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Technical Support Document (TSD): Preparation of
Emissions Inventories for the 2019 North American
Emissions Modeling Platform


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EPA-454/B-22-012
August 2022

Technical Support Document (TSD) Preparation of Emissions Inventories for the 2019 North American

Emissions Modeling Platform

U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC


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Authors:

Alison Eyth (EPA/OAR)
Jeff Vukovich (EPA/OAR)
Caroline Farkas (EPA/OAR)
Janice Godfrey (EPA/OAR)
Karl Seltzer (EPA/OAR)


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TABLE OF CONTENTS

LIST OF TABLES	VII

LIST OF FIGURES	IX

LIST OF APPENDICES	X

ACRONYMS	XI

1	INTRODUCTION	14

2	EMISSIONS INVENTORIES AND APPROACHES	16

2.1	Point sources (ptegu, pt_oilgas, ptnonipm, airports)	20

2.1.1	EGUsector (ptegu)	22

2.1.2	Point source oil and gas sector (pt oilgas)	23

2.1.3	Non-IPM sector (ptnonipm)	26

2.1.4	A ircraft and ground support equipment (airports)	25

2.2	Nonpoint sources (afdust, fertilizer, livestock, np_oilgas, rwc, np_solvents, nonpt)	27

2.2.1	Area fugitive dust sector (afdust)	27

2.2.2	Agricultural Livestock (livestock)	34

2.2.3	Agricultural Fertilizer (fertilizer)	35

2.2.4	Nonpoint Oil and Gas Sector (np oilgas)	38

2.2.5	Residential Wood Combustion (rwc)	39

2.2.6	Solvents (np solvents)	40

2.2.7	Nonpoint (nonpt)	42

2.3	Onroad Mobile sources (onroad)	43

2.3.1	Inventory Development using SMOKE-MOVES	44

2.3.2	Onroad Activity Data Development	46

2.3.3	MOVES Emission Factor Table Development	48

2.3.4	Onroad California Inventory Development (onroad ca)	51

2 A Nonroad Mobile sources (cmv, rail, nonroad)	52

2.4.1	Category 1, Category 2 Commercial Marine Vessels (cmv_clc2)	52

2.4.2	Category 3 Commercial Marine Vessels (cmv_c3)	57

2.4.3	Railway Locomotives (rail)	60

2.4.4	Nonroad Mobile Equipment (nonroad)	65

2.5	Fires (ptfire-rx, ptfire-wild, ptagfire)	68

2.5.1	Wild and Prescribed Fires (ptfire-rx, ptfire-wild)	68

2.5.2	Point source Agriculture Fires (ptagfire)	74

2.6	Biogenic Sources (beis)	76

2.7	Sources Outside of the United States	77

2.7.1	Point Sources in Canada and Mexico (othpt, canadaag, canada_og2D)	78

2.7.2	Fugitive Dust Sources in Canada (othafdust, othptdust)	78

2.7.3	Nonpoint and Nonroad Sources in Canada and Mexico (othar)	79

2.7.4	Onroad Sources in Canada and Mexico (onroadcan, onroadjnex)	79

2.7.5	Fires in Canada and Mexico (ptfire othna)	79

2.7.6	Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury	79

3	EMISSIONS MODELING	81

3.1	Emissions Modeling Overview	81

3.2	Chemical Speciation	84

3.2.1	VOC speciation	93

3.2.1.1	County specific profile combinations	96

3.2.1.2	Additional sector specific considerations for integrating HAP emissions from inventories into speciation	97

3.2.1.3	Oil and gas-related speciation profiles	99

3.2.1.4	Mobile source related VOC speciation profiles	100

3.2.2	PM speciation	105

3.2.2.1	Mobile source related PM2.5 speciation profiles	107

3.2.2.2	Diesel PM	108

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3.2.3	NO x speciation	108

3.2.4	Creation of Sulfuric Acid Vapor (SULF)	109

3.2.5	Speciation of Metals and Mercury	110

3.3	Temporal Allocation	112

3.3.1	Use ofFFlO format for finer than annual emissions	113

3.3.2	Temporal allocation for non-EGU sources (ptnonipm)	114

3.3.3	Electric Generating Utility temporal allocation (ptegu)	114

3.3.4	Airport Temporal allocation (airports)	118

3.3.5	Residential Wood Combustion Temporal allocation (rwc)	121

3.3.6	Agricultural Ammonia Temporal Profiles (livestock)	125

3.3.7	Oil and gas temporal allocation (np oilgas)	126

3.3.8	Onroad mobile temporal allocation (onroad)	126

3.3.9	Nonroad mobile temporal allocation (nonroad)	131

3.3.10	Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire)	132

3.4	Spatial Allocation	134

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	140

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	23

Table 2-3. Point source oil and gas sector emissions for 2017 and 2019	24

Table 2-4. SCCs for the airports sector	25

Table 2-5. Afdust sector SCCs	27

Table 2-6. Total impact of 2019 fugitive dust adjustments to unadjusted inventory	30

Table 2-7. SCCs for the livestock sector	34

Table 2-8. Source of input variables for EPIC	37

Table 2-9. Nonpoint oil and gas emissions for 2017 and 2019	38

Table 2-10. SCCs for the residential wood combustion sector	39

Table 2-11. Non-VCPy SCCs in the np_solvents sector	41

Table 2-12. MOVES vehicle (source) types	44

Table 2-13. Fraction of IHS Vehicle Populations to Retain	50

Table 2-14. SCCs for cmv_clc2 sector	53

Table 2-15. Vessel groups in the cmv_clc2 sector	55

Table 2-16. SCCs for cmv_c3 sector	58

Table 2-17. SCCs for the Rail Sector	60

Table 2-18. 2017-to-2019 projection factors for rail	60

Table 2-19. Alaska counties/census areas for which nonroad equipment sector-specific emissions were

removed	68

Table 2-20. SCCs included in the ptfire sector for the 2019 platform	69

Table 2-21. SCCs included in the ptagfire sector	75

Table 2-22. Meteorological variables required by BEIS 3.7	76

Table 3-1. Key emissions modeling steps by sector	82

Table 3-2. Descriptions of the platform grids	83

Table 3-3. Emission model species produced for CB6R3AE7 for CMAQ	85

Table 3-4. Additional HAP Gaseous model species produced for CMAQ multipollutant specifically for

toxics modeling (not used within CB6)	87

Table 3-5. Additional HAP Particulate* model species produced for CMAQ multipollutant modeling	88

Table 3-6. PAH/POM pollutant groups	88

Table 3-7. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM)

for each platform sector	95

Table 3-8. MOVES integrated species in M-profiles	98

Table 3-9. Basin/Region-specific profiles for oil and gas	99

Table 3-10. TOG MOVES-SMOKE Speciation for nonroad emissions	101

Table 3-11. Select mobile-related VOC profiles	102

Table 3-12. Onroad M-profiles	102

Table 3-13. MOVES process IDs	103

Table 3-14. MOVES Fuel subtype IDs	104

Table 3-15. MOVES regclass IDs	104

Table 3-16. Brake and tire PM2.5 profiles from Schauer (2006)	107

Table 3-17. Nonroad PM2.5 profiles	108

Table 3-18. NOx speciation profiles	109

Table 3-19. Sulfate split factor computation	109

Table 3-20. SO2 speciation profiles	110

Table 3-21. Particle size speciation of Metals	110

Table 3-22. Speciation of Mercury	Ill

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Table 3-23. Temporal settings used for the platform sectors in SMOKE	112

Table 3-24. U.S. Surrogates available for the 2019 modeling platforms	136

Table 3-25. Off-Network Mobile Source Surrogates	137

Table 3-26. Spatial Surrogates for Oil and Gas Sources	138

Table 3-27. Selected 2019 CAP emissions by sector for U.S. Surrogates (short tons in 12US1)	138

Table 3-28. Canadian Spatial Surrogates	141

Table 3-29. 2019 CAPs Allocated to Mexican and Canadian Spatial Surrogates for 12US1 (short tons).... 142

Table 4-1. National by-sector CAP emissions for the 2019 platform, 12US1 grid (tons/yr)	146

Table 4-2. National by-sector VOC HAP emissions for the 2019 platform, 12US1 grid (tons/yr)	147

Table 4-3. National by-sector Diesel PM and metal emissions for the 2019 platform, 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	33

Figure 2-2. "Bidi" modeling system used to compute emissions from fertilizer application	36

Figure 2-3. Map of Representative Counties	49

Figure 2-4. Areas of Transponder Data Request for 2019	54

Figure 2-5. 2017 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT)	61

Figure 2-6. Class I Railroads in the United States	62

Figure 2-7. Class II and III Railroads in the United States5	63

Figure 2-8. Amtrak Routes with Diesel-powered Passenger Trains	64

Figure 2-9. Processing flow for fire emission estimates in the 2019 inventory	72

Figure 2-10. Default fire type assignment by state and month where data are only from satellites	73

Figure 2-11. Blue Sky Modeling Framework	74

Figure 3-1. Emissions modeling domain (12US1) and air quality modeling domain (12US2)	84

Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation	95

Figure 3-3. Profiles composited for PM gas combustion related sources	106

Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources	106

Figure 3-5. Eliminating unmeasured spikes in CEMS data	115

Figure 3-6. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification	116

Figure 3-7. Example Daily Temporal Profiles for the LADCO Region and the Gas Fuel Type	117

Figure 3-8. Example Diurnal Temporal Profiles for the MANE-VU Region and the Coal Fuel Type	117

Figure 3-9. Non-CEMS EGU Temporal Profile Application Counts	118

Figure 3-10. Diurnal Profile for all Airport SCCs	119

Figure 3-11. Weekly profile for all Airport SCCs	120

Figure 3-12. Monthly Profile for all Airport SCCs	120

Figure 3-13. Alaska Seaplane Profile	121

Figure 3-14. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold	122

Figure 3-15. RWC diurnal temporal profile	123

Figure 3-16. Data used to produce a diurnal profile for hydronic heaters	124

Figure 3-17. Day-of-week temporal profiles for hydronic heaters and recreational RWC	124

Figure 3-18. Annual-to-month temporal profiles for hydronic heaters 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	127

Figure 3-21. Sample onroad diurnal profiles for Fulton County, GA	128

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	133

Figure 3-28. Prescribed and Wildfire diurnal temporal profiles	134

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List of Appendices

Appendix A: CB6 Assignment for 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 and later 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

BSP	Blue Sky Pipeline

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

E0, E10, E85	0%, 10% and 85% Ethanol blend gasoline, respectively

ECA	Emissions Control Area

ECCC	Environment and Climate Change Canada

EF	Emission Factor

EGU	Electric Generating Units

EIA	Energy Information Administration

EIS	Emissions Inventory System

EPA	Environmental Protection Agency

EMFAC	EMission FACtor (California's onroad mobile model)

EPIC	Environmental Policy Integrated Climate modeling system

FAA	Federal Aviation Administration

FCCS	Fuel Characteristic Classification System

FEST-C	Fertilizer Emission Scenario Tool for CMAQ

FF10	Flat File 2010

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FINN

Fire Inventory from the National Center for Atmospheric Research

FIPS

Federal Information Processing Standards

FHWA

Federal Highway Administration

HAP

Hazardous Air Pollutant

HMS

Hazard Mapping System

HPMS

Highway Performance Monitoring System

ICI

Industrial/Commercial/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

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ppmv

Parts per million by volume

PSAT

Particulate Matter Source Apportionment Technology

RACT

Reasonably Available Control Technology

RBT

Refinery to Bulk Terminal

RIA

Regulatory Impact Analysis

RICE

Reciprocating Internal Combustion Engine

RWC

Residential Wood Combustion

RPD

Rate-per-vehicle (emission mode used in SMOKE-MOVES)

RPH

Rate-per-hour for hoteling (emission mode used in SMOKE-MOVES)

RPHO

Rate-per-hour for off-network idling (emission mode used in SMOKE-



MOVES)

RPP

Rate-per-profile (emission mode used in SMOKE-MOVES)

RPS

Rate-per-start (emission mode used in SMOKE-MOVES)

RPV

Rate-per-vehicle (emission mode used in SMOKE-MOVES)

RVP

Reid Vapor Pressure

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 2019. The platform is based on the 2017 National
Emissions Inventory (2017 NEI) published in January 2021 (EPA, 2021) along with other data specific to
the year 2019. 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 2019 modeling platform, including the emission inventories, the ancillary data files, and
the approaches used to transform inventories for use in air quality modeling.

The modeling platform includes all criteria air pollutants and precursors (CAPs), two groups of hazardous
air pollutants (HAPs) and diesel particulate matter. The first group of HAPs are those explicitly used by
the chemical mechanism in the Community Multiscale Air Quality (CMAQ) model (Appel, 2018) for
ozone/particulate matter (PM): chlorine (CI), hydrogen chloride (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) that are included in CMAQ for the purposes of air quality
modeling for a HAP+CAP platform.

Emissions were prepared for the Community Multiscale Air Quality (CMAQ) model
(https://www.epa.gov/cmaq) version 5.3.2', 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 "2019ge", where 2019 is the year modeled, g
represents that it was based on the 2017 NEI, and e represents that it was the fifth version of a 2017 NEI-
based platform.

Emissions were also prepared for an air dispersion modeling system: American Meteorological
Society/Environmental Protection Agency Regulatory Model (AERMOD) (EPA, 2018). AERMOD was
run for all NEI HAPs (about 130 more than covered by CMAQ) across all 50 states, Puerto Rico and the
Virgin Islands (EPA, 2022). This TSD focuses on the CMAQ aspects of the modeling platform.

The effort to create the emission inputs for this study included development of emission inventories to
represent emissions during the year of 2019, along with application of emissions modeling tools to
convert the inventories into the format and resolution needed by CM AQ 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

1 CMAQ version 5.3.2: https://doi.org/10.5281/zenodo.4081737; httos://www. epa.gov/cmaa/cmaa-models-0. CMAQ v5.3.2 is
also available from the Community Modeling and Analysis System (CMAS) at: http://www.cmascenter.org..

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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 fires, biogenic sources,
fertilizer, point sources, and onroad and n on road mobile sources. Where available, continuous emission
monitoring system (CEMS) data were used for electric generating unit (EGU) emissions. Most of the
remaining emission inventories were adjusted to represent 2019, primarily using 2017-specific emissions
as a starting point.

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 4.8.1 was used to create
CMAQ-ready emissions files for a 12-krn grid covering the continental U.S. Additional information about
SMOKE is available from http://www.cmascenter.org/smoke.

The gridded meteorological model used to provide input data for the emissions modeling was developed
using the Weather Research and Forecasting Model (WRF,

https://ral.ucar.edu/solutions/products/weather-research-and-forecasting-model-wrf) version 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 2019 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 "19k." The full case
abbreviation includes this suffix following the emissions portion of the case name to fully specify the
abbreviation of the case as "2019ge_cb6_19k."

Following the emissions modeling steps to prepare emissions for CMAQ and AERMOD, both models
were run for the modeling domain covering the Continental United States. 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. 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/2019-emissions-modeling-platform.

This document contains five sections and several appendices. Section 2 describes the emission
inventories input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used to
process the emission inventories into air quality model-ready inputs. Data summaries are provided in
Section 4. 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
January 2021 version of the 2017 NEI along with the point source inventory for 2019 and other year
2019-specific data. 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-inventory-nei-
technical-support-document-tsd (EPA, 2021).

The full NEI including all emissions source categories is developed every three years, with 2017 being the
most recent year represented wih a full NEI. S/L/T agencies are required to submit large point sources to
the NEI in interim years, including the year 2019. Where available, point source data representing 2019
were used for this study. Point sources in the 2017 NEI that did not have data submitted for the year 2019
and that were not marked as closed were pulled forward from the 2017 NEI into the 2019 point source
inventory. The SMARTFIRE2 system and the BlueSky Pipeline (https://uithub.com/pnvvairfire/blueskv)
emissions modeling system were used to develop year 2019 fire emissions. SMARTFIRE2 categorizes
all fires as either prescribed burning or wildfire categories, and the BlueSky Pipeline system includes fuel
loading, consumption and emission factor estimates for both types of fires. Onroad and nonroad mobile
source emissions were developed for this project for the year 2019 by running MOVES3
(https://www.epa.gov/moves).

With the exception of onroad, nonroad and fire emissions, Canadian emissions were based on the 2019
inventories developed for EPA's Air Quality Time Series (EQUATES) project (Foley, 2020). For
Mexico, year 2016 inventories were projected to 2019. The latest year for which Canada and Mexico
inventories were provided was 2016, although the onroad and nonroad emissions were adjusted to
represent the year 2019 and some additional adjustments to the Canadian emissions were made for
EQUATES.

The emissions modeling process, performed using SMOKE v4.8.1, 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 and fertilizer emissions, the CMAQ model allows for these emissions to be included in the
CM AQ-ready emissions inputs, or to be computed within CMAQ itself (the "inline" option). This study

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uses the inline biogenic emissions option and the CMAQ bidirectional ammonia process for fertilizer
emissions.

Table 2-1 presents the sectors in the emissions modeling platform used to develop the year 2019
emissions for this project. The sector abbreviations are provided in italics; these abbreviations are used in
the SMOKE modeling scripts, the inventory file names, and throughout the remainder of this section.

Table 2-1. Platform sectors used in the Emissions Modeling Process

Platform Sector:

abbreviation

NEI Data
Category

Description and resolution of the data input to SMOKE

EGU units:

ptegu

Point

2019 NEI point source EG Us. replaced with hourly Continuous
Emissions Monitoring System (CEMS) values for NOx and SO;, and
the remaining pollutants temporally allocated according to CEMS heat
input where the units are matched to the NEI. Emissions for all
sources not matched to CEMS data come from 2019 NEI point
inventory. Annual resolution for sources not matched to CEMS data,
hourly for CEMS sources. EG Us closed in 2019 are not part of the
inventorv.

Point source oil and
gas:

ptoilgas

Point

2019 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. Production-related sources that did not have 2019 data
were pulled forward from the 2017 NEI and adjusted to 2019. Annual
resolution.

Aircraft and ground
support equipment:

airports

Point

2017 NEI point source emissions from airports, including aircraft and
airport ground support emissions, adjusted to 2019 using Terminal
Area Forecast (TAF) data. Airport-specific factors were used where
available, state average factors were used for regional airports, and no
change was made to militarv aircraft from 2017. Annual resolution.

Remaining non-
EGU point:

ptnonipm

Point

All 2019 NEI point source records not matched to the airports, ptegu,
or pt_oilgas sectors. Closures were reviewed and implemented based
on the most recent submissions to the Emissions Inventory System
(EIS). Includes 2017 NEI rail yard emissions, adjusted to 2019 using
same projection factors as the rail sector. Annual resolution.

Livestock:

livestock

Nonpoint

2017 NEI nonpoint livestock emissions adjusted to 2019 using USDA
survey data. Livestock includes ammonia and other pollutants (except
PM2.5). County and annual resolution.

Agricultural
Fertilizer

Nonpoint

2019 agricultural fertilizer ammonia emissions computed inline within
CMAQ.

Agricultural fires
with point
resolution: ptagfire

Nonpoint

Agricultural fire sources for year 2019 were developed by EPA as
point and day-specific emissions.2 Only EPA-developed ag. fire data
are used in this study, thus 2017 NEI state submissions are not
included. Agricultural fires are in the nonpoint data category of the
NEI, but in the modeling platform, they are treated as day-specific
point sources. Updated HAP-augmentation factors were applied.

2 Only EPA-developed agricultural fire data were included in this study; data submitted by states to the NEI were excluded.

17


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Platform Sector:

abbreviation

NEI Data
Category

Description and resolution of the data input to SMOKE

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; with paved road
dust adjusted to 2019 based on vehicle miles traveled (VMT). The
emissions modeling system applies a transport fraction reduction and a
zero-out based on 2019 gridded hourly meteorology (precipitation and
snow/ice cover). Emissions are county and annual resolution.

Biogenic:

beis

Nonpoint

Year 2019 emissions from biogenic sources. These were left out of the
CMAQ-ready merged emissions, in favor of inline biogenic emissions
produced during the CMAQ model run itself. Version 3.7 of the
Biogenic Emissions Inventory System (BEIS) was used with Version
5 of the Biogenic Emissions Landuse Database (BELD5).

Category 1, 2 CMV:

cmv_clc2

Nonpoint

2019 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

2019 Category 3 (C3) commercial marine vessel (CMV) emissions
based on AIS data. Point and hourly resolution.

Locomotives :
rail

Nonpoint

Line haul rail locomotives emissions for year 2017, projected to 2019
using annual energy outlook (AEO) and additional factors supplied by
ERTAC. County and annual resolution.

Nonpoint source oil
and gas:
npoilgas

Nonpoint

Nonpoint 2017 NEI sources from oil and gas-related processes,
projected to 2019 using based on U.S. Energy Information
Administration (EIA) and Railroad Commission of Texas (TXRRC)
historical production data. County and annual resolution.

Residential Wood
Combustion:

rwc

Nonpoint

2017 NEI nonpoint sources with residential wood combustion (RWC)
processes were used as is, with no projection to represent 2019.
County and annual resolution.

Solvents:

npsolvents

Nonpoint

Emissions of solvents for the year 2019 (Seltzer, 2021). Includes
household cleaners, personal care products, adhesives, architectural
and aerosol coatings, printing inks, and pesticides. Annual and county
resolution.

Remaining
nonpoint:

nonpt

Nonpoint

2017 NEI nonpoint sources not included in other platform sectors. No
adjustments were made to represent 2019. County and annual
resolution.

Nonroad:

nonroad

Nonroad

2019 nonroad equipment emissions developed with MOVES3,
including the updates made to spatial apportionment that were
developed with the 2016vl platform. MOVES3 was used for all states
except California and Texas. California submitted their own emissions
for the 2017 NEI that were adjusted to 2019 based on interpolations
between 2017 and 2023. Texas provided 2017 and 2020 emissions
which were interpolated to 2019. County and monthly resolution.

Onroad:

onroad

Onroad

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, vehicle starts, off network idling, long-haul truck hoteling,
and brake and tire wear. Activity data were projected from 2017 to
2019 using factors developed using data from Federal Highway
Administration and state departments of transportation. MOVES3 was
run for 2019 to generate emission factors.

18


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Platform Sector:

abbreviation

NEI Data
Category

Description and resolution of the data input to SMOKE

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 MOVES3. Volatile organic compound (VOC)
HAP emissions derived from California-provided VOC emissions and
MOVES-based speciation. 2019 was interpolated between 2017 and
2023 emissions from EMFAC2017.

