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Technical Support Document (TSD): Preparation of
Emissions Inventories for the 2017 North American
Emissions Modeling Platform
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EPA-454/B-22-002
February 2022
Technical Support Document (TSD) Preparation of Emissions Inventories for the 2017 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)
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TABLE OF CONTENTS
LIST OF TABLES VII
LIST OF FIGURES IX
LIST OF APPENDICES X
ACRONYMS XI
1 INTRODUCTION 14
2 EMISSIONS INVENTORIES AND APPROACHES 16
2.1 Point sources (ptegu, pt_oilgas, ptnonipm, airports) 19
2.1.1 EGUsector (ptegu) 21
2.1.2 Point source oil and gas sector (pt oilgas) 22
2.1.3 Non-IPM sector (ptnonipm) 23
2.1.4 A ircraft and ground support equipment (airports) 23
2.2 Nonpoint sources (afdust, fertilizer, livestock, np_oilgas, rwc, solvents, nonpt) 24
2.2.1 Area fugitive dust sector (afdust) 24
2.2.2 Agricultural Livestock and Fertilizer (ag) 31
2.2.3 Nonpoint Oil and Gas Sector (np oilgas) 35
2.2.4 Residential Wood Combustion (rwc) 35
2.2.5 Nonpoint (nonpt) 36
2.3 Onroad Mobile sources (onroad) 37
2.3.1 Inventory Development using SMOKE-MOVES 38
2.3.2 Onroad Activity Data Development 40
2.3.3 MOVES Emission Factor Table Development 41
2.3.4 Onroad California Inventory Development (onroad ca) 44
2.4 Nonroad Mobile sources (cmv, rail, nonroad) 45
2.4.1 Category 1, Category 2 Commercial Marine Vessels (cmv_clc2) 45
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3) 49
2.4.3 Railway Locomotives (rail) 52
2.4.4 Nonroad Mobile Equipment (nonroad) 62
2.5 Fires (ptfire, ptagfire) 68
2.5.1 Wild and Prescribed Fires (ptfire) 68
2.5.2 Point source Agriculture Fires (ptagfire) 73
2.6 Biogenic Sources (beis) 76
2.7 Sources Outside of the United States 79
2.7.1 Point Sources in Canada and Mexico (othpt) 80
2.7.2 Fugitive Dust Sources in Canada (othafdust, othptdust) 81
2.7.3 Nonpoint and Nonroad Sources in Canada and Mexico (othar) 81
2.7.4 Onroad Sources in Canada and Mexico (onroadcan, onroadjnex) 81
2.7.5 Fires in Canada andMexico (ptfire othna) 81
2.7.6 Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury 82
3 EMISSIONS MODELING 83
3.1 Emissions modeling Overview 83
3.2 Chemical Speciation 87
3.2.1 VOC speciation 94
3.2.1.1 County specific profile combinations 97
3.2.1.2 Additional sector specific considerations for integrating HAP emissions from inventories into speciation 98
3.2.1.3 Oil and gas related speciation profiles 100
3.2.1.4 Mobile source related VOC speciation profiles 101
3.2.2 PM speciation 106
3.2.2.1 Mobile source related PM2.5 speciation profiles 108
3.2.3 NO x speciation 109
3.2.4 Creation of Sulfuric Acid Vapor (SULF) 110
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3.2.5 Speciation of Metals and Mercury Ill
3.3 Temporal Allocation 113
3.3.1 Use ofFFlO format for finer than annual emissions 114
3.3.2 Electric Generating Utility temporal allocation (ptegu) 115
3.3.3 Airport Temporal allocation (airports) 119
3.3.4 Residential Wood Combustion Temporal allocation (rwc) 121
3.3.5 Agricultural Ammonia Temporal Profiles (ag) 125
3.3.6 Oil and gas temporal allocation (np oilgas) 126
3.3.7 Onroadmobile temporal allocation (onroad) 127
3.3.8 Nonroad mobile temporal allocation(nonroad) 131
3.3.9 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire) 133
3.4 Spatial Allocation 135
3.4.1 Spatial Surrogates for U.S. emissions 135
3.4.2 Allocation method for airport-related sources in the U.S. 140
3.4.3 Surrogates for Canada and Mexico emission inventories 141
4 EMISSION SUMMARIES 145
5 REFERENCES 149
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List of Tables
Table 2-1. Platform sectors used in the Emissions Modeling Process 17
Table 2-2. Point source oil and gas sector NAICS Codes 22
Table 2-3. SCCs for the airports sector 23
Table 2-4. Afdust sector SCCs 24
Table 2-5. Total impact of 2017 fugitive dust adjustments to unadjusted inventory 27
Table 2-6. SCCs for the livestock sector 31
Table 2-7. Source of input variables for EPIC 34
Table 2-8. SCCs for the residential wood combustion sector 36
Table 2-9. MOVES vehicle (source) types 38
Table 2-10. Fraction of IHS Vehicle Populations to Retain for 2017 NEI 43
Table 2-11. SCCs for cmv_clc2 sector 46
Table 2-12. Vessel groups in the cmv_clc2 sector 48
Table 2-13. SCCs for cmv_c3 sector 50
Table 2-14. SCCs for the Rail Sector 53
Table 2-15. Class I Railroad Reported Locomotive Fuel Use Statistics for 2017 54
Table 2-16. 2017 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal) 55
Table 2-17. Surface Transportation Board R-l Fuel Use Data - 2017 57
Table 2-18. 2017 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)4 57
Table 2-19. Expenditures and fuel use for commuter rail 59
Table 2-20. Submitted nonroad input tables by agency 66
Table 2-21. Alaska counties/census areas for which nonroad equipment sector-specific emissions were
removed 67
Table 2-22. SCCs included in the ptfire sector for the 2017 inventory 68
Table 2-23. SCCs included in the ptagfire sector 73
Table 2-24. Assumed field size of agricultural fires per state(acres) 75
Table 2-25. Hourly Meteorological variables required by BEIS 3.7 78
Table 3-1. Key emissions modeling steps by sector 84
Table 3-2. Descriptions of the platform grids 86
Table 3-3. Emission model species produced for CB6R3AE7 for CMAQ 87
Table 3-4. Additional HAP Gaseous model species produced for CMAQ multipollutant specifically for
toxics modeling (not used within CB6) 90
Table 3-5. Additional HAP Particulate* model species produced for CMAQ multipollutant modeling 91
Table 3-6. PAH/POM pollutant groups 91
Table 3-7. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM)
for each platform sector 96
Table 3-8. Ethanol percentages by volume by Canadian province 98
Table 3-9. MOVES integrated species in M-profiles 99
Table 3-10. Basin/Region-specific profiles for oil and gas 101
Table 3-11. TOG MOVES-SMOKE Speciation for nonroad emissions used for the 2017 Platform 102
Table 3-12. Select mobile-related VOC profiles 2017 103
Table 3-13. Onroad M-profiles 103
Table 3-14. MOVES process IDs 105
Table 3-15. MOVES Fuel subtype IDs 105
Table 3-16. MOVES regclass IDs 106
Table 3-17. Brake and tire PM2.5 profiles from Schauer (2006) 108
Table 3-18. Nonroad PM2.5 profiles 109
Table 3-19. NOx speciation profiles 110
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Table 3-20. Sulfate split factor computation 110
Table 3-21. SO2 speciation profiles Ill
Table 3-22. Particle size speciation of Metals Ill
Table 3-23. Speciation of Mercury 112
Table 3-24. Temporal settings used for the platform sectors in SMOKE 113
Table 3-25. U.S. Surrogates available for the 2017 modeling platforms 136
Table 3-26. Off-Network Mobile Source Surrogates 137
Table 3-27. Spatial Surrogates for Oil and Gas Sources 138
Table 3-28. Selected 2017 CAP emissions by sector for U.S. Surrogates (short tons in 12US1) 139
Table 3-29. Canadian Spatial Surrogates 141
Table 3-30. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 12US2) 142
Table 4-1. National by-sector CAP emissions for the 2017gb case, 12US1 grid (tons/yr) 146
Table 4-2. National by-sector VOC HAP emissions for the 2017gb case, 12US1 grid (tons/yr) 147
Table 4-3. National by-sector Diesel PM and metal emissions for the 2017gb case, 12US1 grid (tons/yr).. 148
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List of Figures
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and
cumulative 30
Figure 2-2. "Bidi" modeling system used to compute Fertilizer Application emissions 33
Figure 2-3. Representative Counties in 2017 42
Figure 2-4. 2017NEI geographical extent (solid) and U.S. ECA (dashed) 47
Figure 2-5. 2017 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT) 54
Figure 2-6. Class I Railroads in the United States 55
Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States 58
Figure 2-8. Class II and III Railroads in the United States5 59
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains 61
Figure 2-10. Processing flow for fire emission estimates in the 2017 inventory 71
Figure 2-11. Default fire type assignment by state and month where data are only from satellites 72
Figure 2-12. Blue Sky Modeling Framework 73
Figure 2-13. Normbeis3 data flows 78
Figure 2-14. Tmpbeis3 data flow diagram 79
Figure 3-1. Air quality modeling domains 86
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation 96
Figure 3-3. Profiles composited for PM gas combustion related sources 107
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources 107
Figure 3-5. Eliminating unmeasured spikes in CEMS data 115
Figure 3-6. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification 117
Figure 3-7. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type 118
Figure 3-8. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type 118
Figure 3-9. Non-CEMS EGU Temporal Profile Application Counts 119
Figure 3-10. Diurnal Profile for all Airport SCCs 120
Figure 3-11. Weekly profile for all Airport SCCs 120
Figure 3-12. Monthly Profile for all Airport SCCs 121
Figure 3-13. Alaska Seaplane Profile 121
Figure 3-14. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold 123
Figure 3-15. RWC diurnal temporal profile 123
Figure 3-16. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr) 124
Figure 3-17. Day-of-week temporal profiles for OHH and Recreational RWC 125
Figure 3-18. Annual-to-month temporal profiles for OHH and recreational RWC 125
Figure 3-19. Example of animal NH3 emissions temporal allocation approach (daily total emissions) 126
Figure 3-20. Example of temporal variability of NOx emissions 128
Figure 3-21. Sample onroad diurnal profiles for Fulton County, GA 129
Figure 3-22. Methods to Populate Onroad Speeds and Temporal Profiles by Road Type 129
Figure 3-23. Regions for computing Region Average Speeds and Temporal Profiles 130
Figure 3-24. Example of Temporal Profiles for Combination Trucks 131
Figure 3-25. Example Nonroad Day-of-week Temporal Profiles 132
Figure 3-26. Example Nonroad Diurnal Temporal Profiles 132
Figure 3-27. Agricultural burning diurnal temporal profile 134
Figure 3-28. Prescribed and Wildfire diurnal temporal profiles 134
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List of Appendices
Appendix A: CB6 Assignment for New Species
Appendix B: Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE versions 4.5
and later that were used in the 2016 alpha platforms
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
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Acronyms
AADT
Annual average daily traffic
AE6
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
AEO
Annual Energy Outlook
AERMOD
American Meteorological Society/Environmental Protection Agency
Regulatory Model
AIS
Automated Identification System
APU
Auxiliary power unit
BEIS
Biogenic Emissions Inventory System
BELD
Biogenic Emissions Land use Database
BenMAP
Benefits Mapping and Analysis Program
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAMD
EPA's Clean Air Markets Division
CAMx
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CB05
Carbon Bond 2005 chemical mechanism
CB6
Version 6 of the Carbon Bond mechanism
CBM
Coal-bed methane
CDB
County database (input to MOVES model)
CEMS
Continuous Emissions Monitoring System
CISWI
Commercial and Industrial Solid Waste Incinerators
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
CNG
Compressed natural gas
CO
Carbon monoxide
CONUS
Continental United States
CoST
Control Strategy Tool
CRC
Coordinating Research Council
CSAPR
Cross-State Air Pollution Rule
EO, E10, E85
0%, 10% and 85% Ethanol blend gasoline, respectively
ECA
Emissions Control Area
ECCC
Environment and Climate Change Canada
EF
Emission Factor
EGU
Electric Generating Units
EIA
Energy Information Administration
EIS
Emissions Inventory System
EPA
Environmental Protection Agency
EMFAC
EMission FACtor (California's onroad mobile model)
EPIC
Environmental Policy Integrated Climate modeling system
FAA
Federal Aviation Administration
FCCS
Fuel Characteristic Classification System
FEST-C
Fertilizer Emission Scenario Tool for CMAQ
FF10
Flat File 2010
FINN
Fire Inventory from the National Center for Atmospheric Research
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FIPS
Federal Information Processing Standards
FHWA
Federal Highway Administration
HAP
Hazardous Air Pollutant
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
I/M
Inspection and Maintenance
IMO
International Marine Organization
IPM
Integrated Planning Model
LADCO
Lake Michigan Air Directors Consortium
LDV
Light-Duty Vehicle
LPG
Liquified Petroleum Gas
MACT
Maximum Achievable Control Technology
MARAMA
Mid-Atlantic Regional Air Management Association
MATS
Mercury and Air Toxics Standards
MCIP
Meteorology-Chemistry Interface Processor
MMS
Minerals Management Service (now known as the Bureau of Energy
Management, Regulation and Enforcement (BOEMRE)
MOVES
Motor Vehicle Emissions Simulator
MSA
Metropolitan Statistical Area
MTBE
Methyl tert-butyl ether
MWC
Municipal waste combustor
MY
Model year
NAAQS
National Ambient Air Quality Standards
NAICS
North American Industry Classification System
NBAFM
Naphthalene, Benzene, Acetaldehyde, Formaldehyde and Methanol
NCAR
National Center for Atmospheric Research
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NESCAUM
Northeast States for Coordinated Air Use Management
NH3
Ammonia
NLCD
National Land Cover Database
NO A A
National Oceanic and Atmospheric Administration
NONROAD
OTAQ's model for estimation of nonroad mobile emissions
NOx
Nitrogen oxides
NSPS
New Source Performance Standards
OHH
Outdoor Hydronic Heater
ONI
Off network idling
OTAQ
EPA's Office of Transportation and Air Quality
ORIS
Office of Regulatory Information System
OKI)
EPA's Office of Research and Development
OSAT
Ozone Source Apportionment Technology
pcSOA
Potential combustion Secondary Organic Aerosol
PFC
Portable Fuel Container
PM2.5
Particulate matter less than or equal to 2.5 microns
PM10
Particulate matter less than or equal to 10 microns
POA
Primary Organic Aerosol
PPm
Parts per million
ppmv
Parts per million by volume
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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 (emission mode used in SMOKE-MOVES)
RPP
Rate-per-profile (emission mode used in SMOKE-MOVES)
RPV
Rate-per-vehicle (emission mode used in SMOKE-MOVES)
RVP
Reid Vapor Pressure
see
Source Classification Code
SMARTFIRE2
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
version 2
SMOKE
Sparse Matrix Operator Kernel Emissions
SOi
Sulfur dioxide
SOA
Secondary Organic Aerosol
SIP
State Implementation Plan
SPDPRO
Hourly Speed Profiles for weekday versus weekend
S/L/T
state, local, and tribal
TAF
Terminal Area Forecast
TCEQ
Texas Commission on Environmental Quality
TOG
Total Organic Gas
TSD
Technical support document
USD A
United States Department of Agriculture
VIIRS
Visible Infrared Imaging Radiometer Suite
VOC
Volatile organic compounds
VMT
Vehicle miles traveled
VPOP
Vehicle Population
WRAP
Western Regional Air Partnership
WRF
Weather Research and Forecasting Model
2014NEIv2
2014 National Emissions Inventory (NEI), version 2
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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 2017 based on the 2017 National Emissions
Inventory (2017 NEI) published in January 2021 (EPA, 2021). The air quality modeling platform consists
of all the emissions inventories and ancillary data files used for emissions modeling, as well as the
meteorological, initial condition, and boundary condition files needed to run the air quality model. This
document focuses on the emissions modeling component of the 2017 modeling platform, including the
emission inventories, the ancillary data files, and the approaches used to transform inventories for use in
air quality modeling. Many of the emissions inventory components of this air quality modeling platform
are based on the 2017 NEI, although there are some differences between the platform inventories and the
2017 NEI emissions in support of the emissions modeling process.
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 (HC1), benzene, acetaldehyde,
formaldehyde, methanol, naphthalene (the last five are also 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) added to CMAQ for the purposes of air quality modeling for the
2017 HAP+CAP platform.
Emissions were prepared for the Community Multiscale Air Quality (CMAQ) model
(https://www.epa.gov/cmaq) version 5.3.11, which was used to model ozone (O3) particulate matter (PM),
and H APs. CMAQ requires hourly and gridded emissions of the following inventory pollutants: carbon
monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOC), sulfur dioxide (SO:),
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 HC1 and CI, and can model additional HAPs as described in Section 3.
The short abbreviation for the modeling case name was "2017gb", where 2017 is the year modeled, g
represents that it was based on the 2017 NEI, and b represents that it was the second version of the
2017NEI-based platform.
Emissions were also prepared for an air dispersion modeling system: American Meteorological
Society/Environmental Protection Agency Regulatory Model (AERMOD) (EPA, 2018). AERMOD was
run for all NEI HAPs (about 130 more than covered by CMAQ) across all 50 states, Puerto Rico and the
Virgin Islands. This TSD focuses on the CMAQ aspects of the modeling platform.
1 CMAQv5.3.1 was run in a multi-pollutant configuration using the CB6R3 chemical mechanism with AER07, nonvolatile
primary organic aerosols (POA) and without pcSOA (The case abbreviation was 2017gb_MP_cb6ae7_17j=12US2).
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The effort to create the 2017 emission inputs for this study included development of emission inventories
for input to a 2017 modeling case, along with application of emissions modeling tools to convert the
inventories into the format and resolution needed by CMAQ and AERMOD. The emissions modeling
platform includes point sources, nonpoint sources, commercial marine vessels (CMV), onroad and
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 from Canada and Mexico are used in the CMAQ modeling but are not part of the
NEI. Year-specific emissions were used for all source categories, including fires and continuous emission
monitoring system (CEMS) data for electric generating units (EGUs).
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 4.7 was used to create
CMAQ-ready emissions files for a 12-km national grid. 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 3.8,
Advanced Research WRF core (Skamarock, et al., 2008). The WRF Model is a mesoscale numerical
weather prediction system developed for both operational forecasting and atmospheric research
applications. The WRF was run for 2017 over a domain covering the continental U.S. at a 12km
resolution with 35 vertical layers. The run for this platform included high resolution sea surface
temperature data from the Group for High Resolution Sea Surface Temperature (GHRSST) (see
https://www.ghrsst.org/) and is given the EPA meteorological case abbreviation "17j " The full case
abbreviation includes this suffix following the emissions portion of the case name to fully specify the
abbreviation of the case as "2017gb_17j."
Following the emissions modeling steps to prepare emissions for CMAQ and AERMOD, both models
were run. 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 are not run in AERMOD. Only AERMOD
was run in Alaska, Hawaii, Puerto Rico and the Virgin Islands. 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. Information about the emissions and associated data files for
this platform are available from this section of the air emissions modeling website
https://www.epa.gov/air-emissions-modeling/2017-emissions-modeling-platform.
This document contains five sections and several appendices. Section 2 describes the 2017 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.
Section 5 provides references. The Appendices provide additional details about specific technical
methods or data.
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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
version of the 2017 NEI released in January 2021. The NEI includes five main data categories: a)
nonpoint (formerly called "stationary area") sources; b) point sources; c) nonroad mobile sources; d)
on road 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 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 2017 NEI Technical Support Document describes in detail the development of the
2017 emission inventories and is available at https://www.epa.gov/air-emissions-inventories/2017-
national-emissions-inventorv-nei-technical-support-document-tsd (EPA, 2021).
Point source data from the 2017 NEI, including data submitted to EIS by S/L/T agencies, were used for
this study. EPA used the SMARTFIRE2 system and the BlueSky emissions modeling framework to
develop year 2017 fire emissions. SMARTFIRE2 categorizes all fires as either prescribed burning or
wildfire categories, and the BlueSky framework includes emission factor estimates for both types of fires.
Onroad and nonroad mobile source emissions for year 2017 were developed by running MOVES2014b
(https://www.epa.gov/moves). Canadian emissions interpolated to the year 2017 from 2015 and 2023
were used, and Mexican emissions were for the year 2016.
The emissions modeling process, performed using SMOKE v4.7, apportions the emissions inventories
into the grid cells used by CMAQ and temporalizes the emissions into hourly values. In addition, the
pollutants in the inventories (e.g., NOx, PM and VOC) are split into the chemical species needed by
CMAQ. For the purposes of preparing the CMAQ- ready emissions, the NEI emissions inventories by
data category are 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 are separated into sectors for groups of related emissions source categories
that are run through all of the SMOKE programs, except the final merge, independently from emissions
categories in the other sectors. The final merge program called Mrggrid combines low-level sector-
specific gridded, sped at ed and temporalized emissions to create the final CMAQ-ready emissions inputs.
For biogenic emissions, the CMAQ model allows for biogenic emissions to be included in the CM AQ-
ready emissions inputs, or for biogenic emissions to be computed within CMAQ itself (the "inline"
option). This study uses the inline biogenic emissions option.
Table 2-1 presents the sectors in the emissions modeling platform used to develop the year 2017
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.
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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
2017 NEI point source EG Us. replaced with hourly 2017 Continuous
Emissions Monitoring System (CEMS) values for NOx and SO; where
the units are matched to the NEI. Emissions for all sources not
matched to CEMS data come from 2017 NEI point inventory. Annual
resolution for sources not matched to CEMS data, hourly for CEMS
sources.
Point source oil and
gas:
ptoilgas
Point
2017 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 ofNatural Gas. Includes U.S. offshore oil
production. Annual resolution.
Aircraft and ground
support equipment:
airports
Point
2017 NEI point source emissions from airports, including aircraft and
airport ground support emissions. Annual resolution. The January
2021 version of 2017 NEI corrected the aircraft emissions in the April
2020 release of the 2017 NEI.
Remaining non-
EGU point:
ptnonipm
Point
All 2017 NEI point source records not matched to the airports, ptegu,
or pt_oilgas sectors. Includes 2017-specific rail yard emissions.
Annual resolution.
Agricultural
fertilizer:
ag
Nonpoint
2017 NEI nonpoint livestock and fertilizer application emissions.
Livestock includes ammonia and other pollutants (except PM2.5).
Fertilizer includes only ammonia. County and annual resolution.
Agricultural fires
with point
resolution: ptagfire
Nonpoint
Agricultural fire sources for year 2017 that were developed by EPA as
point and day-specific emissions.2 Agricultural fires are in the
nonpoint data category of the NEI, but in the modeling platform, they
are treated as day-specific point sources.
Area fugitive dust:
afdustadj
Nonpoint
PMio and PM2 5 fugitive dust sources from the 2017 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 2017 gridded hourly meteorology (precipitation and snow/ice
cover). Emissions are county and annual resolution.
Biogenic:
beis
Nonpoint
Year 2017 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.
Category 1, 2 CMV:
cmv_clc2
Nonpoint
2017 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
Within state and federal waters, 2017 NEI Category 3 commercial
marine vessel (CMV) emissions based on AIS data. Outside of state
and federal waters, emissions are based on AIS data in selected areas,
and are gapfilled with emissions from the Emissions Control Area
(ECA) inventory. Point and hourly resolution.
Locomotives :
rail
Nonpoint
Line haul rail locomotives emissions for year 2017. County and
annual resolution.
2 Only EPA-developed agricultural fire data were included in this study; data submitted by states to the NEI were excluded.
17
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Nonpoint source oil
and gas:
np oilgas
Nonpoint
Nonpoint 2017 NEI sources from oil and gas-related processes.
County and annual resolution.
Residential Wood
Combustion:
rwc
Nonpoint
2017 NEI nonpoint sources with residential wood combustion (RWC)
processes. County and annual resolution.
Remaining
nonpoint:
nonpt
Nonpoint
2017 NEI nonpoint sources not included in other platform sectors,
including solvents. County and annual resolution.
Nonroad:
nonroad
Nonroad
2017 nonroad equipment emissions developed with MOVES2014b.
MOVES was used for all states except California, which submitted
their own emissions for the 2017 NEI. County and monthly
resolution.
Onroad:
onroad
Onroad
2017 onroad mobile source gasoline and diesel vehicles from parking
lots and moving vehicles. Includes the following emission processes:
exhaust, extended idle, auxiliary power units, evaporative, permeation,
refueling, and brake and tire wear. For all states except California,
developed using winter and summer MOVES emission factors tables
produced by MOVES2014b.
Onroad California:
onroadcaadj
Onroad
California-provided 2017 CAP and metal HAP onroad mobile source
gasoline and diesel vehicles from parking lots and moving vehicles
based on Emission Factor (EMFAC) 2017, gridded and temporalized
based on outputs from MOVES2014b. Volatile organic compound
(VOC) HAP emissions derived from California-provided VOC
emissions and MOVES-based speciation.
Point source fires-
ptfire
Events
Point source day-specific wildfires and prescribed fires for 2017
computed using SMARTFIRE 2 and BlueSky.
Non-US. Fires:
ptfireothna
N/A
Point source day-specific wildfires and agricultural fires outside of the
U.S. for 2017 from vl.5 of the Fire INventory (FINN) from National
Center for Atmospheric Research (NCAR, 2017 and Wiedinmyer, C.,
2011) for Canada, Mexico, Caribbean, Central American, and other
international fires.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
N/A
Area fugitive dust sources from Canada interpolated to 2017 between
2015 and 2023, with transport fraction and snow/ice adjustments based
on 2017 meteorological data. Annual and province resolution.
Other Point Fugitive
dust sources not
from the NEI:
othptdust
N/A
2017 point source fugitive dust sources from Canada interpolated
between 2015 and 2023, with transport fraction and snow/ice
adjustments based on 2017 meteorological data. Annual and province
resolution.
Other point sources
not from the NEI:
othpt
N/A
2017 Canada point source emissions interpolated between 2015 and
2023, and Mexico point source emissions for 2016 (provided by
SEMARNAT). Annual and monthly resolution.
Other non-NEI
nonpoint and
nonroad:
othar
N/A
Year 2017 Canada interpolated between 2015 and 2023 (province
resolution) and projected year 2016 Mexico (municipio resolution,
provided by SEMARNAT) nonpoint and nonroad mobile inventories,
annual resolution.
Other non-NEI
onroad sources:
onroad can
N/A
Monthly onroad mobile inventory for Canada interpolated to 2017
between 2015 and 2023 (province resolution).
18
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Other non-NEI
onroad sources:
onroad mex
N/A
Monthly onroad mobile inventory from MOVES-Mexico (municipio
resolution) for 2017.
Other natural emissions are also merged in with the above sectors: ocean chlorine, 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). In CMAQ, the species name is "CL2".
The sea salt emissions were developed with version 4.1 of the OCEANIC pre-processor that comes with
the CAMx model. The preprocessor estimates time/space-varying emissions of aerosol sodium, chloride
and sulfate; gas-phase chlorine and bromine associated with sea salt; gaseous halo-methanes; and
dimethyl sulfide (DMS). These additional oceanic emissions are incorporated into the final model-ready
emissions files for CAMx.
The emission inventories in SMOKE input formats for the platform are available from EPA's Air
Emissions Modeling website: https://www.epa.gov/air-emissions-modeling/2017-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 2017 platform.
The types of reports include state summaries of inventory pollutants and model species by modeling
platform sector and county annual totals by modeling platform sector.
2.1 Point sources (ptegu, pt_oiigas, 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 (ptoilgas 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, with 2017 being the most recently finished complete NEI. A
comprehensive description about the development of the 2017 NEI is available in the 2017 NEI TSD
(EPA, 2021). Point inventories are also available in EIS for intermediate years such as 2018. In the
intermediate point inventories, states are required to update larger sources with emissions for the interim
year, while sources not updated by states are either carried forward from the most recent 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 2017 into the Flat File 2010 (FF10) format that is compatible with SMOKE (see
https://www.cmascenter.Org/smoke/documentation/4.7/html/ch08s02s08.htmn and was then split into
several sectors for modeling. After dropping sources without specific locations (i.e., the FIPS code ends in
777), initial versions of inventories for the other three 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 are split out from the other sources to facilitate the use of distinct SMOKE temporal processing
and future-year projection techniques. The oil and gas sector emissions (pt oilgas) and airport emissions
(airports) were processed separately for summary tracking purposes and distinct projection techniques
from the remaining non-EGU emissions (ptnonipm).
19
-------
The inventory pollutants processed through SMOKE for the ptegu, ptoilgas, ptnonipm, and airports
sectors included: CO, NOx, VOC, SO:, NH.,, PMio, and PM2.5 and the following HAPs: HC1 (pollutant
code = 7647010), CI (code = 7782505), and several dozen other HAPs listed in Section 3. NBAFM
pollutants from the point sectors were utilized for the HAP+CAP version of the platform. For cases not
focused on HAPs, they are not used and instead are speciated from VOC without the use (i.e., integration)
of VOC HAP pollutants from the inventory.
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 2017 CEMS data, hourly CEMS NOx and SO2
emissions for 2017 from EPA's Acid Rain Program were used rather than annual inventory emissions.
For all other pollutants (e.g., VOC, PM2.5, HQ), annual emissions were used as-is from the annual
inventory but were allocated to hourly values using heat input from the CEMS data. For the unmatched
units in the ptegu sector, annual emissions were allocated to daily values using IPM region- and pollutant-
specific profiles, and similarly, region- and pollutant-specific diurnal profiles were applied to create
hourly emissions.
The non-EGlJ stationary point source (ptnonipm) emissions were input 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, . . .].
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.
20
-------
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 EG Us in the 2017 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 (1PM) to develop future year EGU emissions. It was necessary to put these
EG Us into a separate sector in the platform because EG Us 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 are placed into the
ptoilgas or ptnonipm sectors. For studies with future year cases, 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
future year modeling scenarios if emissions for units are projected by IPM are not properly matched to the
units in the point source inventory.
The ptegu emissions inventory is a subset of the point source flat file exported from the Emissions
Inventory System (EIS). The 2017 point source emissions were selected from the 2017 NEIFinal dataset
on June 18, 2020 which included submissions from states up through that time. 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 described in the list below:
• 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 are replaced with the hourly CEMS emissions in base
year modeling. For other pollutants at matched units, the hourly CEMS heat input data are used to
allocate the NEI annual emissions to hourly values. All stack parameters, stack locations, and Source
Classification Codes (SCC) for these sources come from the flat file. If CEMS data exists for a unit, but
the unit is not matched to the NEI, the CEMS data for that unit are not used in the modeling platform.
However, if the source exists in the NEI and is not matched to a CEMS unit, the emissions from that
source are still modeled using the annual emission value in the NEI temporally allocated to hourly values.
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
http://ampd.epa.gov/ampd near the bottom of the "Prepackaged Data" tab. Many smaller emitters in the
CEMS program cannot be matched to the NEI due to inconsistencies in the way a unit is defined between
the NEI and CEMS datasets, or due to uncertainties in source identification such as inconsistent plant
names in the two data systems. 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.
21
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Matches from the NEI to ORIS codes and the NEEDS database were improved in the 2017 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 ptoilgas sector consists of point source oil and gas emissions in United States, primarily pipeline-
transportation and some upstream exploration and production. Sources in the pt oilgas sector consist of
sources which are not electricity generating units (EGUs) and which have a North American Industry
Classification System (NAICS) code corresponding to oil and gas exploration, production, pipeline-
transportation or distribution. The pt oilgas sector was separated from the ptnonipm sector by selecting
sources with specific NAICS codes shown in Table 2-2. The use of NAICS to separate out the point oil
and gas emissions forces all sources within a facility to be in this sector, as opposed to ptegu where
sources within a facility can be split between ptnonipm and ptegu sectors.
Table 2-2. Point source oil and gas sector NAICS Codes
NAICS
NAICS description
2111
Oil and Gas Extraction
211111
Crude Petroleum and Natural 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
22
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2.1.3 Non-IPM sector (ptnonipm)
With some exceptions, the non-IPM (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 the non-EGU sources of the
2017 NEI point inventory; however, it is likely that some low-emitting EGUs not matched to the NEEDS
database or to CEMS data may be found 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 "777" 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 2017 NEI
point inventory. Emissions from rail yards are included in the ptnonipm sector.
2.1.4 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. For 2017, Texas and California submitted aircraft emissions. Additional information about
aircraft emission estimates can be found in section 3.2.2 of the 2017 NEI TSD. The SCCs included in the
airport sector are shown in Table 2-3.
Table 2-3. SCCs for the airports sector
sec
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2260008005
Mobile Sources
Off-highway Vehicle
Gasoline
Airport Ground
Support
Equipment
2-Stroke Airport
Ground Support
Equipment
2265008005
Mobile Sources
Off-highway Vehicle
Gasoline
Airport Ground
Support
Equipment
4-Stroke Airport
Ground Support
Equipment
2267008005
Mobile Sources
Off-highway Vehicle
LPG
Airport Ground
Support
Equipment
LPG Airport Ground
Support Equipment
2268008005
Mobile Sources
Off-highway Vehicle
CNG
Airport Ground
Support
Equipment
CNG Airport Ground
Support Equipment
2270008005
Mobile Sources
Off-highway Vehicle
Diesel
Airport Ground
Support
Equipment
Airport Ground
Support Equipment
2275001000
Mobile Sources
Aircraft
Military Aircraft
Total
2275020000
Mobile Sources
Aircraft
Commercial
Aircraft
Total: All Types
2275050011
Mobile Sources
Aircraft
General Aviation
Piston
23
-------
S( (
Tier 1 description
Tier 2 (k'scriplion
Tier 3 (k'scriplion
Tier 4 (k'scriplion
2275050012
Mobile Sources
Aircraft
General Aviation
Turbine
2275060011
Mobile Sources
Aircraft
Air Taxi
Piston
2275060012
Mobile Sources
Aircraft
Air Taxi
Turbine
2275070000
Mobile Sources
Aircraft
Aircraft Auxiliary
Power Units
Total
40600307
Chemical
Evaporation
Transportation and
Marketing of Petroleum
Products
Gasoline Retail
Operations -
Stage I
Underground Tank:
Breathing and
Emptying
20200102
Internal
Combustion
Engines
Industrial
Distillate Oil
(Diesel)
Reciprocating
2.2 Nonpoint sources (afdust, fertilizer, livestock, npoilgas, rwc,
solvents, nonpt)
This section describes the stationary nonpoint sources in the NEI nonpoint data category. Locomotives,
CI and C2 CMV, and C3 CMV are included in the NEI nonpoint data category, but are mobile sources
that are described in Section 2.4.
Nonpoint tribal emissions submitted to the NEI are dropped during spatial processing with SMOKE due
to the configuration of the spatial surrogates. Part of the reason for this is to prevent possible double-
counting with county-level emissions and also because spatial surrogates for tribal data are not currently
available. These omissions are not expected to have an impact on the results of the air quality modeling at
the 12-km resolution used for this platform.
The following subsections describe how the sources in the NEI nonpoint inventory were separated into
modeling platform sectors, along with any data that were updated replaced with non-NEI data.
