Technical Support Document (TSD)
Preparation of Emissions Inventories for the Version 7.2
2016 North American Emissions Modeling Platform
September 2019
Contacts:
Alison Eyth, Jeff Vukovich, Caroline Farkas, Madeleine Strum
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Emissions Inventory and Analysis Group
Research Triangle Park, North Carolina
-------
TABLE OF CONTENTS
LIST OF TABLES Ill
LIST OF FIGURES IV
LIST OF APPENDICES IV
ACRONYMS V
1 INTRODUCTION 1
2 EMISSION INVENTORIES AND APPROACHES 3
2.1 Summary of 2016 Base Year Emission Inventories 3
2.2 Inventory Differences from the 2016 beta platform 7
2.2.1 Prescribed Fires Spatial Reallocation 7
2.2.2 Adjustments to Canadian Emissions 12
2.2.3 Moving sources from ptnonipm to ptegu and other EGU refinements 13
2.3 Summary of 2028 Future Year Emission Inventories 14
3 EMISSIONS MODELING 18
3.1 Emissions modeling Overview 18
3.2 Chemical Speciation 21
3.2.1 VOC speciation 24
3.2.1.1 County specific profile combinations 27
3.2.1.2 Additional sector specific considerations for integrating HAP emissions from inventories into speciation 28
3.2.1.3 Oil and gas related speciation profiles 30
3.2.1.4 Mobile source related VOC speciation profiles 32
3.2.2 PM speciation 37
3.2.2.1 Mobile source related PM2.5 speciation profiles 38
3.2.3 NOxspeciation 40
3.2.4 Creation of Sulfuric Acid Vapor (SULF) 40
3.3 Temporal Allocation 42
3.3.1 Use of FF10 format for finer than annual emissions 43
3.3.2 Electric Generating Utility temporal allocation (ptegu) 44
3.3.2.1 Base year temporal allocation of EGUs 44
3.3.3 Airport Temporal allocation (ptnonipm) 46
3.3.4 Residential Wood Combustion Temporal allocation (rwc) 48
3.3.5 Agricultural Ammonia Temporal Profiles (ag) 52
3.3.6 Oil and gas temporal allocation (np oilgas) 53
3.3.7 Onroad mobile temporal allocation (onroad) 53
3.3.8 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire) 57
3.4 Spatial Allocation 60
3.4.1 Spatial Surrogates for U.S. emissions 60
3.4.2 Allocation method for airport-related sources in the U.S. 66
3.4.3 Surrogates for Canada and Mexico emission inventories 66
3.5 Preparation of Emissions for the CAMx model 69
3.5.1 Development of CAMx Emissions for Standard CAMx Runs 69
3.5.2 Development of CAMx Emissions for Two- Way Nested CAMx Runs in This Study 72
3.5.3 Development of CAMx Emissions for Source Apportionment CAMx Runs 73
4 EMISSION SUMMARIES 77
5 REFERENCES 82
11
-------
List of Tables
Table 2-1. Platform sectors for the 2016 regional haze emissions modeling case 4
Table 2-2. Units moved from ptnoipm to ptegu in the regional haze cases 13
Table 2-3. Overview of projection methods for the 2028 regional haze cases 14
Table 3-1. Key emissions modeling steps by sector 19
Table 3-2. Descriptions of the platform grids 21
Table 3-3. Emission model species produced for CB6 for CMAQ 22
Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM)
for each platform sector 26
Table 3-5. Ethanol percentages by volume by Canadian province 28
Table 3-6. MOVES integrated species in M-profiles 29
Table 3-7. Basin/Region-specific profiles for oil and gas 31
Table 3-8. TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a used for the 2016
Platform 32
Table 3-9. Select mobile-related VOC profiles 2016 33
Table 3-10. Onroad M-profiles 34
Table 3-11. MOVES process IDs 35
Table 3-12. MOVES Fuel subtype IDs 36
Table 3-13. MOVES regclass IDs 36
Table 3-14. SPECIATE4.5 brake and tire profiles compared to those used in the 201 lv6.3 Platform 39
Table 3-15. Nonroad PM2.5 profiles 40
Table 3-16. NOx speciation profiles 40
Table 3-17. Sulfate split factor computation 41
Table 3-18. SO2 speciation profiles 41
Table 3-19. Temporal settings used for the platform sectors in SMOKE 42
Table 3-20. U.S. Surrogates available for the 2016 alpha and beta modeling platforms 61
Table 3-21. Off-Network Mobile Source Surrogates 62
Table 3-22. Spatial Surrogates for Oil and Gas Sources 63
Table 3-23. Selected 2016 CAP emissions by sector for U.S. Surrogates (short tons in 12US1) 64
Table 3-24. Canadian Spatial Surrogates 66
Table 3-25. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3) 67
Table 3-26. Emission model species mappings for CMAQ and CAMx 71
Table 3-27. Sector tags for 2028fg PSAT modeling 74
Table 4-1. National by-sector CAP emissions summaries for the 2016fg case, 12US1 grid 78
Table 4-2. National by-sector CAP emissions summaries for the 2028fg case, 12US1 grid 79
Table 4-3. National by-sector CAP emissions summaries for the 2016fg case, 36US3 grid 80
Table 4-4. National by-sector CAP emissions summaries for the 2028fg case, 36US3 grid 81
111
-------
List of Figures
Figure 2-1. National wildland and prescribed fires for 2016 beta (March 2016, short tons) 8
Figure 2-2. Georgia Prescribed Fire Emissions Concentrated at County Centroids 9
Figure 2-3. Georgia Prescribed Fire Emissions after re-gridding 9
Figure 2-4. Kansas Prescribed Fire Emissions Concentrated at County Centroids 10
Figure 2-5. Kansas Prescribed Fire Emissions after re-gridding 10
Figure 2-6. Corrected annual prescribed fires for 2016 regional haze 11
Figure 2-7. Wildland fires for 2016 regional haze 11
Figure 2-5. Example of gridding artifact that existed in some Canadian emissions in 2016 beta 12
Figure 2-6. Emissions after the gridding artifact was removed 13
Figure 3-1. Air quality modeling domains 20
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation 26
Figure 3-3. Profiles composited for the new PM gas combustion related sources 37
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources 38
Figure 3-5. Eliminating unmeasured spikes in CEMS data 44
Figure 3-6. Seasonal diurnal profiles for EGU emissions in a Virginia Region 45
Figure 3-7. Diurnal Profile for all Airport SCCs 46
Figure 3-8. Weekly profile for all Airport SCCs 47
Figure 3-9. Monthly Profile for all Airport SCCs 47
Figure 3-10. Alaska Seaplane Profile 48
Figure 3-11. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold 49
Figure 3-12. RWC diurnal temporal profile 50
Figure 3-13. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr) 51
Figure 3-14. Day-of-week temporal profiles for OHH and Recreational RWC 51
Figure 3-15. Annual-to-month temporal profiles for OHH and recreational RWC 52
Figure 3-16. Example of animal NH3 emissions temporal allocation approach, summed to daily emissions 53
Figure 3-17. Example of temporal variability of NOx emissions 54
Figure 3-18. Sample onroad diurnal profiles for Fulton County, GA 55
Figure 3-19. Counties for which MOVES Speeds and Temporal Profiles could be Populated 56
Figure 3-20. Example of Temporal Profiles for Combination Trucks 57
Figure 3-21. Agricultural burning diurnal temporal profile 59
Figure 3-22. Prescribed and Wildfire diurnal temporal profiles 59
List of Appendices
Appendix A: CB6 Assignment for New Species
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE4.5 that were used in the
2014 v7.2 Platform
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
iv
-------
Acronyms
AE5
CMAQ Aerosol Module, version 5, introduced in CMAQ v4.7
AE6
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
AEO
Annual Energy Outlook
AERMOD
American Meteorological Society/Environmental Protection Agency
Regulatory Model
NBAFM
Naphthalene, Benzene, Acetaldehyde, Formaldehyde and Methanol
BEIS
Biogenic Emissions Inventory System
BELD
Biogenic Emissions Land use Database
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAMD
EPA's Clean Air Markets Division
CAMx
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CB05
Carbon Bond 2005 chemical mechanism
CBM
Coal-bed methane
CEMS
Continuous Emissions Monitoring System
CEPAM
California Emissions Projection Analysis Model
CISWI
Commercial and Industrial Solid Waste Incinerators
CI
Chlorine
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
CO
Carbon monoxide
CSAPR
Cross-State Air Pollution Rule
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
FCCS
Fuel Characteristic Classification System
FF10
Flat File 2010
FIPS
Federal Information Processing Standards
FHWA
Federal Highway Administration
HAP
Hazardous Air Pollutant
HC1
Hydrochloric acid
HDGHG
Heavy-Duty Vehicle Greenhouse Gas
Hg
Mercury
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
V
-------
ICR
Information Collection Request
I/M
Inspection and Maintenance
IMO
International Marine Organization
IPM
Integrated Planning Model
ITN
Itinerant
LADCO
Lake Michigan Air Directors Consortium
LDGHG
Light-Duty Vehicle Greenhouse Gas
LPG
Liquified Petroleum Gas
MACT
Maximum Achievable Control Technology
MARAMA
Mid-Atlantic Regional Air Management Association
MATS
Mercury and Air Toxics Standards
MCIP
Meteorology-Chemistry Interface Processor
MMS
Minerals Management Service (now known as the Bureau of Energy
Management, Regulation and Enforcement (BOEMRE)
MOVES
Motor Vehicle Emissions Simulator
MSA
Metropolitan Statistical Area
MSAT2
Mobile Source Air Toxics Rule
MTBE
Methyl tert-butyl ether
MWRPO
Mid-west Regional Planning Organization
NCD
National County Database
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NESCAUM
Northeast States for Coordinated Air Use Management
NESHAP
National Emission Standards for Hazardous Air Pollutants
NH3
Ammonia
NLCD
National Land Cover Database
NLEV
National Low Emission Vehicle program
nm
nautical mile
NMIM
National Mobile Inventory Model
NO A A
National Oceanic and Atmospheric Administration
NODA
Notice of Data Availability
NONROAD
OTAQ's model for estimation of nonroad mobile emissions
NOx
Nitrogen oxides
NSPS
New Source Performance Standards
NSR
New Source Review
OAQPS
EPA's Office of Air Quality Planning and Standards
OHH
Outdoor Hydronic Heater
OTAQ
EPA's Office of Transportation and Air Quality
ORIS
Office of Regulatory Information System
OKI)
EPA's Office of Research and Development
ORL
One Record per Line
OTC
Ozone Transport Commission
PADD
Petroleum Administration for Defense Districts
PFC
Portable Fuel Container
PM2.5
Particulate matter less than or equal to 2.5 microns
PM10
Particulate matter less than or equal to 10 microns
ppb, ppm
Parts per billion, parts per million
RBT
Refinery to Bulk Terminal
RFS2
Renewable Fuel Standard
RIA
Regulatory Impact Analysis
vi
-------
RICE
RWC
RPO
RVP
SCC
SESARM
SESQ
SMARTFIRE
SMOKE
SOi
SOA
SIP
SPDPRO
TAF
TCEQ
TOG
TSD
USD A
VOC
VMT
VPOP
WRAP
WRF
Reciprocating Internal Combustion Engine
Residential Wood Combustion
Regional Planning Organization
Reid Vapor Pressure
Source Classification Code
Southeastern States Air Resource Managers
Sesquiterpenes
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
Sparse Matrix Operator Kernel Emissions
Sulfur dioxide
Secondary Organic Aerosol
State Implementation Plan
Hourly Speed Profiles for weekday versus weekend
Terminal Area Forecast
Texas Commission on Environmental Quality
Total Organic Gas
Technical support document
United States Department of Agriculture
Volatile organic compounds
Vehicle miles traveled
Vehicle Population
Western Regional Air Partnership
Weather Research and Forecasting Model
vii
-------
1 Introduction
The U.S. Environmental Protection Agency (EPA), working in conjunction with the National Emissions
Inventory Collaborative, developed an air quality modeling platform for criteria air pollutants to represent
the years of 2016 and 2028. The starting point for the 2016 inventory was the 2014 National Emissions
Inventory (NEI), version 2 (2014NEIv2), although many inventory sectors were updated to represent the
year 2016 through the incorporation of 2016-specific state and local data along with nationally-applied
adjustment methods. The year 2028 inventory was developed starting with the 2016 inventory using
sector-specific methods as described below.
The air quality modeling platform used for regional haze-related analyses 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 data and techniques including the emission inventories, the ancillary data files,
and the approaches used to transform inventories for use in air quality modeling.
The National Emissions Inventory Collaborative is a partnership between state emissions inventory staff,
multi-jurisdictional organizations (MJOs), federal land managers (FLMs), EPA, and others to develop a
North American air pollution emissions modeling platform with a base year of 2016 for use in air quality
planning. The Collaborative planned for three versions of the 2016 platform: alpha, beta, and Version 1.0.
For the regional haze-related emissions modeling documented in this TSD, the emissions values for most
sectors are the same as those in the Inventory Collaborative 2016beta Emissions Modeling Platform,
available from http://views.cira.colostate.edu/wiki/wiki/10197. The specification sheets posted on the
2016beta platform release page provide many details regarding the inventories and emissions modeling
techniques in addition to those addressed in this TSD.
This 2016 emissions modeling platform includes all criteria air pollutants (CAPs) and precursors, and a
group of hazardous air pollutants (HAPs). 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 modeling domain includes the lower 48 states and parts of Canada and Mexico. The modeling cases
for this platform were developed for the Comprehensive Air Quality Model with Extensions (CAMx).
However, the emissions modeling process first prepares outputs in the format used by CMAQ, after which
those emissions data are converted to the formats needed by CAMx.
The 2016 platform used in this study consists of a 2016 base case and a 2028 case with the abbreviations
2016fg_16j and 2028fg_16j, respectively. An additional 2028 case that included source apportionment by
inventory sector named 2028fg_secsa_16j was also developed. This platform accounts for atmospheric
chemistry and transport within a state of the art photochemical grid model. In the case abbreviation
2016fg_16j, 2016 is the year represented by the emissions; the "f' represents the base year emissions
modeling platform iteration, which here shows that it is 2014NEI-based (whereas for 2011 NEI-based
platforms, this letter was "e"); and the "g" stands for the seventh configuration of emissions modeled for a
2014-NEI based modeling platform.
The platform includes point sources, nonpoint sources, commercial marine vessels (CMV), onroad and
nonroad mobile sources, and fires for the U.S., Canada, and Mexico. 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 represented as hourly emissions by vehicle type, fuel type process and road
1
-------
type. In contrast, the onroad emissions in the modeling platform and the NEI are developed using the
same inputs, but the NEI emissions are aggregated to vehicle type/fuel type totals and annual temporal
resolution while the platform emissions have more finely resolved SCCs and temporal resolution.
Temporal, spatial and other changes in emissions between the NEI and the emissions input into the
platform are described primarily in the beta platform specification sheets. Emissions from Canada and
Mexico are used for the platform but are not part of the NEI.