Point source
prescribed fires:

ptfire-rx

Events

Point source day-specific prescribed fires for 2019 computed using
SMARTFIRE 2 and Blue Sky Pipeline. The ptfire emissions were run
as two separate sectors: ptfire-rx (prescribed, including Flint Hills /
grasslands) and ptfire-wild.

Point source
wildfires: ptfire-wild

Events

Point source day-specific wildfires for 2019 computed using
SMARTFIRE 2 and BlueSky Pipeline.

Non-US. Fires:
ptfireothna

N/A

Point source day-specific wildfires and agricultural fires outside of the
U.S. for 2019 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 from EQUATES 2016 with
transport fraction and snow/ice adjustments based on 2019
meteorological data. Annual and province resolution.

Other Point Fugitive
dust sources not
from the NEI:
othptdust

N/A

Point source fugitive dust sources from Canada from EQUATES 2016
with transport fraction and snow/ice adjustments based on 2019
meteorological data. Annual and province resolution.

Other point sources
not from the NEI:
othpt

N/A

Canada and Mexico point source emissions from EQUATES 2016.
Canada point sources were provided by ECCCC and Mexico point
source emissions for 2016 were provided by SEMARNAT. Mexico
sources were projected to 2019 based on national emissions trends
from the Community Emissions Data System (CEDS). Annual and
monthly resolution.

Other non-NEI
nonpoint and
nonroad:

othar

N/A

For Canada except nonroad, EQUATES 2016. Projected Canada
nonroad to 2019 based on US MOVES3 2019/2016 ratios. EQUATES
2016 Mexico (municipio resolution, provided by SEMARNAT)
nonpoint and nonroad mobile inventories were projected to 2019
based on national emissions trends from the Community Emissions
Data System (CEDS). Annual and monthly resolution.

Other non-NEI
onroad sources:

onroadcan

N/A

Monthly onroad mobile inventory for Canada from EQUATES 2016
projected to 2019 using US onroad trends. Separate trends applied
to refueling (gas/diesel) and non-refueling (gas/diesel and
LD/HD). Province resolution.

Other non-NEI
onroad sources:

onroad mex

N/A

Monthly onroad mobile inventory from MOVES-Mexico (municipio
resolution) for 2017, adjusted to 2019 using interpolation between
2017 and 2020.

Other natural emissions are also merged in with the above sectors, including ocean chlorine and sea salt.
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb)
concentrations in oceanic air masses (Bullock and Brehme, 2002).

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Data at 12 km resolution were available and were not modified other than the name "CHLORINE" was
changed to "CL2" because that is the name required by the CMAQ model.

The emission inventories in SMOKE input formats for the platform are available from EPA's Air
Emissions Modeling website: https://www.epa.gov/air-emissions-modeling/2019-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 2019 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 2019. In the
intermediate point inventories, states are required to update larger sources with emissions for the interim
year, while sources not updated by states for the interim year are either carried forward from the most
recent triennial NEI or marked as closed and removed.

In preparation for modeling, the complete set of point sources in the NEI was exported from EIS for the
year 2019 into the Flat File 2010 (FF10) format that is compatible with SMOKE (see
https://www.cmascenter.Org/smoke/documentation/4.8.l/html/ch08s02s08.htmn and was then split into
several sectors for modeling. The 20220325 version of the point FF10 file was used for the CMAQ and
AERMOD modeling. In the flat file, sources without specific locations (i.e., the FIPS code ends in 777)
were dropped and inventories for the other point source sectors were created from the remaining point
sources. The point sectors are: EGUs (ptegu), point source oil and gas extraction-related sources
(pt oilgas), airport emissions (airports), and the remaining non-EGUs (ptnonipm). The EGU emissions
were split out from the other sources to facilitate the use of distinct SMOKE temporal processing and
future-year projection techniques. The oil and gas sector emissions (pt oilgas) and airport emissions
(airports) were processed separately for summary tracking purposes and distinct projection techniques
from the remaining non-EGU emissions (ptnonipm).

In some cases, data about facility or unit closures are entered into EIS after the inventory modeling
inventory flat were reviewed and implemented based on the most recent submissions to EIS. Prior to
processing through SMOKE, submitted closures were reviewed and if closed sources were found in the
inventory, those were removed.

For the 2019 platform, an analysis of point source stack parameters (e.g., stack height, diameter,
temperature, and velocity) was performed after some specific examples of unrealistic stack parameters as
default values were noticed. The defaulted values were noticed in data submissions for the states of

20


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Illinois, Louisiana, Michigan, Pennsylvania, Texas, and Wisconsin. Where these defaults were detected
and deemed to be unreasonable for the specific process, the affected stack parameters were replaced by
values from the currently available PSTK file that is input to SMOKE. PSTK contains default stack
parameters by source classification code (SCC). These updates impacted the ptnonipm and ptoilgas
inventories.

The inventory pollutants processed through SMOKE for input to CMAQ for the ptegu, ptoilgas,
ptnonipm, and airports sectors included: CO, NOx, VOC, SO:, NH.,, PMio, and PM2.5 and the following
HAPs: HQ (pollutant code = 7647010), CI (code = 7782505), and several dozen other HAPs listed in
Section 3. NBAFM pollutants from the point sectors were utilized. For AERMOD, additional HAPS
were included as described in the 2019 AirToxScreen TSD.

The ptnonipm, pt oilgas, and airports sector emissions were provided to SMOKE as annual emissions.
For sources in the ptegu sector that could be matched to 2019 CEMS data, hourly CEMS NOx and SO2
emissions for 2019 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

21


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resided, but no more specific details related to the location of the sources were available.

Each of the point sectors is processed separately through SMOKE as described in the following
subsections.

2.1.1 EGU sector (ptegu)

The ptegu sector contains emissions from EG Us in the 2019 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 EGlJs 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 2019 ptegu emissions inventory is a subset of the point source flat file exported from the Emissions
Inventory System (EIS). In the point source flat file, emission records for sources that have been matched
to the NEEDS database have a value filled into the IPM YN column based on the matches stored within
EIS. Thus, unit-level emissions were split into a separate EGU flat file for units that have a populated
(non-null) ipm_yn field. A populated ipm_yn field indicates that a match was found for the EIS unit in the
NEEDS v6 database. Updates were made to the flat file output from EIS as 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.

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Matches from the NEI to ORIS codes and the NEEDS database were improved in the platform where
applicable. In some cases, NEI units in EIS match to many CAMD units. In these cases, a new entry was
made in the flat file with a "_M_" in the ipm_yn field of the flat file to indicate that there are "multiple"
ORIS IDs that match that unit. This helps facilitate appropriate temporal allocation of the emissions by
SMOKE. Temporal allocation for EGUs is discussed in more detail in the Ancillary Data section below.

The EGU flat file was split into two flat files: those that have unit-level matches to CEMS data using the
oris facility code and oris boiler id fields and those that do not so that different temporal profiles could
be applied. In addition, the hourly CEMS data were processed through v2.1 of the CEMCorrect tool to
mitigate the impact of unmeasured values in the data.

2.1.2 Point source oil and gas sector (pt_oilgas)

The ptoilgas sector was separated from the ptnonipm sector by selecting sources with specific North
American Industry Classification System (NAICS) codes shown in Table 2-2. The emissions and other
source characteristics in the pt oilgas sector are submitted by states, while EPA developed a dataset of
nonpoint oil and gas emissions for each county in the U.S. with oil and gas activity that was available for
states to use. Nonpoint oil and gas emissions can be found in the np oilgas sector. The pt oilgas sector
includes emissions from offshore oil platforms. Where available, the point source emissions submitted as
part of the 2019 NEI process were used.

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

For sources that otherwise would be pulled forward with 2017 emissions values because 2019-specific
emissions were not available, projection factors by NAICS and state derived from historical production

23


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data from EIA. The factors were applied to those 2017 sources to adjust the emissions to make them
more representative of 2019. Texas historical production data by Texas Railroad district
(http://webapps.rrc.texas.gov/PDQ/generalReportAction.do) were used to derive and apply district-
specific factors instead of state-specific. State (plus TX Railroad Commission district) factors were
applied to production-related NAICS. Transportation NAICS were projected using nationally derived
production-related factors for oil and gas. All other NAICS were held constant from 2017 NEI. All Tribal
data and offshore emissions were held constant from 2017 NEI. More information on the development of
the 2017 NEI oil and gas emissions can be found in Section 4.17 of the 2017 NEI TSD. The point oil and
gas emissions for 2017 and 2019 are shown in Table 2-3.

Table 2-3. Point source oil and gas sector emissions for 2017 and 2019







2019 - 2017





2019-2017

State

2017 NOx

2019 NOx

NOx

2017 VOC

2019 VOC

VOC

Alabama

8,861

9,668

807

1,332

1,483

151

Alaska

39,424

38,248

-1,176

2,301

1,925

-376

Arizona

2,326

2,672

346

198

206

8

Arkansas

4,697

5,043

346

316

338

22

California

3,318

3,383

65

2,767

2,782

15

Colorado

14,209

16,748

2,539

15,102

17,815

2,713

Connecticut

172

101

-71

73

54

-19

Delaware

11

11

0

4

4

0

Florida

6,197

5,634

-563

678

657

-21

Georgia

3,163

3,179

16

463

458

-5

Idaho

1,131

1,412

281

34

42

8

Illinois

5,402

7,813

2,411

734

1,325

591

Indiana

3,540

2,513

-1,027

246

237

-9

Iowa

5,769

6,987

1,218

344

394

50

Kansas

23,622

25,984

2,362

2,634

3,250

616

Kentucky

9,673

9,269

-404

1,268

1,313

45

Louisiana

29,025

31,365

2,340

9,259

12,818

3,559

Maine

17

29

12

48

50

2

Maryland

206

220

14

14

66

52

Massachusetts

245

217

-28

56

44

-12

Michigan

10,933

13,840

2,907

1,431

1,378

-53

Minnesota

3,002

3,751

749

169

174

5

Mississippi

11,288

11,487

199

1,478

1,602

124

Missouri

3,544

5,056

1,512

176

236

60

Montana

1,321

815

-506

1,065

1,135

70

Nebraska

3,834

4,762

928

325

391

66

Nevada

195

262

67

32

32

0

New lersey

216

181

-35

114

111

-3

New Mexico

15,002

16,337

1,335

4,979

5,152

173

New York

1,186

1,125

-61

340

337

-3

North Carolina

578

1,694

1,116

141

183

42

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2019 - 2017





2019-2017

State

2017 NOx

2019 NOx

NOx

2017 VOC

2019 VOC

VOC

North Dakota

4,511

4,648

137

1,316

2,594

1,278

Ohio

11,154

8,549

-2,605

1,381

1,523

142

Oklahoma

48,951

50,990

2,039

32,923

38,628

5,705

Oregon

684

855

171

58

72

14

Pennsylvania

5,193

4,708

-485

1,073

1,029

-44

Rhode Island

69

42

-27

39

14

-25

South Carolina

401

182

-219

156

119

-37

South Dakota

409

511

102

11

14

3

Tennessee

3,857

4,969

1,112

539

407

-132

Texas

51,558

53,856

2,298

21,058

22,973

1,915

Utah

1,938

2,211

273

686

722

36

Virginia

2,404

4,279

1,875

236

414

178

Washington

812

741

-71

41

45

4

West Virginia

8,891

8,705

-186

2,187

2,723

536

Wisconsin

545

348

-197

48

53

5

Wyoming

10,205

10,010

-195

15,736

15,888

152

Offshore to EEZ

49,962

49,962

0

38,833

38,833

0

Tribal Data

7,746

7,470

-276

2,030

2,047

17

2.1.3 Aircraft and ground support equipment (airports)

Emissions at airports were separated from other sources in the point inventory based on sources that have
the facility source type of 100 (airports). The airports sector includes all aircraft types used for public,
private, and military purposes and aircraft ground support equipment. The Federal Aviation
Administration's (FAA) Aviation Environmental Design Tool (AEDT) is used to estimate emissions for
this sector. 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. Data from the 2020
Terminal Area Forecast (TAF) were used to project 2017 NEI emissions to 2019. EPA used airport-
specific factors where available. Regional airports were projected using state average factors. Military
airports were unchanged from 2017. An update for the 2019 platform was that airport emissions were
spread out into multiple 12km grid cells when the airport runways were determined to overlap multiple
grid cells. Otherwise, airport emissions for a specific airport are confined to one air quality model grid
cell. The SCCs included in the airport sector are shown in Table 2-4.

Table 2-4. SCCs for the airports sector

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

25


-------
S( (

Tier 1 description

Tier 2 (k'scriplion

Tier 3 (k'scriplion

Tier 4 (k'scriplion

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

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

2.1.4 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 emissions
sources and rail yards. However, it is likely that some low-emitting EGUs not matched to units the
NEEDS database or to CEMS data are in the ptnonipm sector.

The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on
paved or unpaved roads at industrial facilities, coal handling at coal mines, and grain elevators. Sources
with state/county FIPS code ending with "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 2019 NEI
point inventory. Unlike in the 2018 platform, instead of removing solvent emissions from the ptnonipm
sector, solvent emissions from point sources are instead removed from the npsolvents sector to prevent
double counting, so that all point sources can be retained in the modeling as point sources rather than as
area sources. The modeling was based on a version of the point flat file which included corrections to how
the selection was implemented in EIS, updates from the state/local review, and updates specific to
ethylene oxide. The np solvents sector is described in more detail in Section 2.2.6.

26


-------
Emissions from rail yards are included in the ptnonipm sector. Railyards were projected to 2019 from the
2017 NEI railyard inventory using factors derived from the Annual Energy Outlook 2018
(http s: //www, ei a. gov/outl ooks/archive/aeo 18/).

2.2 N on point sources (a fdust, fertilizer, livestock, npoilgas, rwc,
npsolvents, nonpt)

This section describes the stationary nonpoint sources in the NEI nonpoint data category. Locomotives,
CI and C2 CMV, and C3 CMV are included in the NEI nonpoint data category but are mobile sources
that are described in Section 2.4. The 2017 NEI TSD includes documentation for the nonpoint data.

Nonpoint tribal emissions submitted to the NEI are dropped during spatial processing with SMOKE due
to the configuration of the spatial surrogates. Part of the reason for this is to prevent possible double-
counting with county-level emissions and also because spatial surrogates for tribal data are not currently
available. These omissions are not expected to have an impact on the results of the air quality modeling at
the 12-km resolution used for this platform.

The following subsections describe how the sources in the NEI nonpoint inventory were separated into
modeling platform sectors, along with any data that were updated (replaced) with non-NEI data.

2.2.1 Area fugitive dust sector (afdust)

The area-source fugitive dust (afdust) sector contains PMio and PM2.5 emission estimates for nonpoint
SCCs identified by EPA as dust sources. Categories included in the afdust sector are paved roads,
unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production,
and mining and quarrying. It does not include fugitive dust from grain elevators, coal handling at coal
mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as
point sources so they are properly located. Table 2-5 is a listing of the Source Classification Codes
(SCCs) in the afdust sector.

Table 2-5. Afdust sector SCCs

sec

Tier 1
(k'scriplion

Tier 2 (k-M'riplinn

Tier 3 (k-seriplinn

Tier 4 (k-M'riplinn

2275085000

Mobile Sources

Aircraft

Unpaved Airstrips

Total

2294000000

Mobile Sources

Paved Roads

All Paved Roads

Total: Fugitives











2296000000

Mobile Sources

Unpaved Roads

All Unpaved Roads

Total: Fugitives











2311010000

Industrial
Processes

Construction: SIC
15 -17

Residential

Total











2311020000

Industrial
Processes

Construction: SIC
15 -17

Industrial/Commercial/
Institutional

Total

2311030000

Industrial
Processes

Construction: SIC
15 -17

Road Construction

Total

2325000000

Industrial
Processes

Mining and
Quarrying: SIC 14

All Processes

Total

2325060000

Industrial
Processes

Mining and
Quarrying: SIC 10

Lead Ore Mining and Milling

Total

27


-------
sec

Tier 1
description

Tier 2 (k-si'riplinn

Tier 3 (k-M'riplinn

Tier 4 (k-si'riplinn

2801000000

Miscellaneous
Area Sources

Ag. Production -
Crops

Agriculture - Crops

Total

2801000003

Miscellaneous
Area Sources

Ag. Production -
Crops

Agriculture - Crops

Tilling

2801000005

Miscellaneous
Area Sources

Ag. Production -
Crops

Agriculture - Crops

Harvesting

2801000007

Miscellaneous
Area Sources

Ag. Production -
Crops

Agriculture - Crops

Loading

2801000008

Miscellaneous
Area Sources

Ag. Production -
Crops

Agriculture - Crops

Transport

2805001000

Miscellaneous
Area Sources

Ag. Production -
Livestock

Beef cattle - finishing operations
on feedlots (drylots)

Dust Kicked-up by Hooves
(use 28-05-020, -001, -002,
or -003 for Waste

2805001100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Beef cattle - finishing operations
on feedlots (drylots)

Confinement

2805001200

Miscellaneous
Area Sources

Agriculture
Production -
Livestock

Beef cattle - finishing operations
on feedlots (drylots)

Manure handling and
storage

2805001300

Miscellaneous
Area Sources

Agriculture
Production -
Livestock

Beef cattle - finishing operations
on feedlots (drylots)

Land application of manure

2805002000

Miscellaneous
Area Sources

Ag. Production -
Livestock

Beef cattle production composite

Not Elsewhere Classified

2805003100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Beef cattle - finishing operations
on pasture/range

Confinement

2805007100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - layers with
dry manure management systems

Confinement

2805007300

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - layers with
dry manure management systems

Land application of manure

2805008100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - layers with
wet manure management systems

Confinement

2805008200

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - layers with
wet manure management systems

Manure handling and
storage

2805008300

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - layers with
wet manure management systems

Land application of manure

2805009100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - broilers

Confinement

2805009200

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - broilers

Manure handling and
storage

2805009300

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - broilers

Land application of manure

2805010100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - turkeys

Confinement

2805010200

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - turkeys

Manure handling and
storage

2805010300

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry production - turkeys

Land application of manure

2805018000

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle composite

Not Elsewhere Classified

2805019100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - flush dairy

Confinement

2805019200

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - flush dairy

Manure handling and
storage

28


-------
sec

Tier 1
description

Tier 2 (k-si'riplinn

Tier 3 (k-M'riplinn

Tier 4 (k-si'riplinn

2805019300

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - flush dairy

Land application of manure

2805020002

Miscellaneous
Area Sources

Ag. Production -
Livestock

Cattle and Calves Waste
Emissions

Beef Cows

2805021100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - scrape dairy

Confinement

2805021200

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - scrape dairy

Manure handling and
storage

2805021300

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - scrape dairy

Land application of manure

2805022100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - deep pit dairy

Confinement

2805022200

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - deep pit dairy

Manure handling and
storage

2805022300

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - deep pit dairy

Land application of manure

2805023100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - drylot/pasture dairy

Confinement

2805023200

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - drylot/pasture dairy

Manure handling and
storage

2805023300

Miscellaneous
Area Sources

Ag. Production -
Livestock

Dairy cattle - drylot/pasture dairy

Land application of manure

2805025000

Miscellaneous
Area Sources

Ag. Production -
Livestock

Swine production composite

Not Elsewhere Classified
(see also 28-05-039, -047, -
053)

2805030000

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry Waste Emissions

Not Elsewhere Classified
(see also 28-05-007, -008, -
009)

2805030007

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry Waste Emissions

Ducks

2805030008

Miscellaneous
Area Sources

Ag. Production -
Livestock

Poultry Waste Emissions

Geese

2805035000

Miscellaneous
Area Sources

Ag. Production -
Livestock

Horses and Ponies Waste
Emissions

Not Elsewhere Classified

2805039100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Swine production - operations
with lagoons (unspecified animal
age)

Confinement

2805039200

Miscellaneous
Area Sources

Ag. Production -
Livestock

Swine production - operations
with lagoons (unspecified animal
age)

Manure handling and
storage

2805039300

Miscellaneous
Area Sources

Ag. Production -
Livestock

Swine production - operations
with lagoons (unspecified animal
age)

Land application of manure

2805040000

Miscellaneous
Area Sources

Ag. Production -
Livestock

Sheep and Lambs Waste
Emissions

Total

2805045000

Miscellaneous
Area Sources

Ag. Production -
Livestock

Goats Waste Emissions

Not Elsewhere Classified

2805047100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Swine production - deep-pit house
operations (unspecified animal
age)

Confinement

2805047300

Miscellaneous
Area Sources

Ag. Production -
Livestock

Swine production - deep-pit house
operations (unspecified animal
age)

Land application of manure

29


-------
sec

Tier 1
description

Tier 2 description

Tier 3 description

Tier 4 description

2805053100

Miscellaneous
Area Sources

Ag. Production -
Livestock

Swine production - outdoor
operations (unspecified animal
age)

Confinement

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.

Paved road dust emissions were projected from the 2017 NEI (January 2021 version) to 2019 based on
county-level VMT trends. All other afdust SCCs were held constant from the 2017 NEI. For the data
compiled into the 2017 NEI, meteorological adjustments are applied to paved and unpaved road SCCs but
not transport adjustments. This is because the modeling platform applies meteorological adjustments and
transport adjustments based on unadjusted NEI values. For the 2019 platform, the meteorological
adjustments that were applied (to paved and unpaved road SCCs) were backed out in order reapply them
in SMOKE. The FF10 that is run through SMOKE consists of 100% unadjusted emissions, and after
SMOKE all afdust sources have both transport and meteorological adjustments applied according to year
2019 meteorology. The total impacts of the transport fraction and meteorological adjustments are shown
in Table 2-6.

Table 2-6. Total impact of 2019 fugitive dust adjustments to unadjusted inventory

State

I n;id.jiisted

PMio

I n;id.jiisted
I'M;.?

Chiiii^c in
PM io

Chiiii^c in
PM:?

PIMio
Reduction

PM:.?
Reduction

Alabama

305,723

41,233

-226,882

-30,594

74%

74%

Arizona

182,678

24,595

-70,907

-9,336

39%

38%

Arkansas

394,331

54,609

-298,345

-40,764

76%

75%

California

309,117

39,089

-142,624

-17,506

46%

45%

Colorado

282,549

41,231

-157,591

-22,073

56%

54%

Connecticut

24,371

4,017

-18,153

-3,008

74%

75%

Delaware

15,495

2,387

-8,946

-1,378

58%

58%

District of
Columbia

2,917

411

-1,925

-272

66%

66%

Florida

401,015

56,239

-202,591

-28,525

51%

51%

Georgia

296,289

42,312

-221,610

-31,451

75%

74%

30


-------
SliUc

I iiiidjiislcd

PMio

I iiiidjiislcd
I'M;.?

( liiinui- in
PMio

( liiinui- in
I'M;?