2.2.1 Area fugitive dust 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 2-4 is a listing of the Source Classification Codes
(SCCs) in the afdust sector.
Table 2-4. Afdust sector SCCs
sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2275085000
Mobile Sources
Aircraft
Unpaved Airstrips
Total
2294000000
Mobile Sources
Paved Roads
All Paved Roads
Total: Fugitives
2294000002
Mobile Sources
Paved Roads
All Paved Roads
Total: Sanding/Salting -
Fugitives
2296000000
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
2311000000
Industrial
Processes
Construction: SIC
15 -17
All Processes
Total
24
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sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2311010000
Industrial
Processes
Construction: SIC
15 -17
Residential
Total
2311010070
Industrial
Processes
Construction: SIC
15 -17
Residential
Vehicle Traffic
2311020000
Industrial
Processes
Construction: SIC
15 -17
Industrial/Commercial/
Institutional
Total
2311030000
Industrial
Processes
Construction: SIC
15 -17
Road Construction
Total
2325000000
Industrial
Processes
Mining and
Quarrying: SIC 14
All Processes
Total
2325060000
Industrial
Processes
Mining and
Quarrying: SIC 10
Lead Ore Mining and Milling
Total
2801000000
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Total
2801000003
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Tilling
2801000005
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Harvesting
2801000007
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Loading
2801000008
Miscellaneous
Area Sources
Ag. Production -
Crops
Agriculture - Crops
Transport
2805001000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Dust Kicked-up by Hooves
(use 28-05-020, -001,-002,
or -003 for Waste
2805001100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Confinement
2805001200
Miscellaneous
Area Sources
Agriculture
Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Manure handling and storage
2805001300
Miscellaneous
Area Sources
Agriculture
Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Land application of manure
2805002000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle production composite
Not Elsewhere Classified
2805003100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Beef cattle - finishing operations
on pasture/range
Confinement
2805007100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
dry manure management systems
Confinement
2805007300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
dry manure management systems
Land application of manure
2805008100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Confinement
2805008200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Manure handling and storage
2805008300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - layers with
wet manure management systems
Land application of manure
2805009100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Confinement
2805009200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Manure handling and storage
2805009300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - broilers
Land application of manure
25
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sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2805010100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Confinement
2805010200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Manure handling and storage
2805010300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry production - turkeys
Land application of manure
2805018000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle composite
Not Elsewhere Classified
2805019100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Confinement
2805019200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Manure handling and storage
2805019300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - flush dairy
Land application of manure
2805020002
Miscellaneous
Area Sources
Ag. Production -
Livestock
Cattle and Calves Waste
Emissions
Beef Cows
2805021100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Confinement
2805021200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Manure handling and storage
2805021300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - scrape dairy
Land application of manure
2805022100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Confinement
2805022200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Manure handling and storage
2805022300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - deep pit dairy
Land application of manure
2805023100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Confinement
2805023200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Manure handling and storage
2805023300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Dairy cattle - drylot/pasture dairy
Land application of manure
2805025000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production composite
Not Elsewhere Classified
(see also 28-05-039, -047, -
053)
2805030000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Not Elsewhere Classified
(see also 28-05-007, -008, -
009)
2805030007
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Ducks
2805030008
Miscellaneous
Area Sources
Ag. Production -
Livestock
Poultry Waste Emissions
Geese
2805035000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Horses and Ponies Waste
Emissions
Not Elsewhere Classified
2805039100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Confinement
2805039200
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Manure handling and storage
26
-------
sec
Tier 1
description
Tier 2
description
Tier 3 description
Tier 4 description
2805039300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - operations
with lagoons (unspecified animal
age)
Land application of manure
2805040000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Sheep and Lambs Waste
Emissions
Total
2805045000
Miscellaneous
Area Sources
Ag. Production -
Livestock
Goats Waste Emissions
Not Elsewhere Classified
2805047100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - deep-pit house
operations (unspecified animal
age)
Confinement
2805047300
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - deep-pit house
operations (unspecified animal
age)
Land application of manure
2805053100
Miscellaneous
Area Sources
Ag. Production -
Livestock
Swine production - outdoor
operations (unspecified animal
age)
Confinement
Area Fugitive Dust Transport Fraction
The afdust sector is separated from other nonpoint sectors to allow for the application of a "transport
fraction," and meteorological/precipitation reductions. These adjustments are applied using a script that
applies land use-based gridded transport fractions based on landscape roughness, followed by another
script that zeroes out emissions for days on which at least 0.01 inches of precipitation occurs or there is
snow cover on the ground. The land use data used to reduce the NEI emissions determines the amount of
emissions that are subject to transport. This methodology is discussed in Pouliot, et al., 2010, and in
"Fugitive Dust Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). Both the
transport fraction and meteorological adjustments are based on the gridded resolution of the platform (i.e.,
12km grid cells); therefore, different emissions will result if the process were applied to different grid
resolutions. A limitation of the transport fraction approach is the lack of monthly variability that would
be expected with seasonal changes in vegetative cover. While wind speed and direction are not accounted
for in the emissions processing, the hourly variability due to soil moisture, snow cover and precipitation is
accounted for in the subsequent meteorological adjustment.
For the data compiled into the 2017 NEI, meteorological adjustments are applied to paved and unpaved
road SCCs but not transport adjustments. The meteorological adjustments that were applied (to paved
and unpaved road SCCs) in the 2017 NEI were backed out so that the entire sector could be processed
consistently in SMOKE and the same grid-specific transport fractions and meteorological adjustments
could be applied sector-wide. Thus, the FF10 that is run through SMOKE consists of 100% unadjusted
emissions, and after SMOKE all afdust sources have both transport and meteorological adjustments
applied. The total impacts of the transport fraction and meteorological adjustments are shown in Table
2-5.
Table 2-5. Total impact of 2017 fugitive dust adjustments to unadjusted inventory
Stiite
I iiiidjusled
PMio
I iiiidjusled
I'M: ?
Chiiii^c in
PMio
< liiiniic in
I'M:?
PMio
Reduction
PM:;
Reduction
Alabama
306,333
41,208
-219,542
-29,507
72%
72%
Arizona
181,281
24,286
-65,373
-8,554
36%
35%
Arkansas
394,400
54,447
-267,830
-36,303
68%
67%
27
-------
SliUc
I iiiidjiislcd
PMio
I iiiidjiislcd
I'M;.?
( liiinui- in
PMio
( liiinui- in
I'M;?
PMio
Reduction
I'M:.?
Reduction
California
308,755
39,031
-139,766
-17,141
45%
44%
Colorado
281,900
41,042
-141,886
-20,014
50%
49%
Connecticut
24,294
4,006
-19,271
-3,193
79%
80%
Delaware
15,439
2,389
-8,928
-1,387
58%
58%
District of
Columbia
2,889
408
-1,710
-241
59%
59%
Florida
400,265
55,825
-228,186
-31,951
57%
57%
Georgia
297,192
42,391
-209,598
-29,711
71%
70%
Idaho
566,771
65,495
-326,528
-36,556
58%
56%
Illinois
1,111,225
159,957
-651,722
-93,185
59%
58%
Indiana
144,386
26,992
-93,202
-17,509
65%
65%
Iowa
388,258
57,032
-213,452
-31,290
55%
55%
Kansas
673,092
89,596
-301,317
-39,741
45%
44%
Kentucky
177,203
28,924
-128,645
-20,948
73%
72%
Louisiana
179,517
27,344
-117,168
-17,681
65%
65%
Maine
71,790
8,794
-61,563
-7,547
86%
86%
Maryland
74,608
11,975
-46,675
-7,512
63%
63%
Massachusetts
62,397
9,528
-49,509
-7,506
79%
79%
Michigan
296,244
38,935
-219,817
-28,620
74%
74%
Minnesota
426,434
59,889
-273,682
-37,804
64%
63%
Mississippi
452,433
55,232
-315,607
-38,169
70%
69%
Missouri
1,349,732
159,648
-859,469
-101,449
64%
64%
Montana
504,573
66,714
-291,508
-36,985
58%
55%
Nebraska
519,938
71,861
-237,323
-32,437
46%
45%
Nevada
139,660
18,453
-47,509
-6,254
34%
34%
New Hampshire
20,788
4,369
-17,840
-3,745
86%
86%
New Jersey
32,729
6,110
-22,004
-4,068
67%
67%
New Mexico
214,191
26,689
-84,194
-10,460
39%
39%
New York
236,016
33,300
-187,271
-26,287
79%
79%
North Carolina
237,441
32,066
-161,402
-21,829
68%
68%
North Dakota
391,394
60,485
-210,528
-32,057
54%
53%
Ohio
275,901
43,306
-190,273
-29,888
69%
69%
Oklahoma
607,313
82,670
-318,882
-42,469
53%
51%
Oregon
615,351
69,327
-430,557
-47,620
70%
69%
Pennsylvania
135,465
24,345
-99,228
-18,095
73%
74%
Rhode Island
4,662
781
-3,426
-574
73%
73%
South Carolina
120,214
16,666
-79,803
-11,128
66%
67%
South Dakota
215,704
38,411
-102,272
-18,006
47%
47%
28
-------
Stall'
I nad.jnsli'd
PMio
I nad.jnsli'd
I'M:.?
Chanel- in
PMio
Chan^i- in
I'M:?
PMio
Reduction
PM:;
Reduction
Tennessee
142,090
26,135
-99,696
-18,430
70%
71%
Texas
1,341,326
194,418
-636,491
-90,195
47%
46%
Utah
170,358
21,697
-87,785
-11,011
52%
51%
Vermont
77,400
8,614
-67,399
-7,478
87%
87%
Virginia
125,942
20,303
-89,914
-14,586
71%
72%
Washington
232,450
37,800
-138,429
-22,434
60%
59%
West Virginia
85,671
11,064
-72,688
-9,395
85%
85%
Wisconsin
183,820
31,237
-128,407
-21,796
70%
70%
Wyoming
547,211
61,352
-285,754
-31,623
52%
52%
Domain Total
(12km CONUS)
15,364,447
2,112,542
-9,051,028
-1,232,372
59%
58%
Alaska
108,119
11,760
-99,193
-10,717
92%
91%
Hawaii
18,117
2,367
-9,740
-1,305
54%
55%
Puerto Rico
1,147,381
153,203
-1,129,242
-150,989
98%
99%
Virgin Islands
1,787
247
-891
-123
50%
50%
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. Impact of adjustments to fugitive dust emissions due to transport fraction,
precipitation, and cumulative
2017gb_hapcap afdust annual : PM25, xportfrac adjusted - unadjusted
i> 83
41
-20
>.
- 0
8
--20
-41
-83
Max: 0.0 Min: -
mm
2017gb_hapcap afdust annual : PM2_5, precip adjusted - xportfrac adjusted
30
-------
2017gb_hapcap afdust annual : PM2_5, xportfrac + precip adjusted - unadjusted
2.2.2 Agricultural Livestock and Fertilizer (ag)
The livestock portion of the ag sector includes NHS emissions from fertilizer and emissions of all
pollutants other than PM2.5 from livestock in the nonpoint (county-level) data category of the 2017NEI.
PM2.5 from livestock are in the Area Fugitive Dust (afdust) sector. Combustion emissions from
agricultural equipment, such as tractors, are in the nonroad sector. The livestock emissions include VOC
and HAP VOC in addition to NHS. PM2.5 from livestock are in the afdust sector. The livestock SCCs
included in the ag sector are shown in Table 2-6. The livestock SCCs 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.
Table 2-6. 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
31
-------
S( (
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
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
Agricultural livestock emissions in the platform are based on the 2017 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 2017 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 2017 U.S. Department of Agriculture (USDA) National Agricultural Statistics
Service (NASS) survey. Details on the approach are provided in Section 4.5 of the 2017 NEI TSD. Note
that the "ag" sector does not include 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 "ag"
sector also includes livestock emissions from all pollutants other than PM2.5.
Fertilizer emissions
The "ag" sector includes all of the NH3 emissions from fertilizer. As described in the 2017 NEI TSD,
fertilizer emissions for 2017 are based on the FEST-C model (https://www.cmascenter.org/fest-c/). The
bidirectional version of CMAQ (v5.3) and the Fertilizer Emissions Scenario Tool for CMAQ FEST-C
(vl.3) were used to estimate ammonia (NH3) emissions from agricultural soils. The approach to estimate
year-specific fertilizer emissions consists of these steps:
• Run FEST-C and CMAQ model with bidirectional ("bidi") NH3 exchange to produce nitrate
(NO3), Ammonium (NH4+, including Urea), and organic (manure) nitrogen (N) fertilizer usage
estimates, and gaseous ammonia NH3 emission estimates respectively.
• Calculate county-level emission factors as the ratio of bidirectional CMAQ NH3 fertilizer
emissions to FEST-C total N fertilizer application.
• Assign the NH3 emissions to one SCC: ".. .Miscellaneous Fertilizers" (2801700099).
FEST-C is the software program that processes land use and agricultural activity data to develop inputs
for the CMAQ model when run with bidirectional exchange. FEST-C reads land use data from the
Biogenic Emissions Landuse Dataset (BELD), meteorological variables from the Weather Research and
Forecasting (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. Then CMAQ v5.3 was run with the Surface Tiled
32
-------
Aerosol and Gaseous Exchange (STAGE) deposition option with bidirectional exchange to estimate
fertilizer and biogenic NIi3 emissions.
Figure 2-2. "Bidi" modeling system used to compute emissions from fertilizer application
The Fertilizer Emission Scenario Tool for CMAQ
(FEST-C)
Fertilizer Activity Data
The following activity parameters were input into the EPIC model:
• Grid cell meteorological variables from WRF
• Initial soil profiles/soil selection
• Presence of 21 major crops: irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn,
silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans,
spring wheat, winter wheat, canola, and other crops (e.g., lettuce, tomatoes, etc.)
• Fertilizer sales to establish the type/composition of nutri ents 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 2017
using a national 12-km rectangular grid covering the continental U.S. The meteorological parameters in
Table 2-7 were used as EPIC model inputs.
33
-------
Table 2-7. Source of input variables for EPIC
EPIC input variable
Variable Source
Daily Total Radiation (MJ/m2)
WRF
Daily Maximum 2-m Temperature (C)
WRF
Daily minimum 2-m temperature (C)
WRF
Daily Total Precipitation (mm)
WRF
Daily Average Relative Humidity (unitless)
WRF
Daily Average 10-m Wind Speed (m s"1)
WRF
Daily Total Wet Deposition Oxidized N (g/ha)
CMAQ
Daily Total Wet Deposition Reduced N (g/ha)
CMAQ
Daily Total Dry Deposition Oxidized N (g/ha)
CMAQ
Daily Total Dry Deposition Reduced N (g/ha)
CMAQ
Daily Total Wet Deposition Organic N (g/ha)
CMAQ
Initial soil nutrient and pH conditions in EPIC were based on the 1992 USD A Soil Conservation Service
(CSC) Soils-5 survey. The EPIC model then was run for 25 years using current fertilization and
agricultural cropping techniques to estimate soil nutrient content and pH for the 2017 EPIC/WRF/CMAQ
simulation.
The presence of crops in each model grid cell was determined using USD A Census of Agriculture data
(2012) and USGS National Land Cover data (2011). These two data sources were used to compute the
fraction of agricultural land in a model grid cell and the mix of crops grown on that land.
Fertilizer sales data and the 6-month period in which they were sold were extracted from the 2014
Association of American Plant Food Control Officials (AAPFCO,
http://www.aapfco.org/publications.htmn. AAPFCO data were used to identify the composition (e.g.,
urea, nitrate, organic) of the fertilizer used, and the amount applied is estimated using the modeled crop
demand. These data were useful in making a reasonable assignment of what kind of fertilizer is being
applied to which crops.
Management activity data refers to data used to estimate representative crop management schemes. The
USD A Agricultural Resource Management Survey (ARMS,
https://www.nass.usda.gov/Survevs/Guide to NASS Survevs/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.).
For livestock and fertilizer, meteorological-based temporalization (described in Section 3.3.5) is used for
month-to-day and day-to-hour temporalization. Monthly profiles for livestock are based on the daily data
underlying the EPA estimates from the development of the 2014 NEI Version 2 (2014NEIv2). The
fertilizer inventory includes monthly emissions from FEST-C and uses the same meteorological-based
month-to-hour profiles as livestock in the same way as was done for other recent platforms.
34
-------
2.2.3 Nonpoint Oil and Gas Sector (np_oilgas)
The nonpoint oil and gas (np oilgas) sector includes oil and gas exploration and production sources, both
onshore and offshore (state-owned only). The EPA estimated emissions for all counties with 2017 oil and
gas activity data with the Oil and Gas Tool, and many S/L/T agencies also submitted nonpoint oil and gas
data. Where S/L/T submitted nonpoint CAPs but no HAPs, the EPA augmented the HAPs using HAP
augmentation factors (county and SCC level) created from 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 is given to the speciation, spatial
allocation, and monthly temporalization of nonpoint oil and gas emissions, instead of relying on older,
more generalized profiles.
EPA Oil and Gas Tool
EPA updated the Nonpoint Oil and Gas Emission Estimation Tool (i.e., the "tool") to estimate emissions
for 2017. Year 2017 oil and gas activity data was supplied to EPA by state air agencies and where state
data is not supplied to EPA, EPA populates the 2017 inventory with the best available data. The tool is an
Access database that utilizes county-level activity data (e.g., oil production and well counts), operational
characteristics (types and sizes of equipment), and emission factors to estimate emissions. The tool
creates a CSV-formatted emissions dataset covering all national nonpoint oil and gas emissions. This
dataset is then converted to FF10 format for use in SMOKE modeling. This 2017 NEI Tool document
can be found at: https://gaftp.epa.gov/air/nei/2017/doc/supporting data/nonpoint/. More details on the
inputs for and running of the tool for 2017 are provided in the 2017 NEI TSD.
2.2.4 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 2-8.
Some improvements to RWC emissions estimates were made for the 2017 NEI and were included in this
study. 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 are 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 are converted to tons using
county-level density factors from the U.S. Forest Service. RWC emissions were calculated by multiplying
35
-------
the tons of wood burned by emissions factors. For more information on the development of the residential
wood combustion emissions, see Section 4.15 of the 2017 NEI TSD
Table 2-8. SCCs for the residential wood combustion sector
S( (
Tier 1 Description
Tier 2
Description
Tier 3
Description
Tier 4 Description
2104008100
Stationary Source
Fuel Combustion
Residential
Wood
Fireplace: general
2104008210
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts;
non-EPA certified
2104008220
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts;
EPA certified; non-catalytic
2104008230
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts;
EPA certified; catalytic
2104008300
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding,
general
2104008310
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding,
non-EPA certified
2104008320
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding,
EPA certified, non-catalytic
2104008330
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: freestanding,
EPA certified, catalytic
2104008400
Stationary Source
Fuel Combustion
Residential
Wood
Woodstove: pellet-fired,
general (freestanding or FP
insert)
2104008510
Stationary Source
Fuel Combustion
Residential
Wood
Furnace: Indoor, cordwood-
fired, non-EPA certified
2104008530
Stationary Source
Fuel Combustion
Residential
Wood
Furnace: Indoor, pellet-fired,
general
2104008610
Stationary Source
Fuel Combustion
Residential
Wood
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
2.2.5 Nonpoint (nonpt)
Stationary nonpoint sources that were not subdivided into the afdust, ag, npoilgas, or rwc sectors were
assigned to the "nonpt" sector. Locomotives and CMV mobile sources from the 2017 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;
36
-------
• chemical manufacturing;
• industrial processes such as commercial cooking, metal production, mineral processes, petroleum
refining, wood products, fabricated metals, and refrigeration;
• solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic
marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances,
and motor vehicles;
• solvent utilization for degreasing of furniture, metals, auto repair, electronics, and manufacturing;
• solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care
products, household products, adhesives and sealants;
• solvent utilization for asphalt application, roofing, and pesticide application;
• storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline
service stations, aviation, and marine vessels;
• storage and transport of chemicals;
• waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;
and
• miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair
shops.
The nonpt sector includes emission estimates for Portable Fuel Containers (PFCs), also known as "gas
cans " The PFC inventory consists of five distinct sources of PFC emissions, further distinguished by
residential or commercial use. The five sources are: (1) displacement of the vapor within the can; (2)
spillage of gasoline while filling the can; (3) spillage of gasoline during transport; (4) emissions due to
evaporation (i.e., diurnal emissions); and (5) 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 6.5.1 of the 2017 NEI TSD. In addition, a number of
states submitted 2017-specific activity data for incorporation into this platform.
Except for California, all onroad emissions are 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 meteorological data. Specifically, EPA used MOVES 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
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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.
2.3.1 Inventory Development using SMOKE-MOVES
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 2-9. SMOKE-MOVES was run for specific modeling grids. Emissions for the contiguous
U.S. states and Washington, D.C., were computed for a grid covering those areas. Emissions for Alaska,
Hawaii, Puerto Rico, and the U.S. Virgin Islands were computed by running SMOKE-MOVES for
distinct grids covering each of those regions and are included in the onroad nonconus sector. In some
summary reports these non-CONUS emissions are aggregated with emissions from the onroad sector.
Table 2-9. MOVES vehicle (source) types
MOM'.S vehicle Ivpe
Description
II P.MS vehicle Ivpe
11
Motorcycle
10
21
Passenger Car
25
31
Passenger Truck
25
32
Light Commercial Truck
25
41
Intercity Bus
40
42
Transit Bus
40
43
School Bus
40
51
Refuse Truck
50
52
Single Unit Short-haul Truck
50
53
Single Unit Long-haul Truck
50
54
Motor Home
50
61
Combination Short-haul Truck
60
62
Combination Long-haul Truck
60
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 2017-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 are generated are selected according to their state,
elevation, fuels, age distribution, ramp fraction, and inspection and maintenance programs. Each county
is then mapped to a representative county based on its similarity to the representative county with respect
to those attributes. For this study, there are 296 representative counties in the continental U.S. A detailed
discussion of the representative counties is in the 2017 NEI TSD, Section 6.8.2.
Once representative counties have been identified, emission factors are 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 selects the appropriate
MOVES emissions rates for each county, hourly temperature, SCC, and speed bin and then multiplies the
emission rate by appropriate activity data. For on-roadway emissions, vehicle miles travelled (VMT) is
the activity data, vehicle population (VPOP) is used for many off-network processes, and hoteling hours
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are used to develop emissions for extended idling of combination long-haul trucks. These calculations are
done for every county and grid cell in the continental U.S. for each hour of the year.
The SMOKE-MOVES process for creating the model-ready emissions consists of the following steps:
1) Determine which counties will be used to represent other counties in the MOVES runs.
2) Determine which months will be used to represent other month's fuel characteristics.
3) Create inputs needed only by MOVES. MOVES requires county-specific information on
vehicle populations, age distributions, and inspection-maintenance programs for each of the
representative counties.
4) Create inputs needed both by MOVES and by SMOKE, including temperatures and activity
data.
5) Run MOVES to create emission factor tables for the temperatures found in each county.
6) Run SMOKE to apply the emission factors to activity data (VMT, VPOP, 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 are processed in four processing streams that are merged together into the onroad
sector emissions after each of the four 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 (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; and
• 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.
The onroad emissions inputs for the platform are based on the 2017 NEI, described in more detail in
Section 6 of the 2017 NEI TSD. These inputs include:
• MOVES County databases (CDBs) including Low Emission Vehicle (LEV) table
• Representative counties
• Fuel months
• Meteorology
• Activity data (VMT, VPOP, speed, HOTELING)
Representative counties, fuel months and other inputs were consistent with those in the 2017 NEI.
States that submitted activity data and development of the EPA default activity data sets for VMT, VPOP,
and hoteling hours are described in detail in the 2017 NEI TSD and supporting documents. Hoteling hours
activity are 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), and hours of hoteling,
to calculate emissions. These datasets are collectively known as "activity data". For each of these activity
datasets, first a national dataset was developed; this national dataset is called the "EPA default" dataset.
The default dataset started with the 2017 NEI activity data, which was 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)
Representative counties, fuel months and other inputs were consistent with those in the 2017 NEI.
States that submitted activity data and development of the EPA default activity data sets for VMT, VPOP,
and hoteling hours are described in detail in the 2017 NEI TSD (EPA, 2021) and supporting documents.
Speed Activity (SPEED/SPDIST)
In SMOKE 4.7, SMOKE-MOVES was updated to use speed distributions similarly to how they are used
when running MOVES in inventory mode. This new speed distribution file, called SPDIST, specifies the
amount of time spent in each MOVES speed bin for each county, vehicle (aka source) type, road type,
weekday/weekend, and hour of day. This file contains the same information at the same resolution as the
Speed Distribution table used by MOVES but is reformatted for SMOKE. Using the SPDIST file results
in a SMOKE emissions calculation that is more consistent with MOVES than the old hourly speed profile
(SPDPRO) approach, because emission factors from all speed bins can be used, rather than interpolating
between the two bins surrounding the single average speed value for each hour as is done with the
SPDPRO approach.
As was the case with the previous SPDPRO approach, the SPEED inventory that includes a single overall
average speed for each county, SCC, and month, must still be read in by the SMOKE program Smkinven.
SMOKE requires the SPEED dataset to exist even when speed distribution data are available, even though
only the speed distribution data affects the selection of emission factors. The SPEED and SPDIST for
2017NEI are based on a combination of the CRC A-100 (CRC, 2017) project data and 2017 NEI MOVES
CDBs.
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 8,760 hours in the year 2017; therefore, the maximum number of possible hoteling hours in a
particular county is equal to 8,760 * the number of parking spaces in that county. Hoteling hours were
capped at that theoretical maximum value for 2017 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 parking spaces,
even if fewer parking spaces are found in the parking space dataset. Therefore, hoteling hours are never
reduced below 105,408 hours for the year in any county. If the unreduced hoteling hours were already
40
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below that maximum, the hours were left unchanged; in other words, hoteling activity are 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). New Jersey's submittal of
hoteling activity specified a 30% APU split, and this was used throughout NJ. For the rest of the country,
a 12.4% APU split was used, meaning that during 12.4% of the hoteling hours auxiliary power units are
assumed to be running.
2.3.3 MOVES Emission Factor Table Development
MOVES2014b was run in emission rate mode to create emission factor tables using CB6 speciation for
2017, for all representative counties and fuel months. MOVES was run for all counties in Alaska, Hawaii,
and Virgin Islands, and for a single representative county in Puerto Rico.
The county databases CDBs used to run MOVES to develop the emission factor tables were those used
for the 2017 NEI and therefore included any updated data provided and accepted for the 2017 NEI
process. The 2017 NEI development included an extensive review of the various tables including speed
distributions were performed. Where state speed profiles, speed distributions, and temporal profiles data
were not accepted from S/L submissions, those data were obtained from the CRC A-100 study. Each
county in the continental U.S. was classified according to its state, altitude (high or low), fuel region, the
presence of inspection and maintenance programs, the mean light-duty age, and the fraction of ramps. A
binning algorithm was executed to identify "like counties", and then specific requests for representative
county groups by states for the 2017 NEI were honored. The result was 296 representative counties for
CONUS and 38 for Alaska, Hawaii, Puerto Rico, and the US Virgin Islands, as shown in Figure 2-3.
41
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Figure 2-3. Representative Counties in 2017
Reference County Groups 2017 NEI
Age distributions are a key input to MOVES in determining emission rates. The age distributions for 2017
were updated based on vehicle registration data obtained from the CRC A-l 15 project (CRC, 2019),
subject to reductions for older vehicles determined according to CRC A-l 15 methods but using additional
age distribution data that became available as part of the 2017 NEI submitted input data. One of the
findings of CRC project A-l 15 is that 1HS 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 IMS data). The CRC project dealt with the
discrepancy by releasing datasets based on raw (unadjusted) information and adjusted sets of age
distributions, where the adjustments reflected the differences in population by model year of 2014 IHS
data and 2014 submitted data from a single state.
For the 2017 NEI, EPA repeated the CRC's assessment of IHS vs. state vehicles by age, but with updated
information from the 2017 NEI and for more states. The 2017 light-duty vehicle (LDV) populations from
the CRC A-l 15 project were compared by model year to the populations submitted by state/local (S/L)
agencies for the 2017 NEI. The comparisons by model year were used to develop adjustment factors that
remove older age LDVs from the IHS dataset. Out of 31 S/L agencies that provided age distribution and
vehicle population data for the 2017 NEI, sixteen agencies provided LDV population and age
distributions with snapshot dates of January 2017, July 2017, or 2018. The other fifteen agencies had
42
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either unknown or older (back to 2013) data pull dates, so were compared to the 2017 IHS data. The
vehicle populations by model year were compared with IHS data for each of the sixteen agencies for
source type 21 (passenger cars) and for source type 31 plus 32 (light trucks) together. Prior to finalizing
the activity data, the S/L agency populations of source type 21 and light trucks to match IHS car and
light-duty truck splits by county so that vehicles of the same model and year were consistently classified
into MOVES source types throughout the country. The IHS population of vehicles were found to be
higher than the pooled state data by 6.5 percent for cars and 5.9 percent for light trucks.
To adjust for the additional vehicles in the IHS data, vehicle age distribution adjustment factors as one
minus the fraction of vehicles to remove from IHS to equal the state data, with two exceptions: (1) the
model year range 2006/2007 to 2017 receives no adjustment and (2) the model year 1987 receives a
capped adjustment that equals the adjustment to 1988. Table 2-10 below shows the fraction of vehicles to
keep by model year based on this analysis. The adjustments were applied to the 2017 IHS-based age
distributions from CRC project A-l 15 prior to use in this 2017 platform. In addition, the age distributions
to ensure the "tail" of the distribution corresponding to age 30 years and older vehicles did not exceed
20% of the fleet. After limiting the age distribution tails, the age distributions were renormalized to
ensure they summed to one (1). In addition, antique license plate vehicles were removed based on the
registration summary from IHS. Nationally, the prevalence of antique plates is only 0.8 percent, but as
high as 6 percent in some states (e.g., Mississippi).
Table 2-10. Fraction of IHS Vehicle Populations to Retain for 2017 NEI
Model Year
(illS
l.ilihl
pre-1989
0.675
0.769
1989
0.730
0.801
1990
0.732
0.839
1991
0.740
0.868
1992
0.742
0.867
1993
0.763
0.867
1994
0.787
0.842
1995
0.776
0.865
1996
0.790
0.881
1997
0.808
0.871
1998
0.819
0.870
1999
0.840
0.874
2000
0.838
0.896
2001
0.839
0.925
2002
0.864
0.921
2003
0.887
0.942
2004
0.926
0.953
2005
0.941
0.966
2006
1
0.987
2007-2017
1
1
In addition to removing the older and antique plate vehicles from the IHS data, 25 counties found to be
outliers because their fleet age was significantly younger than in typical counties. The outlier review was
limited to LDV source types 21,31, and 32. Many rural counties have outliers for low-population source
types such as Transit Bus and Refuse Truck due to small sample sizes, but these do not have much of an
impact on the inventory overall and reflect sparse data in low-population areas and therefore do not
require correction.
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The most extreme examples of LDV outliers were Light Commercial Truck age distributions where over
50 percent of the population in the entire county is 0 and 1 years old. These sorts of young fleets can
happen if the headquarters of a leasing or rental company is the owner/entity of a relatively large number
of vehicles relative to the county-wide population. While the business owner of thousands of new
vehicles may reside in a single county, the vehicles likely operate in broader areas without being
registered where they drive. To avoid creating artificial low spots of LDV emissions in these outlier
counties, data for all counties with more than 35% new vehicles were excluded from the final set of
grouped age distributions that went into the CDBs.
The final age distributions were then grouped using a population-weighted average of the source type
populations of each county in the representative county group. The resulting end-product was age
distributions for each of the 13 source types in each of the representative counties for 2017. The long-
haul truck source types 53 (Single Unit) and 62 (Combination Unit) are based on a nationwide average
due to the long-haul nature of their operation.
To create the emission factors, MOVES was run separately for each representative county and fuel month
and for each temperature bin needed for calendar year 2017. The CDBs used to run MOVES include the
state-specific control measures such as the California low emission vehicle (LEV) program, except that
fuels were updated to represent calendar year 2017. In addition, the range of temperatures run along with
the average humidities used were specific to the year 2017. 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)
California is the only state agency for which submitted onroad emissions were used in the 2017 NEI and
this study. California uses their own emission model, EMFAC, which 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 provided their CAP and HAP emissions by county using
EPA SCCs after applying an agreed-to EIC to SCC mapping. California provided EMFAC2017-based
onroad emissions inventories for 2017 that were used for this study. To preserve MOVES speciation in
California, VOC HAP emissions provided by California were not used in modeling; instead, HAP-to-
VOC ratios based on MOVES speciation were used in combination with California-provided VOC
emissions to estimate new VOC HAP emissions. Emissions for other HAPs, including metals and PAHs,
were used as provided by California.
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
platform was able to reflect the unique rules in California, 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 are run in a separate sector called
"onroad ca."
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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.
Unlike in previous platforms, the California data separated off and on-network emissions and
extended idling. However, the on-network did not provide specific road types, and California's
emissions 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 "onroadcaadj " Note that in emission
summaries, the emissions from the "onroad" and "onroad ca adj" sectors are 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_c1c2)
The cmv_clc2 sector contains Category 1 and 2 CMV emissions from the 2017 NEI. 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 (port or underway shape ids) and by SCC and emission
type (e.g., hoteling, maneuvering). This sub-county data in the NEI are used to create spatial surrogates.
For more information on CMV sources in the 2017 NEI, see Section 4.21 of the 2017 NEI TSD. CI and
C2 emissions that occur outside of state waters are not assigned to states. 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
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
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the Atlantic and Pacific coasts. The cmv_clc2 sources in the 2017 inventory are categorized as operating
either in-port or underway and as main and auxiliary engines are encoded using the SCCs listed in Table
2-11.
Table 2-11. SCCs for cmv clc2 sector
sec
Tier 1 Description
Tier 2 Description
Tier 3 Dcscriplion
Tier-l Dcscriplion
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 2017 NEI,3 The 2017 NEI emissions were
developed based signals from Automated Identification System (AIS) transmitters. AIS is a tracking
system used by vessels to enhance navigation and avoid collision with other AIS transmitting vessels.
The USEPA Office of Transportation and Air Quality received AIS data from the U.S. Coast Guard
(USCG) to quantify all ship activity which occurred between January 1 and December 31, 2017. The
provided AIS data extends beyond 200 nautical miles from the U.S. coast (Figure 2-4). This boundary is
roughly equivalent to the border of the U.S Exclusive Economic Zone and the North American EC A,
although some non-EC A activity are captured as well.
