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Technical Support Document (TSD):
Preparation of Emissions Inventories for the
Version 7.1 2015 Emissions Modeling Platform
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EPA-454/B-20-011
July 2019
Technical Support Document (TSD): Preparation of Emissions Inventories for the Version 7.1
2015 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 IV
LIST OF FIGURES V
LIST OF APPENDICES V
ACRONYMS VI
1 INTRODUCTION 1
2 2015 EMISSION INVENTORIES AND APPROACHES 2
2.1 Point sources (ptegu, pt_oilgas and ptnonipm) 7
2.1.1 EGUsector (ptegu) 9
2.1.2 Point source oil and gas sector (pt oilgas) 10
2.1.3 Non-IPM sector (ptnonipm) 12
2.2 NONPOINT SOURCES (AFDUST, AG, AGFIRE, PTAGFIRE, NP_OILGAS, RWC, NONPT) 12
2.2.1 Area fugitive dust sector (afdust) 13
2.2.2 Agricultural sector (ag) 20
2.2.3 Agricultural fires (ptagfire) 23
2.2.4 Nonpoint source oil and gas sector (np oilgas) 23
2.2.5 Residential wood combustion sector (rwc) 24
2.2.6 Other nonpoint sources sector (nonpt) 25
2.3 Onroad mobile sources (onroad) 25
2.3.1 Onroad (onroad) 26
2.4 2014 NONROAD MOBILE SOURCES (CMV, RAIL, NONROAD) 31
2.4.1 Category 1, Category 2, Category 3 Commercial Marine Vessels (cmv_clc2, cmv_c3) 31
2.4.2 Railroad sources: (rail) 34
2.4.3 Nonroad mobile equipment sources: (nonroad) 34
2.5 "OtherEmissions": non-U.S. sources 35
2.5.1 Point sources from Canada and Mexico (othpt) 35
2.5.2 Area and nonroad mobile sources from Canada and Mexico (othar, othafdust) 36
2.5.3 Onroad mobile sources from Canada and Mexico (onroadcan, onroadjnex) 36
2.5.4 Fires from Canada and Mexico (ptfire othna) 37
2.6 Fires (ptfire) 37
2.7 Biogenic sources (beis) 38
2.8 SMOKE-ready non-anthropogenic inventory for chlorine 42
3 EMISSIONS MODELING SUMMARY 43
3.1 Emissions modeling Overview 43
3.2 Chemical Speciation 46
3.2.1 VOC speciation 48
3.2.1.1 County specific profile combinations 51
3.2.1.2 Additional sector specific considerations for integrating HAP emissions from inventories into speciation 52
3.2.1.3 Oil and gas related speciation profiles 54
3.2.1.4 Mobile source related VOC speciation profiles 56
3.2.2 PM speciation 60
3.2.2.1 Mobile source related PM2.5 speciation profiles 62
3.2.3 NO x speciation 63
3.2.4 Creation of Sulfuric Acid Vapor (SULF) 64
3.3 Temporal Allocation 65
3.3.1 Use of FF10 format for finer than annual emissions 67
3.3.2 Electric Generating Utility temporal allocation (ptegu) 67
3.3.2.1 Base year temporal allocation of EGUs 67
3.3.3 Airport Temporal allocation (ptnonipm) 71
3.3.4 Residential Wood Combustion Temporal allocation (rwc) 73
3.3.5 Agricultural Ammonia Temporal Profiles (ag) 77
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3.3.6 Oil and gas temporal allocation (npoilgas) 78
3.3.7 Onroad mobile temporal allocation (onroad) 78
3.3.8 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire) 82
3.4 Spatial Allocation 84
3.4.1 Spatial Surrogates for U.S. emissions 85
3.4.2 Allocation method for airport-related sources in the U.S. 91
3.4.3 Surrogates for Canada and Mexico emission inventories 91
4 REFERENCES 95
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List of Tables
Table 2-1. Platform sectors for the 2015 emissions modeling platform 4
Table 2-2. 2015 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.) 6
Table 2-3. 2015 Continental United States 12US1 Emissions by Sector (tons/yr in 48 states + D.C.) 7
Table 2-4. Point source oil and gas sector NAICS Codes 10
Table 2-5. Oil and gas sector 2015 projection factors 11
Table 2-6. SCCs in the afdust platform sector for NEI2014v2; nonzero emissions 13
Table 2-7. SCCs in the afdust platform sector for NEI2014v2; zero emissions 15
Table 2-8. Total impact of fugitive dust adjustments to unadjusted 2015 inventory 16
Table 2-9. Livestock SCCs extracted from the NEI to create the ag sector 21
Table 2-10. Fertilizer SCCs extracted from the NEI for inclusion in the "ag" sector 22
Table 2-11. SCCs in the residential wood combustion sector (rwc)* 24
Table 2-12. Onroad emission aggregate processes 27
Table 2-13. Factors applied to project VMT from 2014 to 2015 28
Table 2-14. SCCs extracted for the cmv_clc2 sector 31
Table 2-15. SCCs extracted for the cmv_c3 sector 32
Table 2-16. Growth factors to project the 2002 ECA-IMO inventory to 2011 33
Table 2-17. 2014NEIv2 SCCs extracted for rail sector 34
Table 2-18. 2014 Platform SCCs representing emissions in the ptfire modeling sector 37
Table 2-19. Meteorological variables required by BEIS 3.61 39
Table 3-1. Key emissions modeling steps by sector 44
Table 3-2. Descriptions of the platform grids 46
Table 3-3. Emission model species produced for CB6 for CMAQ 46
Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM)
for each platform sector 50
Table 3-5. MOVES integrated species in M-profiles 53
Table 3-6. Basin/Region-specific profiles for oil and gas 55
Table 3-7. TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a 56
Table 3-8. Select mobile-related VOC profiles 57
Table 3-9. Onroad M-profiles 58
Table 3-10. MOVES process IDs 59
Table 3-11. MOVES Fuel subtype IDs 59
Table 3-12. MOVES regclass IDs 60
Table 3-13. SPECIATE4.5 brake and tire profiles compared to those used in the 201 lv6.3 Platform 62
Table 3-14. Nonroad PM2.5 profiles 63
Table 3-15. NOx speciation profiles 64
Table 3-16. Sulfate split factor computation 65
Table 3-17. SO2 speciation profiles 65
Table 3-18. Temporal settings used for the platform sectors in SMOKE 66
Table 3-19. U.S. Surrogates available for the 2015 modeling platform 85
Table 3-20. Off-Network Mobile Source Surrogates 87
Table 3-21. Spatial Surrogates for Oil and Gas Sources 88
Table 3-22. Selected 2015 CAP emissions by sector for U.S. Surrogates (12US1 domain totals) 89
Table 3-23. Canadian Spatial Surrogates 91
Table 3-24. CAPs Allocated to Mexican and Canadian Spatial Surrogates for 2015, 12US1 domain 92
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List of Figures
Figure 2-1. Impact of adjustments to 2014 fugitive dust emissions due to transport fraction, precipitation,
and cumulative 19
Figure 2-2. Illustration of regional modeling domains in ECA-IMO study 33
Figure 2-3. Annual NO emissions output from BEIS 3.61 for 2014 40
Figure 2-4. Annual isoprene emissions output from BEIS 3.61 for 2014 40
Figure 2-5. Annual acetaldehyde emissions output from BEIS 3.61 for 2014 41
Figure 2-6. Annual formaldehyde emissions output from BEIS 3.61 for 2014 41
Figure 3-1. Air quality modeling domains 45
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation 50
Figure 3-3. Profiles composited for the new PM gas combustion related sources 61
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources 61
Figure 3-5. Eliminating unmeasured spikes in CEMS data 68
Figure 3-6. Seasonal diurnal profiles for EGU emissions in a Virginia Region 68
Figure 3-7. IPM Regions used to Create Temporal Profiles for EGUs without CEMS 70
Figure 3-8. Month-to-day profiles for different fuels in a West Texas Region 70
Figure 3-9. Diurnal Profile for all Airport SCCs 71
Figure 3-10. Weekly profile for all Airport SCCs 72
Figure 3-11. Monthly Profile for all Airport SCCs 72
Figure 3-12. Alaska Seaplane Profile 73
Figure 3-13. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold 74
Figure 3-14. RWC diurnal temporal profile 75
Figure 3-15. Diurnal profile for OHH, based on heat load (BTU/hr) 76
Figure 3-16. Day-of-week temporal profiles for OHH and Recreational RWC 76
Figure 3-17. Annual-to-month temporal profiles for OHH and recreational RWC 77
Figure 3-18. Example of animal NH3 emissions temporal allocation approach, summed to daily emissions 78
Figure 3-19. Example of temporal variability of NOx emissions 79
Figure 3-20. Sample onroad diurnal profiles for Fulton County, GA 80
Figure 3-21. Counties for which MOVES Speeds and Temporal Profiles could be Populated 81
Figure 3-22. Example of Temporal Profiles for Combination Trucks 82
Figure 3-23. Agricultural burning diurnal temporal profile 83
Figure 3-24. Prescribed and Wildfire diurnal temporal profiles 84
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
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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
Bgal
Billion gallons
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1/C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAEP
Committee on Aviation Environmental Protection
CAIR
Clean Air Interstate Rule
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
CEC
North American Commission for Environmental Cooperation
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
EO, 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
FAPRI
Food and Agriculture Policy and Research Institute
FASOM
Forest and Agricultural Section Optimization Model
FCCS
Fuel Characteristic Classification System
FF10
Flat File 2010
FIPS
Federal Information Processing Standards
FHWA
Federal Highway Administration
HAP
Hazardous Air Pollutant
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HC1
Hydrochloric acid
HDGHG
Heavy-Duty Vehicle Greenhouse Gas
Hg
Mercury
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
IIWC
Hazardous Waste Combustion
HWI
Hazardous Waste Incineration
ICAO
International Civil Aviation Organization
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
ICR
Information Collection Request
IDA
Inventory Data Analyzer
I/M
Inspection and Maintenance
IMO
International Marine Organization
IPAMS
Independent Petroleum Association of Mountain States
IPM
Integrated Planning Model
ITN
Itinerant
LADCO
Lake Michigan Air Directors Consortium
LDGHG
Light-Duty Vehicle Greenhouse Gas
LPG
Liquefied 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
Mgal
Million gallons
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
NIF
NEI Input Format
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
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OTAQ
EPA's Office of Transportation and Air Quality
ORIS
Office of Regulatory Information System
ORD
EPA's Office of Research and Development
ORL
One Record per Line
OTC
Ozone Transport Commission
PADD
Petroleum Administration for Defense Districts
PF
Projection Factor, can account for growth and/or controls
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
SEMAP
Southeastern Modeling, Analysis, and Planning
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
SI
Spark-ignition
SIP
State Implementation Plan
SPDPRO
Hourly Speed Profiles for weekday versus weekend
SPPD
Sector Policies and Programs Division
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) developed an air quality modeling platform for air
toxics and criteria air pollutants that represents the year 2015 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 2015 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 2015 for some emissions sectors.