PMio
Reduction

I'M:.?
Reduction

Idaho

567,238

65,579

-309,299

-34,601

55%

53%

Illinois

1,113,281

160,628

-795,705

-113,834

71%

71%

Indiana

145,852

27,266

-108,290

-20,364

74%

75%

Iowa

388,689

57,217

-288,740

-42,362

74%

74%

Kansas

671,034

89,491

-362,556

-47,414

54%

53%

Kentucky

177,719

29,040

-138,584

-22,580

78%

78%

Louisiana

180,636

27,639

-128,333

-19,570

71%

71%

Maine

71,411

8,760

-58,419

-7,182

82%

82%

Maryland

75,179

12,041

-47,908

-7,705

64%

64%

Massachusetts

62,941

9,664

-46,285

-7,022

74%

73%

Michigan

295,296

38,885

-215,083

-28,314

73%

73%

Minnesota

426,656

60,102

-311,323

-43,482

73%

72%

Mississippi

450,550

55,090

-343,086

-41,839

76%

76%

Missouri

1,345,973

159,831

-1,009,469

-119,626

75%

75%

Montana

503,803

66,808

-343,134

-44,300

68%

66%

Nebraska

518,897

71,883

-277,678

-37,564

54%

52%

Nevada

138,115

18,381

-54,526

-7,235

39%

39%

New Hampshire

20,851

4,383

-17,036

-3,570

82%

81%

New Jersey

33,085

6,207

-21,603

-3,997

65%

64%

New Mexico

213,114

26,553

-88,902

-11,014

42%

41%

New York

235,820

33,306

-183,374

-25,761

78%

77%

North Carolina

237,850

32,255

-171,865

-23,424

72%

73%

North Dakota

392,477

60,825

-269,361

-41,224

69%

68%

Ohio

273,742

42,761

-211,550

-33,102

77%

77%

Oklahoma

605,743

82,607

-335,543

-44,769

55%

54%

Oregon

611,291

68,882

-393,222

-43,504

64%

63%

Pennsylvania

136,536

24,510

-102,909

-18,752

75%

77%

Rhode Island

4,579

756

-3,100

-508

68%

67%

South Carolina

120,753

16,860

-85,074

-11,943

70%

71%

South Dakota

216,915

38,680

-148,482

-26,229

68%

68%

Tennessee

142,974

26,279

-106,647

-19,662

75%

75%

Texas

1,348,851

196,540

-702,631

-100,132

52%

51%

Utah

170,499

21,811

-99,452

-12,537

58%

57%

Vermont

76,845

8,552

-68,243

-7,582

89%

89%

Virginia

126,330

20,377

-99,572

-16,187

79%

79%

Washington

233,795

38,104

-118,973

-19,266

51%

51%

West Virginia

85,395

11,036

-76,326

-9,867

89%

89%

Wisconsin

184,730

31,429

-137,894

-23,449

75%

75%

31


-------
Stall'

I nad.jnsli'd

PMio

I nad.jnsli'd
I'M:.?

Chanel- in
PMio

Clian^i' in
I'M:?

PMio
Reduction

PM:;
Reduction

Wyoming

545,571

61,280

-343,987

-38,276

63%

62%

Domain Total
(12km CONUS)

15,365,503

2,118,419

-9,973,736

-1,364,656

65%

64%

Alaska

107,903

11,775

N/A

N/A

N/A

N/A

Hawaii

18,276

2,389

N/A

N/A

N/A

N/A

Puerto Rico

1,133,401

150,742

N/A

N/A

N/A

N/A

Virgin Islands

1,777

245

N/A

N/A

N/A

N/A

For categories other than paved and unpaved roads, where states submitted afdust data, it was assumed
that the state-submitted data were not met-adjusted and therefore the meteorological adjustments were
applied. Thus, if states submitted data that were met-adjusted for sources other than paved and unpaved
roads, these sources would have been adjusted for meteorology twice. Even with that possibility, air
quality modeling shows that, in general, dust is frequently overestimated in the air quality modeling
results.

Figure 2-1 illustrates the impact of each step of the adjustment. The reductions due to the transport
fraction adjustments alone are shown at the top of the figure. The reductions due to the precipitation
adjustments alone are shown in the middle of the figure. The cumulative emission reductions after both
transport fraction and meteorological adjustments are shown at the bottom of the figure. The top plot
shows how the transport fraction has a larger reduction effect in the east, where forested areas are more
effective at reducing PM transport than in many western areas. The middle plot shows how the
meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust
emissions.

32


-------
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction,

precipitation, and cumulative

2019qe afdust annual : PM2 5, xportfrac adjusted - unadjusted

Max: 0.0 Min: -1830.507

2019ge afdust annual : PM2 5, precip adjusted - xportfrac adjusted

Max: 0.0 Min: -2880.HT



> 50

37

25

12

0

-12
-25
-37
< -50

33


-------
2019ge afdust annual : PM2 5, xportfrac + precip adjusted - unadjusted

2.2.2 Agricultural Livestock (livestock)

The livestock SCCs are shown in Table 2-7. 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. The sector does not include quite all of the livestock NH3 emissions, as there
is a very small amount of NH3 emissions from livestock in the ptnonipm inventory (as point sources). In
addition to NH3. the sector includes livestock emissions from all pollutants other than PM2.5. PM2.5 from
livestock are in the afdust sector.

Agricultural livestock emissions in the 2019 platform were projected from the 2017 NEI (January 2021
version), which is a mix of state-submitted data and EPA estimates. USDA Survey data for 2017 and
2019 was used to create projection factors (https://quickstats.nass.usda.gov/). 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.

Table 2-7. SCCs for the livestock sector

see

Tier 1 description

Tier 2 description

Tier 3 description

Tier 4 description

2805002000

Miscellaneous Area
Sources

Ag. Production -
Livestock

Beef cattle production
composite

Not Elsewhere Classified

34


-------
S( (

Tier 1 description

Tier 2 description

Tier 3 description

Tier 4 description

2805007100

Miscellaneous Area
Sources

Ag. Production-
Livestock

Poultry production - layers
with dry manure management
systems

Confinement

2805009100

Miscellaneous Area
Sources

Ag. Production -
Livestock

Poultry production - broilers

Confinement

2805010100

Miscellaneous Area
Sources

Ag. Production -
Livestock

Poultry production - turkeys

Confinement

2805018000

Miscellaneous Area
Sources

Ag. Production -
Livestock

Dairy cattle composite

Not Elsewhere Classified

2805025000

Miscellaneous Area
Sources

Ag. Production-
Livestock

Swine production composite

Not Elsewhere Classified
(see also 28-05-039, -047, -
053)

2805035000

Miscellaneous Area
Sources

Ag. Production -
Livestock

Horses and Ponies Waste
Emissions

Not Elsewhere Classified

2805040000

Miscellaneous Area
Sources

Ag. Production -
Livestock

Sheep and Lambs Waste
Emissions

Total

2805045000

Miscellaneous Area
Sources

Ag. Production -
Livestock

Goats Waste Emissions

Not Elsewhere Classified

Agricultural livestock emissions in the 2019 platform are based on the 2017 NEI (January 2021 version),
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.

For livestock, meteorological-based temporalization (described in Section 3.3.10) 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 2014NEIv2.

2.2.3 Agricultural Fertilizer (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/). Unlike most of the other emissions that are input to the CMAQ
model, fertilizer emissions are actually output from a run of CMAQ in bi-directional mode and
summarized for inclusion with the rest of the emissions. The bidirectional version of CMAQ (v5.3) and
the Fertilizer Emissions Scenario Tool for CMAQ FEST-C (vl.3) were used to estimate ammonia (NH3)
emissions from agricultural soils. The computed emissions were saved during the CMAQ run for the
purposes of summaries and other model runs that did not use the bidirectional method.

FEST-C is the software program that processes land use and agricultural activity data to develop inputs
for the CMAQ model when run with bidirectional exchange. FEST-C reads land use data from the
Biogenic Emissions Landuse Dataset (BELD), meteorological variables from the Weather Research and
Forecasting (WRF) model, and nitrogen deposition data from a previous or historical average CMAQ
simulation. FEST-C, then uses the Environmental Policy Integrated Climate (EPIC) modeling system
(https://epicapex.tamu.edu/epicf) to simulate the agricultural practices and soil biogeochemistry and
provides information regarding fertilizer timing, composition, application method and amount.

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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
Aerosol and Gaseous Exchange (STAGE) deposition option with bidirectional exchange to estimate
fertilizer and biogenic NH3 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 nutrients applied

•	Management scenarios for the 10 USDA production regions. These include irrigation, tile
drainage, intervals between forage harvest, fertilizer application method (injected versus surface
applied), and equipment commonly used in these production regions.

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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 2-8 were used as EPIC model inputs.

Table 2-8. Source of input variables for EPIC

EPIC input variable

Variable Source

Daily Total Radiation (MJ/m2)

WRF

Daily Maximum 2-m Temperature (C)

WRF

Daily minimum 2-m temperature (C)

WRF

Daily Total Precipitation (mm)

WRF

Daily Average Relative Humidity (unitless)

WRF

Daily Average 10-m Wind Speed (m s"1)

WRF

Daily Total Wet Deposition Oxidized N (g/ha)

CMAQ

Daily Total Wet Deposition Reduced N (g/ha)

CMAQ

Daily Total Dry Deposition Oxidized N (g/ha)

CMAQ

Daily Total Dry Deposition Reduced N (g/ha)

CMAQ

Daily Total Wet Deposition Organic N (g/ha)

CMAQ

Initial soil nutrient and pH conditions in EPIC were based on the 1992 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.).

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2.2.4 Nonpoint Oil and Gas Sector (np_oilgas)

The nonpoint oil and gas (np oilgas) sector includes onshore and offshore oil and gas emissions. The
EPA estimated emissions for all counties with 2019 oil and gas activity data with 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.

The 2017NEI version of the Nonpoint Oil and Gas Emission Estimation Tool (i.e., the "tool") was used to
estimate emissions for 2019. Year 2019 oil and gas activity data was supplied to EPA by Enverus'
activity database (www.enverus.com) and from some state air agencies . 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 created a CSV-
formatted emissions dataset covering all national nonpoint oil and gas emissions. This dataset was then
converted to the FF10 format for use in SMOKE modeling. More details on the inputs for and running of
the tool using year 2017 as an example are provided in the 2017 NEI TSD. Table 2-9 shows the nonpoint
oil and gas NOx and VOC emissions for 2017 and 2019 by state.

Table 2-9. Nonpoint oil and gas emissions for 2017 and 2019







2019 - 2017





2019-2017

State

2017 NOx

2019 NOx

NOx

2017 VOC

2019 VOC

VOC

Alabama

3,415

4,129

714

8,981

10,826

1,845

Alaska

482

2,152

1,670

981

10,752

9,771

Arizona

9

12

3

47

35

-12

Arkansas

7,230

6,879

-351

8,950

7,821

-1,129

California

3,523

591

-2,932

25,808

30,607

4,799

Colorado

30,558

31,631

1,073

103,462

74,143

-29,319

Florida

17

23

6

737

673

-64

Idaho

9

6

-3

178

58

-120

Illinois

13,355

13,492

137

53,539

56,596

3,057

Indiana

4,208

2,608

-1,600

15,332

11,996

-3,336

Kansas

51,851

33,117

-18,734

96,242

69,658

-26,584

Kentucky

12,972

12,802

-170

37,210

36,515

-695

Louisiana

19,202

20,982

1,780

54,829

55,832

1,003

Maryland

0

0

0

0

1

1

Michigan

9,349

8,119

-1,230

16,991

14,937

-2,054

Mississippi

1,732

1,579

-153

9,771

9,099

-672

Missouri

539

457

-82

1,216

1,032

-184

Montana

2,446

2,684

238

31,081

32,793

1,712

Nebraska

356

378

22

2,337

2,383

46

Nevada

2

2

0

149

148

-1

New Mexico

38,065

43,059

4,994

154,849

209,578

54,729

New York

601

666

65

5,410

6,303

893

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2019 - 2017





2019-2017

State

2017 NOx

2019 NOx

NOx

2017 VOC

2019 VOC

VOC

North Dakota

17,626

26,248

8,622

362,287

469,426

107,139

Ohio

2,783

1,813

-970

19,540

16,130

-3,410

Oklahoma

50,221

43,681

-6,540

154,292

177,368

23,076

Oregon

14

12

-2

26

21

-5

Pennsylvania

40,779

48,685

7,906

109,104

136,552

27,448

South Dakota

110

110

0

1,676

1,550

-126

Tennessee

835

783

-52

2,796

2,619

-177

Texas

225,191

165,065

-60,126

894,796

911,109

16,313

Utah

13,130

8,034

-5,096

69,903

61,403

-8,500

Virginia

3,485

3,481

-4

8,569

9,508

939

West Virginia

25,775

21,631

-4,144

85,457

75,943

-9,514

Wyoming

19,750

17,454

-2,296

77,573

88,712

11,139

2.2.5 Residential Wood Combustion (rwc)

The residential wood combustion (rwc) sector includes residential wood burning devices such as
fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor hydronic
heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepots and
chimeneas. Free standing woodstoves and inserts are further differentiated into three categories:
1) conventional (not EPA certified); 2) EPA certified, catalytic; and 3) EPA certified, noncatalytic.
Generally speaking, the conventional units were constructed prior to 1988. Units constructed after 1988
have to meet EPA emission standards and they are either catalytic or non-catalytic. As with the other
nonpoint categories, a mix of S/L and EPA estimates were used. The EPA's estimates use updated
methodologies for activity data and some changes to emission factors. The source classification codes
(SCCs) in the RWC sector are listed in Table 2-10.

The 2019 platform RWC emissions are unchanged from the data in the 2017 NEI. 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
2019. 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 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 2-10. SCCs for the residential wood combustion sector

S( (

Tier 1 Description

Tier 2
Description

Tier 3
Description

Tier 4 Description

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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

2.2.6 Solvents (np_solvents)

The np solvents sector is a diverse collection of emission sources whose emissions are driven by
evaporation. Included in this sector are everyday items, such as cleaners, personal care products,
adhesives, architectural and aerosol coatings, printing inks, and pesticides. These sources exclusively
emit organic gases and feature origins spanning residential, commercial, institutional, and industrial
settings. The organic gases that evaporate from these sources often fulfill other functions than acting as a
traditional solvent (e.g., propellants, fragrances, emollients).

Here, emissions from this sector are derived using the volatile chemical products in python (VCPy)
framework (Seltzer et al., 2021). The VCPy framework is based on the principle that the magnitude and
speciation of organic emissions from this sector are directly related to (1) the mass of chemical products
used, (2) the composition of these products, (3) the physiochemical properties of their constituents that
govern volatilization, and (4) the timescale available for these constituents to evaporate. National product
usage is preferentially estimated using economic statistics from the U.S. Census Bureau's Annual Survey

40


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of Manufacturers (U.S. Census Bureau, 2019), commodity prices from the U.S. Department of
Transportation's 2012 Commodity Flow Survey (U.S. Department of Transportation, 2015) and the U.S.
Census Bureau's Paint and Allied Products Survey (U.S. Census Bureau, 2011), and producer price
indices, which scale commodity prices to target years, are retrieved from the Federal Reserve Bank of St.
Louis (U.S. Bureau of Labor Statistics, 2020). In circumstances where the aforementioned datasets are
unavailable, default usage estimates are derived using functional solvent usage reported by a business
research company (The Freedonia Group, 2016) or in sales reported in a California Air Resources Board
(CARB) California-specific survey (CARB, 2019). The composition of products is estimated by
generating composites from various CARB surveys from 2007 through 2019, along with profiles reported
in the U.S. EPA's SPECIATE database (EPA, 2019). The physiochemical properties of all organic
components are generated from the quantitative structure-activity relationship model OPERA (Mansouri
et al., 2018) and the characteristic evaporation timescale of each component is estimated using previously
published methods (Khare and Gentner, 2018; Weschler and Nazaroff, 2008).

National-level emissions are then allocated to the county-level using several proxies. Most emissions are
allocated using population as a spatial surrogate. This includes all cleaners, personal care products,
adhesives, architectural coatings, and aerosol coatings. Industrial coatings, allied paint products, printing
inks, and dry cleaning emissions are allocated using county-level employment statistics from the U.S.
Census Bureau's County Business Patterns (U.S. Census Bureau, 2019) and follow the same mapping
scheme used in the U.S. EPA's 2017 National Emissions Inventory (EPA, 2021). Agricultural pesticides
are allocated using county-level agricultural pesticide use, as taken from the 2017 NEI.

The version of VCPy used for this platform includes additional product aggregations, variation in the
VOC-content of products to reflect state-level area source rules relevant to the solvent sector, and the
adoption of an indoor emissions pathway. To compute VCP emissions indoors, each product category is
assigned an indoor usage fraction. All coating and industrial products are assigned a 50% indoor emission
fraction, all pesticides and automotive aftermarket products are assigned a 0% indoor emission fraction,
and all consumer and cleaning products are assigned a 100% indoor emission fraction. The lone exception
are daily use personal care products, which are assumed to have a 50% indoor emission fraction. This
indoor emission assignment enables the mass transfer coefficient to vary between indoor and outdoor
conditions. Typically, the mass transfer coefficent indoors is smaller than the mass transfer coefficient
outdoors due to more stagnant atmospheric conditions, and the newest version of the modeling framework
reflects these dynamics. Indoor product usage utilizes a mass transfer coefficient of 5 m/hr, and the
remaining outdoor portion is assigned a mass transfer coefficient of 30 m/hr (Weschler and Nazaroff,
2008; Khare and Gentner, 2018).

The npsolvents sector also includes emissions from the 2017 NEI not covered by VCPy. This includes
some State, Locality, and Tribal emission submissions for other CAPs, such as CO, NOX, and PM2.5. In
addition, there are some SCCs not covered by VCPy but included in the np solvents sector. Here,
emissions for all of these sources are taken from the 2017 NEI and these SCCs are listed in Table 2-11.

Table 2-11. Non-VCPy SCCs in the np solvents sector

sec

Description

2401050000

Solvent Utilization;Surface Coating;Miscellaneous Finished Metals: SIC 34 - (341 +
3498);Total: All Solvent Types

2440020000

Solvent Utilization;Miscellaneous Industrial;Adhesive (Industrial) Application;Total: All
Solvent Types

41


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sec

Description

2461020000

Sohcnl L LilizuLion.Miscellaneous \on-indiislnal. Commcicial.Asphall Application. All
Processes;Total: All Solvent Types

2461021000

Solvent Utilization;Miscellaneous Non-industrial: Commercial;Cutback Asphalt;Total:
All Solvent Types

2461022000

Solvent Utilization;Miscellaneous Non-industrial: Commercial;Emulsified
Asphalt;Total: All Solvent Types

2461023000

Solvent Utilization;Miscellaneous Non-industrial: Commercial;Asphalt Roofing;Total:
All Solvent Types

2461800001

Solvent Utilization;Miscellaneous Non-industrial: Commercial;Pesticide Application:
All Processes;Surface Application

Finally, since emissions from solvents occur from both point and nonpoint SCCs, point source subtraction
is required to ensure emissions from this sector are not double-counted. Point source subtraction for this
sector is performed at the county-level using uncontrolled point source emissions. As such, assumptions
related to the control efficiency of the point sources must be made. In most some cases, metadata
indicates that the point source emission estimates submitted to the NEI feature 80-90% control
efficiencies. Therefore, uncontrolled point source emission calculations are calculated, as necessary, using
the submitted point source emissions, engineering judgement, and an assumed control efficiency. If point
source subtraction results in negative emissions, emissions will zero out emissions for that source
category in that county. The mapping of nonpoint SCCs to point SCCs follows the same crosswalk
implemented for the 2020 NEI.

2.2.7 Nonpoint (nonpt)

The 2019 platform nonpt sector inventory is mostly unchanged from the January 2021 version of the 2017
NEI. Stationary nonpoint sources that were not subdivided into the afdust, livestock, fertilizer, np oilgas,
rwc or np solvents sectors were assigned to the "nonpt" sector. Locomotives and CMV mobile sources
from the 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;

•	chemical manufacturing;

•	industrial processes such as commercial cooking, metal production, mineral processes, petroleum
refining, wood products, fabricated metals, and refrigeration;

•	storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline
service stations, aviation, and marine vessels;

•	storage and transport of chemicals;

•	waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;
and

•	miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair
shops.

The nonpt sector includes emission estimates for Portable Fuel Containers (PFCs), also known as "gas
cans " The PFC inventory consists of 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)

42


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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.

Volatile chemical product (aka solvent) SCCs were placed into the solvents sector. The EPA incorporated
new methods to estimate emissions of VOC and associated HAPs from the solvents sector, for this 2019
modeling platform (See section 3.2.1 for details). The new methods result in improved emissions
estimates for the nonpoint (county-wide) solvent emissions. The new emissions method results in
improved VOC and HAP estimates for nonpoint categories of coatings, pesticides, adhesives and sealants,
oil & gas exploration solvent use, dry cleaning, printing inks, cleaning products, personal care products,
and other miscellaneous solvent uses.

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 NEITSD.

For the 2019 modeling platform, VMT were projected from 2017 to 2019 based mostly on Federal
Highways administration (FHWA) annual VMT changes at the county level. In a few cases, state
Department of Transportation (DOT) data were used instead of FHWA data. Other activity data (i.e.,
starts, on-network idling, VPOP, and hoteling) are projected by applying a ratio of 2017-based
VMT/activity ratios to the 2019 VMT. In addition, a number of states submitted 2017-specific activity
data for incorporation into this platform. Finally, a new MOVES run for 2019 was done using MOVES3.

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 2019 meteorological data. Specifically, EPA used MOVES3
inputs for representative counties, vehicle miles traveled (VMT), vehicle population (VPOP), and hoteling
hours data for all counties, along with tools that integrated the MOVES model with SMOKE. In this way,
it was possible to take advantage of the gridded hourly temperature data available from meteorological
modeling that are also used for air quality modeling. The onroad source classification codes (SCCs) in the
modeling platform are more finely resolved than those in the National Emissions Inventory (NEI). The
NEI SCCs distinguish vehicles and fuels. The SCCs used in the model platform also distinguish between
emissions processes (i.e., off-network, on-network, and extended idle), and road types.