3 Category 1 and 2 Commercial Marine Vessel 2017 Emissions Inventory (ERG, 2019).
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Figure 2-4. 2017NEI geographical extent (solid) and U.S. ECA (dashed)
The AIS data were compiled into five-minute intervals by the USCG, providing a reasonably refined
assessment of a vessel's movement. For example, using a five-minute average, a vessel traveling at 25
knots would be captured every two nautical miles that the vessel travels. For slower moving vessels, the
distance between transmissions would be less. The ability to track vessel movements through AIS data
and link them to attribute data, has allowed for the development of an inventory of very accurate emission
estimates. These AIS data were used to define the locations of individual vessel movements, estimate
hours of operation, and quantify propulsion engine loads. The compiled AIS data also included the
vessel's International Marine Organization (IMO) number and Maritime Mobile Service Identifier
(MMSI); which allowed each vessel to be matched to their characteristics obtained from the Clarksons
ship registry (Clarksons, 2018).
USEPA used the engine bore and stroke data to calculate cylinder volume. Any vessel that had a
calculated cylinder volume greater than 30 liters was incorporated into the USEPA's new Category 3
Commercial Marine Vessel (C3CMV) model. The remaining records were assumed to represent Category
1 and 2 (C1C2) or non-ship activity. The C1C2 AIS data were quality assured including the removal of
duplicate messages, signals from pleasure craft, and signals that were not from CMV vessels (e.g., buoys,
helicopters, and vessels that are not self-propelled). Following this, there were 422 million records
remaining.
The emissions were calculated for each time interval between consecutive AIS messages for each vessel
and allocated to the location of the message following to the interval. Emissions were calculated
according to Equation 2-1.
g
E miss ions interval = Time (hr)interval x Power {kW) x EF(-j^p x LLAF Equatlon 2-1
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Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and
emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF
represents the low load adjustment factor, a unitless factor which reflects increasing propulsive emissions
during low load operations. Time indicates the activity duration time between consecutive intervals.
Next, vessels were identified in order determine their vessel type, and thus their vessel group, power
rating, and engine tier information which are required for the emissions calculations. See the 2017 NEI
documentation for more details on this process. Following the identification, 108 different vessel types
were matched to the C1C2 vessels. Vessel attribute data was not available for all these vessel types, so the
vessel types were aggregated into 13 different vessel groups for which surrogate data were available as
shown in Table 2-12. 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 2-12. Vessel groups in the cmv_clc2 sector
Vessel Group
NEI Area Ship Count
Bulk Carrier
37
Commercial Fishing
1,147
Container Ship
7
Ferry Excursion
441
General Cargo
1,498
Government
1,338
Miscellaneous
1,475
Offshore support
1,149
Reefer
13
Ro Ro
26
Tanker
100
Tug
3,994
Work Boat
77
Total in Inventory:
11.302
As shown in Equation 2-1, power is an important component of the emissions computation. Vessel-
specific installed propulsive power ratings and service speeds were pulled from 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 2-11.
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,
48
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PMio, PM2.5, CO, CO2, SO2 and VOC were developed using Tables 3-7 through 3-10 in USEPA's (2008)
Regulatory Impact Analysis on engines less than 30 liters per cylinder. To compile these emissions
factors, population-weighted average emission factors were calculated per tier based on C1C2 population
distributions grouped by engine displacement. Boiler emission factors were obtained from an earlier
Swedish Environmental Protection Agency study (Swedish EPA, 2004). If the year of manufacture was
unknown then it was assumed that the vessel was Tier 0, such that actual emissions may be less than those
estimated in this inventory. Without more specific data, the magnitude of this emissions difference cannot
be estimated.
Propulsive emissions from low-load operations were adjusted to account for elevated emission rates
associated with activities outside the engines' optimal operating range. The emission factor adjustments
were applied by load and pollutant, based on the data compiled for the Port Everglades 2015 Emission
Inventory.4 Hazardous air pollutants and ammonia were added to the inventory according to
multiplicative factors applied either to VOC or PM2.5.
The stack parameters used for cmv_clc2 are a stack height of 1 ft, stack diameter of 1 ft, stack
temperature of 70°F, and a stack velocity of 0.1 ft/s. These parameters force emissions into layer 1.
For more information on the emission computations for 2017, see the supporting documentation for the
2017 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, so those files were also generated for 2017.
2.4.2 Category 3 Commercial Marine Vessels (cmv_c3)
This sector contains large engine CMV emissions. Category 3 (C3) marine diesel engines are those at or
above 30 liters per cylinder, typically these are the largest engines rated at 3,000 to 100,000 hp. C3
engines are typically used for propulsion on ocean-going vessels including container ships, oil tankers,
bulk carriers, and cruise ships. Emissions control technologies for C3 CMV sources are limited due to the
nature of the residual fuel used by these vessels.5 The cmv_c3 sector contains sources that traverse state
and federal waters; along with sources in waters not covered by the NEI in surrounding areas of Canada,
Mexico, and international waters.
The cmv_c3 sources that operate outside of state waters but within the federal Emissions Control Area
(ECA) are encoded with a FIPS state code of 85, with the "county code" digits representing broad regions
such as the Atlantic, Gulf of Mexico, and Pacific. The ECA areas include parts of the Gulf of Mexico,
and parts of the Atlantic and Pacific coasts. CMV C3 sources around Puerto Rico, Hawaii and Alaska,
which are outside the ECA areas, are included in the inventory but are in separate files from the emissions
4 USEPA. EPA and Port Everglades Partnership: Emission Inventories and Reduction Strategies. US Environmental
Protection Agency, Office of Transportation and Air Quality, June 2018.
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100UKV8.pdf.
5 https://www.epa.gov/regulations-emissions-vehicles-and-engines/regulations-emissions-marine-vessels.
49
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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 2-13. and distinguish
between diesel and residual fuel, in port areas versus underway, and main and auxiliary engines. In
addition to C3 sources in state and federal waters, the cmv_c3 sector includes emissions in waters not
covered by the NEI (FIPS = 98) and taken from the "ECA-IMO-based" C3 CMV inventory.6 The ECA-
IMO inventory is also used for allocating the FlPS-level emissions to geographic locations for regions
within the domain not covered by the AIS selection boxes as described in the next section.
Table 2-13. SCCs for cmv c3 sector
sec
Tier 1
Description
Tier 2
Description
Tier 3
Description
Tier-l 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
1 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 2017 NEI, the EPA received Automated Identification System (AIS) data from
United States Coast Guard (USCG) to quantify all ship activity which occurred between January 1 and
December 31, 2017. The International Maritime Organization's (IMO's) International Convention for the
Safety of Life at Sea (SOLAS) requires AIS to be fitted aboard all international voyaging ships with gross
tonnage of 300 or more, and all passenger ships regardless of size.7 In addition, the USCG has mandated
that all commercial marine vessels continuously transmit AIS signals while transiting U.S. navigable
6 https://www.epa.gOv/sites/production/files/2017-08/documents/2014v7.0 2014 emismod tsdvl.pdf.
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.
50
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waters. As the vast majority of C3 vessels meet these requirements, any omitted from the inventory due to
lack of AIS adoption are deemed to have a negligible impact on national C3 emissions estimates. The
activity described by this inventory reflects ship operations within 200 nautical miles of the official U.S.
baseline. This boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the
North American EC A, although some non-ECA activity is captured as well (Figure 2-4).
The 2017 NEI data were computed based on the AIS data from the USGS for the year of 2017. The AIS
data were coupled with ship registry data that contained engine parameters, vessel power parameters, and
other factors such as tonnage and year of manufacture which helped to separate the C3 vessels from the
C1C2 vessels. Where specific ship parameters were not available, they were gap-filled. The types of
vessels that remain in the C3 data set include bulk carrier, chemical tanker, liquified gas tanker, oil tanker,
other tanker, container ship, cruise, ferry, general cargo, fishing, refrigerated vessel, roll-on/roll-off, tug,
and yacht.
Prior to use, the AIS data were reviewed - data deemed to be erroneous were removed, and data found to
be at intervals greater than 5 minutes were interpolated to ensure that each ship had data every five
minutes. The five-minute average data provide a reasonably refined assessment of a vessel's movement.
For example, using a five-minute average, a vessel traveling at 25 knots would be captured every two
nautical miles that the vessel travels. For slower moving vessels, the distance between transmissions
would be less.
The emissions were calculated for each C3 vessel in the dataset for each 5-minute time range and
allocated to the location of the message following to the interval. Emissions were calculated according to
Equation 2-2.
g
Emissionsinterval = Time (hr)interval x Power(kW) x x LLAF Equation 2-2
Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and
emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF
represents the low load adjustment factor, a unitless factor which reflects increasing propulsive emissions
during low load operations. Time indicates the activity duration time between consecutive intervals.
Emissions were computed according to a computed power need (kW) multiplied by the time (hr) and by
an engine-specific emission factor (g/kWh) and finally by a low load adjustment factor that reflects
increasing propulsive emissions during low load operations.
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
8 Ammonia (NH3) was also added by SMOKE in the speciation step.
51
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the center of each grid cell, and they were already hourly, no other processing was needed within
SMOKE. SMOKE requires an annual inventory file to go along with the hourly data, so those files were
also generated for 2017.
On January 1st, 2015, the EC A initiated a fuel sulfur standard which regulated large marine vessels to use
fuel with 1,000 ppm sulfur or less. These standards are reflected in the cmv_c3 inventories.
There were some areas needed for modeling that the AIS request boxes did not cover (see Figure 2-4).
These include a portion of the St. Lawrence Seaway transit to the Great Lakes, a small portion of the
Pacific Ocean far offshore of Washington State, portions of the southern Pacific Ocean around off the
coast of Mexico, and the southern portion of the Gulf of Mexico that is within the 36-km domain used for
air quality modeling. In addition, a determination had to be made regarding whether to use the existing
Canadian CMV inventory or the more detailed AlS-based inventory. The AlS-based inventory was used
in the areas for which data were available, and the areas not covered were gap-filled with inventory data
from the 2016beta platform (EPA and NEIC, 2019), which included data from Environment Canada and
the 2011 ECA-IMO C3 inventory.
For the gap-filled areas not covered by AIS selections or the Environment Canada inventory, the 2016
nonpoint C3 inventory was converted to a point inventory to support plume rise calculations for C3
vessels. The nonpoint emissions were allocated to point sources using a multi-step allocation process
because not all of the inventory components had a complete set of county-SCC combinations. In the first
step, the county-SCC sources from the nonpoint file were matched to the county-SCC points in the 2011
ECA-IMO C3 inventory. The ECA-IMO inventory contains multiple point locations for each county-
SCC. The nonpoint emissions were allocated to those points using the PM2.5 emissions at each point as a
weighting factor.
For cmv_c3 underway emissions without a matching FIPS in the ECA-IMO inventory were allocated
using the 12 km 2014 offshore shipping activity spatial surrogate (surrogate code 806). Each county with
underway emissions in the area inventory was allocated to the centroids of the cells associated with the
respective county in the surrogate. The emissions were allocated using the weighting factors in the
surrogate.
The resulting point emissions centered on each grid cell were converted to an annual point 2010 flat file
format (FF10). A set of standard stack parameters were assigned to each release point in the cmv_c3
inventory. The assigned stack height was 65.62 ft, the stack diameter was 2.625 ft, the stack temperature
was 539.6 °F, and the velocity was 82.02 ft/s. Emissions were computed for each grid cell needed for
modeling.
2.4.3 Railway Locomotives (rail)
The rail sector includes all locomotives in the NEI nonpoint data category. This 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.
Typically in the NEI, yard locomotive emissions are split between the nonpoint and point categories, but
52
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for this study, all yard locomotive emissions are represented as point sources and included in the
ptnonipm sector.
This study uses 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. More detailed information on
the development of the 2017 rail inventory for this study is available in the 2017 NEI TSD. The
inventory SCCs are shown in Table 2-14.
Table 2-14. SCCs for the Rail Sector
sec
Sector
Description: Mobile Sources prefix lor all
2285002006
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
2285002007
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
2285002008
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains
(Amtrak)
2285002009
rail
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
2285002010
rail
Railroad Equipment; Diesel; Yard Locomotives (nonpoint)
28500201
rail
Railroad Equipment; Diesel; Yard Locomotives (point)
Class I Line-haul Methodology
In 2008, air quality planners in the eastern US formed the Eastern Technical Advisory Committee
(ERTAC) for solving persistent emissions inventory issues. This work is the fourth inventory created by
the ERTAC rail group. For the 2017 inventory, the Class I railroads granted ERTAC Rail permission to
use the confidential link-level line-haul activity GIS data layer maintained by the Federal Railroad
Administration (FRA) for 2016. In addition, the Association of American Railroads (AAR) provided
national emission tier fleet mix information. This allowed ERTAC Rail to calculate weighted emission
factors for each pollutant based on the percentage of the Class I line-haul locomotives in each USEPA
Tier level category. These two datasets, along with 2017 Class I line-haul fuel use data reported to the
Surface Transportation Board (Table 2-15), were used to create a link-level Class I emissions inventory,
based on a methodology recommended by Sierra Research. Rail Fuel Consumption Index (RFCI) is a
measure of fuel use per ton mile of freight. This link-level inventory is nationwide in extent, but it can be
aggregated at either the state or county level.
53
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Table 2-15. Class I Railroad Reported Locomotive Fuel Use Statistics for 2017
Class I Railroads
2017 R-l Report Line-haul Gross
Ton-Mile
RFCI
Adjusted
RFCI
(ton-miles/gal)
and Fuel Use Activity Data
(ton-miles/gal)
Line-Haul*
Gross Ton-Miles
BNSF
1,322,859,935
1,270,332,339,000
960
850
Canadian National
110,554,757
130,733,042,000
1,183
998
Canadian Pacific
64,373,234
68,787,636,000
1,069
1,260
CSX Transportation
392,481,373
428,879,185,000
1,093
1,075
Kansas City
66,461,739
67,085,372,000
1,009
907
Southern
Norfolk Southern
430,036,855
415,580,691,000
966
920
Union Pacific
927,616,712
981,451,930,000
1,058
1,062
Totals:
3,314,384,605
3,362,850,195,000
1,015
959
* Includes work trains; Adjusted RFCI values calculated from FRA gross ton-mile data. RFCI total is ton-mile weighted mean.
Annual default emission factors for locomotives based on operating patterns ("duty cycles") and the
estimated nationwide fleet mixes for both switcher and line-haul locomotives are available. However,
Tier level fleet mixes vary significantly between the Class I and Class II/III railroads. As can be seen in
Figure 2-5 and Figure 2-6, Class I railroad activity is highly regionalized in nature and is subject to
variations in terrain across the country which can have a significant impact on fuel efficiency and overall
fuel consumption.
Figure 2-5. 2017 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT)
Traffic Density
0.02 - 4.99 MGT
5.00 - 9.99 MGT
10.00 - 19.99 MGT
20.00 -39.99 MGT
40.00 -59.99 MGT
60.00 -99.99 MGT
>= 100.00 MGT
54
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Figure 2-6. Class I Railroads in the United States
For the 2017 inventory, the AAR provided a national line-haul Tier fleet mix profile representing the
entire Class I locomotive fleet. A locomotive's Tier level determines its allowable emission rates based
on the year when it was built and/or re-manufactured. The national fleet mix data was then used to
calculate weighted average in-use emissions factors for the line-haul locomotives operated by the Class I
railroads as shown in Table 2-16.
Table 2-16. 2017 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal)
AAR
Tier Level
Fleet Mix
Ratio
PMio
HC
NOx
CO
Uncontrolled (pre-1973)
0.035628
6.656
9.984
270.4
26.624
Tier 0(1973-2001)
0.170656
6.656
9.984
178.88
26.624
Tier 0+ (Tier 0 rebuilds)
0.151779
4.16
6.24
149.76
26.624
Tier 1 (2002-2004)
0.018282
6.656
9.776
139.36
26.624
Tier 1+ (Tier 1 rebuilds)
0.243995
4.16
6.032
139.36
26.624
Tier 2 (2005-2011)
0.112198
3.744
5.408
102.96
26.624
Tier 2+ (Tier 2 rebuilds)
0.098125
1.664
2.704
102.96
26.624
Tier 3 (2012-2014)
0.123549
1.664
2.704
102.96
26.624
Tier 4 (2015 and later)
0.045789
0.312
0.832
20.8
26.624
2017 Weighted EF's
1.000000
3.944
5.901
134.770
26.624
Based on values in EPA Technical Highlights: Emission Factors for Locomotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April
2009.
Weighted Emission Factors (EF) per pollutant for each gallon of fuel used (grams/gal or lbs/gal) were
calculated for the US Class I locomotive fleet based on the percentage of line-haul locomotives certified
at each regulated Tier level (Equation 2-3).
55
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EFit x Equation 2-3
7=1
where:
EFi = Weighted Emission Factor for pollutant i for Class I locomotive fleet (g/gal).
EFiT = Emission Factor for pollutant i for locomotives in Tier T (g/gal).
fr = Percentage of the Class I locomotive fleet in Tier T expressed as a ratio.
While actual engine emissions will vary within Tier level categories, the approach described above likely
provides reasonable emission estimates, as locomotive diesel engines are certified to meet the emission
standards for each Tier. It should be noted that actual emission rates may increase over time due to
engine wear and degradation of the emissions control systems. In addition, locomotives may be operated
in a manner that differs significantly from the conditions used to derive line-haul duty-cycle estimates.
Emission factors for other pollutants are not Tier-specific because these pollutants are not directly
regulated by USEPA's locomotive emission standards. PM2.5 was assumed to be 97% of PM10, the ratio
of volatile organic carbon (VOC) to (hydrocarbon) HC was assumed to be 1.053, and the emission factors
used for sulfur dioxide (SO2) and ammonia (NH3) were 0.0939 g/gal and 83.3 mg/gal, respectively. The
2017 SO2 emission factor is based on the nationwide adoption of 15 ppm ultra-low sulfur diesel (ULSD)
fuel by the rail industry.
The remaining steps to compute the Class 1 rail emissions involved calculating class I railroad-specific
rail fuel consumption index values and calculating emissions per link. The final link-level emissions for
each pollutant were then aggregated by state/county FIPS code and then converted into an FF10 file
format for input to SMOKE. Detailed documentation methodology for this work is available in the
Specification Sheet: Rail 2017 National Emissions Inventory on the 2017 NEI supporting data FTP site.
Rail yard Methodology
Rail yard emissions were computed based on fuel use and/or yard switcher locomotive counts for the class
I rail companies for all of the rail yards on their systems. Three railroads provided complete rail yard
datasets: BNSF, UP, and KCS. CSX provided switcher counts for its 14 largest rail yards. This reported
activity data was matched to existing yard locations and data stored in USEPA's Emissions Inventory
System (EIS) database. All existing EIS yards that had activity data assigned for prior years, but no
reported activity data for 2017 were zeroed out. New yard data records were generated for reported
locations that were not found in EIS. Special care was made to ensure that the new yards added to EIS
did not duplicate existing data records. Data for non-Class I yards was carried forward from the 2014
NEI. Georgia provided updates on seven rail yards that were incorporated into 2017.
Since the railroads only supplied switcher counts, average fuel use per switcher values was calculated for
each railroad. This was done by dividing each company's 2017 R-l yard fuel use total by the number of
switchers reported for each railroad. These values were then used to allocate fuel use to each yard based
on the number of switchers reported for that location. Table 2-17 summarizes the 2017 yard fuel use and
switcher data for each Class I railroad. The emission factors used for rail yard switcher engines are
shown in Table 2-18.
-------
Table 2-17. Surface Transportation Board R-l Fuel Use Data - 2017
Kiiili'Oiid
2017 Ysinl
l- IK'l I SO (liill)
Iriiwiliricri
SwilclRTS
HK I AC per SuiU'licr 1'iicl
I so (mil)
BNSF
43,946,592
437
luu,5t>4
CSXT
38,404,906
416
92,305
CN
6,893,180
103
66,924
KCS
3,143,526
176
17,860
NS
30,730,245
457
67,243
CPRS
1,267,536
70
18,108
UP
87,707,002
1286
68,201
All Class I's
212,092,987
2,945
61,601
Table 2-18. 2017 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)4
Tier Level
AAR l leel
Mix Uafio
I'M in
IK
NOx
CO
Uncontrolled (pre-1973)
0.2601
6.688
15.352
264.48
27.816
Tier 0 (1973-2001)
0.2361
6.688
15.352
191.52
27.816
Tier 0+ (Tier 0 rebuilds)
0.2599
3.496
8.664
161.12
27.816
Tier 1 (2002-2004)
0.0000
6.536
15.352
150.48
27.816
Tier 1+ (Tier 1 rebuilds)
0.0476
3.496
8.664
150.48
27.816
Tier 2 (2005-2011)
0.0233
2.888
7.752
110.96
27.816
Tier 2+ (Tier 2 rebuilds)
0.0464
1.672
3.952
110.96
27.816
Tier 3 (2012-2014)
0.1018
1.216
3.952
68.4
27.816
Tier 4 (2015 and later)
0.0247
0.228
1.216
15.2
27.816
2017Weighted EF's
0.9999
4.668
11.078
178.1195
27.813
Based on values in EPA Technical Highlights: Emission Factors for Locomotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April
2009. AAR fleet mix ratios did not add up to 1.0000, which caused a small error for the CO weighted emission factor as shown above.
In addition to the Class I rail yards, Emission estimates were calculated for four large Class III railroad
hump yards which are among the largest classification facilities in the United States. These four yards are
located in Chicago (Belt Railway of Chicago-Clearing and Indiana Harbor Belt-Blue Island) and Metro-
East St. Louis (Alton & Southern-Gateway and Terminal Railroad Association of St. Louis-Madison).
Figure 2-7 shows the spatial distribution of active yards in the 2016 version 1 (2016vl) and 2017 NEI
inventories.
57
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Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States
Smr£« F,«a«rai RaAturJ termnafratah
Class II and III Methodology
There are approximately 560 Class II and III Railroads operating in the United States, most of which are
members of the American Short Line and Regional Railroad Association (ASLRRA). While there is a lot
of information about individual Class II and III railroads available online, a significant amount of effort
would be required to convert this data into a usable format for the creation of emission inventories. In
addition, the Class II and III rail sector has been in a constant state of flux ever since the railroad industry
was deregulated under the Staggers Act in 1980. Some states have conducted independent surveys of
their Class II and III railroads and produced emission estimates, but no national level emissions inventory
existed for this sector of the railroad industry prior to ERTAC Rail's work for the 2008 NEI.
Class II and III railroad activities account for nearly 4 percent of the total locomotive fuel use in the
combined ERTAC Rail emission inventories and for approximately 35 percent of the industry's national
freight rail track mileage. These railroads are widely dispersed across the country and often utilize older,
higher emitting locomotives than their Class I counterparts Class II and III railroads provide
transportation services to a wide range of industries. Individual railroads in this sector range from small
switching operations serving a single industrial plant to large regional railroads that operate hundreds of
miles of track. Figure 2-8 shows the distribution of Class II and III railroads and commuter railroads
across the country. This inventory will be useful for regional and local modeling, helps identify where
Class II and III railroads may need to be better characterized, and provides a strong foundation for the
58
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future development of a more accurate nationwide short line and regional railroad emissions inventory. A
picture of the locations of class II and III railroads is shown in Figure 2-8. Detailed documentation
methodology for this work is available in the Specification Sheet: Rail 2017 National Emissions
Inventory on the 2017 Supplemental data FTP site.
Figure 2-8. Class II and III Railroads in the United States3
Commuter Rail Methodology
Commuter rail emissions were calculated in the same way as the Class II and III railroads. The primary
difference is that the fuel use estimates were based on data collected by the Federal Transit
Administration (FTA) for the National Transit Database. For the 2017 NEI, 2016 fuel use was estimated
for each of the commuter railroads shown in Table 2-19 by multiplying the fuel and lube cost total by
0.95, then dividing the result by Metra's average diesel fuel cost of $1.93/gallon. These fuel use estimates
were replaced with reported fuel use statistics for MARC (Maryland), MBTA (Massachusetts), Metra
(Illinois), and NJT (New Jersey). The commuter railroads were separated from the Class II and III
railroads so that the appropriate SCC codes could be entered into the emissions calculation sheet.
Table 2-19. Expenditures and fuel use for commuter rail
IRA
Code
System
Cities Served
Propulsion
Type
DOT Fuel &
Lube Costs
Reported/Estimated
Fuel Use
ACEX
Altamont Corridor
Express
San Jose / Stockton
Diesel
$889,828
437,998.24
CMRX
Capital MetroRail
Austin
Diesel
No data
n/a
DART
A-Train
Denton
Diesel
$0
0.00
59
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1 R\
Code
S\stem
Chios Sen I'd
Propulsion
T\|)i'
DOT 1 ik'I \
1 -ii ho Cosls
Ki'pnrk'd/r.MiniiiU'd
1 m l I si-
DRTD
Denver RTD: A&B
Lines
Denver
Electric
$0
lt.00
JPBX
Caltrain
San Francisco / San Jose
Diesel
$7,002,612
3,446,881.55
LI
MTA Long Island Rail
Road
New York
Electric and
Diesel
$13,072,158
6,434,481.92
MARC
MARC Train
Baltimore / Washington, D.C.
Diesel and
Electric
$4,648,060
4.235.297.57
MBTA
MBTA Commuter Rail
Boston / Worcester / Providence
Diesel
$37,653,001
12.142.826.00
MNCW
MTA Metro-North
Railroad
New York / Yonkers / Stamford
Electric and
Diesel
$13,714,839
6,750,827.49
NICD
NICTD South Shore
Line
Chicago / South Bend
Electric
$181,264
0.00
NIRC
Metra
Chicago
Diesel and
Electric
$52,460,705
25.757.673.57
NJT
New Jersey Transit
New
York / Newark / Trenton / Philadelphia
Electric and
Diesel
$38,400,031
16,991,164.00
NMRX
New Mexico Rail
Runner
Albuquerque / Santa Fe
Diesel
$1,597,302
786,236.74
CFCR
SunRail
Orlando
Diesel
$856,202
421,446.58
MNRX
Northstar Line
Minneapolis
Diesel
$708,855
348,918.26
Not
Coded
SMART
San Rafael-Santa Rosa (Opened 2017)
Diesel
n/a
0.00
NRTX
Music City Star
Nashville
Diesel
$456,099
224,504.69
SCAX
Metrolink
Los Angeles / San Bernardino
Diesel
$19,245,255
9,473,052.98
SDNR
NCTD Coaster
San Diego / Oceanside
Diesel
$1,489,990
733,414.77
SDRX
Sounder Commuter
Rail
Seattle / Tacoma
Diesel
$1,868,019
919,491.22
SEPA
SEPTA Regional Rail
Philadelphia
Electric
$483,965
0.00
SLE
Shore Line East
New Haven
Diesel
No data
n/a
TCCX
Tri-Rail
Miami / Fort Lauderdale / West Palm
Beach
Diesel
$5,166,685
2,543,186.92
TREX
Trinity Railway
Express
Dallas / Fort Worth
Diesel
No data
n/a
UTF
UTA FrontRunner
Salt Lake City / Provo
Diesel
$4,044,265
1,990,700.39
VREX
Virginia Railway
Express
Washington, D.C.
Diesel
$3,125,912
1,538,661.35
WSTX
Westside Express
Service
Beaverton
Diesel
No data
n/a
*Reported fuel use values were used for MARC, MBTA, Metra, and New Jersey Transit.
60
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Intercity Passenger Methodology (Amtrak)
2016 and 2017 marked the first times that a nationwide intercity passenger rail emissions inventory was
created for Amtrak. The calculation methodology mimics that used for the Class II and III and commuter
railroads with a few modifications. Since link-level activity data for Amtrak was unavailable, the default
assumption was made to evenly distribute Amtrak's 2016 reported fuel use across all of it diesel-powered
route-miles shown in Figure 2-9. Participating states were instructed that they could alter the fuel use
distribution within their jurisdictions by analyzing Amtrak's 2016 national timetable and calculating
passenger train-miles for each affected route. Illinois and Connecticut chose to do this and were able to
derive activity-based fuel use numbers for their states based on Amtrak's 2016 reported average fuel use
of 2.2 gallons per passenger train-mile. In addition, Connecticut provided supplemental data for selected
counties in Massachusetts, New Hampshire, and Vermont. Amtrak also submitted company-specific fleet
mix information and company-specific weighted emission factors were derived. Amtrak's emission rates
were 25% lower than the default Class II and III and commuter railroad emission rate. Detailed
documentation methodology for this work is available in the Specification Sheet: Rail 2017 National
Emissions Inventory on the 2017 NEI supporting data FTP site.
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains
5F«faad Rtirtiad . Xnt HI*
Other Data Sources
The California Air Resources Board (CARB) provided rail inventories for inclusion in the 2017 NEI.
C ARB's rail inventories were used in California, in place of the national dataset described above. For rail
yards, the national point source rail yard dataset was used to allocate CARB-submitted rail yard emissions
to point sources where possible. That is, for each California county with at least one rail yard in the
61
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national dataset, the emissions in the national rail yard dataset were adjusted so that county total rail yard
emissions matched the CARB dataset. In other words, county total rail yard emissions from CARB are
used, but the locations of rail yards are based on the national methodology. There are three counties with
CARB-submitted rail yard emissions, but no rail yard locations in the national dataset; for those counties,
the rail yard emissions were included in the rail sector using SCC 2285002010.
HAPs were not provided with the rail inventory but were augmented for the NEI. For VOC speciation, the
EPA preferred augmenting the inventory with HAPs and using those HAPs for integration, rather than
running the sector as a no-integrate sector.
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 MOVES2014b9 which incorporates
the NONROAD model. MOVES2014b 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. MOVES was used
for all states other than California, which developed their own emissions using their own tools. VOC and
PM speciation profile assignments are determined by MOVES and applied by SMOKE. The fuels data in
MOVES3 for nonroad vehicles is slightly updated from the MOVES2014b fuels for nonroad vehicles.
MOVES2014b 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 part of the NEI
but are used for speciation. MOVES2014b provides estimates of NONHAPTOG along with the speciation
profile code for the NONHAPTOG emission source. This was accomplished by using NHTOG#### as
the pollutant code in the Flat File 2010 (FF10) inventory file that can be read into SMOKE, where #### is
a speciation profile code. For California, NHTOG####-VOC and HAP-VOC ratios from MOVES-based
emissions were applied to VOC emissions from California so that VOC emissions in California can be
speciated consistently with other states.
MOVES2014b, unlike MOVES2014a, also provides estimates of PM2.5 by speciation profile code for the
PM2.5 emission source, using PM25_#### as the pollutant code in the FF10 inventory file, where #### is
a speciation profile code. To facilitate calculation of 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.
9 https://www.epa.gov/moves.
62
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• 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 that are not needed for air quality modeling, such as
dioxins and furans, were removed from the inventory.
• To reduce the size of the inventory further, all emissions for sources (identified by county/SCC)
for which total CAP emissions are less than 1*10"10 were removed from the inventory. The
MOVES model attributes a very tiny amount of emissions to sources that are actually zero, for
example, snowmobile emissions in Florida. Removing these sources from the inventory reduces
the total size of the inventory by about 7%.
• Gas and particulate components of HAPs that come out of MOVES separately, such as
naphthalene, were combined.
• VOC was renamed VOC INV so that SMOKE does not speciate both VOC and NONHAPTOG,
which would result in a double count.
• PM25TOTAL, referenced above, was also created at this stage of the process.
• 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 methodology for developing Agricultural equipment allocation data for the 2016vl platform was
developed by the North Carolina Department of Environmental Quality (NCDEQ). EPA updated the
Construction equipment allocation data used in MOVES for the 2016vl platform and the same updated
data were used in the 2017 NEI.
NCDEQ compiled regional and state-level Agricultural sector fuel expenditure data for 2016 from the US
Department of Agriculture, National Agricultural Statistics Service (NASS), August 2018 publication,
"Farm Production Expenditures 2017 Summary."10 This resource provides expenditures for each of 5
major regions that cover the Continental U.S., as well as state-level data for 15 major farm producing
states. Because of the limited coverage of the NASS source relative to that in MOVES, it was necessary to
identify a means for estimating the 2016 Agricultural sector allocation data for the following States and
Territories from a different source: Alaska, Hawaii, Puerto Rico, and U.S. Virgin Islands. The approach
for these areas is described below.
For the Continental U.S., NCDEQ first allocated the remainder of the regional fuel expenditures to states
in each region for which state-level data are not reported. For this allocation, NCDEQ relied on 2012 fuel
expenditure data from NASS' 2012 Census of Agriculture (note that 2017 data were not yet available at
10 Accessed from htto://usda.mannlib.Cornell.edu/MannUsda/viewDocumentlnfo.do?documentID= 1066. November 2018.
63
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the time of this effort).11 The next step to developing county-level allocation data for agricultural
equipment was to multiply the state-level fuel expenditure estimates by county-level allocation ratios.
These allocation ratios were computed from county-level fuel expenditure data from the NASS' 2012
Census of Agriculture. There were 17 counties for which fuel expenditure data were withheld in the
Census of Agriculture. For these counties, NCDEQ allocated the fuel expenditures that were not
accounted for in the applicable state via a surrogate indicator of fuel expenditures. For most states, the
2012 Census of Agriculture's total machinery asset value was the surrogate indicator used to perform the
allocation. This indicator was found to have the strongest correlation to agricultural sector fuel
expenditures based on analysis of 2012 state-level Census of Agriculture values for variables analyzed
(correlation coefficient of 0.87).12 Because the analyzed surrogate variables were not available for the two
counties in New York without fuel expenditure data, farm sales data from the 2012 Census of Agriculture
were used in the allocation procedure for these counties.
For Alaska and Hawaii, NCDEQ estimated 2016 state-level fuel production expenditures by first applying
the national change in fuel expenditures between 2012 and 2016 from NASS' "Farm Production
Expenditures" summary publications to 2012 state expenditure data from the 2012 Census of Agriculture.
Next, NCDEQ applied an adjustment factor to account for the relationship between national 2012 fuel
expenditures as reported by the Census of Agriculture and those reported in the Farm Production
Expenditures Summary. Hawaii's state-level fuel expenditures were allocated to counties using the same
approach as the states in the Continental U.S. (i.e., county-level fuel expenditure data from the NASS'
2012 Census of Agriculture). Alaska's fuel expenditures total was allocated to counties using a different
approach because the 2012 Census of Agriculture reports fuel expenditures data for a different list of
counties than the one included in MOVES. To ensure consistency with MOVES, NCDEQ allocated
Alaska's fuel expenditures based on the current allocation data in MOVES, which reflect 2002 harvested
acreage data from the Census of Agriculture.
Because NCDEQ did not identify any source of fuel expenditures data for Puerto Rico or the U.S. Virgin
Islands, the county allocation percentages that are represented by the 2002 MOVES allocation data were
used for these territories.13
For the Construction sector, by default MOVES2014b used estimates of 2003 total dollar value of
construction by county to allocate national Construction equipment populations to the state and local
levels.14 The 2016 National Emissions Inventory Collaborative (NEIC) Nonroad Work Group sought to
update the surrogate data used to geographically allocate Construction equipment with a more recent data
source thought to be more reflective of emissions-generating Construction equipment activity at the
county level: acres disturbed by residential, non-residential, and road construction activity.
