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Technical Support Document (TSD):
Preparation of Emissions Inventories for the
Version 7.1 2016 North American Emissions
Modeling Platform
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EP A-454/B-20-010
August 2019
Technical Support Document (TSD): Preparation of Emissions Inventories for the Version 7.1
2016 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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TABLE OF CONTENTS
LIST OF TABLES Ill
LIST OF FIGURES IV
LIST OF APPENDICES V
ACRONYMS V
1 INTRODUCTION 1
2 2016 EMISSION INVENTORIES AND APPROACHES 3
2.1 Point sources (ptegu, pt_oilgas and ptnonipm) 6
2.1.1 EGUsector (ptegu) 10
2.1.2 Point source oil and gas sector (pt oilgas) 11
2.1.3 Non-IPM sector (ptnonipm) 12
2.2 2016 NONPOINT SOURCES (AFDUST, AG, AGFIRE, PTAGFIRE, NP_OILGAS, RWC, NONPT) 13
2.2.1 Area fugitive dust sector (afdust) 13
2.2.2 Agricultural sector (ag) 20
2.2.3 Agricultural fires (ptagfire) 25
2.2.4 Nonpoint source oil and gas sector (npoilgas) 25
2.2.5 Residential wood combustion sector (rwc) 26
2.2.6 Other nonpoint sources sector (nonpt) 27
2.3 2016 ONROAD MOBILE SOURCES (ONROAD) 27
2.3.1 Onroad (onroad) 28
2.4 2014 NONROAD MOBILE SOURCES (CMV, RAIL, NONROAD) 32
2.4.1 Category 1, Category 2, Category 3 Commercial Marine Vessels (cmv_clc2, cmv_c3) 32
2.4.2 Railroad sources: (rail) 36
2.4.3 Nonroad mobile equipment sources: (nonroad) 36
2.5 "OtherEmissions": non-U.S. sources 37
2.5.1 Point sources from Canada and Mexico (othpt) 37
2.5.2 Area and nonroad mobile sources from Canada and Mexico (othar, othafdust) 38
2.5.3 Onroad mobile sources from Canada and Mexico (onroadcan, onroadmex) 38
2.5.4 Fires from Canada and Mexico (ptfire othna) 39
2.6 Fires (ptfire) 39
2.7 Biogenic sources (beis) 40
2.8 SMOKE-ready non-anthropogenic inventory for chlorine 44
3 EMISSIONS MODELING SUMMARY 45
3.1 Emissions modeling Overview 45
3.2 Chemical Speciation 48
3.2.1 VOC speciation 51
3.2.1.1 County specific profile combinations 54
3.2.1.2 Additional sector specific considerations for integrating HAP emissions from inventories into speciation 55
3.2.1.3 Oil and gas related speciation profiles 57
3.2.1.4 Mobile source related VOC speciation profiles 58
3.2.2 PM speciation 63
3.2.2.1 Mobile source related PM2.5 speciation profiles 65
3.2.3 NOxspeciation 66
3.2.4 Creation of Sulfuric Acid Vapor (SULF) 67
3.3 Temporal Allocation 68
3.3.1 Use of FF10 format for finer than annual emissions 69
3.3.2 Electric Generating Utility temporal allocation (ptegu) 70
3.3.2.1 Base year temporal allocation of EGUs 70
3.3.3 Airport Temporal allocation (ptnonipm) 74
3.3.4 Residential Wood Combustion Temporal allocation (rwc) 76
3.3.5 Agricultural Ammonia Temporal Profiles (ag) 80
3.3.6 Oil and gas temporal allocation (np oilgas) 81
3.3.7 Onroad mobile temporal allocation (onroad) 81
3.3.8 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire) 85
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3.4 Spatial Allocation 88
3.4.1 Spatial Surrogates for U.S. emissions 88
3.4.2 Allocation method for airport-related sources in the U.S. 95
3.4.3 Surrogates for Canada and Mexico emission inventories 95
3.5 Preparation of Emissions for the CAMx model 98
4 EMISSION SUMMARIES 102
5 REFERENCES 104
List of Tables
Table 2-1. Platform sectors for the 2016v7.1 (alpha) emissions modeling platform 4
Table 2-2. Release parameter changes to the SMOKE Modeling flat file for point sources-For point sources
with stack releases (ERPtype NOT equal to "1") 7
Table 2-3. Release parameter changes to the SMOKE Modeling flat file for point sources-For Fugitive
Release Points 8
Table 2-4. Release parameter changes to the SMOKE Modeling flat file for point sources-For Coke Ovens,
any release point that emits coke oven emissions (pollutant code 140) 8
Table 2-5. Description of Comments added to SMOKE Modeling flat file used when defaulting or changing
values from the NEI 9
Table 2-6. Point source oil and gas sector NAICS Codes 11
Table 2-7. Oil and gas sector 2016 projection factors 11
Table 2-8. SCCs in the afdust platform sector from NEI2014v2: nonzero emissions 13
Table 2-9. SCCs in the afdust platform sector from NEI2014v2: zero emissions 15
Table 2-10. Total impact of 2016 fugitive dust adjustments to unadjusted inventory 16
Table 2-11. Livestock SCCs extracted from the NEI to create the ag sector 20
Table 2-12. Fertilizer SCCs extracted from the NEI for inclusion in the "ag" sector 21
Table 2-13. Environment variables needed for an EPIC simulation 23
Table 2-14. SCCs in the residential wood combustion sector (rwc)* 26
Table 2-15. Onroad emission aggregate processes 29
Table 2-16. Factors applied to project VMT from 2014 to 2016 30
Table 2-17. 2014NEI SCCs extracted for the cmv_clc2 sector 33
Table 2-18. 2014NEI SCCs extracted for the cmv_c3 sector 33
Table 2-19. Growth factors to project the 2002 ECA-IMO inventory to 2011 34
Table 2-20. SCCs used for the rail sector 36
Table 2-21. 2014 Platform SCCs representing emissions in the ptfire modeling sector 40
Table 2-22. Meteorological variables required by BEIS 3.61 41
Table 3-1. Key emissions modeling steps by sector 46
Table 3-2. Descriptions of the platform grids 48
Table 3-3. Emission model species produced for CB6 for CMAQ 49
Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM)
for each platform sector 53
Table 3-5. MOVES integrated species in M-profiles 56
Table 3-6. Basin/Region-specific profiles for oil and gas 57
Table 3-7. TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a used for the 2016
Platform 59
Table 3-8. Select mobile-related VOC profiles 2016 60
Table 3-9. Onroad M-profiles 60
Table 3-10. MOVES process IDs 61
Table 3-11. MOVES Fuel subtype IDs 62
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Table 3-12. MOVES regclass IDs 62
Table 3-13. SPECIATE4.5 brake and tire profiles compared to those used in the 201 lv6.3 Platform 65
Table 3-14. Nonroad PM2.5 profiles 66
Table 3-15. NOx speciation profiles 66
Table 3-16. Sulfate split factor computation 67
Table 3-17. SO2 speciation profiles 68
Table 3-18. Temporal settings used for the platform sectors in SMOKE 68
Table 3-19. U.S. Surrogates available for the 2016 alpha modeling platform 89
Table 3-20. Off-Network Mobile Source Surrogates 90
Table 3-21. Spatial Surrogates for Oil and Gas Sources 91
Table 3-22. Selected 2016 CAP emissions by sector for U.S. Surrogates (CONUS domain totals) 92
Table 3-23. Canadian Spatial Surrogates 95
Table 3-24. CAPs Allocated to Mexican and Canadian Spatial Surrogates 96
Table 3-25. Emission model species mappings for CMAQ and CAMx 99
Table 4-1. National by-sector CAP emissions summaries for the 2016 alpha platform, 12US1 grid 103
List of Figures
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and
cumulative 19
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions 23
Figure 2-3. Illustration of regional modeling domains in ECA-IMO study 35
Figure 2-4. Annual NO emissions output from BEIS 3.61 for 2014 42
Figure 2-5. Annual isoprene emissions output from BEIS 3.61 for 2014 42
Figure 2-6. Annual acetaldehyde emissions output from BEIS 3.61 for 2014 43
Figure 2-7. Annual formaldehyde emissions output from BEIS 3.61 for 2014 43
Figure 3-1. Air quality modeling domains 47
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation 53
Figure 3-3. Profiles composited for the new PM gas combustion related sources 64
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources 64
Figure 3-5. Eliminating unmeasured spikes in CEMS data 71
Figure 3-6. Seasonal diurnal profiles for EGU emissions in a Virginia Region 71
Figure 3-7. IPM Regions used to Create Temporal Profiles for EGUs without CEMS 73
Figure 3-8. Month-to-day profiles for different fuels in a West Texas Region 73
Figure 3-9. Diurnal Profile for all Airport SCCs 74
Figure 3-10. Weekly profile for all Airport SCCs 75
Figure 3-11. Monthly Profile for all Airport SCCs 75
Figure 3-12. Alaska Seaplane Profile 76
Figure 3-13. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold 77
Figure 3-14. RWC diurnal temporal profile 78
Figure 3-15. Diurnal profile for OHH, based on heat load (BTU/hr) 79
Figure 3-16. Day-of-week temporal profiles for OHH and Recreational RWC 79
Figure 3-17. Annual-to-month temporal profiles for OHH and recreational RWC 80
Figure 3-18. Example of animal NH3 emissions temporal allocation approach, summed to daily emissions 81
Figure 3-19. Example of temporal variability of NOx emissions 82
Figure 3-20. Sample onroad diurnal profiles for Fulton County, GA 83
Figure 3-21. Counties for which MOVES Speeds and Temporal Profiles could be Populated 84
Figure 3-22. Example of Temporal Profiles for Combination Trucks 85
Figure 3-23. Agricultural burning diurnal temporal profile 87
Figure 3-24. Prescribed and Wildfire diurnal temporal profiles 87
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List of Appendices
Appendix A: Nonpoint Oil and Gas NEI SCCs
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE4.5 that were used in the
2014 v7.1 Platform
Appendix C: CB6 Assignment for New Species
Appendix D: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
Acronyms
AE5
CMAQ Aerosol Module, version 5, introduced in CMAQ v4.7
AE6
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
AEO
Annual Energy Outlook
AERMOD
American Meteorological Society/Environmental Protection Agency
Regulatory Model
NBAFM
Naphthalene, Benzene, Acetaldehyde, Formaldehyde and Methanol
BEIS
Biogenic Emissions Inventory System
BELD
Biogenic Emissions Land use Database
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAMD
EPA's Clean Air Markets Division
CAMx
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CB05
Carbon Bond 2005 chemical mechanism
CBM
Coal-bed methane
CEMS
Continuous Emissions Monitoring System
CEPAM
California Emissions Projection Analysis Model
CISWI
Commercial and Industrial Solid Waste Incinerators
CI
Chlorine
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
CO
Carbon monoxide
CSAPR
Cross-State Air Pollution Rule
E0, E10, E85
0%, 10% and 85% Ethanol blend gasoline, respectively
EBAFM
Ethanol, Benzene, Acetaldehyde, Formaldehyde and Methanol
ECA
Emissions Control Area
EEZ
Exclusive Economic Zone
EF
Emission Factor
EGU
Electric Generating Units
EIS
Emissions Inventory System
EISA
Energy Independence and Security Act of 2007
EPA
Environmental Protection Agency
EMFAC
Emission Factor (California's onroad mobile model)
FAA
Federal Aviation Administration
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FCCS
Fuel Characteristic Classification System
FF10
Flat File 2010
FIPS
Federal Information Processing Standards
FHWA
Federal Highway Administration
HAP
Hazardous Air Pollutant
HC1
Hydrochloric acid
HDGHG
Heavy-Duty Vehicle Greenhouse Gas
Hg
Mercury
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
ICR
Information Collection Request
I/M
Inspection and Maintenance
IMO
International Marine Organization
IPM
Integrated Planning Model
ITN
Itinerant
LADCO
Lake Michigan Air Directors Consortium
LDGHG
Light-Duty Vehicle Greenhouse Gas
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
MSAT2
Mobile Source Air Toxics Rule
MTBE
Methyl tert-butyl ether
MWRPO
Mid-west Regional Planning Organization
NCD
National County Database
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NESCAUM
Northeast States for Coordinated Air Use Management
NESHAP
National Emission Standards for Hazardous Air Pollutants
NH3
Ammonia
NLCD
National Land Cover Database
NLEV
National Low Emission Vehicle program
nm
nautical mile
NMIM
National Mobile Inventory Model
NO A A
National Oceanic and Atmospheric Administration
NODA
Notice of Data Availability
NONROAD
OTAQ's model for estimation of nonroad mobile emissions
NOx
Nitrogen oxides
NSPS
New Source Performance Standards
NSR
New Source Review
OAQPS
EPA's Office of Air Quality Planning and Standards
OHH
Outdoor Hydronic Heater
OTAQ
EPA's Office of Transportation and Air Quality
ORIS
Office of Regulatory Information System
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OKI)
EPA's Office of Research and Development
ORL
One Record per Line
OTC
Ozone Transport Commission
PADD
Petroleum Administration for Defense Districts
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
ppb, ppm
Parts per billion, parts per million
RBT
Refinery to Bulk Terminal
RFS2
Renewable Fuel Standard
RIA
Regulatory Impact Analysis
RICE
Reciprocating Internal Combustion Engine
RWC
Residential Wood Combustion
RPO
Regional Planning Organization
RVP
Reid Vapor Pressure
see
Source Classification Code
SESARM
Southeastern States Air Resource Managers
SESQ
Sesquiterpenes
SMARTFIRE
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
SMOKE
Sparse Matrix Operator Kernel Emissions
SO2
Sulfur dioxide
SOA
Secondary Organic Aerosol
SIP
State Implementation Plan
SPDPRO
Hourly Speed Profiles for weekday versus weekend
TAF
Terminal Area Forecast
TCEQ
Texas Commission on Environmental Quality
TOG
Total Organic Gas
TSD
Technical support document
ULSD
Ultra Low Sulfur Diesel
USD A
United States Department of Agriculture
VOC
Volatile organic compounds
VMT
Vehicle miles traveled
VPOP
Vehicle Population
WRAP
Western Regional Air Partnership
WRF
Weather Research and Forecasting Model
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1 Introduction
The U.S. Environmental Protection Agency (EPA), working in conjunction with the National Emissions
Inventory Collaborative, developed an air quality modeling platform for air toxics and criteria air
pollutants that represents the year 2016 based on the 2014 National Emissions Inventory (NEI), version 2
(2014NEIv2). 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 2016 "alpha" modeling platform, which includes the emission inventories, the ancillary
data files, and the approaches used to transform inventories for use in air quality modeling. Many
emissions inventory components of this air quality modeling platform are based on the 2014NEIv2,
including projections to year 2016 for some emissions sectors.
This 2016 modeling platform includes all criteria air pollutants and precursors (CAPs), and a group of
hazardous air pollutants (HAPs) and diesel particulate matter. The group of HAPs are those explicitly
used by the chemical mechanism in the Community Multiscale Air Quality (CMAQ) model for
ozone/particulate matter (PM): chlorine (CI), hydrogen chloride (HC1), benzene, acetaldehyde,
formaldehyde, methanol, naphthalene.
The National Emissions Inventory Collaborative is a partnership between state emissions inventory staff,
multi-jurisdictional organizations (MJOs), federal land managers (FLMs), EPA, and others to develop a
North American air pollution emissions modeling platform with a base year of 2016 for use in air quality
planning. The Collaborative planned for three versions of the 2016 platform: alpha, beta, and Version 1.0.
The version of the platform described in this document is the "2016 alpha platform", although this study
incorporated additional fixes and updates that were not part of the original 2016 alpha platform that was
released in March, 2018. The 2016 alpha platform was used to support air quality modeling applications
using CMAQ version 5.2 and CAMx version 6.5. The modeling domain includes the lower 48 states and
parts of Canada and Mexico.
The CMAQ model requires hourly and gridded emissions of chemical species that correspond to CAPs
and specific HAPs. The chemical mechanism used by CMAQ for this platform is called Carbon Bond
version 6 -CMAQ (CB6-CMAQ) and includes important reactions for simulating ozone formation,
nitrogen oxides (NOx) cycling, and formation of secondary aerosol species. It is basically the same as the
CB6 used in the 201 lv6.3 platform described in (Hildebrandt Ruiz and Yarwood, 2013) except that CB6-
CMAQ removes naphthalene from the lumped species group "XYL" and treats it explicitly. The CAMx
model uses a similar, but slightly different, chemical mechanism, as described in Section 2.1.
The 2016 alpha platform consists of one 'complete' emissions case: the 2016 base case, i.e., 2016fe_16j.
This platform accounts for atmospheric chemistry and transport within a state of the art photochemical
grid model. In the case abbreviation 2016fe_16j, 2016 is the year represented by the emissions; the "f"
represents the base year platform iteration, which in this case is 2014 (the previous platform, which was a
2011-based platform, was "e"); where the "e" stands for the fifth set of emissions modeled for a 2014-
based modeling platform.
The emissions data in the 2016 platform are primarily based on the 2014NEIv2 for point sources,
nonpoint sources, commercial marine vessels (CMV), onroad and nonroad mobile sources, and fires.
Some platform categories are based on more disaggregated data than are made available in the NEI. For
example, in the platform, onroad mobile source emissions are representated as hourly emissions by
vehicle type, fuel type process and road type. In contrast, the onroad emissions in the 2014NEI are
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developed using the same inputs, but those emissions are aggregated to vehicle type/fuel type totals and
annual temporal resolution. In addition, emissions from Canada and Mexico are used for the platform but
are not part of the NEI. Temporal, spatial and other changes in emissions between the 2014NEI and the
emissions input into the platform are described in Section 2 of this TSD. Point source emissions include
some updates for the year 2016.
The primary emissions modeling tool used to create the air quality model-ready emissions was the Sparse
Matrix Operator Kernel Emissions (SMOKE) modeling system (http ://www.smoke-model.org/), version
4.5 (SMOKE 4.5) with some updates. Emissions files were created for a 36-km national grid, 36US3, and
two 12-km national grids, "12US1" and "12US2", all of which include all of the contiguous states and
parts of Canada and Mexico as shown in Figure 3-1.
The gridded meteorological model used to provide input data for the emissions modeling was developed
using the Weather Research and Forecasting Model (WRF, http://wrf-model.org) 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 2016 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 label "16j " The full case name includes this abbreviation following the emissions
portion of the case name to fully specify the name of the case as "2016fe_16j."
This document contains five sections and several appendices. Section 2 describes the 2016 inventories
input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used with the
emission inventories. 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 2016 Emission Inventories and Approaches
This section describes the 2016 emissions data that make up the 2016 alpha platform. The starting point
for the stationary source emission inputs is the 2014NEIv2 or more detailed temporal/spatial resolution
data used to build the NEI, with some sectors projected to 2016, and other adjustments made to support
modeling as described here. Documentation for the 2014NEIv2, including a TSD, is available at
https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventorv-nei-technical-support-
document-tsd.
The NEI data for CAPs are largely compiled from data submitted by state, local and tribal (S/L/T) air
agencies. HAP emissions data are also from the S/L/T agencies, but, are often augmented by the EPA
because they are voluntarily submitted. The EPA uses the Emissions Inventory System (EIS) to compile
the NEI. The EIS includes hundreds of automated quality assurance (QA) checks to help improve data
quality, and also supports tracking release point (e.g., stack) coordinates separately from facility
coordinates. The EPA collaborated extensively with S/L/T agencies to ensure a high quality of data in the
2014NEI. A targeted review of the data was conducted between the 2014NEIvl and 2014NEIv2 using
initial risk projections to identify potential outliers.
The 2014 NEI includes five data categories: point sources, nonpoint (formerly called "stationary area")
sources, nonroad mobile sources, onroad mobile sources, and events consisting of fires. The NEI uses 60
sectors to further describe the emissions, with an additional biogenic sector generated from a summation
of the gridded, hourly biogenic data used in the emissions modeling platform. In addition to the NEI data,
emissions from the Canadian and Mexican inventories and several other non-NEI data sources are
included in the 2016 platform.
Compared to the 2014v7.1 emissions modeling platform, which is based directly on the 2014NEIv2, the
2016v7.1 alpha emissions modeling platform includes emissions for the year 2016 for some data
categories. The point source emission inventories for platform include partially updated emissions for
2016. Agricultural and wildland fire emissions represent the year 2016. Most area source sectors use
2014NEIv2 emissions estimates except for commercial marine vehicles (CMV), fertilizer emissions, oil
and gas emissions, and onroad and nonroad mobile source emissions. For CMV, SO2 emissions were
updated to reflect new rules on sulfur emissions that took effect in the year 2015. For fertilizer ammonia
emissions, a 2016-specific emissions inventory is used in this platform. Area source oil and gas emissions
were projected from 2014NEIv2 to better represent 2016.
Onroad and nonroad mobile source emissions were developed using the Motor Vehicle Emission
Simulator (MOVES). MOVES2014a was used with S/L inputs, where provided, in combination with
EPA-generated default data. Onroad emissions for the 2016 alpha platform were developed based on
emissions factors output from MOVES2014a for the year 2016 run with inputs derived from the
2014NEIv2 including activity data projected to the year 2016. MOVES2014a replaced the National
Mobile Inventory Model (NMIM) as the interface for using the NONROAD2008 model, thus ensuring
that the gasoline fuels used for nonroad equipment are consistent with those used for onroad vehicles and
using newer data to estimate the HAPs than had been used in NMIM.
For the purposes of preparing the air quality model-ready emissions, the NEI was split into finer-grained
sectors used for emissions modeling. The significance of an emissions modeling or "platform sector" is
that the data are run through the SMOKE programs independently from the other sectors except for the
final merge (Mrggrid). The final merge program combines the sector-specific gridded, speciated, hourly
emissions together to create CMAQ-ready emission inputs.
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Table 2-1 presents the sectors in the 2016 platform and how they generally relate to the 2014NEIv2 as a
starting point. The platform sector abbreviations are provided in italics. These abbreviations are used in
the SMOKE modeling scripts, inventory file names, and throughout the remainder of this document.
Table 2-1. Platform sectors for the 2016v7.1 (alpha) emissions modeling platform
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
EGU units:
ptegu
Point
Point source EGUs for 2016 from the Emissions Inventory System
(EIS), based on 2014NEIv2 with some sources updated to 2016. The
inventory emissions are replaced with hourly 2016 Continuous
Emissions Monitoring System (CEMS) values for NOx and SO2 for
any units that are matched to the NEI, and other pollutants for matched
units are scaled from the 2016 point inventory using CEMS heat input.
Emissions for all sources not matched to CEMS data come from the
raw inventory. Annual resolution for sources not matched to CEMS
data, hourly for CEMS sources.
Point source oil and
gas:
ptoilgas
Point
Point sources for 2016 that include oil and gas production and related
processes based on facilities with the following NAICS: 2111,21111,
211111,211112 (Oil and Gas Extraction); 213111 (Drilling Oil and
Gas Wells); 213112 (Support Activities for Oil and Gas Operations);
2212, 22121, 221210 (Natural Gas Distribution); 48611, 486110
(Pipeline Transportation of Crude Oil); 4862, 48621, 486210 (Pipeline
Transportation of Natural Gas). Includes offshore oil and gas
platforms in the Gulf of Mexico (FIPs=85). Oil and gas point sources
that were not already updated to 2016 in the baseline inventory were
projected from 2014 to 2016. Annual resolution.
Remaining non-
EGU point:
ptnonipm
Point
All 2016 point source records not matched to the ptegu or pt_oilgas
sectors. Includes all aircraft and airport ground support emissions and
some rail yard emissions. Annual resolution.
Agricultural:
ag
Nonpoint
Nonpoint livestock and fertilizer application emissions. Livestock
includes ammonia and other pollutants (except PM2.5) and is from
2014NEIv2. Fertilizer includes only ammonia and is estimated for
2016 using the FEST-C model. County and monthly resolution.
Agricultural fires
with point
resolution: ptagfire
Nonpoint
2016 agricultural fire sources that were developed by EPA as point
sources with day-specific emissions. They are in the nonpoint NEI
data category, but in the platform, they are treated as point sources.
Area fugitive dust:
afdust
Nonpoint
PM10 and PM2 5 fugitive dust sources from the 2014NEIv2 nonpoint
inventory; including building construction, road construction,
agricultural dust, and road dust. The NEI emissions are reduced
during modeling according to a transport fraction and a meteorology-
based (precipitation and snow/ice cover) zero-out. County and annual
resolution.
Biogenic:
beis
Nonpoint
Year 2016, hour-specific, grid cell-specific emissions generated from
the BEIS3.61 model within SMOKE, including emissions in Canada
and Mexico using BELD v4.1 land use data (slightly updated from the
BELDv4.1 used in 2014v7.0).
Category 1, 2 CMV:
cmv_clc2
Nonpoint
Category 1 (CI) and category 2 (C2) commercial marine vessel (cmv)
emissions sources from the 2014NEIv2 nonpoint inventory, except
that it does not use emissions from the 2014 NEI in Federal Waters.
For 2016 modeling, SO2 emissions are reduced by 90% compared to
2014NEIv2. County and annual resolution.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Category 3 CMV:
cmv_c3
Nonpoint
Category 3 (C3) cmv emissions converted to point sources based on
the center of the grid cells. Includes C3 emissions in U.S. state and
Federal waters, and also all non-U.S. C3 emissions except those in
Canadian waters. For 2016 modeling, SO2 emissions are reduced by
90% compared to 2014NEIv2.
locomotives:
rail
Nonpoint
Rail locomotives emissions from the 2014NEIv2. County and annual
resolution.
Remaining
nonpoint:
nonpt
Nonpoint
2014NEIv2 nonpoint sources not included in other platform sectors.
County and annual resolution.
Nonpoint source oil
and gas:
np oilgas
Nonpoint
2014NEIv2 nonpoint sources from oil and gas-related processes,
projected to 2016. County and annual resolution.
Residential Wood
Combustion:
rwc
Nonpoint
2014NEIv2 nonpoint sources from residential wood combustion
(RWC) processes. County and annual resolution.
Nonroad:
nonroad
Nonroad
2016 nonroad equipment emissions developed with the MOVES2014a
using NONROAD2008 version NR08a and new HAP emission factors
than had been used in the 2011NEI and 2014NEIv2. MOVES was
used for all states except California, which submitted their own
emissions. County and monthly resolution.
Onroad:
onroad
Onroad
2016 onroad mobile source gasoline and diesel vehicles from moving
and non-moving vehicles that drive on roads, along with vehicle
refueling. Includes the following modes: exhaust, extended idle,
auxiliary power units, evaporative, permeation, refueling, and brake
and tire wear. For all states except California, developed using winter
and summer MOVES emissions tables produced by MOVES2014a.
Onroad California:
onroadcaadj
Onroad
2016 California-provided CAP onroad mobile source gasoline and
diesel vehicles submitted to the NEI, gridded and temporalized using
MOVES2014a. Volatile organic compound (VOC) HAP emissions
derived from California-provided VOC emissions and MOVES-based
speciation.
Point source fires-
ptjire
Events
Point source day-specific wildfires and prescribed fires for 2016
computed using SMARTFIRE2 for both flaming and smoldering
processes (i.e., SCCs 281XXXX002). Smoldering is forced into layer
1 (by adjusting heat flux).
Non-US. fires:
ptfireothna
N/A
Point source day-specific wildfires and prescribed fires for 2016
provided by Environment Canada with data for missing months, and
for Mexico and Central America, filled in using fires from the Fire
INventory (FINN) from National Center for Atmospheric Research
(NCAR) fires (NCAR, 2016 and Wiedinmyer, C., 2011).
Other dust sources
not from the 2014
NEI:
othafdust
N/A
Fugitive dust sources from Canada's 2013 and 2025 inventories
(interpolated to 2016). A transport fraction adjustment is applied
along with a meteorology-based (precipitation and snow/ice cover)
zero-out. Also includes afdust emissions in Alaska, Hawaii, Puerto
Rico, and Virgin Islands from 2014NEIv2. County and annual
resolution.
Other point sources
not from the 2014
NEI:
othpt
N/A
Point sources from Canada's 2013 and 2025 inventories (interpolated
to 2016) and for Mexico 2014 and 2018 inventories projected from
their 2008 inventory and then interpolated to 2016, annual resolution.
5
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Other non-NEI
nonpoint and
nonroad:
othar
N/A
Year 2016 Canada (province or sub-province resolution) emissions,
interpolated from 2013 and 2025: monthly for agricultural ammonia
and nonroad sources; annual for rail, CMV and other nonpoint Canada
sectors. Year 2016 Mexico (municipio resolution), interpolated
between year 2014 and 2018 projections from their 2008 inventory:
annual nonpoint and nonroad mobile inventories.
Other non-NEI
onroad sources:
onroadcan
N/A
Monthly year 2016 Canada (province resolution or sub-province
resolution, depending on the province) onroad mobile inventory,
interpolated from 2013 and 2025. Also includes onroad emissions in
Alaska, Hawaii, Puerto Rico, and Virgin Islands from 2014NEIv2.
Other non-NEI
onroad sources:
onroad mex
N/A
Monthly year 2016 Mexico (municipio resolution) onroad mobile
inventory, interpolated from 2014 and 2018 inventories developed
with MOVES-Mexico.