MOVES3 includes the following updates from MOVES2014b:

• Updated emission rates:

o Updated heavy-duty (HD) diesel running emission rates based on manufacturer in-use

testing data from hundreds of HD trucks
o Updated HD gasoline and compressed natural gas (CNG) trucks
o Updated light-duty (LD) emission rates for hydrocarbons (HC), CO, NOx, and PM

43


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•	Includes updated fuel information

•	Incorporates HD Phase 2 Greenhouse Gas (GHG) rule, allowing for finer distinctions among HD
vehicles

•	Accounts for glider vehicles that incorporate older engines into new vehicle chassis

•	Accounts for off-network idling - emissions beyond the idling that is already considered in the
MOVES drive cycle

•	Includes revisions to inputs for hoteling

•	Adds starts as a separate type of rate and activity data

2.3.1 Inventory Development using SMOKE-MOVES

Except for California, onroad emissions were computed with SMOKE-MOVES by multiplying specific
types of vehicle activity data by the appropriate emission factors. This section includes discussions of the
activity data and the emission factor development. The vehicles (aka source types) for which MOVES
computes emissions are shown in Table 2-12. 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, typically computed by
running SMOKE-MOVES for distinct grids covering each of those regions, have not been processed for
2019.

Table 2-12. MOVES vehicle (source) types

MOYKS vehicle Ivpe

Description

II P.MS vehicle Ivpe

11

Motorcycle

10

21

Passenger Car

25

31

Passenger Truck

25

32

Light Commercial Truck

25

41

Intercity Bus

40

42

Transit Bus

40

43

School Bus

40

51

Refuse Truck

50

52

Single Unit Short-haul Truck

50

53

Single Unit Long-haul Truck

50

54

Motor Home

50

61

Combination Short-haul Truck

60

62

Combination Long-haul Truck

60

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 2019-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 294 representative counties in the continental U.S. and a total

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of 332 including the non-CONUS areas. The representative counties that were used for the 2019 platform
are the same as in the 2016 version 2 modeling platform, and very close to what was used in EPA's Air
Quality Time Series (EQUATES) project for 2016/2017. The EPA added some additional representative
counties to the set used for EQUATES to account for altitude and variations in I&M programs and fuels.

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; off-network processes use vehicle population (VPOP), vehicle starts, and hours of off-
network idling (ONI); and hoteling hours are used to develop emissions for extended idling of
combination long-haul trucks. These calculations 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, STARTS, off-network
idling, and HOTELING) to calculate emissions based on the gridded hourly temperatures in the
meteorological data.

7)	Aggregate the results to the county-SCC level for summaries and quality assurance.

The onroad emissions are processed in six processing streams that are merged together into the onroad
sector emissions after each of the six streams have been processed:

•	rate-per-distance (RPD) uses VMT as the activity data plus speed and speed profile information to
compute on-network emissions from exhaust, evaporative, permeation, refueling, and brake and
tire wear processes;

•	rate-per-vehicle (RPV) uses VPOP activity data to compute off-network emissions from exhaust,
evaporative, permeation, and refueling processes;

•	rate-per-profile (RPS) uses STARTS activity data to compute off-network emissions from vehicles
starts;

•	rate-per-profile (RPP) uses VPOP activity data to compute off-network emissions from
evaporative fuel vapor venting, including hot soak (immediately after a trip) and diurnal (vehicle
parked for a long period) emissions;

•	rate-per-hour (RPH) uses hoteling hours activity data to compute off-network emissions for idling
of long-haul trucks from extended idling and auxiliary power unit process: and

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•	rate-per-hour off-network idling (RPHO) uses off network idling hours activity data to compute
off-network idling emissions for all types of vehicles.

The onroad emissions inputs to MOVES for the 2019 platform are based on the 2017 NEI, described in
more detail in Section 6 of the 2017 NEI TSD. These inputs include:

•	Key parameters in the MOVES County databases (CDBs) including Low Emission Vehicle (LEV)
table

•	Fuel months

•	Activity data (e.g., VMT, VPOP, speed, HOTELING)

Fuel months and other inputs were consistent with those in the 2017 NEI. Age distributions in the
MOVES databases were adjusted to represent the year 2019. 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.

2.3.2 Onroad Activity Data Development

SMOKE-MOVES uses vehicle miles traveled (VMT), vehicle population (VPOP), vehicle starts, hours of
off-network idling (ONI), and hours of hoteling, to calculate emissions. These datasets are collectively
known as "activity data". For each of these activity datasets, first a national dataset was developed; this
national dataset is called the "EPA default" dataset. The default dataset started with the 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)

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.
For the 2019 modeling platform, VMT were projected from 2017 to 2019 based mostly on Federal
Highways administration (FHWA) annual VMT changes at the county level. In Georgia, state Department
of Transportation (DOT) data were used instead of FHWA data. In Oklahoma, human population trends
were used for 2017 to 2018 and FHWA-based factors were used to project from 2018 to 2019.

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.

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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.

Hotelina Hours (HOTEUNG)

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 2019; 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
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). For 2019 modeling with
MOVES3, a 6.9% APU split is used nationwide, meaning that during 6.9% of the hoteling hours auxiliary
power units are assumed to be running.

Starts

Onroad "start" emissions are the instantaneous exhaust emissions that occur at the engine start (e.g., due
to the fuel rich conditions in the cylinder to initiate combustion) as well as the additional running exhaust
emissions that occur because the engine and emission control systems have not yet stabilized at the
running operating temperature. Operationally, start emissions are defined as the difference in emissions
between an exhaust emissions test with an ambient temperature start and the same test with the
engine and emission control systems already at operating temperature. As such, the units for start
emission rates are instantaneous grams/start.

MOVES3 uses vehicle population information to sort the vehicle population into source bins defined
by vehicle source type, fuel type (gas, diesel, etc.), regulatory class, model year and age. The model uses
default data from instrumented vehicles (or user-provided values) to estimate the number of starts for
each source bin and to allocate them among eight operating mode bins defined by the amount of time
parked ("soak time") prior to the start. Thus, MOVES3 accounts for different amounts of cooling of the
engine and emission control systems. Each source bin and operating mode has an associated g/start

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emission rate. Start emissions are also adjusted to account for fuel characteristics, LD inspection and
maintenance programs, and ambient temperatures.

2019 STARTS = 2019 VMT * (2017 STARTS/ 2017 VMT by county&SCC6)

Off-network Idling Hours

After creating VMT inputs for SMOKE-MOVES, Off-network idle (ONI) activity data were also needed.
ONI is defined in MOVES as time during which a vehicle engine is running idle and the vehicle is
somewhere other than on the road, such as in a parking lot, a driveway, or at the side of the road. This
engine activity contributes to total mobile source emissions but does not take place on the road network.
Examples of ONI activity include:

light duty passenger vehicles idling while waiting to pick up children at school or to pick up
passengers at the airport or train station,

single unit and combination trucks idling while loading or unloading cargo or making
deliveries, and

vehicles idling at drive-through restaurants.

Note that ONI does not include idling that occurs on the road, such as idling at traffic signals, stop signs,
and in traffic—these emissions are included as part of the running and crankcase running exhaust
processes on the other road types. ONI also does not include long-duration idling by long-haul
combination trucks (hoteling/extended idle), as that type of long duration idling is accounted for in other
MOVES processes.

ONI activity hours were calculated based on VMT. For each representative county, the ratio of ONI hours
to onroad VMT (on all road types) was calculated using the MOVES ONI Tool by source type, fuel type,
and month. These ratios are then multiplied by each county's total VMT (aggregated by source type, fuel
type, and month) to get hours of ONI activity.

2.3.3 MOVES Emission Factor Table Development

MOVES3 was run in emission rate mode to create emission factor tables using CB6 speciation for 2019,
for all representative counties and fuel months. The county databases used to run MOVES to develop the
emission factor tables included the state-specific control measures such as the California LEV program,
and fuels represented the year 2019. The range of temperatures run along with the average humidities
used were specific to the year 2019. The remaining settings for the CDBs are documented in the 2017
NEITSD. To create the emission factors, MOVES was run separately for each representative county and
fuel month for each temperature bin needed for the calendar year 2019. The MOVES results were post-
processed into CSV-formatted emission factor tables that can be read by SMOKE-MOVES. Additionally,
MOVES was run for all counties in Alaska, Hawaii, and Virgin Islands, and for a single representative
county in Puerto Rico.

The county databases CDBs used to run MOVES to develop the emission factor tables were those used
for the 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

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county in the continental U.S. was classified according to its state, altitude (high or low), fuel region, the
presence of inspection and maintenance programs, the mean light-duty age, and the fraction of ramps. A
binning algorithm was executed to identify "like" counties. The result was 294 representative counties for
CONUS and 38 for Alaska, Hawaii, Puerto Rico, and the US Virgin Islands. The representative counties
for CONUS, which were the same as those used in 2016v2 platform, are shown in Figure 2-3.

Figure 2-3. Map of Representative Counties

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 1HS data).

For the 2017 NEI, EPA repeated the CRC's assessment of UTS 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

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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
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 2-13 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 30 and up bins, 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 2-13. Fraction of IHS Vehicle Populations to Retain

Model Year

Cars

Light

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

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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.

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 2017 NEI age distributions were then grouped using a population-weighted average of the source
type populations of each county in the representative county group, and were updated to represent the
year 2019. The resulting end-product was age distributions for each of the 13 source types in each of the
representative counties. 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 2019. 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 2019. In addition, the range of temperatures run along with
the average humidities used were specific to the year 2019. 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 uses their own emission model, EMFAC, to develop onroad emissions inventories and provides
those inventories to EPA. EMFAC uses emission inventory codes (EICs) to characterize the emission
processes instead of SCCs. The EPA and California worked together to develop a code mapping to better
match EMFAC's EICs to EPA MOVES' detailed set of SCCs that distinguish between off-network and
on-network and brake and tire wear emissions. This detail is needed for modeling but not for the NEI.
California provided set of emissions for 2023 as part of the 2016vl platform development. EPA
interpolated between 2017 and 2023 to calculate the 2019 onroad emissions for California. The
California inventory had CAPs only and did not have NH3 or refueling emissions. The EPA added NH3 to
the CARB inventory by using the state total NH3 from MOVES and allocating it at the county level based
on CO. California refueling emissions were computed by multiplying California refueling emissions from
the 2017 NEI by projection factors developed by dividing county total refueling from the 2019 MOVES3
run by the county total refueling from the EQUATES 2017 run (which also used MOVES3). CARB
VOCs were speciated to VOC HAPs using MOVES VOC speciation. All other HAPs (e.g., metals and
PAHs) are from MOVES.

The California onroad mobile source emissions were created through a hybrid approach of combining
state-supplied annual emissions with EPA-developed SMOKE-MOVES runs. Through this approach, the
platform was able to reflect the California-developed emissions, while leveraging the more detailed SCCs

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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
"onroadca."

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. Category 1 and 2 vessels use diesel
fuel. Some examples of CMV sources included in cmv_clc2 are fishing vessels, tug boats, and oil and gas
platform support vessels. For more information on the CMV sources, see the supplemental
documentation for 2020 NEI CMV3. 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. The C1C2 CMV emissions were
computed for 2019 using methods compatible with the 2020 NEI.

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.

3 https://gaftp.epa.gOv/Air/nei/2020/doc/supporting_data/nonpoint/CMV/

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The cmv_clc2 inventory sector contains sources that traverse state and federal waters along with
emissions from surrounding areas of Canada, Mexico, and international waters. The cmv_clc2 sources
are modeled as point sources but using plume rise parameters that cause the emissions to be released in
the ground layer of the air quality model.

The cmv_clc2 sources within state waters are identified in the inventory with the Federal Information
Processing Standard (FIPS) county code for the state and county in which the vessel is registered. The
cmv_clc2 sources that operate outside of state waters but within the Emissions Control Area (ECA) are
encoded with a state FIPS code of 85. The ECA areas include parts of the Gulf of Mexico, and parts of
the Atlantic and Pacific coasts. The cmv_clc2 sources are categorized as operating either in-port or
underway and as main and auxiliary engines are encoded using the SCCs listed in Table 2-14.

Table 2-14. 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 2019 using methods compatible with the 2020
NEI.4 The 2019 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, 2019. To ensure coverage for all of the areas needed by the NEI, the requested and
provided AIS data extend beyond 200 nautical miles from the U.S. coast. The area covered by the NEI is
shown in Figure 2-4 (a). This boundary is roughly equivalent to the border of the U.S Exclusive
Economic Zone and the North American ECA, although some non-EC A activity are captured as well.
Two types of AIS data were received: satellite (S-AIS) and terrestrial (T-AIS). The same request boxes
were used for the 2019 modeling platform and for the 2020 NEI. The counts of data received for S-AIS
and T-AIS for the 2020 NEI are shown in Figure 2-4 (b).

4 https://gaftp.epa.gOv/Air/nei/2020/doc/supporting_data/nonpoint/CMV/.

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¦

b) Areas of AIS request boxes and amount of data received

Figure 2-4. NEI Commercial Marine Vessel Boundaries and Automatic Identification System

Request Boxes for 2029

a) NEI (solid) and ECA (dashed) geographical extent

K' Eft

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

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(MMSI); which allowed each vessel to be matched to their characteristics obtained from the Clarksons
ship registry (Clarksons, 2021).

The engine bore and stroke data were used to calculate cylinder volume. Any vessel that had a calculated
cylinder volume greater than 30 liters was incorporated into the USEPA's new Category 3 Commercial
Marine Vessel (C3CMV) model. The remaining records were assumed to represent Category 1 and 2
(C1C2) or non-ship activity. The C1C2 AIS data were quality assured including the removal of duplicate
messages, signals from pleasure craft, and signals that were not from CMV vessels (e.g., buoys,
helicopters, and vessels that are not self-propelled).

The emissions were calculated for each time interval between consecutive AIS messages for each vessel
and allocated to the location of the message following to the interval. Emissions were calculated
according to Equation 2-1.

g

Emissionsinterval = Time (hr)interval x Power(kW) x EFQj^) x LLAF Equation 2-1

Power is calculated for the propulsive (main), auxiliary, and auxiliary boiler engines for each interval and
emission factor (EF) reflects the assigned emission factors for each engine, as described below. LLAF
represents the low load adjustment factor, a unitless factor which reflects increasing propulsive emissions
during low load operations. Time indicates the activity duration time between consecutive intervals.

Next, vessels were identified in order determine their vessel type, and thus their vessel group, power
rating, and engine tier information which are required for the emissions calculations. See the 2020 NEI
documentation for more details on this process. Following the identification, 108 different vessel types
were matched to the C1C2 vessels. Vessel attribute data 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 2-155. For the 2020 NEI, 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 2-15. 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

5 The number of matched vessels shown here were computed for the 2020 NEI, but the numbers should be similar for the 2019
modeling platform.

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Vessel Group

NEI Area Ship Count

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 2-14.

The final components of the emissions computation equation are the emission factors and the low load
adjustment factor. The emission factors used in this inventory take into consideration the EPA's marine
vessel fuel regulations as well as exhaust standards that are based on the year that the vessel was
manufactured to determine the appropriate regulatory tier. Emission factors in g/kWhr by tier for NOx,
PMio, PM2.5, CO, CO2, SO2 and VOC were developed using Tables 3-7 through 3-10 in USEPA's (2008)
Regulatory Impact Analysis on engines less than 30 liters per cylinder. To compile these emissions
factors, population-weighted average emission factors were calculated per tier based on C1C2 population
distributions grouped by engine displacement. Boiler emission factors were obtained from an earlier
Swedish Environmental Protection Agency study (Swedish EPA, 2004). If the year of manufacture was
unknown then it was assumed that the vessel was Tier 0, such that actual emissions may be less than those
estimated in this inventory. Without more specific data, the magnitude of this emissions difference cannot
be estimated.

Propulsive emissions from low-load operations were adjusted to account for elevated emission rates
associated with activities outside the engines' optimal operating range. The emission factor adjustments
were applied by load and pollutant, based on the data compiled for the Port Everglades 2015 Emission
Inventory.6 Hazardous air pollutants and ammonia were added to the inventory according to
multiplicative factors applied either to VOC or PM2.5.

The stack parameters used for cmv_clc2 are a stack height of 1 ft, stack diameter of 1 ft, stack
temperature of 70°F, and a stack velocity of 0.1 ft/s. These parameters force emissions into layer 1.

6 USEPA. EPA and Port Everglades Partnership: Emission Inventories and Reduction Strategies. US Environmental
Protection Agency, Office of Transportation and Air Quality, June 2018.
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100UKV8.pdf.

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For more information on the emission computations for 2019, see the supporting documentation for the
2020 NEI C1C2 CMV emissions. The cmv_clc2 emissions were aggregated to total hourly values in
each grid cell and run through SMOKE as point sources. SMOKE requires an annual inventory file to go
along with the hourly data, so the annual inventory file was also generated for 2019.

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.7 The cmv_c3 sector contains sources that traverse state
and federal waters; along with sources in waters not covered by the NEI in surrounding areas of Canada,
Mexico, and international waters. For more information on the 2019 CMV sources, see the supplemental
documentation for the 2020 NEI CMV8.

The cmv_c3 sources that operate outside of state waters but within the federal Emissions Control Area
(ECA) are encoded with a FIPS state code of 85, with the "county code" digits representing broad regions
such as the Atlantic, Gulf of Mexico, and Pacific. The ECA areas include parts of the Gulf of Mexico,
and parts of the Atlantic and Pacific coasts. CMV C3 sources around Puerto Rico, Hawaii and Alaska,
which are outside the ECA areas, are included in the inventory but are in separate files from the emissions
around the continental United States (CONUS). The cmv_c3 sources in the inventory are categorized as
operating either in-port or underway and are encoded using the SCCs listed in Table 2-16. and distinguish
between diesel and residual fuel, in port areas versus underway, and main and auxiliary engines.

7	https://www.epa.gov/regulations-emissions-vehicles-and-engines/regulations-emissions-marine-vessels.

8	https://gaftp.epa.gov/Air/nei/2020/doc/supporting data/nonpoint/CMV.

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Table 2-16. SCCs for cmv c3 sector

sec

Tier 1
Description

Tier 2 Description

Tier 3
Description

Tier-l Description

228UUU21U3

Mobile
Sources

Marine \ essels,
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 2019 CMV inventory, 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, 2019. 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.9 In addition,
the USCG has mandated that all commercial marine vessels continuously transmit AIS signals while
transiting U.S. navigable waters. As the vast majority of C3 vessels meet these requirements, any omitted
from the inventory due to lack of AIS adoption are deemed to have a negligible impact on national C3
emissions estimates. The activity described by this inventory reflects ship operations within 200 nautical
miles of the official U.S. baseline. This boundary is roughly equivalent to the border of the U.S Exclusive
Economic Zone and the North American EC A, although some non-ECA activity is captured as well
(Figure 2-4).

The 2019 CMV data were computed based on the AIS data from the USGS for the year of 2019. 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.

9 International Maritime Organization (IMO) Resolution MSC.99(73) adopted December 12th. 2000 and entered into force July
1st, 2002; as amended by SOLAS Resolution CONF.5/32 adopted December 13th, 2002.

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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,10 but since the data were already in the form of point sources
at the center of each grid cell, and they were already hourly, no other processing was needed within
SMOKE. SMOKE requires an annual inventory file to go along with the hourly data, so the annual files
were also generated for 2019.

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.

Figure 2-4A 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.

10 Ammonia (NH3) was also added by SMOKE in the speciation step.

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2.4.3 Railway Locomotives (rail)

The rail sector includes all locomotives in the NEI nonpoint data category including line haul locomotives
on Class 1, 2, and 3 railroads along with emissions from commuter rail lines and Amtrak. The rail sector
excludes railway maintenance locomotives and point source yard locomotives. Railway maintenance
emissions are included in the nonroad sector. The point source yard locomotives are included in the
ptnonipm sector. Typically in the NEI, yard locomotive emissions are split between the nonpoint and
point categories, but 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. Pollutant-specific factors were
applied on top of the activity-based changes for the Class I rail. The inventory SCCs are shown in Table
2-17. More detailed information on the development of the 2017 NEI rail inventory for this study is
available in the 2017 NEI TSD and in the Specification Sheet: Rail 2017 National Emissions Inventory on
the 2017 NEI supporting data FTP site. The 2017 NEI rail inventory was projected to 2019 using activity-
based factors show in Table 2-18.

Table 2-17. SCCs for the Rail Sector

sec

Sector

Description: Mobile Sources prefix for all

2285002006

Rail

Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations

2285002007

Rail

Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations

2285002008

Rail

Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains
(Amtrak)

2285002009

Rail

Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines

2285002010

Rail

Railroad Equipment; Diesel; Yard Locomotives (nonpoint)

28500201

Rail

Railroad Equipment; Diesel; Yard Locomotives (point)

Table 2-18. 2017-to-2019 projection factors for rail

Category

NOx

PM

YOC

other
pollutants

Class I

-8.65%

-5.33%

-17.05%

-1.18%

Class II/III and Yards

-1.18%

-1.18%

-1.18%

-1.18%

Passenger/Commuter

+3.83%

+3.83%

+3.83%

+3.83%

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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 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.

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)

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Figure 2-6. Class I Railroads in the United States

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 2-7 shows the di stribution of Class II and III railroads and commuter railroads
across the country. This inventory will be useful for regional and local modeling, helps identify where
Class II and III railroads may need to be better characterized, and provides a strong foundation for the
future development of a more accurate nationwide short line and regional railroad emissions inventory. A
picture of the locations of class II and III railroads is shown in Figure 2-7.

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Figure 2-7. Class II and III Railroads in the United States5

Commuter Rail Methodology

Commuter rail emissions were calculated in the same way as the Class II and III railroads. The primary
difference is that the fuel use estimates were based on data collected by the Federal Transit
Administration (FTA) for the National Transit Database. For the 2017 NEI, 2016 fuel use was estimated
for each of the commuter railroads 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.

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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 2-8. 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.

Figure 2-8. Amtrak Routes with Diesel-powered Passenger Trains

5F«faad Rtlmd takrmiwijwien . Xn 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
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

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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 MOVES3 which incorporates the
NONROAD model. MOVES3 incorporated updated nonroad engine population growth rates, nonroad
Tier 4 engine emission rates, and sulfur levels of nonroad diesel fuels. MOVES provides a complete set of
HAPs and incorporates updated nonroad emission factors for HAPs. MOVES3 was used for all states
other than California, which uses their own model, and the Texas Commission on Environmental Quality
(TCEQ), which provided their own emissions. California nonroad emissions were provided by the
California Air Resources Board (CARB) for the 2017 NEI. The 2019 California nonroad emissions were
interpolated from the 2017 NEI and a 2023 projection from the 2016vl modeling platform, with HAP
augmentation. For Texas, the EPA interpolated to 2019 between data provided for 2017 and 2020 and
applied HAP augmentation.