The nonpoint sector of the National Emissions Inventory (NEI) includes estimates of Construction Dust
(PM2.5), for which acreage disturbed by residential, non-residential, and road construction activity is a
function.15 The 2017 NEI Technical Support Document (EPA, 2021) includes a description of the
methods used to estimate acreage disturbed at the county level by residential, non-residential, and road
construction activity, for the 50 states.
11 Accessed from https://www.nass.usda.gov/Publications/AgCensus/2012/. November 2018.
12 Other variables analyzed were inventory of tractors and inventory of trucks.
13 For reference, these allocations were 0.0639 percent for Puerto Rico and 0.0002 percent for the U.S. Virgin Islands.
14 https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P1004LDX.pdf.
15 https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventorv-nei-data.
64
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Acreage disturbed by residential, non-residential, and road construction were summed together to arrive at
a single value of acreage disturbed by Construction activities at the county level. County-level acreage
disturbed were then summed together to arrive at acreage disturbed at the state level. State totals were
then summed to arrive at a national total of acreage disturbed by Construction activities.
Puerto Rico and the U.S. Virgin Islands are not included in the Construction equipment geographic
allocation update, so their relative share of the national population of Construction equipment remains the
same as MOVES2014b defaults.
For both the Agricultural and Construction equipment sectors, the surrogatequant and surrogateyearlD
fields in the model's nr state surrogate table, which allocates equipment from the state- to the county-level,
were populated with the county-level surrogates described above (fuel expenditures in 2016 for
Agricultural equipment; acreage disturbed by construction activity in 2014 for Construction equipment).
In addition, the nrbaseyearequippopulation table, which apportions the model's national equipment
populations to the state level, was adjusted so that each state's share of the MOVES base-year national
populations of Agricultural and Construction equipment is proportional to each state's share of national
acreage disturbed by construction activity (Construction equipment) and agricultural fuel expenditures
(Agricultural equipment). Additionally, the model" s nr surrogate table, which defines the surrogate data
used in the nr state surrogate table, was updated to reflect the changes to the Agricultural and Construction
equipment sectors made as part of the 2016vl platform development process.
Updated nrsurrogate, nr state surrogate, 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/).
State-Supplied Nonroad Data
As shown Table 2-20. several state and local agencies provided nonroad inputs for use in the 2017 NEI.
Additionally, per the table footnotes, EPA reviewed data submitted by state and local agencies for the
2014 and 2017 National Emissions Inventories and utilized that information where appropriate.
65
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Table 2-20. Submitted nonroad input tables by agency
Mill
ciri
Stale or
( ouniMies) in
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4
ARIZONA -
Maricopa Co.
A
X
A
A
A
A
A
9
CONNECTICU
A
13
GEORGIA
A
A
16
IDAHO
c
17
ILLINOIS
D
18
INDIANA
c
D
19
IOWA
c
D
26
MICHIGAN
c
D
27
MINNESOTA
A
c
D
29
MISSOURI
D
36
NEW YORK
A
A
X
A
A
A
A
A
39
OHIO
c
D
48
TEXAS
A
A
X
A
A
A
49
UTAH
B
A
A
A
A
E
53
WASHINGTO
A
A
A
55
WISCONSIN
D
A Submitted data.
r>
Submitted data with modification: deleted records that were not snowmobile source types 1002-1010.
c 2014NEIv2 data used for 2017 NEI.
D Spreadsheet "ladco_nei2017_nrmonthallocation.xlsx" (see discussion below)
17
Submitted data with modification: deleted records that were not the snowmobile surrogate ID 14.
v
Submitted data not used in 2017 NEI. The GA NRFuelSupply table is only used to divide counties into groups.
Emissions Inside California
California nonroad emissions were provided by CARB for 2017.
All California nonroad inventories are annual, with monthly temporalization applied in SMOKE.
Emissions for oil field equipment (SCCs ending in -10010) were removed from the California inventory
in order to prevent a double count with the np oilgas sector. VOC and PM2.5 emissions were allocated to
speciation profiles, and VOC HAPs were created, using MOVES data in California. For example, ratios
of VOC (PM2.5) by speciation profile to total VOC (PM2.5), and ratios of VOC HAPs to total VOC, were
calculated by county and SCC from the MOVES run in California, and then applied CARB-provided
VOC (PM2.5) in the inventory so that California nonroad emissions could be speciated consistently with
the rest of the country.
66
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Nonroad Updates from State Comments
The 2016 Nonroad Collaborative workgroup received a small number of comments on the 2016beta
inventory (EPA and NEIC, 2019), all of which were addressed and implemented in the 2017 NEI nonroad
inventory:
• Georgia Department of Natural Resources: utilize updated geographic allocation factors
(,nrstatesurrogate table) for the Commercial, Lawn & Garden (commercial, public, and
residential), Logging, Manufacturing, Golf Carts, Recreational, Railroad Maintenance Equipment
and A/C/Refrigeration sectors, using data from the U.S. Census Bureau and U.S. Forest Service.
• Lake Michigan Air Directors Consortium (LADCO): update seasonal allocation of agricultural
equipment activity (nrmonthallocation table) for Illinois, Indiana, Iowa, Michigan, Minnesota,
Missouri, Ohio, and Wisconsin.
• Texas Commission on Environmental Quality: replace MOVES nonroad emissions for Texas
with emissions calculated with TCEQ's TexN2 model.
• Alaska Department of Environmental Conservation: remove emissions as calculated by
MOVES for several equipment sector-county/census areas combinations in Alaska, due to an
absence of nonroad activity (see Table 2-21).
Table 2-21. Alaska counties/census areas for which nonroad equipment sector-specific emissions
were removed
\<>nro:ul Kqiiipmenl Sector
Counties/Census A rests (I'll'S) lor which equipment
seeloi- emissions :irc remoxeil in 201ft
Agricultural
Aleutians East (02013), Aleutians West (02016), Bethel
Census Area (02050), Bristol Bay Borough (02060),
Dillingham Census Area (02070), Haines Borough (02100),
Hoonah-Angoon Census Area (02105), Ketchikan Gateway
(02130), Kodiak Island Borough (02150), Lake and
Peninsula (02164), Nome (02180), North Slope Borough
(02185), Northwest Arctic (02188), Petersburg Borough
(02195), Pr of Wales-Hyder Census Area (02198), Sitka
Borough (02220), Skagway Borough (02230), Valdez-
Cordova Census Area (02261), Wade Hampton Census Area
(02270), Wrangell City + Borough (02275), Yakutat City +
Borough (02282), Yukon-Koyukuk Census Area (02290)
Logging
Aleutians East (02013), Aleutians West (02016), Nome
(02180), North Slope Borough (02185), Northwest Arctic
(02188), Wade Hampton Census Area (02270)
Railway Maintenance
Aleutians East (02013), Aleutians West (02016), Bethel
Census Area (02050), Bristol Bay Borough (02060),
Dillingham Census Area (02070), Haines Borough (02100),
Hoonah-Angoon Census Area (02105), Juneau City +
Borough (02110), Ketchikan Gateway (02130), Kodiak
67
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Nonroiul Kqiiipmenl Sector
Counties/Census A rests (MI'S) lor which equipment
sectoi- emissions sire remo\eil in 201ft
Island Borough (02150), Lake and Peninsula (02164), Nome
(02180),), North Slope Borough (02185), Northwest Arctic
(02188), Petersburg Borough (02195), Pr ofWales-Hyder
Census Area (02198), Sitka Borough (02220), Southeast
Fairbanks (02240), Wade Hampton Census Area (02270),
Wrangell City + Borough (02275), Yakutat City + Borough
(02282), Yukon-Koyukuk Census Area (02290)
2.5 Fires (ptfire, ptagfire)
Multiple types of fires are represented in the modeling platform. These include wild and prescribed fires
that are grouped into the ptfire sector, and agricultural fires that comprise the ptagfire sector. All ptfire
and ptagfire fires are in the United States. Fires outside of the United States are described in the
ptfire othna sector later in this document.
2.5.1 Wild and Prescribed Fires (ptfire)
Wildfire and prescribed burning emissions are contained in the ptfire sector. The ptfire sector has emissions
provided at geographic coordinates (point locations) and has daily emissions values. The ptfire 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 sources are shown in Table 2-22. The ptfire inventory includes separate
SCCs for the flaming and smoldering combustion phases for wildfire and prescribed burns. Note that
prescribed grassland fires or Flint Hills, Kansas have their own SCC (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 2017 inventory
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
Inputs to SMARTFIRE for 2017 include (see Section 7 of the 2017 NEI TSD for more details):
• The National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping System
(HMS) fire location information
68
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• GeoMAC (Geospatial Multi-Agency Coordination), an online wildfire mapping application
designed for fire managers to access maps of current fire locations and perimeters in the
United States
• The Incident Status Summary, also known as the "ICS-209", used for reporting specific
information on fire incidents of significance
• Incident reports including dates of fire activity, acres burned, and fire locations from the
National Association of State Foresters (NASF)
• Hazardous fuel treatment reduction polygons for prescribed bums from the Forest Service
Activity Tracking System (FACTS)
• Fire activity on federal lands from the United States Fish and Wildlife Service (USFWS)
• Wildfire and prescribed date, location, and locations from S/L/T activity submitters
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 2017 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) and BlueSky Framework.
GeoMAC (Geospatial Multi-Agency Coordination) is an online wildfire mapping application designed for
fire managers to access maps of current U.S. fire locations and perimeters. The wildfire perimeter data is
based upon input from incident intelligence sources from multiple agencies, GPS data, and infrared (IR)
imagery from fixed wing and satellite platforms.
The Incident Status Summary, also known as the "ICS-209" is used for reporting specific information on
significant fire incidents. The ICS-209 report is a critical interagency incident reporting tool giving daily
'snapshots' of the wildland fire management situation and individual incident information which include
fire behavior, size, location, cost, and other information. Data from two tables in the ICS-209 database
were merged and used for the 2016vl ptfire inventory: the
SIT209_HISTORY_INCIDENT_209_REPORTS table contained daily 209 data records for large fires,
and the SIT209 HISTORY INCIDENTS table contained summary data for additional smaller fires.
The National Association of State Foresters (NASF) is a non-profit organization composed of the
directors of forestry agencies in the states, U.S. territories, and District of Columbia to manage and protect
state and private forests, which encompass nearly two-thirds of the nation's forests. The NASF compiles
fire incident reports from agencies in the organization and makes them publicly available. The NASF fire
information includes dates of fire activity, acres burned, and fire location information.
Monitoring Trends in Burn Severity (MTBS) is an interagency program whose goal is to consistently map
the burn severity and extent of large fires across the U.S. from 1984 to present. The MTBS data includes
all fires 1,000 acres or greater in the western United States and 500 acres or greater in the eastern United
States. The extent of coverage includes the continental U.S., Alaska, Hawaii, and Puerto Rico. Fire
69
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occurrence and satellite data from various sources are compiled to create numerous MTBS fire products.
The MTBS Burned Areas Boundaries Dataset shapefiles include year 2017 fires and that are classified as
either wildfires, prescribed burns or unknown fire types. The unknown fire type shapes were omitted in
the 2016vl inventory development due to temporal and spatial problems found when trying to use these
data.
The US Forest Service (USFS) compiles a variety of fire information every year. Year 2016 data from the
USFS Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and
used for 2016vl emissions inventory development. This database includes information about activities
related to fire/fuels, silviculture, and invasive species. The FACTS database consists of shapefiles for
prescribed burns that provide acres burned and start and ending time information.
The US Fish and Wildland Service (USFWS) also compiles wildfire and prescribed burn activity on their
federal lands every year. Year 2017 data were acquired from USFWS through direct communication with
USFWS staff and were used for 2017 emissions inventory development. The USFWS fire information
provided fire type, acres burned, latitude-longitude, and start and ending times.
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 2017 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 2-10 is a schematic of the data processing stream for the inventory of wildfire and prescribed burn
sources. The ptfire inventory sources were estimated using Satellite Mapping Automated Reanalysis Tool
for Fire Incident Reconciliation version 2 (SMARTFIRE2) and Blue Sky Framework. SMARTFIRE2 is
an algorithm and database system that operate within a geographic information system (GIS).
SMARTFIRE2 combines multiple sources of fire information and reconciles them into a unified GIS
database. It reconciles fire data from space-borne sensors and ground-based reports, thus drawing on the
strengths of both data types while avoiding double-counting of fire events. At its core, SMARTFIRE2 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 2017 inventory, the national and S/L/T fire information was input into SMARTFIRE2 and then
merged and associated based on user-defined weights for each fire information dataset. The output from
SMARTFIRE2 was daily acres burned by fire type, and latitude-longitude coordinates for each fire. The
fire type assignments were made using the fire information datasets. If the only information for a fire was
a satellite detect for fire activity, then the flow described in Figure 2-11 was used to make fire type
assignment by state and by month.
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Figure 2-10. Processing flow for fire emission estimates in the 2017 inventory
Input Data Sets
(state/local/tribal and national data sets)
Fuel Moisture and
Fuel Loading Data
*
Smoke Modeling (BlueSky Framework)
Daily smoke emissions
for each fire
Emissions Post-Processing
Final Wildland Fire Emissions Inventory
Data Preparation
O 0
Data Aggregation and Reconciliation
(SmartFire2)
Daily fire locations
with fire size and type
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Figure 2-11. Default fire type assignment by state and month where data are only from satellites.
Default Fire Type
Assignment
WF Months
¦ 4,5,6,7
~ 5,6,7,8
~ 5,6,7,8,9,10
~ 6,7,8
¦ None
The second system used to estimate emissions is the BlueSky Modeling Framework version 3.5 (revision
#38169). 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 3-2. The Fire Emissions
Production Simulator (FEPS) in the BlueSky Framework generates all the CAP emission factors for
wildland fires used in the 2017 inventoiy. HAP emission factors were obtained from Urbanski's (2014)
work and applied by region and by fire type.
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Figure 2-12. Blue Sky Modeling Framework
The FCCSv3 cross-reference was implemented along with the LANDFIREvl.4 (at 200 meter resolution)
to provide better fuel bed information for the BlueSky Framework (BSF). The LANDFIREvl .4 was
aggregated from the native resolution and projection to 200 meter using a nearest-neighbor methodology.
Aggregation and reprojection was required for the proper function on BSF.
The final products from this process are annual and daily FF 10-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 2017 NEI in a similar way to the emissions in ptfire. State-provided agricultural fire data from
the NEI are not used in this study.
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
SCC
Description
2801500000
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Unspecified crop type and Burn Method
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see
Description
2801500141
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Field Crop is Bean (red): Headfire Burning
2801500150
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Field Crop is Corn: Burning Techniques Not Important
2801500151
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Double Crop Winter Wheat and Corn
2801500152
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; DoubleCrop Corn and Soybeans
2801500160
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; Field Crop is Cotton: Burning Techniques Not Important
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
2801500263
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; DoubleCrop Winter Wheat and Cotton
2801500264
Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning -
whole field set on fire; DoubleCrop Winter Wheat and Soybeans
The EPA estimated biomass burning emissions using remote sensing data. These estimates were then
reviewed by the states and revised as resources allowed. As many states did not have the resources to
estimate emissions for this sector, remote sensing was necessary to fill in the gaps for regions where there
was no other source of data. Crop residue emissions result from either pre-harvest or post-harvest burning
of agricultural fields. The crop residue emission inventory for 2017 is day-specific and includes
geolocation information by crop type. The method employed and described here is based on the same
methods employed in the 2017 NEI. It should be noted that grassland fires were moved from the
agricultural burning inventory sector to the prescribed and wildland fire sector . This was done to prevent
double-counting of fires and because there are wild grassland fires in some parts of the USA.
Daily, year-specific agricultural burning emissions were derived from HMS fire activity data, which
contains the date and location of remote-sensed anomalies. As point source inventories, the locations of
the fires are identified with latitude-longitude coordinates for specific fire events. The HMS activity data
were filtered using 2016 USD A cropland data layer (CDL). Satellite fire detects over agricultural lands
were assumed to be agricultural burns and assigned a crop type. Detects that were not over agricultural
lands were output to a separate file for use in the point source wildfire (ptfire) inventory sector. Each
detect was assigned an average size of between 40 and 80 acres based on crop type. The assumed field
sizes are found in Table 2-24.
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Table 2-24. Assumed field size of agricultural fires per state(acres)
Suite
Held Si/e
Alabama
40
Arizona
80
Arkansas
40
California
120
Colorado
80
Connecticut
40
Delaware
40
Florida
60
Georgia
40
Idaho
120
Illinois
60
Indiana
60
Iowa
60
Kansas
80
Kentucky
40
Louisiana
40
Maine
40
Maryland
40
Massachusetts
40
Michigan
40
Minnesota
60
Mississippi
40
Missouri
60
Montana
120
Nebraska
60
Nevada
40
New Hampshire
40
New Jersey
40
New Mexico
80
New York
40
North Carolina
40
North Dakota
60
Ohio
40
Oklahoma
80
Oregon
120
Pennsylvania
40
Rhode Island
40
South Carolina
40
South Dakota
60
Tennessee
40
Texas
80
Utah
40
Vermont
40
Virginia
40
Washington
120
West Virginia
40
Wisconsin
40
Wyoming
80
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Another feature of the ptagfire database is that the satellite detections for 2017 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 2017 meteorological Weather Research Forecast (WRF) model simulation.
The locations of fire detections were then compared with this daily snow cover file. For any day in which
a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the
inventory. Due to the inconsistent reporting of fire detections for year 2016 from the Visible Infrared
Imaging Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as
VIIRS or Suomi National Polar-orbiting Partnership satellite were excluded. In addition, certain crop
types (corn and soybeans) 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.
Emissions factors were applied to each daily fire to calculate criteria and hazardous pollutant values.
These factors vary by crop type. In all prior NEIs for this sector, the HAP emission factors and the VOC
emission factors were known to be inconsistent. Corrections have been made to the application of the
HAP emissions factors for the 2017 NEI. Please see section 7.4.4 in the 2017 NEI TSD for the details of
the corrections.
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.
The state of Georgia provided their own estimates of agricultural crop residue burning and completely
replaced the emission estimates by the EPA. The HMS information was replaced with the state-supplied
activity data and the emissions were recomputed for this state. See section 4.12 in 2017 NEI TSD for
more details on agricultural crop residue burning.
For this modeling platform, a SMOKE update allows the use of HAP integration for speciation for
PTDAY inventories. The 2017 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 17j version of the 2017 meteorology data used for the air
quality modeling and were developed using the Biogenic Emission Inventory System version 3.7
(BEIS3.7) within CMAQ. The BEIS3.7 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 BEIS 3.7 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). The new algorithm requires additional
meteorological variables over previous versions of BEIS.
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BEIS3.7 was used in conjunction with Version 5 of the Biogenic Emissions Landuse Database (BELD5).
The BELD5 is based on an updated version of the USDA-USFS Forest Inventory and Analysis (FIA)
vegetation speciation-based data from 2001 to 2017 from the FIA version 8.0. Canopy coverage is based
on the 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). The FIA includes approximately 250,000 representative plots
of species fraction data that are within approximately 75 km of one another in areas identified as forest by
the MODIS canopy coverage. For land areas outside the conterminous United States, 500 meter grid
spacing land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is used.
BELDv5 also incorporates the following datasets:
• Newer version of the Forest Inventory and Analysis (FIA version 8.0
https://www.fia.fs.fed.us/library/database-documentation/index.php)
Agricultural land use from the 2017 US Department of Agriculture (USDA) crop data layer
(https://www.nass.usda.gov/Research_and_Science/Cropland/SARSla.php)
Global Moderate Resolution Imaging Spectroradiometer (MODIS) 20 category data with
enhanced lakes and Fraction of Photosynthetically Active Radiation (FPAR) for vegetation
coverage from National Center for Atmospheric Research (NCAR)
(https://www2.mmm.ucar.edu/wrf/users/download/get sources wps geog.html)
o Note BELD4.1 used 2011 USGS National Land Cover Data (NLCD) limited to the USA
and MODIS 20 category land use for the rest of the world.
Canadian BELD land use (https://www.epa.gov/sites/default/files/2019-
08/documents/800am zhang 2 O.pdf).
The FIA database reports on status and trends in forest area and location; in the species, size, and health
of trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates
by various products; and in forest land ownership. The FIA database version 8.0 includes recent updates
of these data through the year 2017 (from 2001). Earlier versions of BELD used an older version of the
FIA database that had included data only through the year 2014. Canopy coverage is based on the
MODIS 20 category data. The FIA includes approximately 250,000 representative plots of species
fraction data that are within approximately 75 km of one another in areas identified as forest by the
MODIS canopy coverage. For all land areas in the United States, 500-meter grid spacing land cover data
from the MODIS is used.
Like BELD4, the processing of the BELD5 data follows the spatial allocation methods of Bash et al.
2016. However, MODIS land use categories and FPAR are used in the place of NLCD land use and forest
coverage. MODIS land use has the additional broadleaf evergreen and deciduous needleleaf land use
types and only one developed land use type. BELD4.1 used lookup tables for species leaf biomass. In
BELD5, allometric relationships from the FIA v8.0 database (https://www.fia.fs.fed.us/library/database-
documentation/index.php) were utilized to estimate foliage biomass per species. This resulted in better
agreement with measured foliage biomass. BVOC emissions are understood to originate from foliage
thus these biomass changes directly impacted the BEIS emission factors.
BEIS3.7 has some important updates from BEIS 3.61. These include the incorporation BELD5 and
updates to biomass emissions factors. BEIS3.7 includes a two-layer canopy model. Layer structure varies
with light intensity and solar zenith angle. Both layers of the canopy model include estimates of sunlit
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and shaded leaf area based on solar zenith angle and light intensity, direct and diffuse solar radiation, and
leaf temperature (Bash et al., 2016). The new algorithm requires additional meteorological variables over
previous versions of BEIS. The variables output from the Meteorology-Chemistry Interface Processor
(MCIP) that are used for BEIS3.7 processing are shown in Table 2-25. The BEIS3 modeling for year
2017 included processing for a 12km domain (12US1) (see Figure 3-1). The 12US2 modeling domain
can also be supported by taking a subset or window of the 12US1 BEIS3 emissions dataset.
Table 2-25. Hourly Meteorological variables required by BEIS 3.7
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2 m
RC
convective precipitation
RGRND
solar radiation reaching surface
RN
non-convective precipitation
RSTOMI
inverse of bulk stomatal resistance
SLYTP
soil texture type by USD A category
SOIM1
volumetric soil moisture in top cm
SOIT1
soil temperature in top cm
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 m
SMOKE-BEIS3 modeling system consists of two programs named: 1) Normbeis3 and 2) Tmpbeis3.
Normbeis3 uses emissions factors and BELD5 landuse to compute gridded normalized emissions for
chosen model domain (see Figure 2-13). The emissions factor file contains leaf-area-indices (LAI), dry
leaf biomass, winter biomass factor, indicator of specific leaf weight, and normalized emission fluxes for
35 different species/compounds. The BELD5 file is the gridded landuse for 200+ different landuse
types. The output gridded domain is the same as the input domain for the land use data. Output
emission fluxes (B3GRD) are normalized to 30 °C, and isoprene and methyl-butenol fluxes are also
normalized to a photosynthetic active radiation of 1000 |imol/m2s.
Figure 2-13. Normbeis3 data flows
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The normalized emissions output from Normbeis3 (B3GRD) are input into Tmpbeis3 along with the
MCIP meteorological data, chemical speciation profile to use for desired chemical mechanism, and
BIOSEASON file used to indicate how each day in the year being modeled should be treated, either as
summer or winter. Figure 2-14 illustrates the data flows for the Tmpbeis3 program. The output from
Tmpbeis includes gridded, speciated, hourly emissions both in moles/second (B3GTS L) and tons/hour
(B3GTSS).
Figure 2-14. Tmpbeis3 data flow diagram.
( Program )
F* I
j Optional j
Shows inpul or autpul
Biogenic emissions do not use an emissions inventory and do not have SCCs. The gridded land use data,
gridded meteorology, an emissions factor file, and a speciation profile are further described in the
speciation section.
Biogenic emissions computed with BEIS 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.
2.7 Sources Outside of the United States
The emissions from Canada and Mexico are included as part of the emissions modeling sectors: othpt,
othar, othafdust, othptdust, onroadcan, and onroadmex. The "oth" refers to the fact that these emissions
are usually "other" than those in the U.S. state-county geographic FIPS, and the remaining characters
provide the SMOKE source types: "pt" for point, "ar" for area and nonroad mobile, "afdust" for area
fugitive dust (Canada only), and "ptdust" for point fugitive dust (Canada only). The onroad emissions for
Canada and Mexico are in the onroad can and onroad mex sectors, respectively.
Environment and Climate Change Canada (ECCC) provided a set of inventories for the year 2015. ECCC
also provided data for the year 2023, either in the form of standalone inventories, or projection factors to
apply to the 2015 data. Those 2015 and 2023 inventories were interpolated to the year 2017 for all
Canadian inventories used in this study except for fires and CMV.
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ECCC provided the following 2015 inventories for use in this study:
Agricultural livestock and fertilizer, point source format (othpt sector)
Agricultural fugitive dust, point source format (othptdust sector)
Other area source dust (othafdust sector)
Airports, point source format (othpt sector)
Onroad (onroad can sector)
- Nonroad and rail (othar sector)
CMV, provided as area sources but converted to point (othpt sector)
Other area sources (othar sector)
Other point sources, including oil and gas (othpt sector)
ECCC provided all CMV emissions as an area source inventory. The 2017 NEI CMV included most
coastal waters of Canada and Mexico with emissions derived from AIS data. These NEI emissions were
used for all areas of Canada and Mexico in which they were available 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
points at the center of the 12km grid cells. Activity and corresponding emissions along the St. Lawrence
Seaway were not included in the NEI. This region was gapfilled with emissions provided by ECCC that
were apportioned to point sources on the centroids of 12km grid cells using the Canadian commercial
marine vessel surrogate (CA 945).
In addition to emissions inventories, the ECCC 2015 dataset also included temporal profiles, and
shapefiles for creating spatial surrogates. These updated profiles and surrogates were used for this study.
Other than the CB6 species of NBAFM present in the speciated point source data, there are no explicit
HAP emissions in these Canadian inventories.
2.7.1 Point Sources in Canada and Mexico (othpt)
Canadian point source inventories were interpolated to 2017 between 2015 and 2023 emission levels.
These inventories include emissions for airports and other point sources. One of the Canadian point
source inventories in the othpt sector includes pre-speciated VOC emissions for the CB6 mechanism.
However, this inventory did not include all species needed for the CB6 mechanism for CMAQ;
specifically, CH4, SOAALK, NAPH, and XYLMN were missing. For the NAPH species, naphthalene
emissions from a supplemental HAP inventory provided by ECCC were used. Then, XYL was converted
to XYLMN by subtracting NAPH. Finally, CH4 and SOAALK were speciated from total VOC (also
provided by ECCC) using traditional speciation profiles by SCC. There are also other sources in Canada,
such as oil and gas, for which we do not have pre-speciated VOC emissions and for which we apply VOC
speciation within SMOKE.
Point sources in Mexico were compiled based on inventories projected from the Inventario Nacional de
Emisiones de Mexico, 2016 (Secretaria de Medio Ambiente y Recursos Naturales (SEMARNAT)). 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, and latitude and longitude
coordinates were verified and adjusted if they were not consistent with the reported municipality. Only
CAPs are covered in the Mexico point source inventory.
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2.7.2 Fugitive Dust Sources in Canada (othafdust, othptdust)
Canadian fugitive dust inventories from tilling and harvest were interpolated to 2017 between 2015 and
2023 emission levels. The Canadian inventory included fugitive dust emissions that do not incorporate
either a transportable fraction or meteorological-based adjustments. To properly account for this, a
separate sector called othafdust (for area sources) and othptdust (for point sources) were created and
modeled using the same adjustments as are done for U.S. sources. Since fugitive dust emissions were
provided in both area and point format, these emissions needed to be processed as through SMOKE two
separate sectors, one for area sources and one for point sources.
A transport fraction adjustment that reduces dust emissions based on land cover types was applied to both
point and nonpoint dust emissions, along with a meteorology-based (precipitation and snow/ice cover)
zero-out of emissions when the ground is snow covered or wet.
2.7.3 Nonpoint and Nonroad Sources in Canada and Mexico (othar)
Canadian inventories for nonpoint sources and nonroad sources were interpolated to 2017 between 2015
and 2023 emission levels. These inventories include rail emissions and other nonroad sources, along with
livestock ammonia and VOC emissions.
For Mexican area and nonroad sources, emission projections based on Mexico's 2016 inventory were
used for area, point and nonroad sources (Secretaria de Medio Ambiente y Recursos Naturales
(SEMARNAT)). The resulting inventory was written using English units to the nonpoint FF10 format
that could be read by SMOKE. Note that unlike the U.S. inventories, there are no explicit HAPs in the
nonpoint or nonroad inventories for Canada and Mexico and, therefore, all HAPs are created from
speciation. Similar to the point inventories, Mexican area and nonroad inventories were projected from
2008 to the years 2014 and 2018, and then emissions values were interpolated to year 2016 values for this
study.
2.7.4 Onroad Sources in Canada and Mexico (onroad_can, onroad_mex)
Canadian inventories for onroad sources were interpolated to 2017 between 2015 and 2023 levels.
Emissions inventories were provided for refueling and other onroad emissions.
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). Mexico onroad inventories were generated by MOVES
for the years 2014 and 2017.
2.7.5 Fires in Canada and Mexico (ptfire_othna)
Annual 2017 wildland emissions for Mexico, Canada, Central America, and Caribbean nations are
included in the ptfire othna sector. Canadian fires, along with fires in Mexico, Central America, and the
Caribbean, were developed from Fire Inventory from NCAR (FINN) 2017 vl.5 daily fire emissions. 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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For FINN fires, listed vegetation type codes of 1 and 9 are defined as agricultural burning, all other fire
detections and assumed to be wildfires. All wildland fires that are not defined as agricultural are assumed
to be wildfires rather than prescribed. FINN fire detects less than 50 square meters (0.012 acres) are
removed from the inventory. The locations of FINN fires are geocoded from latitude and longitude to
FIPS code.
2.7.6 Ocean Chlorine, 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. The CL2 emissions are constant in all ocean grid cells. These data
are unchanged from the data in 2016vl and are passed to both CMAQ and CAMx. Separately from the
ocean chlorine, CMAQ computes sea salt particulate emissions inline during the model run.
For mercury, the same volcanic mercury emissions were used as in the last several modeling platforms.
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. al, 2004 and Seigneur et. al, 2001).
Because of mercury bidirectional flux within the latest version of CMAQ, the only natural mercury
emissions included in the merge are from volcanoes.
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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 4.7 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.cmasceiiter.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 inventory;
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instead, activity data and emission factors are used in combination with meteorological data to compute
hourly emissions.
Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used. These
sectors are the only ones with emissions in aloft layers based on plume rise. The term "in-line" means
that the plume rise calculations are done inside of the air quality model instead of being computed by
SMOKE. In all of the "in-line" sectors, all sources are output by SMOKE into point source files which
are subject to plume rise calculations in the air quality model. In other words, no emissions are output to
layer 1 gridded emissions files from those sectors as has been done in past platforms. The air quality
model computes the plume rise using stack parameters, the Briggs algorithm, and the hourly emissions in
the SMOKE output files for each emissions sector. The height of the plume rise determines the model
layers into which the emissions are placed. The plume top and bottom are computed, along with the
plumes' distributions into the vertical layers that the plumes intersect. The pressure difference across each
layer divided by the pressure difference across the entire plume is used as a weighting factor to assign the
emissions to layers. This approach gives plume fractions by layer and source. Day-specific point fire
emissions are treated differently in CMAQ. After plume rise is applied, there are emissions in every layer
from the ground up to the top of the plume.
Table 3-1. Key emissions modeling steps by sector.
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust adj
Surrogates
Yes
Annual
ag
Surrogates
Yes
annual
airports
Point
Yes
Annual
None
beis
Pre-gridded
land use
in BEIS3.7
computed hourly
cmv clc2
Point
Yes
hourly
in-line
cmv c3
Point
Yes
hourly
in-line
nonpt
Surrogates &
area-to-point
Yes
Annual
nonroad
Surrogates
Yes
monthly
np oilgas
Surrogates
Yes
Annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
onroadcaadj
Surrogates
Yes
monthly activity,
computed hourly
onroad can
Surrogates
Yes
monthly
onroad mex
Surrogates
Yes
monthly
othafdust ad]
Surrogates
Yes
annual
othar
Surrogates
Yes
annual &
monthly
84
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Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
othpt
Point
Yes
annual &
monthly
in-line
othptdust adj
Point
Yes
monthly
None
ptagfire
Point
Yes
daily
in-line
pt oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire
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
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 platform, biogenic emissions were processed in SMOKE and included in the gridded
CMAQ-ready emissions. When CAMx is the targeted air quality model, BEIS is run within SMOKE and
the resulting emissions are included with the ground-level emissions input to CAMx.
Emissions were developed for the 12-km resolution domain 12US2. More specifically, SMOKE was run
on the 12US1 domain and emissions were extracted from 12US1 data files to create 12US2 emission. The
domains are shown in Figure 3-1. All grids use a Lambert-Conformal projection, with Alpha = 33°, Beta =
45° and Gamma = -97°, with a center of X = -97° and Y = 40°. Table 3-2 describes the grids for the three
domains.
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Table 3-2. Descriptions of the platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig, xcell,
ycell, ncols, nrows, nthik
Continental
36km grid
36 km
Entire conterminous
US, almost all of
Mexico, most of
Canada (south of
60°N)
36US3
'LAM 40N97W, -2952000, -2772000,
36.D3, 36.D3, 172, 148, 1
Continental
12km grid
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_45 9X299
'LAM 40N97W',-2556000,-1728000,
12.D3, 12.D3, 459, 299, 1
US 12 km or
"smaller"
CONUS-12
12 km
Smaller 12km
CONUS plus some of
Mexico/Canada
12US2
'LAM 40N97W',-2412000 ,
-1620000, 12.D3, 12.D3, 396, 246, 1
Figure 3-1. Air quality modeling domains
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3.2 Chemical Speciation
The emissions modeling step for chemical speciation creates the "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual chemical
compounds (i.e., "explicit species") or groups of species (i.e., "lumped species"). The chemical
mechanism used for the 2017 platform is the CB6R3AE7 mechanism (Yarwood, 2010, Luecken, 2019).
In CB6R3AE7 the species added compared to older versions of CB6 are acetic acid (AACD), acetone
(ACET), alpha pinene (APIN), formic acid (FACD), naphthalene (NAPH), and intermediate volatility
organic compounds (IVOC; 3xl02 jag m3 < C* < 3xl06 jag m3; note C* is the saturation vapor pressure).
This mapping uses a new systematic methodology for mapping low volatility compounds. Compounds
with very low volatility (C* < 3xl02 jag m3) are mapped to model species NVOL. In previous mappings,
some of these low vapor pressure compounds were mapped to CB6 species. The mechanism and mapping
are described in more detail in a memorandum describing the mechanism files supplied with the
Speciation Tool, the software used to create the CB6 profiles used in SMOKE. It should be noted that the
onroad mobile sector does not use this newer mapping because the speciation is done within MOVES and
the mapping change was made after MOVES had been run. This platform generates the PM2.5 model
species associated with the CMAQ Aerosol Module version 7 (AE7).