The emission inventories in SMOKE input formats for the 2016 alpha platform are available from the
EPA's Air Emissions Modeling website for the alpha platform: https://www.epa.gov/air-emissions-
modeling/2014-2016-version-7-air-emissions-modeling-platforms. under the section entitled "2016v7.1
(alpha) Platform" The 2016 alpha platform "README" file indicates the particular zipped files
associated with each platform sector. A number of reports (i.e., summaries) are available with the data
files for the 2016 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.
The remainder of Section 2 provides details about the data contained in each of the 2016 platform sectors.
Different levels of detail are provided for different sectors depending on the availability of reference
information for the data, the degree of changes or manipulation of the data needed to prepare it for input
to SMOKE, and whether the 2016 platform emissions are significantly different from the 2014NEIv2.
2.1 Point sources (ptegu, pt_oilgas and ptnonipm)
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).
This section describes NEI point sources within the contiguous U.S. and the offshore oil platforms which
are processed by SMOKE as point source inventories, as described in Section 2.5.1. A comprehensive
description of how EGU emissions were characterized and estimated in the 2014 NEI is located in Section
3.4 in the 2014NEIv2 TSD.
A complete NEI is developed every three years, with 2014 being the most recently finished complete NEI.
A comprehensive description about the development of the 2014NEIv2 is available in the 2014NEIv2
TSD. Point inventories are also available in EIS for interim years such as 2016. In this interim point
inventory, larger sources are updated with emissions for year 2016, while other sources are either carried
forward from 2014NEIv2 or are closed.
In preparation for modeling, the complete set of point sources in the NEI was exported from EIS for the
year 2016 into the Flat File 2010 (FF10) format that is compatible with SMOKE
(https://www.cmascenter.Org/smoke/documentation/4.5/html/ch08s02s08.html). For the 2016 alpha
platform, the export of point source emissions from EIS, including stack parameters and locations, was
performed on March 27, 2018. At that time, EIS did not include a complete set of emissions for
6
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hydrochloric acid (HCL) or chlorine (CL2) for 2016, which are needed for CMAQ modeling. These
pollutants typically are augmented within EIS for sources which do not already have them. Augmentation
of HCL and CL2 had only been partially performed within EIS at that time and had not yet incorporated
the Toxics Release Inventory (TRI) dataset, which includes HCL and CL2 emissions by facility for 2016.
For facilities where HCL and CL2 emissions in the 2016 inventory were greater than or equal to the
emissions reported in the TRI dataset, inferring that the HCL and CL2 emissions in the 2016 inventory
were already complete, no additional changes were made to the inventory. For sources where the TRI
emissions were greater than the EIS emissions, HCL and CL2 emissions were added to the inventory for
those facilities, using stack parameters, source coordinates, and other point source identifiers from the
2014NEIv2 point inventory where possible. HCL and CL2 records in the 2016 point inventory that were
augmented based on the TRI dataset are indicated with data set id = "HCL_CL2_Augment_TRr\ The
only other major differences between the 2016 point inventory exported from EIS and the inventory used
for modeling involve identification of additional EGUs and matching CEMs for the ptegu sector, and
removal of EGUs for which there are no CEMS NOx emissions in 2016.
As in the 2014v7.0 and 2014v7.1 platforms, all changes to release parameters that would occur in
SMOKE as a result of missing values or values outside SMOKE internally set ranges in the FF10 file
prior to SMOKE run were incorporated. This was done for two reasons: 1) to provide better transparency
in the FF10 files with respect to the data used in the model, and 2) to ensure that emission inputs are
consistent across CMAQ and AERMOD models since both use the FF10 as the starting point. Because
SMOKE uses metric units (i.e., m and K) for defaults, these are converted to the English units (ft and F)
as specified by the FF10 file format. Out-of-range criteria were changed from v7.0 to be consistent with
the EIS quality assurance checks (as opposed to the default ranges in SMOKE). Other than velocities, for
which the EIS range for flowrate and velocity were inconsistent, no parameters in the NEI had to be
changed due to not falling within the EIS range. Out of range values existed because the flow range
checks in EIS allow some velocities to be above or below the range and we ran the velocity check after
computing the missing flowrates.
Table 2-2 through Table 2-4 show conditions for which changes are made to the NEI values in the
modeling file. The "Records changed" column indicates how many records were changed in the
2014NEIv2 version of the point file and provide the keywords used in the FF10 that indicate that a release
parameter was changed and the situation. Table 2-5 describes the comment incorporated into the SMOKE
made for each change. Even though SMOKE does not use the fugitive release point parameters for
CMAQ, they are included in the table for completeness.
Table 2-2. Release parameter changes to the SMOKE Modeling flat file for point sources-For point
sources with stack releases (ERPtype NOT equal to "1")
Field
Existing
value
New value
Conditions/notes
Records changed
Stkhgt
missing
Use SMOKE
defaults
None
Stkdiam
missing
Use SMOKE
defaults
None
7
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Field
Existing
value
New value
Conditions/notes
Records changed
Stkvel
missing
calculate from
stkflow and
stkdiam if not
missing; otherwise
use SMOKE
defaults
vel =
4* stkflow/(pi* stkdiamA2)
If the flow and diam are
missing such that you
cannot compute, then use
new value based pstk or
global defaults.
1,473,185
No pstk values used
ERPVelCompute
Stktemp
missing
Use SMOKE
defaults
None
Stkhgt
Outside
EIS range
use minimum value
or maximum value
in feet
Less than 1 ft (0.3048 m)
or greater than 1300 ft
(396 m)
None
Stkdiam
Outside
EIS range
use minimum value
or maximum value
in ft
Less than 0.001 ft
(0.0003048 m) or greater
than 300 ft (91.4 m)
None
Stkvel
Outside
EIS range
use minimum value
or maximum value
in ft/s
Less than O.OOlft/s
(0.0003048 m/s) or
greater than 1000 ft/s
(304.8 m/s)
Below min: 18,817
Above max: 11,742
ERPVelRange
stktemp
Outside
SMOKE
tolerance
use minimum value
or maximum value
in F
Less than -30 F (-34.4 C
or 248.15 K) or greater
than 4000 F (2204.4 C or
2477.6 K)
None
Table 2-3. Release parameter changes to the SMOKE Modeling flat file for point sources-For
Fugitive Release Points
Field
Existing
value
New value
Conditions/notes
Records changed
fug width ydim
missing
32.808 ft
3,856,867; ERPFugMissing
fug length xdim
missing
32.808 ft
3,888,847; ERPFugMissing
fug angle
missing
0
3,932,478; ERPFugMissing
fug_height
missing
10 ft
fug_width_ydim and
fug_length_xdim are
missing
3,556,330; ERPFugMissing
fug_height
missing
0
WHEN fug_width_ydim
and fug length xdim are
not missing and > 0
12,742
ERPFugHeightO
Table 2-4. Release parameter changes to the SMOKE Modeling flat file for point sources-For Coke
Ovens, any release point that emits coke oven emissions (pollutant code 140)
Field
Existing value
New value
Conditions/notes
Records changed
stkhgt
< 126 ft
126 ft
erptype NOT = "1"
2159; ERPCokeovenl26
fug height
< 126 ft
126 ft
erptype = "1"
2829; ERPCokeovenl26
fug length xdim
<50 ft
50 ft
erptype = "1"
2767; ERPCokeovenFug50
fug width ydim
<50 ft
50 ft
erptype = "1"
2767; ERPCokeovenFug50
8
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Table 2-5. Description of Comments added to SMOKE Modeling flat file used when defaulting or
changing values from the NEI
Comment
Description
ERPHtRange
height in the inventory was out of range
ERPDi amRange
diameter in the inventory was out of range
ERPVelRange
velocity in the inventory or velocity calculated from the flowrate in the inventory was out of range
ERPTempRange
Temperature in the inventory was out of range
ERPFugMissing
fugitive height, length and width are missing or fugitive length and/or width are missing
ERPFugHeightO
fugitive height in the inventory was set to 0 because the width and length were not missing
ERPCokeovenl26
fugitive or stack height of release point emitting coke oven emissions was less than 126 ft
ERPCokeovenFug50
fugitive length or width was less than 50 ft.
After incorporating the above changes, the flat file was modified to remove sources without specific
locations (i.e., their FIPS code ends in 777). Then the point source FF10 was divided into three NEI-
based platform point source sectors: the EGU sector (ptegu), point source oil and gas extraction-related
emissions (pt oilgas), and the remaining non-EGU sector also called the non-IPM (ptnonipm) sector. The
split was done at the unit level for ptegu and facility level for pt oilgas such that a facility may have units
and processes in both ptnonipm and ptegu, but, cannot be in both pt oilgas and any other point sector.
The EGU emissions are split out from the other sources to facilitate the use of distinct SMOKE temporal
processing and future-year projection techniques. The oil and gas sector emissions (pt oilgas) were
processed separately for summary tracking purposes and distinct future-year projection techniques from
the remaining non-EGU emissions (ptnonipm).
The inventory pollutants processed through SMOKE for all point source sectors were: carbon monoxide
(CO), NOx, VOC, sulfur dioxide (SO2), ammonia (NH3), particles less than 10 microns in diameter
(PM10), and particles less than 2.5 microns in diameter (PM2.5), hydrochloric acid (HC1), and chlorine
(CI2). The NBAFM species are explicit in the CB6-CMAQ chemical mechanism, but for point sources in
the 2016 alpha platform, are generated through VOC speciation, as is normally done for non-toxics
modeling applications. To prevent double counting of mass, NBAFM pollutants are dropped from the
inventory by SMOKE. This is called the "no-integrate" VOC speciation case and is discussed in detail in
Section 3.2.1.1.
The ptnonipm and pt oilgas sector emissions were provided to SMOKE as annual emissions. For those
ptegu sources with CEMS data that could be matched to the 2016 inventory, hourly CEMS NOx and SO2
emissions were used rather than the annual total NEI emissions. For all other pollutants at matched units,
the annual emissions were used as-is from the NEI, but, were allocated to hourly values using heat input
from the CEMS data. For the sources in the ptegu sector not matched to CEMS data, daily emissions
were created using an approach described in Section 2.1.1. For non-CEMS units other than municipal
waste combustors and cogeneration units, IPM region- and pollutant-specific diurnal profiles were applied
to create hourly emissions.
9
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2.1.1 EGU sector (ptegu)
The ptegu sector contains emissions from EGUs in the 2016 alpha point inventory that could be matched
to units found in the National Electric Energy Data System (NEEDS) v5.16 database. The matching was
prioritized according to the amount of the emissions produced by the source. In the SMOKE point 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.
Higher generation capacity units in the ptegu sector are matched to 2016 CEMS data from EPA's Clean
Air Markets Division (CAMD) via ORIS facility codes and boiler ID. For the matched units, SMOKE
replaces the 2016 emissions of NOx and SO2 with the CEMS emissions, thereby ignoring the annual
values specified in the NEI. 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 NEI (except those changed as
discussed in Table 2-2). Because these attributes are obtained from the NEI, the chemical speciation of
VOC and PM2.5 for the sources is selected based on the SCC or in some cases, based on unit-specific data.
If CEMS data exists for a unit, but the unit is not matched to the NEI, the CEMS data for that unit is 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. The EGU flat file inventory is split into a flat file with CEM
matches and a flat file without CEM matches to support analysis and temporalization.
In the SMOKE point 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 in SMOKE-
ready format is available at http://ampd.epa.gov/ampd/ near the bottom of the "Prepackaged Data" tab.
Many smaller emitters in the CEMS program are not identified with ORIS facility or boiler IDs that can
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. Also, 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 NEEDS database than have CEMS data.
The temporal allocation of EGU units matched to CEMS is based on the CEMS data, whereas regional
profiles are used for most of the remaining units. More detail can be found in Section 3.3.2.
Some EIS units match to multiple CAMD units based on cross-reference information in the EIS alternate
identifier table. The multiple matches are used to take advantage of hourly CEM data when a CAMD unit
specific entry is not available in the inventory. Where a multiple match is made the EIS unit is split and
the ORIS facility and boiler IDs are replaced with the individual CAMD unit IDs. The split EIS unit NOX
and S02 emissions annual emissions are replaced with the sum of CEM values for that respective unit.
All other pollutants are scaled from the EIS unit into the split CAMD unit using the fraction of annual
heat input from the CAMD unit as part of the entire EIS unit. The NEEDS ID in the "ipm_yn" column of
the flat file is updated with a "_M_" between the facility and boiler identifiers to signify that the EIS unit
had multiple CEMs matches.
For sources not matched to CEMS data, except for municipal waste combustors (MWC) waste-to-energy
and cogeneration units, daily emissions were computed from the NEI annual emissions using average
CEMS data profiles specific to fuel type, pollutant2, and IPM region. To allocate emissions to each hour
of the day, diurnal profiles were created using average CEMS data for heat input specific to fuel type and
2 The year to day profiles use NOx and SO2 CEMS for NOx and SO2, respectively. For all other pollutants, they use heat input
CEMS data.
10
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IPM region. See Section 3.5.2 for more details on the temporal allocation approach for ptegu sources.
MWC and cogeneration units were specified to use uniform temporal allocation such that the emissions
are allocated to constant levels for every hour of the year. These sources do not use hourly CEMs, and
instead use a PTDAY file with the same emissions for each day, combined with a uniform hourly
temporal profile applied by SMOKE
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 NAICS
codes shown in Table 2-6. 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. More information on the development of the 2014 oil and gas emissions
can be found in Section 4.16 of the 2014NEIv2 TSD. The pt oilgas sector includes emissions from
offshore oil platforms.
Table 2-6. Point source oil and gas sector NAICS Codes
NAICS
NAICS description
2111,21111
Oil and Gas Extraction
211111
Crude Petroleum and Natural Gas Extraction
211112
Natural Gas Liquid Extraction
213111
Drilling Oil and Gas Wells
213112
Support Activities for Oil and Gas Operations
2212, 22121, 221210
Natural Gas Distribution
4862,48621,486210
Pipeline Transportation of Natural Gas
48611, 486110
Pipeline Transportation of Crude Oil
The pt oilgas inventory is a combination of sources with updated emissions for 2016, and sources with
emissions carried forward from 2014NEIv2 with no updates. For this study, sources already updated for
the year 2016 in EIS were used as-is. The emissions carried forward from 2014NEIv2 were projected to
2016. Projection factors for 2016 are based on historical state crude and natural gas production data from
the U.S. Energy Information Administration (EIA), which is available at these two links:
http://www. eia. gov/dnav/ng/ng sum Isum a epgO fgw mmcf a. htm;
htty://www.eia.gov/dnav/vet/yet crd crydn adc mbbl a.htm. Separate factors are calculated for each
state, and for sources related to oil production, gas production, or a combination of oil and gas. These
factors, which are listed in Table 2-7, were applied to CO, NOx, and VOC emissions only from sources
carried forward from the 2014NEIv2 pt_oilgas inventory. The table does not list every state; emissions in
states that do not have projection factors listed were held constant. The complete 2016 pt oilgas
inventory used for this study consists of both sources already updated to 2016 within EIS (used directly),
and sources carried forward from 2014NEIv2 (projected to 2016).
Table 2-7. Oil and gas sector 2016 projection factors
State
Oil projection factor
Gas projection factor
Average projection factor
Alabama
0.825
0.910
0.868
Alaska
0.989
1.019
1.004
Arizona
0.143
0.443
0.293
Arkansas
1.125
0.733
0.929
11
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State
Oil projection factor
Gas projection factor
Average projection factor
California
0.909
0.858
0.883
Colorado
1.214
1.035
1.125
Florida
0.868
1.080
0.974
Illinois
0.905
1.132
1.018
Indiana
0.725
0.938
0.831
Kansas
0.769
0.850
0.809
Kentucky
0.769
0.984
0.876
Louisiana
0.821
0.890
0.855
Maryland
1.000
1.700
1.700
Michigan
0.758
0.874
0.816
Mississippi
0.837
0.891
0.864
Missouri
0.628
0.333
0.480
Montana
0.776
0.881
0.828
Nebraska
0.740
1.273
1.007
Nevada
0.877
1.000
0.938
New Mexico
1.171
1.014
1.093
New York
0.624
0.666
0.645
North Dakota
0.958
1.314
1.136
Ohio
1.475
2.810
2.142
Oklahoma
1.055
1.059
1.057
Oregon
1.000
0.820
0.820
Pennsylvania
0.921
1.248
1.085
South Dakota
0.783
0.661
0.722
Tennessee
0.779
0.681
0.730
Texas
1.018
0.939
0.979
Utah
0.746
0.802
0.774
Virginia
0.500
0.900
0.700
West Virginia
0.987
1.289
1.138
Wyoming
0.953
0.925
0.939
2.1.3 Non-IPM sector (ptnonipm)
With minor exceptions, the ptnonipm sector contains the point sources that are not in the ptegu or
pt oilgas sectors. For the most part, the ptnonipm sector reflects the non-EGU sources of the NEI point
inventory; however, it is likely that some small low-emitting EGUs not matched to the NEEDS database
or to CEMS data are present in the ptnonipm sector. The larger sources in this sector have 2016-specific
emissions, while emissions for smaller sources that were not submitted for the 2016 NEI were pulled
forward from the 2014NEIv2.
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 EIS but are not included in any modeling sectors.
These sources typically represent mobile (i.e., 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 thus 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.
12
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2.2 2016 nonpoint sources (afdust, ag, agfire, ptagfire, np oilgas, rwc,
nonpt)
Several modeling platform sectors were created from the 2014NEIv2 nonpoint inventory. This section
describes the stationary nonpoint sources. Locomotives, CI and C2 CMV, and C3 CMV are also
included the 2014NEIv2 nonpoint data category, but, are mobile sources that are described in Sections
2.4.1 and 2.4.2 as the cmv_clc2, cmv_c3, and rail sectors. The 2014NEIv2 TSD, available from
https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventorv-nei-technical-support-
document-tsd. includes documentation for the nonpoint sector of the 2014NEIv2.
The nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE due to the
configuration of the spatial surrogates. 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 2014NEIv2 nonpoint inventory were separated
into 2016 modeling platform sectors, along with any data that were replaced with non-NEI data or
projected for 2016.
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.
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, followed by another script that zeroes out emissions for
hours 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 (e.g., 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.
The sources in the afdust sector are for SCCs and pollutant codes (i.e., PM10 and PM2.5) considered to be
"fugitive" dust sources. These SCCs are provided in Table 2-8. Table 2-9 shows the SCCs that would
have also been included in this sector if they had emissions in the 2014 NEI.
Table 2-8. SCCs in the afdust platform sector from NEI2014v2: nonzero emissions
see
SCC Description
2294000000
Mobile Sources;Paved Roads;All Paved Roads;Total: Fugitives
13
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see
SCC Description
2294000002
Mobile Sources;Paved Roads;All Paved Roads;Total: Sanding/Salting - Fugitives
2296000000
Mobile Sources;Unpaved Roads;All Unpaved Roads;Total: Fugitives
2311000000
Industrial Processes;Construction: SIC 15 - 17;A11 Processes;Total
2311010000
Industrial Processes;Construction: SIC 15 - 17;Residential;Total
2311010070
Industrial Processes;Construction: SIC 15 - 17;Residential;Vehicle Traffic
2311020000
Industrial Processes;Construction: SIC 15 -
17 ;Industrial/Commercial/Institutional;Total
2311030000
Industrial Processes;Construction: SIC 15 - 17;Road Construction;Total
2325000000
Industrial Processes;Mining and Quarrying: SIC 14;A11 Processes;Total
2325060000
Industrial Processes;Mining and Quarrying: SIC 10;Lead Ore Mining and
Milling;Total
2801000003
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture -
Crops;Tilling
2801000005
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture -
Crops;Harvesting
2801000007
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture -
Crops;Loading
2801000008
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture -
Crops;Transport
2805001000
Miscellaneous Area Sources; Agriculture 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;Agriculture Production - Livestock;Beef cattle - finishing
operations on feedlots (drylots);Confinement
2805001300
Miscellaneous Area Sources;Agriculture Production - Livestock;Beef cattle - finishing
operations on feedlots (drylots);Land application of manure
2805002000
Miscellaneous Area Sources;Agriculture Production - Livestock;Beef cattle production
composite;Not Elsewhere Classified
2805003100
Miscellaneous Area Sources;Agriculture Production - Livestock;Beef cattle - finishing
operations on pasture/range;Confinement
2805007100
Miscellaneous Area Sources;Agriculture Production - Livestock;Poultry production -
layers with dry manure management systems;Confinement
2805009100
Miscellaneous Area Sources;Agriculture Production - Livestock;Poultry production -
broilers; Confinement
2805010100
Miscellaneous Area Sources;Agriculture Production - Livestock;Poultry production -
turkeys; Confinement
2805018000
Miscellaneous Area Sources;Agriculture Production - Livestock;Dairy cattle
composite;Not Elsewhere Classified
2805020002
Miscellaneous Area Sources;Agriculture Production - Livestock;Cattle and Calves
Waste Emissions;Beef Cows
2805023100
Miscellaneous Area Sources;Agriculture Production - Livestock;Dairy cattle -
drylot/pasture dairy;Confinement
2805030000
Miscellaneous Area Sources;Agriculture Production - Livestock;Poultry Waste
Emissions;Not Elsewhere Classified (see also 28-05-007, -008, -009)
2805030007
Miscellaneous Area Sources;Agriculture Production - Livestock;Poultry Waste
Emissions;Ducks
2805030008
Miscellaneous Area Sources;Agriculture Production - Livestock;Poultry Waste
Emissions;Geese
2805035000
Miscellaneous Area Sources;Agriculture Production - Livestock;Horses and Ponies
Waste Emissions;Not Elsewhere Classified
14
-------
see
SCC Description
2805039100
Miscellaneous Area Sources;Agriculture Production - Livestock;Swine production -
operations with lagoons (unspecified animal age);Confinement
2805040000
Miscellaneous Area Sources;Agriculture Production - Livestock;Sheep and Lambs
Waste Emissions;Total
2805045000
Miscellaneous Area Sources;Agriculture Production - Livestock;Goats Waste
Emissions;Not Elsewhere Classified
2805047100
Miscellaneous Area Sources;Agriculture Production - Livestock;Swine production -
deep-pit house operations (unspecified animal age);Confinement
2805053100
Miscellaneous Area Sources;Agriculture Production - Livestock;Swine production -
outdoor operations (unspecified animal age);Confinement
Table 2-9. SCCs in the afdust platform sector from NEI2014v2: zero emissions
SCC
SCC Description
2275085000
Mobile Sources; Aircraft; Unpaved Airstrips; Total
2801000000
Miscellaneous Area Sources; Agriculture Production - Crops; Agriculture - Crops; Total
2805001200
Miscellaneous Area Sources; Agriculture Production - Livestock; Beef cattle - finishing
operations on feedlots (drylots); Manure handling and storage
2805007300
Miscellaneous Area Sources; Agriculture Production - Livestock; Poultry production -
layers with dry manure management systems; Land application of manure
2805008100
Miscellaneous Area Sources; Agriculture Production - Livestock; Poultry production -
layers with wet manure management systems; Confinement
2805008200
Miscellaneous Area Sources; Agriculture Production - Livestock; Poultry production -
layers with wet manure management systems; Manure handling and storage
2805008300
Miscellaneous Area Sources; Agriculture Production - Livestock; Poultry production -
layers with wet manure management systems; Land application of manure
2805009200
Miscellaneous Area Sources; Agriculture Production - Livestock; Poultry production -
broilers; Manure handling and storage
2805009300
Miscellaneous Area Sources; Agriculture Production - Livestock; Poultry production -
broilers; Land application of manure
2805010200
Miscellaneous Area Sources; Agriculture Production - Livestock; Poultry production -
turkeys; Manure handling and storage
2805010300
Miscellaneous Area Sources; Agriculture Production - Livestock; Poultry production -
turkeys; Land application of manure
2805019100
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle - flush dairy;
Confinement
2805019200
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle - flush dairy;
Manure handling and storage
2805019300
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle - flush dairy;
Land application of manure
2805021100
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle - scrape
dairy; Confinement
2805021200
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle - scrape
dairy; Manure handling and storage
2805021300
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle - scrape
dairy; Land application of manure
2805022100
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle - deep pit
dairy; Confinement
2805022200
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle - deep pit
dairy; Manure handling and storage
15
-------
2805022300
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle - deep pit
dairy; Land application of manure
2805023200
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle -
drylot/pasture dairy; Manure handling and storage
2805023300
Miscellaneous Area Sources; Agriculture Production - Livestock; Dairy cattle -
drylot/pasture dairy; Land application of manure
2805025000
Miscellaneous Area Sources; Agriculture Production - Livestock; Swine production
composite; Not Elsewhere Classified (see also 28-05-039, -047, -053)
2805039200
Miscellaneous Area Sources; Agriculture Production - Livestock; Swine production -
operations with lagoons (unspecified animal age); Manure handling and storage
2805039300
Miscellaneous Area Sources; Agriculture Production - Livestock; Swine production -
operations with lagoons (unspecified animal age); Land application of manure
2805047300
Miscellaneous Area Sources; Agriculture Production - Livestock; Swine production - deep-
pit house operations (unspecified animal age); Land application of manure
For the data compiled into the 2014NEIv2, meteorological adjustments are applied to paved and unpaved
road SCCs but not transport adjustments. For the 2014NEIvl, the meteorological adjustments were
inadvertently not applied. This created a large difference between the 2014NEIvl and 2014NEIv2 dust
emissions but did not impact the modeling platform. This is because the modeling platform applies
meteorological adjustments and transport adjustments based on unadjusted NEI values (for both vl and
v2). For the 2014NEIv2, the meteorological adjustments that were applied (to paved and unpaved road
SCCs) had to be backed out in order reapply them in SMOKE. Because it was determined that some
counties in the v2 did not have the adjustment applied, their emissions were used as-is. Thus, the FF10
that is run through SMOKE consists of 100% unadjusted emissions, and after SMOKE all afdust sources
have both transport and meteorological adjustments applied. The 2016 alpha platform uses the same
unadjusted afdust emissions inventory as the 2014v7.1 platform, except that meteorological adjustments
are based on 2016 meteorology instead of 2014 meteorology.
The total impacts of the transport fraction and meteorological adjustments are shown in Table 2-10 after
backing out the meteorological adjustment applied in the 2014NEIv2. The amount of the reduction
ranges from about 94 percent in New Hampshire to about 23 percent in Nevada. The afdust emissions
adjustments are similar to previous platforms. In the 201 lv6.3 the reduction ranged from 29 percent in
Nevada to 93 percent in New Hampshire.
Figure 2-1 illustrates the impact of each step of the adjustment, using the 2014v7.0 platform afdust sector
as an example. The reductions due to the transport fraction adjustments alone are shown at the top of
Figure 2-1. The reductions due to the precipitation adjustments are shown in the middle of Figure 2-1.
The cumulative emission reductions after both transport fraction and meteorological adjustments are
shown at the bottom of Figure 2-1. 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. These plots are from 2014; similar plots for 2016 would look
slightly different depending on the meteorology for each year, but the general pattern would be the same.