MOVES creates a monthly emissions inventory for criteria air pollutants (CAPs) and a full set of HAPs,
plus additional pollutants such as NONHAPTOG and ETHANOL, which are not part of the NEI but are
used for speciation. MOVES provides estimates of NONHAPTOG along with the speciation profile code
for the NONHAPTOG emission source. This was accomplished by using NHTOG#### as the pollutant
code in the Flat File 2010 (FF10) inventory file that can be read into SMOKE, where #### is a speciation
profile code. For California and Texas, NHTOG####-VOC and HAP-VOC ratios from MOVES-based
emissions were applied to VOC emissions so that VOC emissions can be speciated consistently with other
states.

MOVES also provides estimates of PM2.5 by speciation profile code for the PM2.5 emission source,
using PM25_#### as the pollutant code in the FF10 inventory file, where #### is a speciation profile
code. To facilitate calculation of PMC within SMOKE, and to help create emissions summaries, an
additional pollutant representing total PM2.5 called PM25TOTAL was added to the inventory. As with
VOC, PM25_####-PM25TOTAL ratios were calculated and applied to PM2.5 emissions in California
and Texas so that PM2.5 emissions in California and Texas can be speciated consistently with other
states.

MOVES3 outputs emissions data in county-specific databases, and a post-processing script converts the
data into FF10 format. Additional post-processing steps were performed as follows:

•	County-specific FFlOs were combined into a single FF10 file.

•	Emissions were aggregated from the more detailed SCCs modeled in MOVES to the SCCs
modeled in SMOKE. A list of the aggregated SMOKE SCCs is in Appendix A of the 2016vl
platform nonroad specification sheet (NEIC, 2019).

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•	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). The construction
equipment allocation data used in MOVES for this platform

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.11 The 2016 National Emissions Inventory Collaborative (NEIC) Nonroad Work Group updated 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.

More information on the development of the updates to agricultural and construction equipment
allocations is available in Section 2.4.4 of the Technical Support Document (TSD): Preparation of
Emissions Inventories for the 2017 North American Emissions Modeling Platform (EPA, 2022a).

Updated nrsurrogate, nrstate 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/). Note that these are not included in
MOVES3.

Emissions Inside California and Texas

11 https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P1004LDX.pdf.

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California nonroad emissions were provided by CARB for 2017NEI, and for several years including 2016
and 2023 as part of the 2016 version 1 modeling platform. The 2017 and 2023 datasets provided by
CARB were used to estimate California nonroad emissions for 2019. Specifically, county-level trends by
pollutant were calculated for the six year period from 2017 to 2023, converted (interpolated) to a one year
trend, and then applied to the 2017 emissions to estimate 2019. Trends based on county totals were used
instead of more specific trends (e.g. by SCC) because of possible differences in SCC delineations between
the different CARB datasets.

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.

Texas nonroad emissions were provided by TCEQ for years 2017 and 2020, and then interpolated to 2019
for each county, SCC, and pollutant. The Texas nonroad inventories are seasonal (summer, fall, winter,
spring), split to monthly by dividing the seasonal total by three for each month. As in California, VOC
and PM2.5 emissions were allocated to speciation profiles, and VOC HAPs were created, using MOVES
data in Texas.

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 and the 2019 inventory used for this study:

•	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 2-19).

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Table 2-19. Alaska counties/census areas for which nonroad equipment sector-specific emissions

were removed

Nonroiul Kqiiipmenl Sector

Counties/Census Are;is (MI'S) lor which equipment
sectoi- emissions sire remo\eil 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-Kovukuk Census Area (02290)

Logging

Aleutians East (02013), Aleutians West (02016), Nome
(02180), North Slope Borough (02185), Northwest Arctic
(02188), Wade Hampton Census Area (02270)

Railway Maintenance

Aleutians East (02013), Aleutians West (02016), Bethel
Census Area (02050), Bristol Bay Borough (02060),
Dillingham Census Area (02070), Haines Borough (02100),
Hoonah-Angoon Census Area (02105), Juneau City +
Borough (02110), Ketchikan Gateway (02130), Kodiak
Island Borough (02150), Lake and Peninsula (02164), Nome
(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-rx, ptfire-wild, ptagfire)

Multiple types of fires are represented in the modeling platform. These include wild and prescribed fires
that are grouped into the ptfire-wild and ptfire-rx sectors, respectively, and agricultural fires that comprise
the ptagfire sector. All ptfire and ptagfire fires are in the United States. Fires outside of the United States
are described in the ptfire othna sector later in this document.

2.5.1 Wild and Prescribed Fires (ptfire-rx, ptfire-wild)

Wildfire and prescribed burning emissions are contained in the ptfire-wild and ptfire-rx sectors, respectively. The
ptfire sector has emissions provided at geographic coordinates (point locations) and has daily emissions values.
The ptfire sector excludes agricultural burning and other open burning sources that are included in the ptagfire
sector. Emissions are day-specific and include satellite-derived latitude/longitude of the fire's origin and other
parameters associated with the emissions such as acres burned and fuel load, which allow estimation of plume rise.

The SCCs used for the ptfire-rx and ptfire-wild sources are shown in Table 2-20. The ptfire-rx and ptfire-
wild inventories include separate SCCs for the flaming and smoldering combustion phases for wildfire

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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 2-20.

Table 2-20. SCCs included in the ptfire sector for the 2019 platform

SCC

Description

2810001001

Forest Wildfires; Smoldering; Residual smoldering only (includes grassland
wildfires)

2810001002

Forest Wildfires; Flaming (includes grassland wildfires)

2811015001

Prescribed Forest Burning; Smoldering; Residual smoldering only

2811015002

Prescribed Forest Burning; Flaming

2811020002

Prescribed Rangeland Burning

2811021000

Prescribed Rangeland Burning - Tallgrass Prairie

Fire Information Data

Inputs to SMARTFIRE2 for 2019 include:

•	The National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping System
(HMS) fire location information

•	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

•	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)

•	Bum scar/fire activity shapefiles for wildfires and some prescribed bums from Monitoring
trends in burn severity (MTBS) website (https://www.mtbs.gov/direct-download)

•	Prescribed burn activity on federal lands from the Department of Interior (DOl)

•	Prescribed burn activity from California Air Resources Board (CARB) specifically from their
Prescribed Fire Incident Reporting System (PF1RS)

•	Prescribed burn activity from Texas Parks and Wildlife Division (TPWD)

•	Active fire perimeters from Bureau of Land Management (BLM)

•	Wildfire and prescribed date, location, and locations from a few S/L/T activity submitters
(includes Georgia, Florida and Kanas(Flint Hills only))

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

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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 2019 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 Pipeline. New size assumptions for HMS detects
were used in SMARTFIRE2 for 2019 to accommodate new, higher resolution satellites that came online.
A geospatial analysis of HMS detects that overlapped perimeter datasets in space and time was used by
general land classification to calculate the acres per detect assumptions.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.

Monitoring Trends in Burn Severity (MTBS) is an interagency program whose goal is to consistently map
the burn severity and extent of large fires across the U.S. from 1984 to present. The MTBS data includes
all fires 1,000 acres or greater in the western United States and 500 acres or greater in the eastern United
States. The extent of coverage includes the continental U.S., Alaska, Hawaii, and Puerto Rico. Fire
occurrence and satellite data from various sources are compiled to create numerous MTBS fire products.
The MTBS Burned Areas Boundaries Dataset shapefiles include year 2018 fires and that are classified as
either wildfires, prescribed burns or unknown fire types. The unknown fire type shapes were omitted in
the 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 2019 data from the
USFS Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and
used for emissions inventory development. This database includes information about activities related to
fire/fuels, silviculture, and invasive species. The FACTS database consists of shapefiles for prescribed
burns that provide acres burned and start and ending time information.

The US Fish and Wildland Service (USFWS) also compiles wildfire and prescribed burn activity on their
federal lands every year. Year 2019 data were acquired from USFWS through direct communication with
USFWS staff and were used for 2019 platform development. The USFWS fire information provided fire
type, acres burned, latitude-longitude, and start and ending times. The Department of Interior provided
prescribed burn information for the other bureaus/deparments in DOI (Bureau of Land Management,
Bureau of Indian Affairs and National Parks System). Perimeter shapefiles of year 2019 active fires on
Bureau of Land Management (BLM) land in the western US was also used to capture small, mid-size, and
large fires.

The state-specific activity data acquired and used included data from California, Florida, Georgia, Kansas
and Texas. Activity from Florida and Georgia contained wild and prescribed fires for the entire states.

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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 2019 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 2-20. The lofted smoldering emissions were
assigned to the flaming emissions SCCs in Table 2-20.

Figure 2-9 is a schematic of the data processing stream for the inventory of wildfire and prescribed burn
sources. The ptfire-rx and ptfire-wild inventory sources were estimated using Satellite Mapping
Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and Blue Sky
Pipeline. SMARTFIRE2 is an algorithm and database system that 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 2019 platform, the national and S/L/T fire information was input into SMARTFIRE2 and then
merged and associated based on user-defined weights for each fire information dataset. The output from
SMARTFIRE2 was daily acres burned by fire type, and latitude-longitude coordinates for each fire. The
fire type assignments were made using the fire information datasets. If the only information for a fire was
a satellite detect for fire activity, then the flow described in Figure 2-10 was used to make fire type
assignment by state and by month.

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Figure 2-9. Processing flow for fire emission estimates in the 2019 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 2-10. 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 Pipeline. The framework
supports the calculation of fuel loading and consumption, and emissions using various models depending
on the available inputs as well as the desired results. The contiguous United States and Alaska, where
Fuel Characteristic Classification System (FCCS) fuel loading data are available, were processed using
the modeling chain described in Figure 2-1. The Fire Emissions Production Simulator (FEPS) in the
BlueSky Pipeline generates all the CAP emission factors for wildland fires used in the 2019 study. HAP
emission factors were obtained from Urbanski's (2014) work and applied by region and by fire type.

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Figure 2-11. Blue Sky Modeling Framework

The FCCSv3 cross-reference was implemented along with the LANDFIREv2 (at 200 meter resolution) to
provide better fuel bed information for the BlueSky Pipeline (BSP). The LANDFIREv2 was aggregated
from the native resolution and projection to 200 meter using a nearest-neighbor methodology.
Aggregation and reprojection was required for the proper function on BSP.

The final products from this process are annual and daily FFlO-formatted emissions inventories. These
SMOKE-ready inventory files contain both CAPs and HAPs. The BAFM HAP emissions from the
inventory were used directly in modeling and were not overwritten with VOC speciation profiles (i.e., an
"integrate HAP" use case).

2.5.2 Point source Agriculture Fires (ptagfire)

In the NEI, agricultural fires are stored as county-annual emissions and are part of the nonpoint data
category. For this study agricultural fires are modeled as day specific fires derived from satellite data for
the year 2019 in a similar way to the emissions in ptfire. The state of Florida provided their own
emissions (separate from the other states) for this study.

Daily year-specific agricultural burning emissions are derived from HMS fire activity data, which
contains the date and location of remote-sensed anomalies. The activity is filtered using the 2019 USDA
cropland data layer (CDL). Satellite fire detects over agricultural lands are assumed to be agricultural
burns and assigned a crop type. Detects that are not over agricultural lands are output to a separate file for
use in the ptfire sector. Each detect is assigned an average size of between 40 and 80 acres based on crop
type. Grassland/pasture fires are in the ptfire sector for this modeling platform. Depending on their origin,
grassland fires are in both ptfire-rx and ptfire-wild sectors because both fire types do involve grassy fuels.

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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 2-21.

Table 2-21. 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

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

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Another feature of the ptagfire database is that the satellite detections for 2019 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 2019 meteorological Weather Research Forecast (WRF) model simulation.
The locations of fire detections were then compared with this daily snow cover file. For any day in which
a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the
inventory. Due to the inconsistent reporting of fire detections from the Visible Infrared Imaging
Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as VIIRS or
Suomi National Polar-orbiting Partnership satellite were excluded. In addition, certain crop types (corn
and soybeans) have been excluded from these specific midwestern states: Iowa, Kansas, Indiana, Illinois,
Michigan, Missouri, Minnesota, Wisconsin, and Ohio. The reason for these crop types being excluded is
because states have indicated that these crop types are not burned.

Heat flux for plume rise was calculated using the size and assumed fuel loading of each daily agricultural
fire. This information is needed for a plume rise calculation within a chemical transport modeling system.

The daily agricultural and open burning emissions were converted from a tabular format into the
SMOKE-ready daily point flat file format. The daily emissions were also aggregated into annual values
by location and converted into the annual point flat file format.

For this modeling platform, a SMOKE update allows the use of HAP integration for speciation for
PTDAY inventories. The 2019 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 19k version of the 2019 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. The variables output from the Meteorology-
Chemistry Interface Processor (MCIP) that are used to convert WRF outputs to CMAQ inputs are shown
in Table 2-22.

Table 2-22. 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 per met TSTEP

RGRND

solar rad reaching surface

RN

nonconvective precipitation per met TSTEP

RSTOMI

inverse of bulk stomatal resistance

SLYTP

soil texture type by USD A category

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Variable

Description

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

BEIS 3.7 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:

Canadian BELD land use, updates to Version 4 of the Biogenic Emissions Landuse Database
(BELD4) for Canada and Impacts on Biogenic VOC Emissions
(https://www.epa.gov/sites/default/files/2019-08/documents/8Q0am zhang 2 O.pdf).

2017 30 meter USD A Cropland Data Layer (CDL) data
(http://www.nass.usda.gov/research/Cropland/Release/).

Biogenic emissions computed with BEIS were used to review and prepare summaries, but were left out of
the CMAQ-ready merged emissions in favor of inline biogenics produced during the CMAQ model run
itself using the same algorithm described above but with finer time steps within the air quality model.

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, canadaag, and canada_og2D. 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. Canadian agricultural and low-level (2-D) oil and gas emissions are split into separate
sectors from other Canada point sources to reduce the size of the othpt sector.

Emissions in these sectors were taken from the EQUATES 2016 inventories. Environment and Climate
Change Canada (ECCC) provided the following inventories for use in EQUATES 2016 and 2017
modeling, which are described in more detail below:

Agricultural livestock and fertilizer, point source format (canada ag sector)

CMV were provided as area sources but converted to point (not currently used)

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Agricultural fugitive dust, point source format (othptdust sector)

Other area source dust (othafdust sector)

Onroad (onroad can sector)

- Nonroad and rail (othar sector)

Oil and gas surces (low-level in canada_og2D sector, elevated in othpt sector)

Other area sources (othar sector)

Airports (othpt sector)

Other point sources (othpt sector)

Canadian CMV inventories that had been included in this sector in past modeling platforms are no longer
needed in the cmv_clc2 and cmv_c3 sectors.

Temporal profiles, and shapefiles for creating spatial surrogates, were provided by ECCC in a previous
Canadian emissions dataset and were reused 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, canada_ag, canada_og2D)

Canadian point source inventories provided by ECCC for the EQUATES project for 2016 were used as-is
for 2019. These inventories include emissions for airports and other point sources. The Canadian point
source inventory is pre-speciated for the CB6 chemical mechanism. Point sources in Mexico were
compiled 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. For this study, Mexico emissions were projected from 2016 to 2019
using projection factors derived from the Community Emissions Data System (CEDS). The CEDS dataset
includes emissions totals for 2016 and 2019 for Mexico and other countries by emissions sector. For each
CEDS sector and pollutant, a 2016-to-2019 projection factor was calculated based on the Mexico national
total emissions from each year. Each SCC in the Mexico inventory was mapped to a CEDS sector so that
the appropriate projecton factor is applied.

Due to the large number of points in the Canada inventories, the agricultural sources were split into a
separate sector called canada ag so that the sources could be placed into layer 1 as plume rise calculations
were not needed. Similarly, there were a very large number of Canadian oil and gas point sources, many
of which would be appropriate modeled in layer 1. These sources were placed into the canada_og2D
sector for layer 1 modeling. Reducing the size of the othpt sector sped up the air quality model run.

2.7.2	Fugitive Dust Sources in Canada (othafdust, othptdust)

Fugitive dust sources of particulate matter emissions excluding land tilling from agricultural activities,
were provided by Environment and Climate Change Canada (ECCC) as part of their 2016 emission
inventory. Different source categories were provided as gridded point sources and area (nonpoint) source
inventories. Gridded point source emissions resulting from land tilling due to agricultural activities were
provided as part of the ECCC 2016 emission inventory. The provided wind erosion emissions were
removed. The othptdust emissions have a monthly resolution. A transport fraction adjustment that reduces
dust emissions based on land cover types was applied to both point and nonpoint dust emissions, along

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with a meteorology-based (precipitation and snow/ice cover) zero-out of emissions when the ground is
snow covered or wet. The EQUATES 2016 inventory was used as-is with 2019 meteorology applied.

2.7.3	Nonpoint and Nonroad Sources in Canada and Mexico (othar)

ECCC provided year 2016 Canada province, and in some cases sub-province, resolution emissions from
for nonpoint and nonroad sources (othar). The nonroad sources were monthly while the nonpoint and rail
emissions were annual. The 2016 Canada nonroad emissions were projected to 2019 using US MOVES-
based trends. For Mexico, year 2016 Mexico nonpoint and nonroad inventories at the municipio
resolution provided by SEMARNAT were projected to 2019 using projection factors derived from the
Community Emissions Data System (CEDS). More information on CEDS is provided in Section 2.7.1.
All Mexico inventories were annual resolution.

2.7.4	Onroad Sources in Canada and Mexico (onroad_can, onroad_mex)

The onroad emissions for Canada and Mexico are in the onroad can and onroadmex sectors,
respectively. Emissions for Canada come from the EQUATES 2016 (2016 was the latest year provided
by Environment and Climate Change Canada (ECCC)) and were projected from 2016 to 2019 using US
MOVES-based trends.

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 2017 and 2020, and then interpolated to 2019 for this study.

2.7.5	Fires in Canada and Mexico (ptfire_othna)

Annual 2019 wildland fire 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.

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 12 km resolution were
available and were not modified other than the model-species name "CHLORINE" was changed to "CL2"
to support CMAQ modeling.

For mercury, the volcanic mercury emissions that were used in the last several modeling platforms were
not included in this study. The emissions were originally developed for a 2002 multipollutant modeling
platform with coordination and data from Christian Seigneur and Jerry Lin for 2001 (Seigneur et. al, 2004
and Seigneur et. al, 2001). ). The volcanic emissions from the most recent eruption were not included in
the because they have diminished by the year 2019. Thus no volcanic emissions were included.

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Because of mercury bidirectional flux within the latest version of CMAQ, no other natural mercury
emissions are included in the emissions merge step.

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3 Emissions Modeling

The CMAQ and CAMx air quality models require hourly emissions of specific gas and particle species
for the horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain). To
provide emissions in the form and format required by the model, it is necessary to "pre-process" the "raw"
emissions (i.e., emissions input to SMOKE) for the sectors described above in Section 2. In brief, the
process of emissions modeling transforms the emissions inventories from their original temporal
resolution, pollutant resolution, and spatial resolution into the hourly, speciated, gridded and vertical
resolution required by the air quality model. Emissions modeling includes temporal allocation, spatial
allocation, and pollutant speciation. Emissions modeling sometimes includes the vertical allocation (i.e.,
plume rise) of point sources, but many air quality models also perform this task because it greatly reduces
the size of the input emissions files if the vertical layers of the sources are not included.

As seen in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary across
sectors and may be hourly, daily, monthly, or annual total emissions. The spatial resolution may be
individual point sources; totals by county (U.S.), province (Canada), or municipio (Mexico); or gridded
emissions. This section provides some basic information about the tools and data files used for emissions
modeling as part of the modeling platform.

3.1 Emissions Modeling Overview

SMOKE version 4.8.1 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



airports

Point

Yes

Annual

None

beis

Pre-gridded
land use

in BEIS3.7

computed hourly



fertilizer

Surrogates

No

computed hourly



livestock

Surrogates

Yes

Annual



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



np solvents

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



othpt

Point

Yes

annual &
monthly

in-line

othptdust ad]

Point

Yes

monthly

None

ptagfire

Point

Yes

daily

in-line

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Platform sector

Spatial

Speciation

Inventory
resolution

Plume rise

pt oilgas

Point

Yes

annual

in-line

Ptegu

Point

Yes

daily & hourly

in-line

ptfire-rx

Point

Yes

daily

in-line

ptfire-wild

Point

Yes

daily

in-line

ptfire othna

Point

Yes

daily

in-line

ptnonipm

Point

Yes

annual

in-line

rail

Surrogates

Yes

annual



rwc

Surrogates

Yes

annual



Note that SMOKE has the option of grouping sources so that they are treated as a single stack when
computing plume rise. For the modeling cases discussed in this document, no grouping was performed
because grouping combined with "in-line" processing will not give identical results as "offline"
processing (i.e., when SMOKE creates 3-dimensional files). This occurs when stacks with different stack
parameters or latitude and longitudes are grouped, thereby changing the parameters of one or more
sources. The most straightforward way to get the same results between in-line and offline is to avoid the
use of stack grouping.

Biogenic emissions can be modeled two different ways in the CMAQ model. The BEIS model in SMOKE
can produce gridded biogenic emissions that are then included in the gridded CMAQ-ready emissions
inputs, or alternatively, CMAQ can be configured to create "in-line" biogenic emissions within CMAQ
itself. For this study, the in-line biogenic emissions option was used, and so biogenic emissions from
BEIS were not included in the gridded CMAQ-ready emissions.

For this study, SMOKE was run for the larger 12-km CONtinental United States "CONUS" modeling
domain (12US1) shown in Figure 3-1, but the air quality model was run on the smaller 12-km domain
(12US2). More specifically, SMOKE was run on the 12US1 domain and emissions were extracted from
12US1 data files to create 12US2 emissions. The grids used a Lambert-Conforrnal projection, with Alpha
= 33, Beta = 45 and Gamma = -97, with a center of X = -97 and Y = 40. Later sections provide details on
the spatial surrogates and area-to-point data used to accomplish spatial allocation with SMOKE. Table
3-2 describes the grids.

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
12km grid

12 km

Entire conterminous
US plus some of
Mexico/Canada

12US1_459X299

'LAM 40N97W', -2556000, -1728000,
12.D3, 12.D3, 459, 299, 1

US 12 km or

"smaller"

CONUS-12

12 km

Smaller 12km
CONUS plus some of
Mexico/Canada

12US2

'LAM 40N97W', -2412000 ,
-1620000, 12.D3, 12.D3, 396, 246, 1

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Figure 3-1. Emissions modeling domain (121JS1) and air quality modeling domain (12US2)

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 this platform is the CB6R3AE7 mechanism (Yarwood, 2010, Luecken, 2019). We
used an updated version of CB6 that we refer to as "CB6R3AE7" which includes four new species that
were not in the previous version of CB6: AACD, FACD, APIN, and IVOC. This mapping uses a new
systematic methodology for mapping low volatility compounds. Compounds with veiy low vapor
pressure are mapped to model species NVOL and intermediate volatility compounds are mapped to a
species called IVOC. 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) which has the same PM2.5 model species as version 6 (AE6). The AE7 mechanism is built on the

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AE6 and identical in terms of model species and mechanism definition but requires that alpha-pinene
(APIN) be separate from all other monoterpenes (TERP) and not included in TERP to avoid double
counting.