The primary emissions modeling tool used to create the air quality model-ready emissions was the Sparse
Matrix Operator Kernel Emissions (SMOKE) modeling system (http ://www.smoke-model.org/), version
4.6 (SMOKE 4.62) with some updates. Emissions files were created for a 36-km national grid and for a
12-km national grid, both of which include the contiguous states and parts of Canada and Mexico as
shown in Figure 3-1.
The gridded meteorological model used to provide input data for the emissions modeling was developed
using the Weather Research and Forecasting Model (WRF, http://wrf-model.org) version 3.8, Advanced
Research WRF core (Skamarock, et al., 2008). The WRF Model is a mesoscale numerical weather
prediction system developed for both operational forecasting and atmospheric research applications. The
WRF was run for 2016 over a domain covering the continental U.S. at a 12km resolution with 35 vertical
layers. The run for this platform included high resolution sea surface temperature data from the Group for
High Resolution Sea Surface Temperature (GHRSST) (see https://www.ghrsst.org/) and is given the EPA
meteorological case label "16j." The full case name includes this abbreviation following the emissions
portion of the case name to fully specify the name of the case as "2016fg_16j."
This document contains five sections and several appendices. Section 2 describes the 2016 and 2028
inventories input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used
with the emission inventories. Data summaries are provided in Section 4. Section 5 provides references.
The Appendices provide additional details about specific technical methods or data.
2 It was determined after the modeling for this study was complete that a library used by SMOKE 4.6 was not initializing the
earth ellipsoid to match the spherical earth that is used for the air quality modeling as it had in previous versions. This could
result in shifting of the locations for point sources to change by up to about 1-km, which in some cases could change the
specific grid cell assigned to the source. The total emission would not change, only the modeling grid cell assigned for some
sources. If further studies are performed with emission inputs from this study, EPA results can be reproduced using the version
of SMOKE provided with the beta platform. If studies are not concerned with reproducing the EPA results, it is recommended
that SMOKE 4.7 be used which corrects this issue.
2
-------
2 Emission Inventories and Approaches
This section summarizes the year 2016 and 2028 emissions data that make up the regional haze platform.
This section provides details about the data contained in each of the platform sectors for the base year and
the future year. Differences between the 2016 beta platform and the regional haze platform are also
discussed.
2.1 Summary of 2016 Base Year Emission Inventories
The starting point for many emission inputs is the 2014NEIv2 although in some cases with more detailed
temporal/spatial resolution data, although the emissions have been updated to better represent the year
2016. Documentation for the 2014NEIv2, including a TSD, is available at https://www.epa.gov/air-
emissions-inventories/2014-national-emissions-inventorv-nei-technical-support-document-tsd. In addition
to the NEI-based data for the broad categories of point, nonpoint, onroad, nonroad, and events (i.e., fires),
emissions from the Canadian and Mexican inventories and several other non-NEI data sources are
included in the 2016 platform.
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 collaborates extensively with S/L/T agencies to ensure a high quality of data in the
NEI. Using the 2014NEIv2 as a starting point, the National Inventory Collaborative worked to develop a
modeling platform that more closely represents the year 2016. All emissions modeling sectors were
modified in some way to better represent the year 2016 for the beta platform, which was slightly adjusted
to prepare the regional haze platform used in this study. In terms of emissions totals, only the Canadian
fugitive dust emissions differ from those in the beta platform.
The point source emission inventories for the platform include partially updated emissions for 2016.
Agricultural and wildland fire emissions represent the year 2016. Most nonpoint source sectors started
with 2014NEIv2 emissions and were adjusted to better represent the year 2016. Fertilizer emissions,
nonpoint oil and gas emissions, and onroad and nonroad mobile source emissions represent the year 2016.
For commercial marine vessel (CMV) emissions, SO2 emissions were updated to reflect new rules on
sulfur emissions that took effect in the year 2015. For fertilizer ammonia emissions, a 2016-specific
emissions inventory is used in this platform. Nonpoint oil and gas emissions were developed using 2016-
specific data for oil and gas wells and their 2016 production levels.
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
nationally available data sets. Onroad emissions for the platform were developed based on emissions
factors output from MOVES2014a for the year 2016, run with inputs derived from the 2014NEIv2
including activity data (e.g., vehicle miles traveled and vehicle populations) projected to the year 2016.
MOVES2014b was used to generate nonroad emissions because it included important updates related to
nonroad engine population growth rates.
For the purposes of preparing the air quality model-ready emissions, emissions from the five NEI data
categories are split into finer-grained sectors used for emissions modeling. The significance of an
emissions modeling or "platform sector" is that the data are run through the SMOKE programs
independently from the other sectors except for the final merge (Mrggrid). The final merge program
3
-------
combines the sector-specific gridded, speciated, hourly emissions together to create CMAQ-ready
emission inputs. For studies that use CAMx, these CMAQ-ready emissions inputs are then converted into
the formats needed by CAMx.
Table 2-1 presents an overview the sectors in the 2016 platform and how they generally relate to the
2014NEIv2 as their 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. Through the Collaborative workgroups, state and local agencies provided
data used in the development of most sectors.
Table 2-1. Platform sectors for the 2016 regional haze emissions modeling case
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
EGU units:
Ptegu
Point
Point source electric generating units (EGUs) for 2016 from the
Emissions Inventory System (EIS), based on 2014NEIv2 with some
sources updated to 2016. Includes some specific S/L updates. The
inventory emissions are replaced with hourly 2016 Continuous
Emissions Monitoring System (CEMS) values for NOx and SO2 for
any units that are matched to the NEI, and other pollutants for matched
units are scaled from the 2016 point inventory using CEMS heat input.
Emissions for all sources not matched to CEMS data come from the
raw inventory. Annual resolution for sources not matched to CEMS
data, hourly for CEMS sources.
Point source oil and
gas:
ptoilgas
Point
Point sources for 2016 including S/L updates for oil and gas
production and related processes based on facilities with the following
NAICS: 2111,21111,211111,211112 (Oil and Gas Extraction);
213111 (Drilling Oil and Gas Wells); 213112 (Support Activities for
Oil and Gas Operations); 2212, 22121, 221210 (Natural Gas
Distribution); 48611, 486110 (Pipeline Transportation of Crude Oil);
4862, 48621, 486210 (Pipeline Transportation of Natural Gas).
Includes offshore oil and gas platforms in the Gulf of Mexico
(FIPs=85). Oil and gas point sources that were not already updated to
year 2016 in the baseline inventory were projected from 2014 to 2016.
Annual resolution.
Remaining non-
EGU point:
Ptnonipm
Point
All 2016 point source inventory records not matched to the ptegu or
pt_oilgas sector, including updates submitted by state and local
agencies. Aircraft and airport ground support emissions not submitted
for 2016 were adjusted to year 2016 using FAA data. Year 2016 rail
yard emissions were developed by the rail workgroup. Annual
resolution.
Agricultural:
Ag
Nonpoint
Nonpoint livestock and fertilizer application emissions. Livestock
includes ammonia and other pollutants (except PM2.5) and was
projected from 2014NEIv2 based on animal population data from the
U.S. Department of Agriculture (USDA) National Agriculture
Statistics Service Quick Stats, where available. Fertilizer includes
only ammonia and is estimated for 2016 using the FEST-C model.
County and monthly resolution.
Agricultural fires
with point
resolution: ptagfire
Nonpoint
2016 agricultural fire sources based on EPA-developed data with state
updates, represented as point source day-specific emissions. They are
in the nonpoint NEI data category, but in the platform, they are treated
as point sources. Mostly at daily resolution with some state-submitted
data at monthly resolution.
4
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Area fugitive dust:
Afdust
Nonpoint
PMio and PM2 5 fugitive dust sources from the 2014NEIv2 nonpoint
inventory with paved road dust grown to 2016 levels; including
building construction, road construction, agricultural dust, and road
dust. The NEI emissions are reduced during modeling according to a
transport fraction (newly computed for the beta platform) and a
meteorology-based (precipitation and snow/ice cover) zero-out.
County and annual resolution.
Biogenic:
Beis
Nonpoint
Year 2016, hour-specific, grid cell-specific emissions generated from
the BEIS3.61 model within SMOKE, including emissions in Canada
and Mexico using BELD v4.1 "water fix" land use data (including
improved treatment of water grid cells).
Category 1, 2 CMV:
cmv_clc2
Nonpoint
Category 1 (CI) and category 2 (C2) commercial marine vessel
(CMV) emissions sources projected to 2016 from the 2014NEIv2
nonpoint inventory based on factors from the Regulatory Impact
Analysis (RIA) Control of Emissions of Air Pollution from
Locomotive Engines and Marine Compression Ignition Engines Less
than 30 Liters per Cylinder3. County and annual resolution.
Category 3 CMV:
cmv_c3
Nonpoint
Category 3 (C3) CMV emissions converted to point sources based on
the center of the grid cells. Includes C3 emissions in U.S. state and
Federal waters, and also all non-U.S. C3 emissions except those in
Canadian waters. Emissions are projected to 2016 from 2014NEIv2
based on factors derived from U.S. Army Corps of Engineers Entrance
and Clearance data and information about the ships entering the ports.
Locomotives:
rail
Nonpoint
Rail locomotives emissions developed by the rail workgroup based on
2016 activity and emission factors. Includes freight and commuter rail
emissions and incorporates state and local feedback. County and
annual resolution.
Remaining
nonpoint:
nonpt
Nonpoint
2014NEIv2 nonpoint sources not included in other platform sectors
with sources proportional to human population activity data grown to
year 2016; incorporates state and local feedback. County and annual
resolution.
Nonpoint source oil
and gas:
np oilgas
Nonpoint
2016 nonpoint oil and gas emissions output from the NEI oil and gas
tool along with state and local feedback. County and annual resolution.
Residential Wood
Combustion:
rwc
Nonpoint
2014NEIv2 nonpoint sources from residential wood combustion
(RWC) processes projected to the year 2016. County and annual
resolution.
Nonroad:
nonroad
Nonroad
2016 nonroad equipment emissions developed with the MOVES2014b
model which incorporates updated equipment growth rates. MOVES
was used for all states except California, which submitted emissions.
County and monthly resolution.
3 https://nepis.epa.gov/Exe/ZvPDF.cgi/P10023S4.PDF?Dockev=P 10023S4.PDF
5
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Onroad:
onroad
Onroad
2016 onroad mobile source gasoline and diesel vehicles from moving
and non-moving vehicles that drive on roads, along with vehicle
refueling. Includes the following modes: exhaust, extended idle,
auxiliary power units, evaporative, permeation, refueling, and brake
and tire wear. For all states except California, developed using winter
and summer MOVES emissions tables produced by MOVES2014a
coupled with activity data projected to year 2016 or provided by S/Ls.
SMOKE-MOVES was used to compute emissions from the emission
factors and activity data.
Onroad California:
onroadcaadj
Onroad
2016 California-provided CAP onroad mobile source gasoline and
diesel vehicles based on the EMFAC model, which ere gridded and
temporalized using MOVES2014a results. Volatile organic compound
(VOC) HAP emissions derived from California-provided VOC
emissions and MOVES-based speciation.
Point source fires-
ptjire
Events
Point source day-specific wildfires and prescribed fires for 2016
computed using SMARTFIRE2 for both flaming and smoldering
processes (i.e., SCCs 281XXXX002). Smoldering is forced into layer
1 (by adjusting heat flux). Incorporates state inputs. Daily resolution.
Non-US. fires:
ptfireothna
N/A
Point source day-specific wildfires and prescribed fires for 2016
provided by Environment Canada with data for missing months, and
for Mexico and Central America, filled in using fires from the Fire
INventory (FINN) from National Center for Atmospheric Research
(NCAR) fires (NCAR, 2016 and Wiedinmyer, C., 2011). Daily
resolution.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
N/A
Fugitive dust sources of particulate matter emissions excluding land
tilling from agricultural activities, from Environment and Climate
Change Canada (ECCC) 2015 emission inventory, except that for
regional haze, construction dust emissions were reduced to levels
compatible with their 2010 inventory. A transport fraction adjustment
is applied along with a meteorology-based (precipitation and snow/ice
cover) zero-out. Also includes afdust emissions in Alaska, Hawaii,
Puerto Rico, and Virgin Islands from 2014NEIv2. County and annual
resolution.
Other Point Fugitive
dust sources not
from the NEI:
othptdust
N/A
Fugitive dust sources of particulate matter emissions from land tilling
from agricultural activities, from Environment and Climate Change
Canada (ECCC) 2015 emission inventory, but for regional haze wind
erosion emissions were removed. A transport fraction adjustment is
applied along with a meteorology-based (precipitation and snow/ice
cover) zero-out. Data were originally provided on a rotated 10-km grid
for beta, but were smoothed for regional so as to avoid the artifact of
grid lines in the processed emissions. Monthly resolution.
Other point sources
not from the NEI:
othpt
N/A
Point sources from the ECCC 2015 emission inventory, including
agricultural ammonia, along with emissions from Mexico's 2008
inventory projected to 2014 and 2018 and then interpolated to 2016.
Agricultural data were originally provided on a rotated 10-km grid for
beta, but were smoothed for regional so as to avoid the artifact of grid
lines in the processed emissions. Monthly resolution for Canada
agricultural and airport emissions, annual resolution for the remainder
of Canada and all of Mexico.
6
-------
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Other non-NEI
nonpoint and
nonroad:
othar
N/A
Year 2015 Canada (province or sub-province resolution) emissions
from the ECCC inventory: monthly for nonroad sources; annual for
rail, CMV and other nonpoint Canada sectors. Year 2016 Mexico
(municipio resolution) emissions, interpolated from 2014 and 2018
inventories that were projected from their 2008 inventory: annual
nonpoint and nonroad mobile inventories.
Other non-NEI
onroad sources:
onroadcan
N/A
Monthly year 2015 Canada (province resolution or sub-province
resolution, depending on the province) from the ECCC onroad mobile
inventory. Also includes onroad emissions in Alaska, Hawaii, Puerto
Rico, and Virgin Islands from 2014NEIv2.
Other non-NEI
onroad sources:
onroad mex
N/A
Monthly year 2016 Mexico (municipio resolution) onroad mobile
inventory based on MOVES-Mexico runs for 2014 and 2018 then
interpolated to 2016.
Other natural emissions are also merged in with the above sectors: ocean chlorine and sea salt. The ocean
chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb) concentrations in
oceanic air masses (Bullock and Brehme, 2002). In CMAQ, the species name is "CL2". The sea salt
emissions were developed with version 4.1 of the OCEANIC pre-processor that comes with the CAMx
model. The preprocessor estimates time/space-varying emissions of aerosol sodium, chloride and sulfate;
gas-phase chlorine and bromine associated with sea salt; gaseous halo-methanes; and dimethyl sulfide
(DMS). These additional oceanic emissions are incorporated into the final model-ready emissions files for
CAMx.