Table 2-10. Total impact of 2016 fugitive dust adjustments to unadjusted inventory
State
Unadjusted
* PMio
Unadjusted
* PM2 5
Change in
PMio
Change in
PM25
PMio
Reduction
PMis
Reduction
Alabama
531,293
62,937
-438,671
-51,997
83%
83%
16
-------
State
Unadjusted
* PMio
Unadjusted
* PM2 5
Change in
PMio
Change in
PM25
PMio
Reduction
PMis
Reduction
Arizona
263,125
32,553
-87,885
-10,853
33%
33%
Arkansas
319,496
49,010
-228,479
-34,266
71%
70%
California
312,634
41,077
-144,062
-18,498
46%
45%
Colorado
240,391
36,454
-139,202
-20,316
58%
55%
Connecticut
23,464
3,341
-20,583
-2,936
88%
88%
Delaware
14,316
2,456
-10,454
-1,800
73%
73%
District of
Columbia
2,547
367
-1,932
-277
75%
75%
Florida
715,494
81,268
-445,103
-50,357
63%
62%
Georgia
552,231
65,601
-458,075
-54,088
83%
83%
Idaho
449,835
55,636
-299,519
-36,055
67%
65%
Illinois
994,307
143,485
-647,183
-93,160
65%
64%
Indiana
713,793
83,925
-531,386
-62,342
75%
74%
Iowa
384,852
59,826
-241,936
-37,560
63%
62%
Kansas
610,450
98,980
-295,548
-46,914
48%
47%
Kentucky
311,270
42,672
-244,594
-33,289
79%
78%
Louisiana
265,757
35,626
-191,357
-25,271
72%
71%
Maine
37,846
5,854
-34,175
-5,310
90%
91%
Maryland
103,136
16,220
-80,553
-12,624
77%
77%
Massachusetts
147,627
18,236
-127,438
-15,662
87%
86%
Michigan
388,603
48,408
-307,739
-38,155
79%
79%
Minnesota
403,260
61,397
-296,519
-44,688
73%
72%
Mississippi
432,583
53,230
-350,713
-42,515
81%
80%
Missouri
1,597,370
184,016
-1,170,745
-134,315
74%
73%
Montana
431,167
61,792
-275,027
-37,722
64%
61%
Nebraska
347,803
55,013
-176,311
-27,704
51%
50%
Nevada
159,216
22,770
-37,590
-5,235
23%
23%
New
Hampshire
21,762
4,476
-20,281
-4,169
94%
94%
New Jersey
39,910
9,017
-31,436
-7,082
79%
79%
New Mexico
487,322
53,646
-169,159
-18,644
35%
35%
New York
266,587
44,926
-225,543
-38,051
85%
85%
North Carolina
201,723
29,163
-166,360
-24,065
82%
82%
North Dakota
472,269
82,353
-285,360
-49,598
60%
60%
Ohio
926,270
115,558
-714,546
-88,754
77%
77%
Oklahoma
448,827
67,546
-234,212
-34,417
52%
51%
Oregon
656,174
73,388
-510,850
-55,666
78%
76%
Pennsylvania
239,408
37,266
-204,741
-31,915
85%
85%
Rhode Island
4,773
759
-3,723
-592
78%
78%
17
-------
State
Unadjusted
* PMio
Unadjusted
* PM2 5
Change in
PMio
Change in
PM25
PMio
Reduction
PMis
Reduction
South Carolina
161,909
21,449
-126,440
-16,760
78%
78%
South Dakota
337,913
62,999
-192,859
-35,808
57%
56%
Tennessee
292,101
42,813
-236,307
-34,482
81%
80%
Texas
1,253,345
178,124
-639,339
-88,138
51%
49%
Utah
207,734
26,019
-111,731
-13,796
54%
53%
Vermont
22,131
3,212
-20,038
-2,898
91%
90%
Virginia
283,722
36,631
-239,744
-30,957
85%
85%
Washington
239,794
41,136
-140,928
-24,047
59%
58%
West Virginia
122,180
15,017
-112,762
-13,862
92%
92%
Wisconsin
687,613
89,370
-532,980
-68,885
78%
77%
Wyoming
239,512
29,074
-131,571
-15,750
55%
54%
Domain Total
18,366,850
2,486,092
-12,333,687
-1,642,242
67%
66%
* Unadjusted" here does not mean raw 2014NEIv2, it means 2014NEIv2 with met adjustments backed out
as appropriate (i.e. the inventory that was fed into SMOKE)
18
-------
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction,
precipitation, and cumulative
2014fa Xportfrac - Unadjusted Annual Afdust PM25
Max: 0,0 Min: -1771.085
2014fa Precip and Xportfrac Adjusted - Xportfrac Annual Afdust PM2 5
Max: 0-0006 Min: -534.8289
19
-------
2014fa Precip and Xportfrac Adjusted - Unadjusted Annual Afdust PM25
C 0'»: ' ll 1 ¦¦'0 '¦
2.2.2 Agricultural sector (ag)
The "ag" sector includes NH3 emissions from fertilizer from 2016, and emissions of all pollutants other
than PM2.5 from livestock from 2014NEIv2, in the nonpoint (county-level) data category. PM2.5 from
livestock are in the afdust sector. The livestock and fertilizer emissions in this sector are based only on the
SCCs starting with 2805. The livestock SCCs are shown in Table 2-11 and 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 fertilizer SCCs are shown in Table 2-12 and consist of 15 specific types of ammonia-based fertilizer
and one for miscellaneous fertilizers. The "ag" sector includes all of the NH3 emissions from fertilizer
from the NEI. However, the "ag" sector does not include all of the livestock NH3 emissions, as there is a
very small amount of NH3 emissions from livestock in the ptnonipm inventory (as point sources) in
California (883 tons; less than 0.5 percent of state total) and Wisconsin (356 tons; about 1 percent of state
total). In addition to NH3. the "ag" sector also includes livestock emissions from all pollutants other than
PM2.5. Note that PM2.5 from livestock are in the afdust sector.
Table 2-11. Livestock SCCs extracted from the NEI to create the ag sector
see
SCC Description*
NH3+
other
pollutants
2805001100
Beef cattle - finishing operations onfeedlots (drylots);Confinement
2805001200
Beef cattle - finishing operations on feedlots (drylots);Manure handling and storage
2805001300
Beef cattle - finishing operations on feedlots (dry lots);Land application of manure
Yes
2805002000
Beef cattle production composite: Not Elsewhere Classified
Yes
2805003100
Beef cattle - finishing operations on pasture/range; Confinement
2805007100
Poultry production - layers with dry manure management systems;Confinement
Yes
2805007300
Poultry production - layers with dry manure management svstems:! .and application of manure
20
-------
SCC
SCC Description*
NH3+
other
pollutants
2805008100
Poultry production - layers with wet manure management systems;Confinement
Yes
2805008200
Poultry production - layers with wet manure management systems;Manure handling and
storage
2805008300
Poultry production - layers with wet manure management systems;Land application of manure
2805009100
Poultry production - broilers;Confinement
Yes
2805009200
Poultry production - broilers ;Manure handling and storage
2805009300
Poultry production - broilers;Land application of manure
2805010100
Poultry production - turkeys;Confinement
yes
2805010200
Poultry production - turkeys;Manure handling and storage
yes
2805010300
Poultry production - turkeys;Land application of manure
2805018000
Dairy cattle composite;Not Elsewhere Classified
yes
2805019100
Dairy cattle - flush dairy;Confinement
yes
2805019200
Dairy cattle - flush dairy;Manure handling and storage
2805019300
Dairy cattle - flush dairy;Land application of manure
2805020002
Cattle and Calves Waste Emissions :Beef Cows
2805021100
Dairy cattle - scrape dairy;Confinement
yes
2805021200
Dairy cattle - scrape dairy;Manure handling and storage
2805021300
Dairy cattle - scrape dairy;Land application of manure
2805022100
Dairy cattle - deep pit dairy;Confinement
yes
2805022200
Dairy cattle - deep pit dairy;Manure handling and storage
2805022300
Dairy cattle - deep pit dairy;Land application of manure
2805023100
Dairy cattle - drylot/pasture dairy;Confinement
2805023200
Dairy cattle - drylot/pasture dairy;Manure handling and storage
2805023300
Dairy cattle - drylot/pasture dairy;Land application of manure
2805025000
Swine production composite;Not Elsewhere Classified (see also 28-05-039, -047, -053)
yes
2805030000
Poultry Waste Emissions;Not Elsewhere Classified (see also 28-05-007, -008, -009)
yes
2805030007
Poultry Waste Emissions;Ducks
2805030008
Poultry Waste Emissions;Geese
2805035000
Horses and Ponies Waste Emissions;Not Elsewhere Classified
yes
2805039100
Swine production - operations with lagoons (unspecified animal age);Confinement
yes
2805039200
Swine production - operations with lagoons (unspecified animal age);Manure handling and
storage
2805039300
Swine production - operations with lagoons (unspecified animal age);Land application of
manure
2805040000
Sheep and Lambs Waste Emissions;Total
yes
2805045000
Goats Waste Emissions;Not Elsewhere Classified
yes
2805047100
Swine production - deep-pit house operations (unspecified animal age);Confinement
yes
2805047300
Swine production - deep-pit house operations (unspecified animal age);Land application of
manure
2805053100
Swine production - outdoor operations (unspecified animal age) Confinement
* All SCC Descriptions begin "Miscellaneous Area Sources;Agriculture Production - Livestock"
Table 2-12. Fertilizer SCCs extracted from the NEI for inclusion in the "ag" sector
SCC
SCC Description*
2801700001
Anhydrous Ammonia
2801700002
Aqueous Ammonia
2801700003
Nitrogen Solutions
2801700004
Urea
2801700005
Ammonium Nitrate
21
-------
SCC
SCC Description*
2801700006
Ammonium Sulfate
2801700007
Ammonium Thiosulfate
2801700010
N-P-K (multi-grade nutrient fertilizers)
2801700011
Calcium Ammonium Nitrate
2801700012
Potassium Nitrate
2801700013
Diammonium Phosphate
2801700014
Monoammonium Phosphate
2801700015
Liquid Ammonium Polyphosphate
2801700099
Miscellaneous Fertilizers
* All descriptions include "Miscellaneous Area Sources;
Agriculture Production - Crops; Fertilizer Application" as
the beginning of the description.
Fertilizer emissions for 2016 are based on the FEST-C model. The bidirectional version of CMAQ (v5.3)
and the Fertilizer Emissions Scenario Tool for CMAQ FEST-C (vl.3) were used to estimate ammonia
(NH3) emissions from agricultural soils. The approach to estimate 2016
fertilizer emissions consists of these steps:
• Run FEST-C and CMAQ model with bidirectional ("bidi") NH3 exchange to produce nitrate
(NO3), Ammonium (NH4+, including Urea), and organic (manure) nitrogen (N) fertilizer usage
estimates, and gaseous ammonia NH3 emission estimates respectively.
• Calculate county-level emission factors as the ratio of bidirectional CMAQ NH3 fertilizer
emissions to FEST-C total N fertilizer application.
• Assign the NH3 emissions to one SCC: ".. .Miscellaneous Fertilizers" (2801700099).
The Fertilizer Emission Scenario Tool for CMAQ (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 model, and nitrogen deposition data
from a previous or historical average CMAQ simulation. FEST-C, then uses the USDA's Environmental
Policy Integrated Climate (EPIC) modeling system to simulate the agricultural practices and soil
biogeochemistry and provides information regarding fertilizer timing, composition, application method
and amount.
22
-------
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions
The Fertilizer Emission Scenario Tool for CMAQ
(FEST-C)
Crops
Meteorology
| Deposition
Fertilizer N
The system works for:
•Any domains covering the CONUS,
southern Canada and northern Mexico.
• Four WRF projections (longitude/latitude,
Lambert conformal conic, Universal polar
stereographic, and Mercator).
Non-Fertilizer
NEI Emission
Inventories
WRF
Spatial Allocator
Tools
BELD4
(NLCD/MODIS,
Trees, Crops)
CMAQ
Bi-directional
NH3 Flux
modeling
Agri. Ecosystem
Assessment
(yield, soil erosion, water
quantity/quality)
Environmental
Policy Integrated
Climate (EPIC)
Java-based
Fertilizer Tool
Interface
The following activity parameters were input into the EPIC model:
• Grid cell meteorological variables from WRF (see Table 3)
• 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.
We used the WRF meteorological model to provide grid cell meteorological parameters for 2016 using a
national 12-km rectangular grid covering the continental U.S. The meteorological parameters in Table
2-13 were used as EPIC model inputs.
Table 2-13. Environment variables needed for an EPIC simulation
EPIC input variable
Variable Source
Daily Total Radiation (MJ m2 )
WRF
Daily Maximum 2-m Temperature (C)
WRF
23
-------
EPIC input variable
Variable Source
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 are based on the 1992 USD A Soil Conservation Service
(CSC) Soils-5 survey. The EPIC model then is run for 25 years using current fertilization and agricultural
cropping techniques to estimate soil nutrient content and pH for the 2016 EPIC/WRF/CMAQ simulation.
The presence of crops in each model grid cell was determined through the use of 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). AAPFCO data are 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 are 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. We
used the USD A Agricultural Resource Management Survey (ARMS) to provide management activity
data. These data cover 10 USD A 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.).
The emission factors were derived from the 2016 CMAQ FEST-C outputs. Total fertilizer emission
factors for each month and county were computed by taking the ratio of total fertilizer NH3 emissions
(short tons) to total nitrogen fertilizer application (short tons). 12 km by 12 km gridded NH3 emissions
were mapped to a county shape file polygon. The cell was assigned to a county if the grid centroid fell
within the county boundary.
Agricultural emissions from livestock are based on the 2014NEIv2, which is a mix of state-submitted data
and EPA estimates, and are unchanged from the 2014v7.1 platform. The EPA estimates in 2014NEIv2
were revised from 2014NEIvl, using refined methodologies and/or data. Livestock emissions utilized
improved animal population data. VOC livestock emissions, new for this sector compared to the
2014v7.0 platform, were estimated by multiplying a national VOC/NH3 emissions ratio by the county
NH3 emissions. HAP emissions used HAP-to-VOC factors from livestock profiles in the SPECIATE
database (EPA, 2016). The 2014NEI 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 2012 and 2014 U.S. Department of Agriculture (USDA) agricultural census data. Details on the
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approach are provided in Section 4.5 of the 2014NEIvl TSD; updates for 2014NEIv2 (the new population
estimates) are provided in Section 4.5 of the 2014NEIv2 TSD.
For livestock, meteorological-based temporal allocation (described in Section 3.5.5) is used for month-to-
day and day-to-hour temporal allocation. Monthly profiles are based on the daily data underlying the
EPA estimates. This was different from 2014v7.0 where the daily data underlying the NEI were used for
generating daily emissions. Fertilizer uses different state-specific year-to-month profiles than livestock
but uses the same meteorological-based month-to-hour profiles as livestock. These monthly profiles have
not changed from previous platforms.
2.2.3 Agricultural 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 2016, processed as point sources in support of CMAQ inline plume rise in a similar way to the
emissions in ptfire, except with the sector name "ptagfire". State-provided agricultural fire data from the
2014NEIv2 are not used in this study.
Heat flux and acres burned were provided by George Pouliot of EPA's Office of Research and
Development. Based on field reconnaissance of J. McCarty (2013, personal communication), a "typical"
agricultural field size was assumed for each burn location, which varied by region of the country between
40 and 80 acres. The assumed field sizes can be found at http://www.epa.gov/sites/production/files/2015-
06/draft 2014 ag grasspasture emissions nei mav62015.xlsx. The heat flux calculation for each
agricultural fire depends on estimated field size burned and the fuel loading by SCC (tons/acre). The fuel
load estimate is also provided in the above spreadsheet. The ptagfire emissions estimated by the EPA are
at point source and day-specific resolution. EPA data were developed using a multiple satellite detection
database and crop level land use information. For the NEI, these are summed to the county and national
level, but because they are computed at this finer temporal resolution, the more detailed data were used
for this platform.
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. New agricultural field burning SCCs
were added to the 2014 NEI to account for grass/pasture burning (also known as rangeland burning)
which is included the agriculture field burning sector of the NEI.
For this modeling platform, a SMOKE update allows the use of HAP integration for speciation for
PTDAY inventories. The 2016 agricultural fire inventory does not include emissions for HAPs, however,
so this feature was not used for this study.
2.2.4 Nonpoint source oil and gas sector (np_oilgas)
The nonpoint oil and gas (np oilgas) sector contains onshore and offshore oil and gas emissions. The
EPA estimated emissions for all counties with 2014 oil and gas activity data with the Oil and Gas Tool,
and many S/L/T agencies also submitted nonpoint oil and gas data. Where S/L/T submitted nonpoint
CAPS but no HAPs, the EPA augmented the HAPs using HAP augmentation factors (county and SCC
level) created from the Oil and Gas Tool. The types of sources covered include drill rigs, workover rigs,
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artificial lift, hydraulic fracturing engines, pneumatic pumps and other devices, storage tanks, flares, truck
loading, compressor engines, and dehydrators. The SCCs that comprise this sector are listed in
Appendix A.
The 2014NEIv2 nonpoint oil and gas inventory was projected to 2016 for this study. The methodology
and projection factors for npoilgas projections were the same as for pt oilgas, except that 2016
projections were applied to the entire 2014NEIv2 np oilgas inventory. Projection factors for 2016 are
based on the same EIA crude and natural gas production data as the point oil and gas projections
discussed in Section 2.1.2. Separate factors are calculated for each state, and for sources related to oil
production, gas production, or a combination of oil and gas. These factors, which are listed in Table 2-7,
were applied to CO, NOx, and VOC emissions from the 2014NEIv2 np_oilgas inventory.
2.2.5 Residential wood combustion sector (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 firepits and
chimneas. 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, the conventional units were constructed prior to 1988. Units constructed after 1988 had to
meet EPA emission standards and they are either catalytic or non-catalytic. The SCCs in the rwc sector
are listed in Table 2-14.
Residential wood combustion emissions for the 2016 alpha platform are from 2014NEIv2. As with the
other nonpoint categories, a mix of S/L and EPA estimates were used. The 2014NEIv2 EPA estimates
included adjustments to appliance fractions to account for that not all appliances burn 100% wood (they
also can burn natural gas and propane) and some changes to emission factors. For more information on
the development of the residential wood combustion emissions, see Section 4.14 of the 2014NEIv2 TSD.
Table 2-14. SCCs in the residential wood combustion sector (rwc)*
see
SCC Description
2104008100
SSFC;Residential;Wood;Fireplace: general
2104008210
SSFC;Residential;Wood;Woodstove: fireplace inserts; non-EPA certified
2104008220
SSFC;Residential;Wood;Woodstove: fireplace inserts; EPA certified; non-catalytic
2104008230
SSFC;Residential;Wood;Woodstove: fireplace inserts; EPA certified; catalytic
2104008310
SSFC;Residential;Wood;Woodstove: freestanding, non-EPA certified
2104008320
SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, non-catalytic
2104008330
SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, catalytic
2104008400
SSFC;Residential;Wood;Woodstove: pellet-fired, general (freestanding or FP insert)
2104008510
SSFC;Residential;Wood;Furnace: Indoor, cordwood-fired, non-EPA certified
2104008610
SSFC;Residential;Wood;Hydronic heater: outdoor
2104008700
SSFC;Residential;Wood;Outdoor wood burning device, NEC (fire-pits, chimeas, etc)
2104009000
SSFC;Residential;Firelog;Total: All Combustor Types
* SSFC=Stationary Source Fuel Combustion
The spatial and temporal allocation for the rwc sector follow the same approach as in the 2014v7.1
platform. The temporal allocation of annual rwc emissions to day of year uses a meteorological-based
approach for most SCCs as discussed in Section 3.5.4. For the 2016 alpha platform, day-of-year
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temporalization is based on 2016 meteorology. All SCCs in this sector are spatially allocated using low
intensity residential land (code 300).
2.2.6 Other nonpoint sources sector (nonpt)
Stationary nonpoint sources that were not subdivided into the afdust, ag, npoilgas, or rwc sectors were
assigned to the "nonpt" sector. Locomotives and CMV mobile sources from the 2014NEIv2 nonpoint
inventory are not included in this sector and are described in Section 2.4.1. There are too many SCCs in
the nonpt sector to list all of them individually, but the types of sources in the nonpt sector include:
• stationary source fuel combustion, including industrial, commercial, and residential and orchard
heaters;
• commercial sources such as commercial cooking;
• industrial processes such as chemical manufacturing, metal production, mineral processes,
petroleum refining, wood products, fabricated metals, and refrigeration;
• solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic
marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances,
and motor vehicles;
• solvent utilization for degreasing of furniture, metals, auto repair, electronics, and manufacturing;
• solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care
products, household products, adhesives and sealants;
• solvent utilization for asphalt application and roofing, and pesticide application;
• storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline
service stations, aviation, and marine vessels;
• storage and transport of chemicals;
• waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;
• miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair
shops.
For the 2016 alpha platform, the emissions inventory for the nonpt sector is from 2014NEIv2 and is the
same as in the 2014v7.1 platform.
2.3 2016 onroad mobile sources (onroad)
Onroad mobile source emissions result from motorized vehicles that are normally operated 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 between 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). Except for California, all onroad emissions are generated using
the SMOKE-MOVES emissions modeling framework that leverages MOVES generated emission factors
(http://www.epa.gov/otaq/models/moves/index.htm), county and SCC-specific activity data, and hourly
meteorological data. The onroad SCCs in the modeling platform are more resolved than those in the NEI,
because the NEI SCCs distinguish vehicles and fuels, but in the platform, they also distinguish between
off-network, extended idle, and the various MOVES road-types. For more details on the approach and for
a summary of the inputs submitted by states, see the section 6.5 of the 2014NEIv2 TSD. The 2016 alpha
platform includes emission factors processed by MOVES for the year 2016, and projections of
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2014NEIv2 vehicle miles traveled, vehicle population, and hoteling (extended idling) hours activity data
to 2016.
2.3.1 Onroad (onroad)
For the continental U.S., the EPA uses a modeling framework that accounts for the temperature sensitivity
of the on-road emissions. Specifically, the EPA used MOVES inputs for representative counties, vehicle
miles traveled (VMT), vehicle population (VPOP), and hoteling 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 information available from meteorology modeling used for air quality modeling. The
"SMOKE-MOVES" integration tool was developed by the EPA in 2010 and is used for regional air
quality modeling of onroad mobile sources.
SMOKE-MOVES requires that emission rate "lookup" tables be generated by MOVES, which
differentiates 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., the EPA used an automated process to run MOVES to produce emission factors for a series of
temperatures and speeds for a set of "representative counties," to which every other county is mapped.
Representative counties are used because it is impractical to generate a full suite of emission factors for
the more than 3,000 counties in the U.S. 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 age distributions and
vehicle fuel types, rather than choose the value based on the representative county, a weighted average
was computed. For the 2016 alpha platform, there are 303 representative counties, same as in the
2014v7.1 platform. A detailed discussion of the representative counties is in the 2014NEIv2 TSD, Section
6.8.2.
Once representative counties have been identified, emission factors are generated by running MOVES for
each representative county and for two "fuel months" - January to represent winter months, and July to
represent summer months - because different types of fuels are used in each season. SMOKE selects the
appropriate MOVES emissions rates for each county, hourly temperature, SCC, and speed bin and
multiplies the emission rate by appropriate activity data: VMT (vehicle miles travelled), VPOP (vehicle
population), or HOTELING (hours of extended idle) to produce emissions. 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, speed distribution, temporal profiles, 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 and speeds that exist in each
county during the modeled period.
6) Run SMOKE to apply the emission factors to activity data (VMT, VPOP, and HOTELING) to
calculate emissions based on the gridded hourly temperatures in the meteorological data.
7) Aggregate the results to the county-SCC level for summaries and QA.
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The onroad emissions are processed in four processing streams that are merged together into the onroad
sector emissions after each of the four streams have been processed:
• rate-per-distance (RPD) uses VMT as the activity data plus speed and speed profile information to
compute on-network emissions from exhaust, evaporative, permeation, refueling, and brake and
tire wear processes;
• rate-per-vehicle (RPV) uses VPOP activity data to compute off-network emissions from exhaust,
evaporative, permeation, and refueling processes;
• rate-per-profile (RPP) uses VPOP activity data to compute off-network emissions from
evaporative fuel vapor venting, including hot soak (immediately after a trip) and diurnal (vehicle
parked for a long period) emissions; and
• rate-per-hour (RPH) uses hoteling hours activity data to compute off-network emissions for idling
of long-haul trucks from extended idling and auxiliary power unit process.
The list of emission modes and SCCs differ between the platform and the NEI. Both SMOKE-MOVES
runs were generated at the same level of detail, but the NEI emissions were aggregated into 2 all-inclusive
modes: refueling and all other modes. In addition, the NEI SCCs were aggregated over roads to all
parking and all road emissions. The list of modes (or aggregate processes) and the corresponding
MOVES processes mapped to them are listed in Table 2-15.
Table 2-15. Onroad emission aggregate processes
Aggregate process
Description
MOVES process IDs
40
All brake and tire wear
9; 10
53
All extended idle exhaust
17;90
62
All refueling
18; 19
72
All exhaust and evaporative except refueling and hoteling
1;2;11;12;13;15;16
91
Auxiliary Power Units
91
The onroad emissions inputs for the platform are based on the 2014NEIv2, described in more detail in
Section 6 of the 2014NEIv2 TSD. These inputs include:
• MOVES County databases (CDBs) including Low Emission Vehicle (LEV) table
• Representative counties
• Fuel months
• Meteorology
• Activity data (VMT, VPOP, speed, HOTELING)
Representative counties and fuel months are the same as for the 2014NEIv2, while other inputs were
updated for the year 2016. The activity data was projected from 2014 to 2016 using the following
procedure. First, VMT was projected using factors calculated from FHWA VM-2 data
(https://www.fhwa.dot.gov/policyinformation/statistics/2014/vm2.cfm,
https://www.fhwa.dot.gov/policvinformation/statistics/2016/vm2.cfm). Year-to-year projection factors
were calculated by state, with separate factors for urban and rural road types, and then applied to the
2014NEIv2 VMT. In some states, a single state-wide projection factor for all road types was computed,
usually in states with large discrepancies in how activity is split between urban and rural road types in the
FHWA data as compared to the 2014NEIv2 VMT dataset. States for which a single projection factor was
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applied state-wide are: Alaska, Georgia, Indiana, Louisiana, Maine, Massachusetts, Nebraska, New
Mexico, New York, North Dakota, Tennessee, Virginia, and West Virginia. Furthermore, in Texas and
Utah, a single state-wide projection factor was calculated based on state-wide VMT totals provided by
each state's Department of Transportation3. VMT projection factors for all states are in Table 2-16.
Table 2-16. Factors applied to project VMT from 2014 to 2016
Rural
Urban
State
roads
roads
Alabama
5.36%
5.47%
Alaska
8.27%
8.27%
Arizona
1.07%
6.35%
Arkansas
4.80%
5.36%
California
1.06%
2.39%
Colorado
5.97%
6.67%
Connecticut
1.33%
1.45%
Delaware
4.42%
6.75%
District of
Columbia
0.00%
2.68%
Florida
10.27%
6.64%
Georgia
10.10%
10.10%
Hawaii
6.14%
4.21%
Idaho
5.51%
7.80%
Illinois
3.40%
1.96%
Indiana
5.02%
5.02%
Iowa
6.17%
6.05%
Kansas
2.42%
6.52%
Kentucky
2.52%
3.26%
Louisiana
-5.49%
7.10%
Maine
3.75%
3.75%
Maryland
4.98%
4.75%
Massachusetts
7.42%
7.42%
Michigan
5.62%
0.66%
Minnesota
2.66%
2.97%
Mississippi
1.83%
4.96%
Missouri
4.70%
4.17%
Montana
3.32%
4.34%
Nebraska
5.54%
5.54%
Nevada
8.30%
5.30%
New Hampshire
5.00%
3.65%
New Jersey
5.41%
2.83%
3 Sources of Texas data: https://ftp.dot.state.tx.us/pub/txdot-info/trf7crash statistics/2014/01 .pdf.
https://ftp.dot.state.tx.us/pub/txdot-info/trf/crash statistics/2015/0l.pdf
Sources of Utah data: https://www.udot.utah.gov/main/uconowner. gf?n=32396326443209656 ,
https://www.udot.utah. gov/main/uconowner. gf?n=27035817009129993
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Rural
Urban
State
roads
roads
New Mexico
10.01%
10.01%
New York
-4.90%
-4.90%
North Carolina
7.47%
8.41%
North Dakota
-7.35%
-7.35%
Ohio
4.61%
5.42%
Oklahoma
4.72%
1.23%
Oregon
8.05%
4.84%
Pennsylvania
-4.30%
4.73%
Rhode Island
3.26%
3.26%
South Carolina
9.70%
8.89%
South Dakota
3.23%
2.64%
Tennessee
6.29%
6.29%
Texas
7.82%
7.82%
Utah
11.62%
11.62%
Vermont
5.55%
2.24%
Virginia
-4.93%
9.78%
Washington
6.86%
4.43%
West Virginia
2.21%
2.21%
Wisconsin
4.15%
9.32%
Wyoming
-1.38%
-1.53%
Puerto Rico
0.00%
0.00%
Virgin Islands
0.00%
0.00%
Once the VMT dataset was finalized for 2016, VPOP activity for 2016 was calculated by applying
VMT/VPOP ratios based on 2014NEIv2 to the projected 2016 VMT for each county, fuel, and vehicle
type. Hoteling hours activity for 2016 was calculated in a similar manner, by applying 2014NEIv2-based
VMT/hoteling ratios to the projected 2016 VMT, but only for VMT from long-haul combination trucks
on restricted roads.
An additional step was taken for the refueling emissions. Colorado submitted point emissions for
refueling for some counties4. For these counties, the EPA zeroed out the onroad estimates of refueling
(i.e., SCCs =220xxxxx62) so that the states' point emissions would take precedence. The onroad
refueling emissions were zeroed out using the adjustment factor file (CFPRO) and Movesmrg.
For more detailed information on the methods used to develop the 2014 onroad mobile source emissions
and the input data sets, see the 2014NEIv2 TSD.
California is the only state agency for which submitted onroad emissions were used in the 2014 NEIv2
and 2016 alpha platform. California uses their own emission model, EMFAC, which uses emission
inventory codes (EICs) to characterize the emission processes instead of SCCs. The EPA and California
worked together to develop a code mapping to better match EMFAC's EICs to EPA MOVES' detailed set
of SCCs that distinguish between off-network and on-network and brake and tire wear emissions. This
4 There were 52 counties in Colorado that had point emissions for refueling. Outside Colorado, it was determined that
refueling emissions in the 2014 NEIv2 point did not significantly duplicate the refueling emissions in onroad.
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detail is needed for modeling but not for the NEI. This code mapping is provided in
"2014vl EICtoEPASCCmapping.xlsx." which is found in the supporting data for the 2014 NEI v2 TSD
(ftp://newftp.epa.gov/air/nei/2014/doc/2014v2 supporti n "data/ on road/). California provided their CAP
and HAP emissions by county using EPA SCCs after applying the mapping. There was one change made
after the mapping: the vehicle/fuel type combination gas intercity buses (first 6 digits of the SCC =
220141), that is not generated using MOVES, was changed to gasoline single unit short-haul trucks
(220152) for consistency with the modeling inventory. California provided EMFAC2014-based onroad
emissions inventories for 2014 and 2017; emissions inventories from those two years were interpolated to
2016 values for this platform.