Table 3-3 lists the model species produced by SMOKE in the CMAQ platform used for this study.
Species assignments for CB05 and CB6 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

so2

S02

Sulfur dioxide

so2

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

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

85


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Inventory Pollutant

Model Species

Model species description

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

PM2.5

PNH4

Ammonium

PM2.5

PSI

Silica

PM2.5

PTI

Titanium

86


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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 5.2 database (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, 2022).
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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•	Non-methane organic gases (NMOG), which are total organic gases with methane subtracted from
it, is included as a pollutant in the emissions output files to assist with the use of these data with
future versions of the CMAQ model.

•	Several new VOC and PM2.5 profiles slated for the final version of SPECIATE 5.2 were used.

•	PM2.5 speciation process for nonroad mobile use profiles assigned within MOVES3 (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.

Speciation profiles and cross-references for this study platform are available in the SMOKE input files for
the platform. Emissions of VOC and PM2.5 emissions by county, sector, and profile for all sectors other
than onroad mobile can be found in the sector summaries for the case. 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, 2020).

Updates to speciation profiles for VOC and PM2.5 that had been added in SPECIATE 5.1 and 5.2 were
used. 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 PM2.5 the following profile and cross-reference updates were made for the 2018 platform:

•	Corrected the speciation profile assignment for several SCCs which should have been mapped to
the Heat Treating speciation profile for PM2.5 according to comments in the cross-reference file.

•	Updated the profile for sugar cane burning in the ptagfire sector.

•	Updated the wildfire and prescribed fire profiles.

•	Updated SCC 30400740 to use the Natural Gas combustion profile (95475).

For PM2.5, the following speciation profile and cross-reference updates were made for the 2019 platform:

•	Updated the speciation profile assignments for two pulp and paper SCCs, changing from the
overall default profile to the wood products drying profile (91 128).

•	Changed SCC 3 1000208 from the surface coating profile (91 129) to the petroleum industry
average profile (91145).

•	Assignments for new PM2.5 SCCs in the 2019 point inventory were included.

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For VOC the following profile updates were made for the 2017 platform:

•	Volatile consumer products - recent methods to estimate emissions of Volatile Organic
Compounds (VOC) and associated Hazardous Air Pollutants (HAPs) from Volatile Chemical
Products (VCPs) aka solvents were used in this modeling platform. These methods result in
improved emissions estimates for the nonpoint (county-wide) solvent emissions. This emissions
method results in improved VOC and HAP estimates for nonpoint categories of coatings,
pesticides, adhesives and sealants, oil & gas exploration solvent use, dry cleaning, printing inks,
cleaning products, personal care products, and other miscellaneous solvent uses. See section 3.2.1
for more details.

•	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

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

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For VOC the following cross reference updates were made for the 2018 platform:

•	Changed all 8746 to G8746 (Profile name: Rice Straw and Wheat Straw Burning Composite of
G4420 and G4421)

•	Changed 2104008230/330 from 1084 to 4642 to match all other RWC SCCs

•	For solvents, updated all speciation profiles for SCCs in the VCPy inventory

•	Changed 2680001000 from 0000 to G95241 TOG

•	Uinta Basin oil/gas profiles:

o Replaced profile 95417 with either UTUBOGC- (2310010300, 231001 1500, 23101 1 1401,
2310010700, 2310010400, 31000107) or UTUBOGD (other SCCs)

o Replaced profile 95418 with UTUBOGF

o Replaced profile 95419 with UTUBOGE

•	PA gas profiles: Replaced all 8949 with PAG A S01 (F1PS 42059 only), PAGAS02 (F1PS 42019
only), PAGAS03 (F1PS 42125 only). To do this, we first replaced existing county-specific 8949
profiles with the new PAG AS profiles for these three counties in the ERG COMBO GSREF. This
covered 5 SCCs. For all SCCs other than those 5 SCCs, where the national profile assignment is
8949. New county-specific profile assignments were made to the appropriate PAG AS profile for
each of the three counties, added to the Ramboll basin specific GSREF (since that GSREF is also
county-specific and not combo).

•	Colorado 23 10030300: Set Archuleta/La Plata to SU1ROGWT (counties are in Southern Ute
reservation), rest of Colorado to DJTFLR95

•	Colorado 2310030220: Set to DJTFLR95 (formerly FLR99)

•	Colorado 23 10021010: Set Archuleta/La Plata to SU1ROGCT (counties are in Southern Ute
reservation), rest of Colorado to 95398

•	Changed 23 1000055 1 (CBM produced water) to a new profile, CBMPWWY. Speciation Tool
inputs for this profile tool run by GD1T. Documentation: Profiles are means from WY tests in
SPECIATE, newly composited. Reference: https://doi.org/10.1016/j.scitotenv.2017.1 1.161
Reference: Lyman, Seth N.; Mansfield, Marc L.; Tran, Huy N. Q.; Evans, Jordan D.; Jones,
Colleen; O'Neil, Trevor; Bowers, Ric; Smith, Ann; and Keslar, Cara, "Emissions of organic
compounds from produced water ponds I: Characteristics and speciation" (2018). Chemistry and
Biochemistry Faculty Presentations. Paper 154. Contact: Art Diem and Jeff Vukovich of the
EPA's Office of Air Quality Planning and Standards (OAQPS)

•	Assignments for new VOC SCCs in the 2018 point inventory were included along with changes to
VOC profiles for 16 point SCCs.

For VOC, the following speciation profile and cross-reference updates were made for the 2019 platform:

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•	Speciation profiles were regenerated using version 5.2 of the SPECIATE database, and with the
latest version of the Speciation T ool which includes greater number precision. SPECIATE 5.2
includes several new speciation profiles for solvents, and the cross-reference was updated to use
those profiles.

•	The definition of the model species SOAALK was changed and is now consistent with the
definition from the original SOAALK publication (Pye and Pouliot, 2012). Now, SOAALK is
defined as linear and branched alkanes with more than 8 carbons and cyclic alkanes with more
than 6 carbons. Compared to the 2017 and 2018 platforms, the SOAALK emissions are now
generally lower.

•	Updated the speciation profile assignments for pulp and paper. Two pulp and paper SCCs were
updated from the overall default profile to the pulp and paper industry composite profile (95326),
and four other SCCs were updated from profile 95326 to the pulp and paper pi ay wood veneer
dryer profile (1 189).

•	For oil and gas, the portion of emissions for SCC 23 10010200 which was speciated using profile
2487 was changed to profile 95247.

•	All emissions which were previously speciated with profile 101 1 were changed to profile 95404.
This affects SCCs associated with oil production fugitive leaks and venting.

•	All emissions which were previously speciated with profile 1207 were changed to profile 95782.
This affects produced water from oil and gas production.

•	Assignments for new VOC SCCs in the 2019 point inventory were included.

3.2.1 VOC speciation

The speciation of VOC includes HAP emissions from the emission inventories 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."

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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 integration12). 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.13 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
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, the "no-integrate" sources are treated differently from a criteria
pollutant-focused (CAP) platform. For this 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

12	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.

13	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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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.

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

airports

No integration, use NBAFM in inventory

beis

N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species

cmv clc2

No integration, no NBAFM in inventory, create NBAFM from VOC speciation

cmv c3

No integration, no NBAFM in inventory, create NBAFM from VOC speciation

fertilizer

N/A - sector contains no VOC

livestock

Partial integration (NBAFM)

nonpt

Partial integration (NBAFM)

nonroad

Full integration (internal to MOVES)

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Platform
Sector

Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)

np oilgas

Partial integration (NBAFM)

np solvents

Partial integration (NBAFM)

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 CB6R3AE6

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

pt oilgas

No integration, use NBAFM in inventory

ptagfire

Full integration (NBAFM)

ptegu

No integration, use NBAFM in inventory

ptfire-rx

Partial integration (NBAFM)

ptfire-wild

Partial integration (NBAFM)

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

Full integration (NBAFM)

Integration for the mobile sources estimated from MOVES (onroad and nonroad sectors) 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

In previous platforms, the GSPROCOMBO 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., EO 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 this 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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In Canada, ECCC provided estimates of ethanol mixes by Canadian province. These estimates were used
to develop a GSPROCOMBO for Canadian gasoline onroad emissions. For example, a province where
the average ethanol mix is 6% would have 60% E10 speciation and 40% E0 speciation. A 10% ethanol
mix would imply 100% E10 speciation. In Mexico, only E0 speciation profiles are used, but the
GSPRO COMBO feature is still used in Mexico for inventories where VOC emissions are not explicitly
defined by mode (e.g., exhaust versus evaporative). Here, the GSPRO COMBO specifies a mix of
exhaust and evaporative speciation profiles. Using the GSPRO COMBO to split total VOC into exhaust
and evaporative components is no longer necessary for Canadian mobile sources, whose inventories
include the mode in the pollutant, or for Mexico onroad sources, where VOC speciation is calculated by
the MOVES model. The GSPRO COMBO is still used for Mexican nonroad sources which do not have
modes in the inventory.

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 3-7 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
(e.g., PAR, OLE, etc).14 SMOKE essentially calculates the model-ready species by using the appropriate
emission factor without further speciation.15 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 3-8. 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.

14	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.

15	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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Table 3-8. 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 3-8. 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 and Texas where the state provided emissions, MOVES-style
speciation has been implemented, with NONHAPTOG and PM2.5 pre-split by profiles and with all the
HAPs needed for VOC speciation augmented based on MOVES data in California. This means that the
nonroad emissions in California and Texas 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[1]-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.

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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 3-9.

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 this platform. These fractions can vary by
county FIPS, because they depend on the level of controls, which is an input to the Speciation Tool.

Table 3-9. 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



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Profile Code

Description

Region
(if not in
profile
name)

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



PAGAS01

Oil and Gas-Produced Gas Composition from Gas Wells-Greene Co, PA



PAGAS02

Oil and Gas-Produced Gas Composition from Gas Wells-Butler Co, PA



PAGAS03

Oil and Gas-Produced Gas Composition from Gas Wells-Washington Co, PA



SUIROGCT

Flash Gas from Condensate Tanks - Composite Southern Ute Indian Reservation



SUIROGWT

Flash Gas from Produced Water Tanks - Composite Southern Ute Indian
Reservation



UTUBOGC

Raw Gas from Oil Wells - Composite Uinta basin



UTUBOGD

Raw Gas from Gas Wells - Composite Uinta basin



UTUBOGE

Flash Gas from Oil Tanks - including Carbonyls - Composite Uinta basin



UTUBOGF

Flash Gas from Condensate Tanks - including Carbonyls - Composite Uinta basin



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
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 are listed in Table 3-10. They include new profiles
(i.e., those that begin with "953") for 2-stroke and 4-stroke gasoline engines running on E0 and E10 and

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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 3-10. TOG MOVES-SMOKE Speciation for nonroad emissions

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

16

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 E0 and E10 fuel use, but beginning with
2014v7.0 platform, the appropriate allocation of E0 and E10 fuels is done by MOVES.

Combination profiles reflecting a combination of E10 and E0 fuel use 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 this platform, all of these sources get E10 speciation.

Table 3-11 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 GSPRO COMBO file.

16 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 3-11. Select mobile-related VOC profiles

Sector

Sub-category

Profile

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 3-12 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 3-13 through Table 3-15 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 3-12. Onroad M-profiles

Profile

Profile Description

Model Years

ProcessID

FuelSubTypelD

RegClassID

1001M

CNG Exhaust

1940-2050

1,2,15,16

30

48

4547M

Diesel Headspace

1940-2050

11

20,21,22

0

4547M

Diesel Headspace

1940-2050

12,13,18,19

20,21,22

10,20,30,40,41,
42,46,47,48

8753M

E0 Evap

1940-2050

12,13,19

10

10,20,30,40,41,42,
46,47,48

8754M

E10 Evap

1940-2050

12,13,19

12,13,14

10,20,30,40,41,
42,46,47,48

8756M

Tier 2 E0 Exhaust

2001-2050

1,2,15,16

10

20,30

8757M

Tier 2 E10 Exhaust

2001-2050

1,2,15,16

12,13,14

20,30

8758M

Tier 2 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

102


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Profile

Profile Description

Model Years

ProcessID

FuelSubTypelD

RegClassID

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

9117

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,
1818

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.

Table 3-13. MOVES process IDs

Process ID

Process Name

1

Running Exhaust*

2

Start Exhaust

9

Brakewear

10

Tire wear

11

Evap Permeation

17	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.

18	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.

103


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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

* Off-network idling is a process in M0VES3 that is part of processes 1 and 15
but assigned to road type 1 (off-network) instead of types 2-5

Table 3-14. MOVES Fuel subtype IDs

Fuel Subtype ID

Fuel Subtype Descriptions

10

Conventional Gasoline

11

Reformulated Gasoline (RFG)

12

Gasohol (E10)

13

Gasohol (E8)

14

Gasohol (E5)

15

Gasohol (E15)

18

Ethanol (E20)

20

Conventional Diesel Fuel

21

Biodiesel (BD20)

22

Fischer-Tropsch Diesel (FTD100)

30

Compressed Natural Gas (CNG)

50

Ethanol

51

Ethanol (E85)

52

Ethanol (E70)

Table 3-15. MOVES regclass IDs

Reg. Class ID

Regulatory Class Description

0

Doesn't Matter

10

Motorcycles

104


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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. Most of the PM
profiles come from the 911XX series (Reff et. al, 2009), which include updated AE6 speciation.19
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.

19 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.

105


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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

106


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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.20 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 3-16 shows the differences in the v7.1 (alpha) and
201 lv6.3 profiles.

Table 3-16. 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

20 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/.

107


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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 3-17.

Table 3-17. 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

108


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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 3-18 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=P100F lA5.pdf. Exhaust
Emission Rates for Heavv-Dutv Onroad Vehicles in MOVES3 and Exhaust Emission Rates for Light-
Duty Onroad Vehicles in MOVES3.

Table 3-18. 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 3-19; a summary of the profiles is provided
in Table 3-20.

Table 3-19. Sulfate split factor computation

fuel

SCCs

Profile

Fraction

Fraction as

Split factor (mass





Code

as S02

Sulfate

fraction)

109


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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 3-20. 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 3-21 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 3-21. Particle size speciation of Metals

Source Type

Profile

pollutant

Fine

coarse

Onroad

OARS

Arsenic

.95

.05

110


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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

Table 3-22 provides the mercury profiles used for sources using SCC-based speciation factors for electric
generating units (i.e., the ptegu sector) and other sources (EPA, 2020).

Table 3-22. 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

HGMET

Metal production

0.8

0.15

0.005

111


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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 3-23 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 3-23. Temporal settings used for the platform sectors in SMOKE

Platform sector
short name

Inventory
resolutions

Monthly

profiles

used?

Daily

temporal

approach

Merge

processing

approach

Process holidays
as separate days

afdust adj

Annual

Yes

week

all

Yes

Airports

Annual

Yes

week

week

Yes

Beis

Hourly



n/a

all

No

cmv clc2

Annual & hourly



All

all

No

cmv c3

Annual & hourly



All

all

No

Fertilizer

Monthly



met-based

All

Yes

livestock

Annual

Yes

met-based

All

Yes

nonpt

Annual

Yes

week

week

Yes

nonroad

Monthly



mwdss

mwdss

Yes

np oilgas

Annual

Yes

aveday

aveday

No

onroad

Annual & monthly1



all

all

Yes

onroad ca adi

Annual & monthly1



all

all

Yes

othafdust adi

Annual

Yes

week

all

No

othar

Annual & monthly

Yes

week

week

No

onroad can

Monthly



week

week

No

112


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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



week

week

No

othpt

Annual & monthly

Yes

mwdss

mwdss

No

othptdust adj

Monthly



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



all

all

No

ptfire-rx

Daily



all

all

No

ptfire-wild

Daily



all

all

No

ptfire othna

Daily



all

all

No

rail

Annual

Yes

aveday

aveday

No

rwc

Annual

No3

met-based3

all

No3

np solvents

annual

Yes

aveday

aveday

No

'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, 2019, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2018). For anthropogenic sectors, emissions from December 2019
were used to fill in surrogate emissions for the end of December 2018. For biogenic emissions, December
2018 emissions were computed using year 2018 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.

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SMOKE prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual-to-month temporal allocation applied to it;
rather, it should only have month-to-day and diurnal temporal allocation. This becomes particularly
important when specific sectors have a mix of annual, monthly, daily, and/or hourly inventories. The
flags that control temporal allocation for a mixed set of inventories are discussed in the SMOKE
documentation. The modeling platform sectors that make use of monthly values in the FF10 files are
nonroad, onroad (for activity data), onroad can, onroadmex, othar, othpt, and othptdust. Commercial
marine vessels in cmv_c3 and cmv_clc2 use hourly data in the FF10 files.

3.3.2	Temporal allocation for non-EGU sources (ptnonipm)

Some sectors use straightforward temporal profiles not based on meteorology or other factors. For the
ptfire, ptagfire, and ptfire othna sectors, the inventories are in the daily point fire format, so temporal
profiles are only used to go from day-specific to hourly emissions. For all agricultural burning, the
diurnal temporal profile used reflected the fact that burning occurs during the daylight. This puts most of
the emissions during the workday and suppresses the emissions during the middle of the night. This
diurnal profile was used for each day of the week for all agricultural burning emissions in all states.

An update in the 2018 platform carried into this platform was an analysis of monthly temporal profiles for
non-EGU point sources in the ptnonipm sector. A number of profiles were found to be not quite flat over
the months but were so close to flat that the difference was not meaningful. These profiles were replaced
in the cross reference to point instead to the flat monthly profile. The codes for the profiles that were
replaced were: 202, 214, 220, 221, 222, 223, 227, 257, 263, 264, 265, 266, 267, 269, 271, 272, 279, 280,
295, 302, 303, 304, 305, 306, 309, 310, 327, 329, 332, and 333.

3.3.3	Electric Generating Utility temporal allocation (ptegu)

Electric generating unit (EGU) sources matched to ORIS units were temporally allocated to hourly
emissions needed for modeling using the hourly CEMS data for units that could be matched to the CEMS
emissions. Those hourly data were processed through v2.1 of the CEMCorrect tool to mitigate the impact
of unmeasured values in the data. An example of before and after the application of the tool is shown in
Figure 3-5.

The temporal allocation procedure for EGUs in the base year is differentiated by whether or not the unit
could be directly matched to a unit with CEMS data via its ORIS facility code and boiler ID. Note that
for units matched to CEMS data, annual totals of their emissions input to CMAQ may be different than
the values in the annual inventory because the CEMS data 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).

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Figure 3-5. Eliminating unmeasured spikes in CEMS data

2017 January Unit 469_5



2000



1800



1600

&_
3

1400

O
.n

1200

LO
£

1000

X

800

O

Z

600



400



200



0

^Hr^foa^Ln^Hr^rocr)Ln^HP>.rocr)Ln^HP>.fiocr)Ln^HP>.rocr)Ln^HP>.rocr)
T-i^rtO(T>rM^rr--(T>rMLnr--orM
^H^H^H^HrNjrNjrNjrMrorororO'^-^r^r^rLnLnLnLntDtDtDP>.p>.

January 2017 Hour
RawCEM	Corrected

The region, fuel, and type (peaking or non-peaking) must be identified for each input EGU with CEMS
data that are used for generating profiles. The identification of peaking units was done using hourly heat
input data from the 2019 base year and the two previous years (2017 and 2018). The heat input was
summed for each year. Equation 1 shows how the annual heat input value is converted from heat units
(BTU/year) to power units (MW) using the NEEDS v6 derived unit-level heat rate (BTU/kWh). In
equation 2 a capacity factor is calculated by dividing the annual unit MW value by the NEEDS v6 unit
capacity value (MW) multiplied by the hours in the year. A peaking unit was defined as any unit that had
a maximum capacity factor of less than 0.2 for every year (2017, 2018, and 2019) and a 3-year average
capacity factor of less than 0.1.

Equation 1. Annual unit power output

8760 Hourly HI	,

^i=o (rtii} *1000 ( kw )

Annual Unit Output (MW) = 					

NEEDS Heat Rate (jzwji)

Equation 2. Unit capacity factor

_	„	Annual Unit Output (MW)

Capacity Factor =

NEEDS

Unit Capacity (^p)*8760 (h)

Input regions were determined from one of the eight EGU modeling regions based on MJO and climate
regions. Regions were used to group units with similar climate-based load demands. Region assignment is
made on a state level, where all units within a state were assigned to the appropriate region. Unit fuel
assignments were made using the primary NEEDS v6 fuel. Units fueled by bituminous, subbituminous, or
lignite 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

115


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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).

Figure 3-6. Temporal Profile Input Unit Counts by Fuel and Peaking Unit Classification

Small EGU 2016 Temporal Profile Input Unit Counts

(pcjkf^fncmpcj.jng:

coa: 0 /11	"V

,jas:ll-/-25	I
D«l: B / 0 / ^
Othei:0/Q

Nom^Ceittal

r. ,¦ ,i

HW4E-VU

(p«kiwllnW(^a«):

'Wcs—'		

(peatonj'nonpcsMrgi:
COS: 0/ 3
$ai:99/ 137
'Oil: 0 I 0
ufor.Oi*

50mOm«!

(pwking/'rur^KHting]:

.OMlU.US	

¦(peaking/Tonpe'ak
coal: 1) 166""'

Ol: 71/« \
other; 0 / SJ \

South

(CfeikiKii'norpiJlung):

¦C®l: 0/97 I
9«;»3T33?-L,

	

other; 0/4 \

WDOO

(pMbng/rwp«Juni)}:
TbSjJL/155

4Lii4

EGU Regions
:¦ LADCO
¦ MANE-VU
I Horthiwest
I ISESARM
1 I South
[ I Soulhivest
If I West

(H West North Central

The daily and diurnal profiles were calculated for each region, fuel, and peaking type group from the year
2019 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.

116


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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

0.040

0.035

Nonpeaking
Peaking

0.000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2017

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

		Summer Nonpeaking

		Summer Peaking

		Winter Nonpeaking

		Winter Peaking

117


-------
SMOKE uses a cross-reference file to select a monthly, daily, and diurnal profile for each source. For this
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
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 2019 unit counts should be similar.