The emission inventories in SMOKE input formats for the regional haze platform are available from
EPA's Air Emissions Modeling website for the alpha platform: https://www.epa.gov/air-emissions-
modeling/2014-2016-version-7-air-emissions-modeling-platforms, under the section entitled "2016v7.2
(beta and regional haze) Platform". The platform "README" file indicates the particular zipped files
associated with each platform sector. A number of reports (i.e., summaries) are available with the data
files for the 2016 platform. The types of reports include state summaries of inventory pollutants and
model species by modeling platform sector and county annual totals by modeling platform sector.
Additional types of data including outputs from SMOKE and inputs to CAMx will be available from the
Intermountain West Data Warehouse.
2.2 Inventory Differences from the 2016 beta platform
This section describes how the regional haze cases differ from the 2016 beta platform case 2016ff Note
that most of the emissions updates are spatial allocation changes only and do not change the emissions
totals that would be seen in summaries, although the dust emissions in Canada were lowered.
2.2.1 Prescribed Fires Spatial Reallocation
Prescribed fire data were submitted for the beta platform by certain states, and in these data some Kansas
(Flint Hills grasslands) and Georgia prescribed fire emissions were located at county centroids, which was
not realistic. These issues are illustrated in Figure 2-1. For the regional haze platform, these emissions
were re-gridded to spread the emissions out to other parts of each county. The emissions were placed in
areas with appropriate types of land use for these types of fires: 2011 National Land Cover Database
(NLCD) forest land in Georgia and 2011 NLCD grass-land in Kansas. Note that the total of these
emissions did not change as a result of the regridding process - only the spatial allocation. Examples of
7
-------
fires before and after the spatial reallocation are shown in Figures 2-1 through 2-6. 2016 annual wildland
fires are shown in Figure 2-7 for reference.
In addition, to support 36/12km two-way nesting with CAMx that was used for this study, the gridded
36km and 12km point source prescribed fire emissions file had to be combined to use the appropriate
resolution in each area of the grid. This issue only affects 2-way nested CAMx model runs and is
described further in Section 3.5.2.
Figure 2-1. National wildland and prescribed fires for 2016 beta (March 2016, short tons)
-------
Figure 2-2. Georgia Prescribed Fire Emissions Concentrated at County Centroids
2016beta GA RX PM25 Month 01
Figure 2-3. Georgia Prescribed Fire Emissions after re-gridding
2016beta GA RX PM25 Month 01
9
-------
2016beta KS Flint Hills Original PM2_5
Figure 2-4. Kansas Prescribed Fire Emissions Concentrated at County Centroids
2016beta KS Flint Hills Reapportion PM2_5
Figure 2-5. Kansas Prescribed Fire Emissions after re-gridding
10
-------
Figure 2-6. Corrected annual prescribed fires for 2016 regional haze
2016 Regional Haze Wildland Prescribed Fire PM2.5
2.054e+04
Max:
Figure 2-7. Wildland fires for 2016 regional haze
2016 Regional Haze Wildland Fire PM2.5
n
-------
2.2.2 Adjustments to Canadian Emissions
In the 2016ff (beta) model run, very high modeled "soil particulate matter (PM)" concentrations were
noted in the spring in Alberta and Saskatchewan. ECCC confirmed that they had also seen this issue and
had adjusted some of their dust emission categories downward as a result of the modeled PM being high
compared to monitors in the area. They noted that the emissions inventory method for these emissions had
changed in recent inventories. To reduce this issue of high PM, adjustments to construction dust were
made to make those more consistent with the ECCC 2010 inventory, and wind erosion dust was removed
because this category is not included in the US emissions.
In addition, several categories of agricultural Canadian emissions were received in a gridded format.
Since the Canadian grids and the EPA grids did not match, a "waffle" pattern was observed in some of the
EPA gridded data. EPA re-gridded the raw Canadian data to reflect the EPA 36km and 12km grids
without the waffling. The pattern in the original beta platform and an example of the regridded data are
shown in Figure 2-5 and Figure 2-6, respectively (note that the plots are on different scales). More
specifically, spatial apportionment factors were calculated using the area of overlap between the 10 km
Canada Lambert grid and a 4 km resolution grid with the same boundaries and grid projection as the
36US3 modeling domain. The 2015 Canada point dust emissions were placed into the 10 km grid cells
based on the inventory latitude and longitude and aggregated by province and location. The spatial
factors were then applied to allocate the emissions to the 4 km grid cells. Centroid latitude and longitudes
for each respective emitting 4 km grid cell were used to fill the location information of the resulting point
Flat File. The 4 km resolution inventories were then aggregated to 12 km resolution in order to reduce the
size of the inventory.
Figure 2-8. Example of gridding artifact that existed in some Canadian emissions in 2016 beta
2Q16ff othptdust adj annual : PM2 5
^ /-a *,
h j
*
| f ;¦ fe * A,
i Or
y j>
; a r
12
-------
2016fq othptdust
Figure 2-9. Emissions after the gridding artifact was removed
2016fq othptdust adi annual : PM2 5
2.2.3 Moving sources from ptnonipm to ptegu and other EGU refinements
Following the 2016ff (aka beta) modeling, some sources in the ptnonipm inventory were found in an
output from the Integrated Planning Model (IPM), which is a model used to estimate future year EGU
emissions.. As a result, they were matched to the sources in the database that is input to IPM and were
moved from ptnonipm to ptegu sector. If the sources were left in the ptnonipm inventory they would be
double-counted with emissions output from IPM when modeling future years. The units moved from
ptnonipm to ptegu are listed in Table 2-2. In addition, a newer version of SMOKE was used to process
emissions for the regional haze case that corrects cases when CEMS data have NOx missing for the entire
year. This is important in certain areas for the base year, such as a source in Utah, and more so in future
year cases.
Table 2-2. Units moved from ptnoipm to ptegu in the regional haze cases
EIS Facility ID
EIS Unit ID
NEEDS ID
ORIS Facility Code
ORIS Boiler ID
5783911
119254913
50837 B UNIT1
50837
UNIT1
5783911
119255013
50837 B UNIT2
50837
UNIT2
533611
91819113
57898 B BLR3
57898
BLR3
533611
91819213
57898 B BLR4
57898
BLR4
533611
91819313
57898 B BLR5
57898
BLR5
7869811
87378813
50878 B UNIT1
50878
UNIT!
13
-------
EIS Facility ID
EIS Unit ID
NEEDS ID
ORIS Facility Code
ORIS Boiler ID
8057311
112375613
50630 B BLR1
50630
BLR1
8057311
112375613
50630 B BLR2
50630
BLR2
3109711
124523513
59254 G GENS1
59254
GENS1
4837411
90282913
3456 G 5CA1
3456
5CA1
12807411
123689413
54775 B BLR 10
54775
BLR 10
12807411
123689013
54775 B BLR11
54775
BLR11
7663611
12450313
58205 B 1
B
1
6719911
12840813
56119 G 300
56119
300
5632711
69997613
56152 G CTG1
56152
CTG1
5633011
20889913
10167 G GEN1
10167
GEN1
6940911
14044213
55596 G 0001
55596
0001
6940911
124476513
55596 G 0002
55596
0002
6940911
124476613
55596 G 0003
55596
0003
6940911
82780713
55596 G 0004
55596
0004
2.3 Summary of 2028 Future Year Emission Inventories
This section describes how the 2028 future year emissions inventories were developed for the 2016 beta
and regional haze platforms. For the 2028 modeling, emissions for some sectors were kept the same as
those used in the 2016 air quality modeling, while others were projected to future year levels that
represent 2028. Emissions for the following sectors are the same for the base and future year: beis,
ptagfire, ptfire, ptfireothna, ocean_cl2, and sea salt. All remaining sectors have been projected to 2028 as
summarized in Table 2-3. Additional information regarding the projection techniques applied to each
sector can be found in the 2016 beta platform specification sheets.
Table 2-3. Overview of projection methods for the 2028 regional haze cases
Platform Sector:
abbreviation
Description of Projection Method for regional haze case
EGU units:
Ptegu
The Integrated Planning Model (IPM) was run to create the 2028 emissions. The
2030 model output year from the November, 2018 version of the IPM platform
was used (https://www.epa.20v/airmarkets/power-sector-modelins-platform-v6-
novcmbcr-2018). Emission inventory Flat Files for input to SMOKE were
generated using post-processed IPM output data. Temporal allocation for future
year emissions is discussed in the EGU-IPM specification sheet for the 2016 beta
platform.
Point source oil and
gas:
ptoilgas
First, known closures were applied to the 2016 pt_oilgas sources. Production-
related sources were then grown from 2016 to 2017 using historic production data.
The production-related sources were then grown to 2028 based on growth factors
derived from the Annual Energy Outlook (AEO) 2018 data for oil, natural gas, or a
combination thereof. The grown emissions were then controlled to account for the
impacts of relevant New Source Performance Standards (NSPS).
14
-------
Platform Sector:
abbreviation
Description of Projection Method for regional haze case
Remaining non-
EGU point:
Ptnonipm
First, known closures were applied to the 2016 ptnonipm sources. Closures were
obtained from the Emission Inventory System (EIS) and also submitted by the
states of Alabama, North Carolina, and Ohio. Industrial sources were grown using
factors derived from the AEO 2018. Airport emissions were grown using factors
derived from the Terminal Area Forecast (TAF) (see
https://www.faa.gov/data research/aviation/taf A Rail vard emissions were
grown using the same factors as line haul locomotives in the rail sector. Controls
were then applied to account for relevant NSPS for reciprocating internal
combustion engines (RICE), gas turbines, and process heaters. Reductions due to
consent decrees that had not been fully implemented by 2016 were also applied,
along with specific comments received by S/L/Ts.
Agricultural:
Ag
Livestock were projected based on factors created from USDA National livestock
inventory projections published in February 2018
(https://www.ers.usda.eov/webdocs/publications/87459/oce-2018-1.pdf?v=0).
Fertilizer emissions were held constant at year 2016 levels.
Area fugitive dust:
Afdust
Paved road dust was grown to 2028 levels based on the growth in VMT from 2016
to 2028. The remainder of the sector including building construction, road
construction, agricultural dust, and road dust was held constant. The projected
emissions are reduced during modeling according to a transport fraction (newly
computed for the beta platform) and a meteorology-based (precipitation and
snow/ice cover) zero-out as they are for the base year.
Category 1, 2 CMV:
cmv_clc2
Category 1 (CI) and category 2 (C2) CMV emissions sources outside of California
were projected to 2028 based on factors from the Regulatory Impact Analysis
(RIA) Control of Emissions of Air Pollution from Locomotive Engines and Marine
Compression Ignition Engines Less than 30 Liters per Cylinder. California
emissions were projected based on factors provided by the state.
Category 3 CMV:
cmv_c3
Category 3 (C3) CMV emissions were projected using a forthcoming EPA report
on projected bunker fuel demand. The report projects bunker fuel consumption by
region out to the year 2030. Bunker fuel usage was used as a surrogate for marine
vessel activity. Factors based on the report were used for all pollutants except
NOx. Growth factors for NOx emissions were handled separately to account for
the phase in of Tier 3 vessel engines. The NOx growth rates from the EPA C3
Regulatory Impact Assessment (RIA) were refactored to use the new bunker fuel
usage growth rates. The assumptions of changes in fleet composition and
emissions rates from the C3 RIA were preserved and applied to the new bunker
fuel demand growth rates for 2028 to arrive at the final growth rates.
Locomotives:
rail
Passenger and freight were projected using separate factors with each factor
applied to all pollutants. The factors are based on AEO2018, except the 2016-to-
2017 trend for freight was based on historical fuel use for those years instead of
AEO2018. In other words, the freight projection factors are based on 2016-to-2017
fuel use growth plus AEO2018 projections for 2017-to-future. Passenger train
emissions were grown to 2028 by 16% and freight trains by 4.7%.
Remaining
nonpoint:
nonpt
Industrial emissions were grown according to factors derived from AEO 2018.
Portions of the nonpt sector were grown using factors based on expected growth in
human population. Controls were applied to reflect relevant NSPS rules (i.e.,
reciprocating internal combustion engines (RICE), natural gas turbines, and
process heaters). Emissions were also reduced to account fuel sulfur rules in the
mid-Atlantic and northeast.
15
-------
Platform Sector:
abbreviation
Description of Projection Method for regional haze case
Nonpoint source oil
and gas:
npoilgas
Production-related sources grown starting with an average of 2014 and 2016
production data. Emissions were initially projected to 2017 using historical data
and then grown to 2028 based on factors generated based on AEO2018. Based on
the SCC, factors related to oil, gas, or combined growth were used. Coalbed
methane SCCs were projected independently. Controls were then applied to
account for NSPS for oil and gas and RICE.
Residential Wood
Combustion:
rwc
RWC emissions were projected from 2014 to 2028 based on growth and control
assumptions compatible with EPA's 201 lv6.3 platform, which accounts for
growth, retirements, and NSPS, although implemented in the Mid-Atlantic
Regional Air Management Association (MARAMA)'s growth tool. RWC
emissions in California, Oregon, and Washington were held constant.
Nonroad:
nonroad
Outside California, the MOVES2014b model was run to create nonroad emissions
for 2028 without any state inputs. The fuels used are specific to the future year, but
the meteorological data represented the year 2016. For California, datasets
provided by the California Air Resources Board (CARB) circa 2017 were used.
Onroad:
onroad
Activity data were projected from 2016 to 2028 based on factors derived from
AEO 2018. Where S/Ls provided activity data, those data were used. To create the
emission factors, MOVES2014a was run for the year 2028, with 2016 met. data
and fuels, but with the remaining inputs consistent with those used in 2014NEIv2.
The future year activity data and emission factors were then combined using
SMOKE-MOVES to produce the 2028 emissions.
Onroad California:
onroadcaadj
CARB-provided emissions were used for California, but they were gridded and
temporalized using MOVES2014a-based data output from SMOKE-MOVES.
Volatile organic compound (VOC) HAP emissions derived from California-
provided VOC emissions and MOVES-based speciation.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
Othafdust emissions for future years were provided by ECCC. The emissions were
extracted from a broader nonpoint source inventory. Adjustments to construction
dust were made to make those more consistent with the 2016 and ECCC 2010
inventories. Mexico emissions are not included in this sector.
Other Point Fugitive
dust sources not
from the NEI:
othptdust
Wind erosion emissions were removed from the point fugitive dust inventory prior
to regional haze modeling. Base year 2015 inventories with the rotated grid pattern
removed were projected to 2028 based on factors provided by ECCC. A transport
fraction adjustment is applied to the projected inventories along with a
meteorology-based (precipitation and snow/ice cover) zero-out.
Other point sources
not from the NEI:
othpt
For agricultural sources that were originally developed on the rotated 10-km grid,
the reallocated base year emissions were projected to 2028 using projection factors
based on data provided by ECCC and applied by province, pollutant, and ECCC
sub-class code. Airports were also projected from 2016 using ECCC-based factors.
For the remaining sources in this sector, ECCC provided future year inventories.