The California onroad mobile source emissions were created through a hybrid approach of combining
state-supplied annual emissions with EPA-developed SMOKE-MOVES runs. Through this approach, the
platform was able to reflect the unique rules in California, while leveraging the more detailed SCCs and
the highly resolved spatial patterns, temporal patterns, and speciation from SMOKE-MOVES. The basic
steps involved in temporally allocating onroad emissions from California based on SMOKE-MOVES
results were:
1) Run CA using EPA inputs through SMOKE-MOVES to produce hourly 2016 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 estimates5. These were calculated for
each county/SCC/pollutant combination. 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 2014 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, Category 3 Commercial Marine Vessels
(cmv_c1c2, cmv_c3)
The cmv_clc2 and cmv_c3 sectors contain commercial marine vessel (CMV) emissions. The cmv_clc2
sector contains Category 1 and 2 (CI and C2) CMV emissions that traverse state and Federal waters and
that are in the 2014 NEIv2. The cmv_c3 sector contains Category 3 emissions that traverse state and
5 These ratios were created for all matching pollutants. These ratios were duplicated for all appropriate modeling species. For
example, the EPA used the NOx ratio for NO, NO2, HONO and used the PM2 5 ratio for PEC, PNO3, POC, PSO4, etc. (For
more details on NOx and PM speciation, see Sections 3.2.2, and 3.2.3. For VOC model-species, the EPA used VOC ratios.)
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Federal waters (in the NEI) plus C3 in waters not covered by the NEI. The CI and C2 emissions were
split from C3 to allow the C3 to be modeled as point sources with plume rise.
All NEI emissions from these sectors that are in state waters are annual and at county-SCC resolution;
however, in the NEI they are provided at the sub-county level (port or underway shape ids) and by SCC
and emission type (e.g., hoteling, maneuvering). NEI emission estimates are a mix of state-submitted
values and EPA-developed emissions in areas where states did not submit. The emissions developed by
EPA use a "bottom up" procedure based on activity details from the U.S. Coast Guard and Army Corps of
Engineers databases. For the 2014NEIv2, emissions developed by the Lake Michigan Air Directors
Consortium (LADCO) were used for several states in the region: Illinois, Indiana, Iowa, Minnesota,
Michigan, Missouri, Ohio and Wisconsin. In addition, Delaware submitted data for v2. See section 4.19
of the 2014NEIv2 TSD for a description of the methodology and updates to commercial marine vessels in
the 2014NEIv2.
The NEI includes CMV outside of state waters, but that are in Federal waters (FIPS = 85). These areas
include parts of the Gulf of Mexico and East and West Coasts. The U.S. Federal waters around Puerto
Rico and Alaska are outside the CONUS modeling domain and are not used in the platform. The Federal
Waters emissions are also categorized as port or underway shapes.
For the 2016 alpha platform, cmv_clc2 emissions from the 2014NEIv2 were used as-is, with the
exception of SO2 emissions, which were reduced by 90% from 2014NEIv2 levels in accordance with
ECA-IMO emissions standards for 2016. However, it should be noted that this reduction was not
appropriate for CI and C2 ships, because those ships use diesel fuel and not residual fuel; however, since
SO2 emissions levels for CI and C2 ships are small in 2014NEIv2, this further reduction had a small
impact.
Table 2-17 provides the SCCs extracted from the NEI for the cmv_clc2 sector. For the purpose of the
NEI, it is assumed that CI and C2 vessels typically used distillate fuels.
Table 2-17. 2014NEI SCCs extracted for the cmv clc2 sector
SCC
Sector
Description: Mobile Sources prefix for all
2280002100
Cmv
Marine Vessels; Commercial; Diesel; Port
2280002200
Cmv
Marine Vessels; Commercial; Diesel; Underway
The sources in the cmv_clc2 sector are gridded from the county estimates. For the 2016 alpha platform,
ports for cl/c2 use a surrogate based on Ports NEI2014 activity (surrogate 820), and underway emissions
use a surrogate based on 2013 shipping density (surrogate 808).
Table 2-18 provides the SCCs extracted from the NEI for the cmv_c3 sector. For the purpose of the NEI,
it is assumed that C3 vessels typically use residual blends; however, in California, the larger C3 vessels
are required to use cleaner diesel fuel in state waters and were thus mapped to CI and C2 vessels. In the
future, these SCCs will change to properly categorize C3 vessels that use diesel fuel appropriately.
Table 2-18. 2014NEI SCCs extracted for the cmv c3 sector
SCC
Sector
Description: Mobile Sources prefix for all
2280003100
cmv
Marine Vessels, Commercial; Residual; Port emissions
2280003200
cmv
Marine Vessels, Commercial; Residual; Underway emissions
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The cmv_c3 sector sources are treated as point sources. This allows plume rise to be computed so that
emissions can be allocated to air quality model layers higher than layer 1. A set of fixed stack parameters
were assigned to every CMV point source: 65.62 ft (20 m) height, 2.625 ft (0.8 m) diameter, 82.02 ft/s (25
m/s) velocity and 539.5 F (282 C).
The 2016 alpha platform C3 emissions are from 2014NEIv2 within U.S. state and federal waters (FIPS =
85). SO2 emissions in the cmv_c3 sector were reduced by 90% from 2014NEIv2 levels within state and
federal waters, in accordance with ECA-IMO emissions standards for 2016.
The "ECA-IMO-based" C3 CMV inventory is used for waters not covered by the NEI (with FIPS
assigned to 98001) and is used for allocating the county-level NEI emissions to geographic locations.
These data are described below.
The EPA-"ECA-IMO-based" emissions were developed based on a 4-km resolution ASCII raster format
dataset that preserves shipping lanes. This dataset has been used since the ECA-IMO project began in
2005, although it was then known as the Sulfur Emissions Control Area (SECA). The ECA-IMO
emissions consist of large marine diesel engines (at or above 30 liters/cylinder) that, until recently, were
allowed to meet relatively modest emission requirements and, as a result, these ships would often burn
residual fuel in that region. The emissions in this sector are comprised of primarily foreign-flagged
ocean-going vessels, referred to as C3 CMV ships. The cmv inventory sector includes these ships in
several intra-port modes (i.e., cruising, hoteling, reduced speed zone, maneuvering, and idling) and an
underway mode, and includes near-port auxiliary engine emissions.
An overview of the C3 EC A Proposal to the International Maritime Organization project (EPA-420-F-10-
041, August 2010) and future-year goals for reduction of NOx, SO2, and PM C3 emissions can be found
at: http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf. The resulting ECA-IMO coordinated
strategy, including emission standards under the Clean Air Act for new marine diesel engines with per-
cylinder displacement at or above 30 liters, and the establishment of ECA is available from
http://www.epa.gov/oms/oceanvessels.htm. The base-year ECA inventory is 2002 and consists of these
CAPs: PM10, PM2.5, CO, CO2, NH3, NOx, SOx (assumed to be SO2), and hydrocarbons (assumed to be
VOC). The EPA developed regional growth (activity-based) factors that were applied to create the 2011
inventory from the 2002 data. These growth factors are provided in Table 2-19. The geographic regions
listed in the table are shown in Figure 2-3. The East Coast and Gulf Coast regions were divided along a
line roughly through Key Largo (longitude 80° 26' West). Technically, the Exclusive Economic Zone
(EEZ) FIPS are not really "FIPS" state-county codes but, are treated as such in the inventory and
emissions processing.
Table 2-19. Growth factors to project the 2002 ECA-IMO inventory to 2011
Region
EEZ FIPS
NOx
PM10
pm25
VOC
CO
SO2
Outside ECA
98001
1.341
1.457
1.457
1.457
1.457
1.457
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Figure 2-3. Illustration of regional modeling domains in ECA-IMO study
The emissions were converted to SMOKE point source inventory format as described in
http://www3.epa.gov/ttn/chief/conference/eil7/session6/mason.pdf. allowing for the emissions to be allocated to modeling layers
above the surface layer. As described in the paper, the ASCII raster dataset was converted to latitude-longitude, mapped to
state/county FIRS codes that extended up to 200 nautical miles (nm) from the coast, assigned stack parameters, and monthly ASCII
raster dataset emissions were used to create monthly temporal profiles. All non-US, non-EEZ emissions (i.e., in waters considered
outside of the 200 nm EEZ and, hence, out of the U.S. and Canadian ECA-IMO controllable domain) were simply assigned a dummy
state/county FIPS code=98001 and were projected to year 2011 using the "Outside ECA" factors in Table 2-18.
No data from this inventory were used for State waters which extend approximately 3 to 10 miles offshore
or FIPs beginning with 85, since these were taken from the 2014NEIv2. However, the "ECA-IMO-
based" inventory was used to convert the NEI emissions to point sources. Also, the SMOKE-ready data
have been cropped from the original ECA-IMO entire northwestern quarter of the globe to cover only the
large continental U.S. 36-km "36US3" air quality model domain, the largest Continental U.S. domain
used by the EPA in recent years. Emissions in Canadian Federal waters are also removed from the ECA-
IMO-based inventory to prevent a double count with a separate C3 emissions inventory provided by
Environment Canada.
The original ECA-IMO inventory did not delineate between ports and underway emissions (or other C3
modes such as hoteling, maneuvering, reduced-speed zone, and idling). However, a U.S. ports spatial
surrogate dataset was used to assign the ECA-IMO emissions to ports and underway SCCs 2280003100
and 2280003200, respectively. This had no effect on temporal allocation or speciation because all C3
CMV emissions, unclassified/total, port and underway, share the same temporal and speciation profiles.
See Section 3.2.1.3 for more details on C3 speciation in the cmv sector and Section 3.5.8 for details on
temporal allocation.
A hierarchical process was used for generating the geographic coordinates of the points. The ECA
inventory was used as a first choice, port polygons as a next choice (for port SCCs), and then gridding
surrogates where there is not county overlap between the C3 emissions and the ECA or port polygons.
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2.4.2 Railroad sources: (rail)
The rail sector includes all locomotives in the NEI nonpoint data category, SCCs are shown in Table 2-20.
This sector excludes railway maintenance locomotives and point source yard locomotives. Railway
maintenance emissions are included in the nonroad sector. The point source yard locomotives are
included in the ptnonipm sector.
The nonpoint rail data, which for 2016 alpha platform are from 2014NEIv2, are a mix of S/L and EPA
data. EPA estimates cover only SCCs 2285002006 and 2285002007. Revised and/or new data were
provided by some states for the 2014NEIv2. The EPA data were completely replaced from the vl
estimates, which had been carried forward from the 2011 NEI. The updated EPA data were developed by
the Eastern Regional Technical Advisory Committee's (ERTAC) rail group. The group coordinated with
the Federal Rail Administration to collect link-based activity data and apply the equipment-specific
emission factors appropriate. For more information on locomotive sources in the NEI, see Section 4.20 of
the 2014NEIv2 TSD.
Table 2-20. SCCs used for the rail sector
SCC
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
2.4.3 Nonroad mobile equipment sources: (nonroad)
The nonroad equipment emissions in the platform and the NEI result primarily from running the
MOVES2014a model, which incorporates the NONROAD2008 model. MOVES2014a replaces NMIM,
which was used for 2011 and earlier NEIs. MOVES2014a provides a complete set of HAPs and
incorporates updated nonroad emission factors for HAPs. MOVES2014a was used for all states other
than California, which uses their own model. Additional details on the development of the 2014NEI
nonroad emissions are available in Section 5 of the 2014NEIv2 TSD. A separate MOVES2014a run was
performed for the year 2016 and is the basis for nonroad emissions in the 2016 alpha platform. This study
includes a corrected nonroad inventory which represents year 2016 emissions. This corrected inventory
was developed in May 2018, two months after the 2016 alpha platform was originally published.
The magnitude of the annual emissions in the nonroad inventory used here are similar to the emissions in
the nonroad data category of the 2014NEIv2. Unlike the NEI, the platform has monthly emission totals,
which are provided by MOVES2014a, and contain additional pollutants used in the emissions modeling.
The emissions in the modeling platform include NONHAPTOG and ETHANOL, and these are not
included in the NEI. NONHAPTOG is the difference between total organic gases (TOG) and explicit
species that are estimated separately such as benzene, toluene, styrene, ethanol, and numerous other
compounds and are integrated into the chemical speciation process. MOVES2014a provides estimates of
NONHAPTOG along with the speciation profile code for the NONHAPTOG emission source. This is
accomplished by using NHTOG#### as the pollutant code in the FF10 inventory file, where #### is a
speciation profile code. Since speciation profiles are applied by SCC and pollutant, no changes to
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SMOKE were needed to use the FF10 with this profile information. This approach is not used for
California, because their model provides VOC and traditional speciation is performed instead.
Nonroad emissions for California submitted to NEI were developed using the California Emissions
Projection Analysis Model (CEPAM) that supports various California off-road regulations.
Documentation of the CARB offroad mobile methodology, including CMV sector data, is provided at:
http://www.arb.ca.gOv/msei/categories.htm#offroad motor vehicles. The CARB-supplied nonroad annual
inventory emissions values were temporalized to monthly values using monthly temporal profiles applied
in SMOKE by SCC. Some VOC emissions were added to California to account for situations when VOC
HAP emissions were included in the inventory, but VOC emissions were either less than the sum of the
VOC HAP emissions, or were missing entirely. These additional VOC emissions were computed by
summing benzene, acetaldehyde, formaldehyde, and naphthalene for the specific sources. California
nonroad inventories were available for years 2014 and 2017; emissions for those two years were
interpolated to 2016 values for this platform.
2.5 "Other Emissions": non-U.S. sources
The emissions from Canada and Mexico are included as part of five emissions modeling sectors: othpt,
othar, othafdust, onroadcan, and onroadmex. The "oth" refers to the fact that these emissions are
usually "other" than those in the NEI, 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). Because
Canada and Mexico onroad mobile emissions are modeled differently from each other, they are separated
into two sectors: onroad can and onroad mex.
2.5.1 Point sources from Canada and Mexico (othpt)
For Canadian point sources, 2013 and 2025 emissions provided by Environment Canada were
interpolated to year 2016 for facilities included in both the 2013 and 2025 datasets. Sources that were
only in the 2013 dataset and not in 2025 (i.e. closures) were omitted from the 2016 dataset. Sources that
were only in the 2025 dataset and not in 2013 (i.e. newly opened facilities) were included in the 2016
inventory with emissions set to 2025 values, except for the Bonnybrook Energy Centre facility in Alberta,
which as of 2018 has not opened and thus was left out of the 2016 inventory. These Canadian point
source inventories included VOC emissions with CB6 speciation, although the CB6 VOCs differed
slightly from the version of CB6 in CMAQ. Environment Canada also provided total unspeciated VOC,
which was added to the inventory as VOC INV and was speciated for ACET, CH4 and CB6-CMAQ
species not covered in the CB6-speciated inventory (XYLMN, NAPH and SOAALK). Airport emissions
were provided by month. Temporal profiles were provided for all source categories. Other than the CB6
species of NBAFM present in the speciated NPRI data, there are no explicit HAP emissions in this
inventory.
Point sources in Mexico were compiled based on inventories projected from the the Inventario Nacional
de Emisiones de Mexico, 2008 (ERG, 2017). 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. Mexican point inventories were projected from 2008 to the
years 2014 and 2018, and then those emissions values were interpolated to the year 2016 for this platform.
Only CAPs are included in the Mexico point source inventory.
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2.5.2 Area and nonroad mobile sources from Canada and Mexico (othar,
othafdust)
For Canadian area and nonroad sources, year-2013 and year-2025 emissions provided by Environment
Canada were interpolated to year 2016, including CMV emissions for most pollutants. SO2 emissions
from CMV were set to 2025 values for 2016 modeling, because 2025 SO2 CMV emissions in Canada
more accurately reflect 2016 marine sulfur emissions rules than do emissions from a 2013-to-2025
interpolation. For all pollutants other than SO2, CMV emissions were interpolated from 2013 and 2025 to
2016. Agricultural ammonia and nonroad emissions inventories from Canada are monthly; rail, CMV and
other nonpoint Canada sectors are annual. The following Canadian area inventories are sub-province:
agricultural ammonia (for all provinces) and nonroad (Quebec, Ontario, and BC only). The ag inventory
goes all the way down to census division. For nonroad, Quebec/Ontario/BC resolution is by "region", not
by census division, with only a couple of regions in each province.
The Canadian inventory included fugitive dust emissions that do not incorporate either a transportable
fraction or meteorological-based adjustments. To properly account for this, a separate sector called
othafdust was created and modeled using the same adjustments as are done for U.S. sources (see Section
2.2.1 for more details). Updated Shapefiles used for creating spatial surrogates for Canada were also
provided.
The 2016 alpha platform includes modeling for the 36US3 domain (see Section 3.4), which includes a
portion of Southeast Alaska. The U.S. afdust emissions for 36US3 are based on the 12US1 onroad
emissions, and thus do not include Southeast Alaska. Therefore, we include the 2014NEIv2 afdust Alaska
emissions inventory when processing the othafdust sector for 36US3. So for 36US3 only, the othafdust
sector includes emissions in both Canada and part of Alaska.
For Mexican area and nonroad sources, emission projections based on Mexico's 2008 inventory were
used for area and nonroad sources (ERG, 2017). The resulting inventory was written using English units
to the nonpoint FF10 format that could be read by SMOKE. Note that unlike the U.S. inventories, there
are no explicit HAPs in the nonpoint or nonroad inventories for Canada and Mexico and, therefore, all
HAPs are created from speciation. Similar to the point inventories, Mexican area and nonroad inventories
were projected from 2008 to the years 2014 and 2018, and then emissions values were interpolated to year
2016 values for this platform.
2.5.3 Onroad mobile sources from Canada and Mexico (onroad_can,
onroad_mex)
For Canada onroad emissions, month-specific year-2013 and year-2025 emissions provided by
Environment Canada were interpolated to year 2016. This inventory is sub-province in Ontario (4
regions) and BC (2 regions), and province elsewhere. There are no explicit HAPs in the onroad
inventories for Canada, and therefore, NBAFM HAPs are created from speciation.
For Mexico onroad emissions, a version of the MOVES model for Mexico was run that provided the same
VOC HAPs and speciated VOCs as for the U.S. MOVES model (ERG, 2016a). This includes NBAFM
plus several other VOC HAPs such as toluene, xylene, ethylbenzene and others. Except for VOC HAPs
that are part of the speciation, no other HAPs are included in the Mexico onroad inventory (such as
particulate HAPs nor diesel particulate matter). Mexico onroad inventories were generated by MOVES
for the years 2014 and 2017, and then emissions values were interpolated to the year 2016 for this
platform.
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The 2016 alpha platform includes modeling for the 36US3 domain (see Section 3.4), which includes a
portion of Southeast Alaska. The U.S. onroad emissions for 36US3 are based on the 12US1 onroad
emissions, and thus do not include Southeast Alaska. Therefore, we include the 2014NEIv2 onroad
Alaska emissions inventory when processing the onroadcan sector for 36US3. So for 36US3 only, the
onroadcan sector includes emissions in both Canada and part of Alaska.
2.5.4 Fires from Canada and Mexico (ptfire_othna)
Annual 2016 wildland emissions for Mexico, Canada, Central America, and Caribbean nations in the
2016 alpha platform were developed from a combination of FINN (Fire Inventory from NCAR) daily fire
emissions and fire data provided by Environment Canada when available. Environment Canada
emissions were used for Canada wildland fire emissions for April through November and FINN fire
emissions were used to fill in the annual gaps from January through March and December. Only CAP
emissions are provided in the ptfire othna sector inventories.
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 wild fires rather than prescribed. FINN fire detects less than 50 square meters (0.012 acres) are
removed from the inventory. The locations of FINN fires are geocoded from latitude and longitude to
FIPS code.
2.6 Fires (ptfire)
In the 2016 alpha platform, wildfires and prescribed burning emissions are contained in the ptfire sector
which contain emissions from flaming and smoldering combustion phases. Fire emissions are specified at
geographic coordinates (point locations) and have daily emissions values.
The point source day-specific emission estimates for 2016 fires were developed using SMARTFIRE 2
(Sullivan, et al., 2008), which uses the National Oceanic and Atmospheric Administration's (NOAA's)
Hazard Mapping System (HMS) fire location information as input. Additional inputs include the
CONSUME v4.1 software application (Joint Fire Science Program, 2009) and the Fuel Characteristic
Classification System (FCCS) fuel-loading database to estimate fire emissions from wildfires and
prescribed bums on a daily basis. The method involves the reconciliation of 1CS-209 reports (Incident
Status Summary Reports), GeoMAC perimeter Shapefiles, USFS fire information, and USFWS fire
information data with satellite-based fire detections to determine spatial and temporal information about
the fires. A functional diagram of the SMARTFIRE 2 process of reconciling fires with ICS-209 reports is
available in the documentation (Raffuse, et al., 2007). Once the fire reconciliation process is completed,
the emissions are calculated using the U.S. Forest Service's CONSUME v4.1 fuel consumption model
and the FCCS v2 fuel-loading database in the BlueSky Framework (Ottmar, et. al., 2007).
A difference between the fires for this study and those in the NEI is that the proportion of emissions
allocated to flaming versus smoldering SCCs were adjusted. Flaming fractions were calculated for each
fire based on the flaming and smoldering consumption divided by the total consumption. Smoldering
fractions were calculated by dividing the residual consumption by the total consumption. The fractions
were then applied to the 2016 fire emissions to obtain revised emissions for the flaming and smoldering
SCCs. The total emissions by state were unchanged, but they were reapportioned to the flaming and
smoldering SCCs to facilitate a more realistic plume rise for fires.
Large fires of more than 20,000 acres in a single day were split using GeoM AC
(https://www.geomac.gov/) fire shapes, where available, or otherwise using a circle centered on the detect
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1 at/1 on based on 12US2 grid cell overlap. The resulting split fires have emissions and area apportioned
from the original fire into the grid cells based on fraction of area overlap between the fire shape and the
cell. The idea is to prevent all of the emissions from a very large fire from going into a single grid cell,
when in reality the fire emissions were more dispersed than a single point. The area of each of the
"subfires" was computed in proportion to the overlap with that grid cell. These "subfires" were given new
names that were the same as the original, but with "_a", "_b", "_c", and "_d" appended as needed.
The SMOKE-ready inventory files created from the raw daily fires for 2016 contain CAPs only, and so the
BAFM HAP emissions were obtained using VOC speciation profiles (i.e., a "no-integrate noHAP" use
case).
The ptfire sector excludes agricultural burning and other open burning sources that are included in the
nonpt sector. The NEI SCCs for the ptfire sector are shown in Table 2-21.
Table 2-21. 2014 Platform SCCs representing emissions in the ptfire modeling sector
SCC
SCC Description*
2810001001
Other Combustion-as Event; Forest Wildfires; Smoldering
2810001002
Other Combustion-as Event; Forest Wildfires; Flaming
2811015001
Other Combustion-as Event; Prescribed Forest Burning; Smoldering
2811015002
Other Combustion-as Event; Prescribed Forest Burning; Flaming
* The first tier level of the SCC Description is "Miscellaneous Area Sources."
2.7 Biogenic sources (beis)
Biogenic emissions were developed using the Biogenic Emission Inventory System version 3.61
(BEIS3.61) within SMOKE using the "16j" version of 2016 meteorology. The BEIS3.61 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. Biogenic emissions can be processed within SMOKE (the "offline" option), or
within CMAQ using the same inputs as SMOKE (the "inline" option). For the 2016 alpha platform, the
offline option was used for CMAQ modeling, and so the model-ready emissions input to CMAQ include
biogenics.
For the 2014NEIv2, land use changes were made for the states of Florida, Texas and Washington to
correct an error with the land use fractions which did not sum to 1; but the version remained named
BELD4.1. The same land use version is used for 2016 alpha platform.
The BEIS3.61 was used in conjunction with the modified Version 4.1 of the Biogenic Emissions Landuse
Database (BELD4) and incorporates a canopy two-layer canopy model to estimate leaf-level temperatures
(Pouliot and Bash, 2015). In the BEIS 3.61 two-layer canopy model, the layer structure varies with light
intensity and solar zenith angle. 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.
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Table 2-22. Meteorological variables required by BEIS 3.61
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 sfc
RN
nonconvective precipitation per met TSTEP
RSTOMI
inverse of bulk stomatal resistance
SLYTP
soil texture type by USD A category
SOIM1
volumetric soil moisture in top cm
SOIT1
soil temperature in top cm
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 m
The BELD version 4.1 is based on an updated version of the USDA-USFS Forest Inventory and Analysis
(FIA) vegetation speciation based data from 2001 to 2014 from the FIA version 5.1. Canopy coverage is
based on the Landsat satellite National Land Cover Database (NLCD) product from 2011. 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 NLCD canopy coverage. The 2011 NLCD
provides land cover information with a native data grid spacing of 30 meters. For land areas outside the
conterminous United States, 500 meter grid spacing land cover data from the Moderate Resolution
Imaging Spectroradiometer (MODIS) is used. BELDv4.1 also incorporates the following:
• 30 meter NASA's Shuttle Radar Topography Mission (SRTM) elevation data
(http://www2.jpl.nasa.gov/srtm/) to more accurately define the elevation ranges of the vegetation
species than in previous versions; and
• 2011 30 meter USD A Cropland Data Layer (CDL) data
(http://www.nass.usda.gov/research/Cropland/Release/).
To provide a sense of the scope and spatial distribution of the emissions, plots of annual BEIS outputs for
NO, isoprene, acetaldehyde, and formaldehyde associated with the 2014v7.0 platform are shown in Figure
2-4, Figure 2-5, Figure 2-6, and Figure 2-7, respectively. The land use changes made in 2014v7.1 and
alpha platform would not impact these v7.0-based figures. Biogenic emissions for 2016 are different from
2014 in terms of temporalization and magnitude but, are similar spatially.
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Figure 2-4. Annual NO emissions output from BE IS 3.61 for 2014
2014fa_nata beis NO emissions, annual
^lax 106.3774 Min: 0.0
Figure 2-5. Annual isoprene emissions output from BEIS 3.61 for 2014
2014fa_nata beis ISOP emissions, annual
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Figure 2-6. Annual acetaldehyde emissions output from BEIS 3.61 for 2014
2014fa_nata beis ALD2 emissions, annual
Figure 2-7. Annual formaldehyde emissions output from BEIS 3.61 for 2014
2014fa nata beis FORM emissions, annual
37
33
29
25
&
20 g
B
16
12
8
4
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2.8 SMOKE-ready non-anthropogenic inventory for chlorine
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb)
concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution
were available and were not modified other than the model-species name "CHLORINE" was changed to
"CL2" to support CMAQ modeling.
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3 Emissions Modeling Summary
The CMAQ model requires 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 resolution required by the
air quality model. Emissions modeling includes temporal allocation, spatial allocation, and pollutant
speciation. In some cases, emissions modeling also includes the vertical allocation 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, county/province/municipio totals, or gridded emissions and varies by sector.
This section provides some basic information about the tools and data files used for emissions modeling
as part of the modeling platform. In Section 2, the emissions inventories and how they differ from the the
previous platform are described. In Section 3, the descriptions of data are limited to the ancillary data
SMOKE uses to perform the emissions modeling steps. Note that all SMOKE inputs for the 2016 alpha
platform are available from the Air Emissions Modeling website (https://www.epa.gov/air-emissions-
modeling/2016-alpha-platform).
SMOKE version 4.5 was used to process the raw emissions inventories into emissions inputs for each
modeling sector into a format compatible with CMAQ. 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 QA 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.
3.1 Emissions modeling Overview
When preparing emissions for the air quality model, emissions for each sector are processed separately
through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sector-
specific 2-D gridded emissions across sectors. The SMOKE settings in the run scripts and the data in the
SMOKE ancillary files control the approaches used by the individual SMOKE programs for each sector.
Table 3-1 summarizes the major processing steps of each platform sector. The "Spatial" column shows
the spatial approach used: "point" indicates that SMOKE maps the source from a point location (i.e.,
latitude and longitude) to a grid cell; "surrogates" indicates that some or all of the sources use spatial
surrogates to allocate county emissions to grid cells; and "area-to-point" indicates that some of the
sources use the SMOKE area-to-point feature to grid the emissions (further described in Section 3.4.2).
The "Speciation" column indicates that all sectors use the SMOKE speciation step, though 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; instead,
activity data and emission factors are used in combination with meteorological data to compute hourly
emissions.
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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. The air quality model computes the plume rise using stack parameters and the hourly emissions
in the SMOKE output files for each emissions sector. The height of the plume rise determines the model
layer into which the emissions are placed. The othpt sector has only "in-line" emissions, meaning that all
of the emissions are treated as elevated sources and there are no emissions for those sectors in the two-
dimensional, layer-1 files created by SMOKE. Other inline-only sectors are: cmv_c3, ptegu, ptfire,
ptfire othna, ptagfire. 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 ad]
Surrogates
Yes
annual
ag
Surrogates
Yes
monthly
beis
Pre-gridded
land use
in BEIS3 .61
computed hourly
cmv clc2
Surrogates
Yes
annual
cmv c3
Point
Yes
annual
in-line
nonpt
Surrogates &
area-to-point
Yes
annual
nonroad
Surrogates &
area-to-point
Yes
monthly
np oilgas
Surrogates
Yes
annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
onroadcaadj
Surrogates
Yes
monthly activity,
computed hourly
othafdust adj
Surrogates
Yes
annual
othar
Surrogates
Yes
annual &
monthly
onroad can
Surrogates
Yes
monthly
onroad mex
Surrogates
Yes
monthly
othpt
Point
Yes
annual &
monthly
in-line
ptagfire
Point
Yes
daily
in-line
pt oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire
Point
Yes
daily
in-line
ptfire othna
Point
Yes
daily
in-line
ptnonipm
Point
Yes
annual
in-line
rail
Surrogates
Yes
annual
rwc
Surrogates
Yes
annual
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
46
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itself. For this platform, biogenic emissions were processed in SMOKE and included in the gridded
CMAQ-ready emissions.