Table 3-3 lists the model species produced by SMOKE in the CMAQ platform used for this study16.
Updates to species assignments for CB05 and CB6 were made for the 2014v7.1 platform and are
described in Appendix A. Table 3-4 and Table 3-5 list additional CMAQ model species generated
specifically for HAP toxics modeling. Pollutants groups exist for chromium VI (hexavalent), cresol
cresylic acid (mixed isomers), cynanide compounds, glycol ethers, nickel compounds, PAHPOM,
polychlorinated biphenyls (aroclors), and xylenes (mixed isomers). Table 3-6 lists a mapping of
polycyclic aromatic hydrocarbons (PAHs) to the PAH groups used in CMAQ modeling.
Table 3-3. Emission model species produced for CB6R3AE7 for CMAQ
Inventory Pollutant
Model Species
Model species description
Cl2
CL2
Atomic gas-phase chlorine
HC1
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOx
NO
Nitrogen oxide
NOx
N02
Nitrogen dioxide
NOx
HONO
Nitrous acid
S02
S02
Sulfur dioxide
S02
SULF
Sulfuric acid vapor
nh,
NH3
Ammonia
nh,
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 (not part of CB05)
VOC
CH4
Methane
16 This table includes the NBAFM species because they are generated through VOC speciation for sectors with no HAP inventories (e.g.
Canada) although these species will not be generated through speciation for most sectors.
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Inventory Pollutant
Model Species
Model species description
VOC
ETH
Ethene
VOC
ETHA
Ethane
VOC
ETHY
Ethyne
VOC
ETOH
Ethanol
VOC
FACD
Formic acid
VOC
FORM
Formaldehyde
VOC
IOLE
Internal olefin carbon bond (R-C=C-R)
VOC
ISOP
Isoprene
VOC
IVOC
Intermediate volatility organic compounds
VOC
KET
Ketone Groups
VOC
MEOH
Methanol
VOC
NAPH
Naphthalene
VOC
NVOL
Non-volatile compounds
VOC
OLE
Terminal olefin carbon bond (R-C=C)
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 <10 microns
PM2.5
PEC
Particulate elemental carbon <2.5 microns
PM2.5
PN03
Particulate nitrate <2.5 microns
PM2.5
POC
Particulate organic carbon (carbon only) <2.5 microns
PM2.5
PS04
Particulate Sulfate <2.5 microns
PM2.5
PAL
Aluminum
PM2.5
PCA
Calcium
PM2.5
PCL
Chloride
PM2.5
PFE
Iron
PM2.5
PK
Potassium
PM2.5
PH20
Water
PM2.5
PMG
Magnesium
PM2.5
PMN
Manganese
PM2.5
PMOTHR
PM2.5 not in other AE6 species
PM2.5
PNA
Sodium
PM2.5
PNA
Sodium
PM2.5
PNCOM
Non-carbon organic matter
88
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Inventory Pollutant
Model Species
Model species description
PM2.5
PNH4
Ammonium
PM2.5
PSI
Silica
PM2.5
PTI
Titanium
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Table 3-4. Additional HAP Gaseous model species produced for CMAQ multipollutant specifically
for toxics modeling (not used within CB6)
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
BUTADIENE 13
Carbon tetrachloride
CARBONTET
Carbonyl Sulfide
CARB SULFIDE
Chloroform
CHCL3
Chloroprene
CHLOROPRENE
1,4-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-1,6-diisocyanate
HEXAMETH DIIS
Hexane
HEXANE
Hydrazine
HYDRAZINE
Maleic Anyhydride
MAL ANYHYDRIDE
Methyl Chloride
METHCLORIDE
Methylene chloride (Dichloromethane)
CL2 ME
Specific PAHs assigned with URE =0
PAH 000E0
Specific PAHs assigned with URE =9.6E-06 (previously 1.76E-5)
PAH 176E5
Specific PAHs assigned with URE =4.8E-05 (previously 8.8E-5)
PAH 880E5
Specific PAHs assigned with URE =9.6E-05 (previously 1.76E-4)
PAH 176E4
Specific PAHs assigned with URE =9.6E-04 (previously 1.76E-3)
PAH 176E3
Specific PAHs assigned with URE =9.6E-03 (previously 1.76E-2)
PAH 176E2
Specific PAHs assigned with URE =0.01 (previously 1.01E-2)
PAH 101E2
Specific PAHs assigned with URE =1.14E-1
PAH 114E1
Specific PAHs assigned with 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 ETHANE 1122
Tetrachloroethylene (Perchloroethylene)
CL4 ETHE
Toluene
TOLU
2,4-Toluene diisocyanate
TOL DIIS
Trichloroethylene
CL3 ETHE
Triethylamine
TRIETHYLAMINE
m-xylene, o-xylene, p-xylene, xylenes (mixed isomers)
XYLENES
Vinyl chloride
CL ETHE
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Table 3-5. Additional HAP Particulate* model species produced for CMAQ multipollutant
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 TIT
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
1 Mercury is multi-phase
Table 3-6. PAH/POM pollutant groups
PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(jig/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-DimethylbenzfalAnthracene
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,11 Pyrene
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 -Methylchry sene
9.6E-04
PAH 176E3
41637905
Methylchrysene
9.6E-04
PAH 176E3
53703
Dibenzo [ a,h] Anthracene
9.6E-04
PAH 176E4
193395
Indenof 1,2,3-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 1 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 [ al Anthracene
9.6E-05
PAH 176E5
207089
Benzo [k] Fluoranthene
9.6E-06
PAH 176E5
218019
Chrysene
9.6E-06
PAH 176E5
86748
Carbazole
9.6E-06
PAH 192E3
8007452
Coal Tar
9.9E-04
PAH 880E5
130498292
PAH, total
4.8E-05
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PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(jig/m3)
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
PAH/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
Benzofluoranthene s
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 speciation factors that are the basis of the chemical speciation approach were
developed from a draft version of the SPECIATE 5.0 database (EPA, 2019; https://www.epa.gov/air-
emissions-modeling/speciate ). the EPA's repository of TOG and PM speciation profiles of air pollution
sources. The SPECIATE database development and maintenance is a collaboration involving the EPA's
Office of Research and Development (ORD), Office of Transportation and Air Quality (OTAQ), and the
Office of Air Quality Planning and Standards (OAQPS), in cooperation with Environment Canada (EPA,
2016). The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical
compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation profiles
for PM2.5.
As with previous platforms, some Canadian point source inventories are provided from Environment
Canada as pre-speciated emissions; although not all CB6 species are provided, the inventories were not
supplemented with missing species due to the minimal impact of supplementation.
Some key features and updates to speciation from previous platforms include the following (the
subsections below contain more details on the specific changes):
• Use of the CBR3AE7 mechanism, as described earlier
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• Several new VOC and PM2.5 profiles added to SPECIATE 5.0 or slated for the next version of
SPECIATE were used.
• PM2.5 speciation process for nonroad mobile has been updated. Similar to VOC, PM2.5 profiles are
now assigned within MOVES2014b which outputs the emissions with those assignments.
• As with previous platforms, some Canadian point source inventories are provided from
Environment Canada as pre-speciated emissions, and not all CB6 species were provided; missing
species were supplemented by speciating total VOC.
• A new GSPROCOMBO file was developed for use in Canada to account for ethanol mixes in
Canadian gasoline.
Speciation profiles and cross-references for this study platform are available in the SMOKE input files for
the platform. Totals of each model species by state and sector can be found in the state-sector totals
workbook for this case.
For onroad mobile sources, speciation is done in MOVES, to allow for profiles that vary by model year,
which is not part of the SCC code, to be used. Therefore, cross-references or emissions summaries by
profile for onroad mobile sources are not provided. These profiles are documented in a MOVES technical
report on speciation (EPA, 2018).
Updates to speciation profiles for VOC and PM2.5, were largely from the update of the SPECIATE
database to version SPECIATE 5.0 in June 2019, which added numerous organic gas and PM
profiles. We also used several profiles added to SPECIATE 5.1, which was released in the summer of
2020. In addition, we changed profile assignments to incorporate data provided by states or correct errors
in previous assignments.
For PM2.5 the following profile updates were made for the 2017 platform:
• Corrected the wildfire and prescribed fire profile due to error in compositing (the previous profile
included creosote in the average)
• Updated the profile for aircraft
• Corrected several profile assignments for the petroleum industry
For VOC the following profile updates were made for the 2017 platform:
• Consumer products - replaced the profiles developed from the CARB 1997 consumer products
survey with profiles developed from the CARB 2010 survey update
• Oil and Gas - used additional region-specific profiles or updated assignments
o Used county-specific profiles gas for several Wyoming counties developed from data
provided by the Wyoming DEQ
o Used Willi son Basin gas composition data, separate profiles for the Montana and North
Dakota portions of the basin, based on data developed by the Western Regional Air
Partnership WRAP
o Used Central Montana Uplift area gas composition data, based on data developed by the
WRAP
o Updated Uinta basin profile assignments (based on data provided by Utah)
o Used Utah and Wyoming oil and gas produced water pond profiles
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o Updated profile assignments (by county and SCC) for nonpoint oil and gas sources that
account for the portion of VOC estimated to come from flares. These were updated using
results from the Oil and Gas estimation tool run that was used for the 2017 NEI
o Updated profile assignment for miscellaneous engines to use internal combustion engine
natural gas profile
• Commercial Marine vessel - changed profile assignment to an existing Pre-Tier 1 nonroad diesel
profile because the previous profile was missing key species (aldehydes)
• Livestock - updated profile assignments
• Agricultural burning - updated profiles for rice straw and wheat straw burning, and used new
sugar cane burning profile
3.2.1 VOC speciation
The speciation of VOC includes HAP emissions from the NEI in the speciation process. Instead of
speciating VOC to generate all of the species listed in Table 3-3, emissions of five specific HAPs:
naphthalene, benzene, acetaldehyde, formaldehyde and methanol (collectively known as "NBAFM") from
the NEI were "integrated" with the NEI VOC. The integration combines these HAPs with the VOC in a
way that does not double count emissions and uses the HAP inventory directly in the speciation process.
The basic process is to subtract the specified HAPs emissions mass from the VOC emissions mass, and to
then use a special "integrated" profile to speciate the remainder of VOC to the model species excluding
the specific HAPs. The EPA believes that the HAP emissions in the NEI are often more representative of
emissions than HAP emissions generated via VOC speciation, although this varies by sector.
The NBAFM HAPs were chosen for integration because they are the only explicit VOC HAPs in CMAQ
version 5.2. Explicit means that they are not lumped chemical groups like PAR, IOLE and several other
CB6 model species. These "explicit VOC HAPs" are model species that participate in the modeled
chemistry using the CB6 chemical mechanism. The use of inventory HAP emissions along with VOC is
called "HAP-CAP integration."
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats,
including PTDAY (the format used for the ptfire and ptagfire sectors). The ability to use integration with
the PTDAY format is used for the ptfire sector in the 2017 platform, but not for the ptagfire sector which
does not include HAPs. SMOKE allows the user to specify the particular HAPs to integrate via the
INVTABLE. This is done by setting the "VOC or TOG component" field to "V" for all HAP pollutants
chosen for integration. SMOKE allows the user to also choose the specific sources to integrate via the
NHAPEXCLUDE file (which actually provides the sources to be excluded from integration17). For the
"integrated" sources, SMOKE subtracts the "integrated" HAPs from the VOC (at the source level) to
compute emissions for the new pollutant "NONHAPVOC." The user provides NONHAPVOC-to-
NONHAPTOG factors and NONHAPTOG speciation profiles.18 SMOKE computes NONHAPTOG and
then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC
species not including the integrated HAPs. After determining if a sector is to be integrated, if all sources
17 Since SMOKE version 3.7, the options to specify sources for integration are expanded so that a user can specify the
particular sources to include or exclude from integration, and there are settings to include or exclude all sources within a sector.
In addition, the error checking is significantly stricter for integrated sources. If a source is supposed to be integrated, but it is
missing NBAFM or VOC, SMOKE will now raise an error.
18 These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a specified list of
pollutants, for example NBAFM.
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have the appropriate HAP emissions, then the sector is considered fully integrated and does not need a
NHAPEXCLUDE file. If, on the other hand, certain sources do not have the necessary HAPs, then an
NHAPEXCLUDE file must be provided based on the evaluation of each source's pollutant mix. The
EPA considered CAP-HAP integration for all sectors in determining whether sectors would have full, no
or partial integration (see Figure 3-2). For sectors with partial integration, all sources are integrated other
than those that have either the sum of NBAFM > VOC or the sum of NBAFM = 0.
For an air toxics platform such as this 2017 case, the "no-integrate" sources are treated differently from a
criteria pollutant-focused (CAP) platform. For this 2017 case, the "no-integrate" approach removes the
specified HAPs from the profile and still use the emissions of these HAPs from the NEI. It is very similar
to the "integrate" case except that it does not renormalize the revised profile. In a CAP platform case, no-
integrate means that no inventory HAPs are used. The explicit HAP model species are instead created by
speciating the "no-integrate" source VOC emissions. In general, HAPs that are explicit in the chemical
mechanism can be generated from either speciation or the inventory. We chose to use the HAPs in the
inventory for this study since these are the data that are used to represent HAP emissions in the U.S.
Also, HAP emissions in the NEI may be developed using more site-specific data (e.g., source testing,
material balance) that would not be reflected by applying a speciation profile to VOC emissions. In
addition, we have applied numerous HAP augmentation measures in the NEI. Since Canada and Mexico
inventories do not contain HAPs, we use the approach of generating the HAPs via speciation, except for
Mexico onroad mobile sources where emissions for integrate HAPs were available.
It should be noted that even though NBAFM were removed from the SPECIATE profiles used to create
the GSPRO for both the NONHAPTOG and no-integrate TOG profiles, there still may be small fractions
for "BENZ", "FORM", "ALD2", and "MEOH" present. This is because these model species may have
come from species in SPECIATE that are mixtures. The quantity of these model species is expected to be
very small compared to the BAFM in the NEI. There are no NONHAPTOG profiles that produce
"NAPH."
In SMOKE, the INVTABLE allows the user to specify the specific HAPs to integrate. To support use of
NBAFM emissions from the inventory for all sectors, regardless of integration status, all sectors used an
INVTABLE in which the inventory NBAFM pollutants are kept and processed through SMOKE.
NBAFM pollutants are labeled as integrate pollutants using the "VOC or TOG component" field, set to
"V" for all five HAP pollutants. For the onroad and nonroad sectors, additional HAPs are labeled as
integrate pollutants, in addition to NBAFM: 1,3 butadiene, acrolein, ethyl benzene, 2,2,4-
Trimethylpentane, hexane, propionaldehyde, styrene, toluene, xylene, and methyl tert-butyl ether
(MTBE). The integrated pollutants for this platform are shown in Table 3-7.
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Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation
CMAQ-CB6 species
Table 3-7. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol
(NBAFM) for each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
afdust
N/A - sector contains no VOC
ag
Partial integration (NBAFM), use NBAFM in inventory for no-integrate sources
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
nonpt
Partial integration (NBAFM), use NBAFM in inventory for no-integrate sources
nonroad
Full integration (internal to MOVES)
np oilgas
Partial integration (NBAFM), use NBAFM in inventory for no-integrate sources
onroad
Full integration (internal to MOVES)
onroad can
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
onroadmex
Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation was
older CB6, so post-SMOKE emissions were converted to CB6R3AE7
othafdust
N/A - sector contains no VOC
othar
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
othpt
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
othptdust
N/A - sector contains no VOC
96
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Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
pt oilgas
No integration, use NBAFM in inventory
ptagfire
Partial integration (NBAFM), use NBAFM in inventory for no-integrate sources
ptegu
No integration, use NBAFM in inventory
ptfire
Partial integration (NBAFM), use NBAFM in inventory for no-integrate sources
ptfire othna
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptnonipm
No integration, use NBAFM in inventory
rail
Full integration (NBAFM)
rwc
Partial integration (NBAFM), use NBAFM in inventory for no-integrate sources
Integration for the mobile sources estimated from MOVES (onroad and nonroad sectors, other than for
California) is done differently. Briefly there are three major differences: 1) for these sources integration
is done using more than just NBAFM, 2) all sources from the MOVES model are integrated, and 3)
integration is done fully or partially within MOVES. For onroad mobile, speciation is done fully within
MOVES3 such that the MOVES model outputs emission factors for individual VOC model species along
with the HAPs. This requires MOVES to be run for a specific chemical mechanism. For this platform
MOVES was run for the CB6R3AE7 mechanism.
For nonroad mobile, speciation is partially done within MOVES such that it does not need to be run for a
specific chemical mechanism. For nonroad, MOVES outputs emissions of HAPs and NONHAPTOG are
split by speciation profile. Taking into account that integrated species were subtracted out by MOVES
already, the appropriate speciation profiles are then applied in SMOKE to get the VOC model species.
HAP integration for nonroad uses the same additional HAPs and ethanol as for onroad.
3.2.1.1 County specific profile combinations
SMOKE can compute speciation profiles from mixtures of other profiles in user-specified proportions via
two different methods. The first method, which uses a GSPROCOMBO file, has been in use since the
2005 platform; the second method (GSPRO with fraction) was used for the first time in the 2014v7.0
platform. The GSPRO COMBO method uses profile combinations specified in the GSPRO COMBO
ancillary file by pollutant (which can include emissions mode, e.g., EXH VOC), state and county (i.e.,
state/county FIPS code) and time period (i.e., month). Different GSPRO COMBO files can be used by
sector, allowing for different combinations to be used for different sectors; but within a sector, different
profiles cannot be applied based on SCC. The GSREF file indicates that a specific source uses a
combination file with the profile code "COMBO." SMOKE computes the resultant profile using the
fraction of each specific profile assigned by county, month and pollutant.
In previous platforms, the GSPRO COMBO feature was used to speciate nonroad mobile and gasoline-
related stationary sources that use fuels with varying ethanol content. In these cases, the speciation
profiles require different combinations of gasoline profiles, e.g. E0 and E10 profiles. Since the ethanol
content varies spatially (e.g., by state or county), temporally (e.g., by month), and by modeling year
(future years have more ethanol), the GSPRO COMBO feature allows combinations to be specified at
various levels for different years. For the 2017 platform, GSPRO COMBO is still used for certain
gasoline-related stationary sources nationwide. GSPRO COMBO is no longer needed for nonroad
sources because nonroad emissions within MOVES have the speciation profiles built into the results, so
there is no need to assign them via the GSREF or GSPRO COMBO feature.
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Starting with the 2016v7.2 beta and regional haze platform, a GSPROCOMBO is used to specify a mix
of EO and E10 fuels in Canada. ECCC provided percentages of ethanol use by province, and these were
converted into EO and E10 splits. For example, Alberta has 4.91% ethanol in its fuel, so we applied a mix
of 49.1% E10 profiles (4.91% times 10, since 10% ethanol would mean 100%) E10), and 50.9% E0 fuel.
Ethanol splits for all provinces in Canada are listed in Table 3-8. The Canadian onroad inventory includes
four distinct FIPS codes in Ontario, allowing for application of different E0/E10 splits in Southern
Ontario versus Northern Ontario. In Mexico, only E0 profiles are used.
Table 3-8. Ethanol percentages by volume by Canadian province
Province
Ethanol % by volume (E10 = 10%)
Alberta
4.91%
British Columbia
5.57%
Manitoba
9.12%
New Brunswick
4.75%
Newfoundland & Labrador
0.00%
Nova Scotia
0.00%
NW Territories
0.00%
Nunavut
0.00%
Ontario (Northern)
0.00%
Ontari o ( S outhern)
7.93%
Prince Edward Island
0.00%
Quebec
3.36%
Saskatchewan
7.73%
Yukon
0.00%
A new method to combine multiple profiles became available in SMOKE4.5. It allows multiple profiles
to be combined by pollutant, state and county (i.e., state/county FIPS code) and SCC. This was used
specifically for the oil and gas sectors (pt oilgas and np oilgas) because SCCs include both controlled
and uncontrolled oil and gas operations which use different profiles.
3.2.1.2 Additional sector specific considerations for integrating HAP
emissions from inventories into speciation
The decision to integrate HAPs into the speciation was made on a sector-by-sector basis. For some
sectors, there is no integration and VOC is speciated directly; for some sectors, there is full integration
meaning all sources are integrated; and for other sectors, there is partial integration, meaning some
sources are not integrated and other sources are integrated. The integrated HAPs are either NBAFM or, in
the case of MOVES (onroad, nonroad, and MOVES-Mexico), a larger set of HAPs plus ethanol are
integrated. Table 3-6 above summarizes the integration method for each platform sector.
Speciation for the onroad sector is unique. First, SMOKE-MOVES is used to create emissions for these
sectors and both the MEPROC and INVTABLE files are involved in controlling which pollutants are
processed. Second, the speciation occurs within MOVES itself, not within SMOKE. The advantage of
using MOVES to speciate VOC is that during the internal calculation of MOVES, the model has complete
information on the characteristics of the fleet and fuels (e.g., model year, ethanol content, process, etc.),
thereby allowing it to more accurately make use of specific speciation profiles. This means that MOVES
produces emission factor tables that include inventory pollutants (e.g., TOG) and model-ready species
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(e.g., PAR, OLE, etc).19 SMOKE essentially calculates the model-ready species by using the appropriate
emission factor without further speciation.20 Third, MOVES' internal speciation uses full integration of
an extended list of HAPs beyond NBAFM (called "M-profiles"). The M-profiles integration is very
similar to NBAFM integration explained above except that the integration calculation (see Figure 3-2) is
performed on emissions factors instead of on emissions, and a much larger set of pollutants are integrated
besides NBAFM. The list of integrated pollutants is described in Table 3-9. An additional run of the
Speciation Tool was necessary to create the M-profiles that were then loaded into the MOVES default
database. Fourth, for California, the EPA applied adjustment factors to SMOKE-MOVES to produce
California adjusted model-ready files. By applying the ratios through SMOKE-MOVES, the CARB
inventories are essentially speciated to match EPA estimated speciation. This resulted in changes to the
VOC HAPs from what CARB submitted to the EPA.
Table 3-9. MOVES integrated species in M-profiles
MOVES ID
Pollutant Name
5
Methane (CH4)
20
Benzene
21
Ethanol
22
MTBE
24
1,3-Butadiene
25
Formaldehyde
26
Acetaldehyde
27
Acrolein
40
2,2,4-Trimethylpentane
41
Ethyl Benzene
42
Hexane
43
Propionaldehyde
44
Styrene
45
Toluene
46
Xylene
185
Naphthalene gas
For the nonroad sector, all sources are integrated using the same list of integrated pollutants as shown in
Table 3-9. The integration calculations are performed within MOVES. For California and Texas, all
VOC HAPs were recalculated using MOVES HAP/VOC ratios based on the MOVES run so that VOC
speciation methodology would be consistent across the country. NONHAPTOG emissions by speciation
profile were also calculated based on MOVES data in California in Texas.
For nonroad emissions in California where the state provided emissions, MOVES-style speciation has
been implemented in 2017, with NONHAPTOG and PM2.5 pre-split by profiles and with all the HAPs
19 Because the EF table has the speciation "baked" into the factors, all counties that are in the county group (i.e., are mapped to
that representative county) will have the same speciation.
20 For more details on the use of model-ready EF, see the SMOKE 3.7 documentation:
https://www.cmascenter.Org/smoke/documentation/3.7/html/.
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needed for VOC speciation augmented based on MOVES data in California. This means in 2017, nonroad
emissions in California are speciated consistently with the rest of the country.
MOVES-MEXICO for onroad used the same speciation approach as for the U.S. in that the larger list of
species shown in Table 3-8 was used. However, MOVES-MEXICO used an older version of the CB6
mechanism sometimes referred to as "CB6-CAMx". That mechanism is missing the XYLMN and
SOAALK species in particular, so post-SMOKE we converted the emissions to CB6-CMAQ as follows:
• XYLMN = XYL[1]-0.966*NAPHTHALENE[1]
• PAR = PAR[l]-0.00001 *NAPHTHALENE[1]
• SOAALK = 0.108*PAR[1]
The CB6R3AE7 mechanism includes other new species which are not part of CB6-CAMx, such as IVOC.
CB6R3AE7-specific species were not added to the MOVES-MEXICO emissions because those extra
species would be expected to have only a minor impact.
For the beis sector, the speciation profiles used by BEIS are not included in SPECIATE. BEIS3.7
includes the species (SESQ) that is mapped to the BEIS model species SESQT (Sesquiterpenes). The
profile code associated with BEIS3.7 for use with CB6R3AE7 is "BC6E7". The difference in the biogenic
profile compared to the previous version of CB6, based on profile code "B10C6", is the explicit treatment
of acetic acid, formic acid, and alpha-pinine emissions in BC6E7. The biogenic speciation files are
managed in the CMAQ Github repository.21
3.2.1.3 Oil and gas related speciation profiles
Several oil and gas profiles were developed or assigned to sources in np oilgas and pt oilgas to better
reflect region-specific differences in VOC composition and whether the process SCC would include
controlled emissions, considering the controls are not part of the SCC. The basin / region-specific
profiles for oil and gas are shown in Table 3-10.
In addition to region-specific assignments, multiple profiles were assigned to particular county/SCC
combinations using the SMOKE feature discussed in 3.2.1.1 that allows multiple profiles to be combined
within the chemical speciation cross reference file (GSREF) by pollutant, state/county, and SCC. Oil and
gas SCCs for associated gas, condensate tanks, crude oil tanks, dehydrators, liquids unloading and well
completions represent the total VOC from the process, including the portions of process that may be
flared or directed to a reboiler. For example, SCC 2310021400 (gas well dehydrators) consists of process,
reboiler, and/or flaring emissions. There are not separate SCCs for the flared portion of the process or the
reboiler. However, the VOC associated with these three portions can have very different speciation
profiles. Therefore, it is necessary to have an estimate of the amount of VOC from each of the portions
(process, flare, reboiler) so that the appropriate speciation profiles can be applied to each portion. The
Nonpoint Oil and Gas Emission Estimation Tool generates an intermediate file which provides flare, non-
flare (process), and reboiler (for dehydrators) emissions for six source categories that have flare
emissions: by county FIPS and SCC code for the U.S. From these emissions the fraction of the emissions
to assign to each profile was computed and incorporated into the 2017 platform. These fractions can vary
by county FIPS, because they depend on the level of controls, which is an input to the Speciation Tool.
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Table 3-10. Basin/Region-specific profiles for oil and gas
Profile Code
Description
Region
(if not in
profile
name)
DJVNT R
Denver-Julesburg Basin Produced Gas Composition from Non-CBM Gas Wells
PNC01 R
Piceance Basin Produced Gas Composition from Non-CBM Gas Wells
PNC02 R
Piceance Basin Produced Gas Composition from Oil Wells
PRBCB R
Powder River Basin Produced Gas Composition from CBM Wells
PRBCO R
Powder River Basin Produced Gas Composition from Non-CBM Wells
PRM01 R
Permian Basin Produced Gas Composition for Non-CBM Wells
SSJCB R
South San Juan Basin Produced Gas Composition from CBM Wells
SSJCO R
South San Juan Basin Produced Gas Composition from Non-CBM Gas Wells
SWFLA R
SW Wyoming Basin Flash Gas Composition for Condensate Tanks
SWVNT R
SW Wyoming Basin Produced Gas Composition from Non-CBM Wells
UNT01 R
Uinta Basin Produced Gas Composition from CBM Wells
WRBCO R
Wind River Basin Produced Gagres Composition from Non-CBM Gas Wells
95087a
Oil and Gas - Composite - Oil Field - Oil Tank Battery Vent Gas
East
Texas
95109a
Oil and Gas - Composite - Oil Field - Condensate Tank Battery Vent Gas
East
Texas
95417
Uinta Basin, Untreated Natural Gas
95418
Uinta Basin, Condensate Tank Natural Gas
95419
Uinta Basin, Oil Tank Natural Gas
95420
Uinta Basin, Glycol Dehydrator
95398
Composite Profile - Oil and Natural Gas Production - Condensate Tanks
Denver-
Julesburg
95399
Composite Profile - Oil Field - Wells
California
95400
Composite Profile - Oil Field - Tanks
California
95403
Composite Profile - Gas Wells
San
Joaquin
CMU01
Oil and Gas - Produced Gas Composition from Gas Wells - Central Montana Uplift
- Montana
WIL01
Oil and Gas - Flash Gas Composition from Tanks at Oil Wells - Williston Basin
North Dakota
WIL02
Oil and Gas - Flash Gas Composition from Tanks at Oil Wells - Williston Basin
Montana
WIL03
Oil and Gas - Produced Gas Composition from Oil Wells - Williston Basin North
Dakota
WIL04
Oil and Gas - Produced Gas Composition from Oil Wells - Williston Basin Montana
3.2.1.4 Mobile source related VOC speciation profiles
The VOC speciation approach for mobile source and mobile source-related source categories is
customized to account for the impact of fuels and engine type and technologies. The impact of fuels also
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affects the parts of the nonpt and ptnonipm sectors that are related to mobile sources such as portable fuel
containers and gasoline distribution.
The VOC speciation profiles for the nonroad sector other than for California are listed in Table 3-11.
They include new profiles (i.e., those that begin with "953") for 2-stroke and 4-stroke gasoline engines
running on EO and E10 and compression ignition engines with different technologies developed from
recent EPA test programs, which also supported the updated toxics emission factor in MOVES2014a
(Reichle, 2015 and EPA, 2015b).
Table 3-11. TOG MOVES-SMOKE Speciation for nonroad emissions used for the 2017 Platform
Profile
Profile Description
Engine
Type
Engine
Technology
Engine
Size
Horse-
power
category
Fuel
Fuel
Sub-
type
Emission
Process
95328
SI 2-stroke E10
SI 2-stroke
All
All
All
Gasoline
E10
exhaust
95330
SI 4-stroke E10
SI 4-stroke
All
All
All
Gasoline
E10
exhaust
95331
CI Pre-Tier 1
CI
Pre-Tier 1
All
All
Diesel
All
exhaust
95332
CI Tier 1
CI
Tier 1
All
All
Diesel
All
exhaust
95333
CI Tier 2
CI
Tier 2 and 3
all
All
Diesel
All
exhaust
95335a
21
CI Tier 2
CI
Tier 4
<56 kW
(75 hp)
S
Diesel
All
exhaust
8775
ACES Phase 1 Diesel
Onroad
CI Tier 4
Tier 4
>=56 kW
(75 hp)
L
Diesel
All
exhaust
8754
E10 Evap
SI
All
all
All
Gasoline
E10
evaporative
8769
E10 evap permeation
SI
All
all
All
Gasoline
E10
permeation
8870
E10 Headspace
SI
All
all
All
Gasoline
E10
headspace
1001
CNG Exhaust
All
all
all
All
CNG
All
exhaust
8860
LPG exhaust
All
all
all
All
LPG
All
exhaust
Speciation profiles for VOC in the nonroad sector account for the ethanol content of fuels across years. A
description of the actual fuel formulations can be found in NEITSD. For previous platforms, the EPA
used "COMBO" profiles to model combinations of profiles for EO and E10 fuel use, but beginning with
2014v7.0 platform, the appropriate allocation of EO and E10 fuels is done by MOVES.
Combination profiles reflecting a combination of E10 and EO fuel use ideally would be used for sources
upstream of mobile sources such as portable fuel containers (PFCs) and other fuel distribution operations
associated with the transfer of fuel from bulk terminals to pumps (BTP), which are in the nonpt sector.
For these sources, ethanol may be mixed into the fuels, in which case speciation would change across
years. The speciation changes from fuels in the ptnonipm sector include BTP distribution operations
inventoried as point sources. Refinery-to-bulk terminal (RBT) fuel distribution and bulk plant storage
(BPS) speciation does not change across the modeling cases because this is considered upstream from the
introduction of ethanol into the fuel. The mapping of fuel distribution SCCs to PFC, BTP, BPS, and RBT
emissions categories can be found in Appendix C. In 2017 platform, all of these sources get E10
speciation.
21 95335a replaced 95335. This correction was made to remove alcohols due to suspected contamination. Additional
information is available in SPECIATE.
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Table 3-12 summarizes the different profiles utilized for the fuel-related sources in each of the sectors.
The term "COMBO" indicates that a combination of the profiles listed was used to speciate that
subcategory using the GSPROCOMBO file.
Table 3-12. Select mobile-related VOC profiles 2017
Sector
Sub-category
Profile
Nonroad non-US
gasoline exhaust
COMBO
8750a Pre-Tier 2 E0 exhaust
8751a Pre-Tier 2 E10 exhaust
nonpt/
ptnonipm
PFC and BTP
8870 ElOHeadspace
nonpt/
ptnonipm
Bulk plant storage (BPS)
and refine-to-bulk terminal
(RBT) sources
8870 ElOHeadspace
The speciation of onroad VOC occurs completely within MOVES. MOVES accounts for fuel type and
properties, emission standards as they affect different vehicle types and model years, and specific
emission processes. Table 3-13 describes the M-profiles available to MOVES depending on the model
year range, MOVES process (processID), fuel sub-type (fuelSubTypelD), and regulatory class
(regClassID). m While MOVES maps the liquid diesel profile to several processes, MOVES only
estimates emissions from refueling spillage loss (processID 19). The other evaporative and refueling
processes from diesel vehicles have zero emissions.
Table 3-14 through Table 3-16 describe the meaning of these MOVES codes. For a specific
representative county and future year, there will be a different mix of these profiles. For example, for HD
diesel exhaust, the emissions will use a combination of profiles 8774M and 8775M depending on the
proportion of HD vehicles that are pre-2007 model years (MY) in that particular county. As that county is
projected farther into the future, the proportion of pre-2007 MY vehicles will decrease. A second
example, for gasoline exhaust (not including E-85), the emissions will use a combination of profiles
8756M, 8757M, 8758M, 8750aM, and 875 laM. Each representative county has a different mix of these
key properties and, therefore, has a unique combination of the specific M-profiles. More detailed
information on how MOVES speciates VOC and the profiles used is provided in the technical document,
"Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles in
MOVES2014" (EPA, 2015c).