This 2015 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 platform was used to support air quality modeling
applications using CMAQ version 5.2. 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. In addition,
many additional HAPs are included to support the NATA analysis. The CAMx model uses a similar, but
slightly different, chemical mechanism, as described in Section Error! Reference source not found..
The 2015 platform consists of one 'complete' emissions case: the 2015 base case, i.e., 2015fd_cb6_15j.
This platform accounts for atmospheric chemistry and transport within a state of the art photochemical
grid model. In the case abbreviation 2015fd_cb6_15j, 2015 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"); the "d" stands for the fourth set of emissions modeled for a 2014-
based modeling platform.
The emissions data in the 2015 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
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 2015.
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://cmascenter.org/smoke), version 4.5
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(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 2015 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 "15j " The full case name includes this abbreviation following the emissions
portion of the case name to fully specify the name of the case as "2015fd_15j."
This document contains five sections and several appendices. Section 2 describes the 2015 inventories
input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used with the
emission inventories. Section 4 provides references. The Appendices provide additional details about
specific technical methods or data.
2 2015 Emission Inventories and Approaches
This section describes the emissions data that make up the 2015 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 2015, and other adjustments made to support modeling as
described here. Documentation for the 2014NEIv2, including a TSD, is available from
https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-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 as a part of the NATA review process.
Point source data for the year 2015 as submitted to EIS were used for this study. EPA used the
SMARTFIRE2 system to develop 2015 fire emissions. SMARTFIRE2 categorizes all fires as either
prescribed burning or wildfire categories and includes improved emission factor estimates for prescribed
burning. Onroad mobile source emissions for year 2015 were developed using MOVES2014a. Nonroad
mobile source emissions were developed by running MOVES2014a for 2015. Canadian emissions reflect
year 2013 and Mexican emissions were interpolated to year 2015.
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. The 2014 NEI is the first use of MOVES for nonroad emissions.
MOVES2014a replaces the National Mobile Inventory Model (NMIM) as the interface for using the
NONROAD2008 model, 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.
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Onroad emissions for the 2015 platform were developed based on emissions factors output from
MOVES2014a for the year 2015 run with inputs derived from the 2014NEIv2.
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 2015 platform.
The methods used to process emissions for this study are similar to those documented for EPA's Version
7.1, 2014 Emissions Modeling Platform that was also used for version 2 of the 2014 National Air Toxics
Assessment (NATA), with some exceptions. One exception is that many fewer HAPs are included in this
platform. Also, many emissions inventories and inputs were updated to the year 2015 for this study. A
technical support document (TSD) for the 2014v7.1 platform is available here https://www.epa.gov/air-
emissions-modeling/2014-version-71 -technical-support-document-tsd (EPA, 2018b) and includes
additional details regarding the data preparation and emissions modeling, with the exception of the HAP
speciation and any updates specific to 2015.
Compared to the 2014v7.1 emissions modeling platform, which is based directly on the 2014NEIv2, the
2015v7.1 emissions modeling platform includes emissions for the year 2015 for some data categories.
The point source emission inventories for platform include partially updated emissions for 2015.
Agricultural and wildland fire emissions represent the year 2015. 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 2015-specific emissions inventory is used in this platform. Area source oil and gas emissions
were projected from 2014NEIv2 to better represent 2015. Onroad and nonroad emissions for the year
2015 were developed based on MOVES2014a outputs for 2015, and the activity data used to compute the
onroad emissions were projected from 2014 to 2015.
The emissions modeling process performed using SMOKE v4.5, apportions the emissions inventories into
the grid cells used by CMAQ and temporalizes the emissions into hourly values. In addition, the
pollutants in the inventories (e.g., NOx, PM and VOC) are split into the chemical species needed by
CMAQ. For the purposes of preparing the air quality model-ready emissions, the NEI was split into finer-
grained sectors used for emissions modeling; and emissions from sources other than the NEI are added,
such as the Canadian, Mexican, and offshore inventories. 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. For biogenic
emissions, the CMAQ model allows for biogenic emissions to be included in the CM AQ-ready emissions
inputs, or for biogenic emissions to be computed within CM AQ itself (the "inline" option). This study
uses the inline biogenics option.
Table 2-1 presents the sectors in the 2015 platform used to develop the year 2015 emissions for this
project, 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. Annual 2015 emission summaries
for the U.S. anthropogenic sectors are shown in Table 2-2 (i.e., biogenic emissions are excluded). Table
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2-3 provides a summary of emissions for the anthropogenic sectors containing Canadian, Mexican and
offshore sources.
Table 2-1. Platform sectors for the 2015 emissions modeling platform
Platform Sector (Abbrev)
NEI Category
Description and resolution of the data input to SMOKE
EGUs (ptegu)
Point
2015 point source EGUs. Replaced with hourly 2015
Continuous Emissions Monitoring System (CEMS) values
for NOX and S02, where the units are matched to the NEI.
Emissions for all sources not matched to CEMS data come
from 2015 NEI point inventory. Annual resolution for
sources not matched to CEMS data, hourly for CEMS
sources.
Point source oil and gas
(pt oilgcts)
Point
2015 NEI point sources that include oil and gas production
emissions processes based on facilities with the following
NAICS: 211* (Oil and Gas Extraction), 2212* (Natural
Gas Distribution), 213111 (Drilling Oil and Gas Wells),
213112 (Support Activities for Oil and Gas Operations),
4861* (Pipeline Transportation of Crude Oil), 4862*
(Pipeline Transportation ofNatural Gas). Includes U.S.
offshore oil production. The portion of the 2015 NEI point
inventory oil and gas inventory that was carried forward
from 2014NEIv2 (i.e. not updated to 2015 in EIS) was
projected to year 2015 estimates. Annual resolution.
Remaining non-EGU point
(ptnonipm)
Point
All 2015 NEI 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.
Point source fire (ptfire)
Fires
Point source day-specific wildfires and prescribed fires for
2015 computed using SMARTFIRE 2. Fires over 20,000
acres on a single day allocated to overlapping grid cells.
Point Source agricultural fires
(ptagfire)
Nonpoint
Agricultural fire sources that were developed by EPA as
point and day-specific emissions; they were put into the
nonpoint data category of the NEI, but in the platform,
they are treated as point sources.
Agricultural (cig)
Nonpoint
2014NEIv2 nonpoint livestock and fertilizer application
emissions. Livestock includes ammonia and other
pollutants (except PM2.5). Fertilizer includes only
ammonia. County and annual resolution.
Area fugitive dust (afdiist adj)
Nonpoint
PMio and PM2.5 fugitive dust sources from the 2014NEIv2
nonpoint inventory; including building construction, road
construction, agricultural dust, and road dust. The
emissions modeling adjustment applies a transport fraction
and a zero-out based on 2015 meteorology (precipitation
and snow/ice cover). County and annual resolution.
Biogenic (beis)
Nonpoint
Biogenic emissions were left out of the CMAQ-ready
merged emissions, in favor of inline biogenics produced
during the CMAQ model run itself.
C1 and C2 commercial marine
(cmv clc2)
Nonpoint
2014NEIv2 Category 1 (CI) and Category 2 (C2),
commercial marine vessel (CMV) emissions. County and
annual resolution.
4
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Platform Sector (Abbrev)
NEI Category
Description and resolution of the data input to SMOKE
C3 commercial marine
(cmv c3)
Nonpoint
Within state and federal waters, 2014NEIv2 Category 3
commercial marine vessel (CMV) emissions. Outside of
state and federal waters, emissions are based on the
Emissions Control Area (ECA) inventory. Point (to allow
for plume rise) 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 oilgcts)
Nonpoint
2014NEIv2 nonpoint sources from oil and gas-related
processes, projected to year 2015 estimates. County and
annual resolution.
Locomotive (rail)
Nonpoint
Rail locomotives emissions from the 2014NEIv2. County
and annual resolution.
Residential Wood Combustion
(rwc)
Nonpoint
2014NEIv2 nonpoint sources with residential wood
combustion (RWC) processes. County and annual
resolution.
Nonroad (nonroad)
Nonroad
2015 nonroad equipment emissions developed with the
MOVES2014a. MOVES was run for 2014 and 2016, and
the resulting emissions were interpolated to 2015. MOVES
was used for all states except California, which submitted
their own emissions for the 2014NEIv2 and for the year
2017, from which 2015 estimates were interpolated.
County and monthly resolution.
Onroad (onroad)
Onroad
2015 onroad mobile source gasoline and diesel vehicles
from parking lots and moving vehicles. 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 emission factors tables
produced by MOVES2014a.
California-provided CAP and metal HAP onroad mobile
source gasoline and diesel vehicles from parking lots and
moving vehicles based on Emission Factor (EMFAC),
Onroad California Onroad gridded and temporalized using MOVES2014a. Volatile
(onroad' ca ctdj) organic compound (VOC) HAP emissions derived from
California-provided VOC emissions and MOVES-based
speciation. California estimates for 2014 and 2017 were
interpolated to 2015 values.
Onroad Canada (onroad can)
Non-US
Monthly year 2013 Canada (province resolution) onroad
mobile inventory.
Onroad Mexico (onroad mex)
Non-US
Monthly year Mexico (municipio resolution) onroad
mobile inventory, with 2015 emissions values interpolated
from 2014 and 2018 inventories.
Other area fugitive dust sources
(othafdiist adj)
Non-US
Area fugitive dust sources from Canada 2013 inventory
with transport fraction and snow/ice adjustments based on
2015 meteorological data. Annual and province
resolution.
5
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Platform Sector (Abbrev)
NEI Category
Description and resolution of the data input to SMOKE
Other nonpoint and nonroad
(othctr)
Non-US
Year 2013 Canada (province resolution) and projected
year 2015 Mexico (municipio resolution, interpolated from
2014 and 2018 values) nonpoint and nonroad mobile
inventories, annual resolution.