Figure 3-9. Non-CEMS EGU Temporal Profile Application Counts

Small EGU 2016 Temporal Profile Application Counts

coal: 0/0 I I
pn : H 11

0 j- 0 I
¦other "0TD~S

 Cogen: S

I I SESARM

I I South

~ SoLitln'«!st

~ west

I West North Central

LADCO

nomas*);
CM1: 6

IS

oiUS®®

EGU Regions
¦ LADCO
~ MAME-VU
I I Northwest

3.3.4 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/svs/AnalvsisAP.asp). A report of

118


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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.

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

119


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Figure 3-11. Weekly profile for all Airport SCCs

Weekly Airport Profile

0.18

Figure 3-12. Monthly Profile for all Airport SCCs
Monthly Airport Profile

0.05
0.04
0.03
0.02
0.01
0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

120


-------
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

3.3.5 Residential Wood Combustion Temporal allocation (rwc)

There are many factors that impact the timing of when emissions occur, and for some sectors this includes
meteorology. The benefits of utilizing meteorology as a method for temporal allocation are: (1) a
meteorological dataset consistent with that used by the AQ model is available (e.g., outputs from WRF);
(2) the meteorological model data are highly resolved in terms of spatial resolution; and (3) the
meteorological variables vary at hourly resolution and can, therefore, be translated into hour-specific
temporal allocation.

The SMOKE program Gentpro provides a method for developing meteorology-based temporal allocation.
Currently, the program can utilize three types of temporal algorithms: annual-to-day temporal allocation
for residential wood combustion (RWC); month-to-hour temporal allocation for agricultural livestock
NH3; and a generic meteorology-based algorithm for other situations. Meteorological-based temporal
allocation was used for portions of the rwc sector and for the entire ag sector.

Gentpro reads in gridded meteorological data (output from MCIP) along with spatial surrogates and uses
the specified algorithm to produce a new temporal profile that can be input into SMOKE. The
meteorological variables and the resolution of the generated temporal profile (hourly, daily, etc.) depend
on the selected algorithm and the run parameters. For more details on the development of these
algorithms and running Gentpro, see the Gentpro documentation and the SMOKE documentation at
http://www.cmascenter.0rg/smoke/documentation/3.l/GenTPRO Technical Summary Aug2012 Final.pd
f and https://www.cmascenter.Org/smoke/documentation/4.5/html/ch05s03s05.html respectively.

For the RWC algorithm, Gentpro uses the daily minimum temperature to determine the temporal
allocation of emissions to days of the year. Gentpro was used to create an annual-to-day temporal profile
for the RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of
the year. On days where the minimum temperature does not drop below a user-defined threshold, RWC
emissions for most sources in the sector are zero. Conversely, the program temporally allocates the
largest percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total
annual emissions do not change, only the distribution of the emissions within the year is affected. The
temperature threshold for RWC emissions was 50 °F for most of the country, and 60 °F for the following

121


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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.

Figure 3-14. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold

RWC temporal profile, Duval County, FL, Jan - Apr

0.04
0.035
0.03

r»

75 0.02

| 0

0.01
0.005

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.

122


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Figure 3-15. RWC diurnal temporal profile

Comparison of RWC diurnal profile

The temporal profiles for hydronic heaters" (i.e., SCCs=2104008610 [outdoor], 2104008620 [indoor], and
2104008620 [pellet-fired]) 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
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.

123


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Figure 3-16. Data used to produce a diurnal profile for hydronic heaters

124


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Figure 3-18. Annual-to-month temporal profiles for hydronic heaters and recreational RWC

Monthly Temporal Activity for OHH & Recreational RWC

¦Fire Pit/Chimenea
¦Outdoor Hydronic Heater

JAN FEB MARAPRMAYJUN JUL AUG SEP OCT NOV DEC

3.3.6 Agricultural Ammonia Temporal Profiles (livestock)

For the ag sector, agricultural GenTPRO temporalization was applied to livestock emissions and to all
pollutants within the sector, not just NFb. The GenTPRO algorithm is based on an equation derived by
Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2014) empirical equation. This equation is based
on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to estimate
diurnal NFb emission variations from livestock as a function of ambient temperature, aerodynamic
resistance, and wind speed. The equations are:

Eu, = [161500/T,/; x e'

,(-1380/1. )

/;'] X ARiJ,

Equation 3-2

PE;,/; = E;,/; / SU1T|(E,,/,)

Equation 3-3

where

•	PE;,/; = Percentage of emissions in county i on hour h

•	Eij, = Emission rate in county i on hour h

•	Tu, = 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.

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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 NH3 emissions temporal allocation approach (daily total emissions)

MN ag NH3 livestock temporal profiles

12.0 	

0.0

1/1/2008 2/1/2008 3/1/2008 4/1/2008 5/1/2008 6/1/2008 7/1/2008 8/1/2008 9/1/2008 10/1/2008 11/1/2008 12/1/2008

-old
-new

3.3.7	Oil and gas temporal allocation (np_oilgas)

Monthly temporalization of np oilgas emissions is based primarily on monthly factors from the Oil and
Gas Tool (OGT). Factors were specific to each county and SCC. For use in SMOKE, each unique set of
factors was assigned a label (OG19 0001 through OG19 6617), and then a SMOKE-formatted
ATPRO MONTHLY and an ATREF were developed. This dataset of monthly temporal factors included
profiles for all counties and SCCs in the Oil and Gas Tool inventory. Because we are using non-tool
datasets in some states, this monthly temporalization dataset did not cover all counties and SCCs in the
entire inventory used for this study. To fill in the gaps in those states, state average monthly profiles for
oil, natural gas, and combination sources were calculated from Energy Information Administration (EIA)
data and assigned to each county/SCC combination not already covered by the OGT monthly temporal
profile dataset. Coal bed methane (CBM) and natural gas liquid sources in those states were assigned flat
monthly profiles where there was not already a profile assignment in the ERG dataset.

3.3.8	Onroad mobile temporal allocation (onroad)

For the onroad sector, the temporal distribution of emissions is a combination of traditional temporal
profiles and the influence of meteorology. This section will discuss both the meteorological influences
and the development of the temporal profiles for this platform.

The "inventories" referred to in Table 3-23 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. Day-of-week and
hour-of-day temporal profiles are also used to temporalize the starts activity used for rate-per-start (RPS)
processes, and the off-network idling (ONI) hours activity used for rate-per-hour-ONI (RPHO) processes.
The inventories for starts and ONI activity contain monthly activity so that monthly temporal profiles are
not needed.

For on-roadway 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

126


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to day of the week, and then to hourly through temporal profiles. The RPD processes also use hourly
speed distributions (SPDIST) as discussed in Section 2.3.2. For onroad, the temporal profiles and SPDIST
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 and
stationary vehicle (RPV, RPH, RPHO, RPS, and RPP) processes use the gridded meteorology (MCIP)
either directly or indirectly. For RPD, RPV, RPH, RPHO, and RPS, Movesmrg determines the
temperature for each hour and grid cell and uses that information to select the appropriate emission factor
for the specified SCC/pollutant/mode combination. For RPP, instead of reading gridded hourly
meteorology, Movesmrg reads gridded daily minimum and maximum temperatures. The total of the
emissions from the combination of these six processes (RPD, RPV, RPH, RPHO, RPS, and RPP)
comprise the onroad sector emissions. The temporal patterns of emissions in the onroad sector are
influenced by meteorology.

Figure 3-20. Example of temporal variability of NOx emissions

2014v2 onroad RPD hourly NOX and VMT: Wake County, NC

IAAMAaaH:!

7/8/140:00 7/9/140:00 7/10/140:00 7/11/140:00 7/12/140:00 7/13/140:00 7/14/140:00 7/15/140:00

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

127

	 VMT

	NOX


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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 vaiy 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
regional average profiles in the CRC A-100 data.

Figure 3-21. Sample on road diurnal profiles for Fulton County, GA

0.09	0.1

road 2	road 3	road 4	road 5	road 2	road 3	road 4	road 5

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Figure 3-22. Methods to Populate Onroad Speeds and Temporal Profiles by Road Type

Group |	l Individual

I	I Midwest Region Average of Single County MSA Counties

I	I Midwest Region non-MSA Average

1	I Northeast Region Average of Single County MSA Counties

I	I Northeast Region non-MSA Average
I I South Region Average of Single County MSA Counties

I	I South Region non-MSA Average

I	I West Region Average of Single County MSA Counties

I	I West Region non-MSA Average

Midwest Region Average of Core Counties inside MSAs
Midwest Region Average of non-Core Counties inside MSAs
Northeast Region Average of Core Counties inside MSAs
Northeast Region Average of non-Core Counties inside MSAs
South Region Average of Core Counties inside MSAs
South Region Average of non-Core Counties inside MSAs
West Region Average of Core Counties inside MSAs
West Region Average of non-Core Counties inside MSAs

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Figure 3-23. Regions for computing Region Average Speeds and Temporal Profiles

State/local-provided data for the 2017 NEI were accepted for use in the 2017 NEI if they were deemed to
be at least as credible as the CRC A-100 data. The 2017 NEI TSD includes more details on which data
were used for which counties. In areas of the contiguous United States where state/local-provided data
were not provided or deemed unacceptable, the CRC A-100 temporal profiles were used, including in
California. 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.

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.

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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 13 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

Temporal profiles for RPHO are based on the same temporal profiles as the on-network processes in
RPD, but since the on-network profiles are road-type-specific and ONI is not road-type-specific, the
RPHO profiles were assigned to use rural unrestricted profiles for counties considered "rural" and urban
unrestricted profiles for counties considered "urban". RPS uses a separate set of temporal profiles
specifically for starts activity. For starts, there is one day-of-week temporal profile for each source type
(e.g. motorcycles, passenger cars, combination long haul trucks), and two hour-of-day temporal profiles
for each source type, one for weekdays and one for weekends. The starts temporal profiles are applied
nationally and are based on the default starts-per-day-per-vehicle and starts-hour-fraction tables from
MOVES.

3.3.9 Nonroad mobile temporal allocation (nonroad)

For nonroad mobile sources, temporal allocation is performed differently for different SCCs. Beginning
with the final 2011 platform 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.

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.

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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 friday saurday 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

hlh2h3h4h5h6h7h8 h9 hl0hllhl2hl3h!4hl5hl6hl7hl8hl9h20h21h22h23h24
26a-New 	27 	25a-New	26

3.3.10 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

132


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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 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.

Figure 3-27. Agricultural burning diurnal temporal profile

Comparison of Agricultural Burning Temporal Profiles

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Industrial processes that are not likely to shut down on Sundays, such as those at cement plants, use
profiles that include emissions on Sundays, while those that would shut down on Sundays use profiles that
reflect Sunday shutdowns.

For the ptfire sectors, the inventories are in the daily point fire format FF10 PTDAY, so temporal profiles
are only used to go from day-specific to hourly emissions. Separate hourly profiles for prescribed and
wildfires were used. For ptfire, state-specific hourly profiles were used, with distinct profiles for
prescribed fires and wildfires. Figure 3-28 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

0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0

US Prescribed Fire diurnal profile: Flaming vs residual
smoldering example

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
FLAME7 10LST	RESID7 10LST

Wildfire diurnal profiles: State

10 11 12 13 14 15 16 17 18 19 20 21 22 23

For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from MOVES. For California, CARB's annual inventory was temporalized to
monthly using monthly temporal profiles applied in SMOKE by SCC. This is an improvement over the
2011 platform, which applied monthly temporal allocation in California at the broader SCC7 level.

3.4 Spatial Allocation

The methods used to perform spatial allocation are summarized in this section. For the modeling
platform, spatial factors are typically applied by county and SCC. Spatial allocation was performed for
each of the modeling grids shown in Section 3.1. To accomplish this, SMOKE used national 12-km
spatial surrogates and a SMOKE area-to-point data file. For the U.S., the EPA updated surrogates to use
circa 2016-2017 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 12-km surrogates cover the entire CONUS domain 12US1
shown in Figure 3-1.

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3.4.1 Spatial Surrogates for U.S. emissions

There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-to-point
approach overrides the use of surrogates for airport refueling sources.

Table 3-24 lists the codes and descriptions of the surrogates. Surrogate names and codes listed in italics
are not directly assigned to any sources in the platform, but are sometimes used to gapfill other surrogates,
or as an input for merging two surrogates to create a new surrogate that is assigned to sources. 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.

Many of the surrogates currently in use were developed for use in the 2014v7.0 platform using recently
available data sets (Adelman, 2016). They include the use of the 2011 National Land Cover Database (the
previous platform used 2006) and development of various development density levels such as open, low,
medium high and various combinations of these. These landuse surrogates largely replaced the FEMA
category surrogates that were used in the 2011 platform. Additionally, onroad surrogates were developed
using more recent average annual daily traffic counts from the highway monitoring performance system
(HPMS). Onroad surrogates for this platform do not distinguish between urban and rural road types,
which prevents issues in areas where there are inconsistent urban and rural definitions between MOVES
and the surrogate data.

Recent surrogate updates include:

A public school surrogate (508) was developed for off-network school buses.

Oil and gas surrogates were updated to represent 2019, including a new surrogate for total gas
produced(689)

Corrections were made to the rail surrogates (261/271).

The transit bus terminal surrogate was re-gapfilled with the NLCD medium+high surrogate (306)

Some gridding cross reference corrections / updates were made including the use of NLCD
medium+high surrogate instead of intercity bus terminals for off network emissions from other
buses.

The 500 series surrogates are no longer used and SCCs that used them (e.g., cigarette smoke,
accidental releases) were remapped to NLCD surrogates.

Onroad surrogates were generated to incorporate 2017 Average Annual Daily Traffic (AADT);

Surrogates for the U.S. were generated using the Surrogate Tool to drive the Spatial Allocator with some
surrogates were developed directly within ArcGIS or using the Surrogate Tools DB. 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/. The file
Surrogate_specifications_2019_platform_US_Can_Mex.xlsx documents the configuration for generating
the surrogates.

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Table 3-24. U.S. Surrogates available for the 2019 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

NT AD 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

689

Gas Produced - Total

305

NLCD Low + Med

691

Well Counts - CBM Wells

306

NLCD Med + High

692

Spud Count -All Wells

307

NLCD All Development

693

Well Count - All Wells

308

NLCD Low + Med + High

694

Oil Production at Oil Wells

309

NLCD Open + Low + Med

695

Well Count - Oil Wells

310

NLCD Total Agriculture

696

Gas Production at Gas Wells

319

NLCD Crop Land

697

Oil Production at Gas Wells

320

NLCD Forest Land

698

Well Count - Gas Wells

321

NLCD Recreational Land

699

Gas Production at CBM Wells

340

NLCD Land

711

Airport Areas

350

NLCD Water

801

Port Areas

500

Commercial Land

805

Offshore Shipping Area

505

Industrial Land

806

Offshore Shipping NE12014 Activity

506

Education

807

Navigable Waterway Miles

508

Public Schools

808

2013 Shipping Density

510

Commercial plus Industrial

820

Ports NEL2014 Activity

535

Residential + Commercial + Industrial + :
Institutional + Government

850

Golf Courses

560

Hospital (COM6)

860

Mines

For the onroad sector, the on-network (RPD) emissions were spatially allocated differently from other off-
network processes (i.e. RPV, RPP, RPHO). Surrogates for on-network processes are based on AADT
data and off network processes (including the off-network idling included in RPHO) are based on land use
surrogates as shown in Table 3-25. Emissions from the extended (i.e., overnight) idling of trucks were
assigned to surrogate 205, which is based on locations of overnight truck parking spaces. The underlying
data for this surrogate were updated during the development of the 2016 platforms to include additional

136


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data sources and corrections based on comments received and these updates were carried into this
platform.

Table 3-25. 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

Other Bus

306

NLCD Med + High

42

Transit Bus

259

Transit Bus Terminals

43

School Bus

508

Public Schools

51

Refuse Truck

306

NLCD Med + High

52

Single Unit Short-haul Truck

306

NLCD Med + High

53

Single Unit Long-haul Truck

306

NLCD Med + High

54

Motor Home

304

NLCD Open + Low

61

Combination Short-haul Truck

306

NLCD Med + High

62

Combination Long-haul Truck

306

NLCD Med + High

For the oil and gas sources in the npoilgas sector, the spatial surrogates were updated to those shown in
Table 3-26 using 2019 data consistent with what was used to develop the nonpoint oil and gas emissions.

The exploration and production of oil and gas has increased in terms of quantities and locations over the
last seven years, primarily through the use of new technologies, such as hydraulic fracturing. Census-
tract, 2-km, and 4-km sub-county Shapefiles were developed, from which the 2019 oil and gas surrogates
were generated. All spatial surrogates for np oilgas are developed based on known locations of oil and
gas activity for year 2019.

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, 2019). 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 2019.

The spatial surrogates, numbered 670 through 699 and also 6831, 6832, and 6833, were processed at
12km resolution and gapfilled with the Surrogate Tool. The surrogates were first gapfilled using fallback
surrogates. For each surrogate, the last two fallbacks were surrogate 693 (Well Count - All Wells) and
304 (NLCD Open + Low). Where appropriate, other surrogates were also 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.

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Table 3-26. 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

Table 3-27 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by sector assigned to each
spatial surrogate.

Table 3-27. Selected 2019 CAP emissions by sector for U.S. Surrogates (short tons in 12US1)

Sector

ID

Description

MI;

NOx

PM25

SO2

VOC

afdust

240

Total Road Miles

0

0

315,096

0

0

afdust

304

NLCD Open + Low

0

0

842,116

0

0

afdust

306

NLCD Med + High

0

0

52,278

0

0

afdust

308

NLCD Low + Med + High

0

0

117,047

0

0

afdust

310

NLCD Total Agriculture

0

0

791,881

0

0

livestock

310

NLCD Total Agriculture

2,602,279

0

0

0

227,985

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-1

208

,055

,039

	3_

,181

,798

0

,255

,203

,599

0

,049

,262

,037

0

299

279

,401

596

,730

356

,423

,749

,188

,963

,811

,015

,213

,675

,840

,186

,738

462

89

,284

870

,964

,159

,758

,017

,318

,697

ID

Description

MI;

NOx

PM2S

100

Population

34,304

0

0

150

Residential Heating - Natural Gas

33,550

204,371

4,041

170

Residential Heating - Distillate Oil

1,531

30,031

3,284

180

Residential Heating - Coal

1

1

190

Residential Heating - LP Gas

98

31,061

163

239

Total Road AADT

22

541

240

Total Road Miles

244

All Unrestricted AADT

271

NTAD Class 12 3 Railroad Density

0

0

0

300

NLCD Low Intensity Development

4,823

19,093

94,548

304

NLCD Open + Low

0

0

0

306

NLCD Med + High

23,668

272,514

245,871

307

NLCD All Development

85

25,798

110,610

308

NLCD Low + Med + High

884

156,033

15,683

310

NLCD Total Agriculture

0

38

319

NLCD Crop Land

0

97

320

NLCD Forest Land

3,953

68

273

650

Refineries and Tank Farms

0

16

711

Airport Areas

801

Port Areas

0

261

NTAD Total Railroad Density

1,807

184

304

NLCD Open + Low

1,620

136

305

NLCD Low + Med

96

14,661

3,879

306

NLCD Med + High

335

160,244

9,947

307

NLCD All Development

101

29,155

15,414

308

NLCD Low + Med + High

565

262,271

21,894

309

NLCD Open + Low + Med

122

21,080

1,240

310

NLCD Total Agriculture

421

305,710

21,805

320

NLCD Forest Land

15

3,281

522

321

NLCD Recreational Land

83

13,038

5,523

350

NLCD Water

192

113,237

4,467

850

Golf Courses

13

2,087

119

860

Mines

2,467

240

670

Spud Count - CBM Wells

0

671

Spud Count - Gas Wells

674

Unconventional Well Completion
Counts

27

20,730

496

678

Completions at Gas Wells

7,874

193

679

Completions at CBM Wells

681

Spud Count - Oil Wells

685

Completions at Oil Wells

377

0

687

Feet Drilled at All Wells

75,545

1,995

689

Gas Produced - Total

460

55

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Sector

ID

Description

MI;

NOx

PM2S

SO2

voc

npoilgas

691

Well Counts - CBM Wells

0

29,113

521

11

30,841

npoilgas

694

Oil Production at Oil Wells

0

3,695

0

31,403

1,052,276

npoilgas

695

Well Count - Oil Wells

0

129,122

3,032

1,465

676,769

npoilgas

696

Gas Production at Gas Wells

0

211

0

1

50,268

npoilgas

698

Well Count - Gas Wells

0

253,031

4,794

127

498,114

npoilgas

699

Gas Production at CBM Wells

0

47

5

0

5,190

npoilgas

6831

Produced water at CBM wells

0

0

0

0

3,695

npoilgas

6832

Produced water at gas wells

0

0

0

0

38,515

npoilgas

6833

Produced water at oil wells

0

0

0

0

46,549

npsolvents

100

Population

0

0

0

0

1,372,923

npsolvents

240

Total Road Miles

0

0

0

0

48,397

npsolvents

306

NLCD Med + High

33

27

300

1

409,967

npsolvents

307

NLCD All Development

24

6

19

5

527,883

npsolvents

308

NLCD Low + Med + High

0

0

129

0

7,970

npsolvents

310

NLCD Total Agriculture

0

0

0

0

149,185

onroad

205

Extended Idle Locations

342

34,291

780

18

4,084

onroad

242

All Restricted AADT

34,157

925,436

25,956

5,041

134,605

onroad

244

All Unrestricted AADT

63,200

1,496,382

55,842

10,503

374,603

onroad

259

Transit Bus Terminals

15

2,037

50

1

439

onroad

304

NLCD Open + Low

0

687

20

0

4,124

onroad

306

NLCD Med + High

921

95,906

3,529

76

20,258

onroad

307

NLCD All Development

3,576

204,352

7,433

960

594,388

onroad

308

NLCD Low + Med + High

204

19,149

569

56

29,121

onroad

508

Public Schools

16

2,189

81

1

544

rail

261

NT AD Total Railroad Density

14

35,834

1,061

31

1,822

rail

271

NTAD Class 12 3 Railroad Density

324

480,174

13,760

644

20,118

rwc

300

NLCD Low Intensity Development

16,369

33,925

297,877

7,937

322,528

3.4.2	Allocation method for airport-related sources in the U.S.

There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground support
equipment, and jet refueling. The modeling platform includes the aircraft and airport ground support
equipment emissions as point sources. For the modeling platform, the EPA used the SMOKE "area-to-
point" approach for only jet refueling in the nonpt sector. The following SCCs use this approach:
2501080050 and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/rocket engine
firing and testing). The ARTOPNT approach is described in detail in the 2002 platform documentation:
http://www3.epa.gov/scram001/reports/Emissions%20TSD%20Voll 02-28-08.pdf. The ARTOPNT file
that lists the nonpoint sources to locate using point data were unchanged from the 2005-based platform.