For Mexico sources, inventories projected from Mexico's 2008 inventory to 2025
and 2030 were interpolated to the year 2028. The Mexico 2014 CMV inventory
was used as-is without any projections.
Other non-NEI
nonpoint and
nonroad:
othar
Future year nonpoint inventories for many parts of this sector were provided by
ECCC and were split into sectors to match those in the base year inventory. For
Canadian nonroad sources, factors were provided from which the future year
inventories could be derived. For Mexico nonpoint and nonroad sources,
inventories projected to 2025 and 2030 from their 2008 inventory were
interpolated to 2028. Mexico CMV emissions were removed so as not to double-
count emissions in the othpt sector.
16
-------
Platform Sector:
abbreviation
Description of Projection Method for regional haze case
Other non-NEI
onroad sources:
onroad can
For Canadian mobile onroad sources, future year inventories were derived from
the base year 2015 inventory and data provided by ECCC. Projection factors were
applied by province, sub-class code, and pollutant.
Other non-NEI
onroad sources:
onroad mex
Monthly year 2028 Mexico (municipio resolution) onroad mobile inventory was
developed based on a run of MOVES-Mexico for 2028.
17
-------
3 Emissions Modeling
The CMAQ and CAMx air quality models require hourly emissions of specific gas and particle species
for the horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain). To
provide emissions in the form and format required by the model, it is necessary to "pre-process" the "raw"
emissions (i.e., emissions input to SMOKE) for the sectors described above in Section 2. In brief, the
process of emissions modeling transforms the emissions inventories from their original temporal
resolution, pollutant resolution, and spatial resolution into the hourly, speciated, gridded resolution
required by the air quality model. Emissions modeling includes temporal allocation, spatial allocation,
and pollutant speciation. Emissions modeling sometimes 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. For additional details that may not be covered in this section, see the
specification sheets provided with the 2016 beta platform as many will contain additional sector-specific
information.
3.1 Emissions modeling Overview
SMOKE version 4.6 was used to process the raw emissions inventories into emissions inputs for each
modeling sector into a format compatible with CMAQ, which were then converted to CAMx. 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.
When preparing emissions for the air quality model, emissions for each sector are processed separately
through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sector-
specific 2-D gridded emissions across sectors. The SMOKE settings in the run scripts and the data in the
SMOKE ancillary files control the approaches used by the individual SMOKE programs for each sector.
Table 3-1 summarizes the major processing steps of each platform sector with the columns as follows.
The "Spatial" column shows the spatial approach used: "point" indicates that SMOKE maps the source
from a point location (i.e., latitude and longitude) to a grid cell; "surrogates" indicates that some or all of
the sources use spatial surrogates to allocate county emissions to grid cells; and "area-to-point" indicates
that some of the sources use the SMOKE area-to-point feature to grid the emissions (further described in
Section 3.4.2).
The "Speciation" column indicates that all sectors use the SMOKE speciation step, though biogenics
speciation is done within the Tmpbeis3 program and not as a separate SMOKE step.
The "Inventory resolution" column shows the inventory temporal resolution from which SMOKE needs
to calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no input inventory;
18
-------
instead, activity data and emission factors are used in combination with meteorological data to compute
hourly emissions.
Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used. These
sectors are the only ones with emissions in aloft layers based on plume rise. The term "in-line" means
that the plume rise calculations are done inside of the air quality model instead of being computed by
SMOKE. 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 ad]
Surrogates
Yes
annual
othar
Surrogates
Yes
annual &
monthly
othpt
Point
Yes
annual &
monthly
in-line
othptdust adj
Point
Yes
monthly
None
ptagfire
Point
Yes
daily
in-line
pt oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire
Point
Yes
daily
in-line
ptfire othna
Point
Yes
daily
in-line
ptnonipm
Point
Yes
annual
in-line
rail
Surrogates
Yes
annual
rwc
Surrogates
Yes
annual
19
-------
Biogenic emissions can be modeled two different ways in the CMAQ model. The BEIS model in SMOKE
can produce gridded biogenic emissions that are then included in the gridded CMAQ-ready emissions
inputs, or alternatively, CMAQ can be configured to create "in-line" biogenic emissions within CMAQ
itself. For this platform, biogenic emissions were processed in SMOKE and included in the gridded
CMAQ-ready emissions. When CAMx is the targeted air quality modeling, BEIS is run within SMOKE
and the resulting emissions are included with the ground-level emissions input to CAMx.
SMOKE has the option of grouping sources so that they are treated as a single stack when computing
plume rise. For this platform, no grouping was performed because grouping combined with "in-line"
processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-
dimensional files). This occurs when stacks with different stack parameters or latitudes/longitudes are
grouped, thereby changing the parameters of one or more sources. The most straightforward way to get
the same results between in-line and offline is to avoid the use of grouping.
SMOKE was am for two modeling domains: a 36-km resolution CONtinental United States "CONUS"
modeling domain (36US3), and the 12-km resolution domain. 12US2. More specifically, SMOKE was
run on the 12US1 domain and emissions were extracted from 12US1 data files to create 12US2 emission.
The domains are shown in Figure 3-1.
Figure 3-1. Air quality modeling domains
-WRF_36IMOAM
BELD4
36US3
12US1
12US2
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.
20
-------
Table 3-2. Descriptions of the platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig, xcell,
ycell, ncols, nrows, nthik
Continental
36km grid
36 km
Entire conterminous
US, almost all of
Mexico, most of
Canada (south of
60°N)
36US3
'LAM 40N97W', -2952000, -2772000,
36.D3, 36.D3, 172, 148, 1
Continental
12km grid
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_459X299
'LAM 40N97W', -2556000, -1728000,
12.D3, 12.D3, 459, 299, 1
US 12 km or
"smaller"
CONUS-12
12 km
Smaller 12km
CONUS plus some of
Mexico/Canada
12US2
'LAM 40N97W', -2412000 , -
1620000, 12.D3, 12.D3, 396, 246, 1
3.2 Chemical Speciation
The emissions modeling step for chemical speciation creates the "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual chemical
compounds (i.e., "explicit species") or groups of species (i.e., "lumped species"). The chemical
mechanism used for the 2016 platform is the CB6 mechanism (Yarwood, 2010). We used a particular
version of CB6 that we refer to as "CMAQ CB6" that breaks out naphthalene from XYL as an explicit
model species, resulting in model species NAPH and XYLMN instead of XYL and uses SOAALK. This
platform generates the PM2.5 model species associated with the CMAQ Aerosol Module version 6 (AE6).
Table 3-3 lists the model species produced by SMOKE in the platform used for this study. Updates to
species assignments for CB05 and CB6 were made for the 2014v7.1 platform and are described in
Appendix A.
21
-------
Table 3-3. Emission model species produced for CB6 for CMAQ
Inventory Pollutant
Model Species
Model species description
Cl2
CL2
Atomic gas-phase chlorine
HC1
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOx
NO
Nitrogen oxide
N02
Nitrogen dioxide
HONO
Nitrous acid
S02
S02
Sulfur dioxide
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
NH3 FERT
Ammonia from fertilizer
voc
ACET
Acetone
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
BENZ
Benzene (not part of CB05)
CH4
Methane
ETH
Ethene
ETHA
Ethane
ETHY
Ethyne
ETOH
Ethanol
FORM
Formaldehyde
IOLE
Internal olefin carbon bond (R-C=C-R)
ISOP
Isoprene
KET
Ketone Groups
MEOH
Methanol
NAPH
Naphthalene
NVOL
Non-volatile compounds
OLE
Terminal olefin carbon bond (R-C=C)
PAR
Paraffin carbon bond
PRPA
Propane
SESQ
Sequiterpenes (from biogenics only)
SOAALK
Secondary Organic Aerosol (SOA) tracer
TERP
Terpenes (from biogenics only)
TOL
Toluene and other monoalkyl aromatics
UNR
Unreactive
XYLMN
Xylene and other polyalkyl aromatics, minus
naphthalene
Naphthalene
NAPH
Naphthalene from inventory
Benzene
BENZ
Benzene from the inventory
Acetaldehyde
ALD2
Acetaldehyde from inventory
Formaldehyde
FORM
Formaldehyde from inventory
Methanol
MEOH
Methanol from inventory
PM10
PMC
Coarse PM >2.5 microns and <10 microns
PM2.5
PEC
Particulate elemental carbon <2.5 microns
PN03
Particulate nitrate <2.5 microns
POC
Particulate organic carbon (carbon only) <2.5 microns
PS04
Particulate Sulfate <2.5 microns
PAL
Aluminum
PCA
Calcium
22
-------
Inventory Pollutant
Model Species
Model species description
PCL
Chloride
PFE
Iron
PK
Potassium
PH20
Water
PMG
Magnesium
PMN
Manganese
PMOTHR
PM2.5 not in other AE6 species
PNA
Sodium
PNCOM
Non-carbon organic matter
PNH4
Ammonium
PSI
Silica
PTI
Titanium
Sea-salt species (non -
PCL
Particulate chloride
anthropogenic)4
PNA
Particulate sodium
The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were
developed from the SPECIATE 4.5 database (https://www.epa.gov/air-emissions-modeling/speciate).
which is the EPA's repository of TOG and PM speciation profiles of air pollution sources. The
SPECIATE database development and maintenance is a collaboration involving the EPA's Office of
Research and Development (ORD), Office of Transportation and Air Quality (OTAQ), and the Office of
Air Quality Planning and Standards (OAQPS), in cooperation with Environment Canada (EPA, 2016).
The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical
compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation profiles
for PM2.5.
Some key features and recent updates to speciation from previous platforms include the following:
• 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 and PM speciation process for nonroad mobile has been updated - profiles are now
assigned within MOVES2014b 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;
4 These emissions are created outside of SMOKE
23
-------
• 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 2015 inventory, not all CB6-
CMAQ species were provided; missing species were supplemented by speciating VOC which was
provided separately.
Speciation profiles and cross-references for this study platform are available in the SMOKE input files for
the 2016 regional haze platform. Emissions of VOC and PM2.5 emissions by county, sector and profile
for all sectors other than onroad mobile can be found in the sector summaries for the case. Totals of each
model species by state and sector can be found in the state-sector totals workbook for this case.
3.2.1 VOC speciation
The speciation of VOC includes HAP emissions from the 2014NEIv2 in the speciation process. Instead
of speciating VOC to generate all of the species listed in Table 3-3, emissions of five specific HAPs:
naphthalene, benzene, acetaldehyde, formaldehyde and methanol (collectively known as "NBAFM") from
the NEI were "integrated" with the NEI VOC. The integration combines these HAPs with the VOC in a
way that does not double count emissions and uses the HAP inventory directly in the speciation process.
The basic process is to subtract the specified HAPs emissions mass from the VOC emissions mass, and to
then use a special "integrated" profile to speciate the remainder of VOC to the model species excluding
the specific HAPs. The EPA believes that the HAP emissions in the NEI are often more representative of
emissions than HAP emissions generated via VOC speciation, although this varies by sector.
The NBAFM HAPs were chosen for integration because they are the only explicit VOC HAPs in the
CMAQ version 5.2. Explicit means that they are not lumped chemical groups like PAR, IOLE and
several other CB6 model species. These "explicit VOC HAPs" are model species that participate in the
modeled chemistry using the CB6 chemical mechanism. The use of inventory HAP emissions along with
VOC is called "HAP-CAP integration."
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats,
including PTDAY (the format used for the ptfire and ptagfire sectors). The ability to use integration with
the PTDAY format was made available in the version of SMOKE used for the 2014v7.1 platform, but this
new feature is not used for the 2016 platform because the ptfire and ptagfire inventories for 2016 do not
include HAPs. SMOKE allows the user to specify the particular HAPs to integrate via the INVTABLE.
This is done by setting the "VOC or TOG component" field to "V" for all HAP pollutants chosen for
integration. SMOKE allows the user to also choose the particular sources to integrate via the
NHAPEXCLUDE file (which actually provides the sources to be excluded from integration5). 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 profiles6. SMOKE computes NONHAPTOG and
then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC
5 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.
6 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.
24
-------
species not including the integrated HAPs. After determining if a sector is to be integrated, if all sources
have the appropriate HAP emissions, then the sector is considered fully integrated and does not need a
NHAPEXCLUDE file. If, on the other hand, certain sources do not have the necessary HAPs, then an
NHAPEXCLUDE file must be provided based on the evaluation of each source's pollutant mix. The
EPA considered CAP-HAP integration for all sectors in determining whether sectors would have full, no
or partial integration (see Figure 3-2). For sectors with partial integration, all sources are integrated other
than those that have either the sum of NBAFM > VOC or the sum of NBAFM = 0.
In this platform, we create NBAFM species from the no-integrate source VOC emissions using speciation
profiles. Figure 3-2 illustrates the integrate and no-integrate processes for U.S. Sources. Since Canada
and Mexico inventories do not contain HAPs, we use the approach of generating the HAPs via speciation,
except for Mexico onroad mobile sources where emissions for integrate HAPs were available.
It should be noted that even though NBAFM were removed from the SPECIATE profiles used to create
the GSPRO for both the NONHAPTOG and no-integrate TOG profiles, there still may be small fractions
for "BENZ", "FORM", "ALD2", and "MEOH" present. This is because these model species may have
come from species in SPECIATE that are mixtures. The quantity of these model species is expected to be
very small compared to the BAFM in the NEI. There are no NONHAPTOG profiles that produce
"NAPH."
In SMOKE, the INVTABLE allows the user to specify the particular HAPs to integrate. Two different
INVTABLE files are used for different sectors of the platform. For sectors that had no integration across
the entire sector (see Table 3-4), EPA created a "no HAP use" INVTABLE in which the "KEEP" flag is
set to "N" for NBAFM pollutants. Thus, any NBAFM pollutants in the inventory input into SMOKE are
automatically dropped. This approach both avoids double-counting of these species and assumes that the
VOC speciation is the best available approach for these species for sectors using this approach. The
second INVTABLE, used for sectors in which one or more sources are integrated, causes SMOKE to keep
the inventory NBAFM pollutants and indicates that they are to be integrated with VOC. This is done by
setting the "VOC or TOG component" field to "V" for all five HAP pollutants. Note for the onroad
sector, "full integration" includes the integration of benzene, 1,3 butadiene, formaldehyde, acetaldehyde,
naphthalene, acrolein, ethyl benzene, 2,2,4-Trimethylpentane, hexane, propionaldehyde, styrene, toluene,
xylene, and MTBE.
