilgas
0
0
0
0
1
0
0
Non-U.S. Total
35,804
35,134
50,947
62,976
5,045
133
426
145
-------
Table 4-3. National by-sector Diesel PM and metal emissions for the 2020 platform, 12US1 grid
(tons/yr)
Sector
Diesel
PMio
Diesel
PM2.5
Chromium
Hex
Arsenic
Cadmium
Nickel
Manganese
Ethylene
Oxide
airports
25
24
--
--
--
--
--
--
cmv_clc2
3,051
2,956
0.00002
0.08
0.70
2.03
0.010
--
cmv_c3
1,639
1,508
0.00001
0.04
0.36
1.04
0.005
--
nonpt
--
--
0.39
8.43
6.07
37.69
12.57
0.99
nonroad
44,626
43,128
0.008
0.74
--
4.58
1.29
--
nP_oilgas
--
--
0.00003
0.01
0.06
0.05
0.03
--
onroad
45,511
41,885
0.07
6.68
--
15.12
34.93
--
ptegu
--
--
4.09
12.64
5.54
66.45
98.93
0.0008
ptnonipm
1,054
998
19.86
27.17
9.85
151.51
505.86
89.78
pt_oilgas
--
--
0.02
0.03
0.29
7.06
2.43
--
rail
10,819
10,459
0.05
11.09
0.0003
41.59
23.82
--
rwc
--
--
--
--
0.11
0.10
0.84
--
Con. U.S. Total
106,725
100,959
24.48
66.90
22.96
327.22
680.72
90.76
Canada
cmv_clc2
441
428
0.000003
0.011
0.10
0.29
0.0014
—
Canada cmv_c3
1,051
967
0.000007
0.03
0.23
0.66
0.003
--
Mexico
cmv_clc2
20
19
0.0000001
0.0005
0.005
0.013
0.00006
—
Mexico cmv_c3
4,088
3,761
0.00003
0.10
0.89
2.58
0.012
--
Offshore
cmv clc2
610
591
0.000004
0.02
0.14
0.41
0.002
—
Offshore cmv_c3
14,334
13,187
0.00010
0.34
3.11
9.06
0.04
--
Table 4-4. Criteria Pollutant emissions in 2020 for Alaska, Hawaii, Puerto Rico, and the U.S. Virgin
Islands (tons/yr)
Sector
Pollutant
AK 2020
HI 2020
PR 2020
VI 2020
afdust_adj
PM10
3,175
5,873
1,510
256
afdust_adj
PM2 5
350
741
224
34
airports
CO
8,616
2,994
1,846
500
airports
NOX
2,793
1,004
333
35
airports
PM10
271
75
35
7
airports
PM2 5
250
69
33
7
airports
S02
304
118
43
6
airports
VOC
2,164
787
346
60
cmv clc2
CO
662
179
133
98
cmv clc2
NH3
2
1
0
0
cmv_clc2
NOX
4,230
1,159
852
633
146
-------
Sector
Pollutant
AK 2020
HI 2020
PR 2020
VI 2020
cmv clc2
PM10
109
30
23
17
cmv clc2
PM2 5
105
29
22
16
cmv clc2
S02
12
2
4
3
cmv clc2
VOC
138
37
28
22
cmv c3
CO
210
57
186
113
cmv c3
NH3
1
0
1
0
cmv c3
NOX
2,389
592
1,867
1,089
cmv c3
PM10
84
11
37
21
cmv c3
PM2 5
77
10
34
19
cmv c3
S02
233
26
87
46
cmv c3
VOC
96
26
85
54
livestock
NH3
21
264
livestock
VOC
2
21
nonpt
CO
41,873
7,412
10,759
338
nonpt
NH3
691
479
415
14
nonpt
NOX
12,445
543
6,033
61
nonpt
PM10
8,698
2,191
3,693
112
nonpt
PM2 5
7,933
2,011
3,140
99
nonpt
S02
854
68
445
5
nonpt
VOC
2,880
8,095
3,717
228
nonroad
CO
40,870
47,700
126,490
4,414
nonroad
NH3
6
8
16
1
nonroad
NOX
1,865
2,818
5,312
267
nonroad
PM10
302
280
704
28
nonroad
PM2 5
283
264
660
27
nonroad
S02
2
3
7
0
nonroad
VOC
6,576
3,635
9,734
385
nP_oilgas
CO
4,592
nP_oilgas
NH3
0
nP_oilgas
NOX
2,419
nP_oilgas
PM10
34
np_oilgas
PM2 5
34
np_oilgas
S02
3,266
np_oilgas
VOC
9,490
np_solvents
VOC
10,384
10,387
22,033
768
onroad
CO
55,147
55,509
83,546
3,160
onroad
NH3
180
263
410
15
onroad
NOX
5,150
5,841
5,950
290
onroad
PM10
398
593
700
28
onroad
PM2 5
187
217
215
10
onroad
S02
11
28
43
2
147
-------
Sector
Pollutant
AK 2020
HI 2020
PR 2020
VI 2020
onroad
voc
3,112
4,684
5,699
238
pt_oilgas
CO
8,174
pt_oilgas
NOX
38,598
pt_oilgas
PM10
1,227
pt_oilgas
PM2 5
391
pt_oilgas
S02
1,474
pt_oilgas
VOC
1,673
ptegu
CO
2,150
1,198
1,784
ptegu
NH3
38
159
0
ptegu
NOX
6,729
15,413
19,925
ptegu
PM10
497
1,236
1,711
ptegu
PM2 5
286
1,096
793
ptegu
S02
1,054
15,959
12,099
ptegu
VOC
180
160
227
ptfire
CO
2,125,080
14,518
1,716
127
ptfire
NH3
34,592
242
30
1
ptfire
NOX
14,287
380
109
11
ptfire
PM10
203,046
1,623
211
14
ptfire
PM2 5
172,073
1,390
159
14
ptfire
S02
11,472
165
13
2
ptfire
VOC
497,265
3,476
953
63
ptnonipm
CO
1,647
413
850
ptnonipm
NH3
25
45
316
ptnonipm
NOX
6,551
2,051
2,952
ptnonipm
PM10
312
336
560
ptnonipm
PM2 5
268
281
271
ptnonipm
S02
1,301
370
2,854
ptnonipm
VOC
590
1,434
231
rail
CO
124
rail
NH3
0
rail
NOX
347
rail
PM10
9
rail
PM2 5
2
rail
S02
0
rail
VOC
18
rwc
CO
201,523
517
2,248
137
rwc
NH3
168
4
26
1
rwc
NOX
4,877
15
40
3
rwc
PM10
37,637
90
357
22
rwc
PM2 5
36,600
89
356
22
rwc
S02
672
1
6
0
148
-------
Sector
Pollutant
AK 2020
HI 2020
PR 2020
VI 2020
rwc
voc
3,680
94
294
20
149
-------
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-23-004
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Agency Research Triangle Park, NC
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