SMOKE has the option of grouping sources so that they are treated as a single stack when computing
plume rise. For this platform, 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 latitudes/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 grouping.
SMOKE was am for three modeling domains: a 36-km resolution CONtinental United States "CONUS"
modeling domain (36US3), and two 12-km resolution domains, 12US1 and 12US2. The domains are
shown in Figure 3-1. Section 3.6 provides the details on the spatial surrogates and area-to-point data used
to accomplish spatial allocation with SMOKE. More specifically, SMOKE was run on the 12US1 domain
and emissions were extracted from 12US1 data files to create 12US2 emission.
Figure 3-1. Air quality modeling domains
-WRF_36NOAM
BELD4
36US3
12US1
All grids use a Lamhert-Conformal projection, with Alpha = 33°, Beta = 45° and Gamma = -97°, with a
center of X = -97° and Y = 40°. Table 3-2 describes the grids for the three domains.
47
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Table 3-2. Descriptions of the platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig, xcell,
ycell, ncols, nrows, nthik
Continental
36km grid
36 km
Entire conterminous
US, almost all of
Mexico, most of
Canada (south of
60°N)
36US3
'LAM 40N97W', -2952000, -2772000,
36.D3, 36.D3, 172, 148, 1
Continental
12km grid
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_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
3.2 Chemical Speciation
The emissions modeling step for chemical speciation creates the "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual chemical
compounds (i.e., "explicit species") or groups of species (i.e., "lumped species"). The chemical
mechanism used for the 2016 platform is the CB6 mechanism (Yarwood, 2010). We used a particular
version of CB6 that we refer to as "CMAQ CB6" that breaks out naphthalene from XYL as an explicit
model species, resulting in model species NAPH and XYLMN instead of XYL and uses SOAALK. This
platform generates the PM2.5 model species associated with the CMAQ Aerosol Module version 6 (AE6).
Table 3-3 lists the model species produced by SMOKE in the platform used for this study. Updates to
species assignments for CB05 and CB6 were made for the 2014v7.1 platform and are described in
Appendix C.
48
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Table 3-3. Emission model species produced for CB6 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
N02
Nitrogen dioxide
HONO
Nitrous acid
S02
S02
Sulfur dioxide
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
NH3 FERT
Ammonia from fertilizer
voc
ACET
Acetone
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
BENZ
Benzene (not part of CB05)
CH4
Methane
ETH
Ethene
ETHA
Ethane
ETHY
Ethyne
ETOH
Ethanol
FORM
Formaldehyde
IOLE
Internal olefin carbon bond (R-C=C-R)
ISOP
Isoprene
KET
Ketone Groups
MEOH
Methanol
NAPH
Naphthalene
NVOL
Non-volatile compounds
OLE
Terminal olefin carbon bond (R-C=C)
PAR
Paraffin carbon bond
PRPA
Propane
SESQ
Sequiterpenes (from biogenics only)
SOAALK
Secondary Organic Aerosol (SOA) tracer
TERP
Terpenes (from biogenics only)
TOL
Toluene and other monoalkyl aromatics
UNR
Unreactive
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
PM10
PMC
Coarse PM >2.5 microns and <10 microns
PM2.5
PEC
Particulate elemental carbon <2.5 microns
PN03
Particulate nitrate <2.5 microns
POC
Particulate organic carbon (carbon only) <2.5 microns
PS04
Particulate Sulfate <2.5 microns
PAL
Aluminum
PCA
Calcium
49
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Inventory Pollutant
Model Species
Model species description
PCL
Chloride
PFE
Iron
PK
Potassium
PH20
Water
PMG
Magnesium
PMN
Manganese
PMOTHR
PM2.5 not in other AE6 species
PNA
Sodium
PNCOM
Non-carbon organic matter
PNH4
Ammonium
PSI
Silica
PTI
Titanium
Sea-salt species (non -
PCL
Particulate chloride
anthropogenic)6
PNA
Particulate sodium
The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were
developed from the SPECIATE 4.5 database (https://www.epa.gov/air-emissions-modeling/speciate).
which is the EPA's repository of TOG and PM speciation profiles of air pollution sources. The
SPECIATE database development and maintenance is a collaboration involving the EPA's Office of
Research and Development (ORD), Office of Transportation and Air Quality (OTAQ), and the Office of
Air Quality Planning and Standards (OAQPS), in cooperation with Environment Canada (EPA, 2016).
The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical
compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation profiles
for PM2.5.
Some key features and updates to speciation from previous platforms include the following (the
subsections below contain more details on the specific changes):
• VOC speciation profile cross reference assignments for point and nonpoint oil and gas sources
were updated to (1) make corrections to the 201 lv6.3 cross references, (2) use new and revised
profiles that were added to SPECIATE4.5 and (3) account for the portion of VOC estimated to
come from flares, based on data from the Oil and Gas estimation tool used to estimate emissions
for the NEI. The new/revised profiles included oil and gas operations in specific regions of the
country and a national profile for natural gas flares;
• the Western Regional Air Partnership (WRAP) speciation profiles used for the np oilgas sector
are the SPECIATE4.5 revised versions (profiles with "_R" in the profile code);
• the VOC speciation process for nonroad mobile has been updated - profiles are now assigned
within MOVES2014a which outputs the emissions with those assignments; also the nonroad
profiles themselves were updated;
• VOC and PM speciation for onroad mobile sources occurs within MOVES2014a except for brake
and tirewear PM speciation which occurs in SMOKE;
• speciation for onroad mobile sources in Mexico is done within MOVES and is more consistent
with that used in the United States;
6 These emissions are created outside of SMOKE
50
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• the PM speciation profile for C3 ships in the US and Canada was updated to a new profile,
5675AE6; and
• As with previous platforms, some Canadian point source inventories are provided from
Environment Canada as pre-speciated emissions; however for the 2013 and 2025 inventories, not
all CB6-CMAQ species were provided; missing species were supplemented by speciating VOC
which was provided separately.
Speciation profiles and cross-references for this study platform are available in the SMOKE input files for
the 2016 alpha 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.
3.2.1 VOC speciation
The speciation of VOC includes HAP emissions from the 2014NEIv2 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 the
CMAQ version 5.2. Explicit means that they are not lumped chemical groups like PAR, IOLE and
several other CB6 model species. These "explicit VOC HAPs" are model species that participate in the
modeled chemistry using the CB6 chemical mechanism. The use of inventory HAP emissions along with
VOC is called "HAP-CAP integration."
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats,
including PTDAY (the format used for the ptfire and ptagfire sectors). The ability to use integration with
the PTDAY format was made available in the version of SMOKE used for the 2014v7.1 platform, but this
new feature is not used for the 2016 platform because the ptfire and ptagfire inventories for 2016 do 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 particular sources to integrate via the
NHAPEXCLUDE file (which actually provides the sources to be excluded from integration7). 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 profiles8. SMOKE computes NONHAPTOG and
then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC
7 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.
8 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.
51
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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.
In this platform, we create NBAFM species from the no-integrate source VOC emissions using speciation
profiles. Figure 3-2 illustrates the integrate and no-integrate processes for U.S. Sources. 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 particular HAPs to integrate. Two different
INVTABLE files are used for different sectors of the platform. For sectors that had no integration across
the entire sector (see Table 3-4), EPA created a "no HAP use" INVTABLE in which the "KEEP" flag is
set to "N" for NBAFM pollutants. Thus, any NBAFM pollutants in the inventory input into SMOKE are
automatically dropped. This approach both avoids double-counting of these species and assumes that the
VOC speciation is the best available approach for these species for sectors using this approach. The
second INVTABLE, used for sectors in which one or more sources are integrated, causes SMOKE to keep
the inventory NBAFM pollutants and indicates that they are to be integrated with VOC. This is done by
setting the "VOC or TOG component" field to "V" for all five HAP pollutants. Note for the onroad
sector, "full integration" includes the integration of benzene, 1,3 butadiene, formaldehyde, acetaldehyde,
naphthalene, acrolein, ethyl benzene, 2,2,4-Trimethylpentane, hexane, propionaldehyde, styrene, toluene,
xylene, and MTBE.
52
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Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation
Emissions Ready for SMOKE
SMOKE
List of "no-integrate" sources
(NHAPEXCLUDE)
Speciation cross '
reference file (GSREF) !
NONHAPVOC to NONHAPTOG
factors (GSCNV)
NONHAPTOG speciation factors (GSPRO)
TOG speciation factors for which NBAFM
compounds removed prior to GSPRO creation
Assign speciation profile to each source
Compute NONHAPVOC = VOC-(N+B+A+F+M) for each
integrate source
Retain VOC for each no-integrate source
Compute NONHAPTOG from NONHAPVOC for each integrate
source
Compute TOG from VOC for each no-integrate source
Compute CMAQ-CB6 Species:
For integrate source: Use (1) NONHAPTOG profiles applied to
NONHAPTOG and (2) N,B,A,F,M from inventory
For no-integrate source: Use (1) non-normalized TOG profiles
appliedtoTOG and (2) N,B,A,F,M from inventory
CMAQ-CB6 species
Table 3-4. 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)
ptegu
No integration, create NBAFM from VOC speciation
ptnonipm
No integration, create NBAFM from VOC speciation
ptfire
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptfire othna
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptagfire
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ag
Partial integration (NBAFM)
afdust
N/A - sector contains no VOC
beis
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
cmv clc2
Full integration (NBAFM)
cmv c3
Full integration (NBAFM)
rail
Partial integration (NBAFM)
nonpt
Partial integration (NBAFM)
nonroad
Full integration (NBAFM in California, internal to MOVES elsewhere)
np oilgas
Partial integration (NBAFM)
othpt
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
pt oilgas
No integration, create NBAFM from VOC speciation
rwc
Partial integration (NBAFM)
53
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Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
onroad
Full integration (internal to MOVES); however, MOVES2014a speciation was CB6-
CAMx, not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-CMAQ
onroad can
No integration, no NBAFM in inventory, create NBAFM from speciation
onroadmex
Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation
was CB6-CAMx, not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-
CMAQ
othafdust
N/A - sector contains no VOC
othar
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
Integration for the mobile sources estimated from MOVES (onroad and nonroad sectors, other than for
California) is done differently. Briefly there are three major differences: 1) for these sources integration
is done using more than just NBAFM, 2) all sources from the MOVES model are integrated and 3)
integration is done fully or partially within MOVES. For onroad mobile, speciation is done fully within
MOVES2014a 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. MOVES was
run for the CB6-CAMx mechanism rather than CB6-CMAQ, so post-SMOKE onroad emissions were
converted to CB6-CMAQ. More specifically, the CB6-CAMx mechanism excludes XYLMN, NAPH,
and SOAALK. After SMOKE processing, we converted the onroad and onroadmex 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]
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
split by speciation profile. Taking into account that integrated species were subtracted out by MOVES
already, the appropriate speciation profiles are then applied in SMOKE to get the VOC model species.
HAP integration for nonroad uses the same additional HAPs and ethanol as for onroad.
3.2.1.1 County specific profile combinations
SMOKE can compute speciation profiles from mixtures of other profiles in user-specified proportions via
two different methods. The first method, which uses a GSPROCOMBO file, has been in use since the
2005 platform; the second method (GSPRO with fraction) was used for the first time in the 2014v7.0
platform. The GSPRO COMBO method uses profile combinations specified in the GSPRO COMBO
ancillary file by pollutant (which can include emissions mode, e.g., EXH VOC), state and county (i.e.,
state/county FIPS code) and time period (i.e., month). Different GSPRO COMBO files can be used by
sector, allowing for different combinations to be used for different sectors; but within a sector, different
profiles cannot be applied based on SCC. The GSREF file indicates that a specific source uses a
combination file with the profile code "COMBO." SMOKE computes the resultant profile using the
fraction of each specific profile assigned by county, month and pollutant.
In previous platforms, the GSPRO COMBO feature was used to speciate nonroad mobile and gasoline-
related stationary sources that use fuels with varying ethanol content. In these cases, the speciation
profiles require different combinations of gasoline profiles, e.g. E0 and E10 profiles. Since the ethanol
content varied 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 allowed combinations to be specified at
various levels for different years. The GSPRO COMBO is no longer needed for nonroad sources outside
54
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of California 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 GSPROCOMBO feature. For the 2016 alpha
platform, GSPRO COMBO is still used for nonroad sources in California and for certain gasoline-related
stationary sources nationwide. The fractions combining the E0 and E10 profiles are based on year 2010
regional fuels and do not vary by month. GSPRO COMBO is not needed for inventory years after 2016,
because the vast majority of fuel is projected to be E10 in future years.
In Canada and Mexico, only E0 speciation profiles are used, but the GSPRO COMBO feature is still used
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.
This is no longer necessary for Canadian mobile sources, whose inventories now include the mode in the
pollutant, or for Mexico onroad sources, where VOC speciation is calculated by the MOVES model. For
the 2016 alpha platform platform, the GSPRO COMBO is still used for Mexican nonroad sources which
do not have modes in the inventory.
A new method to combine multiple profiles became available in SMOKE4.5. It allows multiple profiles
to be combined by pollutant, state and county (i.e., state/county FIPS code) and SCC. This was used
specifically for the oil and gas sectors (pt oilgas and np oilgas) because SCCs include both controlled
and uncontrolled oil and gas operations which use different profiles.
3.2.1.2 Additional sector specific considerations for integrating HAP emissions from
inventories into speciation
The decision to integrate HAPs into the speciation was made on a sector by sector basis. For some
sectors, there is no integration and VOC is speciated directly; for some sectors, there is full integration
meaning all sources are integrated; and for other sectors, there is partial integration, meaning some
sources are not integrated and other sources are integrated. The integrated HAPs are either NBAFM or, in
the case of MOVES (onroad, nonroad and MOVES-Mexico), a larger set of HAPs plus ethanol are
integrated. Table 3-4 above summarizes the integration method for each platform sector.
For the rail sector, the EPA integrated NBAFM for most sources. Some SCCs had zero BAFM and,
therefore, they were not integrated. These were SCCs provided by states for which EPA did not do HAP
augmentation (2285002008, 2285002009 and 2285002010) because EPA does not create emissions for
these SCCs. The VOC for these sources sum to 272 tons, and most of the mass is in California (189 tons)
and Washington state (62 tons).
Speciation for the onroad sector is unique. First, SMOKE-MOVES (see Section 2.3.1) 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)9. SMOKE essentially calculates the model-ready species
by using the appropriate emission factor without further speciation10. Third, MOVES' internal speciation
9 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.
10 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/.
55
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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 3-5. 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 (see Section 2.3.1 for details). 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. Finally,
MOVES speciation used the CAMx version of CB6 which does not split out naphthalene.
Table 3-5. 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 3-5. Outside of California, the integration calculations are performed within MOVES. For
California, integration calculations are handled by SMOKE. The CARB-based nonroad inventory
includes VOC HAP estimates for all sources, so every source in California was integrated as well. Some
sources in the original CARB inventory had lower VOC emissions compared to sum of all VOC HAPs.
For those sources, VOC was augmented to be equal to the VOC HAP sum, ensuring that every source in
California could be integrated. The CARB-based nonroad data includes exhaust and evaporative mode-
specific data for VOC, but, does not contain refueling.
MOVES-MEXICO for onroad used the same speciation approach as for the U.S. in that the larger list of
species shown in Table 3-5 was used. However, MOVES-MEXICO used CB6-CAMx, not CB6-CMAQ,
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]
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For most sources in the rwc sector, the VOC emissions were greater than or equal to NBAFM, and
NBAFM was not zero, so those sources were integrated, although a few specific sources that did not meet
these criteria could not be integrated. In all cases, these sources have SCC= 2104008400 (pellet stoves),
and NBAFM > VOC, but not by a significant amount. This results from the sum of NBAFM emission
factors exceeding the VOC emission factor. In total, the no-integrate rwc sector sources sum to 4.4 tons
VOC and 66 tons of NBAFM.
For the nonpt sector, sources for which VOC emissions were greater than or equal to NBAFM, and
NBAFM was not zero, were integrated. There is a substantial amount of mass in the nonpt sector that is
not integrated: 731,000 tons which is about 20% of the VOC in that sector. It is likely that there would be
sources in nonpt that are not integrated because the emission source is not expected to have NBAFM. In
fact, 390,000 tons of the no-integrate VOC have no NBAFM in the speciation profiles used for these no-
integrate sources. Of the portion of no-integrate VOC with NBAFM there is 3900 tons NBAFM in the
profiles (that are dropped from the profiles per the procedure in Figure 3-2) for these no-integrate sources.
For the biog sector, the speciation profiles used by BEIS are not included in SPECIATE. BEIS3.61
includes the species (SESQ), that is mapped to the model species SESQT. The profile code associated
with BEIS3.61 for use with CB05 is "B10C5," while the profile for use with CB6 is "B10C6." The main
difference between the profiles is the explicit treatment of acetone emissions in B10C6.
3.2.1.3 Oil and gas related speciation profiles
Most of the new VOC profiles from SPECIATE4.5 listed in Appendix B are for the oil and gas sector. A
new national flare profile, FLR99, Natural Gas Flare Profile with DRE >98% was developed from a Flare
Test study and used in the v7.0 platform. For the oil and gas sources in the np oilgas and pt oilgas
sectors, several counties were assigned to newly available basin or area-specific profiles in SPECIATE4.5
that account for measured or modeled from measured compositions specific a particular region of the
country. In the 2011 platform, the only county-specific profiles were for the WRAP, but in the 2014 and
2016 platforms, several new profiles were added for other parts of the country. In addition, some of the
WRAP profiles were revised to correct for errors such as mole fractions being used for mass fractions and
VOCtoTOG factors or replaced with newer data. All WRAP profile codes were renamed to include an
"_R" to distinguish between the previous set of profiles (even those that did not change). For the Uintah
basin and Denver-Julesburg Basin, Colorado, more updated profiles were used instead of the WRAP
Phase III profiles. Table 3-6 lists the region-specific profiles assigned to particular counties or groups of
counties. Although this platform increases the use of regional profiles, many counties still rely on the
national profiles.
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. The profile fractions were computed from
VOC emissions provided in an intermediate file generated by the 2014 Nonpoint Oil and Gas Emission
Estimation Tool and were updated for the version of the Tool used for the 2014NEIv2. The intermediate
file provides flare, non-flare (process), and reboiler (for dehydrators) emissions for six source categories
that have flare emissions: Associated Gas, Condensate Tanks, Crude Oil Tanks, Dehydrators, Liquids
Unloading and Well Completions by county FIPS and SCC code for the U.S. to account for portions of
VOC for a particular VOC that were from controlled emissions or reboiler.
Table 3-6. Basin/Region-specific profiles for oil and gas
Profile
Code
Description
Region (if not in
the profile name)
57
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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
PNC03 R
Piceance Basin Flash Gas Composition for Condensate Tank
PNCDH
Piceance Basin, Glycol Dehydrator
PRBCB R
Powder River Basin Produced Gas Composition from CBM Wells
PRBCO R
Powder River Basin Produced Gas Composition from Non-CBM
Wells
PRM01 R
Permian Basin Produced Gas Composition for Non-CBM Wells
SSJCB R
South San Juan Basin Produced Gas Composition from CBM
Wells
SSJCO R
South San Juan Basin Produced Gas Composition from Non-CBM
Gas Wells
SWFLA R
SW Wyoming Basin Flash Gas Composition for Condensate
Tanks
SWVNT R
SW Wyoming Basin Produced Gas Composition from Non-CBM
Wells
UNT01 R
Uinta Basin Produced Gas Composition from CBM Wells
WRBCO R
Wind River Basin Produced Gagres Composition from Non-CBM
Gas Wells
95087a
Oil and Gas - Composite - Oil Field - Oil Tank Battery Vent Gas
East Texas
95109a
Oil and Gas - Composite - Oil Field - Condensate Tank Battery
Vent Gas
East Texas
95417
Uinta Basin, Untreated Natural Gas
95418
Uinta Basin, Condensate Tank Natural Gas
95419
Uinta Basin, Oil Tank Natural Gas
95420
Uinta Basin, Glycol Dehydrator
95398
Composite Profile - Oil and Natural Gas Production - Condensate
Tanks
Denver-Julesburg
Basin
95399
Composite Profile - Oil Field - Wells
State of California
95400
Composite Profile - Oil Field - Tanks
State of California
95403
Composite Profile - Gas Wells
San Joaquin Basin
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 other than for California are listed in Table 3-7. They
include new profiles (i.e., those that begin with "953") for 2-stroke and 4-stroke gasoline engines running
on EO and E10 and compression ignition engines with different technologies developed from recent EPA
test programs, which also supported the updated toxics emission factor in MOVES2014a (Reichle, 2015
and EPA, 2015b). California nonroad source profiles are presented in Table 3-8.
58
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Table 3-7. TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a used for the
2016 Platform
Profile
Profile Description
Engine
Type
Engine
Technology
Engine
Size
Horse-
power
category
Fuel
Fuel
Sub-
type
Emission
Process
95327
SI 2-stroke E0
SI 2-stroke
all
All
all
Gasoline
E0
exhaust
95328
SI 2-stroke E10
SI 2-stroke
all
All
all
Gasoline
E10
exhaust
95329
SI 4-stroke E0
SI 4-stroke
all
All
all
Gasoline
E0
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
95333
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
8753
E0 Evap
SI
all
all
all
Gasoline
E0
evaporative
8754
E10 Evap
SI
all
all
all
Gasoline
E10
evaporative
8766
E0 evap permeation
SI
all
all
all
Gasoline
E0
permeation
8769
E10 evap permeation
SI
all
all
all
Gasoline
E10
permeation
8869
E0 Headspace
SI
all
all
all
Gasoline
E0
headspace
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 for 2014 can be found in the 2014NEIv2 TSD. 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 are still 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. They are also
used for California nonroad sources. 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 D.
Table 3-8 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for
2016. The term "COMBO" indicates that a combination of the profiles listed was used to speciate that
subcategory using the GSPRO COMBO file.
59
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Table 3-8. Select mobile-related VOC profiles 2016
Sector
Sub-category
2014
Nonroad- California & non US
gasoline exhaust
COMBO
8750a Pre-Tier 2 E0 exhaust
8751a Pre-Tier 2 E10 exhaust
Nonroad-California
gasoline evaporative
COMBO
8753 E0 evap
8754 E10 evap
Nonroad-California
gasoline refueling
COMBO
8869 E0 Headspace
8870 E10 Headspace
Nonroad-California
diesel exhaust
8774 Pre-2007 MY HDD exhaust
Nonroad-California
diesel evap-
orative and diesel refueling
4547 Diesel Headspace
nonpt/
ptnonipm
PFC and BTP
COMBO
8869 E0 Headspace
8870 E10 Headspace
nonpt/
ptnonipm
Bulk plant storage (BPS)
and refine-to-bulk terminal
(RBT) sources
8869 E0 Headspace
The speciation of onroad VOC occurs completely within MOVES. MOVES takes into account fuel type
and properties, emission standards as they affect different vehicle types and model years, and specific
emission processes. Table 3-9 describes all of the M-profiles available to MOVES depending on the
model year range, MOVES process (processID), fuel sub-type (fuelSubTypelD), and regulatory class
(regClassID). Table 3-10 through Table 3-12 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 875laM. 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 3-9. 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
60
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Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
8757M
Tier 2 E10 Exhaust
2001-2050
1,2,15,16
12,13,14
20,30
8758M
Tier 2 E15 Exhaust
1940-2050
1,2,15,16
15,18
10,20,30,40,41,
42,46,47,48
8766M
E0 evap permeation
1940-2050
11
10
0
8769M
E10 evap permeation
1940-2050
11
12,13,14
0
8770M
El5 evap permeation
1940-2050
11
15,18
0
8774M
Pre-2007 MY HDD
exhaust
1940-2006
1,2,15,16,17,90
20, 21, 22
40,41,42,46,47, 48
8774M
Pre-2007 MY HDD
exhaust
1940-2050
91ii
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, 1812
10,40,41,42,46,47,48
Table 3-10. MOVES process IDs
Process ID
Process Name
1
Running Exhaust
2
Start Exhaust
9
Brakewear
10
Tirewear
11
Evap Permeation
12
Evap Fuel Vapor Venting
13
Evap Fuel Leaks
15
Crankcase Running Exhaust
11 91 is the processed for APUs which are diesel engines not covered by the 2007 Heavy-Duty Rule, so the older technology
applieds to all years.
12 The profile assingments 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.
61
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16
Crankcase Start Exhaust
17
Crankcase Extended Idle Exhaust
18
Refueling Displacement Vapor Loss
19
Refueling Spillage Loss
20
Evap Tank Permeation
21
Evap Hose Permeation
22
Evap RecMar Neck Hose Permeation
23
Evap RecMar Supply/Ret Hose Permeation
24
Evap RecMar Vent Hose Permeation
30
Diurnal Fuel Vapor Venting
31
HotSoak Fuel Vapor Venting
32
RunningLoss Fuel Vapor Venting
40
Nonroad
90
Extended Idle Exhaust
91
Auxiliary Power Exhaust
Table 3-11. 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 (El5)
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 3-12. MOVES regclass IDs
Reg. Class ID
Regulatory Class Description
0
Doesn't Matter
10
Motorcycles
20
Light Duty Vehicles
30
Light Duty Trucks
40
Class 2b Trucks with 2 Axles and 4 Tires (8,500 lbs < GVWR <= 10,000 lbs)
41
Class 2b Trucks with 2 Axles and at least 6 Tires or Class 3 Trucks (8,500 lbs < GVWR <= 14,000
lbs)
42
Class 4 and 5 Trucks (14,000 lbs < GVWR <= 19,500 lbs)
62
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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, ethanol may be mixed into the fuels; therefore, county- and month-specific
COMBO speciation was used (via the GSPROCOMBO file). 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. We
speciated PM2.5 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 speciation13.
Starting with the 2014v7.1 platform, we replaced profile 91112 (Natural Gas Combustion - Composite)
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 with the
one that we had been using in the 2014v7.0 and earlier platforms.
13 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.
63
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Figure 3-3. Profiles composited for the new PM 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
10 20 30 40
Weight Percent
50
60
¦ Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
•<»: Natural Gas Combustion - Composite 91112
64
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3.2.2.1 Mobile source related PM2.5 speciation profiles
For the onroad sector, for all processes except brake and tire wear, 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, etc). SMOKE essentially calculates the PM
components by using the appropriate EF without further speciation14. The specific profiles used within
MOVES include two compressed natural gas (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 3-13 shows the differences in the v7.1 and v6.3
profiles.
Table 3-13. SPECIATE4.5 brake and tire profiles compared to those used in the 2011v6.3 Platform
Inventory
Pollutant
Model
Species
V6.3 platform
brakewear profile:
91134
SPECIATE4.5 brakewear
profile: 95462 from
Schauer (2006)
V6.3 platform
tirewear
profile: 91150
SPECIATE4.5 tirewear
profile: 95460 from
Schauer (2006)
PM2 5
PAL
0.00124
0.000793208
6.05E-04
3.32401E-05
PM2 5
PCA
0.01
0.001692177
0.00112
PM2 5
PCL
0.001475
0.0078
PM2 5
PEC
0.0261
0.012797085
0.22
0.003585907
PM2 5
PFE
0.115
0.213901692
0.0046
0.00024779
PM2 5
PH20
0.0080232
0.007506
PM2 5
PK
1.90E-04
0.000687447
3.80E-04
4.33129E-05
PM2 5
PMG
0.1105
0.002961309
3.75E-04
0.000018131
PM2 5
PMN
0.001065
0.001373836
1.00E-04
1.41E-06
PM2 5
PMOTHR
0.4498
0.691704999
0.0625
0.100663209
PM2 5
PNA
1.60E-04
0.002749787
6.10E-04
7.35312E-05
PM2 5
PNCOM
0.0428
0.020115749
0.1886
0.255808124
PM2 5
PNH4
3.00E-05
1.90E-04
PM2 5
PN03
0.0016
0.0015
PM2 5
POC
0.107
0.050289372
0.4715
0.639520309
PM2 5
PSI
0.088
0.00115
PM2 5
PS04
0.0334
0.0311
PM2 5
PTI
0.0036
0.000933341
3.60E-04
5.04E-06
The formulas used based on brake wear profile 95462 and tire wear profile 95460 are as follows:
POC = 0.6395 * PM25TIRE + 0.0503 * PM25BRAKE
PEC = 0.0036 * PM25TIRE + 0.0128 * PM25BRAKE
14 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. org/smoke/documentation/3.7/html/.