25
-------
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation
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
appliedtoTOG 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)
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)
26
-------
Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
onroad
Full integration (internal to MOVES); however, MOVES2014a speciation was CB6-
CAMx, not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-CMAQ
onroad can
No integration, no NBAFM in inventory, create NBAFM from speciation
onroadmex
Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation
was CB6-CAMx, not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-
CMAQ
othafdust
N/A - sector contains no VOC
othptdust
N/A - sector contains no VOC
othar
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
Integration for the mobile sources estimated from MOVES (onroad and nonroad sectors, other than for
California) is done differently. Briefly there are three major differences: 1) for these sources integration
is done using more than just NBAFM, 2) all sources from the MOVES model are integrated and 3)
integration is done fully or partially within MOVES. For onroad mobile, speciation is done fully within
MOVES2014a such that the MOVES model outputs emission factors for individual VOC model species
along with the HAPs. This requires MOVES to be run for a specific chemical mechanism. MOVES was
run for the CB6-CAMx mechanism rather than CB6-CMAQ, so post-SMOKE onroad emissions were
converted to CB6-CMAQ. More specifically, the CB6-CAMx mechanism excludes XYLMN, NAPH,
and SOAALK. After SMOKE processing, we converted the onroad and onroadmex emissions to CB6-
CMAQ as follows:
• XYLMN = XYL[1]-0.966*NAPHTHALENE[1]
• PAR = PAR[1]-0.00001*NAPHTHALENE[1]
• SOAALK = 0.108*PAR[1]
For nonroad mobile, speciation is partially done within MOVES such that it does not need to be run for a
specific chemical mechanism. For nonroad, MOVES outputs emissions of HAPs and NONHAPTOG
split by speciation profile. Taking into account that integrated species were subtracted out by MOVES
already, the appropriate speciation profiles are then applied in SMOKE to get the VOC model species.
HAP integration for nonroad uses the same additional HAPs and ethanol as for onroad.
3.2.1.1 County specific profile combinations
SMOKE can compute speciation profiles from mixtures of other profiles in user-specified proportions via
two different methods. The first method, which uses a GSPROCOMBO file, has been in use since the
2005 platform; the second method (GSPRO with fraction) was used for the first time in the 2014v7.0
platform. The GSPRO COMBO method uses profile combinations specified in the GSPRO COMBO
ancillary file by pollutant (which can include emissions mode, e.g., EXH VOC), state and county (i.e.,
state/county FIPS code) and time period (i.e., month). Different GSPRO COMBO files can be used by
sector, allowing for different combinations to be used for different sectors; but within a sector, different
profiles cannot be applied based on SCC. The GSREF file indicates that a specific source uses a
combination file with the profile code "COMBO." SMOKE computes the resultant profile using the
fraction of each specific profile assigned by county, month and pollutant.
In previous platforms, the GSPRO COMBO feature was used to speciate nonroad mobile and gasoline-
related stationary sources that use fuels with varying ethanol content. In these cases, the speciation
profiles require different combinations of gasoline profiles, e.g. E0 and E10 profiles. Since the ethanol
content varied spatially (e.g., by state or county), temporally (e.g., by month), and by modeling year
(future years have more ethanol), the GSPRO COMBO feature allowed combinations to be specified at
27
-------
various levels for different years. The GSPROCOMBO 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 2016 alpha
platform, GSPRO COMBO is still used for nonroad sources in California and for certain gasoline-related
stationary sources nationwide. The fractions combining the E0 and E10 profiles are based on year 2010
regional fuels and do not vary by month. GSPRO COMBO is not needed for inventory years after 2016,
because the vast majority of fuel is projected to be E10 in future years.
New in the 2016v7.2 beta and regional haze platforms, a GSPRO COMBO is used to specify a mix of E0
and E10 fuels in Canada. ECCC provided percentages of ethanol use by province, and these were
converted into E0 and E10 splits. For example, Alberta has 4.91% ethanol in its fuel, so we applied a mix
of 49.1% E10 profiles (4.91% times 10, since 10% ethanol would mean 100%) E10), and 50.9% E0 fuel.
Ethanol splits for all provinces in Canada are listed in Table 3-5. The Canadian onroad inventory includes
four distinct FIPS codes in Ontario, allowing for application of different E0/E10 splits in Southern
Ontario versus Northern Ontario. In Mexico, only E0 profiles are used.
Table 3-5. Ethanol percentages by volume by Canadian province
Province
Ethanol % by volume (E10 = 10%)
Alberta
4.91%
British Columbia
5.57%
Manitoba
9.12%
New Brunswick
4.75%
Newfoundland & Labrador
0.00%
Nova Scotia
0.00%
NW Territories
0.00%
Nunavut
0.00%
Ontario (Northern)
0.00%
Ontari o ( S outhern)
7.93%
Prince Edward Island
0.00%
Quebec
3.36%
Saskatchewan
7.73%
Yukon
0.00%
A new method to combine multiple profiles became available in SMOKE4.5. It allows multiple profiles
to be combined by pollutant, state and county (i.e., state/county FIPS code) and SCC. This was used
specifically for the oil and gas sectors (pt oilgas and np oilgas) because SCCs include both controlled
and uncontrolled oil and gas operations which use different profiles.
3.2.1.2 Additional sector specific considerations for integrating HAP emissions from
inventories into speciation
The decision to integrate HAPs into the speciation was made on a sector by sector basis. For some
sectors, there is no integration and VOC is speciated directly; for some sectors, there is full integration
meaning all sources are integrated; and for other sectors, there is partial integration, meaning some
sources are not integrated and other sources are integrated. The integrated HAPs are either NBAFM or, in
the case of MOVES (onroad, nonroad and MOVES-Mexico), a larger set of HAPs plus ethanol are
integrated. Table 3-4 above summarizes the integration method for each platform sector.
28
-------
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 is used to create emissions for these
sectors and both the MEPROC and INVTABLE files are involved in controlling which pollutants are
processed. Second, the speciation occurs within MOVES itself, not within SMOKE. The advantage of
using MOVES to speciate VOC is that during the internal calculation of MOVES, the model has complete
information on the characteristics of the fleet and fuels (e.g., model year, ethanol content, process, etc.),
thereby allowing it to more accurately make use of specific speciation profiles. This means that MOVES
produces emission factor tables that include inventory pollutants (e.g., TOG) and model-ready species
(e.g., PAR, OLE, etc)7. SMOKE essentially calculates the model-ready species by using the appropriate
emission factor without further speciation8. 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 3-6. An additional run of the Speciation Tool was necessary to create the
M-profiles that were then loaded into the MOVES default database. Fourth, for California, the EPA
applied adjustment factors to SMOKE-MOVES to produce California adjusted model-ready files. By
applying the ratios through SMOKE-MOVES, the CARB inventories are essentially speciated to match
EPA estimated speciation. This resulted in changes to the VOC HAPs from what CARB submitted to the
EPA. Finally, MOVES speciation used the CAMx version of CB6 which does not split out naphthalene.
Table 3-6. 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
7 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.
8 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/.
29
-------
MOVES ID
Pollutant Name
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 3-6. Outside of California, the integration calculations are performed within MOVES. For
California, integration calculations are handled by SMOKE. The CARB-based nonroad inventory
includes VOC HAP estimates for all sources, so every source in California was integrated as well. Some
sources in the original CARB inventory had lower VOC emissions compared to sum of all VOC HAPs.
For those sources, VOC was augmented to be equal to the VOC HAP sum, ensuring that every source in
California could be integrated. The CARB-based nonroad data includes exhaust and evaporative mode-
specific data for VOC, but, does not contain refueling.
MOVES-MEXICO for onroad used the same speciation approach as for the U.S. in that the larger list of
species shown in Table 3-6 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. BEIS3.61
includes the species (SESQ) that is mapped to the model species SESQT. The profile code associated
with BEIS3.61 for use with CB05 is "B10C5," while the profile for use with CB6 is "B10C6." The main
difference between the profiles is the explicit treatment of acetone emissions in B10C6.
3.2.1.3 Oil and gas related speciation profiles
Most of the recently added VOC profiles from SPECIATE4.5 (listed in Appendix B) are in 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
30
-------
pt oilgas sectors, several counties were assigned to newly available basin or area-specific profiles in
SPECIATE4.5 that account for measured or modeled from measured compositions specific a particular
region of the country. In the 2011 platform, the only county-specific profiles were for the WRAP, but in
the 2014 and 2016 platforms, several new profiles were added for other parts of the country. In addition,
some of the WRAP profiles were revised to correct for errors such as mole fractions being used for mass
fractions and VOCtoTOG factors or replaced with newer data. All WRAP profile codes were renamed to
include an "_R" to distinguish between the previous set of profiles (even those that did not change). For
the Uintah basin and Denver-Julesburg Basin, Colorado, more updated profiles were used instead of the
WRAP Phase III profiles. Table 3-7 lists the region-specific profiles assigned to particular counties or
groups of counties. Although this platform increases the use of regional profiles, many counties still rely
on the national profiles.
In addition to region-specific assignments, multiple profiles were assigned to particular county/SCC
combinations using the SMOKE feature discussed in 3.2.1.1. Oil and gas SCCs for associated gas,
condensate tanks, crude oil tanks, dehydrators, liquids unloading and well completions represent the total
VOC from the process, including the portions of process that may be flared or directed to a reboiler. For
example, SCC 2310021400 (gas well dehydrators) consists of process, reboiler, and/or flaring
emissions. There are not separate SCCs for the flared portion of the process or the reboiler. However, the
VOC associated with these three portions can have very different speciation profiles. Therefore, it is
necessary to have an estimate of the amount of VOC from each of the portions (process, flare, reboiler) so
that the appropriate speciation profiles can be applied to each portion. The Nonpoint Oil and Gas
Emission Estimation Tool generates an intermediate file which file provides flare, non-flare (process), and
reboiler (for dehydrators) emissions for six source categories that have flare emissions: by county FIPS
and SCC code for the U.S. From these emissions we can compute the fraction of the emissions to assign
to each profile. These fractions can vary by county FIPS, because they depend on the level of controls
which is an input to the Speciation Tool.
Table 3-7. 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
31
-------
Profile
Code
Description
Region (if not in
the profile name)
UNT01 R
Uinta Basin Produced Gas Composition from CBM Wells
WRBCO R
Wind River Basin Produced Gagres Composition from Non-CBM
Gas Wells
95087a
Oil and Gas - Composite - Oil Field - Oil Tank Battery Vent Gas
East Texas
95109a
Oil and Gas - Composite - Oil Field - Condensate Tank Battery
Vent Gas
East Texas
95417
Uinta Basin, Untreated Natural Gas
95418
Uinta Basin, Condensate Tank Natural Gas
95419
Uinta Basin, Oil Tank Natural Gas
95420
Uinta Basin, Glycol Dehydrator
95398
Composite Profile - Oil and Natural Gas Production - Condensate
Tanks
Denver-Julesburg
Basin
95399
Composite Profile - Oil Field - Wells
State of California
95400
Composite Profile - Oil Field - Tanks
State of California
95403
Composite Profile - Gas Wells
San Joaquin Basin
3.2.1.4 Mobile source related VOC speciation profiles
The VOC speciation approach for mobile source and mobile source-related source categories is
customized to account for the impact of fuels and engine type and technologies. The impact of fuels also
affects the parts of the nonpt and ptnonipm sectors that are related to mobile sources such as portable fuel
containers and gasoline distribution.
The VOC speciation profiles for the nonroad sector other than for California are listed in Table 3-8. 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 3-9.
Table 3-8. TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a used for the
2016 Platform
Profile
Profile Description
Engine
Type
Engine
Technology
Engine
Size
Horse-
power
category
Fuel
Fuel
Sub-
type
Emission
Process
95327
SI 2-stroke E0
SI 2-stroke
all
All
all
Gasoline
E0
exhaust
95328
SI 2-stroke E10
SI 2-stroke
all
All
all
Gasoline
E10
exhaust
95329
SI 4-stroke E0
SI 4-stroke
all
All
all
Gasoline
E0
exhaust
95330
SI 4-stroke E10
SI 4-stroke
all
All
all
Gasoline
E10
exhaust
95331
CI Pre-Tier 1
CI
Pre-Tier 1
All
all
Diesel
all
exhaust
95332
CI Tier 1
CI
Tier 1
All
all
Diesel
all
exhaust
95333
CI Tier 2
CI
Tier 2 and 3
all
all
Diesel
all
exhaust
95333
CI Tier 2
CI
Tier 4
<56 kW
(75 hp)
S
Diesel
all
exhaust
8775
ACES Phase 1 Diesel
Onroad
CI Tier 4
Tier 4
>=56 kW
(75 hp)
L
Diesel
all
exhaust
32
-------
Profile
Profile Description
Engine
Type
Engine
Technology
Engine
Size
Horse-
power
category
Fuel
Fuel
Sub-
type
Emission
Process
8753
E0 Evap
SI
all
all
all
Gasoline
E0
evaporative
8754
E10 Evap
SI
all
all
all
Gasoline
E10
evaporative
8766
E0 evap permeation
SI
all
all
all
Gasoline
E0
permeation
8769
E10 evap permeation
SI
all
all
all
Gasoline
E10
permeation
8869
E0 Headspace
SI
all
all
all
Gasoline
E0
headspace
8870
E10 Headspace
SI
all
all
all
Gasoline
E10
headspace
1001
CNG Exhaust
All
all
all
all
CNG
all
exhaust
8860
LPG exhaust
All
all
all
all
LPG
all
exhaust
Speciation profiles for VOC in the nonroad sector account for the ethanol content of fuels across years. A
description of the actual fuel formulations for 2014 can be found in the 2014NEIv2 TSD. For previous
platforms, the EPA used "COMBO" profiles to model combinations of profiles for E0 and E10 fuel use,
but beginning with 2014v7.0 platform, the appropriate allocation of E0 and E10 fuels is done by MOVES.
Combination profiles reflecting a combination of E10 and E0 fuel use are still used for sources upstream
of mobile sources such as portable fuel containers (PFCs) and other fuel distribution operations associated
with the transfer of fuel from bulk terminals to pumps (BTP) which are in the nonpt sector. They are also
used for California nonroad sources. For these sources, ethanol may be mixed into the fuels, in which
case speciation would change across years. The speciation changes from fuels in the ptnonipm sector
include BTP distribution operations inventoried as point sources. Refinery-to-bulk terminal (RBT) fuel
distribution and bulk plant storage (BPS) speciation does not change across the modeling cases because
this is considered upstream from the introduction of ethanol into the fuel. The mapping of fuel
distribution SCCs to PFC, BTP, BPS, and RBT emissions categories can be found in Appendix C.
Table 3-9 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for
2016. The term "COMBO" indicates that a combination of the profiles listed was used to speciate that
subcategory using the GSPRO COMBO file.
Table 3-9. Select mobile-related VOC profiles 2016
Sector
Sub-category
2014
Nonroad- California & non US
gasoline exhaust
COMBO
8750a Pre-Tier 2 E0 exhaust
8751a Pre-Tier 2 E10 exhaust
Nonroad-California
gasoline evaporative
COMBO
8753 E0 evap
8754 E10 evap
Nonroad-California
gasoline refueling
COMBO
8869 E0 Headspace
8870 E10 Headspace
Nonroad-California
diesel exhaust
8774 Pre-2007 MY HDD exhaust
Nonroad-California
diesel evap-
orative and diesel refueling
4547 Diesel Headspace
PFC and BTP
COMBO
33
-------
Sector
Sub-category
2014
nonpt/
ptnonipm
8869
8870
E0 Headspace
E10 Headspace
nonpt/
Bulk plant storage (BPS)
and refine-to-bulk terminal
ptnonipm
(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 3-10 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 3-11 through Table 3-13 describe the meaning of these MOVES codes. For a
specific representative county and future year, there will be a different mix of these profiles. For
example, for HD diesel exhaust, the emissions will use a combination of profiles 8774M and 8775M
depending on the proportion of HD vehicles that are pre-2007 model years (MY) in that particular county.