Table 3-13. Onroad M-profiles
Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
1001M
CNG Exhaust
1940-2050
1,2,15,16
30
48
4547M
Diesel Headspace
1940-2050
11
20,21,22
0
4547M
Diesel Headspace
1940-2050
12,13,18,19
20,21,22
10,20,30,40,41,
42,46,47,48
8753M
E0 Evap
1940-2050
12,13,19
10
10,20,30,40,41,42,
46,47,48
8754M
E10 Evap
1940-2050
12,13,19
12,13,14
10,20,30,40,41,
42,46,47,48
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Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
8756M
Tier 2 E0 Exhaust
2001-2050
1,2,15,16
10
20,30
8757M
Tier 2 E10 Exhaust
2001-2050
1,2,15,16
12,13,14
20,30
8758M
Tier 2 E15 Exhaust
1940-2050
1,2,15,16
15,18
10,20,30,40,41,
42,46,47,48
8766M
E0 evap permeation
1940-2050
11
10
0
8769M
E10 evap permeation
1940-2050
11
12,13,14
0
8770M
El5 evap permeation
1940-2050
11
15,18
0
8774M
Pre-2007 MY HDD
exhaust
1940-2006
1,2,15,16,17,90
20, 21, 22
40,41,42,46,47, 48
8774M
Pre-2007 MY HDD
exhaust
1940-2050
9122
20, 21, 22
46,47
8774M
Pre-2007 MY HDD
exhaust
1940-2006
1,2,15,16
20, 21, 22
20,30
8775M
2007+ MY HDD
exhaust
2007-2050
1,2,15,16
20, 21, 22
20,30
8775M
2007+ MY HDD
exhaust
2007-2050
1,2,15,16,17,90
20, 21, 22
40,41,42,46,47,48
8855M
Tier 2 E85 Exhaust
1940-2050
1,2,15,16
50, 51, 52
10,20,30,40,41,
42,46,47,48
8869M
E0 Headspace
1940-2050
18
10
10,20,30,40,41,
42,46,47,48
8870M
E10 Headspace
1940-2050
18
12,13,14
10,20,30,40,41,
42,46,47,48
8871M
El5 Headspace
1940-2050
18
15,18
10,20,30,40,41,
42,46,47,48
8872M
El5 Evap
1940-2050
12,13,19
15,18
10,20,30,40,41,
42,46,47,48
8934M
E85 Evap
1940-2050
11
50,51,52
0
8934M
E85 Evap
1940-2050
12,13,18,19
50,51,52
10,20,30,40,41,
42,46,47,48
8750aM
Pre-Tier 2 E0 exhaust
1940-2000
1,2,15,16
10
20,30
8750aM
Pre-Tier 2 E0 exhaust
1940-2050
1,2,15,16
10
10,40,41,42,46,47,48
875 laM
Pre-Tier 2 E10 exhaust
1940-2000
1,2,15,16
11,12,13,14
20,30
875 laM
Pre-Tier 2 E10 exhaust
1940-2050
1,2,15,16
11,12,13,14,15,
1823
10,40,41,42,46,47,48
95120m
Liquid Diesel
19602060
11
20,21,22
0
95120m
Liquid Diesel
19602060
12,13,18,19
20,21,22
10,20,30,40,41,42,46,47,4
8
95335a
2010+MY HDD
exhaust
20102060
1,2,15,16,17,90
20,21,22
40,41,42,46,47,48
m While MOVES maps the liquid diesel profile to several processes, MOVES only estimates emissions from
refueling spillage loss (processID 19). The other evaporative and refueling processes from diesel vehicles have zero
emissions.
22 91 is the processed for APUs which are diesel engines not covered by the 2007 Heavy-Duty Rule, so the older technology
applies to all years.
23 The profile assignments for pre-2001 gasoline vehicles fueled on E15/E20 fuels (subtypes 15 and 18) were corrected for
MOVES2014a. This model year range, process, fuelsubtype regclass combinate is already assigned to profile 8758.
104
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Table 3-14. MOVES process IDs
Process ID
Process Name
1
Running Exhaust
2
Start Exhaust
9
Brakewear
10
Tire wear
11
Evap Permeation
12
Evap Fuel Vapor Venting
13
Evap Fuel Leaks
15
Crankcase Running Exhaust
16
Crankcase Start Exhaust
17
Crankcase Extended Idle Exhaust
18
Refueling Displacement Vapor Loss
19
Refueling Spillage Loss
20
Evap Tank Permeation
21
Evap Hose Permeation
22
Evap RecMar Neck Hose Permeation
23
Evap RecMar Supply/Ret Hose Permeation
24
Evap RecMar Vent Hose Permeation
30
Diurnal Fuel Vapor Venting
31
HotSoak Fuel Vapor Venting
32
RunningLoss Fuel Vapor Venting
40
Nonroad
90
Extended Idle Exhaust
91
Auxiliary Power Exhaust
Table 3-15. MOVES Fuel subtype IDs
Fuel Subtype ID
Fuel Subtype Descriptions
10
Conventional Gasoline
11
Reformulated Gasoline (RFG)
12
Gasohol (E10)
13
Gasohol (E8)
14
Gasohol (E5)
15
Gasohol (E15)
18
Ethanol (E20)
20
Conventional Diesel Fuel
21
Biodiesel (BD20)
22
Fischer-Tropsch Diesel (FTD100)
30
Compressed Natural Gas (CNG)
50
Ethanol
51
Ethanol (E85)
52
Ethanol (E70)
105
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Table 3-16. MOVES regclass IDs
Reg. Class ID
Regulatory Class Description
0
Doesn't Matter
10
Motorcycles
20
Light Duty Vehicles
30
Light Duty Trucks
40
Class 2b Trucks with 2 Axles and 4 Tires (8,500 lbs < GVWR <= 10,000 lbs)
41
Class 2b Trucks with 2 Axles and at least 6 Tires or Class 3 Trucks (8,500 lbs < GVWR <= 14,000
lbs)
42
Class 4 and 5 Trucks (14,000 lbs < GVWR <= 19,500 lbs)
46
Class 6 and 7 Trucks (19,500 lbs < GVWR <= 33,000 lbs)
47
Class 8a and 8b Trucks (GVWR > 33,000 lbs)
48
Urban Bus (see CFR Sec 86.091 2)
For portable fuel containers (PFCs) and fuel distribution operations associated with the bulk-plant-to-
pump (BTP) distribution, a 10% ethanol mix (E10) was assumed for speciation purposes. Refinery to
bulk terminal (RBT) fuel distribution and bulk plant storage (BPS) speciation are considered upstream
from the introduction of ethanol into the fuel; therefore, a single profile is sufficient for these sources. No
refined information on potential VOC speciation differences between cellulosic diesel and cellulosic
ethanol sources was available; therefore, cellulosic diesel and cellulosic ethanol sources used the same
SCC (30125010: Industrial Chemical Manufacturing, Ethanol by Fermentation production) for VOC
speciation as was used for corn ethanol plants.
3.2.2 PM speciation
In addition to VOC profiles, the SPECIATE database also contains profiles for speciating PM2.5. PM2.5
was speciated into the AE6 species associated with CMAQ 5.0.1 and later versions. Of particular note for
this platform, the nonroad PM2.5 speciation was updated as discussed later in this section. Most of the PM
profiles come from the 911XX series (Reff et. al, 2009), which include updated AE6 speciation.24
Starting with the 2014v7.1 platform, profile 91112 (Natural Gas Combustion - Composite) was replaced
with 95475 (Composite -Refinery Fuel Gas and Natural Gas Combustion). This updated profile is an
AE6-ready profile based on the median of 3 SPECIATE4.5 profiles from which AE6 versions were made
(to be added to SPECIATE5.0): boilers (95125a), process heaters (95126a) and internal combustion
combined cycle/cogen plant exhaust (95127a). As with profile 91112, these profiles are based on tests
using natural gas and refinery fuel gas (England et al., 2007). Profile 91112 which is also based on
refinery gas and natural gas is thought to overestimate EC.
Profile 95475 (Composite -Refinery Fuel Gas and Natural Gas Combustion) is shown along with the
underlying profiles composited in Figure 3-3. Figure 3-4 shows a comparison of the new profile as of the
2014v7.1 platform with the one that we had been using in the 2014v7.0 and earlier platforms.
24 The exceptions are 5675AE6 (Marine Vessel - Marine Engine - Heavy Fuel Oil) used for cmv_c3 and 92018 (Draft
Cigarette Smoke - Simplified) used in nonpt. 5675AE6 is an update of profile 5675 to support AE6 PM speciation.
106
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Figure 3-3. Profiles composited for PIY1 gas combustion related sources
Zinc
Sulfate
Silicon
Potassium
Particulate Non-Carbon Organic Matter
Other Unspeciated PM2.5
Organic carbon
Nitrate
Nickel
Metal-bound Oxygen
Iron
Elemental Carbon
Copper
Chloride ion
Calcium
Bromine Atom
Ammonium
Aluminum
0 10 20 30 40 50
Weight Percent
I Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
Gas-fired process heater exhaust 95126a
¦ Gas-fired internal combustion combined cycle/cogeneration plant exhaust 95127a
Gas-fired boiler exhaust 95125a
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources
Zinc
Sulfate
Silicon
Potassium
Particulate Non-Carbon Organic Matter
Other Unspeciated PM2.5
Organic carbon
Nitrate
Nickel
Metal-bound Oxygen
Iron
Elemental Carbon
Copper
Chloride ion
Calcium
Bromine Atom
Ammonium
Aluminum
20 30 40
Weight Percent
60
I Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
Natural Gas Combustion - Composite 91112
107
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3.2.2.1 Mobile source related PM2.5 speciation profiles
For all processes except brake and tire wear in the onroad sector, PM speciation occurs within MOVES
itself, not within SMOKE (similar to the VOC speciation described above). The advantage of using
MOVES to speciate PM is that during the internal calculation of MOVES, the model has complete
information on the characteristics of the fleet and fuels (e.g., model year, sulfur content, process, etc.) to
accurately match to specific profiles. This means that MOVES produces EF tables that include total PM
(e.g., PMio and PM2.5) and speciated PM (e.g., PEC, PFE). SMOKE essentially calculates the PM
components by using the appropriate EF without further speciation.25 The specific profiles used within
MOVES include two CNG profiles, 45219 and 45220, which were added to SPECIATE4.5. A list of
profiles is provided in the technical document, "Speciation of Total Organic Gas and Particulate Matter
Emissions from On-road Vehicles in MOVES2014" (EPA, 2015c).
For onroad brake and tire wear, the PM is speciated in the moves2smk postprocessor that prepares the
emission factors for processing in SMOKE. The formulas for this are based on the standard speciation
factors from brake and tire wear profiles, which were updated from the v6.3 platform based on data from
a Health Effects Institute report (Schauer, 2006). Table 3-17 shows the differences in the v7.1 (alpha) and
201 lv6.3 profiles.
Table 3-17. Brake and tire PM2.5 profiles from Schauer (2006)
Inventory
Pollutant
Model
Species
SPECIATE4.5
brakewear profile:
95462
SPECIATE4.5
tirewear profile:
95460
PM2 5
PAL
0.000793208
3.32401E-05
PM2 5
PCA
0.001692177
PM2 5
PCL
PM2 5
PEC
0.012797085
0.003585907
PM2 5
PFE
0.213901692
0.00024779
PM2 5
PH20
PM2 5
PK
0.000687447
4.33129E-05
PM2 5
PMG
0.002961309
0.000018131
PM2 5
PMN
0.001373836
1.41E-06
PM2 5
PMOTHR
0.691704999
0.100663209
PM2 5
PNA
0.002749787
7.35312E-05
PM2 5
PNCOM
0.020115749
0.255808124
PM2 5
PNH4
PM2 5
PN03
PM2 5
POC
0.050289372
0.639520309
PM2 5
PSI
PM2 5
PS04
PM2 5
PTI
0.000933341
5.04E-06
For California onroad emissions, adjustment factors were applied to SMOKE-MOVES to produce
California adjusted model-ready files. California did not supply speciated PM, therefore, the adjustment
25 Unlike previous platforms, the PM components (e.g., POC) are now consistently defined between MOVES2014 and CMAQ.
For more details on the use of model-ready EF, see the SMOKE 3.7 documentation:
https://www.cmascenter.0rg/smoke/documentation/3.7/html/.
108
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factors applied to PM2.5 were also applied to the speciated PM components. By applying the ratios
through SMOKE-MOVES, the CARB inventories are essentially speciated to match EPA estimated
speciation.
For nonroad PM2.5, speciation is partially done within MOVES such that it does not need to be run for a
specific chemical mechanism. For nonroad, MOVES outputs emissions of PM2.5 split by speciation
profile. Similar to how VOC and NONHAPTOG are speciated, PM2.5 is now also speciated this way
starting with MOVES2014b. For California, PM2.5 emissions split by speciation profile are estimated
from total PM2.5 based on MOVES data in California, so that PM is speciated consistently across the
country. The PM2.5 profiles assigned to nonroad sources are listed in Table 3-18.
Table 3-18. Nonroad PM2.5 profiles
SPECIATE4.5
Profile Code
SPECIATE4.5 Profile Name
Assigned to Nonroad
sources based on Fuel
Type
8996
Diesel Exhaust - Heavy-heavy duty truck - 2007
model year with NCOM
Diesel
91106
HDDV Exhaust - Composite
Diesel
91113
Nonroad Gasoline Exhaust - Composite
Gasoline
95219
CNG Transit Bus Exhaust
CNG and LPG
3.2.2.2 Diesel PM
Diesel particulate matter is neither a CAP nor HAP as defined by Section 112 of the CAA, however it was
identified as a mobile source air toxic in EPA's 2007 rule, "Control of Hazardous Air Pollutants From
Mobile Sources final rule" (EPA 2007a). Starting with the 2014 NEI, diesel PM emissions are explicitly
included in the NEI using pollutant names DIESEL-PM10 and DIESEL-PM25, respectively. Diesel PM
emissions are tracked for mobile-source, engine-exhaust PMio and PM2.5 emissions from engines burning
diesel or residual-oil fuels. These sources include on-road, nonroad, point-airport-ground support
equipment, point-locomotives, nonpoint locomotives, and all PM from diesel or residual-oil-fueled
nonpoint CMVs. For these sources, DIESEL-PM10 and DIESEL-PM25 are equal to primary PM10-PRI
and PM25-PRI in the NEI. Although stationary engines also can burn diesel fuel, only mobile-related
diesel engine SCCs have diesel PM emissions modeled.
Diesel PM is speciated in SMOKE using the same speciation profiles as primary PM, except that diesel
PM is mapped to the following model species: DIESEL PMC (PMC), DIESEL PMEC (PEC),
DIESEL PMOC (POC), DIESEL PMN03 (PN03), DIESEL PMS04 (PS04), and DIESEL PMFINE
(all other PM2.5 species).
3.2.3 NOx speciation
NOx emission factors and therefore NOx inventories are developed on a NO2 weight basis. For air quality
modeling, NOx is speciated into NO, NO2, and/or HONO. For the non-mobile sources, the EPA used a
single profile "NHONO" to split NOx into NO and NO2.
The importance of HONO chemistry, identification of its presence in ambient air and the measurements of
HONO from mobile sources have prompted the inclusion of HONO in NOx speciation for mobile
sources. Based on tunnel studies, a HONO to NOx ratio of 0.008 was chosen (Sarwar, 2008). For the
109
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mobile sources, except for onroad (including nonroad, cmv, rail, othon sectors), and for specific SCCs in
othar and ptnonipm, the profile "HONO" is used. Table 3-19 gives the split factor for these two profiles.
The onroad sector does not use the "HONO" profile to speciate NOx. MOVES2014 produces speciated
NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables
used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOx. The NO
fraction varies by heavy duty versus light duty, fuel type, and model year. The NO2 fraction is calculated
as the remainder (i.e., NO2 1 - NO - HONO). For more details on the NOx fractions within MOVES, see
EPA report "Use of data from 'Development of Emission Rates for the MOVES Model,' Sierra Research,
March 3, 2010" available at https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockev=P100FlA5.pdf.
Table 3-19. NOx speciation profiles
Profile
Pollutant
species
Molar split factor
HONO
NOX
N02
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
N02
0.1
NHONO
NOX
NO
0.9
3.2.4 Creation of Sulfuric Acid Vapor (SULF)
Since at least the 2002 Platform, sulfuric acid vapor (SULF) has been estimated through the SMOKE
speciation process for coal combustion and residual and distillate oil fuel combustion sources. Profiles
that compute SULF from SO2 are assigned to coal and oil combustion SCCs in the GSREF ancillary file
The profiles were derived from information from AP-42 (EPA, 1998), which identifies the fractions of
sulfur emitted as sulfate and SO2 and relates the sulfate as a function of S02.
Sulfate is computed from SO2 emissions assuming that gaseous sulfate, which is comprised of many
components, is primarily H2SO4. The equation for calculating H2S04is given below.
Emissions of SULF (as H2S04) Equation 3-1
fraction of S emitted as sulfate MW H2S04
= S07 emissions x — - x
fraction of S emitted as S02 MW S02
In the above, MWis the molecular weight of the compound. The molecular weights of H2SO4 and SO2
are 98 g/mol and 64 g/mol, respectively.
This method does not reduce SO2 emissions; it solely adds gaseous sulfate emissions as a function of S02
emissions. The derivation of the profiles is provided in Table 3-20; a summary of the profiles is provided
in Table 3-21.
Table 3-20. Sulfate split factor computation
fuel
SCCs
Profile
Fraction
Fraction as
Split factor (mass
Code
as S02
Sulfate
fraction)
110
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Bituminous
1-0X-002-YY, where X is 1,
2 or 3 and YY is 01 thru 19
and 21-ZZ-002-000 where
ZZ is 02,03 or 04
95014
0.95
0.014
.014/.95 * 98/64 =
0.0226
Subbituminous
1-0X-002-YY, where X is 1,
2 or 3 and YY is 21 thru 38
87514
.875
0.014
.014/.875 * 98/64 =
0.0245
Lignite
1-0X-003-YY, where X is 1,
2 or 3 and YY is 01 thru 18
and 21-ZZ-002-000 where
ZZ is 02,03 or 04
75014
0.75
0.014
.014/.75 * 98/64 =
0.0286
Residual oil
1-0X-004-YY, where X is 1,
2 or 3 and YY is 01 thru 06
and 21-ZZ-005-000 where
ZZ is 02,03 or 04
99010
0.99
0.01
.01/. 99 * 98/64 =
0.0155
Distillate oil
1-0X-005-YY, where X is 1,
2 or 3 and YY is 01 thru 06
and 21-ZZ-004-000 where
ZZ is 02,03 or 04
99010
0.99
0.01
Same as residual oil
Table 3-21. 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
Mercury and other metals from the inventory were speciated for use in modeling. Other than the facility
specific data for one facility provided by Minnesota, the profiles are the same as were used in previous
modeling platforms and are documented in Appendix D of the Technical Support Document for the 2011
National-scale Air Toxics Assessment (https://www.epa.gov/sites/production/files/2015-
12/documents/2011-nata-tsd.pdf). Mercury in the inventory was reported as pollutant code 7439976 and
needs to be speciated into the three forms of mercury used by CMAQ: elemental, divalent gaseous, and
divalent particulate. Metals (other than mercury) were speciated into coarse and fine particulate, which are
needed by CMAQ. Table 3-22 contains summaries of the particle size profiles. Most were applied across
an entire sector or multiple sectors (i.e., the nonroad profiles were applied to the nonroad-related sector
and the stationary profile was applied to the stationary-related sectors). A Minnesota facility and process-
specific profile were added based on data provided by the state during the 2014vl emissions review.
Table 3-22. Particle size speciation of Metals
Source Type
Profile
pollutant
Fine
coarse
Onroad
OARS
Arsenic
.95
.05
111
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Source Type
Profile
pollutant
Fine
coarse
Onroad
ONMN
Manganese
.4375
.5625
Onroad
ONNI
Nickel
.83
.17
Onroad
CRON
Chromhex
.86
.14
Nonroad
NOARS
Arsenic
.83
.17
Nonroad
NONMN
Manganese
.67
.33
Nonroad
NONNI
Nickel
.49
.51
Nonroad
CRNR
Chromhex
.8
.2
Stationary
STANI
Nickel
.59
.41
Stationary
STACD
Cadmium
.76
.24
Stationary
STAMN
Manganese
.67
.33
Stationary
STAPB
Lead
.74
.26
Stationary
STABE
Beryllium
.68
.32
Stationary
CRSTA
Chromhex
.71
.29
Stationary
STARS
Arsenic
.59
.41
Stationary1
MNBE
Beryllium
.15
.85
Stationary1
MNCD
Cadmium
.15
.85
Stationary1
MNMN
Manganese
.15
.85
Stationary1
MNNI
Nickel
.15
.85
Stationary1
MNRS
Arsenic
.15
.85
Stationary1
CRMN
Chromhex
.15
.85
facility specific metal splits at United Taconite LLC - Thunderbird Mine facility in Minnesota as reported by Minnesota
For 2017 toxics modeling, mercury was speciated using new profiles , including unit-specific electric
generating unit profiles (ptegu sector). Table 3-23 provides the mercury profiles used for sources using
SCC-based speciation factors (EPA, 2020).
Table 3-23. Speciation of Mercury
Divalent
Profile Code
Description
Elemental
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
112
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HGMET
Metal production
0.8
0.15
0.005
HGMWI
Medical waste
incineration
0.2
0.6
0.2
HGPETCOKE
Petroleum coke
0.6
0.3
0.1
3.3 Temporal Allocation
Temporal allocation is the process of distributing aggregated emissions to a finer temporal resolution,
thereby converting annual emissions to hourly emissions as is required by CMAQ. While the total
emissions are important, the timing of the occurrence of emissions is also essential for accurately
simulating ozone, PM, and other pollutant concentrations in the atmosphere. Many emissions inventories
are annual or monthly in nature. Temporal allocation takes these aggregated emissions and distributes the
emissions to the hours of each day. This process is typically done by applying temporal profiles to the
inventories in this order: monthly, day of the week, and diurnal, with monthly and day-of-week profiles
applied only if the inventory is not already at that level of detail.
The temporal factors applied to the inventory are selected using some combination of country, state,
county, SCC, and pollutant. Table 3-24 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 LTYPE 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
MTYPE setting (see below for more information).
Table 3-24. 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 adi
Annual
Yes
week
All
Yes
airports
Annual
Yes
week
week
Yes
ag
Annual
Yes
all
all
Yes
beis
Hourly
No
n/a
All
No
cmv clc2
Annual
Yes
aveday
aveday
No
cmv c3
Annual
Yes
aveday
aveday
No
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
No
mwdss
Mwdss
Yes
np oilgas
Annual
Yes
aveday
Aveday
No
onroad
Annual & monthly1
No
all
All
Yes
onroad ca adi
Annual & monthly1
No
all
All
Yes
othafdust adj
Annual
Yes
week
All
No
othar
Annual & monthly
Yes
week
week
No
onroad can
Monthly
No
week
week
No
113
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Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process holidays
as separate days
onroad mex
Monthly
No
week
week
No
othpt
Annual & monthly
Yes
mwdss
mwdss
No
othptdust adj
Monthly
No
week
All
No
pt oilgas
Annual
Yes
mwdss
mwdss
Yes
ptegu
Annual & hourly
Yes2
all
All
No
ptnonipm
Annual
Yes
mwdss
mwdss
Yes
ptagfire
Daily
No
all
All
No
ptfire
Daily
No
all
all
No
ptfire othna
Daily
No
all
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No3
met-based3
all
No3
'Note the annual and monthly "inventory" actually refers to the activity data (VMT, hoteling, and VPOP) for onroad.
VMT and hoteling is monthly and VPOP is annual. The actual emissions are computed on an hourly basis.
2Only units that do not have matching hourly CEMS data use monthly temporal profiles.
3Except for 2 SCCs that do not use met-based speciation
The following values are used in the table. The value "all" means that hourly emissions are computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the
month. The value "mwdss" means hourly emissions for one representative Monday, representative
weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each month.
This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within
the month, but not week-to-week variation within the month. The value "aveday" means hourly
emissions computed for one representative day of each month, meaning emissions for all days within a
month are the same. Special situations with respect to temporal allocation are described in the following
subsections.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2017, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2016). For all anthropogenic sectors, emissions from December
2017 were used to fill in surrogate emissions for the end of December 2016. For biogenic emissions,
December 2016 emissions were computed using year 2016 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;
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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 ag,
nonroad, onroad, onroad can, onroadmex, othar, and othpt.
3.3.2 Electric Generating Utility temporal allocation (ptegu)
The temporal allocation procedure for EGUs in the base year is differentiated by whether or not the unit
could be directly matched to a unit with CEMS data via its ORIS facility code and boiler ID. Note that
for units matched to CEMS data, annual totals of their emissions input to CMAQ may be different than
the annual values in the 2017 annual inventory because the CEMS data replaces the NOx and SO2 annual
inventory data for the seasons in which the CEMS are operating. If a CEMS-matched unit is determined
to be a partial year reporter, as can happen for sources that run CEMS only in the summer, emissions
totaling the difference between the annual emissions and the total CEMS emissions are allocated to the
non-summer months. Prior to use of the CEMS data in SMOKE it is processed through the CEMCorrect
tool. The CEMCorrect tool identifies hours for which the data were not measured as indicated by the data
quality flags in the CEMS data files. Unmeasured data can be filled in with maximum values and thereby
cause erroneously high values in the CEMS data. When data were flagged as unmeasured and the values
were found to be more than three times the annual mean for that unit, the data for those hours are replaced
with annual mean values (Adelman et al., 2012). These adjusted CEMS data were then used for the
remainder of the temporal allocation process described below (see Figure 3-5 for an example).
Figure 3-5. Eliminating unmeasured spikes in CEMS data
2016 January CEMs for 6068 3
2000 -
2016 Original CEMs
2016 Corrected CEMs
M /\- K*--
V \I t
^-Qv
,-Ov
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,-ov
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In modeling platforms prior to 2016 beta, unmatched EGUs were temporally allocated using daily and
diurnal profiles weighted by CEMS values within an IPM region, season, and by fuel type (coal, gas, and
other). All unit types (peaking and non-peaking) were given the same profile within a region, season and
fuel bin. Units identified as municipal waste combustors (MWCs) or cogeneration units (cogens) were
given flat daily and diurnal profiles. Beginning with the 2016 beta platform and continuing for the 2016vl
and 2017 platforms, the small EGU temporalization process was improved to also consider peaking units.
The region, fuel, and type (peaking or non-peaking) were identified for each input EGU with CEMS data
that are used for generating profiles. The identification of peaking units was based on hourly heat input
data from the 2016 base year and the two previous years (2014 and 2015). The heat input was summed for
each year. Equation 3-2 shows how the annual heat input value is converted from heat units (BTU/year) to
power units (MW) using the unit-level heat rate (BTU/kWh) derived from the NEEDS v6 database. In
Equation 3-3 a capacity factor is calculated by dividing the annual unit MW value by the NEEDS v6 unit
capacity value (MW) multiplied by the hours in the year. A peaking unit was defined as any unit that had
a maximum capacity factor of less than 0.2 for every year (2015, 2016, and 2017) and a 3-year average
capacity factor of less than 0.1.
Annual Unit Power Output
y 8760 Hourly HI ^ iMW\
Annual Unit Output {MW) = — M— Equation 3-2
NEEDS Heat Rate (tttjt)
KkWh '
Capacity Factor =
Unit Capacity Factor
Annual Unit Output (MW)
(MW \
NEEDS Unit Capacity ( J * 8760 (h)
MW\ Equation 3-3
Input regions were determined from one of the eight EGU modeling regions based on MJO and climate
regions. Regions were used to group units with similar climate-based load demands. Region assignment is
made on a state level, where all units within a state were assigned to the appropriate region. Unit fuel
assignments were made using the primary NEEDS v6 fuel. Units fueled by bituminous, subbituminous, or
lignite are assigned to the coal fuel type. Natural gas units were assigned to the gas fuel type. Distillate
and residual fuel oil were assigned to the oil fuel type. Units with any other primary fuel were assigned
the "other" fuel type. The number of units used to calculate the daily and diurnal EGU temporal profiles
are shown in Figure 3-6 by region, fuel, and for peaking/non-peaking. Currently there are 64 unique
profiles available based on 8 regions, 4 fuels, and 2 for peaking unit status (peaking and non-peaking).
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Figure 3-6. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification
Small EGU 2016 Temporal Profile Input Unit Counts
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The daily and diurnal profiles were calculated for each region, fuel, and peaking type group from the year
2017 CEMS heat input values. The heat input values were summed for each input group to the annual
level at each level of temporal resolution: monthly, month-of-day, and diurnal. The sum by temporal
resolution value was then divided by the sum of annual heat input in that group to get a set of
temporalization factors. Diurnal factors were created for both the summer and winter seasons to account
for the variation in hourly load demands between the seasons. For example, the sum of all hour 1 heat
input values in the group was divided by the sum of all heat inputs over all hours to get the hour 1 factor.
Each grouping contained 12 monthly factors, up to 31 daily factors per month, and two sets of 24 hourly
factors. The profiles were weighted by unit size where the units with more heat input have a greater
influence on the shape of the profile. Composite profiles were created for each region and type across all
fuels as a way to provide profiles for a fuel type that does not have hourly CEMS data in that region.
Figure 3-7 shows peaking and non-peaking daily temporal profiles for the gas fuel type in the LADCO
region. Figure 3-8 shows the diurnal profiles for the coal fuel type in the Mid-Atlantic Northeast Visibility
Union (MANE-VU) region.
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Figure 3-7. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type
Daily Small EGU Profile for LADCO gas
2016
n_iw
Figure 3-8. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type
Diurnal Small EGU Profile for MANE-VU coal
SMOKE uses a cross-reference file to select a monthly, daily, and diurnal profile for each source. For the
2017 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. As with the input unit the regions are
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assigned using the state from the unit FIPS. The fuel was assigned by SCC to one of the four fuel types:
coal, gas, oil, and other. A fuel type unit assignment is made by summing the VOC, NOX, PM2.5, and
S02 for all SCCs in the unit. The SCC that contributed the highest total emissions to the unit for selected
pollutants was used to assign the unit fuel type. Peaking units were identified as any unit with an oil, gas,
or oil fuel type with a NAICS of 22111 or 221112. Some units may be assigned to a fuel type within a
region that does not have an available input unit with a matching fuel type in that region. These units
without an available profile for their group were assigned to use the regional composite profile. MWC and
cogen units were identified using the NEEDS primary fuel type and cogeneration flag, respectively, from
the NEEDS v6 database. The number of EGU units assigned each profile group are shown by region in
Figure 3-9. In this plot, the unit counts are from 2016, but the 2017 unit counts should be similar.
Figure 3-9. Non-CEMS EGU Temporal Profile Application Counts
Small EGU 2016 Temporal Profile Application Counts
MANE-VU
(peaWnonswi):
TW
EGU Regions
¦ LADO)
I I MANE-VU
~ NortJiwest
~ SESARM
South
I I Souttwest
~ West
CZI West Worth Central
3.3.3 Airport Temporal allocation (airports)
Airport temporal profiles were updated in 2014v7.0 and were kept the same for this platform. All airport
SCCs (i.e., 2275*, 2265008005, 2267008005, 2268008005 and 2270008005) were given the same hourly,
weekly and monthly profile for all airports other than Alaska seaplanes (which are not in the CMAQ
modeling domain). Hourly airport operations data were obtained from the Aviation System Performance
Metrics (ASPM) Airport Analysis website (https://aspm.faa.gov/apm/sv s/ Anal v si s AP. asp). A report of
2014 hourly Departures and Arrivals for Metric Computation was generated. An overview of the ASPM
metrics is at http://aspmhelp.faa.gov/index.php/Aviation Performance Metrics %28APM%29. Figure
3-10 shows the diurnal airport profile.
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Weekly and monthly temporal profiles are based on 2014 data from the FAA Operations Network Air
Traffic Activity System (http://aspm.faa.gov/opsnet/sys/Terminal.asp). A report of all airport operations
(takeoffs and landings) by day for 2014 was generated. These data were then summed to month and day-
of-week to derive the monthly and weekly temporal profiles shown in Figure 3-10, Figure 3-11, and
Figure 3-12. An overview of the Operations Network data system is at
http://aspmhelp.faa.gov/index.php/Operations Network %280PSNET%29. The weekly and monthly
profiles from 2014 are still used in this platform.
Alaska seaplanes, which are outside the CONUS domain use the monthly profile in Figure 3-13. These
were assigned based on the facility ID.
Figure 3-10. Diurnal Profile for all Airport SCCs
Diurnal Airport Profile
Hour
Figure 3-11. Weekly profile for all Airport SCCs
Weekly Airport Profile
0.18
0.08
0.06
0.04
0.02
o L
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Figure 3-12. Monthly Profile for all Airport SCCs
Monthly Airport Profile
0.04
0.03
0.02
0.01
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 3-13. Alaska Seaplane Profile
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3.3.4 Residential Wood Combustion Temporal allocation (rwc)
There are many factors that impact the timing of when emissions occur, and for some sectors this includes
meteorology. The benefits of utilizing meteorology as a method for temporal allocation are: (1) a
meteorological dataset consistent with that used by the AQ model is available (e.g., outputs from WRF);
(2) the meteorological model data are highly resolved in terms of spatial resolution; and (3) the
meteorological variables vary at hourly resolution and can, therefore, be translated into hour-specific
temporal allocation.
The SMOKE program Gentpro provides a method for developing meteorology-based temporal allocation.
Currently, the program can utilize three types of temporal algorithms: annual-to-day temporal allocation
for residential wood combustion (RWC); month-to-hour temporal allocation for agricultural livestock
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NH3; and a generic meteorology-based algorithm for other situations. Meteorological-based temporal
allocation was used for portions of the rwc sector and for the entire ag sector.
Gentpro reads in gridded meteorological data (output from MCIP) along with spatial surrogates and uses
the specified algorithm to produce a new temporal profile that can be input into SMOKE. The
meteorological variables and the resolution of the generated temporal profile (hourly, daily, etc.) depend
on the selected algorithm and the run parameters. For more details on the development of these
algorithms and running Gentpro, see the Gentpro documentation and the SMOKE documentation at
http://www.cmascenter.Org/smoke/documentation/3.l/GenTPRQ Technical Summary Aug2012 Final, pd
f and https://www.cmascenter.Org/smoke/documentation/4.5/html/ch05s03s05.html respectively.
For the RWC algorithm, Gentpro uses the daily minimum temperature to determine the temporal
allocation of emissions to days of the year. Gentpro was used to create an annual-to-day temporal profile
for the RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of
the year. On days where the minimum temperature does not drop below a user-defined threshold, RWC
emissions for most sources in the sector are zero. Conversely, the program temporally allocates the
largest percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total
annual emissions do not change, only the distribution of the emissions within the year is affected. The
temperature threshold for RWC emissions was 50 °F for most of the country, and 60 °F for the following
states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas. The algorithm is as follows:
IfTd >=Tt: no emissions that day
If Td < Tt: daily factor = 0.79*(Tt -Td)
where (Td = minimum daily temperature; Tt = threshold temperature, which is 60 degrees F in southern
states and 50 degrees F elsewhere).
Once computed, the factors are normalized to sum to 1 to ensure that the total annual emissions are
unchanged (or minimally changed) during the temporal allocation process.
Figure 3-14 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.
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Figure 3-14. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold
The diurnal profile used for most RWC sources (see Figure 3-15) 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.
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]) and "Outdoor wood burning device, NEC (fire-pits, chimeneas, etc.)" (i.e.,
"recreational RWC," SCC=2104008700) are not based on temperature data, because the meteorologically
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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. As shown in Figure
3-17, the NESCAUM report describes how for individual units, OHH are highly variable day-to-day but
that in the aggregate, these emissions have no day-of-week variation. In contrast, the day-of-week profile
for recreational RWC follows a typical "recreational" profile with emissions peaked on weekends.