65
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PN03 = 0.000 * PM25TIRE + 0.000 * PM25BRAKE
PS04 = 0.0 * PM25TIRE + 0.0 * PM25BRAKE
PNH4 = 0.000 * PM25TIRE + 0.0000 * PM25BRAKE
PNCOM = 0.2558 * PM25TIRE + 0.0201 * PM25BRAKE
For California onroad emissions, adjustment factors were applied to SMOKE-MOVES to produce
California adjusted model-ready files (see Section 2.3.1 for details). California did not supply speciated
PM, therefore, the adjustment 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 done in SMOKE similarly to nonpoint and point categories based on the
GSREF SCC-to-speciation profile cross reference file. There are only 3 unique PM2.5 profiles assigned to
the hundreds of nonroad SCCs.
Table 3-14. Nonroad PM2.5 profiles
SPECIATE4.5
Profile Code
SPECIATE4.5 Profile Name
Assigned to Nonroad
sources based on Fuel
Type
91106
HDDV Exhaust - Composite
Diesel
91113
Nonroad Gasoline Exhaust - Composite
Gasoline
91156
Residential Natural Gas Combustion - Composite
LPG, CNG
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
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 3-15 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 = 1 - NO - HONO. For more details on the NOx fractions within MOVES, see
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockev=P100F lA5.pdf.
Table 3-15. NOx speciation profiles
Profile
pollutant
species
split factor
HONO
NOX
N02
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
N02
0.1
66
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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 assuming that gaseous sulfate, which is comprised of many components, is
primarily H2SO4. The equation for calculating FhSO/jis given below.
Emissions of SULF (as H2S04)
fraction of S emitted as sulfate MW H2S04
= SO2 emissions x — ; — ; - ——— x —.,..r
fraction of S emitted as SO2 MW SO2
In the above, MW is 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 3-16; a summary of the profiles is provided
in Table 3-17.
Table 3-16. Sulfate split factor computation
fuel
SCCs
Profile
Code
Fraction
as S02
Fraction as
sulfate
Split factor (mass
fraction)
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
67
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Table 3-17. 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
3.3 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 3-18 summarizes the temporal aspects of emissions modeling by
comparing the key approaches used for temporal processing across the sectors. In the table, "Daily
temporal approach" refers to the temporal approach for getting daily emissions from the inventory using
the SMOKE Temporal program. The values given are the values of the SMOKE L TYPE setting. The
"Merge processing approach" refers to the days used to represent other days in the month for the merge
step. If this is not "all," then the SMOKE merge step runs only for representative days, which could
include holidays as indicated by the right-most column. The values given are those used for the SMOKE
M TYPE setting (see below for more information).
Table 3-18. 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
ag
Monthly
No
all
all
No
beis
Hourly
No
n/a
all
No
cmv clc2
Annual
Yes
aveday
aveday
No
cmv c3
Annual
Yes
aveday
aveday
No
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
No
mwdss
mwdss
Yes
np oilgas
Annual
Yes
week
week
Yes
onroad
Annual & monthly1
No
all
all
Yes
68
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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 ca adj
Annual & monthly1
No
all
all
Yes
othafdust adj
Annual
Yes
week
all
No
othar
Annual & monthly
Yes
week
week
No
onroad can
Monthly
No
week
week
No
onroad mex
Monthly
No
week
week
No
othpt
Annual & monthly
Yes
mwdss
mwdss
No
pt oilgas
Annual
Yes
mwdss
mwdss
Yes
ptegu
Annual & hourly
Yes2
all
all
No
ptnonipm
Annual
Yes
mwdss
mwdss
Yes
ptagfire
Daily
No
all
all
No
ptfire
Daily
No
all
all
No
ptfire othna
Daily
No
all
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No3
met-based3
all
No3
1 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, 2016, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2015). For most sectors, emissions from December 2016
(representative days) were used to fill in emissions for the end of December 2015. For biogenic
emissions, December 2015 emissions were processed using 2015 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.
69
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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 ag,
nonroad, onroad, onroad can, onroadmex, othar, and othpt.
3.3.2 Electric Generating Utility temporal allocation (ptegu)
3.3.2.1 Base year temporal allocation of EGUs
The 2016 annual EGU emissions not matched to CEMS sources use region/fuel specific profiles based on
average hourly emissions for the region and fuel. Peaking units were removed during the averaging to
minimize the spikes generated by those units. The non-matched units are allocated to hourly emissions
using the following three-step methodology: annual value to month, month to day, and day to hour. First,
the CEMS data were processed using a tool that reviewed the data quality flags that indicate the data were
not measured. Unmeasured data can be filled in with maximum values and thereby cause erroneously
high values in the CEMS data. The CEMCorrect tool identifies hours for which the data were not
measured. When those values are 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). Winter and summer seasons are included in the development of the diurnal profiles as
opposed to using data for the entire year because analysis of the hourly CEMS data revealed that there
were different diurnal patterns in winter versus summer in many areas. Typically, a single mid-day peak
is visible in the summer, while there are morning and evening peaks in the winter as shown in Figure 3-6.
The temporal allocation procedure is differentiated by whether or not the source could be directly
matched to a CEMS unit via ORIS facility code and boiler ID. Note that for units matched to CEMS data,
annual totals of their emissions input to CMAQ may be different than the annual values in 2016 because
the CEMS data replaces the NOx and SO2 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.
70
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Figure 3-5. Eliminating unmeasured spikes in CEMS data
400
300
1—
3
O
^ 200
tn
_Q
100
0
Figure 3-6. Seasonal diurnal profiles for EGU emissions in a Virginia Region
Diurnal CEMS Profile for PJM Dom Gas
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Winter Summer Annual
For sources not matched to CEMS units, temporal profiles are calculated that are used by SMOKE to
allocate the annual emissions to hourly values. For these units, the allocation of the inventory annual
emissions to months is done using average fuel-specific annual-to-month factors generated for each of the
2014 CEM 2398 1101 Month 1
n
n
1
..i
. .i
1.1.
oi|
"**1
|
101 201 301 401 501 601 701
Hour
— Raw CEM — Corrected
u.uu
0.055
0.05
£
O
£ 0.045
I—
J-
c 0.04
I—
3
21 0.035
0.03
n mc
71
-------
64 IPM regions shown in Figure 3-7. These factors are based on 2016 CEMS data only. In each region,
separate factors were developed for the fuels: coal, natural gas, and "other," where the types of fuels
included in "other" vary by region. Separate profiles were computed for NOx, SO2, and heat input. An
overall composite profile was also computed and used when there were no CEMS units with the specified
fuel in the region containing the unit. For both CEMS-matched units and units not matched to CEMS,
NOx and SO2 CEMS data are used to allocate NOx and SO2 emissions to monthly emissions,
respectively, while heat input data are used to allocate emissions of all pollutants from monthly to daily
emissions.
Daily temporal allocation of units matched to CEMS was performed using a procedure similar to the
approach to allocate emissions to months in that the CEMS data replaces the inventory data for each
pollutant. For units without CEMS data, emissions were allocated from month to day using IPM-region
and fuel-specific average month-to-day factors based on the 2016 CEMS heat data. Separate month-to-
day allocation factors were computed for each month of the year using heat input for the fuels coal,
natural gas, and "other" in each region. For CEMS matched units, NOx and SO2 CEMS data are used to
replace inventory NOx and SO2 emissions, while CEMS heat input data are used to allocate all other
pollutants. An example of month-to-day profiles for gas, coal, and an overall composite for a region in
western Texas is shown in Figure 3-8.
For units matched to CEMS data, hourly emissions use the hourly CEMS values for NOx and SO2, while
other pollutants are allocated according to heat input values. For units not matched to CEMS data,
temporal profiles from days to hours are computed based on the season-, region- and fuel-specific average
day-to-hour factors derived from the CEMS data for those fuels and regions using the appropriate subset
of data. For the unmatched units, CEMS heat input data are used to allocate all pollutants (including NOx
and SO2) because the heat input data was generally found to be more complete than the pollutant-specific
data. SMOKE then allocates the daily emissions data to hours using the temporal profiles obtained from
the CEMS data for the analysis base year (i.e., 2016 in this case).
Certain sources without CEMS data, such as specific municipal waste combustors (MWCs) and
cogeneration facilities (cogens), were assigned a flat temporal profile by source. The emissions for these
sources have an equal value for each hour of the year.
72
-------
Figure 3-7. IPM Regions used to Create Temporal Profiles for EGUs without CEMS
NENG_ME
M1S_MAPP
WEC_CALN /
PJM
COMD
SPP_NEBR
>JM_EMAC
S_VACA
WECCAZ
WECC_NM
ERC.REST
S„D_WOTA
Figure 3-8. Month-to-day profiles for different fuels in a West Texas Region
Daily temporal fraction: ERC_WEST_NOX_7
0.10
0.08
0.06
E 0.04
0.02
0.00
day
73
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3.3.3 Airport Temporal allocation (ptnonipm)
Airport temporal profiles were updated in 2014v7.0 and were kept the same for 2014v7.1 and 2016 alpha
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/sys/AnalysisAP.asp). A report of 2014 hourly Departures and Arrivals for
Metric Computation was generated. An overview of the ASPM metrics is at
http://aspmhelp.faa.gov/index.php/Aviation Performance Metrics %28APM%29. Figure 3-9 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 3-9, Figure 3-10, and Figure
3-11. An overview of the Operations Network data system is at
http://aspmhelp.faa.gov/index.php/Operations Network %28QPSNET%29.
Alaska seaplanes, which are outside the CONUS domain use the same monthly profile as in the 2011
platform shown in Figure 3-12. These were assigned based on the facility ID.
Figure 3-9. Diurnal Profile for all Airport SCCs
Diurnal Airport Profile
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
0
5
10
15
20
Hour
74
-------
Figure 3-10. Weekly profile for all Airport SCCs
Weekly Airport Profile
0.18
Figure 3-11. Monthly Profile for all Airport SCCs
Monthly Airport Profile
o.i
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
75
-------
Figure 3-12. Alaska Seaplane Profile
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3.3.4 Residential Wood Combustion Temporal allocation (rwc)
There are many factors that impact the timing of when emissions occur, and for some sectors this includes
meteorology. The benefits of utilizing meteorology as a method for temporal allocation are: (1) a
meteorological dataset consistent with that used by the AQ model is available (e.g., outputs from WRF);
(2) the meteorological model data are highly resolved in terms of spatial resolution; and (3) the
meteorological variables vary at hourly resolution and can, therefore, be translated into hour-specific
temporal allocation.
The SMOKE program Gentpro provides a method for developing meteorology-based temporal allocation.
Currently, the program can utilize three types of temporal algorithms: annual-to-day temporal allocation
for residential wood combustion (RWC); month-to-hour temporal allocation for agricultural livestock
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. 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
76
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states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas.
Figure 3-13 illustrates the impact of changing the temperature threshold for a warm climate county. The
plot shows the temporal fraction by day for Duval County, Florida, for the first four months of 2007. The
default 50 °F threshold creates large spikes on a few days, while the 60 °F threshold dampens these spikes
and distributes a small amount of emissions to the days that have a minimum temperature between 50 and
60 °F.
Figure 3-13. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold
RWC temporal profile, Duval County, FL, Jan - Apr
0.04
0.035
0.03
I 0.025
60F, alternate formula
— 50F, default formula
75 0.02
o
i 0.015
0.01
0.005
0
o
¦3-
o o o
o
The diurnal profile for used for most RWC sources (see Figure 3-14) 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
(http://www.marama.org/publications folder/ResWoodCombustion/Final report.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.
77
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Figure 3-14. RWC diurnal temporal profile
Comparison of RWC diurnal profile
0.12
0.1
0.08
0.06
Q_
0.04
0.02
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
The temporal allocation for "Outdoor Hydronic Heaters" (i.e., "OHH," SCC=2104008610) and "Outdoor
wood burning device, NEC (fire-pits, chimneas, etc.)" (i.e., "recreational RWC," SCC=21040087000) is
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 OHH, 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 produce the diurnal profile for OHH, shown in Figure 3-15, are based on a conventional
single-stage heat load unit burning red oak in Syracuse, New York. As shown in Figure 3-16, 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 3-17. The OHH 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.
78
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Figure 3-15. Data used to produce the diurnal profile for OHH, based on heat load (BTU/hr)
Heat Load (BTU/hr)
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
nx ^ tfr a
-------
Figure 3-17. Annual-to-month temporal profiles for OHH 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
3.3.5 Agricultural Ammonia Temporal Profiles (ag)
For the agricultural livestock NFb algorithm, the GenTPRO algorithm is based on an equation derived by
Jesse Bash of the EPA's ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation is
based on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to
estimate diurnal NFb emission variations from livestock as a function of ambient temperature,
aerodynamic resistance, and wind speed. The equations are:
EUi = [161500/T,/; x e(~1380/T,/,)] x AR,/;
PE;,/; = Ea, / Sum(E, /,)
where
• PE;,/; = Percentage of emissions in county i on hour h
• Eij, = Emission rate in county i on hour h
• Tij, = Ambient temperature (Kelvin) in county i on hour h
• Vi,/; = Wind speed (meter/sec) in county i (minimum wind speed is 0.1 meter/sec)
• 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 3-18 compares the daily emissions for Minnesota from the "old" approach (uniform
monthly profile) with the "new" approach (GenTPRO generated month-to-hour profiles) for 2014.
Although the GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the
same between the two approaches.
80
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Figure 3-18. Example of animal NH3 emissions temporal allocation approach, summed to daily
emissions
2014fd Minnesota ag NH3 livestock daily temporal profiles
1600
1400
~ 1200
;S 1000
l/l
g 800
* 600
400
200
0
1/1/2014 2/1/2014 3/4/2014 4/4/2014 5/5/2014 6/5/2014 7/6/2014 8/6/2014 9/6/2014 10/7/201411/7/2014 12/8/2014
month^ hourly
approach approach
m
X
For the 2016 alpha platform, the GenTPRO approach is applied to all sources in the ag sector, NFb and
non- NFb, livestock and fertilizer. Monthly profiles are based on the daily-based EPA livestock
emissions and are the same as were used in 2014v7.0. Profiles are by state/SCC_category, where
SCC_category is one of the following: beef, broilers, layers, dairy, swine.
3.3.6 Oil and gas temporal allocation (np_oilgas)
For the 2014v7.1 platform, the monthly oil and gas temporal profiles by county and SCC were updated to
use 2014 activity information. However, these profiles are based on year-specific activity which cannot
necessarily be applied for other years such as 2016. Therefore, in the 2016 alpha platform, the entire
npoilgas sector uses flat monthly temporalization. Weekly and diurnal profiles are flat and are based on
comments received on a version of the 2011 platform.
3.3.7 Onroad mobile temporal allocation (onroad)
For the onroad sector, the temporal distribution of emissions is a combination of traditional temporal
profiles and the influence of meteorology. This section will discuss both the meteorological influences
and the development of the temporal profiles for this platform.
The "inventories" referred to in Table 3-18 consist of activity data for the onroad sector, not emissions.
For the off-network emissions from the RPP and RPV processes, the VPOP activity data is annual and
does not need temporal allocation. For processes that result from hoteling of combination trucks (RPH),
the HOTELING inventory is annual and was temporalized to month, day of the week, and hour of the day
through temporal profiles.
For on-roadway RPD processes, the VMT activity data is annual for some sources and monthly for other
sources, depending on the source of the data. Sources without monthly VMT were temporalized from
annual to month through temporal profiles. VMT was also temporalized from month to day of the week,
and then to hourly through temporal profiles. The RPD processes require a speed profile (SPDPRO) that
consists of vehicle speed by hour for a typical weekday and weekend day. For onroad, the temporal
81
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profiles and SPDPRO will impact not only the distribution of emissions through time but also the total
emissions. Because SMOKE-MOVES (for RPD) calculates emissions based on the VMT, speed and
meteorology, if one shifted the VMT or speed to different hours, it would align with different
temperatures and hence different emission factors. In other words, two SMOKE-MOVES runs with
identical annual VMT, meteorology, and MOVES emission factors, will have different total emissions if
the temporal allocation of VMT changes. Figure 3-19 illustrates the temporal allocation of the onroad
activity data (i.e., VMT) and the pattern of the emissions that result after running SMOKE-MOVES. In
this figure, it can be seen that the meteorologically varying emission factors add variation on top of the
temporal allocation of the activity data.
Meteorology is not used in the development of the temporal profiles, but rather it impacts the calculation
of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the
hourly level by grid cell. More specifically, the on-network (RPD) and the off-network parked vehicle
(RPV, RPH, and RPP) processes use the gridded meteorology (MCIP) either directly or indirectly. For
RPD, RPV, and RPH, Movesmrg determines the temperature for each hour and grid cell and uses that
information to select the appropriate emission factor for the specified SCC/pollutant/mode combination.
For RPP, instead of reading gridded hourly meteorology, Movesmrg reads gridded daily minimum and
maximum temperatures. The total of the emissions from the combination of these four processes (RPD,
RPV, RPH, and RPP) comprise the onroad sector emissions. The temporal patterns of emissions in the
onroad sector are influenced by meteorology.
Figure 3-19. Example of temporal variability of NOx emissions
4 -
2014v2 onroad RPD hourly NOX and VMT: Wake County, NC
3
3.5 J
3 3 J
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1 13
aj 2.5 J
A A
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A J
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. 1 \
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7/8/1.
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10:00
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
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.
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The CRC A-100 dataset includes temporal profiles for individual counties, Metropolitan Statistical Areas
(MSAs), and entire regions (e.g. West, South). For counties without county or MSA temporal profiles
specific to itself, regional temporal profiles are used. Temporal profiles also vary by each of the MOVES
road types, and there are distinct hour-of-day profiles for each day of the week. Plots of hour-of-day
profiles for passenger vehicles in Fulton County, GA, are shown in Figure 3-20. 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 3-21 shows which counties have temporal profiles specific to that county, and which counties use
regional average profiles.
Figure 3-20. Sample onroad diurnal profiles for Fulton County, GA
Monday
Fulton Co
Friday
Fulton Co
passenger
passenger
o.i
0.09
0.09
0.08
0.07
0.07
0.06
0.06
0.05
0.05
0.04
0.04
0.03
0.03
0.02
0.02
0.01
0.01
24
road 2 road 3 road 4 road 5
road 2 road 3 road 4
road 5
Saturday
Fulton Co
Sunday
Fulton Co
passenger
passenger
Monday Fulton Co passenger Friday Fulton Co passenger
0.1 0.09
0-09 . 0.08
008 L~X\. 0 07
007 0.06 LA \
0.01 _ xjv 0.01 ^ y
0 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 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 road 2 road 3 road 4 road 5
Saturday Fulton Co passenger Sunday Fulton Co passenger
0.09
0.09
0.08
0.06
0.05
0.04
0.03
0.02
001 ^
1 2 3 4 5 6 7
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
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 road 2 road 3 road 4 road 5
83
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Figure 3-21. Counties for which MOVES Speeds and Temporal Profiles could be Populated
Group I | Individual
Midwest Region Average of Single County MSA Counties
Midwest Region non-MSA Average
~ Northeast Region Average of Single County MSA Counties
~ Northeast Region non-MSA Average
! South Region Average of Single County MSA Counties
U South Region non-MSA Average
B West Region Average of Single County MSA Counties
H 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
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 3-22.
The CRC A-100 temporal profiles were used in the entire contiguous United States, except in California.
All California temporal profiles were carried over from 2014v7.0, although California hoteling uses CRC
A-100-based profiles just like the rest of the country, since CARB didn't have a hoteling-specific profile.
Monthly profiles in all states (national profiles by broad vehicle type) were also carried over from
2014v7.0 and applied directly to the VMT. For California, CARB supplied diurnal profiles that varied by
vehicle type, day of the week15, and air basin. These C ARB-specific profiles were used in developing
EPA estimates for California. Although the EPA adjusted the total emissions to match California-
submitted emissions for 2016, the temporal allocation of these emissions took into account both the state-
specific VMT profiles and the SMOKE-MOVES process of incorporating meteorology. For more details
on the adjustments to California's onroad emissions, see Section 2.3.1.
15 California's diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and
Tuesday, Wednesday. Thursday had the same profile.
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Monday
Figure 3-22. Example of Temporal Profiles for Combination Trucks
Fulton Co combo Friday Fulton Co combo
5 S 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
Saturday
Fulton Co
combo
Sunday
Fulton Co
combo
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
— road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
3.3.8 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm,
ptfire)
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to
reduce the total emissions based on meteorological conditions. These adjustments are applied through
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and
then subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the
ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are
subject to transport. This methodology is discussed in (Pouliot et al., 2010,
http://www3.epa.gov/ttn/chief/conference/eil9/session9/pouliot pres.pdf). 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. For this reason, to ensure consistency between grid resolutions, afdust emissions for the
36US3 grid are aggregated from the 12US1 emissions. Application of the transport fraction and
meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid modeling as
compared to ambient samples.
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Biogenic emissions in the beis sector vary by every day of the year because they are developed using
meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions are
computed using appropriate emission factors according to the vegetation in each model grid cell, while
taking the meteorological data into account.
For the cmv sectors, emissions are allocated with flat day of week and flat hourly profiles. Updated
monthly profiles were developed for the LADCO states using link-level NOx emissions for ship traffic
provided by LADCO. These data were based on activities reported by ship AIS (transponder) devices.
Monthly NOx emissions were normalized to create temporal profiles for each lake. For the port SCCs, an
in-port profile was developed as the average of the maneuvering and hoteling emissions. The cruising
emissions were used for the underway SCCs. As some of the lakes did not include complete data for the
in-port sources (Ontario, Canada, St. Claire), a hybrid profile was created as an average of the in-port
NOx emissions for Lakes Michigan, Huron, Superior, and Erie. A resulting 22 profiles were developed
and applied to CI, C2 and C3 ships based county and SCC (i.e., port versus underway). Only new
monthly profiles were developed from these data because the weekly and diurnal variation were deemed
to be comparable to the existing EPA profiles. For non-LADCO areas, CI and C2 monthly profiles are
flat and C3 monthly profiles are highest (but not significantly different from the rest of the year) in the
summer.
For the rail sector, new monthly profiles were developed for the 2014 platform. Monthly temporal
allocation for rail freight emissions is based on AAR Rail Traffic Data, Total Carloads and Intermodal, for
2014. For passenger trains, monthly temporal allocation is based on rail passenger miles data for 2014
from the Bureau of Transportation Statistics. Rail emissions are allocated with flat day of week profiles,
and most emissions are allocated with flat hourly profiles. These 2014-based profiles are used for 2016
modeling.
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
3-23 (McCarty et al., 2009). This puts most of the emissions during the work day and suppresses the
emissions during the middle of the night.
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Figure 3-23. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
0.18
0.16
0.14
New McCarty Profile
OLD EPA
0.12
c
o
o.i
u
re
ro
10.08
0.06
0.04
0.02
12345678 9 10111213141516171819 202122 23 24
Industrial processes that are not likely to shut down on Sundays, such as those at cement plants, use
profiles that include emissions on Sundays, while those that would shut down on Sundays use profiles that
reflect Sunday shutdowns.
For the ptfire sectors, the inventories are in the daily point fire format FF10 PTDAY. Separate hourly
profiles for prescribed and wildfires were used. Figure 3-24 below shows the profiles used for each state
for the 2014v7.0 and 2014v7.1 modeling platforms. They are similar but not the same and vary according
to the average meteorological conditions in each state. The 2016 alpha platform uses the ptfire diurnal
profiles form 2014v7.1 platform.
Figure 3-24. Prescribed and Wildfire diurnal temporal profiles
US Prescribed fire diurnal profiles: State
Wildfire diurnal profiles: State
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
87
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monthly using monthly temporal profiles applied in SMOKE by SCC. This is an improvement over the
2011 platform, which applied monthly temporal allocation in California at the broader SCC7 level.
3.4 Spatial Allocation
The methods used to perform spatial allocation are summarized in this section. For the modeling
platform, spatial factors are typically applied by county and SCC. As described in Section 3.1, spatial
allocation was performed for a national 36-km domain and two national 12-km domains. To accomplish
this, SMOKE used national 36-km and 12-km spatial surrogates and a SMOKE area-to-point data file.
For the U.S., the EPA updated surrogates to use circa 2014 data wherever possible. For Mexico, updated
spatial surrogates were used as described below. For Canada, updated surrogates were provided by
Environment Canada for 2014v7.1. The U.S., Mexican, and Canadian 36-km and 12-km surrogates cover
the entire CONUS domain 12US1 shown in Figure 3-1. The 36US3 domain includes a portion of Alaska,
and since Alaska emissions are typically not included in air quality modeling, special considerations are
taken to include Alaska emissions in 36-km modeling.
Documentation of the origin of the spatial surrogates for the platform is provided in the workbook
US_SpatialSurrogate_Workbook_v07172018 which is available with the reports for the 2014v7.1
platform. The remainder of this subsection summarizes the data used for the spatial surrogates and the
area-to-point data which is used for airport refueling.
3.4.1 Spatial Surrogates for U.S. emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 36-km and 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-
to-point approach overrides the use of surrogates for an airport refueling sources. Table 3-19 lists the
codes and descriptions of the surrogates. Surrogate names and codes listed in italics are not directly
assigned to any sources for the 2016 alpha platform, but they are sometimes used to gapfill other
surrogates, or as an input for merging two surrogates to create a new surrogate that is used.
Many surrogates were updated or newly developed for use in the 2014v7.0 platform (Adelman, 2016).
They include the use of the 2011 National Land Cover Database (the previous platform used 2006) and
development of various development density levels such as open, low, medium high and various
combinations of these. These landuse surrogates largely replaced the FEMA category surrogates that
were used in the 2011 platform. Additionally, onroad surrogates were developed using average annual
daily traffic counts from the highway monitoring performance system (HPMS). Previously, the "activity"
for the onroad surrogates was length of road miles. This and other surrogates are described in a reference
(Adelman, 2016).
Several surrogates were updated or developed as new surrogates for 2014v7.1, and used in 2016 alpha
platform:
cl/c2 ships at ports uses a surrogate based on 2014 NEI ports activity data based on use of the
2014NEIvl (surrogate 820); previously, just the port shapes (801) were used.
cl/c2 ships underway uses a 2013-shipping density surrogate (surrogate 808); previously Offshore
Shipping NEI2014 Activity (806) was used.
Oil and gas surrogates were updated to correct errors found after they were used for 2014v7.0;
88
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Onroad spatial allocation uses surrogates that do not distinguish between urban and rural road
types, correcting the issue arising in some counties due to the inconsistent urban and rural
definitions between MOVES and the surrogate data;
Correction was made to the water surrogate to gap fill missing counties using 2006 NLCD.
The surrogates for the U.S. were mostly generated using the Surrogate Tool to drive the Spatial Allocator,
but a few surrogates were developed directly within ArcGIS or using scripts that manipulate spatial data
in PostgreSQL . The tool and documentation for the Surrogate Tool is available at
https://www.cmascenter.Org/sa-tools/documentation/4.2/SurrogateToolUserGuide 4 2.pdf.
Table 3-19. U.S. Surrogates available for the 2016 alpha modeling platform
Code
Surrogate Description
1 Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2) I
505
Industrial Land
100
Population ]
506
Education
110
Housing
507
Heavy Light Construction Industrial Land
131
urban Housing
510
Commercial plus Industrial
132
Suburban Housing 1
515
Commercial plus Institutional Land
134
Rural Housing
520
Commercial plus Industrial plus Institutional
137
Housing Change \
525
Golf Courses plus Institutional plus
Industrial plus Commercial
140
Housing Change and Population
526
Residential - Non-Institutional
150
Residential Heating - Natural Gas
527
Single Family Residential
160
Residential Heating - Wood \
535
Residential + Commercial + Industrial +
Institutional + Government
170
Residential Heating - Distillate Oil
540
Retail Trade (COM1)
180
Residential Heating - Coal
545
Personal Repair (COM3)
190
Residential Heating - LP Gas
555
Professional/Technical (COM4) plus General
Government (GOV1)
201
Urban Restricted Road Miles
560
Hospital (COM6)
202
Urban Restricted AADT
575
Light and High Tech Industrial (1ND2 +
IND5)
205
Extended Idle Locations
580
Food Drug Chemical Industrial (1ND3)
211
Rural Restricted Road Miles j
585
Metals and Minerals Industrial (LND4)
212
Rural Restricted AADT \
590
Heavy Industrial (1ND1)
221
Urban Unrestricted Road Miles
595
Light Industrial (1ND2)
222
Urban Unrestricted AADT
596
Industrial plus Institutional plus Hospitals
231
Rural Unrestricted Road Miles ;
650
Refineries and Tank Farms
232
Rural Unrestricted AADT ^
670
Spud Count - CBM Wells
239
Total Road AADT
671
Spud Count - Gas Wells
240
Total Road Miles
672
Gas Production at Oil Wells
241
Total Restricted Road Miles \
673
Oil Production at CBM Wells
242
All Restricted AADT
674
Unconventional Well Completion Counts
243
Total Unrestricted Road Miles s
676
Well Count - All Producing
244
All Unrestricted AADT
677
Well Count - All Exploratory
258
Intercity Bus Terminals
678
Completions at Gas Wells
259
Transit Bus Terminals
679
Completions at CBM Wells
260
Total Railroad Miles
681
Spud Count - Oil Wells
261
NT AD Total Railroad Density
683
Produced Water at All Wells
271
NTAD Class 12 3 Railroad Density
685
Completions at Oil Wells
272
NT AD Amtrak Railroad Density \
686
Completions at All Wells
89
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Code
Surrogate Description
I Code
Surrogate Description
273
NTAD Commuter Railroad Density \
6X7
Feet Drilled at All Wells
275
ERTACRail Yards
691
Well Counts - CBM Wells
280
Class 2 and 3 Railroad Miles ?