As that county is projected farther into the future, the proportion of pre-2007 MY vehicles will decrease.
A second example, for gasoline exhaust (not including E-85), the emissions will use a combination of
profiles 8756M, 8757M, 8758M, 8750aM, and 875laM. Each representative county has a different mix
of these key properties and, therefore, has a unique combination of the specific M-profiles. More detailed
information on how MOVES speciates VOC and the profiles used is provided in the technical document,
"Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles in
MOVES2014" (EPA, 2015c).
Table 3-10. Onroad M-profiles
Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
1001M
CNG Exhaust
1940-2050
1,2,15,16
30
48
4547M
Diesel Headspace
1940-2050
11
20,21,22
0
4547M
Diesel Headspace
1940-2050
12,13,18,19
20,21,22
10,20,30,40,41,
42,46,47,48
8753M
E0 Evap
1940-2050
12,13,19
10
10,20,30,40,41,42,
46,47,48
8754M
E10 Evap
1940-2050
12,13,19
12,13,14
10,20,30,40,41,
42,46,47,48
8756M
Tier 2 E0 Exhaust
2001-2050
1,2,15,16
10
20,30
8757M
Tier 2 E10 Exhaust
2001-2050
1,2,15,16
12,13,14
20,30
8758M
Tier 2 E15 Exhaust
1940-2050
1,2,15,16
15,18
10,20,30,40,41,
42,46,47,48
8766M
E0 evap permeation
1940-2050
11
10
0
8769M
E10 evap permeation
1940-2050
11
12,13,14
0
8770M
El5 evap permeation
1940-2050
11
15,18
0
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
919
20, 21, 22
46,47
8774M
Pre-2007 MY HDD
exhaust
1940-2006
1,2,15,16
20, 21, 22
20,30
9 91 is the processed for APUs which are diesel engines not covered by the 2007 Heavy-Duty Rule, so the older technology
applieds to all years.
34
-------
Profile
Profile Description
Model Years
ProcessID
FuelSubTypelD
RegClassID
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, 1810
10,40,41,42,46,47,48
Table 3-11. 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
10 The profile assingments for pre-2001 gasoline vehicles fueled on E15/E20 fuels (subtypes 15 and 18) were corrected for
MOVES2014a. This model year range, process, fuelsubtype regclass combinate is already assigned to profile 8758.
35
-------
40
Nonroad
90
Extended Idle Exhaust
91
Auxiliary Power Exhaust
Table 3-12. MOVES Fuel subtype IDs
Fuel Subtype ID
Fuel Subtype Descriptions
10
Conventional Gasoline
11
Reformulated Gasoline (RFG)
12
Gasohol (E10)
13
Gasohol (E8)
14
Gasohol (E5)
15
Gasohol (El5)
18
Ethanol (E20)
20
Conventional Diesel Fuel
21
Biodiesel (BD20)
22
Fischer-Tropsch Diesel (FTD100)
30
Compressed Natural Gas (CNG)
50
Ethanol
51
Ethanol (E85)
52
Ethanol (E70)
Table 3-13. 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.
36
-------
3.2.2 PM speciation
In addition to VOC profiles, the SPECIATE database also contains profiles for speciating PM2.5. PM2.5
was speciated into the AE6 species associated with CMAQ 5.0.1 and later versions. Of particular note for
the 2016v7.2 beta and regional haze platforms, the nonroad PM2.5 speciation was updated as discussed
later in this section. Most of the PM profiles come from the 911XX series (Reff et. al, 2009), which
include updated AE6 speciation11. 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 as of the
2014v7.1 platform with the one that we had been using in the 2014v7.0 and earlier platforms.
Figure 3-3. Profiles composited for the new PM gas combustion related sources
Zinc
S u If ate
Silicon ~
Potassium
Particulate Non-Carbon Organic Matter
Other Unspeciated PM2.5
Organic carbon -
Nitrate SS
Nickel
Metal-bound Oxygen —
Iron T"-
Elemental Carbon
Copper
Chloride ion
Calcium T
Bromine Atom
Ammonium
Aluminum
0 10 20 30 40 SO 60
Weight Percent
¦ Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
Gas-fired process heater exhaust 95126a
¦ Gas-fired internal combustion combined cycte/cogeneratson plant exhaust 95127a
Gas fired boiter exhaust 95125a
11 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.
37
-------
Figure 3-4. Comparison of PM profiles used for Natural gas combustion related sources
Zinc
Sulfate
Silicon
Potassium
Particulate Non-Carbon Organic Matter
Other Unspeciated PM2.5
Organic carbon
Nitrate
Nickel
Metal-bound Oxygen
Iron
Elemental Carbon
Copper
Chloride ion
Calcium
Bromine Atom
Ammonium
Aluminum
20 30 40
Weight Percent
2 Composite -Refinery Fuel Gas and Natural Gas Combustion 95475
Natural Gas Combustion - Composite 91112
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 speciation12. 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 3-14 shows the differences in the v7.1 and v6.3
profiles.
12 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/.
38
-------
Table 3-14. 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
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. 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 partially done within MOVES such that it does not need to be run for a
specific chemical mechanism. For nonroad, MOVES outputs emissions of PM2.5 split by speciation
profile. Similar to how VOC and NONHAPTOG are speciated, PM2.5 is now also speciated this way
starting with MOVES2014b. California nonroad emissions, which are not from MOVES, continue to be
speciated the traditional way with speciation profiles assigned by SMOKE using the GSREF cross-
reference. The PM2.5 profiles assigned to nonroad sources are listed in Table 3-15.
39
-------
Table 3-15. Nonroad PM2.5 profiles
SPECIATE4.5
Profile Code
SPECIATE4.5 Profile Name
Assigned to Nonroad
sources based on Fuel
Type
8996
Diesel Exhaust - Heavy-heavy duty truck - 2007
model year with NCOM
Diesel
91106
HDDV Exhaust - Composite
Diesel
91113
Nonroad Gasoline Exhaust - Composite
Gasoline
91156
Residential Natural Gas Combustion
CNG and LPG
(California only)
95219
CNG Transit Bus Exhaust
CNG and LPG
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 3-16 gives the split factor for these two profiles.
The onroad sector does not use the "HONO" profile to speciate NOx. MOVES2014 produces speciated
NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables
used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOx. The NO
fraction varies by heavy duty versus light duty, fuel type, and model year.
The NO2 fraction = 1 - NO - HONO. For more details on the NOx fractions within MOVES, see
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockev=P100F lA5.pdf.
Table 3-16. 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.
40
-------
Sulfate is computed from SO2 assuming that gaseous sulfate, which is comprised of many components, is
primarily H2SO4. The equation for calculating FhSO/jis given below.
Emissions of SULF (as H2S04)
fraction of S emitted as sulfate MW H2S04
= SO2 emissions x — ; — ; - ——— x —.,..r
fraction of S emitted as SO2 MW SO2
In the above, MW is the molecular weight of the compound. The molecular weights of H2SO4 and SO2
are 98 g/mol and 64 g/mol, respectively.
This method does not reduce SO2 emissions; it solely adds gaseous sulfate emissions as a function of S02
emissions. The derivation of the profiles is provided in Table 3-17; a summary of the profiles is provided
in Table 3-18.
Table 3-17. 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 3-18. 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
41
-------
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 3-19 summarizes the temporal aspects of emissions modeling by
comparing the key approaches used for temporal processing across the sectors. In the table, "Daily
temporal approach" refers to the temporal approach for getting daily emissions from the inventory using
the SMOKE Temporal program. The values given are the values of the SMOKE L TYPE setting. The
"Merge processing approach" refers to the days used to represent other days in the month for the merge
step. If this is not "all," then the SMOKE merge step runs only for representative days, which could
include holidays as indicated by the right-most column. The values given are those used for the SMOKE
M TYPE setting (see below for more information).
Table 3-19. Temporal settings used for the platform sectors in SMOKE
Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process holidays
as separate days
afdust adj
Annual
Yes
week
All
Yes
ag
Monthly
No
all
All
No
beis
Hourly
No
n/a
All
No
cmv clc2
Annual
Yes
aveday
aveday
No
cmv c3
Annual
Yes
aveday
aveday
No
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
No
mwdss
mwdss
Yes
np oilgas
Annual
Yes
week
week
Yes
onroad
Annual & monthly1
No
all
all
Yes
onroad ca adj
Annual & monthly1
No
all
all
Yes
othafdust adj
Annual
Yes
week
all
No
othar
Annual & monthly
Yes
week
week
No
onroad can
Monthly
No
week
week
No
onroad mex
Monthly
No
week
week
No
othpt
Annual & monthly
Yes
mwdss
mwdss
No
othptdust adj
Monthly
No
week
all
No
pt oilgas
Annual
Yes
mwdss
mwdss
Yes
ptegu
Annual & hourly
Yes2
all
all
No
ptnonipm
Annual
Yes
mwdss
mwdss
Yes
ptagfire
Daily
No
all
all
No
ptfire
Daily
No
all
all
No
42
-------
Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process holidays
as separate days
ptfire othna
Daily
No
all
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No3
met-based3
all
No3
'Note the annual and monthly "inventory" actually refers to the activity data (VMT, hoteling and VPOP) for onroad.
VMT and hoteling is monthly and VPOP is annual. The actual emissions are computed on an hourly basis.
2Only units that do not have matching hourly CEMS data use monthly temporal profiles.
3Except for 2 SCCs that do not use met-based speciation
The following values are used in the table. The value "all" means that hourly emissions are computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the
month. The value "mwdss" means hourly emissions for one representative Monday, representative
weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each month.
This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within
the month, but not week-to-week variation within the month. The value "aveday" means hourly
emissions computed for one representative day of each month, meaning emissions for all days within a
month are the same. Special situations with respect to temporal allocation are described in the following
subsections.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 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.
43
-------
3.3.2 Electric Generating Utility temporal allocation (ptegu)
3.3.2.1 Base year temporal allocation of EGUs
The 2016 annual EGU emissions not matched to CEMS sources use region/fuel specific profiles based on
average hourly emissions for the region and fuel. Peaking units were removed during the averaging to
minimize the spikes generated by those units. The non-matched units are allocated to hourly emissions
using the following three-step methodology: annual value to month, month to day, and day to hour. First,
the CEMS data were processed using a tool that reviewed the data quality flags that indicate the data were
not measured. Unmeasured data can be filled in with maximum values and thereby cause erroneously
high values in the CEMS data. The CEMCorrect tool identifies hours for which the data were not
measured. When those values are found to be more than three times the annual mean for that unit, the
data for those hours are replaced with annual mean values (Adelman et al., 2012). These adjusted CEMS
data were then used for the remainder of the temporal allocation process described below (see Figure 3-5
for an example). Winter and summer seasons are included in the development of the diurnal profiles as
opposed to using data for the entire year because analysis of the hourly CEMS data revealed that there
were different diurnal patterns in winter versus summer in many areas. Typically, a single mid-day peak
is visible in the summer, while there are morning and evening peaks in the winter as shown in Figure 3-6.
The temporal allocation procedure is differentiated by whether or not the source could be directly
matched to a CEMS unit via ORIS facility code and boiler ID. Note that for units matched to CEMS data,
annual totals of their emissions input to CMAQ may be different than the annual values in 2016 because
the CEMS data replaces the NOx and SO2 inventory data for the seasons in which the CEMS are
operating. If a CEMS-matched unit is determined to be a partial year reporter, as can happen for sources
that run CEMS only in the summer, emissions totaling the difference between the annual emissions and
the total CEMS emissions are allocated to the non-summer months.
Figure 3-5. Eliminating unmeasured spikes in CEMS data
400
300
&_
3
o
200
LO
£2
100
0
2014 CEM 2398 1101 Month 1
n
n
I
Ik
.1
j^i j
-1
101 201 301 401 501 601 701
Hour
— Raw CEM — Corrected
44
-------
Figure 3-6. Seasonal diurnal profiles for EGU emissions in a Virginia Region
Diurnal CEMS Profile for PJM Dom Gas
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Winter Summer Annual
For sources not matched to CEMS units, temporal profiles are calculated that are used by SMOKE to
allocate the annual emissions to hourly values. For these units, the allocation of the inventory annual
emissions to months is done using average fuel-specific annual-to-month factors generated for regions
with similar climate. These factors are based on 2016 CEMS data only. In each region, separate factors
were developed for the fuels: coal, natural gas, and "other," where the types of fuels included in "other"
vary by region. Separate profiles were computed for NOx, SO2, and heat input. An overall composite
profile was also computed and used when there were no CEMS units with the specified fuel in the region
containing the unit. For both CEMS-matched units and units not matched to CEMS, NOx and SO2 CEMS
data are used to allocate NOx and SO2 emissions to monthly emissions, respectively, while heat input data
are used to allocate emissions of all pollutants from monthly to daily emissions.
Daily temporal allocation of units matched to CEMS was performed using a procedure similar to the
approach to allocate emissions to months in that the CEMS data replaces the inventory data for each
pollutant. For units without CEMS data, emissions were allocated from month to day using IPM-region
and fuel-specific average month-to-day factors based on the 2016 CEMS heat data. Separate month-to-
day allocation factors were computed for each month of the year using heat input for the fuels coal,
natural gas, and "other" in each region. For CEMS matched units, NOx and SO2 CEMS data are used to
replace inventory NOx and SO2 emissions, while CEMS heat input data are used to allocate all other
pollutants.
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
u.uo
0.055
0.05
£
O
£ 0.045
I—
J-
c 0.04
I—
3
21 0.035
0.03
n m c
45
-------
data. SMOKE then allocates the daily emissions data to hours using the temporal profiles obtained from
the CEMS data for the analysis base year (i.e., 2016 in this case).
Certain sources without CEMS data, such as specific municipal waste combustors (MWCs) and
cogeneration facilities (cogens), were assigned a flat temporal profile by source. The emissions for these
sources have an equal value for each hour of the year.
For additional information on EGU temporal allocation, please see the Point-EGU-IPM specification
sheet provided with the 2016 beta platform.
3.3.3 Airport Temporal allocation (ptnonipm)
Airport temporal profiles were updated in 2014v7.0 and were kept the same for 2014v7.1 and 2016 alpha
platform. All airport SCCs (i.e., 2275*, 2265008005, 2267008005, 2268008005 and 2270008005) were
given the same hourly, weekly and monthly profile for all airports other than Alaska seaplanes (which are
not in the CMAQ modeling domain). Hourly airport operations data were obtained from the Aviation
System Performance Metrics (ASPM) Airport Analysis website
(https://aspm.faa.gov/apm/sys/AnalysisAP.asp). A report of 2014 hourly Departures and Arrivals for
Metric Computation was generated. An overview of the ASPM metrics is at
http://aspmhelp.faa.gov/index.php/Aviation Performance Metrics %28APM%29. Figure 3-7 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 3-7, Figure 3-8, and Figure
3-9. 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 3-10. These were assigned based on the facility ID.