Annual-to-month temporal allocation for OHH as well as recreational RWC were computed from the
MDNR 2008 survey and are illustrated in Figure 3-18. 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. In contrast to all other RWC appliances, recreational RWC emissions are used far more
frequently during the warm season.
Figure 3-16. Data used to produce a diurnal profile for hydronic heaters
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Figure 3-17. Day-of-week temporal profiles for hydronic heaters and recreational RWC
Figure 3-18. Annual-to-month temporal profiles for hydronic heaters and recreational RWC
3.3.5 Agricultural Ammonia Temporal Profiles (ag)
For the agricultural livestock NH3 algorithm, the GenTPRO algorithm is based on an equation derived by
Jesse Bash of the EPA's ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation is
based on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to
estimate diurnal NH3 emission variations from livestock as a function of ambient temperature,
aerodynamic resistance, and wind speed. The equations are:
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Ea = [161500/Ti./, x e*-138071,-./,*] x AR,/;
PEi,/; = Ea / Sum(Ei,/;)
Equation 3-4
Equation 3-5
where
• PEa = Percentage of emissions in county i on hour h
• Eij, = Emission rate in county i on hour h
• Tin = Ambient temperature (Kelvin) in county i on hour h
• AR;,/; = Aerodynamic resistance in county i
GenTPRO was run using the "BASH NH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized
to the month. Figure 3-19 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.
Figure 3-19. Example of animal NFb emissions temporal allocation approach (daily total emissions)
2014fd Minnesota ag NH3 livestock daily temporal profiles
1600
1/1/2014 2/1/2014 3/4/2014 4/4/2014 5/5/2014 6/5/2014 7/6/2014 8/6/2014 9/6/2014 10/7/2D1411/7/201412/8/2014
month^ hourly
approach approach
For this platform, the GenTPRO approach is applied to all sources in the ag sector, NH3 and non- NH3,
livestock and fertilizer. Monthly profiles are based on the daily-based EPA livestock emissions and are
the same as were used in 2014v7.0. Profiles are by state/SCC_category, where SCC_category is one of
the following: beef, broilers, layers, dairy, swine.
3.3.6 Oil and gas temporal allocation (np_oilgas)
Monthly oil and gas temporal profiles by county and SCC were updated to use 2017 activity information
for the 2017 platform. Weekly and diurnal profiles are flat and are based on comments received on a
version of the 2011 platform.
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3.3.7 Onroad mobile temporal allocation (onroad)
For the onroad sector, the temporal distribution of emissions is a combination of traditional temporal
profiles and the influence of meteorology. This section will discuss both the meteorological influences
and the development of the temporal profiles for this platform.
The "inventories" referred to in Table 3-24 consist of activity data for the onroad sector, not emissions.
For the off-network emissions from the rate-per-profile (RPP) and rate-per-vehicle (RPV) processes, the
VPOP activity data is annual and does not need temporal allocation. For rate-per-hour (RPH) processes
that result from hoteling of combination trucks, the HOTELING inventory is annual and was
temporalized to month, day of the week, and hour of the day through temporal profiles.
For on-roadway rate-per-distance (RPD) processes, the VMT activity data is annual for some sources and
monthly for other sources, depending on the source of the data. Sources without monthly VMT were
temporalized from annual to month through temporal profiles. VMT was also temporalized from month
to day of the week, and then to hourly through temporal profiles. The RPD processes require a speed
profile (SPDPRO) that consists of vehicle speed by hour for a typical weekday and weekend day. For
onroad, the temporal profiles and SPDPRO will impact not only the distribution of emissions through
time but also the total emissions. Because SMOKE-MOVES (for RPD) calculates emissions based on the
VMT, speed and meteorology, if one shifted the VMT or speed to different hours, it would align with
different temperatures and hence different emission factors. In other words, two SMOKE-MOVES runs
with identical annual VMT, meteorology, and MOVES emission factors, will have different total
emissions if the temporal allocation of VMT changes. Figure 3-20 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.
Meteorology is not used in the development of the temporal profiles, but rather it impacts the calculation
of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the
hourly level by grid cell. More specifically, the on-network (RPD) and the off-network parked vehicle
(RPV, RPH, and RPP) processes use the gridded meteorology (MCIP) either directly or indirectly. For
RPD, RPV, and RPH, Movesmrg determines the temperature for each hour and grid cell and uses that
information to select the appropriate emission factor for the specified SCC/pollutant/mode combination.
For RPP, instead of reading gridded hourly meteorology, Movesmrg reads gridded daily minimum and
maximum temperatures. The total of the emissions from the combination of these four processes (RPD,
RPV, RPH, and RPP) comprise the onroad sector emissions. The temporal patterns of emissions in the
onroad sector are influenced by meteorology.
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Figure 3-20. Example of temporal variability of NOx emissions
A _
2014v2 onroad RPD hourly NOX and VMT: Wake County, NC
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Date and time (GMT)
New VMT day-of-week and hour-of-day temporal profiles were developed for use in the 2014NEIv2 and
later platforms as part of the effort to update the inputs to MOVES and SMOKE-MOVES under CRC A-
100 (Coordinating Research Council, 2017). CRC A-100 data includes profiles by region or county, road
type, and broad vehicle category. There are three vehicle categories: passenger vehicles (11/21/31),
commercial trucks (32/52), and combination trucks (53/61/62). CRC A-100 does not cover buses, refuse
trucks, or motor homes, so those vehicle types were mapped to other vehicle types for which CRC A-100
did provide profiles as follows: 1) Intercity/transit buses were mapped to commercial trucks; 2) Motor
homes were mapped to passenger vehicles for day-of-week and commercial trucks for hour-of-day; 3)
School buses and refuse trucks were mapped to commercial trucks for hour-of-day and use a new custom
day-of-week profile called LOWSATSUN that has a very low weekend allocation, since school buses and
refuse trucks operate primarily on business days. In addition to temporal profiles, CRC A-100 data were
also used to develop the average hourly speed data (SPDPRO) used by SMOKE-MOVES. In areas where
CRC A-100 data does not exist, hourly speed data is based on MOVES county databases.
The CRC A-100 dataset includes temporal profiles for individual counties, Metropolitan Statistical Areas
(MSAs), and entire regions (e.g., West, South). For counties without county or MSA temporal profiles
specific to itself, regional temporal profiles are used. Temporal profiles also vary by each of the MOVES
road types, and there are distinct hour-of-day profiles for each day of the week. Plots of hour-of-day
profiles for passenger vehicles in Fulton County, GA, are shown in Figure 3-21. Separate plots are shown
for Monday, Friday, Saturday, and Sunday, and each line corresponds to a particular MOVES road type
(i.e., road type 2 = rural restricted, 3 = rural unrestricted, 4 = urban restricted, and 5 = urban unrestricted).
Figure 3-22 shows which counties have temporal profiles specific to that county, and which counties use
MSA or regional average profiles. Figure 3-23 shows the regions used for each of the regional average
profiles.
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Figure 3-21. Sample on road diurnal profiles for Fulton County, GA
Figure 3-22. Methods to Populate Onroad Speeds and Temporal Profiles by Road Type
Monday Fulton Co passenger Friday Fulton Co passenger
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
Saturday Fulton Co passenger
0.09
Sunday Fulton Co passenger
o.i
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
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Figure 3-23. Regions for computing Region Average Speeds and Temporal Profiles
The CRC A-100 temporal profiles were used in areas of the contiguous United States that did not submit
temporal profiles of sufficient detail for the 2017 NEI. For this platform, CRC A-100 profiles are used in
most of the country, but day-of-week and hour-of-day profiles based on MOVES CDB submissions for
the 2017NEI are used in Maricopa and Pima counties in Arizona, Delaware, Washington DC, Florida,
some of Georgia, Idaho, Massachusetts, Maryland, Missouri, Clark County Nevada, New Jersey, New
York, Ohio, Pennsylvania, Davidson and Knox counties in Tennessee, Texas, and Virginia.. All
California temporal profiles were carried over from 2014v7.0 platform, although California hoteling uses
CRC A-100-based profiles just like the rest of the country, since CARB didn't have a hoteling-specific
profile. Monthly profiles in all states (national profiles by broad vehicle type) were also carried over from
2014v7.0 and applied directly to the VMT. For California, CARB supplied diurnal profiles that varied by
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vehicle type, day of the week,26 and air basin. These CARB-specific profiles were used in developing
EPA estimates for California. Although the EPA adjusted the total emissions to match California-
submitted emissions, the temporal allocation of these emissions considered both the state-specific VMT
profiles and the SMOKE-MOVES process of incorporating meteorology.
For hoteling, day-of-week profiles are the same as non-hoteling for combination trucks, while hour-of-day
non-hoteling profiles for combination trucks were inverted to create new hoteling profiles that peak
overnight instead of during the day. The combination truck profiles for Fulton County are shown in
Figure 3-24.
Monday
Figure 3-24. Example of Temporal Profiles for Combination Trucks
Fulton Co combo Friday Fulton Co combo
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
Saturday
Fulton Co
combo
Sunday
Fulton Co
combo
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
3.3.8 Nonroad mobile temporal allocation (nonroad)
For nonroad mobile sources, temporal allocation is performed differently for different SCCs. Beginning
with the final 2011 platform and continuing in the 2016 platform, improvements to temporal allocation of
nonroad mobile sources were made to make the temporal profiles more realistically reflect real-world
practices. Some specific updates were made for agricultural sources (e.g., tractors), construction, and
commercial residential lawn and garden sources.
26 California's diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and
Tuesday, Wednesday, Thursday had the same profile.
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Figure 3-25 shows two previously existing temporal profiles (9 and 18) and a newer temporal profile (19)
which has lower emissions on weekends. In this platform, construction and commercial lawn and garden
sources use the new profile 19 which has lower emissions on weekends. Residental lawn and garden
sources continue to use profile 9 and agricultural sources continue to use profile 19.
Figure 3-25. Example N on road 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 fridary
Saturday sundae
Figure 3-26 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 3-26. Example Non road Diurnal Temporal Profiles
Hour of Day Profiles
0.11
o.i
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
hi h2 h3 h4 h5 h6 h7 h8 h9 hl0hllhl2hl3hl4h05hl6hl7hl8hl9h20h21h22h23h24
26a-New 27 25 a-New 26
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3.3.9 Additional sector specific details (afdust, beis, cmv, rail, nonpt,
ptnonipm, ptfire)
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to
reduce the total emissions based on meteorological conditions. These adjustments are applied through
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and
then subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the
ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are
subject to transport. This methodology is discussed in (Pouliot et al., 2010), and in "Fugitive Dust
Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). The precipitation adjustment is
applied to remove all emissions for hours where measurable rain occurs, or where there is snow cover.
Therefore, the afdust emissions vary day-to-day based on the precipitation and/or snow cover for each
grid cell and hour. Both the transport fraction and meteorological adjustments are based on the gridded
resolution of the platform; therefore, somewhat different emissions will result from different grid
resolutions. 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, new monthly profiles were developed for the 2016 platform. Monthly temporal
allocation for rail freight emissions is based on AAR Rail Traffic Data, Total Carloads and Intermodal, for
2016. For passenger trains, monthly temporal allocation is flat for all months. Rail passenger miles data
is available by month for 2016 but it is not known how closely rail emissions track with passenger activity
since passenger trains run on a fixed schedule regardless of how many passengers are aboard, and so a flat
profile is chosen for passenger trains. Rail emissions are allocated with flat day of week profiles, and
most emissions are allocated with flat hourly profiles.
For the ptagfire sector, the inventories are in the daily point fire format FF10 PTDAY. The diurnal
temporal profile for ag fires reflects the fact that burning occurs during the daylight hours - see Figure
3-27 (McCarty et al., 2009). This puts most of the emissions during the work day and suppresses the
emissions during the middle of the night.
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Figure 3-27. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
Industrial processes that are not likely to shut down on Sundays, such as those at cement plants, use
profiles that include emissions on Sundays, while those that would shut down on Sundays use profiles that
reflect Sunday shutdowns.
For the ptfire sectors, the inventories are in the daily point fire format FF10 PTDAY. Separate hourly
profiles for prescribed and wildfires were used. Figure 3-28 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.
Figure 3-28. Prescribed and Wildfire diurnal temporal profiles
For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from MOVES. For California, CARB's annual inventory was temporalized to
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monthly using monthly temporal profiles applied in SMOKE by SCC. This is an improvement over the
2011 platform, which applied monthly temporal allocation in California at the broader SCC7 level.
3.4 Spatial Allocation
The methods used to perform spatial allocation are summarized in this section. For the modeling
platform, spatial factors are typically applied by county and SCC. As described in Section 3.1, spatial
allocation was performed for the 12-km domain. To accomplish this, SMOKE used national 36-km and
12-km spatial surrogates and a SMOKE area-to-point data file. For the U.S., the EPA updated surrogates
to use circa 2014 to 2017 data wherever possible. For Mexico, updated spatial surrogates were used as
described below. For Canada, updated surrogates were provided by Environment Canada for the
2016v7.2 platform. The U.S., Mexican, and Canadian 36-km and 12-km surrogates cover the entire
CONUS domain 12US1 shown in Figure 3-1. The 36US3 domain includes a portion of Alaska, and since
Alaska emissions are typically not included in air quality modeling, special considerations are taken to
include Alaska emissions in 36-km modeling.
Documentation of the origin of the spatial surrogates for the platform is provided in the workbook
US_SpatialSurrogate_Workbook_v07172018 which is available with the reports for the 2014v7.1
platform. The remainder of this subsection summarizes the data used for the spatial surrogates and the
area-to-point data which is used for airport refueling.
3.4.1 Spatial Surrogates for U.S. emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 36-km and 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-
to-point approach overrides the use of surrogates for an airport refueling sources. Table 3-25 lists the
codes and descriptions of the surrogates. Surrogate names and codes listed in italics are not directly
assigned to any sources for the 2017 platform, but they are sometimes used to gapfill other surrogates, or
as an input for merging two surrogates to create a new surrogate that is used.
Many surrogates were updated or newly developed for use in the 2014v7.0 platform (Adelman, 2016).
They include the use of the 2011 National Land Cover Database (the previous platform used 2006) and
development of various development density levels such as open, low, medium high and various
combinations of these. These landuse surrogates largely replaced the FEMA category (500 series)
surrogates that were used in the 2011 platform. Additionally, onroad surrogates were developed using
average annual daily traffic counts from the highway monitoring performance system (HPMS).
Previously, the "activity" for the onroad surrogates was length of road miles. This and other surrogates
are described in a reference (Adelman, 2016).
Several surrogates were updated or developed as new surrogates for the 2016 and 2017 platforms:
Oil and gas surrogates were updated to represent 2017;
Onroad spatial allocation uses surrogates that do not distinguish between urban and rural road
types, correcting the issue arising in some counties due to the inconsistent urban and rural
definitions between MOVES and the surrogate data;
- New onroad surrogates were generated to incorporate 2017 Average Annual Daily Traffic
(AADT);
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Spatial surrogates 201 through 244, which concern road miles, annual average daily traffic
(AADT), and truck stops, were updated for the 2017 platform.
A correction was made to the water surrogate to gap fill missing counties using the 2006 National
Land Cover Database (NLCD).
The surrogates for the U.S. were mostly generated using the Surrogate Tool to drive the Spatial Allocator,
but some surrogates were developed directly within ArcGIS or using the Surrogate Tools. The tool and
documentation for the original Surrogate Tool are available at https://www.cmascenter.org/sa-
tools/documentation/4.2/SurrogateToolUserGuide 4 2.pdf and the tool and documentation for the
Surrogate Tools DB is available from https://www.cmascenter.org/surrogate tools db/.
Table 3-25. U.S. Surrogates available for the 2017 modeling platforms
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2)
650
Refineries and Tank Farms
100
Population
670
Spud Count - CBM Wells
110
Housing
671
Spud Count - Gas Wells
150
Residential Heating - Natural Gas
672
Gas Production at Oil Wells
170
Residential Heating - Distillate Oil
673
Oil Production at CBM Wells
180
Residential Heating - Coal
674
Unconventional Well Completion Counts
190
Residential Heating - LP Gas
676
Well Count - All Producing
205
Extended Idle Locations
677
Well Count - All Exploratory
239
Total Road AADT
678
Completions at Gas Wells
240
Total Road Miles
679
Completions at CBM Wells
242
All Restricted AADT
681
Spud Count - Oil Wells
244
All Unrestricted AADT
683
Produced Water at All Wells
258
Intercity Bus Terminals
6831
Produced Water at CBM Wells
259
Transit Bus Terminals
6832
Produced Water at Gas Wells
260
Total Railroad Miles ;
6833
Produced Water at Oil Wells
261
NT AD Total Railroad Density
685
Completions at Oil Wells
271
NTAD Class 12 3 Railroad Density
686
Completions at All Wells
300
NLCD Low Intensity Development
687
Feet Drilled at All Wells
304
NLCD Open + Low
691
Well Counts - CBM Wells
305
NLCD Low + Med
692
Spud Count - All Wells
306
NLCD Med + High
693
Well Count - All Wells
307
NLCD All Development
694
Oil Production at Oil Wells
308
NLCD Low + Med + High
695
Well Count - Oil Wells
309
NLCD Open + Low + Med
696
Gas Production at Gas Wells
310
NLCD Total Agriculture
697
Oil Production at Gas Wells
319
NLCD Crop Land
698
Well Count - Gas Wells
320
NLCD Forest Land
699
Gas Production at CBM Wells
321
NLCD Recreational Land
711
Airport Areas
340
NLCD Land
801
Port Areas
350
NLCD Water
805
Offshore Shipping Area
500
Commercial Land '
806
Offshore Shipping NE12014 Activity
505
Industrial Land
807
Navigable Waterway Miles
506
Education
808
2013 Shipping Density
510
Commercial plus Industrial s
820
Ports NEI2014 Activity
136
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Code
Surrogate Description
1 *-()('e
Surrogate Description
Residential + Commercial + Industrial +
535
Institutional + Government
1 850
Golf Courses
560
Hospital (COM6)
1 860
Mines
For the onroad sector, the on-network (RPD) emissions were spatially allocated differently from other off-
network processes (e.g., RPV, RPP). On-network used AADT data and off network used land use
surrogates as shown in Table 3-26. Emissions from the extended (i.e., overnight) idling of trucks were
assigned to surrogate 205, which is based on locations of overnight truck parking spaces. This surrogate's
underlying data were updated for use in the 2016 platforms to include additional data sources and
corrections based on comments received. These updates were carried into this platform.
Table 3-26. Off-Network Mobile Source Surrogates
Source type
Source Type name
Surrogate ID
Description
11
Motorcycle
307
NLCD All Development
21
Passenger Car
307
NLCD All Development
31
Passenger Truck
307
NLCD All Development
NLCD Low + Med +
32
Light Commercial Truck
308
High
41
Intercity Bus
258
Intercity Bus Terminals
42
Transit Bus
259
Transit Bus Terminals
43
School Bus
506
Education
51
Refuse Truck
306
NLCD Med + High
52
Single Unit Short-haul Truck
306
NLCD Med + High
53
Single Unit Long-haul Truck
306
NLCD Med + High
54
Motor Home
304
NLCD Open + Low
61
Combination Short-haul Truck
306
NLCD Med + High
62
Combination Long-haul Truck
306
NLCD Med + High
For the oil and gas sources in the np oilgas sector, the spatial surrogates were updated to those shown in
Table 3-27 using 2017 data consistent with what was used to develop the nonpoint oil and gas emissions.
The primary activity data source used for the development of the oil and gas spatial
surrogates was data from Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2017). This
database contains well-level location, production, and exploration statistics at the monthly level.
Due to a proprietary agreement with DI Desktop, individual well locations and ancillary
production cannot be made publicly available, but aggregated statistics are allowed. These data were
supplemented with data from state Oil and Gas Commission (OGC) websites (Alaska, Arizona, Idaho,
Illinois, Indiana, Kentucky, Louisiana, Michigan, Mississippi, Missouri, Nevada, Oregon and
Pennsylvania, Tennessee). In cases when the desired surrogate parameter was not available (e.g., feet
drilled), data for an alternative surrogate parameter (e.g., number of spudded wells) was downloaded and
used. Under that methodology, both completion date and date of first production from HPDI were used to
identify wells completed during 2017. In total, over 1 million unique wells were compiled from the above
data sources. The wells cover 34 states and over 1,100 counties. (ERG, 2018).
The spatial surrogates, numbered 670 through 699 and also 6831, 6832, and 6833, were originally
processed at 4km resolution and without gapfilling. The surrogates were first gapfilled using fallback
surrogates. For each surrogate, the last two fallbacks were surrogate 693 (Well Count - All Wells) and
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304 (NLCD Open + Low). Where appropriate, other surrogates were also part 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. After gapfilling, surrogates were aggregated to 12km and 36km
resolution. All gapfilling and aggregating was performed with the Surrogate Tool.
Table 3-27. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
670
Spud Count - CBM Wells
671
Spud Count - Gas Wells
672
Gas Production at Oil Wells
673
Oil Production at CBM Wells
674
Unconventional Well Completion Counts
676
Well Count - All Producing
677
Well Count - All Exploratory
678
Completions at Gas Wells
679
Completions at CBM Wells
681
Spud Count - Oil Wells
683
Produced Water at All Wells
685
Completions at Oil Wells
686
Completions at All Wells
687
Feet Drilled at All Wells
689
Gas Produced - Total
691
Well Counts - CBM Wells
692
Spud Count - All Wells
693
Well Count - All Wells
694
Oil Production at Oil Wells
695
Well Count - Oil Wells
696
Gas Production at Gas Wells
697
Oil Production at Gas Wells
698
Well Count - Gas Wells
699
Gas Production at CBM Wells
6831
Produced water at CBM wells
6832
Produced water at gas wells
6833
Produced water at oil wells
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-25 were not assigned to any SCCs, although many of the "unused"
surrogates are actually used to "gap fill" other surrogates that are used. When the source data for a
surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in
those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the
emission inventories. Table 3-28 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by
sector assigned to each spatial surrogate.
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Table 3-28. Selected 2017 CAP emissions by sector for U.S. Surrogates (short tons in 12US1)
Sector
ID
Description
MI;
NOx
PM2S
SO2
voc
afdust
240
Total Road Miles
309,376
afdust
304
NLCD Open + Low
842,116
afdust
306
NLCD Med + High
52,278
afdust
308
NLCD Low + Med + High
117,313
afdust
310
NLCD Total Agriculture
791,881
ag
310
NLCD Total Agriculture
3,457,964
227,853
nonpt
100
Population
34,304
0
0
0
1,181,307
nonpt
150
Residential Heating - Natural Gas
33,550
204,371
4,041
1,365
12,055
nonpt
170
Residential Heating - Distillate Oil
1,531
30,031
3,284
11,510
1,039
nonpt
180
Residential Heating - Coal
1
3
1
3
3
nonpt
190
Residential Heating - LP Gas
98
31,061
163
712
1,181
nonpt
239
Total Road AADT
0
22
541
0
297,798
nonpt
240
Total Road Miles
0
0
0
0
39,013
nonpt
244
All Unrestricted AADT
0
0
0
0
101,255
nonpt
271
NT AD Class 12 3 Railroad Density
0
0
0
0
2,203
nonpt
300
NLCD Low Intensity Development
4,823
19,093
94,548
2,882
72,599
nonpt
306
NLCD Med + High
23,713
274,780
246,324
131,747
878,553
nonpt
307
NLCD All Development
109
25,803
110,507
8,260
574,679
nonpt
308
NLCD Low + Med + High
886
156,239
15,825
10,081
65,798
nonpt
310
NLCD Total Agriculture
0
0
38
0
227,051
nonpt
319
NLCD Crop Land
0
0
97
72
299
nonpt
320
NLCD Forest Land
3,953
68
273
0
279
nonpt
505
Industrial Land
0
0
0
0
48
nonpt
535
Residential + Commercial + Industrial +
Institutional + Government
0
2
121
0
36
nonpt
650
Refineries and Tank Farms
0
16
0
0
106,401
nonpt
711
Airport Areas
0
0
0
0
596
nonpt
801
Port Areas
0
0
0
0
6,730
nonroad
261
NT AD Total Railroad Density
3
2,026
212
2
398
nonroad
304
NLCD Open + Low
4
1,763
152
5
2,598
nonroad
305
NLCD Low + Med
94
15,378
3,843
116
108,457
nonroad
306
NLCD Med + High
315
173,548
11,084
399
90,482
nonroad
307
NLCD All Development
100
30,701
15,370
117
170,196
nonroad
308
NLCD Low + Med + High
532
315,346
26,575
525
50,340
nonroad
309
NLCD Open + Low + Med
120
21,204
1,253
149
45,270
nonroad
310
NLCD Total Agriculture
419
353,338
26,071
444
37,536
nonroad
320
NLCD Forest Land
15
4,767
610
14
3,824
nonroad
321
NLCD Recreational Land
83
12,197
6,142
94
230,274
nonroad
350
NLCD Water
189
115,331
5,398
221
320,701
nonroad
850
Golf Courses
13
2,039
118
16
5,617
nonroad
860
Mines
2
2,579
262
3
493
np oilgas
670
Spud Count - CBM Wells
0
0
0
0
152
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Sector
ID
Description
MI;
NOx
PM2S
SO2
voc
np oilgas
671
Spud Count - Gas Wells
0
0
0
0
4,761
np oilgas
672
Gas Production - Oil Wells
0
511
0
205
60,626
np oilgas
674
Unconventional Well Completion Counts
3
20,367
485
202
2,260
np oilgas
678
Completions at Gas Wells
0
7,843
192
3,578
19,743
np oilgas
679
Completions at CBM Wells
0
3
0
105
484
np oilgas
681
Spud Count - Oil Wells
0
0
0
0
28,490
np oilgas
683
Produced Water at All Wells
0
22
0
0
868
np oilgas
685
Completions at Oil Wells
0
825
0
426
38,265
np oilgas
687
Feet Drilled at All Wells
4
37,669
1,172
176
3,639
np oilgas
691
Well Counts - CBM Wells
0
19,926
289
7
15,937
np oilgas
692
Spud Count - All Wells
0
365
12
42
34
np oilgas
693
Well Count - All Wells
0
0
0
0
2
np oilgas
694
Oil Production at Oil Wells
0
4,193
0
6,104
793,175
np oilgas
695
Well Count - Oil Wells
0
141,275
3,476
19,198
484,384
np oilgas
696
Gas Production at Gas Wells
0
40,880
278
4,251
259,234
np oilgas
697
Oil Production - Gas Wells
0
858
0
0
80,817
np oilgas
698
Well Count - Gas Wells
7
324,370
4,475
143
516,440
np oilgas
699
Gas Production at CBM Wells
0
33
5
0
6,090
np oilgas
6831
Produced Water at CBM Wells
0
0
0
0
79,531
np oilgas
6832
Produced Water at Gas Wells
0
0
0
0
17,360
np oilgas
6833
Produced Water at Oil Wells
0
0
0
0
846
onroad
205
Extended Idle Locations
251
89,719
763
41
15,078
onroad
239
Total Road AADT
5,774
onroad
242
All Restricted AADT
34,371
1,093,396
35,386
8,124
187,892
onroad
244
All Unrestricted AADT
64,963
1,701,659
65,959
16,291
460,414
onroad
258
Intercity Bus Terminals
195
2
0
67
onroad
259
Transit Bus Terminals
77
5
0
195
onroad
304
NLCD Open + Low
1,149
38
1
5,411
onroad
306
NLCD Med + High
13,678
267
17
15,343
onroad
307
NLCD All Development
525,284
9,761
817
1,038,858
onroad
308
NLCD Low + Med + High
36,174
633
51
56,651
onroad
506
Education
599
14
1
583
rail
261
NT AD Total Railroad Density
14
34,523
1,022
30
1,755
rail
271
NT AD Class 12 3 Railroad Density
328
522,862
14,493
652
23,949
rwc
300
NLCD Low Intensity Development
16,409
34,095
299,280
7,989
323,683
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:
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2501080050 and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/rocket engine
firing and testing). The ARTOPNT approach is described in detail in the 2002 platform documentation:
http://www3.epa.gov/scram001/reports/Emissions%20TSD%20Voll 02-28-08.pdf. The ARTOPNT file
that lists the nonpoint sources to locate using point data were unchanged from the 2005-based platform.
3.4.3 Surrogates for Canada and Mexico emission inventories
The surrogates for Canada to spatially allocate the Canadian emissions are based on the 2015 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 3-15. The population
surrogate was updated for Mexico for the 2014v7.1 platform. Surrogate code 11, which uses 2015
population data at 1 km resolution, replaces the previous population surrogate code 10. The other
surrogates for Mexico are circa 1999 and 2000 and were based on data obtained from the Sistema
Municpal de Bases de Datos (SIMBAD) de INEGI and the Bases de datos del Censo Economico 1999.
Most of the CAPs allocated to the Mexico and Canada surrogates are shown in Table 3-30.
Table 3-29. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
100
Population
921
Commercial Fuel Combustion
TOTAL INSTITUTIONAL AND
101
total dwelling
923
GOVERNEMNT
104
capped total dwelling
924
Primary Industry
106
ALL INDUST
925
Manufacturing and Assembly
113
Forestry and logging
926
Distribution and Retail (no petroleum)
200
Urban Primary Road Miles
927
Commercial Services
210
Rural Primary Road Miles
932
CANRAIL
211
Oil and Gas Extraction
940
PAVED ROADS NEW
212
Mining except oil and gas
946
Construction and mining
220
Urban Secondary Road Miles
948
Forest
221
Total Mining
951
Wood Consumption Percentage
222
Utilities
955
UNPAVED ROADS AND TRAILS
230
Rural Secondary Road Miles
960
TOTBEEF
233
Total Land Development
970
TOTPOUL
240
capped population
980
TOTSWIN
308
Food manufacturing
990
TOTFERT
321
Wood product manufacturing
996
urban area
323
Printing and related support activities
1251
OFFR TOTFERT
Petroleum and coal products
324
manufacturing
1252
OFFR MINES
Plastics and rubber products
326
manufacturing
1253
OFFR Other Construction not Urban
Non-metallic mineral product
327
manufacturing
1254
OFFR Commercial Services
331
Primary Metal Manufacturing
1255
OFFR Oil Sands Mines
350
Water
1256
OFFR Wood industries CANVEC
141
-------
Code
Canadian Surrogate Description
Code
Description
412
Petroleum product wholesaler-
distributors
1257
OFFR UNPAVED ROADS RURAL
448
clothing and clothing accessories
stores
1258
OFFR Utilities
482
Rail transportation
1259
OFFR total dwelling
562
Waste management and remediation
services
1260
OFFR water
901
AIRPORT
1261
OFFR ALL INDUST
902
Military LTO
1262
OFFR Oil and Gas Extraction
903
Commercial LTO
1263
OFFR ALLROADS
904
General Aviation LTO
1265
OFFR CANRAIL
945
Commercial Marine Vessels
9450
Commercial Marine Vessel Ports
Table 3-30. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 12US2)
Code
Mexican or Canadian Surrogate
Description
MI;
NOx
PM2s
SO2
voc
11
MEX 2015 Population
0
75,091
454
151
204,890
14
MEX Residential Heating - Wood
0
1,942
5,445
162
14,562
16
MEX Residential Heating - Distillate
Oil
1
25
0
0
1
22
MEX Total Road Miles
2,493
316,652
10,056
5,277
63,239
24
MEX Total Railroads Miles
0
20,893
467
183
816
26
MEX Total Agriculture
95,034
17,101
13,147
412
3,565
32
MEX Commercial Land
0
68
1,478
0
24,996
34
MEX Industrial Land
79
2,016
1,173
5
31,706
36
MEX Commercial plus Industrial
Land
4
6,464
327
12
92,441
40
MEX Residential (RES1-
4)+Comercial+Industrial+Institutional
+Government
0
15
66
1
18,980
42
MEX Personal Repair (COM3)
0
0
0
0
4,576
44
MEX Airports Area
0
3,445
51
224
1,343
48
MEX Brick Kilns
0
192
3,849
349
94
50
MEX Mobile sources - Border
Crossing
3
71
2
0
57
100
CAN Population
611
42
515
12
175
101
CAN total dwelling
0
0
0
0
115,985
104
CAN capped total dwelling
264
26,357
1,986
148
1,394
106
CAN ALL INDUST
0
0
2,824
0
0
113
CAN Forestry and logging
81
962
5,294
20
2,715
200
CAN Urban Primary Road Miles
1,162
49,767
1,767
219
5,210
210
CAN Rural Primary Road Miles
457
28,045
964
88
2,223
211
CAN Oil and Gas Extraction
0
30
26
8
366
142
-------
Mexican or Canadian Surrogate
Code
Description
MI;
NOx
PM2s
SO2
voc
212
CAN Mining except oil and gas
0
0
2,369
0
0
220
CAN Urban Secondary Road Miles
2,151
77,473
3,543
455
13,514
221
CAN Total Mining
0
0
32,447
0
0
222
CAN Utilities
27
1,419
12,364
346
16
230
CAN Rural Secondary Road Miles
1,236
54,510
1,953
243
6,198
240
CAN capped population
28
34,842
838
50
64,072
308
CAN Food manufacturing
0
0
15,711
0
9,438
321
CAN Wood product manufacturing
493
3,074
1,142
148
10,455
323
CAN Printing and related support
activities
0
0
0
0
10,199
CAN Petroleum and coal products
324
manufacturing
0
615
779
298
2,906
326
CAN Plastics and rubber products
manufacturing
0
0
0
0
15,320
CAN Non-metallic mineral product
327
manufacturing
0
0
5,302
0
0
331
CAN Primary Metal Manufacturing
0
145
5,442
29
72
412
CAN Petroleum product wholesaler-
distributors
0
0
0
0
35,673
448
CAN clothing and clothing accessories
stores
0
0
0
0
117
482
CAN Rail transportation
1
2,438
52
7
148
562
CAN Waste management and
remediation services
224
1,622
1,896
2,411
10,118
901
CAN AIRPORT
0
75
7
0
7
921
CAN Commercial Fuel Combustion
157
16,140
1,674
1,053
728
CAN TOTAL INSTITUTIONAL
923
AND GOVERNEMNT
0
0
0
0
11,402
924
CAN Primary Industry
0
0
0
0
28,417
925
CAN Manufacturing and Assembly
0
0
0
0
57,090
CAN Distribution and Retail (no
926
petroleum)
0
0
0
0
5,704
927
CAN Commercial Services
0
0
0
0
24,917
932
CAN CANRAIL
33
62,174
1,436
260
2,972
940
CAN PAVED ROADS NEW
0
0
141,178
0
0
946
CAN Construction and mining
0
0
0
0
2,676
951
CAN Wood Consumption Percentage
1,459
16,211
133,259
2,316
188,165
CAN
955
UNPAVED ROADS AND TRAILS
0
0
212,342
0
0
960
CAN TOTBEEF
0
0
792
0
0
970
CAN TOTPOUL
0
0
156
0
0
980
CAN TOTS WIN
0
0
682
0
0
990
CAN TOTFERT
33
3,199
226
6,464
120
996
CAN urban area
0
0
326
0
0
143
-------
Code
Mexican or Canadian Surrogate
Description
MI;
NOx
PM2s
SO2
voc
1251
CAN OFFR TOTFERT
48
42,015
3,041
34
4,082
1252
CAN OFFR MINES
1
627
46
1
95
1253
CAN OFFR Other Construction not
Urban
44
32,470
3,616
31
7,558
1254
CAN OFFR Commercial Services
32
13,624
1,990
27
33,516
1255
OFFR Oil Sands Mines
0
0
0
0
0
1256
CAN OFFR Wood industries
CANVEC
6
3,648
293
4
895
1257
CAN OFFR UNPAVED ROADS
RURAL
20
7,359
702
17
28,618
1258
CAN OFFR Utilities
6
3,876
255
5
833
1259
CAN OFFR total dwelling
13
4,585
582
11
11,645
1260
CAN OFFR water
3
785
86
4
5,445
1261
CAN OFFR ALL INDUST
3
4,823
211
2
898
1262
CAN OFFR Oil and Gas Extraction
0
56
12
0
78
1263
CAN OFFR ALLROADS
2
1,706
168
2
396
1265
CAN OFFR CANRAIL
0
69
7
0
12
144
-------
4 Emission Summaries
Tables 4-1 through 4-3 summarize emissions by sector for the 2017gb case. 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 includes
CMV emissions at U.S. ports. "Offshore" represents CMV emissions that are outside of U.S. state
waters. Canadian CMV emissions are included in the other sector. The total of all US sectors is listed as
"ConU.S. Total."