692
Spud Count - All Wells
300
NLCD Low Intensity Development
693
Well Count - All Wells
301
NL CD Med Intensity Development
694
Oil Production at Oil Wells
302
NLCD High Intensity Development \
695
Well Count - Oil Wells
303
NLCD Open Space \
696
Gas Production at Gas Wells
304
NLCD Open + Low
697
Oil Production at Gas Wells
305
NLCD Low + Med
698
Well Count - Gas Wells
306
NLCD Med + High
699
Gas Production at CBM Wells
307
NLCD All Development
710
Airport Points
308
NLCD Low + Med + High
711
Airport Areas
309
NLCD Open + Low + Med
801
Port Areas
310
NLCD Total Agriculture
805
Offshore Shipping Area
318
NLCD Pasture Land
806
Offshore Shipping NEI2014 Activity
319
NLCD Crop Land
807
Navigable Waterway Miles
320
NLCD Forest Land
808
2013 Shipping Density
321
NLCD Recreational Land
820
Ports NEI2014 Activity
340
NLCD Land
850
Golf Courses
350
NLCD Water
860
Mines
500
Commercial Land
1 890
Commercial Timber
For the onroad sector, the on-network (RPD) emissions were allocated differently from the off-network
(RPP and RPV). On-network used average annual daily traffic (AADT) data and off network used land
use surrogates as shown in Table 3-20. Emissions from the extended (i.e., overnight) idling of trucks were
assigned to surrogate 205, which is based on locations of overnight truck parking spaces. This surrogate's
underlying data were updated for use in the 2014 and 2016 platforms to include additional data sources
and corrections based on comments received on the 2011 NATA.
Table 3-20. Off-Network Mobile Source Surrogates
Source type
Source Type name
Surrogate ID
Description
11
Motorcycle
307
NLCD All Development
21
Passenger Car
307
NLCD All Development
31
Passenger Truck
307
NLCD All Development
NLCD Low + Med +
32
Light Commercial Truck
308
High
41
Intercity Bus
258
Intercity Bus Terminals
42
Transit Bus
259
Transit Bus Terminals
43
School Bus
506
Education
51
Refuse Truck
306
NLCD Med + High
52
Single Unit Short-haul Truck
306
NLCD Med + High
53
Single Unit Long-haul Truck
306
NLCD Med + High
54
Motor Home
304
NLCD Open + Low
61
Combination Short-haul Truck
306
NLCD Med + High
62
Combination Long-haul Truck
306
NLCD Med + High
90
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For the oil and gas sources in the np oilgas sector, the spatial surrogates were updated to those shown in
Table 3-21 using 2014 data consistent with what was used to develop the 2014NEI nonpoint oil and gas
emissions. The primary activity data source used for the development of the oil and gas spatial
surrogates was data from Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2015). 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 (Illinois, Idaho, Indiana,
Kentucky, Missouri, Nevada, Oregon and Pennsylvania, Tennessee). In many cases, the correct surrogate
parameter was not available (e.g., feet drilled), but an alternative surrogate parameter was available (e.g.,
number of spudded wells) and downloaded. Under that methodology, both completion date and date of
first production from HPDI were used to identify wells completed during 2014. In total, over 1.43 million
unique wells were compiled from the above data sources. The wells cover 34 states and 1,158 counties.
(ERG, 2016b). Corrections to these data were made for the 2014v7.1 platform, and carried forward into
the 2016 alpha platform, after errors were discovered in some counties.
Table 3-21. 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
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
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-19 were not assigned to any SCCs, although many of the "unused"
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surrogates are actually used to "gap fill" other surrogates that are used. When the source data for a
surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in
those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the
emission inventories. Table 3-22 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by
sector assigned to each spatial surrogate.
Table 3-22. Selected 2016 CAP emissions by sector for U.S. Surrogates (CONUS domain totals)
Sector
ID
Description
NH3
NOX
PM2 5
S02
VOC
afdust
240
Total Road Miles
—
-
283,210
-
-
afdust
304
NLCD Open + Low
--
-
1,053,145
-
-
afdust
306
NLCD Med + High
-
-
43,636
-
-
afdust
308
NLCD Low + Med + High
-
-
122,943
-
-
afdust
310
NLCD Total Agriculture
-
-
987,447
-
-
ag
310
NLCD Total Agriculture
2,776,255
-
—
-
179,970
cmv_clc2
808
2013 Shipping Density
293
520,571
14,357
421
9,117
cmv_clc2
820
Ports NEI2014 Activity
11
23,201
729
148
972
nonpt
100
Population
32,842
0
0
0
1,222,980
nonpt
150
Residential Heating - Natural Gas
47,819
227,291
3,837
1,494
13,756
nonpt
170
Residential Heating - Distillate Oil
1,861
35,101
3,978
56,026
1,241
nonpt
180
Residential Heating - Coal
20
101
53
1,086
111
nonpt
190
Residential Heating - LP Gas
121
34,432
183
762
1,332
nonpt
239
Total Road AADT
0
25
551
0
274,177
nonpt
240
Total Road Miles
0
0
0
0
34,027
nonpt
242
All Restricted AADT
0
0
0
0
5,451
nonpt
244
All Unrestricted AADT
0
0
0
0
95,292
nonpt
271
NT AD Class 12 3 Railroad Density
0
0
0
0
2,252
nonpt
300
NLCD Low Intensity Development
5,184
27,632
103,906
3,720
74,580
nonpt
304
NLCD Open + Low
0
0
0
0
0
nonpt
306
NLCD Med + High
28,046
200,320
238,731
65,131
948,148
nonpt
307
NLCD All Development
24
46,331
126,722
14,185
596,598
nonpt
308
NLCD Low + Med + High
1,166
185,948
16,915
19,736
65,608
nonpt
310
NLCD Total Agriculture
0
0
37
0
204,819
nonpt
319
NLCD Crop Land
0
0
95
71
293
nonpt
320
NLCD Forest Land
4,143
378
1,289
9
474
nonpt
505
Industrial Land
0
0
0
0
174
nonpt
535
Residential + Commercial + Industrial +
Institutional + Government
5
2
130
0
39
nonpt
560
Hospital (COM6)
0
0
0
0
0
nonpt
650
Refineries and Tank Farms
0
22
0
0
98,989
nonpt
711
Airport Areas
0
0
0
0
282
nonpt
801
Port Areas
0
0
0
0
8,059
nonroad
261
NT AD Total Railroad Density
3
2,380
246
2
457
nonroad
304
NLCD Open + Low
4
2,028
177
5
2,914
nonroad
305
NLCD Low + Med
114
19,450
4,654
154
136,612
92
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nonroad
306
NLCD Med + High
346
207,923
13,328
431
113,689
nonroad
307
NLCD All Development
104
34,233
15,676
124
169,016
nonroad
308
NLCD Low + Med + High
684
378,335
31,078
506
63,783
nonroad
309
NLCD Open + Low + Med
113
20,838
1,250
152
42,930
nonroad
310
NLCD Total Agriculture
493
369,213
26,906
390
43,497
nonroad
320
NLCD Forest Land
19
6,306
1,153
16
8,386
nonroad
321
NLCD Recreational Land
161
22,718
13,568
235
500,251
nonroad
350
NLCD Water
216
142,165
7,301
391
380,972
nonroad
850
Golf Courses
13
2,027
117
18
5,603
nonroad
860
Mines
2
2,670
271
2
516
npoilgas
670
Spud Count - CBM Wells
0
0
0
0
155
npoilgas
671
Spud Count - Gas Wells
0
0
0
0
9,775
npoilgas
672
Gas Production at Oil Wells
0
2,890
0
21,703
117,295
npoilgas
673
Oil Production at CBM Wells
0
52
0
0
3,033
npoilgas
674
Unconventional Well Completion Counts
0
47,074
1,793
237
3,402
npoilgas
678
Completions at Gas Wells
0
3,384
26
6,768
71,343
npoilgas
679
Completions at CBM Wells
0
11
0
483
1,366
npoilgas
681
Spud Count - Oil Wells
0
0
0
0
65,351
npoilgas
683
Produced Water at All Wells
0
11
0
0
87,045
npoilgas
685
Completions at Oil Wells
0
3,000
129
2,266
50,750
npoilgas
687
Feet Drilled at All Wells
0
114,998
3,995
449
9,059
npoilgas
691
Well Counts - CBM Wells
0
28,093
483
12
23,454
npoilgas
692
Spud Count - All Wells
0
9,018
255
113
365
npoilgas
693
Well Count - All Wells
0
0
0
0
186
npoilgas
694
Oil Production at Oil Wells
0
4,874
0
6,337
1,049,085
npoilgas
695
Well Count - Oil Wells
0
110,111
2,892
80
408,969
npoilgas
696
Gas Production at Gas Wells
0
45,335
2,123
163
57,017
npoilgas
697
Oil Production at Gas Wells
0
1,346
0
25
359,070
npoilgas
698
Well Count - Gas Wells
15
303,844
5,457
299
665,379
npoilgas
699
Gas Production at CBM Wells
0
2,151
325
26
4,179
onroad
205
Extended Idle Locations
521
184,705
2,170
74
34,050
onroad
239
Total Road AADT
0
0
0
0
5,935
onroad
242
All Restricted AADT
35,739
1,285,235
40,512
8,435
200,706
onroad
244
All Unrestricted AADT
64,970
1,929,919
75,206
17,813
514,332
onroad
258
Intercity Bus Terminals
0
142
2
0
33
onroad
259
Transit Bus Terminals
0
88
4
0
192
onroad
304
NLCD Open + Low
0
773
17
1
2,546
onroad
306
NLCD Med + High
0
15,208
278
18
17,358
onroad
307
NLCD All Development
0
589,660
11,395
953
1,128,888
onroad
308
NLCD Low + Med + High
0
39,617
662
61
57,232
onroad
506
Education
0
489
17
1
721
rail
261
NT AD Total Railroad Density
4
15,222
368
286
873
rail
271
NT AD Class 12 3 Railroad Density
359
657,335
18,786
415
33,866
93
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
rwc
300
NLCD Low Intensity Development
15,331
30,493
313,945
7,684
338,465
For 36US3 modeling, most U.S. emissions sectors were processed using 36-km spatial surrogates, and if
applicable, 36-km meteorology. Exceptions include:
- For the onroad and onroad ca adj sectors, 36US3 emissions were aggregated from 12US1 by
summing emissions from a 3x3 group of 12-km cells into a single 36-km cell. Differences in 12-
km and 36-km meteorology can introduce differences in onroad emissions, and so this approach
ensures that the 36-km and 12-km onroad emissions are consistent. However, this approach means
that 36US3 onroad does not include emissions in Southeast Alaska; therefore, Alaska onroad
emissions are included in the Canadian onroad sector (onroadcan), as described in Section 2.5.3.
The 36US3 onroad can emissions, including Canada and Alaska, are spatially allocated using 36-
km surrogates and processed with 36-km meteorology.
Similarly to onroad, because afdust emissions incorporate meteorologically-based adjustments,
afdust adj emissions for 36US3 were aggregated from 12US1 to ensure consistency in emissions
between modeling domains. Again, similarly to onroad, this means 36US3 afdust does not include
emissions in Southeast Alaska; therefore, Alaska afdust emissions are included in the Canadian
dust sector (othafdustadj), as described in Section 2.5.2. The 36US3 othafdustadj emissions,
including Canada and Alaska, are spatially allocated using 36-km surrogates and adjusted with 36-
km meteorology.
The ag and rwc sectors are processed using 36-km spatial surrogates, but using temporal profiles
based on 12-km meteorology.
94
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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
Spatial surrogates for allocating Canada and Mexico province/sub-province and municipio level
emissions have been updated in the 2014v7.1 platform and carried forward into the 2016 alpha platform.
A new set of Canada shapefiles were provided by Environment Canada along with cross references
spatially allocate the new 2013 Canadian emissions. Gridded surrogates were generated using the
Surrogate Tool (previously referenced); Table 3-23 provides a list. Due to computational reasons, total
roads (1263) were used instead of the unpaved rural road surrogate provided. The population surrogate
was recently updated for Mexico; 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 3-24.
Table 3-23. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
100
Population
941
PAVED ROADS
101
total dwelling
942
UNPAVED ROADS
106
ALL INDUST
945
Commercial Marine Vessels
113
Forestry and logging
950
Combination of Forest and Dwelling
115
Agriculture and forestry activities
955
UNPAVED ROADS AND TRAILS
200
Urban Primary Road Miles
960
TOTBEEF
210
Rural Primary Road Miles
965
TOTBEEF CD
212
Mining except oil and gas
966
TOTPOUL CD
220
Urban Secondary Road Miles
967
TOTSWIN CD
221
Total Mining
968
TOTFERT CD
222
Utilities
970
TOTPOUL
230
Rural Secondary Road Miles
980
TOTSWIN
240
Total Road Miles
990
TOTFERT
308
Food manufacturing
996
urban area
321
Wood product manufacturing
1211
Oil and Gas Extraction
323
Printing and related support activities
1212
Oil Sands
Petroleum and coal products
324
manufacturing
1251
OFFR TOTFERT
Plastics and rubber products
326
manufacturing
1252
OFFR MINES
95
-------
Code
Canadian Surrogate Description
Code
Description
Non-metallic mineral product
327
manufacturing
1253
OFFR Other Construction not Urban
331
Primary Metal Manufacturing
1254
OFFR Commercial Services
Petroleum product wholesaler-
412
distributors
1255
OFFR Oil Sands Mines
416
Building material and supplies
whol esal er-di stributor s
1256
OFFR Wood industries CANVEC
448
clothing and clothing accessories stores
1257
OFFR Unpaved Roads Rural
562
Waste management and remediation
services
1258
OFFR Utilities
921
Commercial Fuel Combustion
1259
OFFR total dwelling
TOTAL INSTITUTIONAL AND
923
GOVERNEMNT
1260
OFFR water
924
Primary Industry
1261
OFFR ALL INDUST
925
Manufacturing and Assembly
1262
OFFR Oil and Gas Extraction
926
Distribtution and Retail (no petroleum)
1263
OFFR ALLROADS
927
Commercial Services
1264
OFFR OTHERJET
931
OTHERJET
1265
OFFR CANRAIL
932
CANRAIL
—
—
Table 3-24. CAPs Allocated to Mexican and Canadian Spatial Surrogates
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
11
MEX 2015 Population
26,410
125,626
4,309
495
143,747
14
MEX Residential Heating - Wood
0
1,335
17,124
204
117,737
16
MEX Residential Heating - Distillate Oil
0
13
0
3
0
20
MEX Residential Heating - LP Gas
0
5,500
165
0
93
22
MEX Total Road Miles
2,789
361,522
10,292
6,079
72,731
24
MEX Total Railroads Miles
0
24,326
543
213
948
26
MEX Total Agriculture
175,112
134,919
28,631
6,455
10,855
32
MEX Commercial Land
0
75
1,653
0
25,416
34
MEX Industrial Land
4
1,138
2,005
0
121,933
36
MEX Commercial plus Industrial Land
0
2,218
30
6
100,789
38
MEX Commercial plus Institutional Land
2
1,645
70
3
45
40
MEX Residential (RES1-
4)+Comercial+Industrial+Institutional+Government
0
4
11
0
77,571
42
MEX Personal Repair (COM3)
0
0
0
0
5,841
44
MEX Airports Area
0
3,708
105
480
1,270
50
MEX Mobile sources - Border Crossing
5
161
1
3
293
100
CAN Population
740
66
764
14
343
101
CAN total dwelling
408
35,196
2,578
4,752
144,529
106
CAN ALL INDUST
0
0
11,984
0
69
113
CAN Forestry and logging
509
2,797
0
144
7,548
115
CAN Agriculture and forestry activities
51
585
2,932
13
1,717
200
CAN Urban Primary Road Miles
1,874
82,882
3,491
284
10,848
210
CAN Rural Primary Road Miles
758
50,528
1,914
119
4,744
96
-------
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
212
CAN Mining except oil and gas
0
0
3,536
0
0
220
CAN Urban Secondary Road Miles
3,506
126,804
6,784
603
26,809
221
CAN Total Mining
0
0
57,656
0
0
222
CAN Utilities
82
9,163
56,095
3,072
227
230
CAN Rural Secondary Road Miles
1,970
87,734
3,637
311
12,390
240
CAN Total Road Miles
45
67,129
2,442
73
107,438
308
CAN Food manufacturing
0
0
11,480
0
6,207
321
CAN Wood product manufacturing
306
1,980
0
161
8,202
323
CAN Printing and related support activities
0
0
0
0
11,919
324
CAN Petroleum and coal products manufacturing
0
1,089
1,376
433
6,561
326
CAN Plastics and rubber products manufacturing
0
0
0
0
22,854
327
CAN Non-metallic mineral product manufacturing
0
0
6,916
0
0
331
CAN Primary Metal Manufacturing
0
158
5,724
51
74
412
CAN Petroleum product wholesaler-distributors
0
0
0
0
40,364
448
CAN clothing and clothing accessories stores
0
0
0
0
118
562
CAN Waste management and remediation services
224
1,679
2,312
2,351
16,715
921
CAN Commercial Fuel Combustion
204
25,592
2,355
5,315
1,195
923
CAN TOTAL INSTITUTIONAL AND GOVERNEMNT
0
0
0
0
14,349
924
CAN Primary Industry
0
0
0
0
38,003
925
CAN Manufacturing and Assembly
0
0
0
0
72,660
926
CAN Distribtution and Retail (no petroleum)
0
0
0
0
7,168
927
CAN Commercial Services
0
0
0
0
31,818
932
CAN CANRAIL
56
100,494
2,396
354
5,080
941
CAN PAVED ROADS
0
0
315,987
0
0
945
CAN Commercial Marine Vessels
233
159,147
6,606
4,170
14,934
948
CAN Forest
0
21
7
0
236
950
CAN Combination of Forest and Dwelling
1,797
19,917
164,025
2,829
232,213
955
CANUNPAVED ROADS AND TRAILS
0
0
477,941
0
0
960
CAN TOTBEEF
0
0
1,241
0
264,904
965
CANTOTBEEF CD
280,659
0
0
0
0
966
CANTOTPOUL CD
23,920
0
0
0
0
967
CANTOTSWIN CD
68,024
0
0
0
0
968
CANTOTFERT CD
120,207
0
0
0
0
970
CAN TOTPOUL
0
0
182
0
243
980
CAN TOTSWIN
0
0
757
0
2,591
990
CAN TOTFERT
0
3,743
380,161
9,570
150
996
CAN urban area
0
0
1,305
0
0
1211
CAN Oil and Gas Extraction
2
35
240,377
149
937
1212
CAN OilSands
151
2,374
0
693
1,911
1251
CAN OFFR TOTFERT
111
106,004
7,733
81
10,726
1252
CAN OFFR MINES
44
38,370
3,312
32
4,174
1253
CAN OFFR Other Construction not Urban
27
21,854
3,747
20
9,680
1254
CAN OFFR Commercial Services
36
16,814
2,167
30
23,481
1255
CAN OFFR Oil Sands Mines
0
0
0
0
0
1256
CAN OFFR Wood industries CANVEC
14
10,922
1,089
10
1,995
97
-------
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
1257
CAN OFFR Unpaved Roads Rural
35
9,857
1,756
30
69,379
1258
CAN OFFR Utilities
17
8,221
523
15
10,568
1259
CAN OFFR total dwelling
18
5,233
1,441
15
35,810
1260
CAN OFFR water
9
2,247
348
13
21,030
1261
CAN OFFR ALL INDUST
4
3,972
260
3
880
1262
CAN OFFR Oil and Gas Extraction
1
958
54
1
153
1263
CAN OFFR ALLROADS
2
1,015
74
1
523
1264
CAN OFFR OTHERJET
1
782
69
1
71
1265
CAN OFFR CANRAIL
0
77
8
0
14
3.5 Preparation of Emissions for the CAMx model
For this study, we perform air quality modeling with two models: CMAQ, and also the Comprehensive
Air Quality Model with Extensions (CAMx model). Gridded hourly emissions output by the SMOKE
model are used as emissions inputs to the CMAQ model, but they cannot be used directly as emissions
inputs to the CAMx model. Instead, CMAQ-ready emissions must be converted to the format required by
CAMx. The CAMx conversion process consists of the following:
1) Convert all emissions file formats from the I/O API NetCDF format used by CMAQ to the UAM
format used by CAMx, including the merged, gridded low-level emissions files which include
biogenics
2) Shift hourly emissions files from the 25 hour format used by CMAQ to the averaged 24 hour
format used by CAMx
3) Rename and aggregate model species for CAMx
4) Convert 3D wildland and agricultural fire emissions into CAMx point format
5) Merge all inline point source emissions files together for each day, including layered fire
emissions originally from SMOKE
6) Add sea salt aerosol emissions to the converted, gridded low-level emissions files
Conversion of file formats from I/O API to UAM is performed using a program called "cmaq2uam". In
the CAMx conversion process, all SMOKE outputs are passed through this step first. Unlike CMAQ, the
CAMx model does not have an inline biogenics option, and so for the purposes of CAMx modeling,
emissions from SMOKE must include biogenic emissions.
One difference between CMAQ-ready emissions files and CAMx-ready emissions files involves hourly
temporalization. A daily emissions file for CMAQ includes data for 25 hours, where the first hour is 0:00
GMT of a given day, and the last hour is 0:00 GMT of the following day. For the CAMx model, a daily
emissions file must only include data for 24 hours, not 25. Furthermore, to match the hourly configuration
expected by CAMx, each set of consecutive hourly timesteps from CMAQ-ready emissions files must be
averaged. For example, the first hour of a CAMx-ready emissions file will equal the average of the first
two hours from the corresponding CMAQ-ready emissions file, and the last (24fe) hour of a CAMx-ready
emissions file will equal the average of the last two hours (24th and 25th) from the corresponding CMAQ-
ready emissions file. This time conversion is incorporated into each step of the CAMx-ready emissions
conversion process.
98
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The CAMx model uses a slightly different version of the CB6 speciation mechanism than does the
CMAQ model. SMOKE prepares emissions files for the CB6 mechanism used by the CMAQ model
("CB6-CMAQ"), and therefore, the emissions must be converted to the CB6 mechanism used by the
CAMx model ("CB6-CAMx") during the CAMx conversion process. In addition to the mechanism
differences, CMAQ and CAMx also occasionally use different species naming conventions. For CAMx
modeling, we also create additional tracer species. A summary of the differences between CMAQ input
species and CAMx input species for CB6 (VOC), AE6 (PM2.5), and other model species, is provided in
Table 3-25. Each step of the CAMx-ready emissions conversion process includes conversion of CMAQ
species to CAMx species using a species mapping table which includes the mappings in Table 3-25.
Table 3-25. Emission model species mappings for CMAQ and CAMx
Inventory Pollutant
CMAQ Model Species
CAMx Model Species
Cl2
CL2
CL2
HC1
HCL
HCL
CO
CO
CO
NOx
NO
NO
N02
N02
HONO
HONO
so2
S02
S02
SULF
SULF
nh3
NH3
NH3
NH3 FERT
n/a (not used in CAMx)
VOC
ACET
ACET
ALD2
ALD2
ALDX
ALDX
BENZ
BENZ and BNZA (duplicate species)
CH4
CH4
ETH
ETH
ETHA
ETHA
ETHY
ETHY
ETOH
ETOH
FORM
FORM
IOLE
IOLE
ISOP
ISOP and ISP (duplicate species)
KET
KET
MEOH
MEOH
NAPH + XYLMN (sum)
XYL
NVOL
n/a (not used in CAMx)
OLE
OLE
PAR
PAR
PRPA
PRPA
SESQ
SQT
SOAALK
n/a (not used in CAMx)
TERP
TERP and TRP (duplicate species)
TOL
TOL and TOLA (duplicate species)
UNR + NR (sum)
NR
PMio
PMC
CPRM
pm25
PEC
PEC
PN03
PN03
99
-------
Inventory Pollutant
CMAQ Model Species
CAMx Model Species
POC
POC
PS04
PS04
PAL
PAL
PCA
PCA
PCL
PCL
PFE
PFE
PK
PK
PH20
PH20
PMG
PMG
PMN
PMN
PMOTHR
PMOTHR and FPRM (duplicate species)
PNA
NA
PNCOM
PNCOM
PNH4
PNH4
PSI
PSI
PTI
PTI
POC + PNCOM (sum)
POA1
PAL + PCA + PFE +
FCRS1
PMG + PK + PMN +
PSI + PTI (sum)
1 The POA species, which is the sum of POC and PNCOM, is passed to the CAMx model in addition to individual species POC
and PNCOM. The FCRS species, which is also a sum of multiple PM species, is passed to CAMx in addition to each of the
individual component species.
One feature which is part of CMAQ and is not part of CAMx involves plume rise for fires. For CMAQ
modeling, we process fire emissions through SMOKE as inline point sources, and plume rise for fires is
calculated within CMAQ using parameters from the inline emissions files (heat flux, etc). This is similar
to how non-fire point sources are handled, except that the fire parameters are used to calculate plume rise
instead of traditional stack parameters. The CAMx model supports inline plume rise calculations using
traditional stack parameters, but, does not support inline plume rise for fire sources. Therefore, for the
purposes of CAMx modeling, we must have SMOKE calculate plume rise for fires using the Laypoint
program. In this modeling platform, this must be done for the ptfire, ptfire othna, and ptagfire sectors. To
distinguish these layered fire emissions from inline fire emissions, layered fire emissions are processed
with the sector names "ptfire3D", "ptfire_othna3D", and "ptagfire3D". When converting layered fire
emissions files to CAMx format, stack parameters are added to the CAMx-ready fire emissions files to
force the correct amount of fire emissions into each layer for each fire location.
CMAQ modeling uses one gridded low-level emissions file, plus multiple inline point source emissions
files, per day. CAMx modeling also uses one gridded low-level emissions file per day - but instead of
reading multiple inline point source emissions files at once, CAMx can only read a single point source file
per day. Therefore, as part of the CAMx conversion process, all inline point source files are merged into a
single "mrgpt" file per day. The mrgpt file includes the layered fire emissions described in the previous
paragraph, in addition to all non-fire elevated point sources from the cmv_c3, othpt, ptegu, ptnonipm, and
pt oilgas sectors.
The remaining step in the CAMx emissions process is to generate sea salt aerosol emissions, which are
distinct from the ocean chlorine emissions described in Section 2.8. Sea salt emissions do not need to be
included in CMAQ-ready emissions because they are calculated by the model, but, do need to be included
in CAMx-ready emissions. After the merged low-level emissions are converted to CAMx format, sea salt
100
-------
emissions are generated using a program called "seasalt" and added to the low-level emissions. Sea salt
emissions depend on meteorology, vary on a daily and hourly basis, and exist for model species PCL, NA,
PS04, and SS.