Figure 3-7. 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
46
-------
Figure 3-8. Weekly profile for all Airport SCCs
Weekly Airport Profile
0.18
Figure 3-9. Monthly Profile for all Airport SCCs
Monthly Airport: Profile
Jan Feb Mar Apt May Jun Jul Aug Sep Oct Nov Dec
47
-------
Figure 3-10. 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.Org/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
48
-------
states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas.
Figure 3-11 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 3-11. Example of RWC temporal allocation in 2007 using a 50 versus 60 °F threshold
RWC temporal profile, Duval County, FL, Jan - Apr
0.04
0.035
0.03
| 0.025
i °-02
aj 0.015
0.01
0.005
0
The diurnal profile for used for most RWC sources (see Figure 3-12) places more of the RWC emissions
in the morning and the evening when people are typically using these sources. This profile is based on a
2004 MANE-VU survey based temporal profiles
(http://www.marama.org/publications folder/ResWoodCombustion/Final report.pdf). This profile was
created by averaging three indoor and three RWC outdoor temporal profiles from counties in Delaware
and aggregating them into a single RWC diurnal profile. This new profile was compared to a
concentration-based analysis of aethalometer measurements in Rochester, New York (Wang et al. 2011)
for various seasons and days of the week and was found that the new RWC profile generally tracked the
concentration based temporal patterns.
- 60F, clternate formula
-50F, default formula
49
-------
Figure 3-12. RWC diurnal temporal profile
Comparison of RWC diurnal profile
0.12
0.1
0.08
0.06
Q_
0.04
0.02
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
The temporal allocation for "Outdoor Hydronic Heaters" (i.e., "OHH," SCC=2104008610) and "Outdoor
wood burning device, NEC (fire-pits, chimneas, etc.)" (i.e., "recreational RWC," SCC=21040087000) is
not based on temperature data, because the meteorologically-based temporal allocation used for the rest of
the rwc sector did not agree with observations for how these appliances are used.
For OHH, the annual-to-month, day-of-week and diurnal profiles were modified based on information in
the New York State Energy Research and Development Authority's (NYSERDA) "Environmental,
Energy Market, and Health Characterization of Wood-Fired Hydronic Heater Technologies, Final Report"
(NYSERDA, 2012), as well as a Northeast States for Coordinated Air Use Management (NESCAUM)
report "Assessment of Outdoor Wood-fired Boilers" (NESCAUM, 2006). A Minnesota 2008 Residential
Fuelwood Assessment Survey of individual household responses (MDNR, 2008) provided additional
annual-to-month, day-of-week and diurnal activity information for OHH as well as recreational RWC
usage.
Data used to create the diurnal profile for OHH, shown in Figure 3-13, are based on a conventional single-
stage heat load unit burning red oak in Syracuse, New York. As shown in Figure 3-14, 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 3-15. 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.
50
-------
Figure 3-13. Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr)
Heat Load (BTU/hr)
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
nx ^ tfr a
-------
Figure 3-15. Annual-to-month temporal profiles for OHH and recreational RWC
Monthly Temporal Activity for OHH & Recreational RWC
¦Fire Pit/Chimenea
Outdoor Hydronic Heater
JAN FEB MARAPRMAYJUN JUL AUG SEP OCT NOV DEC
3.3.5 Agricultural Ammonia Temporal Profiles (ag)
For the agricultural livestock NFb algorithm, the GenTPRO algorithm is based on an equation derived by
Jesse Bash of the EPA's ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation is
based on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to
estimate diurnal NFb emission variations from livestock as a function of ambient temperature,
aerodynamic resistance, and wind speed. The equations are:
EUi = [161500/T,/; x e(~1380/T,/,)] x AR,/;
PE;,/; = Ea, / Sum(E, /,)
where
• PE;,/; = Percentage of emissions in county i on hour h
• Eij, = Emission rate in county i on hour h
• Tij, = Ambient temperature (Kelvin) in county i on hour h
• Vi,/; = Wind speed (meter/sec) in county i (minimum wind speed is 0.1 meter/sec)
• AR;,/; = Aerodynamic resistance in county i
GenTPRO was run using the "BASH NH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized
to the month. Figure 3-16 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.
52
-------
Figure 3-16. 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
on
g 800
* 600
400
200
0
rn
X
j
Jl
A
j
,
iji
^ /u
LAt
-hF
-i
i J
i/WV
11Y
V!
jF
i\ aA
AM*
\T
I
w V
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 10/7/201411/7/201412/8/2014
-months
approach
¦ hourly
approach
For the 2016 alpha platform, the GenTPRO approach is applied to all sources in the ag sector, NFb and
non- NFb, livestock and fertilizer. Monthly profiles are based on the daily-based EPA livestock
emissions and are the same as were used in 2014v7.0. Profiles are by state/SCC_category, where
SCC_category is one of the following: beef, broilers, layers, dairy, swine.
3.3.6 Oil and gas temporal allocation (np_oilgas)
Monthly oil and gas temporal profiles by county and SCC were updated to use 2016 activity information
for the beta and regional haze cases. 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 3-19 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
emissions. Because SMOKE-MOVES (for RPD) calculates emissions based on the VMT, speed and
53
-------
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 3-17 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 3-17. Example of temporal variability of NOx emissions
4
3.5
U
O
_c
3
l/l
OJ
2.5
E
c
2
o
=
1.5
1-
1
>
>
0.5
0
7/8/140:00
VMT
NOX
2014v2 onroad RPD hourly NOX and VMT: Wake County, IMC
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)
0
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.
54
-------
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 3-18. 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 3-19 shows which counties have temporal profiles specific to that county, and which counties use
regional average profiles.
Figure 3-18. 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
road 5
Saturday
Fulton Co
Sunday
Fulton Co
passenger
passenger
o.oi o.oi ^
o 0
1 2 3 4 5 6 7 S 9 10 11 12 13 14 15 16 17 18 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
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
55
-------
Figure 3-19. Counties for which MOVES Speeds and Temporal Profiles could be Populated
Group I I Individual
I I Midwest Region Average of Single County MSA Counties
I I Midwest Region non-MSA Average
I I Northeast Region Average of Single County MSA Counties
I I Northeast Region non-MSA Average
I I South Region Average of Single County MSA Counties
I I South Region non-MSA Average
I I West Region Average of Single County MSA Counties
I I West Region non-MSA Average
I Midwest Region Average of Core Counties inside MSAs
I 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
3 South Region Average of non-Core Counties inside MSAs
3 West Region Average of Core Counties inside MSAs
3 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 3-20.
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
vehi cle type, day of the week13, and air basin. These C ARB-specific profiles were used in developing
EPA estimates for California. Although the EPA adjusted the total emissions to match California-
submitted emissions for 2016, the temporal allocation of these emissions took into account both the state-
specific VMT profiles and the SMOKE-MOVES process of incorporating meteorology.
13 California's diurnal profiles varied within the week. Monday. Friday, Saturday, and Sunday had unique profiles and
Tuesday, Wednesday. Thursday had the same profile.
56
-------
Monday
Figure 3-20. Example of Temporal Profiles for Combination Trucks
Fulton Co combo Friday Fulton Co combo
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
Saturday
Fulton Co
combo
Sunday
Fulton Co
combo
5 6 7 8 9 10 11 12 13 14 15 16 17 13 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
3.3.8 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm,
ptfire)
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to
reduce the total emissions based on meteorological conditions. These adjustments are applied through
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and
then subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the
ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are
subject to transport. This methodology is discussed in (Pouliot et al., 2010,
http://www3.epa.gov/ttn/chief/conference/eil9/session9/pouliot pres.pdf). and in "Fugitive Dust
Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). The precipitation adjustment is
applied to remove all emissions for hours where measurable rain occurs, or where there is snow cover.
Therefore, the afdust emissions vary day-to-day based on the precipitation and/or snow cover for each
grid cell and hour. Both the transport fraction and meteorological adjustments are based on the gridded
resolution of the platform; therefore, somewhat different emissions will result from different grid
resolutions. For this reason, to ensure consistency between grid resolutions, afdust emissions for the
36US3 grid are aggregated from the 12US1 emissions. Application of the transport fraction and
meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid modeling as
compared to ambient samples.
57
-------
Biogenic emissions in the beis sector vary by every day of the year because they are developed using
meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions are
computed using appropriate emission factors according to the vegetation in each model grid cell, while
taking the meteorological data into account.
For the cmv sectors, emissions are allocated with flat day of week and flat hourly profiles. Updated
monthly profiles were developed for the LADCO states using link-level NOx emissions for ship traffic
provided by LADCO. These data were based on activities reported by ship AIS (transponder) devices.
Monthly NOx emissions were normalized to create temporal profiles for each lake. For the port SCCs, an
in-port profile was developed as the average of the maneuvering and hoteling emissions. The cruising
emissions were used for the underway SCCs. As some of the lakes did not include complete data for the
in-port sources (Ontario, Canada, St. Claire), a hybrid profile was created as an average of the in-port
NOx emissions for Lakes Michigan, Huron, Superior, and Erie. A resulting 22 profiles were developed
and applied to CI, C2 and C3 ships based county and SCC (i.e., port versus underway). Only new
monthly profiles were developed from these data because the weekly and diurnal variation were deemed
to be comparable to the existing EPA profiles. For non-LADCO areas, CI and C2 monthly profiles are
flat and C3 monthly profiles are highest (but not significantly different from the rest of the year) in the
summer.
For the rail sector, new monthly profiles were developed for the 2016 platform. Monthly temporal
allocation for rail freight emissions is based on AAR Rail Traffic Data, Total Carloads and Intermodal, for
2016. For passenger trains, monthly temporal allocation is flat for all months. Rail passenger miles data
is available by month for 2016 but it is not known how closely rail emissions track with passenger activity
since passenger trains run on a fixed schedule regardless of how many passengers are aboard, and so a flat
profile is chosen for passenger trains. Rail emissions are allocated with flat day of week profiles, and
most emissions are allocated with flat hourly profiles.
For the ptagfire sector, the inventories are in the daily point fire format FF10 PTDAY. The diurnal
temporal profile for ag fires reflects the fact that burning occurs during the daylight hours - see Figure
3-21 (McCarty et al., 2009). This puts most of the emissions during the work day and suppresses the
emissions during the middle of the night.
58
-------
Figure 3-21. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
0.18
0.16
0.14
New McCarty Profile
OLD EPA
0.12
c
o
o.i
u
re
| 0.08
0.06
0.04
0.02
12345678 9 10111213141516171819 202122 23 24
Industrial processes that are not likely to shut down on Sundays, such as those at cement plants, use
profiles that include emissions on Sundays, while those that would shut down on Sundays use profiles that
reflect Sunday shutdowns.
For the ptfire sectors, the inventories are in the daily point fire format FF10 PTDAY. Separate hourly
profiles for prescribed and wildfires were used. Figure 3-22 below shows the profiles used for each state
for the 2014v7.0 and 2014v7.1 modeling platforms. They are similar but not the same and vary according
to the average meteorological conditions in each state. The 2016 alpha platform uses the ptfire diurnal
profiles form 2014v7.1 platform.
Figure 3-22. 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
59
-------
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 national 36-km and 12-km domains. To accomplish this, SMOKE used
national 36-km and 12-km spatial surrogates and a SMOKE area-to-point data file. For the U.S., the EPA
updated surrogates to use circa 2014 data wherever possible. For Mexico, updated spatial surrogates were
used as described below. For Canada, updated surrogates were provided by Environment Canada for the
2016v7.2 platform. The U.S., Mexican, and Canadian 36-km and 12-km surrogates cover the entire
CONUS domain 12US1 shown in Figure 3-1. The 36US3 domain includes a portion of Alaska, and since
Alaska emissions are typically not included in air quality modeling, special considerations are taken to
include Alaska emissions in 36-km modeling.
Documentation of the origin of the spatial surrogates for the platform is provided in the workbook
US_SpatialSurrogate_Workbook_v07172018 which is available with the reports for the 2014v7.1
platform. The remainder of this subsection summarizes the data used for the spatial surrogates and the
area-to-point data which is used for airport refueling.
3.4.1 Spatial Surrogates for U.S. emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 36-km and 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-
to-point approach overrides the use of surrogates for an airport refueling sources. Table 3-20 lists the
codes and descriptions of the surrogates. Surrogate names and codes listed in italics are not directly
assigned to any sources for the 2016 alpha platform, but they are sometimes used to gapfill other
surrogates, or as an input for merging two surrogates to create a new surrogate that is used.
Many surrogates were updated or newly developed for use in the 2014v7.0 platform (Adelman, 2016).
They include the use of the 2011 National Land Cover Database (the previous platform used 2006) and
development of various development density levels such as open, low, medium high and various
combinations of these. These landuse surrogates largely replaced the FEMA category surrogates that
were used in the 2011 platform. Additionally, onroad surrogates were developed using average annual
daily traffic counts from the highway monitoring performance system (HPMS). Previously, the "activity"
for the onroad surrogates was length of road miles. This and other surrogates are described in a reference
(Adelman, 2016).
Several surrogates were updated or developed as new surrogates for the 2016v7.1 (aka alpha) platform:
cl/c2 ships at ports uses a surrogate based on 2014 NEI ports activity data based on use of the
2014NEIvl (surrogate 820); previously, just the port shapes (801) were used.
cl/c2 ships underway uses a 2013-shipping density surrogate (surrogate 808); previously Offshore
Shipping NEI2014 Activity (806) was used.
Oil and gas surrogates were updated to correct errors found after they were used for 2014v7.0;
60
-------
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.