Other point sources not from
the NEI (othpt)
Non-US
Point sources from Canada's 2013 inventory, and Mexico
point source emissions for 2015 (interpolated from 2014
and 2018). Annual resolution.
Point source day-specific wildfires and prescribed fires for
2015 are computed from SMARTFIRE 2 in Canada and
Mexico. Caribbean, Central American, and other
international fires are from 2015 vl.5 of the Fire
INventory (FINN) from National Center for Atmospheric
Research (NCAR) fires (NCAR, 2016 and Wiedinmyer,
C., 2011).
Table 2-2. 2015 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.)
Sector
CO
NH3
NOx
PMio
PM2.5
SO2
voc
afdust_adj
6,093,367
857,261
ag
2,823,395
179,970
cmv_clc2
47,183
120
260,338
6,493
6,168
3,453
4,840
cmv_c3
10,885
25
108,268
4,248
3,832
38,826
5,043
nonpt
2,680,775
121,229
758,152
608,827
496,454
162,231
3,672,687
nP_oilgas
686,168
15
719,934
17,746
17,480
38,963
3,206,411
nonroad
12,405,416
2,211
1,375,025
139,410
132,017
3,129
1,622,303
onroad
23,064,322
104,472
4,401,420
285,167
144,312
27,173
2,199,205
ptagfire
382,760
53,353
11,971
62,034
43,724
3,719
23,711
ptfire
21,180,425
347,360
275,352
2,142,471
1,815,654
154,996
4,993,305
ptegu
625,780
19,811
1,451,134
176,516
136,106
2,293,080
33,581
ptnonipm
1,967,804
73,358
1,108,414
418,371
273,483
788,604
834,143
pt_oilgas
190,337
1,244
390,734
12,372
11,856
43,422
142,197
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
Continental
U.S. 65,459,130 3,562,287 11,563,793 10,302,215 4,271,445 3,565,979 17,290,600
Point fires in Mexico and N
Canada (ptfireothna) °n
6
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Table 2-3. 2015 Continental United States 12US1 Emissions by Sector (tons/yr in 48 states + D.C.)
Sector
CO
nh3
NOx
PMio
pm25
so2
VOC
Canada othafdust
2,232,563
438,196
Canada othar
2,892,737
497,580
652,907
425,822
237,155
70,080
1,128,917
Canada onroad_can
2,130,822
8,514
478,221
28,406
21,623
1,614
193,264
Canada othpt
1,102,931
17,323
646,978
91,095
47,322
999,876
782,445
Canada ptfire_othna
10,277,333
290,735
339,883
1,216,218
1,109,341
85,204
2,853,915
Canada Subtotal
16,403,822
814,152
2,117,989
3,994,105
1,853,636
1,156,775
4,958,542
Mexico othar
236,143
203,945
216,175
114,754
53,727
7,661
512,070
Mexico onroad_mex
1,825,267
2,724
437,330
14,935
10,744
6,047
158,562
Mexico othpt
196,410
4,851
456,220
72,957
57,378
509,144
68,615
Mexico ptfire_othna
81,991
1,390
7,168
10,654
8,557
641
28,294
Mexico Subtotal
2,339,811
212,911
1,116,894
213,300
130,406
523,494
767,541
Offshore cmv_clc2
56,393
184
283,431
9,193
8,918
2,236
5,248
Offshore cmv_c3
77,449
68
854,639
47,205
43,618
358,452
34,059
Offshore pt_oilgas
50,046
15
48,688
668
666
502
48,167
2015 Total non-U.S.
18,850,073
1,027,262
3,567,003
4,217,266
1,993,627
1,683,006
5,779,497
The emission inventories in SMOKE input formats for the 2015 platform are available from the EPA's
Air Emissions Modeling website for the platform: https://www.epa.gov/air-emissions-modeling/2014-
2016-version-7-air-emissions-modeling-platforms. under the section entitled "2015v7.1 (alpha) Platform"
The platform "README" file indicates the particular zipped files associated with each platform sector.
The remainder of Section 2 provides details about the data contained in each of the 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 2015 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).
With a couple of minor exceptions, this section describes only NEI point sources within the contiguous
U.S. The offshore oil platform (pt oilgas sector) and category 3 CMV emissions (cmv_c3 sector) are
processed by SMOKE as point source inventories and are discussed later in this section. 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 (EPA, 2018a).
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 (EPA, 2018a). Point inventories are also available in EIS for intermediate years such as 2015. In
this intermediate point inventory, larger sources are updated with emissions for year 2015, while other
sources are either carried forward from 2014NEIv2 or are closed.
7
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In preparation for modeling, the complete set of point sources in the NEI was exported from EIS for the
year 2015 into the Flat File 2010 (FF10) format that is compatible with SMOKE (see
https://www.cmascenter.Org/smoke/documentation/4.5/html/ch08s02s08.htmn and was then split into
several sectors for modeling. The point sectors are: EGUs (ptegu), point source oil and gas extraction-
related sources (ptoilgas) and the remaining non-EGUs (ptnonipm). The EGU emissions are split out
from the other sources to facilitate the use of distinct SMOKE temporal processing and future-year
projection techniques. The oil and gas sector emissions (pt oilgas) were processed separately for
summary tracking purposes and distinct projection techniques from the remaining non-EGU emissions
(ptnonipm).
The ptnonipm and pt oilgas sector emissions were provided to SMOKE as annual emissions. The full
description of how the NEI emissions were developed is provided in the NEI documentation, but a brief
summary of their development follows:
a. CAP and HAP data were provided by States, locals and tribes under the Air Emissions Reporting
Rule (AERR) [the reporting size threshold is larger for inventory years between the triennial inventory
years of 2011,2014, 2017, ...]
b. EPA corrected known issues and filled PM data gaps.
c. EPA added HAP data from the Toxic Release Inventory (TRI) where corresponding data was not
already provided by states/locals.
d. EPA stores and applies matches of the point source units to units with CEMS data and also for all
EGU units modeled by EPA's Integrated Planning Model (IPM).
e. EPA provided data for airports and rail yards.
f. Off-shore platform data were added from the Bureau of Ocean Energy Management (BOEM).
The changes made to the NEI point sources prior to modeling with SMOKE are as follows:
• The tribal data, which do not use state/county Federal Information Processing Standards (FIPS)
codes in the NEI, but rather use the tribal code, were assigned a state/county FIPS code of 88XXX,
where XXX is the 3-digit tribal code in the NEI. This change was made because SMOKE requires
all sources to have a state/county FIPS code.
• Sources that did not have specific counties assigned (i.e., the county code ends in 777) were not
included in the modeling because it was only possible to know the state in which the sources
resided, but no more specific details related to the location of the sources were available.
• Stack parameters for point sources missing this information were filled in prior to modeling in
SMOKE.
Following the removal of point sources without specific locations (i.e., their FIPS code ends in 777), 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.
8
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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).
For sources in the ptegu sector that could be matched to 2015 CEMS data, hourly CEMS NOx and SO2
emissions for 2015 from EPA's Acid Rain Program were used rather than annual inventory emissions.
For all other pollutants (e.g., VOC, PM2.5, HQ), annual emissions were used as-is from the annual
inventory, but, were allocated to hourly values using heat input from the CEMS data. For the unmatched
units in the ptegu sector, annual emissions were allocated to daily values using IPM region- and pollutant-
specific profiles, and similarly, region- and pollutant-specific diurnal profiles were applied to create
hourly emissions.
The 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 platform, these 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.
Each of the point sectors is processed separately through SMOKE as described in the following
subsections.
2.1.1 EGU sector (ptegu)
The ptegu sector contains emissions from EGUs in the 2015 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 2015 CEMS data from EPA's Clean
Air Markets Division (CAMD) via ORIS facility codes and boiler ID. For the matched units, SMOKE
replaces the 2015 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. 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-
9
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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
IPM region. See Section 3.3.2 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 2-4. 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 2-4. 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
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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NAICS
NAICS description
2212, 22121, 221210
Natural Gas Distribution
4862,48621,486210
Pipeline Transportation of Natural Gas
48611, 486110
Pipeline Transportation of Crude Oil
The ptoilgas inventory is a combination of sources with updated emissions for 2015, and sources with
emissions carried forward from 2014NEIv2 with no updates. For this study, sources already updated for
the year 2015 in EIS were used as-is. The emissions carried forward from 2014NEIv2 were projected to
2015. Projection factors for 2015 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:
htty://www.eia.gov/dnav/ng/ng sum Isum a eygO fgw mmcf a.htm;
http://www.eia.gov/dnav/pet/pet crd crpdn 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 2-5, 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 2015 pt oilgas
inventory used for this study consists of both sources already updated to 2015 within EIS (used directly),
and sources carried forward from 2014NEIv2 (projected to 2015).
Table 2-5. Oil and gas sector 2015 projection factors
State
Oil projection factor
Gas projection factor
"Both" projection factor
Alabama
0.987
0.929
0.958
Alaska
0.973
1.002
0.988
Arizona
0.661
0.896
0.778
Arkansas
0.926
0.900
0.913
California
0.983
0.990
0.987
Colorado
1.284
1.028
1.156
Florida
0.991
1.822
1.407
Illinois
0.997
1.078
1.038
Indiana
0.885
1.096
0.990
Kansas
0.918
0.992
0.955
Kentucky
0.848
1.030
0.939
Louisiana
0.915
0.921
0.918
Maryland
1.000
1.900
1.900
Michigan
0.881
0.936
0.909
Mississippi
1.023
1.069
1.046
Missouri
0.760
0.333
0.547
Montana
0.955
0.984
0.970
Nebraska
0.950
1.144
1.047
Nevada
0.889
1.333
1.111
New Mexico
1.182
1.024
1.103
New York
0.798
0.858
0.828
North Dakota
1.088
1.262
1.175
Ohio
1.788
1.966
1.877
Oklahoma
1.121
1.072
1.097
Oregon
1.000
0.743
0.743
11
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State
Oil projection factor
Gas projection factor
"Both" projection factor
Pennsylvania
1.034
1.130
1.082
South Dakota
0.927
0.948
0.937
Tennessee
0.897
0.808
0.852
Texas
1.089
1.016
1.053
Utah
0.908
0.917
0.913
Virginia
0.786
0.955
0.870
West Virginia
1.087
1.233
1.160
Wyoming
1.136
0.999
1.067
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 2015-specific
emissions, while emissions for smaller sources that were not submitted for the 2015 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.