101
-------
4 Emission Summaries
Table 4-1 summarizes emissions by sector for the 2016 alpha platform. This summary is 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. The afdust sector emissions represent the
summaries after application of both the land use (transport fraction) and meteorological adjustments (see
Section 2.2.1); therefore, this sector is called "afdust adj" in these summaries. The onroad sector totals
are post-SMOKE-MOVES totals, representing air quality model-ready emission totals, and include
CARB emissions for California. The cmv sectors include U.S. emissions within state waters only; these
extend to roughly 3-5 miles offshore and includes CMV emissions at U.S. ports. "Offshore" represents
CMV emissions that are outside of U.S. state waters. Canadian CMV emissions are included in the othar
sector. The total of all sectors is listed as "Con U.S. Total." State totals are available in the reports area
on the FTP site for the 2016 alpha platform:
102
-------
Table 4-1. National by-sector CAP emissions summaries for the 2016 alpha platform, 12US1 grid
Sector
CO
NHs
NOx
PMio
PM2.S
SO2
VOC
afdust adj
6,216,650
874,142
ag
2,776,552
__
179,970
cmv clc2
47,183
120
260,338
6,493
6,168
345
4,840
cmv c3
10,885
25
108,268
4,248
3,832
3,883
5,043
nonpt
2,680,775
121,229
758,152
608,827
496,454
162,231
3,672,687
nonroad
12,188,930
2,266
1,206,980
122,107
115,409
2,418
1,464,613
np oilgas
642,086
15
676,194
17,746
17,480
38,963
2,986,288
onroad
20,446,327
101,230
4,045,998
272,855
130,263
27,356
1,961,995
pt oilgas
177,723
4,358
360,231
11,926
11,417
41,639
132,928
ptagfire
592,980
80,344
18,294
96,328
68,096
5,635
36,114
ptegu
672,184
25,012
1,289,229
170,818
140,823
1,544,799
33,453
ptfire
23,642,400
388,237
333,111
2,414,507
2,046,192
180,888
5,580,909
ptnonipm
1,847,809
61,395
1,072,555
407,458
263,816
672,952
808,939
rail
118,367
363
672,558
20,728
19,154
700
34,739
rwc
2,098,907
15,331
30,493
314,466
313,945
7,684
338,465
Con U.S. Total
65,166,557
3,576,477
10,832,402
10,685,156
4,507,190
2,689,491
17,240,985
Canada othafdust
_
_
_
2,182,869
426,384
_
_
Canada othar
2,951,746
497,869
589,490
427,171
236,053
34,377
1,141,438
Canada onroad can
1,903,123
8,153
414,875
25,071
18,246
1,390
162,191
Canada othpt
1,147,803
18,699
600,674
90,358
48,248
869,280
790,253
Canada ptfire othna
761,707
13,036
16,385
84,528
71,778
6,733
185,609
Mexico othar
241,571
201,994
220,491
115,484
54,299
7,717
522,236
Mexico onroad mex
1,828,101
2,789
442,410
15,151
10,836
6,247
158,812
Mexico othpt
205,081
5,049
447,645
73,252
57,437
476,077
71,030
Mexico ptfire othna
386,134
7,499
16,697
45,382
38,527
2,810
132,467
Offshore cmv clc2
56,548
184
284,208
9,219
8,942
224
5,263
Offshore cmv c3
77,632
69
856,659
47,247
43,653
255,222
34,140
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
9,609,498
755,356
3,938,226
3,116,400
1,015,070
1,660,580
3,251,647
103
-------
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108
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109
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Appendix A: Nonpoint Oil and Gas (npoilgas) SCCs
The table below shows the SCCs in the nonpoint oil and gas sector (np oilgas).
see
SCC description
2310000000
Industrial Processes;Oil and Gas Exploration and Production;All Processes;Total: All Processes
2310000220
Industrial Processes;Oil and Gas Exploration and Production;All Processes;Drill Rigs
2310000230
Industrial Processes;Oil and Gas Exploration and Production; All Processes; Workover Rigs
2310000330
Industrial Processes;Oil and Gas Exploration and Production;All Processes;Artificial Lift
2310000550
Industrial Processes;Oil and Gas Exploration and Production;All Processes;Produced Water
2310000660
Industrial Processes;Oil and Gas Exploration and Production;All Processes;Hydraulic Fracturing Engines
2310001000
Industrial Processes;Oil and Gas Exploration and Production;All Processes : On-shore;Total: All Processes
2310002000
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Oil And Gas Production;Total: All
Processes
2310002401
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Oil And Gas Production;Pneumatic
Pumps: Gas And Oil Wells
2310002411
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Oil And Gas
Production;Pressure/Level Controllers
2310002421
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Oil And Gas Production;Cold Vents
2310010000
Industrial Processes;Oil and Gas Exploration and Production;Crude Petroleum;Total: All Processes
2310010100
Industrial Processes;Oil and Gas Exploration and Production;Crude Petroleum;Oil Well Heaters
2310010200
Industrial Processes;Oil and Gas Exploration and Production;Crude Petroleum;Oil Well Tanks - Flashing &
Standing/Working/Breathing
2310010300
Industrial Processes;Oil and Gas Exploration and Production;Crude Petroleum;Oil Well Pneumatic Devices
2310010700
Industrial Processes;Oil and Gas Exploration and Production;Crude Petroleum;Oil Well Fugitives
2310010800
Industrial Processes;Oil and Gas Exploration and Production;Crude Petroleum;Oil Well Truck Loading
2310011000
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Total: All Processes
2310011020
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Storage Tanks: Crude
Oil
2310011100
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Heater Treater
2310011201
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Tank Truck/Railcar
Loading: Crude Oil
2310011500
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Fugitives: All
Processes
2310011501
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Fugitives: Connectors
2310011502
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Fugitives: Flanges
2310011503
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Fugitives: Open
Ended Lines
2310011504
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Fugitives: Pumps
2310011505
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Fugitives: Valves
2310011506
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Fugitives: Other
2310011600
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Production;Artificial Lift Engines
2310012000
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Oil Production;Total: All Processes
2310012020
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Oil Production;Storage Tanks: Crude
Oil
2310012525
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Oil Production;Fugitives, Valves:
Oil/Water
2310012526
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Oil Production;Fugitives, Other:
Oil/Water
110
-------
see
SCC description
2310020000
Industrial Processes;Oil and Gas Exploration and Production;Natural Gas;Total: All Processes
2310020600
Industrial Processes;Oil and Gas Exploration and Production;Natural Gas;Compressor Engines
2310020700
Industrial Processes;Oil and Gas Exploration and Production;Natural Gas;Gas Well Fugitives
2310020800
Industrial Processes;Oil and Gas Exploration and Production;Natural Gas;Gas Well Truck Loading
2310021010
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Storage Tanks:
Condensate
2310021011
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Condensate Tank
Flaring
2310021030
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Tank Truck/Railcar
Loading: Condensate
2310021100
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well Heaters
2310021101
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Natural Gas Fired
2Cycle Lean Burn Compressor Engines < 50 HP
2310021102
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Natural Gas Fired
2Cycle Lean Burn Compressor Engines 50 To 499 HP
2310021103
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Natural Gas Fired
2Cycle Lean Burn Compressor Engines 500+ HP
2310021201
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Natural Gas Fired
4Cycle Lean Burn Compressor Engines <50 HP
2310021202
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Natural Gas Fired
4Cycle Lean Burn Compressor Engines 50 To 499 HP
2310021203
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Natural Gas Fired
4Cycle Lean Burn Compressor Engines 500+ HP
2310021251
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Lateral Compressors
4 Cycle Lean Burn
2310021300
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well Pneumatic
Devices
2310021301
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Natural Gas Fired
4Cycle Rich Burn Compressor Engines <50 HP
2310021302
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Natural Gas Fired
4Cycle Rich Burn Compressor Engines 50 To 499 HP
2310021303
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Natural Gas Fired
4Cycle Rich Burn Compressor Engines 500+ HP
2310021310
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well Pneumatic
Pumps
2310021351
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Lateral Compressors
4 Cycle Rich Burn
2310021400
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well
Dehydrators
2310021402
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Nat Gas Fired 4Cycle
Rich Burn Compressor Engines 50 To 499 HP w/NSCR
2310021403
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Nat Gas Fired 4Cycle
Rich Burn Compressor Engines 500+ HP w/NSCR
2310021411
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well
Dehydrators - Flaring
2310021450
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Wellhead
2310021500
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production; Gas Well Completion
- Flaring
2310021501
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Fugitives: Connectors
2310021502
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Fugitives: Flanges
2310021503
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Fugitives: Open
Ended Lines
2310021504
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Fugitives: Pumps
Ill
-------
see
SCC description
2310021505
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Fugitives: Valves
2310021506
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Fugitives: Other
2310021509
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Fugitives: All
Processes
2310021600
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well Venting
2310021601
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well Venting -
Initial Completions
2310021602
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well Venting -
Recompletions
2310021603
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well Venting -
Blowdowns
2310021604
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well Venting -
Compressor Startups
2310021605
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Gas Well Venting -
Compressor Shutdowns
2310021700
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Miscellaneous
Engines
2310022000
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Gas Production;Total: All Processes
2310022010
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Gas Production;Storage Tanks:
Condensate
2310022051
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Gas Production;Turbines: Natural
Gas
2310022090
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Gas Production;Boilers/Heaters:
Natural Gas
2310022105
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Gas Production;Diesel Engines
2310022410
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Gas Production;Amine Unit
2310022420
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Gas Production;Dehydrator
2310022506
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Gas Production;Fugitives, Other: Gas
2310023010
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Storage Tanks:
Condensate
2310023030
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Tank
Truck/Railcar Loading: Condensate
2310023100
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;CBM Well
Heaters
2310023102
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;CBM Fired
2Cycle Lean Burn Compressor Engines 50 To 499 HP
2310023202
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;CBM Fired
4Cycle Lean Burn Compressor Engines 50 To 499 HP
2310023251
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Lateral
Compressors 4 Cycle Lean Burn
2310023300
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Pneumatic
Devices
2310023302
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;CBM Fired
4Cycle Rich Burn Compressor Engines 50 To 499 HP
2310023310
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Pneumatic
Pumps
2310023351
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Lateral
Compressors 4 Cycle Rich Burn
2310023400
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Dehydrators
2310023509
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives
2310023511
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives:
Connectors
112
-------
see
SCC description
2310023512
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives:
Flanges
2310023513
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives: Open
Ended Lines
2310023515
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives:
Valves
2310023516
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives:
Other
2310023600
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;CBM Well
Completion: All Processes
2310023603
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;CBM Well
Venting - Blowdowns
2310023606
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Mud Degassing
2310030300
Industrial Processes;Oil and Gas Exploration and Production;Natural Gas Liquids;Gas Well Water Tank
Losses
2310030401
Industrial Processes;Oil and Gas Exploration and Production;Natural Gas Liquids;Gas Plant Truck Loading
2310111100
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Exploration;Mud Degassing
2310111401
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Exploration;Oil Well Pneumatic
Pumps
2310111700
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Oil Exploration;Oil Well Completion:
All Processes
2310112401
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Oil Exploration;Oil Well Pneumatic
Pumps
2310121100
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Exploration;Mud Degassing
2310121401
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Exploration;Gas Well Pneumatic
Pumps
2310121700
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Exploration;Gas Well
Completion: All Processes
2310122100
Industrial Processes;Oil and Gas Exploration and Production;Off-Shore Gas Exploration;Mud Degassing
2310321010
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production -
Conventional;Storage Tanks: Condensate
2310321400
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production - Conventional;Gas
Well Dehydrators
2310321603
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production - Conventional;Gas
Well Venting - Blowdowns
2310400220
Industrial Processes;Oil and Gas Exploration and Production;All Processes - Unconventional;Drill Rigs
2310421010
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production -
Unconventional; Storage Tanks: Condensate
2310421100
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production - Unconventional;Gas
Well Heaters
2310421400
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production - Unconventional;Gas
Well Dehydrators
2310421603
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production - Unconventional;Gas
Well Venting - Blowdowns
113
-------
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE4.5 that were used
in the 2016 alpha platform
Profile
SPECIATE
comment
Sector
Pollutant
code
Profile description
version
5.0 (not
Replacement for v4.5
yet
profile 95223; Used 70%
released)
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 (not
Replacement for v4.5
yet
profile 95240. Used 70%
released)
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 (not
Replacement for v4.5
yet
profile 95241. Used 70%
released)
methane, 20% ethane;
the 10% remaining VOC
nonpt
voc
G95241TOG
Swine Farm and Animal Waste
is from profile 95241
nonpt,
5.0 (not
Composite of AE6-ready
ptnonipm,
yet
versions of SPECIATE4.5
pt_oilgas,
Composite -Refinery Fuel Gas and Natural
released)
profies 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
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
114
-------
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
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
115
-------
pt_oilgas,
Oil and Gas-Permian Basin Produced Gas
4.5
ptnoniom
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
116
-------
Appendix C: CB6 Assignment for New Species
September 27,2016
MEMORANDUM
Tr: - -50" E-." =nd rv'sde's OACPi i:A
From: Ross Bearisief awl Greg Yarwaod, Kimball Environ
specie* rTtopp.r,gs for C£6 ar.o CSC5 far use w.th speciate 4.5
Summary
RambaH Environ ftEf reviewed version 4.5 of the spec iT5 database, 3i.f created CBD5 and CS6
mechanism species mappings for newly added compounds. In srd tic ¦,:f? -tapping guidelines for
Carbon Bond fCBj mechanisms were expanded to promote com stancy: - "rent and future work.
Background
Tf« Environmental Prelection Agency's speciate repository contains fas and particulate "natter
spedation profiles of air pollution sources', which ate used in the generation of Emission data for air
:u = ltv rjdeli iuc- 5i C'-'i3..-t:;:.-.c-issce-ts-r 3-'|.c-i9q.-'« 3" CA-*-
fhttp://wi¥A¥.G3Fnxxo;fii|. However, the condensed chemical mechanisms used v."th" - t'-iss
photochemical models utiilie fewer species than SPECIATE to represent fas ansse che ¦>'! =:r;, a-:e;, or as a comb-ration c" rrode- species that rep-==ent
commc structural groups (e.g. £_D'< "tr ether aldehydes, PAR far b kyl grsupsi "able i lists all of
the e*pl-:i" a~» sruetira'. mode" spec as in CiCI ardCBfi mechanisms. ea:h cf «h zb rep"Ka-ts 3
defre: n.-: = " :f carton ats-nrssi t-,"i-'|r:r :a-fcc- :t be ::nseved n 3 esses. Cii cc-tshsfsir
mere-e«;:p' ;i: rc-oa spec sitis* CECr i-d 51 ar?i: 3-3 st-. ::.-a\,s :u: to -ep-e=="t . T--=
CHC5 recesentat on :>f :re five add "lions I CB6 spsries '= provked ir h~= ":nc!>j?i£d >Vi £505 column of
Table 1.
117
-------
in:c the e:=d : * i:-ij:tu-= -pec =: :-r'e s- = r.vr ttoce species that are used to
e; =Hr: :rpr.': p:e: :r=t 5 ¦= net: -==ted tv the -IE = hit-.
NVOt- It viiat ,r; SPEC -"= torrpc.f.ds th«t -eiide srs-rz-rira-Tiv i" the : = ~ tie :hs:-= and
should be excluded from the gas phase mechanism. These contpauiids are mapped by setting
MVQ1 equal to the -nciecirar weight {e.g. decai}romcKJip.:hBny!I oxide's trapped as S59.2
NWt|, which allows for tnt total mass of al NVQLto be deterrrinedl
UNK-Compounds that are urtsb = tc be -"isprsr c: C: ui'gthe 3'-= la bis model species,.. This
approach should be aw: as-: ir =s= =tt;Ltr'\ necsi-sry, a1 stead to a warning message
n the ¦£=-: it':- :::H
T=otle i. Mode sp = :ies .n the cbis ;nd CIS chemical mechanisms.
V : ¦: tl
Zm i:
j-.k-
U air hi -
Caress
¦: 1-= - ir
:e :=.
sin. 9
r? 3: li
i-: j¦:
¦1 L =-t
: t : je; tr
-t=-
Z
¦ : : : :
-- • ¦ .
2
:;
::
ii
3er:tr»
s
teiiPAB.3
yi; i
m, ,i. ,8
Yes
i
Yes
=¦-
E:5" f-5 - if . ifi
2
Yei
=---
E-rrsne
2
s;
z«i' ~^ ;i*" 5"*
2
Mc «i aA^,L
* i
=T0'4
2
-'S3
Yes
=C RiVt
=; —"-al :c 3« fmethsnall
I
res
Yes
:t •
i-aprs'ie '•(Hrtadhsne)
5
Yes
¥es
Ve f*»re
1
"fes
=¦3(3.^
Propane
3
res
Ce nsrse i
l .171 j':.
- : "i" ? :.i " 1
2
*:
*;.
'©L:E
'internal c-fefiit group [si, .Rj.>C=C
-------
Maopir.g guidelines for ncr-eapl'-cit wganicgaies ufng CE modelspecies
SPf CIATE com pounds that sre not treats 3 icily are mapped to CB model species that represent
common structural groups. Table 2 lists t-= carbon number and general mapping guidelines for each
of the structure mode! species.
T 5 ble- 2, General guidelines tar- mapping using CB6 structural mode! species.
:Ef
"¦ant
Csstafts
- -- 1
2,
^ ¦ j ¦ "Br_ „ " 'j ¦ & 1 j ¦ ¦ ¦ " i ¦ ¦ ¦¦ - - ¦ ¦ ^ "i «.• ¦ ¦ ¦
J - i.I ? ! t' !! ! ?!
•€>Ls
fitsrT!9l Etlfsiri n erouD fOLE rEDT,£.!iEn'£s< 4 cor ts&ns flP^cJ fedeft"!!otjbi curt
ffOUD! ImCStf*# F4R 5' £* X"fcffH®rc 1QLE 4" PAR-
¦ons B«: represented as
. mm I carton branches m both-t-s::.:* i
b«1 are togrtiled to
:5S.ET
I
'Cctor^'S {roup tj" pr.njC IS G4.E "f PAR. AikynS jSTCUP'
rs
: -"
i
.•ilksr k sic all", g's JpJ PAR represents I carbon, e.£. butane it 4
iteUMIfor
- ZZ- z
10
ill rr3r;.fe'per,:s art i;:-tea os 1 r=RP
mi
7
''sljent ni stter rc-rcsill.fi arniviBtks.TDL represents 7 carbons and any Bmefetesnal
cartels are rep-1: en tec » ¦tfcfl groups (mostly PA?J,e,g. ettifiiireMniS:is7Qt- »&»..
Cress w -spreseitsS as TOL and PAR.. Kfrsnss are represented using TOL. OlE sie
®AR.
mm
i
..nreactve sre i J\R :u:t- s: qL-re-r s-r jik. g'cap* ; ; r.sa-pe-.tsie is 4 W
C5-3C-.V ; scd »":upj (: ». sretics :id is ris t JNIS_ :iterj-jjp « 5 n?:h,l
s:e:ste j I (-j j»»ni:*r :art-i.: is.5.. t- :f- jc-ier; ar« ; j'lii.
¦zerfcens c* n *rils 5'sl;; i-C:'1
Xfl
S
>; :ne uc<': ;-,d cirt' se rsl>-- arc""iBiici.'»t. -«:rese"iti S tsrtap: nJ in1, asdit spal
ire r«f:-er:ec si & *>^1 grouos ir^ist are
=i=:
Senile compounds that are multifunctional a >¦ d/or include hetero-aitoflns lack obvious CB mappings
We developed guidelines for some of these cc npour i c asses to promote consistent reprasentstic r»
in this wort and tut* re 'tv'-smns. Approaches far several compound classes are explained in Table 2.
¦A = rev= rpe-r g_:d= nes b: - = = :z lie en r,e\. bote: scec e: r ;-;:iA~E £ r b.td : lot
systematically review existing mappings for '>nc.t:: assign0 compounds that could benefit from
developing a guideline.
siftc-ll Eiwiron US Copm-lion, 773San \1wm 3ni, Site 2113, Nawife CA 53SS1 3
**- WW
119
-------
ENVIRON
T=cl~ I, Mapp nggu':-e^ nE=fo -so-i= r-;ffi:l=It :z< ma: cc-ipo.nct :ia =5-5 = =_d str-ci.ia gioup-
!•: t: : t ¦ c
I!*:;-:: i ::t ¦?
I
\5 ¦¦¦::» rt: .-t:e -1 r 1 : ¦
ClticrBbenzerw and
after halcgenBfted
benzenes
Suittefine:
¦ 3:' r: :j«ns-i PAI, 3 i-MF
• i -r iBlogeni- fi -'•»
Examples:
* ^.j.5-cr-"crebsflssfie -1 ba* 3U
« TiEtescfiloratiefHeBes - 6 J VR
;j\ct ,-ie;
~ . Z'.z, withadditionalcarbons represented 6s aifcyl groups (generally
MS
ammples;:
¦ *ff»f:l0|l(3OpBBti:-dis- • - . I .= I 3AI
• - r----
:. is/'P-pwoles
Suirtsline;
¦ 2 OLE with additional eai&ans represented as tfeyt (raifi (generally
= i»-
St i Pip I ESI
• 2-BHItyfflursn - 2 G'-LE, 4 PAR
« 2-P«itpteie - 2 OLE, 5 PAS.
*
« l-Mettytpymrte - 2 OLE. i PAR
- eteracfEfc aromatic
corn pounds
1 iwn-
carbon etnrai
Suifieins;
¦ 1 OLE with rernsiP n; :»rtcs: represerte a is aifcfi groups (generally
¦aAR <
= t;
> E:~ i ;- :- =
• 1-wefcliflpfriictle - i : .E I1-!
¦ 4,3-3*1 etftjrleiaale - i C .1 5 «*
Guideline:
• Tripe tionds are trsatee u »4S fvsss a»e: are we c
-------
JN
Recommendation
I. co*tr =te b s/jteirst^: r= -i&.v :frrsp; -§ y a spec ;• tc 5'=iu-s :c "frr". tv c. *•-=«**
-"5; org gj'rel ¦¦==. ~ = 5=; e ti zr Hiing -irrrpc. "di T"=t a ¦= ;.nri = ¦:: new = D€:ies •. = -e
'*¦: €-.\5d a*: -rvisec tc zrr'-z-te ::-ni!=:=r.c-,'i- = zpi - e azif-ac-ri. z»: the ~a.':rtv 3-"
=.;:ix: *% spec •• rrsrp'-gi .'. = •& -rt -viewed as ft was outside the scope1 cftfais work.
Z. r=-.e-:p a -i=:r.o•:: cgy f 3-.- :• 3isf yi -s*andtfackirig larger organic compounds based on their
•;c =:ii E-, iitr: i -r^'-ned ste, :r 3'.. trotatility} to improve suppiorl for secondary organic aerosol
;soa; rrode-irtj *gt" = .-c =*i tv bssii ia: t.V5i; SCU icrrl, % .hi ;h 1 svs.ia5: € i* bcf C'.-'AC.
and CAfctt. A preliiii-narf investigation of tie possibility of doing so has been performed, and is
discussed in a separate memorandum.
121
-------
Appendix D: 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
Type
Description
4030100
1
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Breathing Loss (67000 Bbl. Tank Size)
4030100
2
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 10: Breathing Loss (67000 Bbl. Tank Size)
4030100
3
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 7: Breathing Loss (67000 Bbl. Tank Size)
4030100
4
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Breathing Loss (250000 Bbl. Tank Size)
4030100
6
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 7: Breathing Loss (250000 Bbl. Tank Size)
4030100
7
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Working Loss (Tank Diameter Independent)
4030110
1
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 13: Standing Loss (67000 Bbl. Tank Size)
4030110
2
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 10: Standing Loss (67000 Bbl. Tank Size)
4030110
3
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 7: Standing Loss (67000 Bbl. Tank Size)
4030110
5
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 10: Standing Loss (250000 Bbl. Tank Size)
4030115
1
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline: Standing Loss - Internal
4030120
2
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor Space;
Gasoline RVP 10: Filling Loss
4030120
3
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor Space;
Gasoline RVP 7: Filling Loss
4040010
1
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
4040010
2
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
4040010
3
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank
4040010
4
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank
4040010
5
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank
4040010
6
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Breathing Loss (250000 Bbl Capacity) - Fixed Roof Tank
4040010
7
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Working Loss (Diam. Independent) - Fixed Roof Tank
4040010
8
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Working Loss (Diameter Independent) - Fixed Roof Tank
4040010
9
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Working Loss (Diameter Independent) - Fixed Roof Tank
4040011
0
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
122
-------
see
Type
Description
4040011
1
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
4040011
2
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss (67000 Bbl Capacity)- Floating Roof Tank
4040011
3
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
4040011
4
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
4040011
5
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
4040011
6
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float RfTnk
4040011
7
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (250000 Bbl Cap.) - Float RfTnk
4040011
8
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
4040011
9
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
4040012
0
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
4040013
0
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
4040013
1
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
4040013
2
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
4040013
3
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
4040014
0
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Ext. Float Roof Tank w/ Secondy Seal
4040014
1
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
4040014
2
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Secondary Seal
4040014
3
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Ext. Floating Roof w/ Secondary Seal
4040014
8
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)
4040014
9
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
4040015
0
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Miscellaneous Losses/Leaks: Loading Racks
4040015
1
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Valves, Flanges, and Pumps
4040015
2
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Collection Losses
4040015
3
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Control Unit Losses
4040016
0
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
4040016
1
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
4040016
2
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
4040016
3
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
123
-------
see
Type
Description
4040017
0
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
4040017
1
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
4040017
2
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
4040017
3
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
4040017
8
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)
4040017
9
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)
4040019
9
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; See
Comment **
4040020
1
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
4040020
2
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
4040020
3
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank
4040020
4
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 13: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
4040020
5
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 10: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
4040020
6
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 7: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
4040020
7
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 13: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank
4040020
8
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 10: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank
4040021
0
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float Rf Tnk
4040021
1
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
4040021
2
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
4040021
3
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
4040023
0
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify
Liquid: Standing Loss - External Floating Roof w/ Primary Seal
4040023
1
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
124
-------
see
Type
Description
4040023
2
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
4040023
3
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
4040024
0
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify
Liquid: Standing Loss - Ext. Floating Roof w/ Secondary Seal
4040024
1
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
4040024
8
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 10/13/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
4040024
9
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify
Liquid: External Floating Roof (Primary/Secondary Seal)
4040025
0
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Loading
Racks
4040025
1
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Valves,
Flanges, and Pumps
4040025
2
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Collection Losses
4040025
3
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Control Unit Losses
4040026
0
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify
Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
4040026
1
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
4040026
2
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
4040026
3
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
4040027
0
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify
Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
4040027
1
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
4040027
2
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
4040027
3
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
4040027
8
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline
RVP 10/13/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal)
125
-------
see
Type
Description
4040027
9
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify
Liquid: Internal Floating Roof (Primary/Secondary Seal)
4040040
1
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products -
Underground Tanks; Gasoline RVP 13: Breathing Loss
4040040
2
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products -
Underground Tanks; Gasoline RVP 13: Working Loss
4040040
3
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products -
Underground Tanks; Gasoline RVP 10: Breathing Loss
4040040
4
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products -
Underground Tanks; Gasoline RVP 10: Working Loss
4040040
5
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products -
Underground Tanks; Gasoline RVP 7: Breathing Loss
4040040
6
BTP/B
PS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products -
Underground Tanks; Gasoline RVP 7: Working Loss
4060010
1
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars
and Trucks; Gasoline: Splash Loading **
4060012
6
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars
and Trucks; Gasoline: Submerged Loading **
4060013
1
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars
and Trucks; Gasoline: Submerged Loading (Normal Service)
4060013
6
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars
and Trucks; Gasoline: Splash Loading (Normal Service)
4060014
1
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars
and Trucks; Gasoline: Submerged Loading (Balanced Service)
4060014
4
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars
and Trucks; Gasoline: Splash Loading (Balanced Service)
4060014
7
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars
and Trucks; Gasoline: Submerged Loading (Clean Tanks)
4060016
2
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars
and Trucks; Gasoline: Loaded with Fuel (Transit Losses)
4060016
3
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars
and Trucks; Gasoline: Return with Vapor (Transit Losses)
4060019
9
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars
and Trucks; Not Classified **
4060023
1
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Cleaned and Vapor Free Tanks
4060023
2
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers
126
-------
see
Type
Description
4060023
3
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Cleaned and Vapor Free Tanks
4060023
4
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Ballasted Tank
4060023
5
BTP/B
PS
Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum Products;Marine
Vessels;Gasoline: Ocean Barges Loading - Ballasted Tank
4060023
6
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Uncleaned Tanks
4060023
7
RBT
Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum Products;Marine
Vessels;Gasoline: Ocean Barges Loading - Uncleaned Tanks
4060023
8
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Uncleaned Tanks
4060023
9
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tankers: Ballasted Tank
4060024
0
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Average Tank Condition
4060024
1
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tanker Ballasting
4060029
9
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Not Classified **
4060030
1
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Splash Filling
4060030
2
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Submerged Filling w/o Controls
4060030
5
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Unloading **
4060030
6
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Balanced Submerged Filling
4060030
7
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Underground Tank Breathing and Emptying
4060039
9
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Not Classified **
4060040
1
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling
Vehicle Gas Tanks - Stage II; Vapor Loss w/o Controls
4060050
1
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Leaks
4060050
2
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Venting
4060050
3
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station
4060050
4
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station Leaks
4060060
2
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer
(Corporate) Fleet Refueling - Stage II; Liquid Spill Loss w/o Controls
127
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see
Type
Description
4060070
1
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer
(Corporate) Fleet Refueling - Stage I; Splash Filling
4060070
2
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer
(Corporate) Fleet Refueling - Stage I; Submerged Filling w/o Controls
4060070
6
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer
(Corporate) Fleet Refueling - Stage I; Balanced Submerged Filling
4060070
7
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer
(Corporate) Fleet Refueling - Stage I; Underground Tank Breathing and Emptying
4068880
1
BTP/B
PS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Fugitive
Emissions; Specify in Comments Field
2501050
120
RBT
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Terminals: All Evaporative
Losses; Gasoline
2501055
120
BTP/B
PS
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Plants: All Evaporative
Losses; Gasoline
2501060
050
BTP/B
PS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1:
Total
2501060
051
BTP/B
PS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1:
Submerged Filling
2501060
052
BTP/B
PS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1:
Splash Filling
2501060
053
BTP/B
PS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1:
Balanced Submerged Filling
2501060
200
BTP/B
PS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Total
2501060
201
BTP/B
PS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Breathing and Emptying
2501995
000
BTP/B
PS
Storage and Transport; Petroleum and Petroleum Product Storage; All Storage Types: Working Loss;
Total: All Products
2505000
120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; All Transport Types; Gasoline
2505020
120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline
2505020
121
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline - Barge
2505030
120
BTP/B
PS
Storage and Transport; Petroleum and Petroleum Product Transport; Truck; Gasoline
2505040
120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Pipeline; Gasoline
2660000
000
BTP/B
PS
Waste Disposal, Treatment, and Recovery; Leaking Underground Storage Tanks; Leaking
Underground Storage Tanks; Total: All Storage Types
128
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129
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-20-010
Environmental Protection Air Quality Assessment Division August 2019
Agency Research Triangle Park, NC
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