State totals and other summaries are available in the reports area on the FTP site for the 2017 platform
(https://gaftp.epa.gov/Air/emismod/2017/).
145
-------
Table 4-1. National by-sector CAP emissions for the 2017gb case, 12US1 grid (tons/yr)
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
afdustadj
6,313,419
880,170
Ag
3,457,964
227,853
airports
479,642
0
128,482
9,725
8,499
16,040
54,104
cmv_clc2
24,150
85
166,625
4,569
4,428
648
6,596
cmv_c3
14,721
41
116,250
2,325
2,139
4,780
9,100
nonpt
1,928,006
102,968
741,729
573,265
475,768
166,650
3,593,050
nonroad
10,478,152
1,890
1,050,224
102,809
97,089
2,105
1,066,205
npoilgas
604,340
13
599,138
10,530
10,384
34,438
2,413,140
onroad
19,241,496
99,587
3,461,929
236,730
112,826
25,344
1,786,266
ptagfire
359,379
86,695
15,907
47,295
29,171
6,069
45,292
ptfire
23,817,819
389,528
357,178
2,460,819
2,087,231
186,731
5,555,642
ptegu
608,586
22,741
1,173,262
136,155
111,723
1,396,988
32,003
ptnonipm
1,351,965
60,677
871,085
380,035
242,454
584,625
739,591
ptoilgas
159,713
2,175
332,011
11,762
11,478
38,812
125,336
rail
109,882
342
557,384
16,021
15,515
682
25,704
rwc
2,160,536
16,409
34,095
300,141
299,280
7,989
323,683
Con. U.S. Total
61,338,388
4,241,116
9,605,297
10,605,600
4,388,157
2,471,900
16,003,565
beis
3,852,455
980,479
25,255,118
CONUS + beis
65,190,843
4,241,116
10,585,776
10,605,600
4,388,157
2,471,900
41,258,684
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada othafdust
1,004,479
176,964
Canada othar
2,707,964
4,785
373,473
309,767
242,457
19,655
827,125
Canada onroadcan
1,602,360
6,667
362,085
25,344
13,290
1,354
129,089
Canada othpt
1,085,244
531,101
639,119
109,295
46,980
981,159
768,720
Canada othptdust
155,197
56,278
Canada ptfireothna
5,337,594
109,386
228,651
744,538
628,852
42,216
1,568,403
Canada CMV
11,103
37
96,590
1,716
1,594
2,939
5,410
Mexico othar
115,441
111,699
60,045
104,881
34,686
1,729
361,626
Mexico onroad mex
1,829,349
2,852
447,216
15,361
10,925
6,441
158,925
Mexico othpt
108,706
1,093
190,439
53,889
37,386
354,772
35,671
Mexico ptfire othna
545,772
10,555
21,773
72,378
61,857
4,527
155,324
Offshore cmv in Federal
waters
33,562
129
296,981
7,243
6,708
28,296
16,345
Offshore cmv outside
Federal waters
24,257
457
267,280
25,795
23,738
189,007
11,520
Offshore pt oilgas
51,866
8
49,959
636
635
462
38,803
Non-U.S. Total
13,453,218
778,770
3,033,612
2,630,518
1,342,350
1,632,557
4,076,960
146
-------
Table 4-2. National by-sector VOC HAP emissions for the 2017gb case, 12US1 grid (tons/yr)
Sector
Acetaldehyde
Benzene
Formaldehyde
Methanol
Naphthalene
Acrolein
1,3-
Butadiene
ag
1,583
457
0
16,222
0
0
0
airports
2,076
930
6,008
867
593
1,178
837
cmv_clc2
64
31
282
0
18
12
7
cmv_c3
89
43
389
0
25
17
9
nonpt
5,043
9,701
5,843
148,098
4,786
222
778
nonroad
10,445
27,383
26,365
1,390
1,796
1,913
4,360
npoilgas
2,722
26,869
23,413
1,578
104
1,602
337
onroad
20,315
42,821
26,511
3,043
3,545
1,831
6,410
ptegu
293
457
2,493
114
22
267
3
ptagfire
2,766
987
2,984
0
0
0
301
ptfire
184,210
55,154
348,557
333,686
53,919
60,736
34,691
ptnonipm
6,012
3,091
6,528
52,233
1,125
787
611
ptoilgas
2,503
1,028
12,452
1,748
25
1,851
261
rail
1,863
535
5,306
0
67
380
44
rwc
8,476
15,634
17,190
0
2,393
821
1,981
Con. U.S. Total
248,462
185,122
484,322
558,981
68,417
71,616
50,631
beis
403,401
0
550,102
2,026,335
0
0
0
CONUS + beis
651,862
185,122
1,034,423
2,585,316
68,417
71,616
50,631
Canada othar
24,392
37,424
19,647
4,567
3,945
0
0
Canada onroad can
2,286
5,658
3,117
0
43
0
0
Canada othpt
2,475
44,342
4,453
42,303
66
0
0
Canada ptfireothna
56,327
36,553
164,688
135,852
0
0
0
Canada CMV
53
27
231
0
15
10
5
Mexico othar
3,148
6,218
2,403
6,187
437
0
0
Mexico onroad mex
685
3,975
1,620
620
238
115
595
Mexico othpt
74
809
2,957
450
12
0
0
Mexico ptfire othna
9,425
3,415
15,409
9,684
0
0
0
Offshore cmv in
Federal waters
160
77
698
0
45
30
17
Offshore cmv outside
Federal waters
113
55
492
0
31
21
12
Offshore pt oilgas
0
0
0
0
0
0
0
Non-U.S. Total
99,137
138,554
215,715
199,663
4,831
177
629
147
-------
Table 4-3. National by-sector Diesel PM and metal emissions for the 2017gb case, 12US1 grid
(tons/yr)
Sector
Diesel
PMio
Diesel
PM2s
Chromium
Hex
Arsenic
Cadmium
Nickel
Manganese
Ethylene
Oxide
airports
76
74
cmv clc2
4,569
4,428
0.00003
0.11
1.05
3.04
0.01
__
cmv c3
2,325
2,139
0.00002
0.06
0.50
1.47
0.01
—
nonpt
0.357
5.96
3.77
26.82
15.56
0.95
nonroad
61,662
59,630
0.004
0.87
1.12
0.68
np oilgas
0.00005
0.01
0.07
0.03
0.02
__
onroad
61,942
57,082
0.038
7.36
5.80
30.99
—
ptegu
6.347
7.01
6.19
64.65
127.33
0.004
ptnonipm
1,115
1,002
25.864
28.28
13.92
185.03
640.53
109.46
pt oilgas
0.027
0.02
0.33
6.75
3.12
rail
16,021
15,515
0.074
15.25
0.03
57.31
32.78
__
rwc
—
0.08
0.07
0.62
—
Con. U.S.
Total
147,709
139,869
32.71
64.94
25.95
352.10
851.64
110.42
Canada CMV
1,476
1,354
0.00001
0.04
0.32
0.93
0.004
—
Offshore CMV
ECA
7,243
6,708
0.00005
0.17
1.58
4.61
0.02
Offshore CMV
non-ECA
25,796
23,739
0.00017
0.61
5.60
16.31
0.08
148
-------
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0Emission%20Estimation%20Tool%20Vl 0%20December 2018.pdf.
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McCarty, J.L., Korontzi, S., Jutice, C.O., and T. Loboda. 2009. The spatial and temporal distribution of
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/media/Files/Publications/Research/Environmental/Wood-Fired-Hvdronic-Heater-Tech.ashx.
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Anthropogenic Fugitive Dust Emission Inventory and Temporal Allocation Using an Updated
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152
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Appendix A: CB6 Assignment for New Species
153
-------
September 27,2016
MEMORANDUM
Tc: a tsort Ey:- End Models ne Strum, OAQPS, EPA
F-rnr P:n E-e = r: = =v =*-• £*e= '• = 'wc:i, R = n_bc : E"vrcn
3yrjjecr: Species Mapp np for C3S and CBC5 for use »th SPECIATE 4.3
Summary
Rar he j.-m ~.EJ reviewed version 4.5 of the SPECIATE database,, and created CBQ5 and CS6
ntszhsnis-i isscies mappings for newly added compounds, in addition, the mapping guidelines for
Carbon Sono s C E! mechanisms were expanded to promote consistency i» current and futon wort.
Background
T" Env r-:rnrerv:a Pritecr-c* • S=EClA"i •= easier. c:r.:a'-j s- s~z tk.^te •-•-arcer
speciation profiles of air pollution sources, which are used in. the generation of emissions data for air
:uiltv '~:de: = iuc =; C'Vi i! ;-f;v. '..c-'iaice^trr o "E. c "iaq.- i r: CA'-'-
fhttp://,«*wxaiTO.coiii). However, the condensed chemical mechanisms uses! within these
chcTrc-s- :s} -tresis is re-.,*r :cec s-ilui s-E'I:ate tc 'er-esen: gss :'5sS chen sir;, 3-id
thus the speciate compounds must be assigned to the aqm model species of the condensed
<"=:heni£"ii ic":"11= ( -isc: nj I- = fe;--eien:a:c- cr|s-ic rhe-ivra :pec as by
the model compounds of the condensed mechanisms.
This memorandum describes how chemical mappings were developed from speciate 4.5
compounds to model species of the CB mechanism, specifically cats
xfitsp'¦/ >• *'** .camjt.com/puW/pdfi/CBK_FiiiaI_Report_12iOBD5.pdf) and CB6
(:http://aqrp.eeer.iJte*is,edu/proiecttnfeWi2_i £/I Z-Dl2/i;-G I2l20FiiHK20Report.pcif;i.
Methods
CB Mode' Spec es
Organic gases are mapped to tie CB mechanism- either as explicitly represented individual
romcr-ntis :e g. -"csre^a rer.vre , :r 52 = :jt; -=t'-:f c-* ^ode :pec'=ith=; 'ep~s:5^t
common structural groups, {e.g. AtPX for ether aldehydes,, par fcralkyl groups). Table i lists ell of
the sxol = "r:VU"U''= nroo= spec e; h CSCE s-d-ZBe ~=c-sr=is"ni. e=;h z* v.,h :h *ep;=i=-T5a
n«"'%e!r of C3r%«n a'enre sftnving for t™ be conserved 'n b" cases. €55 co"t=re few
"¦:r= i;:p' :i: r ;o= :pec tns i cece E'd 5;~ =:ri: 3'a st- _r:_ _bI |-;u: to *ep ';=="t <:e*r-e j. T- =
ZECr 'e: "eisr^s: si :f:-s -~ve sd-r ti r r.= 156 -z = :-a i cr::sr '- :* = ' J?r ZSC5 «::-u i
Table 1.
154
-------
in addition to the explicit and structural species, there are two mo(3 PAR!
rei
i.::
fteetiiMihftfe |eHiinBl|
2
::
Vej
9ENZ
3;-75i»
S
r-c i--: :
>'ei
CH4
Vsfwe
1
ss
¥es
STH
•r:-i i stiylire i
2
;;
res
SFHA
2
:i
Yes
E:~ "* ;:it, i't
" . PAR, 1
Yes
¦ T ;"i :«•
=:*-:tc
2
Yes
s 4
tormalaenyde I'lrsefhsnsll
i
Yes
¦ ¦ :
3
::
Yes
E C1 -
1
'
Yes
•'~ P'3FI-S
2,
1.5 UWIJ
Yes
:: i
i j:
-¦
- tr\er aldehyde group J-C-CHOJ
2
ft:,S
Yes
iiiers!: : -1-" = =i4J
4
Yms
Yes
SET
C=C|-
.&
ss
Yes
»sTBlftnic group
i
" ::
Yes
7=5?'=
Manotefpenu
let
J!
Yes
TOtLKftf tnd njfrjgr p¦>.« qpuf 5f Pf^St35*®'
7
::
JnriBctiv? c&rtscf ""¦"'UC'S ** i5lcc"n,>t* j
1
Yes
;-ii irj :-ri- ,1 *r;
S
s;
::
it ijCs vie:
- -
Very low volatility cnmpipufifis
•
Yea
Yes
yNn Is r?-CfWfi
»
¦:
Yes
"an® v„o. represents 1 g mol" :a«i Imwwolsititi" cornpcuru irs »s; :s i'« 3. based nn ira sat! - .qjjit yR : jifnappsil
intffc: at»: r j t represent any aitm
sai"**»ll Enwrers US CSpcrate, 775 Sl» 3rwe, Suits 2115, Movirto, CA1WS8 2
155
-------
156
-------
Mapping guioePnes for nc-expl cit cgmcpas ua'ng CB iraddspeds
SPECIATEcompc'ufa: =-e "t :rs»:ad explicitly are mapped to CB mode! species that represent
commoiistructii^! ~=b 5 2 i its tie carbon number and general mapping guidelines for each
Tsole 2. Gen=ra; G jide r-es fcr "nac^ng u= ng CB6 structural mo: el ioecies.
:e_:
*;
" art
'JunDs-f a"
C-S.-JC-'iS
Reasserts
AIDS
d.
Aidedyde group. AL3X represents 2 cartons and additional carbons ire represented as
!,• ,'l ;r;i,p! licit; »3', fg zrof :isle: v.de i: a.: > -
4
internal olefin group. IQLE represents 4 cartons and additional arbons are >-e3'es«r ted 15
atfcyl groups [mnstty PAR|, eg. 2-pentene mamma are lOLE # MR.
F' CMfbrr.-
* D.i >» it" : :art;n t^ariJ-ss cr oe-.h j 3es c* tne C3tS't sc~»d are downgraded to
\ET
1
>star.s 5-sjp «.ET rsprsssnts i c:-:c-i sis asdit 3rs cs'ic-i; srs -sprs:sites a. s»-,!
5--¦ t: ™
2
!-yl jntps |ic:t> "i'1 se D^apsie is C.E t Pi'., i-yne :rc jp eg :uwi! isomers are
1
Alkacts ins alkv «~3jps. par represents i carton « 5 Etfaite is 4 See *sr
-=«=
10
ill rsrjtE-aeRes arerepresented i:
7
Toluene ma after monoattyt arnraaha. TQl represents 7 carbons and any additional
curtails are represented as attyl grouts 1 mostly WW|s, eg. ethyfeeivene is fOL - WW.
CrssoS aw mpresented as TOt end PAR. Sffsnes we represented using T31, OLE and
?AR.
I
wireaclwe OHtnmi ere i U1R khJ» as fiustemwry alfcjl groups |eg., eeo-pentiwe is -I «A«
* wl»R|i cart wfii: snd gmups fe.g,, •catic add is PAR + UKSi«, ester groups |ejj, metl*fl
: : - •: - . s ; : .' :
cartons of ntlrils groups
XTfL
S
I*|i:ene teamen and b*b' :c ,!Blk> er:*natiB. «-*5irs:eib S :artsr: s-.c ir, a:3it sral
carbons an represented as lifcjl greifis (motsBir PAa|. eg. trimetnv lbenT2ene aar.srs are
XVi.- -i*
Same compounds that are multifunctional and/or include hetero-atoins lack obvious Ci mappings.
Wt developed guidelines lor same of these compound classes lo promote consistent representation
n :hii .v;r¦: sne ru:. -e = i-ni Ap» ¦ j=cn== for ==vera :c-i::up; zlhizzz sr= E;-:p i -s i - ~ab;= l.
We devetopedf-idesines as needed to address newly added species in SPECIATE <4.5 but did not
systemat cilly review Existing mspp np for "difficult to assign" compounds that :ou a benefit from
-eve opirf 3 guideline.
riswtmli Eriiircri US Copofatix^ "jliSsm ¦MBrin.3cwe, Sute 2113... CASflSSB 3
V -1 itsummmi ?'+J.«3.S53?lff?
157
-------
Table 3, Mapping guide! nai fo-scie difficult to nan ccnpo^nd daises and struck i a groups
¦'i: ¦ •:
Us: >. ::i ¦;
: iL-:
¦;3 "::t :j»c rt:'t;4 -im
ChlBrabentenes anil
other halogenBtei
::
Suitfcfifie:
* 3 or less halogens - l PAH, 3 iJNft
« 4 or more halogens — E U:»m
Em ran leu:
» ..E.3-CI- :rctsn::ne -1B£5 SUBS
» ~»fs:M:-3t«-»sers5 - 6 J\zt
jjic; is:
* . D.E sdci'icrs »itcns rep-sierisd ti » »-fl |raups (generally
= 1 =
Siar:
¦ *elftflcfclftpentadis:rie-I *;OLE»,2 PAR
• i a PAR
=•- ^risf^pwclES
Suiielins:
¦ 2 OLE wfl* icWit"rr.!.= c«'30fis represented m Ml,fl groups (generally
par)
« :¦ = j.: .1 - r-:
* -- : s - I : .E L - -:
- eterocfcfiE arnfnatk:
compounds
tBfrtatftiiig' I non-
c»rbon atoms
• i DlE with remair. :arban& represented *s Blkfl groups (generally
* awsyrwbw - i OLE, 4 SftS
¦ 1-meBiyipf mole - 1 OLE, 2 MR
« 4,5-3in ethyl mi wle -1 Gil, 3 PAH
: ac ni(j|
SuiiSfcie:
¦ Triple bunds are treated: as 3AI unless they are the, only rcsctive
furKtiurmiJ group. :t a oampnund contains mars than one triple bona
¦rat iw other react*- functiBnal groups,, then one of the tfiple donas
is treated as OLE with additional carbons treated is Blkfl groups.
rjcsnoiei
g 4,.,&¦<;«^an...S*1 [Q-. ™ 3 0*^B*
* . f-j- -• - ; : par
¦ ;.S— 6c:bc -,t,; - . OlE. 5
These guidelines »e*e usee tc map the new species from SFBCATE4.5,. and also to revise some
prs'.ic.s ,• "¦3:csd ::irpc.rdr cvera , s tea c- i"5 hew -c-ii-: ECutev- 5 were mapped
and 7 previously nia:ped species were revised based on the new guidelines.
158
-------
lecoramentrtaiori
1. ccnr'sta a systsrm5: r=--iev, :f:*s rr-p2 '| r s spec's: tc = --su-= tv -.-.i:' c_T#nt
guidel *=:. "•= 3=i = e* iThf ccrpc.'ds vst sr= 2=rri nev. zz-eiieswere
'e-. €-.,ed a*: -=-.-i=ec tc r?ns==T=rcv r ~" = zpri :w."::he —.a.: ft-. :r
exi." <¦•§ spec ~s ma apings-were not reviewed is It was outside the scope- of this work.
2. l = .c :p b -ieT^cci'ogv ciaisryh? r: : •=:}¦ 't large:- o ".§5 ¦; : ccto.' :.i "rased :n :hei ¦
vc sti tv iser-. rTsnied •:?, :r o*.v .-c a:-- ty! tc >irpTy= -irpirr: fo~:=cc-:=••, org an ; aerosol
f?CA srcdefing .; r.E t' = .c =:i -t.-basis ==r iCA tic:=;. •."h'rh ¦ avsl'sc = r be:" cvaq
it. CAl-l-L A p ¦=-! "i •- = '•,< rvest »s' :ti :f *- = pen :• ty :'J :rh» =•: h;: perc-fias, a.ic is
jisc-ssecl in 3 -SDsratE memorandum.
U5„Nll¥lt», ZA'Smm 3
159
-------
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE versions 4.5 and
later that were used in the 2016 platforms
Table B-l Profiles first used in 2016beta, 2016vl, and 2016v2 platforms
Sector
Pollutant
Profile code
Profile description
SPECIATE
version
npoilgas.
pt oilgas
VOC
CMU01
Oil and Gas - Produced Gas Composition from Gas Wells -
Central Montana Uplift - Montana
5.1
npoilgas.
pt oilgas
VOC
WIL01
Oil and Gas - Flash Gas Composition from Tanks at Oil
Wells - Williston Basin North Dakota
5.1
npoilgas.
pt oilgas
VOC
WIL02
Oil and Gas - Flash Gas Composition from Tanks at Oil
Wells - Williston Basin Montana
5.1
npoilgas.
pt oilgas
VOC
WIL03
Oil and Gas - Produced Gas Composition from Oil Wells -
Williston Basin North Dakota
5.1
npoilgas.
pt oilgas
VOC
WIL04
Oil and Gas - Produced Gas Composition from Oil Wells -
Williston Basin Montana
5.1
cmv_clc2,
cmv c3
VOC
95331NEIHP
Marine Vessel - 95331 blend with CMV HAP
5.1
Table B-2 Profiles first used in 2016 alpha platform
Profile
SPECIATE
Comment
Sector
Pollutant
code
Profile description
version
5.0
Replacement for v4.5
profile 95223; Used 70%
methane, 20% ethane,
and the 10% remaining
Poultry Production - Average of Production
VOC is from profile
nonpt
VOC
G95223TOG
Cycle with gapfilled methane and ethane
95223
5.0
Replacement for v4.5
profile 95240. Used 70%
methane, 20% ethane;
Nonpt,
Beef Cattle Farm and Animal Waste with
the 10% remaining VOC
ptnonipm
VOC
G95240TOG
gapfilled methane and ethane
is from profile 95240.
5.0
Replacement for v4.5
profile 95241. Used 70%
methane, 20% ethane;
the 10% remaining VOC
nonpt
VOC
G95241TOG
Swine Farm and Animal Waste
is from profile 95241
nonpt,
5.0
Composite of AE6-ready
ptnonipm,
versions of SPECIATE4.5
pt_oilgas,
Composite -Refinery Fuel Gas and Natural
profiles 95125, 95126,
ptegu
PM2.5
95475
Gas Combustion
and 95127
Spark-Ignition Exhaust Emissions from 2-
4.5
stroke off-road engines - E10 ethanol
nonroad
VOC
95328
gasoline
Spark-Ignition Exhaust Emissions from 4-
4.5
stroke off-road engines - E10 ethanol
nonroad
VOC
95330
gasoline
160
-------
Profile
SPECIATE
Comment
Sector
Pollutant
code
Profile description
version
Diesel Exhaust Emissions from Pre-Tier 1
4.5
nonroad
voc
95331
Off-road Engines
Diesel Exhaust Emissions from Tier 1 Off-
4.5
nonroad
voc
95332
road Engines
Diesel Exhaust Emissions from Tier 2 Off-
4.5
nonroad
voc
95333
road Engines
Oil and Gas - Composite - Oil Field - Oil
4.5
nP_oilgas
voc
95087a
Tank Battery Vent Gas
Oil and Gas - Composite - Oil Field -
4.5
nP_oilgas
voc
95109a
Condensate Tank Battery Vent Gas
Composite Profile - Oil and Natural Gas
4.5
nP_oilgas
voc
95398
Production - Condensate Tanks
np_oilgas
voc
95403
Composite Profile - Gas Wells
4.5
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95417
- Untreated Natural Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
nP_oilgas
voc
95418
- Condensate Tank Vent Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95419
- Oil Tank Vent Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95420
- Glycol Dehydrator, Uinta Basin
Oil and Gas -Denver-Julesburg Basin
4.5
Produced Gas Composition from Non-CBM
np_oilgas
voc
DJVNT R
Gas Wells
np_oilgas
voc
FLR99
Natural Gas Flare Profile with DRE >98%
4.5
Oil and Gas -Piceance Basin Produced Gas
4.5
np_oilgas
voc
PNC01 R
Composition from Non-CBM Gas Wells
Oil and Gas -Piceance Basin Produced Gas
4.5
np_oilgas
voc
PNC02 R
Composition from Oil Wells
Oil and Gas -Piceance Basin Flash Gas
4.5
np_oilgas
voc
PNC03 R
Composition for Condensate Tank
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
PNCDH
- Glycol Dehydrator, Piceance Basin
Oil and Gas -Powder River Basin Produced
4.5
np_oilgas
voc
PRBCB R
Gas Composition from CBM Wells
Oil and Gas -Powder River Basin Produced
4.5
np_oilgas
voc
PRBCO R
Gas Composition from Non-CBM Wells
Oil and Gas -Permian Basin Produced Gas
4.5
np_oilgas
voc
PRM01 R
Composition for Non-CBM Wells
Oil and Gas -South San Juan Basin
4.5
Produced Gas Composition from CBM
np_oilgas
voc
SSJCB R
Wells
Oil and Gas -South San Juan Basin
4.5
Produced Gas Composition from Non-CBM
np_oilgas
voc
SSJCO R
Gas Wells
Oil and Gas -SW Wyoming Basin Flash Gas
4.5
np_oilgas
voc
SWFLA R
Composition for Condensate Tanks
Oil and Gas -SW Wyoming Basin Produced
4.5
np_oilgas
voc
SWVNT R
Gas Composition from Non-CBM Wells
Oil and Gas -Uinta Basin Produced Gas
4.5
np_oilgas
voc
UNT01_R
Composition from CBM Wells
161
-------
Profile
SPECIATE
Comment
Sector
Pollutant
code
Profile description
version
Oil and Gas -Wind River Basin Produced
4.5
nP_oilgas
voc
WRBCO R
Gas Composition from Non-CBM Gas Wells
Chemical Manufacturing Industrywide
4.5
pt_oilgas
voc
95325
Composite
pt_oilgas
voc
95326
Pulp and Paper Industry Wide Composite
4.5
pt_oilgas,
4.5
ptnonipm
voc
95399
Composite Profile - Oil Field - Wells
pt_oilgas
voc
95403
Composite Profile - Gas Wells
4.5
Oil and Gas Production - Composite Profile
4.5
pt_oilgas
voc
95417
- Untreated Natural Gas, Uinta Basin
Oil and Gas -Denver-Julesburg Basin
4.5
Produced Gas Composition from Non-CBM
pt_oilgas
voc
DJVNT R
Gas Wells
pt_oilgas,
4.5
ptnonipm
voc
FLR99
Natural Gas Flare Profile with DRE >98%
Oil and Gas -Piceance Basin Produced Gas
4.5
pt_oilgas
voc
PNC01 R
Composition from Non-CBM Gas Wells
Oil and Gas -Piceance Basin Produced Gas
4.5
pt_oilgas
voc
PNC02 R
Composition from Oil Wells
Oil and Gas Production - Composite Profile
4.5
pt_oilgas
voc
PNCDH
- Glycol Dehydrator, Piceance Basin
pt_oilgas,
Oil and Gas -Powder River Basin Produced
4.5
ptnonipm
voc
PRBCO R
Gas Composition from Non-CBM Wells
pt_oilgas,
Oil and Gas -Permian Basin Produced Gas
4.5
ptnonipm
voc
PRM01 R
Composition for Non-CBM Wells
Oil and Gas -South San Juan Basin
4.5
pt_oilgas,
Produced Gas Composition from Non-CBM
ptnonipm
voc
SSJCO R
Gas Wells
pt_oilgas,
Oil and Gas -SW Wyoming Basin Produced
4.5
ptnonipm
voc
SWVNT R
Gas Composition from Non-CBM Wells
Composite Profile - Prescribed fire
4.5
ptfire
voc
95421
southeast conifer forest
Composite Profile - Prescribed fire
4.5
ptfire
voc
95422
southwest conifer forest
Composite Profile - Prescribed fire
4.5
ptfire
voc
95423
northwest conifer forest
Composite Profile - Wildfire northwest
4.5
ptfire
voc
95424
conifer forest
ptfire
voc
95425
Composite Profile - Wildfire boreal forest
4.5
Chemical Manufacturing Industrywide
4.5
ptnonipm
voc
95325
Composite
ptnonipm
voc
95326
Pulp and Paper Industry Wide Composite
4.5
onroad
PM2.5
95462
Composite - Brake Wear
4.5
Used in SMOKE-MOVES
onroad
PM2.5
95460
Composite - Tire Dust
4.5
Used in SMOKE-MOVES
162
-------
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
The table below provides a crosswalk between fuel distribution SCCs and classification type for portable
fuel containers (PFC), fuel distribution operations associated with the bulk-plant-to-pump (BTP), refinery
to bulk terminal (RBT) and bulk plant storage (BPS).
see
Typ
e
Description
40301001
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Breathing Loss (67000 Bbl. Tank Size)
40301002
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 10: Breathing Loss (67000 Bbl. Tank Size)
40301003
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 7: Breathing Loss (67000 Bbl. Tank Size)
40301004
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Breathing Loss (250000 Bbl. Tank Size)
40301006
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 7: Breathing Loss (250000 Bbl. Tank Size)
40301007
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Working Loss (Tank Diameter Independent)
40301101
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 13: Standing Loss (67000 Bbl. Tank Size)
40301102
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 10: Standing Loss (67000 Bbl. Tank Size)
40301103
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 7: Standing Loss (67000 Bbl. Tank Size)
40301105
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 10: Standing Loss (250000 Bbl. Tank Size)
40301151
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline: Standing Loss - Internal
40301202
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor
Space; Gasoline RVP 10: Filling Loss
40301203
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor
Space; Gasoline RVP 7: Filling Loss
40400101
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400102
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400103
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400104
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank
40400105
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank
40400106
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Breathing Loss (250000 Bbl Capacity) - Fixed Roof Tank
40400107
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Working Loss (Diam. Independent) - Fixed Roof Tank
40400108
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Working Loss (Diameter Independent) - Fixed Roof Tank
40400109
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Working Loss (Diameter Independent) - Fixed Roof Tank
40400110
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
163
-------
see
Typ
e
Description
40400111
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
40400112
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss (67000 Bbl Capacity)- Floating Roof Tank
40400113
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400114
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400115
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400116
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float RfTnk
40400117
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (250000 Bbl Cap.) - Float RfTnk
40400118
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400119
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400120
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400130
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
40400131
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400132
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400133
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
40400140
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Ext. Float Roof Tank w/ Secondy Seal
40400141
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400142
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400143
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400148
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
40400149
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
40400150
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Miscellaneous Losses/Leaks: Loading Racks
40400151
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Valves, Flanges, and Pumps
40400152
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Collection Losses
40400153
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Control Unit Losses
40400160
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
40400161
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
40400162
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
164
-------
see
Typ
e
Description
40400163
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
40400170
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400171
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400172
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400173
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400178
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal)
40400179
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)
40400199
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
40400201
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400202
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400203
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400204
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400205
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400206
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400207
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank
40400208
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank
40400210
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float RfTnk
40400211
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400212
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400213
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
165
-------
see
Typ
e
Description
40400230
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
40400231
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400232
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400233
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
40400240
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400241
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400248
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10/13/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
40400249
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
40400250
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Loading Racks
40400251
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Valves, Flanges, and Pumps
40400252
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Collection Losses
40400253
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Control Unit Losses
40400260
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
40400261
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
40400262
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
40400263
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
40400270
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400271
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400272
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
166
-------
see
Typ
e
Description
40400273
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400278
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10/13/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal)
40400279
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)
40400401
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 13: Breathing Loss
40400402
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 13: Working Loss
40400403
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 10: Breathing Loss
40400404
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 10: Working Loss
40400405
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 7: Breathing Loss
40400406
BTP
/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 7: Working Loss
40600101
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading
40600126
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading
40600131
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Normal Service)
40600136
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading (Normal Service)
40600141
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Balanced Service)
40600144
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading (Balanced Service)
40600147
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Clean Tanks)
40600162
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Loaded with Fuel (Transit Losses)
40600163
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Return with Vapor (Transit Losses)
167
-------
see
Typ
e
Description
40600199
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Not Classified
40600231
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Cleaned and Vapor Free Tanks
40600232
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers
40600233
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Cleaned and Vapor Free Tanks
40600234
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Ballasted Tank
40600235
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Ocean Barges Loading - Ballasted Tank
40600236
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Uncleaned Tanks
40600237
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Ocean Barges Loading - Uncleaned Tanks
40600238
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Uncleaned Tanks
40600239
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tankers: Ballasted Tank
40600240
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Average Tank Condition
40600241
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tanker Ballasting
40600299
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Not Classified
40600301
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Splash Filling
40600302
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Submerged Filling w/o Controls
40600305
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Unloading
40600306
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Balanced Submerged Filling
40600307
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Underground Tank Breathing and Emptying
40600399
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Not Classified **
40600401
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling
Vehicle Gas Tanks - Stage II; Vapor Loss w/o Controls
40600501
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Leaks
168
-------
see
Typ
e
Description
40600502
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Venting
40600503
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station
40600504
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station Leaks
40600602
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage II; Liquid Spill Loss w/o Controls
40600701
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Splash Filling
40600702
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Submerged Filling w/o Controls
40600706
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Balanced Submerged Filling
40600707
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Underground Tank Breathing and Emptying
40688801
BTP
/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Fugitive
Emissions; Specify in Comments Field
2501050120
RBT
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Terminals: All Evaporative
Losses; Gasoline
2501055120
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Plants: All Evaporative
Losses; Gasoline
2501060050
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Total
2501060051
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Submerged Filling
2501060052
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Splash Filling
2501060053
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Balanced Submerged Filling
2501060200
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Total
2501060201
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Breathing and Emptying
2501995000
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; All Storage Types: Working
Loss; Total: All Products
2505000120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; All Transport Types; Gasoline
2505020120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline
169
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see
Typ
e
Description
2505020121
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline -
Barge
2505030120
BTP
/BPS
Storage and Transport; Petroleum and Petroleum Product Transport; Truck; Gasoline
2505040120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Pipeline; Gasoline
2660000000
BTP
/BPS
Waste Disposal, Treatment, and Recovery; Leaking Underground Storage Tanks; Leaking
Underground Storage Tanks; Total: All Storage Types
170
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-22-002
Environmental Protection Air Quality Assessment Division February 2022
Agency Research Triangle Park, NC
171
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