2.2 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 2015 modeling platform sectors, along with any data that were replaced with non-NEI data or
projected for 2015.
12
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2.2.1 Area fugitive dust sector (afdust)
The area-source fugitive dust (afdust) sector contains PMio and PM2.5 emission estimates for nonpoint
SCCs identified by EPA as dust sources. Categories included in the afdust sector are paved roads,
unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production,
and mining and quarrying. It does not include fugitive dust from grain elevators, coal handling at coal
mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as
point sources so they are properly located.
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 2-6. Table 2-7 shows the SCCs that would
have also been included in this sector if they had emissions in the 2014 NEI.
Table 2-6. SCCs in the afdust platform sector for NEI2014v2; nonzero emissions
see
SCC Description
2294000000
Mobile Sources;Paved Roads;All Paved Roads;Total: Fugitives
2294000002
Mobile Sources;Paved Roads;All Paved Roads;Total: Sanding/Salting - Fugitives
2296000000
Mobile Sources;Unpaved Roads;All Unpaved Roads;Total: Fugitives
2311000000
Industrial Processes;Construction: SIC 15 - 17;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
13
-------
see
SCC Description
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
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
14
-------
Table 2-7. SCCs in the afdust platform sector for NEI2014v2; zero emissions
see
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
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
15
-------
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 2015 platform uses the same unadjusted
afdust emissions inventory as the 2014v7.1 platform, except that meteorological adjustments are based on
2015 meteorology instead of 2014 meteorology.
The total impacts of the transport fraction and meteorological adjustments are shown in Table 2-8 after
backing out the meteorological adjustment applied in the 2014NEIv2. The amount of the reduction
ranges from about 92 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 2015 would look
slightly different depending on the meteorology for each year, but the general pattern would be the same.
Table 2-8. Total impact of fugitive dust adjustments to unadjusted 2015 inventory
State
Unadjusted
* PMio
Unadjusted
* PM2 5
Change in
PMio
Change in
PM25
PMio
Reduction
PMis
Reduction
Alabama
531,031
62,910
-442,078
-52,383
83%
83%
Arizona
263,092
32,550
-88,123
-10,861
33%
33%
Arkansas
319,532
49,024
-232,430
-34,783
73%
71%
California
312,541
41,065
-140,882
-18,105
45%
44%
Colorado
240,400
36,459
-145,182
-21,243
60%
58%
Connecticut
23,460
3,340
-20,134
-2,873
86%
86%
Delaware
14,318
2,456
-10,415
-1,791
73%
73%
District of
Columbia
2,548
367.2044
-1,895
-272
74%
74%
Florida
715,123
81,227
-445,812
-50,405
62%
62%
Georgia
551,983
65,577
-460,090
-54,298
83%
83%
16
-------
State
Unadjusted
* PMio
Unadjusted
* PM2 5
Change in
PMio
Change in
PM25
PMio
Reduction
PMis
Reduction
Idaho
449,697
55,628
-287,356
-34,630
64%
62%
Illinois
994,459
143,538
-651,846
-93,806
66%
65%
Indiana
713,519
83,903
-517,070
-60,669
72%
72%
Iowa
384,930
59,847
-238,519
-37,058
62%
62%
Kansas
610,575
99,017
-304,968
-48,616
50%
49%
Kentucky
311,212
42,670
-249,008
-33,933
80%
80%
Louisiana
265,714
35,625
-194,577
-25,699
73%
72%
Maine
37,839
5,854
-33,368
-5,193
88%
89%
Maryland
103,187
16,226
-78,412
-12,310
76%
76%
Massachusetts
147,555
18,227
-123,552
-15,169
84%
83%
Michigan
388,475
48,399
-289,455
-35,891
75%
74%
Minnesota
403,298
61,415
-269,429
-40,598
67%
66%
Mississippi
432,447
53,220
-355,506
-43,106
82%
81%
Missouri
1,596,627
183,950
-1,200,402
-137,733
75%
75%
Montana
431,106
61,794
-255,652
-34,895
59%
56%
Nebraska
347,814
55,023
-184,530
-28,892
53%
53%
Nevada
159,219
22,770
-37,373
-5,208
23%
23%
New
Hampshire
21,753
4,474
-19,969
-4,104
92%
92%
New Jersey
39,888
9,012
-30,798
-6,935
77%
77%
New Mexico
487,042
53,617
-179,264
-19,724
37%
37%
New York
266,547
44,918
-218,199
-36,801
82%
82%
North Carolina
201,722
29,165
-167,633
-24,298
83%
83%
North Dakota
472,493
82,404
-269,004
-46,732
57%
57%
Ohio
926,036
115,547
-698,919
-86,693
75%
75%
Oklahoma
448,835
67,558
-261,679
-38,509
58%
57%
Oregon
655,811
73,353
-494,124
-53,749
75%
73%
Pennsylvania
239,436
37,271
-200,011
-31,167
84%
84%
Rhode Island
4,774
758.9918
-3,599
-571
75%
75%
South Carolina
161,860
21,445
-127,773
-16,953
79%
79%
South Dakota
338,107
63,040
-188,679
-35,018
56%
56%
Tennessee
292,095
42,817
-237,706
-34,646
81%
81%
Texas
1,253,281
178,135
-693,229
-95,694
55%
54%
Utah
207,655
26,012
-104,611
-12,893
50%
50%
Vermont
22,127
3,211
-19,645
-2,842
89%
89%
Virginia
283,656
36,627
-239,498
-30,939
84%
84%
Washington
239,770
41,133
-136,452
-23,267
57%
57%
West Virginia
122,147
15,014
-111,989
-13,766
92%
92%
Wisconsin
687,446
89,364
-479,916
-62,117
70%
70%
17
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State
Unadjusted
* PMio
Unadjusted
* PM2 5
Change in
PMio
Change in
PM25
PMio
Reduction
PMis
Reduction
Wyoming
239,402
29,064
-127,537
-15,284
53%
53%
Domain Total
18,363,585
2,486,019
-12,268,300
-1,633,119
67%
66%
* Unadjusted" here does not mean raw 2015, it means 2015 with met adjustments backed out as appropriate
(i.e. the inventory that was fed into SMOKE)
18
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Figure 2-1. Impact of adjustments to 2014 fugitive dust emissions due to transport fraction,
precipitation, and cumulative
1160
120
80
- 40
2014fa Precip and Xportfrac Adjusted - Xportfrac Annual Afdust PM2 5
^ax: 0.0006 Min: -534.8289
,
160
120
80
40
0
AO
-80
-120
-160
03
QJ
>.
1/1
C
8
2014fa Xportfrac - Unadjusted Annual Afdust PM2 5
Max: 0 0 Min: -1771.085
19
-------
2014fa Precip and Xportfrac Adjusted - Unadjusted Annual Afdust PM2 5
C 0'»: ' ll 1 ¦¦'0 '¦
2.2.2 Agricultural sector (ag)
The "ag" sector includes NH3 emissions from fertilizer, 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 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. Year
2014 ag sector emissions from the 2014NEIv2 were used as is for this 2015 study.
The fertilizer SCCs 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 N H3
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 2-9 provides the SCCs for livestock. Of these, all have NH3 and the ones marked in the 3rd column
of the table include additional pollutants. Table 2-10 shows the fertilizer SCCs.
20
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Table 2-9. Livestock SCCs extracted from the NEI to create the ag sector
SCC
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 (drylots);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 systems;Land application of manure
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"
21
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Table 2-10. Fertilizer SCCs extracted from the NEI for inclusion in the "ag" sector
see
SCC Description*
2801700001
Anhydrous Ammonia
2801700002
Aqueous Ammonia
2801700003
Nitrogen Solutions
2801700004
Urea
2801700005
Ammonium Nitrate
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.
Agricultural emissions in the platform are based on the 2014NEIv2, which is a mix of state-submitted
data and EPA estimates. The EPA estimates in 2014NEIv2 were revised from 2014NEIvl, using refined
methodologies and/or data for livestock and fertilizer. Livestock emissions utilized improved animal
population data. VOC livestock emissions, new for this sector, were estimated by multiplying a national
VOC/NH3 emissions ratio by the county NH3 emissions. The 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 approach are provided in Section 4.5 of 2014NEIv2 TSD.
Annual fertilizer emissions were submitted by three states for all or part of the sector as shown in
parentheses: California (57 percent), Illinois (100 percent) and Idaho (100 percent). Georgia had
previously submitted data in vl but used the EPA estimates for v2. The EPA estimates employed a
methodology that uses the bidirectional (bi-di) version of CMAQ (v5.0.2) and the Fertilizer Emissions
Scenario Tool for CMAQ FEST-C (vl.2). The FEST-C and CMAQ simulations were used to directly
estimate emission rates based on 2014 inputs. This is a refinement from the earlier estimates that relied on
emission factors calculated from a 2011 model simulation applied to 2014 FEST-C county level fertilizer
application estimates. Additionally, revised FEST-C estimates of fertilizer application were reduced for
pasture and hay due to estimates of fertilizer use and hay yield being higher than USDA estimates. This
resulted in a reduction of NFb emissions, primarily in the Southeastern U.S. Section 4.5 of the
2014NEIv2 TSD presents the updated approach.
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)
22
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and 2012 and 2014 U.S. Department of Agriculture (USDA) agricultural census data. Details on the
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 3.3.5) 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 2015, 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 2015 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,
23
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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 2015 for this study. The methodology
and projection factors for npoilgas projections were the same as for pt oilgas, except that 2015
projections were applied to the entire 2014NEIv2 np oilgas inventory. Projection factors for 2015 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 2-5,
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 2-11.
Residential wood combustion emissions for the 2015 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 2-11. 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
24
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approach for most SCCs as discussed in Section 3.3.4. For the 2015 platform, day-of-year
temporalization is based on 2015 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.
The nonpt sector includes emission estimates for Portable Fuel Containers (PFCs), also known as "gas
cans." The PFC inventory consists of five distinct sources of PFC emissions, further distinguished by
residential or commercial use. The five sources are: (1) displacement of the vapor within the can; (2)
spillage of gasoline while filling the can; (3) spillage of gasoline during transport; (4) emissions due to
evaporation (i.e., diurnal emissions); and (5) emissions due to permeation. Note that spillage and vapor
displacement associated with using PFCs to refuel nonroad equipment are included in the nonroad
inventory.