3.4.3	Surrogates for Canada and Mexico emission inventories

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

140


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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 3-29.

Table 3-28. 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

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



Waste management and remediation





562

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

141


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Code

Canadian Surrogate Description

Code

Description

904

General Aviation LTO

1265

OFFR CANRAIL

945

Commercial Marine Vessels

9450

Commercial Marine Vessel Ports

Table 3-29. 2019 CAPs Allocated to Mexican and Canadian Spatial Surrogates for 12US1 (short

tons)

Code

Mexican or Canadian Surrogate
Description

MI;

NOx

PM2s

SO2

voc

11

MEX 2015 Population

0

85,151

465

178

223,016

14

MEX Residential Heating - Wood

0

2,333

6,512

190

17,541

16

MEX Residential Heating - Distillate
Oil

1

28

0

0

1

22

MEX Total Road Miles

2,983

359,738

12,086

6,647

71,102

24

MEX Total Railroads Miles

0

19,518

408

185

732

26

MEX Total Agriculture

115,677

18,186

16,239

473

3,847

32

MEX Commercial Land

0

75

1,631

0

27,763

34

MEX Industrial Land

92

2,036

1,257

6

34,866

36

MEX Commercial plus Industrial
Land

5

7,049

305

15

101,386

40

MEX Residential (RES1-

4)+Comercial+Industrial+Institutional

+Government

0

15

61

2

21,041

42

MEX Personal Repair (COM3)

0

0

0

0

5,130

44

MEX Airports Area

0

3,420

48

241

1,294

48

MEX Brick Kilns

0

266

5,297

470

130

50

MEX Mobile sources - Border
Crossing

3

58

2

0

45

100

CAN Population

776

51

604

14

219

101

CAN total dwelling

0

0

0

0

147,322

104

CAN capped total dwelling

347

30,961

2,282

2,473

1,614

106

CAN ALL INDUST





583





113

CAN Forestry and logging

117

1,392

7,471

29

3,938

200

CAN Urban Primary Road Miles

1,572

66,863

2,365

232

7,134

210

CAN Rural Primary Road Miles

628

39,000

1,297

98

3,023

211

CAN Oil and Gas Extraction

1

39

424

41

1,657

212

CAN Mining except oil and gas

0

0

3,051

0

0

220

CAN Urban Secondary Road Miles

2,951

105,604

4,764

482

18,852

221

CAN Total Mining

0

0

13,221

0

0

222

CAN Utilities

55

3,344

2,859

453

63

230

CAN Rural Secondary Road Miles

1,652

71,811

2,515

257

8,255

240

CAN capped population

327

44,524

1,385

52

95,157

308

CAN Food manufacturing

0

0

18,982

0

17,387

321

CAN Wood product manufacturing

785

4,450

1,543

328

15,455

142


-------
Code

Mexican or Canadian Surrogate
Description

MI;

NOx

PM2s

SO2

voc

323

CAN Printing and related support
activities

0

0

0

0

11,693

324

CAN Petroleum and coal products
manufacturing

0

1,179

1,599

462

9,154

326

CAN Plastics and rubber products
manufacturing

0

0

0

0

24,027

327

CAN Non-metallic mineral product
manufacturing

0

0

6,449

0

0

331

CAN Primary Metal Manufacturing

0

156

5,561

29

72

412

CAN Petroleum product wholesaler-
distributors

0

0

0

0

43,724

448

CAN clothing and clothing accessories
stores

0

0

0

0

140

482

CAN Rail transportation

1

4,062

88

1

256

562

CAN Waste management and
remediation services

240

1,924

2,620

2,483

9,199

901

CAN AIRPORT

0

93

9

0

9

921

CAN Commercial Fuel Combustion

187

24,020

2,379

1,414

1,224

923

CAN TOTAL INSTITUTIONAL
AND GOVERNEMNT

0

0

0

0

14,458

924

CAN Primary Industry

0

0

0

0

38,858

925

CAN Manufacturing and Assembly

0

0

0

0

69,488

926

CAN Distribution and Retail (no
petroleum)

0

0

0

0

7,285

927

CAN Commercial Services

0

0

0

0

31,311

932

CAN CANRAIL

48

83,844

1,662

43

3,559

940

CAN PAVED ROADS NEW





27,751





946

CAN Construction and mining

0

0

0

0

9,850

951

CAN Wood Consumption Percentage

957

10,634

107,554

1,519

152,072

955

CAN

UNPAVED ROADS AND TRAILS





383,147





990

CAN TOTFERT

48

4,047

265

6,827

152

996

CAN urban area

0

0

2,994

0

0

1251

CAN OFFR TOTFERT

73

50,505

3,421

48

4,585

1252

CAN OFFR MINES

1

563

38

1

82

1253

CAN OFFR Other Construction not
Urban

68

28,864

3,601

41

10,186

1254

CAN OFFR Commercial Services

43

14,335

2,235

26

35,468

1255

OFFR Oil Sands Mines

0

0

0

0

0

1256

CAN OFFR Wood industries
CANVEC

7

2,103

214

4

853

1257

CAN OFFR UNPAVED ROADS
RURAL

24

10,272

574

14

24,333

1258

CAN OFFR Utilities

8

3,727

176

5

824

1259

CAN OFFR total dwelling

17

5,844

588

10

12,557

143


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Code

Mexican or Canadian Surrogate
Description

MI;

NOx

PM2s

SO2

voc

1260

CAN OFFR water

19

5,604

264

22

20,367

1261

CAN OFFR ALL INDUST

4

5,065

145

2

1,068

1262

CAN OFFR Oil and Gas Extraction

1

570

42

0

168

1263

CAN OFFR ALLROADS

3

1,361

129

2

438

1265

CAN OFFR CANRAIL

0

489

15

0

37

144


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4 Emission Summaries

Tables 4-1 through 4-3 summarize emissions by sector for the 2019 platform. These summaries are
provided at the national level by sector for the contiguous U.S. and for the portions of Canada and Mexico
inside the larger 12km domain (12US1) discussed in Section 3.1. Note that totals for the 12US2 domain
are not available here, but the sum of the U.S. sectors would be essentially the same and only the
Canadian and Mexican emissions would change according to the extent of the grids to the north and south
of the continental United States. The afdust sector emissions here represent the emissions after application
of both the land use (transport fraction) and meteorological adjustments; therefore, this sector is called
"afdust adj" in these summaries. The onroad sector totals are post-SMOKE-MOVES totals, representing
air quality model-ready emission totals, and include CARB emissions for California. Emissions from the
cmv sectors in include separate totals for the U.S., which indludes emissions within state waters only;
these extend to roughly 3-5 miles offshore and includes CMV emissions at U.S. ports. CMV emissions
from outside U.S. state waters, including those within Canadian waters, Mexican waters, and offshore
areas, are summarized in the non-U.S. section of the table. The total of all US sectors is listed as "Con
U.S. Total."

State totals and other summaries are available in the reports area on the FTP site for the 2019 platform
(https:// gaftp. epa. gov/Air/emi smod/2019/).

145


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Table 4-1. National by-sector CAP emissions for the 2019 platform, 12US1 grid (tons/yr)

Sector

CO

NH3

NOX

PM10

PM2 5

S02

VOC

afdustadj







5,391,767

753,763





airports

494,906

0

135,054

9,899

8,661

16,907

55,888

cmv_clc2

17,699

60

117,678

3,191

3,092

596

4,264

cmv_c3

9,509

30

95,659

1,683

1,549

3,864

4,418

fertilizer



1,202,914











livestock



2,602,279









227,985

nonpt

1,927,267

102,898

739,250

572,589

475,154

166,399

818,185

nonroad

10,464,693

1,953

930,665

90,623

85,372

1,168

974,635

npoilgas

629,304

27

520,213

11,180

11,091

37,872

2,581,373

npsolvents

36

58

34

469

448

5

2,516,324

onroad

16,637,063

102,432

2,780,428

215,217

94,256

16,657

1,162,167

ptegu

479,731

20,734

999,436

116,793

98,046

1,017,892

29,201

ptagfire

687,701

146,655

27,373

104,474

66,053

10,683

99,156

ptfire-rx

9,176,460

150,531

163,988

986,337

842,244

80,673

2,200,144

ptfire-wild

2,121,948

34,930

34,299

220,638

186,981

17,581

502,126

ptnonipm

1,359,439

68,482

855,115

380,052

241,176

500,592

760,023

ptoilgas

174,426

3,759

354,632

13,514

13,190

36,934

143,335

rail

108,825

339

516,008

15,304

14,821

675

21,940

rwc

2,152,689

16,369

33,925

298,738

297,877

7,937

322,528

Con. U.S. Total

46,441,694

4,454,448

8,303,756

8,432,468

3,193,775

1,916,436

12,423,693

beis

3,695,221



942,563







25,450,181

CONUS + beis

50,136,915

4,454,448

9,246,319

8,432,468

3,193,775

1,916,436

37,873,874

Can./Mex./Offshore















Sector

CO

NH3

NOX

PM10

PM2 5

S02

VOC

Canada ag



492,799









105,147

Canada oil and gas 2D

666

7

3,232

185

185

3,933

509,228

Canada othafdust







500,478

77,863





Canada othptdust







124,644

43,736





Canada othar

2,180,838

3,818

298,213

222,283

173,889

16,299

721,690

Canada onroadcan

1,587,108

7,125

327,671

24,783

12,312

1,121

132,251

Canada othpt

1,115,145

19,471

650,682

90,031

43,039

989,862

148,178

Canada ptfireothna

1,503,071

30,374

63,142

210,958

178,315

12,143

439,097

Canada cmv

11,009

31

101,469

1,749

1,633

2,997

4,623

Mexico othar

109,035

115,777

50,564

101,331

32,933

1,576

348,466

Mexico onroad mex

1,812,455

2,983

447,355

16,112

11,380

6,832

159,395

Mexico othpt

154,181

1,154

172,530

47,798

33,325

345,440

37,488

Mexico ptfire othna

367,565

7,109

14,666

48,343

41,333

3,026

106,475

Mexico cmv

8,243

185

90,456

10,460

9,625

84,307

3,827

Offshore cmv

49,677

551

499,240

31,100

28,647

225,408

22,602

Offshore pt oilgas

51,866

8

49,959

636

635

462

38,803

Can/Mex/offshore total

8,950,860

681,393

2,769,180

1,430,892

688,848

1,693,406

2,777,271

146


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Table 4-2. National by-sector VOC HAP emissions for the 2019 platform, 12US1 grid (tons/yr)

Sector

Acetaldehyde

Benzene

Formaldehyde

Methanol

Naphthalene

Acrolein

1,3-
Butadiene

airports

2,138

956

6,186

893

603

1,213

861

cmv_clc2

42

20

182

0

12

8

4

cmv_c3

43

21

189

0

12

8

4

livestock

1,546

458



16,102

0





nonpt

5,105

9,269

5,921

5,262

490

223

779

nonroad

9,153

26,147

22,895

1,254

1,607

1,591

4,179

npoilgas

1,215

35,055

13,239

582

36

655

122

npsolvents

56

307

6

40,441

4,053



0

onroad

11,111

22,791

13,937

1,887

1,856

1,095

3,070

ptegu

293

390

2,712

102

22

235

4

ptagfire

7,394

1,612

6,271

930

29

584

679

ptfire-rx

70,282

22,255

135,979

104,320

17,922

28,313

16,906

ptfire-wild

17,345

5,248

31,188

30,989

5,035

5,300

2,639

ptnonipm

5,798

2,929

6,803

52,092

1,039

957

608

ptoilgas

2,849

1,180

14,044

2,056

28

2,093

282

rail

1,593

458

4,536

0

57

325

38

rwc

11,227

15,902

18,584

0

2,695

892

2,119

Con. U.S. Total

147,190

144,997

282,672

256,909

35,497

43,492

32,295

beis

386,933



527,645

2,055,435







CONUS + beis

534,123

144,997

810,317

2,312,343

35,497

43,492

32,295

Canada ag

1,126

149

0

25,776

0

0

0

Canada oil and gas
2D

0

42,586

0

0

0

0

0

Canada othafdust

0

0

0

0

0

0

0

Canada othar

21,509

14,034

17,104

4,586

3,651

0

0

Canada
onroad can

2,157

5,892

2,866

0

46

0

0

Canada othpt

1,486

1,667

5,221

15,927

12

0

0

Canada
ptfire othna

13,088

9,952

44,692

38,508

0





Canada cmv

45

22

197

0

13

9

5

Mexico othar

2,905

5,861

2,250

2,720

404

0

0

Mexico
onroad mex

674

3,885

1,616

665

232

114

576

Mexico othpt

77

846

3,070

474

12

0

0

Mexico
ptfire othna

4,664

2,048

9,202

6,504

0





Mexico cmv

37

18

163

0

10

7

4

Offshore cmv

221

107

965

0

62

42

23

Offshore pt oilgas

0

0

0

0

0

0

0

147


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Non-U.S. total

47,991

87,068

87,346

95,160

4,442

172

608

Table 4-3. National by-sector Diesel PM and metal emissions for the 2019 platform, 12US1 grid

(tons/yr)

Sector

Diesel
PMio

Diesel
PM2s

Chromium
Hex

Arsenic

Cadmiu
m

Nickel

Manganes
e

Ethylen
e Oxide

airports

80

78

-

-

-

-

-

-

cmv_clc2

3,191

3,092

0.00002

0.08

0.73

2.12

0.010

-

cmv_c3

1,683

1,549

0.00001

0.04

0.37

1.06

0.005

-

nonpt

-

-

0.3571

5.95

3.77

26.27

15.54

0.95

nonroad

50,589

48,894

0.004

0.89

-

1.13

0.68

-

npoilgas

-

-

0.00002

0.03

0.10

0.09

0.05

-

onroad

58,015

53,446

0.04

8.09

-

5.94

33.70

-

ptegu

-

-

4.02

14.60

7.81

71.49

119.12

0.0006

ptnonipm

1,105

991

36.30

33.81

12.33

204.86

718.87

115.15

ptoilgas

-

-

0.02

0.02

0.30

6.14

2.54

-

rail

15,304

14,821

0.07

14.56

0.03

54.71

31.29

-

rwc

-

-

-

-

0.08

0.07

0.62

-

Con. U.S.
Total

129,969

122,870

41

78

26

374

922

116

Canada CMV

1,749

1,633

0.000012

0.04

0.39

1.12

0.01

-

Mexico CMV

10,460

9,625

0.00007

0.25

2.27

6.61

0.03

-

Offshore CMV

12,209

11,258

0.00008

0.29

2.66

7.73

0.04

-

148


-------
5 References

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Appendix A: CB6 Assignment for Species

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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.

155


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ENVIRON

in addition to the explicit and structural species, there are two model specie; that a re used to
represent organic gases that are not treated by the CB mechanism:

NVOL — Very low volatility SPEC1ATE compounds that reside predominantly in the particle phase and
should be excluded from the gas phase mechanism. These compounds are mapped by serting
NVOL equal to the molecular weight (e.g. decabramodiphenyl cxide is mapped as 959.2
NVOL), which a Hows for the total mass of all NVOL to be determined,

UN K— Compounds that are unable tc be mapped to CS using the available model species. This

approach should be avoided unless absolutely necessary, and will tead to a warning message
in the speciation tool.

Table l. Model species in the CBQ5 and CB6 chemical mechanisms.







fridwded in



Voael



Number

CBOS



Species



at

(structural

Inducted

Nume

Doits'sticn

CsfUons

mapping]

in CSfi

Expliiit mcdel s oec es

ACET

•4cetar.e {proparonel

1

No (3 PAR|

res

ALDZ

Ac-t: dehyde |etl-rarial|i

2

res

res

BEfJZ

3er,iEne

6

filo(l PAR, 3
JNR|

res

CH4

Vetfiane

1

res

res

ETH

Ethene (ethylene)

2

res

res

ET HA

Ethane

2

Tea

res

ET HT

Sthyne [acetylene)

1

No(l PAR,. 1
C=C<3 R4}

4

Yes

res

•SET

C=Q|

1

Nd (1 PAR|

res

OLE

Terminal OEfir group 'R ,R >C=C|

2

res

res

SAR

^arafinic group (R <«R

1

res

res

TERP

VonoterperfES

13

res

res

TOL

Toluene and other monoalkyl arcriatici

7

res

res

UNR

Jnreacbve carbon groups |e.g., tialcgenated
carbons)

i

res

res

XV L

Xtrtsne and other polyafcyl aran:t :s

$

res

res

Mot napo ec to CB m odel spe:ies

*JVOL

Very law volatility car peunds



res

res

JNK

Jnknown



res

res

' iadi Vi'O. rcDr«cTj 1 g rnal md lew volatility conptLrGs art ass gnsd ta NVCL tassd cn maecUsrweight. UNK is unmapped
and tfiis d3« net rqpressnl any rafiwi.

Entail Entiran US Coporatiori, 773 Son. -Stein Drire, SuitE 2113, taftirto, CA9S3S8	2

vw^u.sasuccDO F+uu.Easjcmw

156


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157


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¦ M'l MH environ

Mapping guidelines for nan-explicit organic gases using CB model species

5PECIATE compound; that sre not treated explicitly &re mapped to CB model species that represent
common structural groups. Table 2 lists the carbon number and general mapp ing guidelines for each
of the structure model species.

Table 2. General Guidelines far mapping using CB6 structural model species.

CK

Species

Name

Number at
CaftlDfIS

Represents

ALOX

2

Aldehyde group. ALU* represents 2 csrbcn: end additions! cartons are represented ss
alkyl groups [mnsHy PAR|, e.g. prapionaldehyde is ALOX + PAR

OLE

4

-itsrnal clelin gre jp.lOLE represents 4 cartons and additional cartons are represented as
alkyl groups [mostly PAR|, e.g. 2-pentsne isomers sre (OLE 4 PAR.

Sxapvont:

* OLE with 2 carton trancf.es cr, bcth sides of the daub a bend are downgraded to
OLE

¦CET

1

<:star,e group. :vl

£

Xylene isomers and other pclyalkyl aromaiics. XVL represents £ cartans and any additional
cartons are represented as alkyl graups (mostly PA^; e.g. trimethyltemene baners are

:«:vLf pa=:

Some compounds thatare multifunctional and/or include hetero-atorns lack obviousCB mappings.
We developed guidelines for some of these compound classes to promote consistent representation
in this work and future revisions. Approaches for several compound classes are explained in Table 3.
We developed guidelines as needed to address newly added specie; in 5PECIATE £.5 but did not
systematically review existing rnapp ng; for "difficult to assign" compounds that could benefit from
developi ng a guideline.

kartell Emiron US Ccpcratcr, 773 SB n Marin 3rive. Sute 2113, Nn.uto, CA34S5B	a

V4S1-4U.BS9JJ7D3 F+14113990707

158


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ENVIRON

Table 3. Mapping guidelines for same difficult to map compound classes and structural groups

Coflipauind

ClBss/S'ructunl

group

CB model species representation

Chlorotemeres and
other halcgenated
Benienes-

Guideline:

*	3 or less halogens -1 PAR, 3 JNR
» 4 or more halogens-6 U^JR

Examples:

*	1,3.3-ChlcirobenMne-1 PAR, 3 UN*

*	Tetrachlorotemeres - 6 UMR



Guideline:

*	1 OLE with additional cartons represented ss aUryl groups (generally
PAH)

Examples:

*	Mefl-iylcydopentadiene-1 OLE, 2 PAR
¦» y«at>tJW0eC'- 1 IC -E. 3 PAR

:urans,'pyrroles

Guideline:

*	2 OLE with additional carbons represented i; alKyl gruLps (generally
PAR)

Examples:

2-ButylfLran - 2 C .E, 4 PAR

*	2-Pentylftiran - 2 OLE, 3 PAR

*	=>Trole -iD.E

*	1-Maiiftyiufc - 2 OLE, 1 PAR

-Etsracfdic aromatic
compounds
containing 2 non-
carton ataru

Guideline:

¦» 1 OLE with remaining cartons represented es olfcyl grcjp: (generally
PAR)

Examples:

*	Ethylpyraiine — 1 OLE, 4 3AR

*	l-metiylpyraiole — 1 OLE, 2 PAH

*	4,3-Oimethy1o>BZBle -1 C .=, 3 PAR

Triple bcrid(.s]>

Guideline:

*	Tnple bends are treated as =AR unless they are the only reactive
¦functional group ra compound contains more than one triple tond
and ra other reactive functional groups, then one cfthe triple tends
s treated as OLE with additional cartons treated as alfcyl groups.

Examples:

¦» l-Penter-S-'iiTie - 1 0 _E, 3 PAR

*	1>HH e>:adien-3-frie - 2 C .E, 2 PAR

*	1.6— eotadivne - l OLE. SPAR

These guidelines were used tc rraptfie new species from SPEICATE45, and alsc tc revise some
previously mapped compounds. Overall, 3 total of 175 new species from 5PECIATEV4.5 were rrspped
and 7 previously mapped species were revised b=s=d on the new guidelines.

=arfcoll Enkiron US Cnporation, 773 San Euferin Urwe, Suite 2113,	Cft3«BB	A

V+1413.099J57DQ F-H 413.SS3J37U7

j'ic-^^y-ccvie-

159


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ENVIRON

Recommendation

1.	Comp lete a systematic review of the mE ppi rig of al I specie; tc ensura conformity with cu rrerit
mapping guidelines. The assignments of existing compounds that are similar to new species were
reviewed and revised to promote consistency in mapping approaches, but the majority of
existing species mappings were not reviewed as it was outside the scope of this work.

2.	Develop a methodology for classifying and tracking larger organic compounds based on their
volatility [semi, intermediate, or low volatility | tc improve support for secondary organic aerosol
|5QA| modeling using the volatility basis set |VBS| SOA model, which is available in both CMAQ
and CAM*. A preliminary investigate of the possioilhy of doing so has been perbrned.. and is
discussed in a separate memorandum.

kartell En-iimn US Cnpnratic4\ 773 an \1orin HrrvE. SutE 2113. Nnvnto, CA 3-SSE
VM413.BSS.D71H F41 J13.399.CT7D7

160

3


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Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE versions 4.5 and

later that were used in the 2016 and later 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





161


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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





162


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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

163


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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

164


-------
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

165


-------
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

166


-------
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

167


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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)

168


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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

169


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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

170


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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

171


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United States	Office of Air Quality Planning and Standards	Publication No. EPA-454/B-22-012

Environmental Protection	Air Quality Assessment Division	August 2022

Agency	Research Triangle Park, NC

172


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