For the 2015 platform, the emissions inventory for the nonpt sector is from 2014NEIv2 and is the same as
in the 2014v7.1 platform.
2.3 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
25
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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 2015 platform includes
emission factors processed by MOVES for the year 2015, and projections of 2014NEIv2 vehicle miles
traveled, vehicle population, and hoteling (extended idling) hours activity data to 2015.
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 originally 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 2015 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.
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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.
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 2-12.
Table 2-12. 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
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• 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 2015. The activity data was projected from 2014 to 2015 using the following
procedure. First, VMT was projected using factors calculated from FHWA VM-2 data
(https://www.fhwa.dot.gov/policvinformation/statistics/2014/vm2.cfm.
https://www.fhwa.dot.gov/policvinformation/statistics/2015/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
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 shown in Table
2-13.
Table 2-13. Factors applied to project VMT from 2014 to 2015
Rural
Urban
State
roads
roads
Alabama
2.28%
2.53%
Alaska
3.89%
3.89%
Arizona
0.31%
5.02%
Arkansas
2.59%
2.54%
California
2.36%
0.51%
Colorado
2.02%
3.36%
Connecticut
1.24%
1.29%
Delaware
4.42%
3.08%
District of
Columbia
0.00%
0.84%
Florida
4.17%
2.73%
Georgia
5.89%
5.89%
Hawaii
3.50%
0.78%
Idaho
1.57%
5.32%
Illinois
1.32%
-0.01%
Indiana
-0.49%
-0.49%
Iowa
4.36%
7.29%
Kansas
0.82%
3.45%
Kentucky
1.66%
1.39%
Louisiana
-0.15%
-0.15%
Maine
2.29%
2.29%
Maryland
2.01%
1.90%
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/trf7crash 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
Massachusetts
2.96%
2.96%
Michigan
2.80%
-0.49%
Minnesota
0.00%
0.00%
Mississippi
-1.35%
4.08%
Missouri
1.50%
1.37%
Montana
1.22%
2.29%
Nebraska
2.49%
2.49%
Nevada
3.70%
2.17%
New Hampshire
1.25%
0.76%
New Jersey
3.00%
0.56%
New Mexico
8.24%
8.24%
New York
-1.57%
-1.57%
North Carolina
2.92%
3.91%
North Dakota
-4.52%
-4.52%
Ohio
1.43%
0.54%
Oklahoma
1.51%
-1.12%
Oregon
6.06%
2.70%
Pennsylvania
-1.44%
2.47%
Rhode Island
1.32%
2.12%
South Carolina
4.30%
3.01%
South Dakota
1.23%
0.72%
Tennessee
5.99%
5.99%
Texas
6.23%
6.23%
Utah
6.62%
6.62%
Vermont
4.50%
1.47%
Virginia
2.02%
2.02%
Washington
3.91%
2.30%
West Virginia
3.71%
3.71%
Wisconsin
3.68%
3.03%
Wyoming
1.74%
0.88%
Puerto Rico
0.00%
0.00%
Virgin Islands
0.00%
0.00%
Once the VMT dataset was finalized for 2015, VPOP activity for 2015 was calculated by applying
VMT/VPOP ratios based on 2014NEIv2 to the projected 2015 VMT for each county, fuel, and vehicle
type. Hoteling hours activity for 2015 were calculated in a similar manner, by applying 2014NEIv2-
based VMT/hoteling ratios to the projected 2015 VMT, but only for VMT from long-haul combination
trucks on restricted roads.
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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 2015 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 detail
is needed for modeling but not for the NEI. This code mapping is provided in
"2014vl_EICtoEPA_SCCmapping.xlsx." which is found in the supporting data for the 2014 NEI v2 TSD
(ftp://newftp.epa.gov/air/nei/2014/doc/2014v2 supportingdata/onroad/Y 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
2015 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 2015 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 "onroad ca adj." Note that in emission
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.
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.)
30
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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
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 2015 platform, cmv_clc2 emissions from the 2014NEIv2 were used as-is. In a future 2015 study,
SO2 emissions were reduced by 90% from 2014NEIv2 levels in accordance with ECA-IMO emissions
standards for 2016, but this reduction was not applied in this 2015 study.
Table 2-14 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 2-14. 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 2015 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).
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Table 2-15 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 2-15. SCCs extracted for the cmv c3 sector
see
Sector
Description: Mobile Sources prefix for all
2280003100
cmv
Marine Vessels, Commercial; Residual; Port emissions
2280003200
cmv
Marine Vessels, Commercial; Residual; Underway emissions
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 2015 platform C3 emissions are from 2014NEIv2 within U.S. state and federal waters (FIPS = 85). In
a future 2015 study, 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. However,
this SO2 cut was not applied for this 2015 study.
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 2-16. The geographic regions
listed in the table are shown in Figure 2-2. 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.
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Table 2-16. Growth factors to project the 2002 ECA-IMO inventory to 2011
Region
EEZ FIPS
NOx
PMio
pm2.5
voc
CO
S02
Outside ECA
98001
1.341
1.457
1.457
1.457
1.457
1.457
Figure 2-2. Illustration of regional modeling domains in ECA-IMO study
GC
The emissions were converted to SMOKE point source inventory format as described in
http://www3.epa.gov/ttn/chief/conference/eil7/session6/mason.pdt. 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 FIPS 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 2-16.
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 3.2.1.2 for more details on C3 speciation in the cmv sector and Section 3.3.8 for details on
temporal allocation.
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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.
2.4.2 Railroad sources: (rail)
The rail sector includes all locomotives in the NEI nonpoint data category, SCCs are shown in Table 2-17.
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 2015 platform are unchanged from those in the 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 as compared to the 2014NEIvl. 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 2-17. 2014NEIv2 SCCs extracted for rail sector
see
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. The basis of nonroad emissions in
the 2015 platform is an interpolation of emissions from separate runs of MOVES2014a for 2014 and
2016. The 2016 run of MOVES2014a that was used for this interpolation was found to not be a true
representation of 2016 emissions and instead represented year 2014 emissions with 2016 meteorology,
therefore the nonroad emissions are representative of year 2014 instead of 2015. This was corrected in a
later version of year 2015 inventories.
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
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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
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 2015 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 2015 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 2015
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 2015 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
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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.
2.5.2 Area and nonroad mobile sources from Canada and Mexico (othar,
othafdust)
For Canadian area and nonroad sources, year-2013 emissions provided by Environment Canada were used
for this 2015 study, including CMV emissions for most pollutants. A later 2015 study recomputed
Canadian emissions based on an interpolation of 2013 and 2025 emissions, but for this 2015 study, the
2013 emissions were used directly. 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.
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
2015 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 emissions provided by Environment Canada
were used for this year 2015 study. 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 2015 for this
platform.
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2.5.4 Fires from Canada and Mexico (ptfire_othna)
Annual 2015 wildland emissions for Mexico, Canada, Central America, and Caribbean nations in the
2015 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)
Wildfire and prescribed burning emissions are contained in the ptfire sector. The ptfire sector has
emissions provided at geographic coordinates (point locations) and has daily emissions values. The ptfire
sector excludes agricultural burning and other open burning sources that are included in the ptagfire
sector. Emissions are day-specific and include satellite-derived latitude/longitude of the fire's origin and
other parameters associated with the emissions such as acres burned and fuel load, which allow estimation
of plume rise.
The 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 2-18.
Table 2-18. 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."
The point source day-specific emission estimates for 2015 fires were developed using SM ARTF1RE 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 burns 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).
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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 2015 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 GeoMAC-
(https://www.geomac.gov/) fire shapes, where available, or otherwise using a circle centered on the detect
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 contain both CAPs and HAPs. The
BAFM HAP emissions from the inventory were obtained using VOC speciation profiles (i.e., a "no-
integrate noHAP" use case).
2.7 Biogenic sources (beis)
Biogenic emissions were developed using the Biogenic Emission Inventory System version 3.61
(BEIS3.61) within SMOKE using the "15j" version of 2015 meteorology. 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 this 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 this 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 2-19.
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Table 2-19. 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/).
For the 2014NEIv2 and this study, 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.
Biogenic emissions computed with BEIS version 3.61 were left out of the CMAQ-ready merged
emissions, in favor of inline biogenics produced during the CMAQ model run itself.
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
2-3, Figure 2-4, Figure 2-5, and Figure 2-6, respectively. The land use changes made in the v7.1 platform
would not impact these v7.0-based figures. The biogenic emissions for 2015 are different from 2014 in
terms of temporalization and magnitude but, are similar spatially.
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Figure 2-3. Annual NO emissions output from BEIS 3.61 for 2014
2014fa_nata beis NO emissions, annual
fri-'Sr-maHssm
^ax 106.3774 Mm. Q 0
Figure 2-4. Annual isoprene emissions output from BEIS 3.61 for 2014
2014fa_nata beis ISOP emissions, annual
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Figure 2-5. Annual acetaldehyde emissions output from BEIS 3.61 for 2014
2014fa_nata beis ALD2 emissions, annual
Hax: 55 1179 Min: 0.0
Figure 2-6. Annual formaldehyde emissions output from BEIS 3.61 for 2014
2014fa_nata beis FORM emissions, annual
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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 2015
platform are available from the Air Emissions Modeling website (https://www.epa.gov/air-emissions-
modeling/2015 -alpha-platform).
SMOKE version 4.5 was used to process the 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 adj
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
onroad can
Surrogates
Yes
monthly
onroad mex
Surrogates
Yes
monthly
othafdust
Surrogates
Yes
annual
othar
Surrogates
Yes
annual &
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
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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 run for the 12US1 modeling domain shown in Figure 3-1 and then the emissions were
extracted for the 12US2 domain prior to running the air quality model for this study. Section 3.4 provides
the details on the spatial surrogates and area-to-point data used to accomplish spatial allocation with
SMOKE.
Figure 3-1. Air quality modeling domains
12US1 Continental US Domain
12US2 Continental FS Domain
All grids use a Lambert-Conformal projection, with Alpha = 33°, Beta = 45° and Gamma = -97°, with a
center of X = -97° and Y = 40°. Table 3-2 describes the grids for the three domains.
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Table 3-2. Descriptions of the platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig, xcell,
ycell, ncols, nrows, nthik
Continental
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 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. The CB6
mechanism is an update of the older CB05 mechanism. Updates to species assignments for CB05 and
CB6 were made for the 2014v7.1 platform and are described in Appendix C.
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
46
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Inventory Pollutant
Model Species
Model species description
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
PM25
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
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 -
anthropogenic)6
PCL
Particulate chloride
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
6 These emissions are created outside of SMOKE
47
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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;
• 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 2015 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.
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.
48
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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 this platform because the ptfire and ptagfire inventories for 2015 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
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. Process of integrating NBAFM with VOC for use in VOC
Speciation). 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. Process of integrating NBAFM with VOC for use in VOC Speciation 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
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.
49
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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.
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation
i Emissions Ready for 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
applied to TOG and (2) N,B,A,F,M from inventory
SMOKE
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)
50
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Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
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)
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[l]-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
51
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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 GSPROCOMBO feature was used to speciate nonroad mobile and gasoline-
related stationary sources that use fuels with varying ethanol content. In these cases, the speciation
profiles require different combinations of gasoline profiles, e.g. EO and E10 profiles. Since the ethanol
content 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
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 GSPRO COMBO feature. For the 2015 platform,
GSPRO COMBO is still used for nonroad sources in California and for certain gasoline-related stationary
sources nationwide. The fractions combining the EO 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 El0 in future years.
In Canada and Mexico, only EO 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
this 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
52
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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
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. Process of integrating NBAFM with VOC for use in VOC Speciation) 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
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/.
53
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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]
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. Because for the NATA case the NBAFM are used from the inventory,
these no-integrate NBAFM emissions were used in the speciation.
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. Process of integrating
NBAFM with VOC for use in VOC Speciation) for these no-integrate sources.
For the biog sector, the speciation profiles used by BEIS are not included in SPECIATE. The 2011
platform uses BEIS3.61, which includes a new species (SESQ) that was 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
2015 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.
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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)
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
55
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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.
Table 3-7. TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a
rofile
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 EO and E10 fuel use,
but beginning with 2014v7.0 platform, the appropriate allocation of EO and E10 fuels is done by MOVES.
Combination profiles reflecting a combination of E10 and EO fuel use 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
56
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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 F of the
2014v7.1 TSD.
Table 3-8 summarizes the different profiles utilized for the fuel-related sources in each of the sectors. The
term "COMBO" indicates that a combination of the profiles listed was used to speciate that subcategory
using the GSPROCOMBO file.
Table 3-8. Select mobile-related VOC profiles
Sector
Sub-category
This platform
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).
57
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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
8757M
Tier 2 E10 Exhaust
2001-2050
1,2,15,16
12,13,14
20,30
8758M
Tier 2 El5 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
11 91 is the processed for APUs which, are diesel engines not covered by the 2007 Heavy-Duty Rule, so the older technology
applied to all years.
12 The profile assignments forpre-2001 gasoline vehicles fueled onE15/E20 fuels (subtypes 15 and 18) were corrected for
MOVES2014a. This model year range, process, fuelsubtype regclass combinate is already assigned to profile 8758.
58
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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
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 (E15)
18
Ethanol (E20)
20
Conventional Diesel Fuel
21
Biodiesel (BD20)
22
Fischer-Tropsch Diesel (FTD100)
30
Compressed Natural Gas (CNG)
50
Ethanol
51
Ethanol (E85)
52
Ethanol (E70)
59
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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)
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 which
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.
60
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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
s 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 1
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 60
Weight Percent
¦ Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
Natural Gas Combustion - Composite 91112
61
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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/.
62
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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,
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockev=P100F lA5.pdf.
63
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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
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/tis 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.
64
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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
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
65
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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 adi
Annual
Yes
week
all
Yes
ag
Monthly
met-based
all
No
beis
Hourly
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
mwdss
mwdss
Yes
np oilgas
Annual
Yes
week
week
Yes
onroad
Annual & monthly1
all
all
Yes
onroad ca adj
Annual & monthly1
all
all
Yes
othafdust adi
Annual
Yes
week
week
No
othar
Annual & monthly
Yes
week
week
No
onroad can
Monthly
week
week
No
onroad mex
Monthly
week
week
No
othpt
Annual & monthly
Yes
mwdss
mwdss
No
ptagfire
Daily
all
all
No
pt oilgas
Annual
Yes
mwdss
mwdss
Yes
ptegu
Annual & hourly
Yes2
all
all
No
ptnonipm
Annual
Yes
mwdss
mwdss
Yes
ptfire
Daily
all
all
No
ptfire othna
Daily
all
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No3
met-based
all
No3
'Note the annual and monthly "inventory" actually refers to the activity data (VMT, hoteling and VPOP) for onroad. VMT and
hoteling is monthly and VPOP is annual. The actual emissions are computed on an hourly basis.
2Only units that do not have matching hourly CEMS data use monthly temporal profiles.
3Except for 2 SCCs that do not use met-based temporalization.
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
66
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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.
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 2015 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 2015 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
67
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that ran 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.
Figure 3-5. Eliminating unmeasured spikes in CEMS data
2014 CEM 2398_1101 Month 1
400
300
3
O
200
100
n
n
1
..i
. .i
1.1.
aij
|
101
201
301 401
Hour
501
601
701
¦Raw CEM
¦Corrected
Figure 3-6. Seasonal diurnal profiles for EGU emissions in a Virginia Region
Diurnal CEMS Profile for PJM Dom Gas
0.06
0.055
0.05
C
O
£ 0.045
I—
J-
c 0.04
I—
3
21 0.035
0.03
0.025
1 2 3 4 5 6 7
¦Winter
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Summer Annual
68
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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
64 IPM regions shown in Figure 3-7. These factors are based on 2015 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 2015 CEMS heat input 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., 2015 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.
69
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Figure 3-7. IPM Regions used to Create Temporal Profiles for EGUs without CEMS
NENG_ME
WECC_MT
MAP
WAUE
NENOCT
WECCALN
SPP_NEBR
PJMSMAC
S_VACA
SPP WEST
WEC_SDGE
S_D_REST
ERC_WEST
S_D_AMSO
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
erc_west gas
E 0.04
0.02
0.00
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
day
70
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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 2015
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
71
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Figure 3-10. Weekly profile for all Airport SCCs
Weekly Airport Profile
0.18
0.1
0.08
0.06
0.04
0.02
0
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
72
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Figure 3-12. Alaska Seaplane Profile
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3.3.4 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
73
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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
60F, alternate formula
50F, default formula
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 (see
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.
74
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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.
The diurnal profile for OHH, shown in Figure 3-15, is 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.
75
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Figure 3-15. 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 ^ ^ ^
Figure 3-16. Day-of-week temporal profiles for OHH and Recreational RWC
Fire Pits/Chimineas Day-of-Week Profile
350
300
250
200
150
100
Fire Pit/Chimenea
Outdoor Hydronic Heater
76
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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 MARAPR MAYJUN 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:
Ea = [161500/T,/; x e("1380/V] 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.
77
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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
;3 1000
1/1
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
X
For this 2015 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 2015. Therefore, in the 2015 platform, the entire np oilgas
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
profiles and SPDPRO will impact not only the distribution of emissions through time but also the total
78
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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
_ a
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7/8/140:00
7/9/140:00
7/10/140:00 7/11/140:00 7/12/140:00 7/13/140:00 7/14/140:00
Date and time (GMT)
7/15/140:00
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.
79
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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
Saturday
Fulton Co
Sunday
Fulton Co
passenger
passenger
oo: J)— vN. o.oi JK—
0 0
1 2 3 4 S 6 7 S 9 10 11 12 13 14 15 16 17 IS 19 20 21 22 23 24 j 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 0.1
0.09
0.08
0.08
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
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
80
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Figure 3-21. Counties for which MOVES Speeds and Temporal Profiles could be Populated
Group |i | Individual
I I Midwest Region Average of Single County MSA Counties
Midwest Region non-MSA Average
Northeast Region Average of Single County MSA Counties
Northeast Region rion-MSA Average
South Region Average of Single County MSA Counties
South Region non-MSA Average
West Region Average of Single County MSA Counties
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 CARB-specific profiles were used in developing
EPA estimates for California. Although the EPA adjusted the total emissions to match California-
submitted emissions, the temporal allocation of these emissions 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.
81
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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 for different grid
resolutions.
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
82
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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 the
2015 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.
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
0.1
10.08
0.06
0.04
0.02
12345678 9 10111213141516171819 202122 23 24
83
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Industrial processes that are not likely to shut down on Sundays, such as those at cement plants, use
profiles that include emissions on Sundays, while those that would shut down on Sundays use profiles that
reflect Sunday shutdowns.
For the ptfire sectors, the inventories are in the daily point fire format FF10 PTDAY. 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 2015 platform uses the same ptfire diurnal as
the 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
monthly using monthly temporal profiles applied in SMOKE by SCC. This is an improvement over the
2011 platform, which applied monthly temporal allocation in California at the broader SCC7 level.
3.4 Spatial Allocation
The methods used to perform spatial allocation are summarized in this section. For the modeling
platform, spatial factors are typically applied by county and SCC. As described in Section 3.1, spatial
allocation was performed for the larger 12-km U.S. domain. To accomplish this, SMOKE used national
12-km spatial surrogates and a SMOKE area-to-point data file. For the U.S., the EPA updated surrogates
to use circa 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 12-km surrogates cover the entire CONUS domain 12US1 shown in Figure 3-1.
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.
84
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3.4.1 Spatial Surrogates for U.S. emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 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 a 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 2015 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 2015 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;
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 2015 modeling platform
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2)
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
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
85
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Code
Surrogate Description |
| Code
Surrogate Description
150
Residential Heating - Natural Gas
1 527
Single Family Residential
160
Residential Heating - Wood j
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 (IND2 +
IND5)
205
Extended Idle Locations
580
Food Drug Chemical Industrial (IND3)
211
Rural Restricted Road Miles
585
Metals and Minerals Industrial (IND4)
212
Rural Restricted AADT \
590
Heavy Industrial (IND1)
221
Urban Unrestricted Road Miles
595
Light Industrial (IND2)
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
676
Well Count-All Producing
244
All Unrestricted AADT
677
Well Count-All Exploratory
258
Intercity Bus Terminals
678
Completions at Gas Wells
259
T ransit Bus T erminals
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
NT AD Class 12 3 Railroad Density
685
Completions at Oil Wells
272
NTAD Amtrak Railroad Density
686
Completions at All Wells
273
NTAD Commuter Railroad Density |
687
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
NLCD Med Intensity Development i
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 j
1 860
Mines
86
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Code
Surrogate Description
i Code
Surrogate Description
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 2015 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
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 2011. 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 2015 platform, after errors were discovered in some counties.
87
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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"
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.
88
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Table 3-22. Selected 2015 CAP emissions by sector for U.S. Surrogates (12US1 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
2,823,395
179,970
cmv clc2
808
2013 Shipping Density
293
520,571
14,357
4,207
9,117
cmv clc2
820
Ports NEI2014 Activity
11
23,201
729
1,482
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
2,584
272
4
500
nonroad
304
NLCD Open + Low
4
2,199
191
6
3,230
nonroad
305
NLCD Low + Med
110
22,782
4,549
146
148,910
nonroad
306
NLCD Med + High
339
240,678
15,532
522
124,752
nonroad
307
NLCD All Development
101
35,965
15,347
132
168,805
nonroad
308
NLCD Low + Med + High
662
454,115
37,672
874
68,825
nonroad
309
NLCD Open + Low + Med
111
22,100
1,249
148
44,312
nonroad
310
NLCD Total Agriculture
475
416,654
31,718
664
47,528
nonroad
320
NLCD Forest Land
19
8,705
1,332
24
8,317
89
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Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nonroad
321
NLCD Recreational Land
157
20,730
15,115
226
553,559
nonroad
350
NLCD Water
215
143,603
8,628
361
447,354
nonroad
850
Golf Courses
13
2,149
115
17
5,662
nonroad
860
Mines
2
2,760
298
4
549
np oilgas
670
Spud Count - CBM Wells
0
0
0
0
179
np oilgas
671
Spud Count - Gas Wells
0
0
0
0
10,213
np oilgas
672
Gas Production at Oil Wells
0
3,114
0
21,703
132,924
np oilgas
673
Oil Production at CBM Wells
0
60
0
0
3,510
np oilgas
674
Unconventional Well Completion Counts
0
49,995
1,793
237
3,633
np oilgas
678
Completions at Gas Wells
0
3,598
26
6,768
71,380
np oilgas
679
Completions at CBM Wells
0
13
0
483
1,581
np oilgas
681
Spud Count - Oil Wells
0
0
0
0
71,799
np oilgas
683
Produced Water at All Wells
0
12
0
0
96,489
np oilgas
685
Completions at Oil Wells
0
3,526
129
2,266
55,417
np oilgas
687
Feet Drilled at All Wells
0
119,951
3,995
449
9,569
np oilgas
691
Well Counts - CBM Wells
0
32,515
483
12
27,146
np oilgas
692
Spud Count - All Wells
0
9,020
255
113
366
np oilgas
693
Well Count - All Wells
0
0
0
0
191
np oilgas
694
Oil Production at Oil Wells
0
5,446
0
6,337
1,148,869
np oilgas
695
Well Count - Oil Wells
0
121,851
2,892
80
452,987
np oilgas
696
Gas Production at Gas Wells
0
48,679
2,123
163
56,273
np oilgas
697
Oil Production at Gas Wells
0
1,405
0
25
379,201
np oilgas
698
Well Count - Gas Wells
15
318,258
5,457
299
679,839
np oilgas
699
Gas Production at CBM Wells
0
2,489
325
26
4,837
onroad
205
Extended Idle Locations
509
182,233
2,501
73
35,634
onroad
239
Total Road AADT
6,780
onroad
242
All Restricted AADT
36,812
1,414,639
45,066
8,378
226,757
onroad
244
All Unrestricted AADT
67,151
2,138,188
82,736
17,676
590,047
onroad
258
Intercity Bus Terminals
153
2
0
35
onroad
259
Transit Bus Terminals
100
4
0
222
onroad
304
NLCD Open + Low
779
19
1
2,595
onroad
306
NLCD Med + High
15,884
317
18
18,741
onroad
307
NLCD All Development
608,367
12,902
965
1,253,173
onroad
308
NLCD Low + Med + High
40,355
744
62
64,388
onroad
506
Education
545
21
1
835
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
rwc
300
NLCD Low Intensity Development
15,331
30,493
313,945
7,684
338,465
90
-------
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 were updated in the 2014v7.1 platform and carried forward into the 2015 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
91
-------
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
412
Petroleum product wholesaler-
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
Waste management and remediation
562
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 for 2015,12US1 domain
Code
Mexican or Canadian Surrogate
Description
nh3
NOx
pm25
so2
voc
11
MEX 2015 Population
26,089
119,206
4,128
473
142,715
14
MEX Residential Heating - Wood
0
1,323
16,963
203
116,625
16
MEX Residential Heating -
Distillate Oil
0
13
0
4
0
20
MEX Residential Heating - LP Gas
0
5,649
171
0
96
22
MEX Total Road Miles
2,725
360,388
10,170
5,886
73,886
24
MEX Total Railroads Miles
0
22,751
508
199
887
26
MEX Total Agriculture
177,847
135,558
28,722
6,492
10,886
32
MEX Commercial Land
0
75
1,634
0
23,657
34
MEX Industrial Land
4
1,109
1,975
0
120,470
36
MEX Commercial plus Industrial
Land
0
2,123
30
5
98,045
38
MEX Commercial plus Institutional
Land
3
1,699
76
3
49
40
MEX Residential (RES 1 -
4)+Comercial+Industrial+Institution
al+Government
0
4
11
0
76,212
42
MEX Personal Repair (COM3)
0
0
0
0
5,773
44
MEX Airports Area
0
3,410
97
441
1,166
50
MEX Mobile sources - Border
Crossing
5
146
1
3
267
100
CAN Population
734
63
743
13
338
92
-------
Code
Mexican or Canadian Surrogate
Description
nh3
NOx
pm25
so2
VOC
101
CAN total dwelling
407
34,757
2,561
4,641
145,170
106
CAN ALL INDUST
0
0
11,653
0
72
113
CAN Forestry and logging
470
2,558
0
145
7,191
115
CAN Agriculture and forestry
activities
51
610
2,944
13
1,710
200
CAN Urban Primary Road Miles
1,959
94,879
4,176
330
12,707
210
CAN Rural Primary Road Miles
788
57,758
2,318
136
5,511
212
CAN Mining except oil and gas
0
0
3,493
0
0
220
CAN Urban Secondary Road Miles
3,669
144,985
7,901
704
31,364
221
CAN Total Mining
0
0
56,431
0
0
222
CAN Utilities
79
9,604
54,336
3,356
201
230
CAN Rural Secondary Road Miles
2,055
100,286
4,328
361
14,470
240
CAN Total Road Miles
44
80,391
2,918
85
129,307
308
CAN Food manufacturing
0
0
11,188
0
5,908
321
CAN Wood product manufacturing
263
1,801
0
132
7,712
323
CAN Printing and related support
activities
0
0
0
0
11,633
324
CAN Petroleum and coal products
manufacturing
0
1,023
1,232
391
6,070
326
CAN Plastics and rubber products
manufacturing
0
0
0
0
23,640
327
CAN Non-metallic mineral product
manufacturing
0
0
6,696
0
0
331
CAN Primary Metal Manufacturing
0
156
5,509
52
74
412
CAN Petroleum product wholesaler-
distributors
0
0
0
0
40,256
448
CAN clothing and clothing
accessories stores
0
0
0
0
113
562
CAN Waste management and
remediation services
220
1,652
2,316
2,282
16,280
921
CAN Commercial Fuel Combustion
192
24,167
2,258
3,890
1,156
923
CAN TOTAL INSTITUTIONAL
AND GOVERNEMNT
0
0
0
0
13,910
924
CAN Primary Industry
0
0
0
0
35,614
925
CAN Manufacturing and Assembly
0
0
0
0
70,395
926
CAN Distribtution and Retail (no
petroleum)
0
0
0
0
7,096
927
CAN Commercial Services
0
0
0
0
30,629
932
CAN CANRAIL
54
120,110
2,796
433
5,984
941
CAN PAVED ROADS
0
0
303,031
0
0
945
CAN Commercial Marine Vessels
226
185,290
6,730
41,449
15,215
948
CAN Forest
0
0
0
0
0
950
CAN Combination of Forest and
Dwelling
1,807
20,074
165,440
2,868
234,530
93
-------
Code
Mexican or Canadian Surrogate
Description
nh3
NOx
pm25
so2
VOC
955
CAN
UNPAVED ROADS AND TRAIL
S
0
0
446,328
0
0
960
CAN TOTBEEF
0
0
1,241
0
264,838
965
CANTOTBEEF CD
280,587
0
0
0
0
966
CANTOTPOUL CD
23,914
0
0
0
0
967
CANTOTSWIN CD
68,007
0
0
0
0
968
CANTOTFERT CD
120,175
0
0
0
0
970
CAN TOTPOUL
0
0
182
0
243
980
CAN TOTS WIN
0
0
757
0
2,590
990
CAN TOTFERT
0
4,244
380,084
9,470
155
996
CAN urban area
0
0
1,275
0
0
1211
CAN Oil and Gas Extraction
2
29
228,601
152
922
1212
CAN OilSands
126
2,053
0
638
1,754
1251
CAN OFFR TOTFERT
109
118,228
8,760
79
10,875
1252
CAN OFFR MINES
42
41,668
3,461
31
4,197
1253
CAN OFFR Other Construction not
Urban
27
23,675
3,900
20
9,548
1254
CAN OFFR Commercial Services
34
17,872
2,209
29
22,802
1255
CAN OFFR Oil Sands Mines
0
0
0
0
0
1256
CAN OFFR Wood industries
CANVEC
14
11,836
1,126
10
1,973
1257
CAN OFFR Unpaved Roads Rural
33
9,929
1,706
29
66,779
1258
CAN OFFR Utilities
16
8,618
533
14
10,248
1259
CAN OFFR total dwelling
17
5,425
1,415
15
34,693
1260
CAN OFFR water
9
2,246
334
13
20,148
1261
CAN OFFR ALL INDUST
4
4,176
267
3
861
1262
CAN OFFR Oil and Gas Extraction
1
1,061
59
1
151
1263
CAN OFFR ALLROADS
2
1,087
76
1
507
1264
CAN OFFR OTHERJET
1
849
71
1
72
1265
CAN OFFR CANRAIL
0
85
8
0
14
94
-------
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99
-------
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
2310020000
Industrial Processes;Oil and Gas Exploration and Production;Natural Gas;Total: All Processes
100
-------
see
SCC description
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
2310021505
Industrial Processes;Oil and Gas Exploration and Production;On-Shore Gas Production;Fugitives: Valves
101
-------
see
SCC description
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
2310023512
Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives:
Flanges
102
-------
see
SCC description
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
103
-------
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE4.5 that were used
in the 2015 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
104
-------
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
105
-------
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
106
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Appendix C: CB6 Assignment for New Species
September 27,2016
MEMORANDUM
T:: - 'SO' E>.:~ and Madeleine Struru, OAQP5,EPA
From: Rcu BeBrtisley and Gr^gYarwood, Rambofi Environ
Subject: i pedes Mappings for C86 and CBOSfor use with SPECtATE 4,5
Summarv
Ramholl Environ |«E| reviewer version 4.5 of the SPEC flTE s'ltsbsse, and created CB05 ir? CSS
mechanism species mappings fc: nev/y a:red corr.pc-'ds. irv addition the maprng guicsiines for
Carbon Bond fcsf mechanisms were expander tc propose cons'stency in current ana fut_re wortc.
Background
Tfee Environmental Protection Agency's Sf ECIATE repository contains fas ami particulate matter
s:s:is: ;n jrafiles of air pollution sources, which are used in the generation of emissions data for air
: usI't ,• -c dels fAQJW J sucfi -is CMAQ |htip://ww»¥xinascenter.ori/ciriaq/| ami CAMS
, <¦ t: • v.'w.cimixoffi). However, the condensed chemical mechinisms used within these-
i-hcr acs-ieal models eliiie fewer species than SPECIATE to represent ps phis* chemistry, art
:h _s the :¦: ECIATE compounds must- be assigned to the AQM mcxiel species of the- corutensed
rnezhsnisins. A chemical mapping is used to shew tie representation of organic chemical species by
the model compounds of t®a canoensed mecnanisms.
T'-jj Tis-r-cErdj- describes hew chemical mappings were developed from SPEOATf 45
compounds to mode! sesdes ™f t*"» €5- ""®ch3n%"" specifically CB05
fhttp://www,camxxoT,/pub\.-lz- rtfs/CEG5_Fi - a'._Rep3rt_l2DBD5.pdf) and CB6
,rt:D://=q-c.:55' wt=:••== rdu- "c.s:t "f :rr:z_ii- II-jII. Ll-ci2'-:2:?'~ = ::IDReport.pdf).
Methods
CB Mode Species
Organic gases.are -13!:; = : :: "= C: f^e;h5^ i*" •=-¦- = ¦ =_- e:;cl ; "ep"=E = "tec nc •. :<.•=!
::r.:c.r.cs ;e.g. A^DZ ffcr Eceta'aehyde;, or as a :omb"r«aticr cs rrode species that rep"B=srt
:o r,rc ¦ struct.ral g-aups i>=.g. ALDX fcr ether aldehydes PAR fur a'kyl groups i. "able i lists ill ?f
the expl ± r.-u"u '3 rroc= spec = = h C2CE =-d IE6 -*5:- = r.iiTS e=:h ~ :h -rp'-==="is
defined n. :f c=r::rs a;:n_jjl :-vi_":r :=-b;- t: be ::nse".-ed n 5 esse:. C:f c"t;rs x:ir
more e:=:p :i: rrode spec s = t CECr =*"d =::i: -D--3 it:. -r:u: to ¦ep"e:="t T"s
€805 re:-eseri:3: 71-"ve ;dc t::r= :5os;s:;ei 1 or:.':;-.- :-= res-r C5C5 crurr ;if
Table i.
107
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S\: to the =:= c!:.* = -r -uciu -a spec = e v-'es: rvr r odd species that are used to
"*•: ¦=: = "¦? rrjir-!-: raiei :hit 5 ¦= net rested - . :he •;& "i=c"=¦¦ iinr
fcVC.- Vr'; k*v vc^t :y SPEC -"= :^nrpc»r,diihct -eside z-re:;riP5.-:!> r the : = ~ :le :h=:-s and
sfcouii be excluded from the gas phase mechanism. These compounds are mapped bv setting
MWOi equal to the molecular weight |e.g, dtecabraniodip'henvd oxide is mapped as S59.2,
-:c- s lc-v= for t~= tors nr = : = 3*' 5 WCL r= sete•r'.nec
b-\\- Czr-po^-di :r=t ;r= i-nsb s te be n=p:e: t: C = u^'sihe e = lab = ~-:de :;e:=i :
approach should be avoided unless absolutely necessary, and will lead fic s warding message
Tacle i, Mads-. spsries n the CBS 5 End CB6 cnar-ica mechsni = n- = ,
V:c?l
Tit: t:
"•'art
,*• i:
BENZ
ZC%'~4
*?NF
4 £ t:
YE
i-Sf-SIE
:c"T'3iisni>-3« i"7Rftsnal»
ispne 12--ne!l", l-S,,3-®iF!afcf!ej
j-r:
I: ""i ¦ ¦ Jl . i7l .
I., r-je
C ? }•: ¦:
"* IBQIIOBllMl 2trGITi«tE5
ifiif'eacfere urtran groups fe«g,, tiBlngenoted
cirtaons'l
"5 : ijCi r::: :p •.: ¦
¦: . .U: ir
:e:=.
JifLIM f
¦r:;: ig-
iMc
-------
Mapping guidel'nes for nc-ekc-l cit organise gases u="ng CB model species
5-EC i~E :c-i;ru-r; t = •« ~.t :r=a:;r : ic :lv srs mapped to CB model species 'that represent
cor-r;c " |-ou;= ";b = 2 I =::?¦= ;=r~ : n -.ruber and general mapping guidelines for each
of the structure nodel speries
T=ble 2, general Guidelines 'far' mapping using C86 structural mode} species..
:e;
j:-t! t:
1 sre
Mumlwr o»
Cs-sc.s
:.t.: ¦ :;e ¦<:
- -- '
:
i ¦ ¦ 1 ' • o • r - ¦ • • - -
OLE
4:
ttsmai grrja icif refian: A :orfccni sra sdc'ticisl ;arbons ere represented bi
s'stp s i^c:t ^ "£*| e e. Z-peitsna ;sr eri iTs O.E x
fn-RfOS."
* If.: .:srt:i brsrjjhes er.oe;t" i-aei 2-* tie cji; s send are tfowngreitel to
CLE
£. ,1
s 2 aAS - t~.s :g:ri;«c :srtcn: i.e.5., t' ^ :r3s*fjne sc~ser; ir- 2 Uisf.
of r :rtl: j-sl": l-CE'i'
K*L
£
<:.,isne Kc-^er: 5-a cTi-pc ,»lt. ar--iB*ie.v jrcie.-ti S :srt or: a-.c ar-v additional
:srfcs"i: ire rep"!ien:ec si s.»¦ ^1 gr3i.pi : 5.nr-et!v,lltS'»i:eri janers ire
x=-=;
Some compounds that a^e multifunctional &r„d|/or include hetero-atoms lack obvious CB mapping's
We developed guidelines for some of these ccnaou^d classes to promote consistent representation
in this woric and future rev jjons. Approaches far several compound classes are explained in Table 3.
We itet b aped guidelines as needed to address newly added species in SPCCIATE 4.5 but did not
systematically rs.-Isv. £&&.rig iir.ap^.r.p for 'ci't.!:: assign xompewndsthat could benefit from
c-. .i :p: ¦; = e. de re.
109
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Tsclr 2. Mspp ng gu'?ei n== fo* so-1= :oni3: cc-ipo.nd :iaiie= 3-d -tr.c!. ia" groups
; :l ¦(
lis::-1.": i ::i t
2'jUp
CB Ri«K)ei species representation
CWDtrabenseBss and
attier hatogenststf
benzenes
Suiaaine:
¦ I pus-i PAR, 1
« a ;r "js'6 nilcee"! -6
= • j"-: t:
* i,s,5-Cr:=rctM;:ie - I =4« 3 U .»
• "i-qfcs?v:er is - 6 J\R
f"ElopBntBrfe!ie.-1. aoi* 2 PAS
• i 10 iE, 3 PAS
: - v/Pymries
Gui::
» : :.s with addilisr i" caraciK represented as aBcyl groups (generally
= 1S
• 2-6utyOTuran — 2 C.E, 4 PAR
¦ 2-PentfI'ur: C.E,3 PAR
« =¦
« i-fclefclpwriffte - 2 OLE, 1 FAR
-sft'3c~»«:hvl:ni3lB-l c.s 3 par
T ; i ri " 3(if
3jic: -it:
• Triple tarnfe are- treated as am unless they are the only reactive
funrtiowl group. ?f a oompnuni' contains mare than one triple Sena
and no aetisr resctfce. functional groups, then one of the triple bonds
is treated m OLE wiBi achStiafN carbons treated a* eNgpt §mupa.
Eli rap las:
¦ L-=sntsp-3-..-e - i C.E 3 =A'
« ..J—: C.i 1 »AR
• ..6~ir.6C ,r;-. O.E J
The,se guidelines were used to Ft a:, (is new species from SPEICATC4.5, and also to revise scaie
previously mapped compounds. Overall, a total of 175 new species from SFEClftTEwcs we *s rs pped
ami 7 previously mapped species were revised based on the new guidelines.
santwis Biiimrtus , _ __ . to*...
V «3.SiS8 J
110
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JN
iecmninoMlatlQfi
1. co^r 5te b ir-steir=t:: r=view :-f r- rrspr -f o" a 1 spec =• t¦: s-!>u< = :c -?;rnity -.-.i:' c.'-s'-t
mapping guidelines. The asagMnerits of existing corr pounds that are simi a- to new species were
'evewed "svisedtc rrcirte cenwrtsTO i" a-fprm-es, tut the maj6rityof
existing species mappings were not reviewed as it was outside the scope of c-his work.
Z. Develop a methodology for classifying and tracking larger organic caifipouiids based on their
..¦c s:ii ty issr\ hT=f-T>ea it;, :t y.-. vc .at:1 ty tc nrp h" i=:c-:=*v organic serosal
;iCA nrc-rs -ng rgt'-s ,-c =:i .t';'bssiji=:iv5S: SOA *icz-=l, -,vhi:r ¦ = i* do:' CVAC.
a" Cm"--'-* a p'==i-i 'sri'vait gs; ;.f, ;f f = pc=z ::i-g sc hsj "==' performed, s-":r is
discussed in a separate memorandum.
Ill
3
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112
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-20-011
Environmental Protection Air Quality Assessment Division July 2019
Agency Research Triangle Park, NC
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