In addition, spatial surrogates 201 through 244, which concern road miles, annual average daily traffic
(AADT), and truck stops, were further updated for the 2016 beta and regional haze platforms. 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 3-20. U.S. Surrogates available for the 2016 alpha and beta modeling platforms
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
Hea\>v 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
150
Residential Heating - Natural Gas
527
Single Familv Residential
160
Residential Heating - H ood
535
Residential + Commercial + Industrial +
Institutional + Government
170
Residential Heating - Distillate Oil
540
Retail Trade (COM1)
180
Residential Heating - Coal
545
Personal Repair (COM3)
190
Residential Heating - LP Gas
555
Professional/Technical (COM4) plus General
Government (GOV1)
201
Urban Restricted Road Miles
560
Hospital (COM6)
202
Urban Restricted AADT
575
Light and High Tech Industrial (1ND2 +
IND5)
205
Extended Idle Locations
580
Food Drug Chemical Industrial (LND3)
211
Rural Restricted Road Miles
585
Metals and Minerals Industrial (LND4)
212
Rural Restricted AADT
590
Hea\>v Industrial (LND1)
221
Urban Unrestricted Road Miles
595
Light Industrial (1ND2)
222
Urban Unrestricted AADT
596
Industrial plus Institutional plus Hospitals
231
Rural Unrestricted Road Miles
650
Refineries and Tank Farms
232
Rural Unrestricted AADT
670
Spud Count - CBM Wells
239
Total Road AADT
671
Spud Count - Gas Wells
240
Total Road Miles
672
Gas Production at Oil Wells
241
Total Restricted Road Miles
673
Oil Production at CBM Wells
242
All Restricted AADT
674
Unconventional Well Completion Counts
243
Total Unrestricted Road Miles
676
Well Count - All Producing
244
All Unrestricted AADT
677
Well Count - All Exploratory
258
Intercity Bus Terminals
678
Completions at Gas Wells
259
Transit Bus Terminals
679
Completions at CBM Wells
260
Total Railroad Miles
681
Spud Count - Oil Wells
261
NT AD Total Railroad Density
683
Produced Water at All Wells
61
-------
Code
Surrogate Description
Code
Surrogate Description
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
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
111
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 NE12014 Activity
319
NLCD Crop Land
807
Na\'igable Waterway Miles
320
NLCD Forest Land
808
2013 Shipping Density
321
NLCD Recreational Land
820
Ports NEI2014 Activity
340
NLCD Land
850
Golf Courses
350
NLCD Water
860
Mines
500
Commercial Land
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 3-21. Emissions from the extended (i.e., overnight) idling of trucks were
assigned to surrogate 205, which is based on locations of overnight truck parking spaces. This surrogate's
underlying data were updated for use in the 2016 platforms to include additional data sources and
corrections based on comments received.
Table 3-21. 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
62
-------
For the oil and gas sources in the np oilgas sector, the spatial surrogates were updated to those shown in
Table 3-22 using 2016 data consistent with what was used to develop the 2016 beta nonpoint oil and gas
emissions. The primary activity data source used for the development of the oil and gas spatial
surrogates was data from Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2017). This
database contains well-level location, production, and exploration statistics at the monthly level.
Due to a proprietary agreement with DI Desktop, individual well locations and ancillary
production cannot be made publicly available, but aggregated statistics are allowed. These data were
supplemented with data from state Oil and Gas Commission (OGC) websites (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 2016. In total, over 1.43 million
unique wells were compiled from the above data sources. The wells cover 34 states and 1,158 counties.
(ERG, 2016b). Corrections to these data were made for the 2014v7.1 platform, and carried forward into
the 2016 alpha platform, after errors were discovered in some counties.
Table 3-22. 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
63
-------
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-20 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 3-23 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by
sector assigned to each spatial surrogate.
Table 3-23. Selected 2016 CAP emissions by sector for U.S. Surrogates (short tons in 12US1)
Sector
ID
Description
NH3
NOX
PM2 5
S02
VOC
afdust
240
Total Road Miles
--
--
295,442
--
"
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
2,856,742
--
--
--
186,274
cmv_clc2
808
2013 Shipping Density
297
489,917
12,963
1,736
8,543
cmv_clc2
820
Ports NEI2014 Activity
11
23,996
735
1,386
985
nonpt
100
Population
32,842
0
0
0
1,244,799
nonpt
150
Residential Heating - Natural Gas
47,820
227,295
3,837
1,494
13,757
nonpt
170
Residential Heating - Distillate Oil
1,865
35,187
3,988
56,230
1,245
nonpt
180
Residential Heating - Coal
20
101
53
1,086
111
nonpt
190
Residential Heating - LP Gas
121
34,439
183
762
1,332
nonpt
239
Total Road AADT
0
25
551
0
274,991
nonpt
240
Total Road Miles
0
0
0
0
34,042
nonpt
242
All Restricted AADT
0
0
0
0
5,451
nonpt
244
All Unrestricted AADT
0
0
0
0
95,312
nonpt
271
NT AD Class 12 3 Railroad Density
0
0
0
0
2,252
nonpt
300
NLCD Low Intensity Development
5,198
27,749
104,168
3,725
75,096
nonpt
306
NLCD Med + High
28,101
200,139
240,282
64,743
955,021
nonpt
307
NLCD All Development
25
46,372
126,828
14,199
602,300
nonpt
308
NLCD Low + Med + High
1,134
185,338
16,837
18,989
65,604
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
99,043
nonpt
711
Airport Areas
0
0
0
0
287
nonpt
801
Port Areas
0
0
0
0
8,059
nonroad
261
NT AD Total Railroad Density
3
2,157
222
2
431
nonroad
304
NLCD Open + Low
4
1,836
159
5
2,988
nonroad
305
NLCD Low + Med
95
16,298
3,866
129
116,725
64
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nonroad
306
NLCD Med + High
306
184,311
11,935
426
96,119
nonroad
307
NLCD All Development
107
33,798
16,275
135
178,932
nonroad
308
NLCD Low + Med + High
491
340,485
29,187
510
53,506
nonroad
309
NLCD Open + Low + Med
131
22,947
1,367
178
49,881
nonroad
310
NLCD Total Agriculture
366
347,896
25,991
408
38,673
nonroad
320
NLCD Forest Land
15
6,020
674
15
3,666
nonroad
321
NLCD Recreational Land
83
11,923
6,353
139
243,437
nonroad
350
NLCD Water
184
121,152
6,929
248
365,285
nonroad
850
Golf Courses
13
2,052
119
18
5,704
nonroad
860
Mines
2
2,698
281
3
522
npoilgas
670
Spud Count - CBM Wells
0
0
0
0
113
npoilgas
671
Spud Count - Gas Wells
0
0
0
0
6,768
npoilgas
674
Unconventional Well Completion Counts
12
19,127
731
9
1,284
npoilgas
678
Completions at Gas Wells
0
274
0
6,743
32,577
npoilgas
679
Completions at CBM Wells
0
3
0
80
395
npoilgas
681
Spud Count - Oil Wells
0
0
0
0
16,718
npoilgas
683
Produced Water at All Wells
0
11
0
0
47,204
npoilgas
685
Completions at Oil Wells
0
254
0
763
27,822
npoilgas
687
Feet Drilled at All Wells
0
38,373
1,391
27
2,785
npoilgas
691
Well Counts - CBM Wells
0
32,341
481
12
27,342
npoilgas
692
Spud Count - All Wells
0
8,884
253
99
353
npoilgas
693
Well Count - All Wells
0
0
0
0
159
npoilgas
694
Oil Production at Oil Wells
0
4,165
0
15,385
1,060,803
npoilgas
695
Well Count - Oil Wells
0
143,918
3,099
34
600,255
npoilgas
696
Gas Production at Gas Wells
0
16,562
1,871
166
431,037
npoilgas
698
Well Count - Gas Wells
0
298,879
6,173
248
645,169
npoilgas
699
Gas Production at CBM Wells
0
2,413
312
25
7,612
onroad
205
Extended Idle Locations
499
177,484
2,129
72
32,817
onroad
239
Total Road AADT
0
0
0
0
6,021
onroad
242
All Restricted AADT
35,855
1,316,007
41,161
8,564
205,314
onroad
244
All Unrestricted AADT
64,487
1,929,809
75,033
17,881
517,975
onroad
258
Intercity Bus Terminals
0
141
2
0
31
onroad
259
Transit Bus Terminals
0
82
4
0
180
onroad
304
NLCD Open + Low
0
762
17
1
2,698
onroad
306
NLCD Med + High
0
15,478
283
18
17,706
onroad
307
NLCD All Development
0
584,068
11,221
945
1,142,084
onroad
308
NLCD Low + Med + High
0
41,226
698
64
60,234
rail
261
NT AD Total Railroad Density
15
33,822
1051
16
1626
rail
271
NT AD Class 12 3 Railroad Density
307
523,394
15,063
346
24,365
rwc
300
NLCD Low Intensity Development
15,491
31,432
318,099
7,929
417,395
For 36US3 modeling in the 2016 alpha and beta / regional haze platforms, most U.S. emissions sectors
were processed using 36-km spatial surrogates, and if applicable, 36-km meteorology. Exceptions
include:
65
-------
- For the onroad and onroad ca adj sectors, 36US3 emissions were aggregated from 12US1 by
summing emissions from a 3x3 group of 12-km cells into a single 36-km cell. Differences in 12-
km and 36-km meteorology can introduce differences in onroad emissions, and so this approach
ensures that the 36-km and 12-km onroad emissions are consistent. However, this approach means
that 36US3 onroad does not include emissions in Southeast Alaska; therefore, Alaska onroad
emissions are included in the Canadian onroad sector (onroadcan). The 36US3 onroadcan
emissions, including Canada and Alaska, are spatially allocated using 36-km surrogates and
processed with 36-km meteorology.
Similarly to onroad, because afdust emissions incorporate meteorologically-based adjustments,
afdust adj emissions for 36US3 were aggregated from 12US1 to ensure consistency in emissions
between modeling domains. Again, similarly to onroad, this means 36US3 afdust does not include
emissions in Southeast Alaska; therefore, Alaska afdust emissions are included in the Canadian
dust sector (othafdustadj). The 36US3 othafdustadj emissions, including Canada and Alaska,
are spatially allocated using 36-km surrogates and adjusted with 36-km meteorology.
The ag and rwc sectors are processed using 36-km spatial surrogates, but using temporal profiles
based on 12-km meteorology.
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 Mexico municipio level emissions have been updated in the 2014v7.1
platform and carried forward into the 2016 alpha platform. For the 2016v7.2 platform, a new set of
Canada shapefiles were provided by Environment Canada along with cross references spatially allocate
the year 2015 Canadian emissions. Gridded surrogates were generated using the Surrogate Tool
(previously referenced); Table 3-24 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 Municipal 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 3-25.
Table 3-24. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
100
Population
923
TOTAL INSTITUTIONAL AND
GOVERNEMNT
101
total dwelling
924
Primary Industry
104
capped total dwelling
925
Manufacturing and Assembly
66
-------
Code
Canadian Surrogate Description
Code
Description
106
ALL INDUST
926
Distribtution and Retail (no petroleum)
113
Forestry and logging
927
Commercial Services
200
Urban Primary Road Miles
932
CANRAIL
210
Rural Primary Road Miles
940
PAVED ROADS NEW
211
Oil and Gas Extraction
945
Commercial Marine Vessels
212
Mining except oil and gas
946
Construction and mining
220
Urban Secondary Road Miles
948
Forest
221
Total Mining
951
Wood Consumption Percentage
222
Utilities
955
UNPAVED ROADS AND TRAILS
230
Rural Secondary Road Miles
960
TOTBEEF
233
Total Land Development
970
TOTPOUL
240
capped population
980
TOTSWIN
308
Food manufacturing
990
TOTFERT
321
Wood product manufacturing
996
urban area
323
Printing and related support activities
1251
OFFR TOTFERT
324
Petroleum and coal products manufacturing
1252
OFFR MINES
326
Plastics and rubber products manufacturing
1253
OFFR Other Construction not Urban
327
Non-metallic mineral product manufacturing
1254
OFFR Commercial Services
331
Primary Metal Manufacturing
1255
OFFR Oil Sands Mines
350
Water
1256
OFFR Wood industries CANVEC
412
Petroleum product wholesaler-distributors
1257
OFFR UNPAVED ROADS RURAL
448
clothing and clothing accessories stores
1258
OFFR Utilities
482
Rail transportation
1259
OFFR total dwelling
562
Waste management and remediation services
1260
OFFR water
901
AIRPORT
1261
OFFR ALL INDUST
902
Military LTO
1262
OFFR Oil and Gas Extraction
903
Commercial LTO
1263
OFFR ALLROADS
904
General Aviation LTO
1265
OFFR CANRAIL
921
Commercial Fuel Combustion
9450
Commercial Marine Vessel Ports
Table 3-25. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3)
Sector
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
S02
voc
othafdust
106
CAN ALL INDUST
—
—
5,632
—
—
othafdust
212
CAN Mining except oil and gas
—
—
684
—
—
othafdust
221
CAN Total Mining
—
—
142,940
—
—
othafdust
222
CAN Utilities
—
—
23,640
—
—
othafdust
940
CAN Paved Roads New
—
—
210,336
—
—
othafdust
955
CAN UNPAVED ROADS AND TRAILS
—
—
389,775
—
—
othafdust
960
CAN TOTBEEF
—
—
1,289
—
—
othafdust
970
CAN TOTPOUL
—
—
184
—
—
othafdust
980
CAN TOTSWIN
—
—
792
—
—
othafdust
990
CAN TOTFERT
—
—
321
—
—
othafdust
996
CAN urban area
—
—
617
—
—
othar
11
MEX 2015 Population
164,464
168,447
13,521
1,164
291,178
67
-------
Sector
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
othar
14
MEX Residential Heating - Wood
0
23,842
305,597
3,658
2,101,03
3
othar
16
MEX Residential Heating - Distillate Oil
2
58
1
16
2
othar
20
MEX Residential Heating - LP Gas
0
26,526
838
0
505
othar
22
MEX Total Road Miles
1
1,046
2
7
2,308
othar
24
MEX Total Railroads Miles
0
63,136
1,407
551
2,494
othar
26
MEX Total Agriculture
713,253
399,070
80,458
18,650
33,742
othar
32
MEX Commercial Land
0
457
7,719
0
106,077
othar
34
MEX Industrial Land
8
3,383
4,833
1
563,953
othar
36
MEX Commercial plus Industrial Land
0
0
0
0
272,155
othar
38
MEX Commercial plus Institutional Land
3
6,740
235
3
148
othar
40
MEX Residential (RESl-4)+Commercial+
Industrial+Institutional+Government
0
16
39
0
331,216
othar
42
MEX Personal Repair (COM3)
0
0
0
0
26,261
othar
44
MEX Airports Area
0
13,429
306
1,561
3,766
othar
50
MEX Mobile sources - Border Crossing
5
161
1
3
293
othar
100
CAN Population
761
54
669
15
241
othar
101
CAN total dwelling
0
0
0
0
150,892
othar
104
CAN Capped Total Dwelling
421
37,205
2,766
206
1,952
othar
113
CAN Forestry and logging
185
2,210
11,310
45
6,246
othar
211
CAN Oil and Gas Extraction
0
31
60
22
925
othar
212
CAN Mining except oil and gas
0
0
3,079
0
0
othar
221
CAN Total Mining
0
0
43
0
0
othar
222
CAN Utilities
34
1,858
0
386
22
othar
308
CAN Food manufacturing
0
0
20,185
0
10,324
othar
321
CAN Wood product manufacturing
874
4,822
1,646
383
16,606
othar
323
CAN Printing and related support activities
0
0
0
0
11,770
othar
324
CAN Petroleum and coal products manufacturing
0
1,205
1,542
486
9,304
othar
326
CAN Plastics and rubber products manufacturing
0
0
0
0
23,283
othar
327
CAN Non-metallic mineral product manufacturing
0
0
6,695
0
0
othar
331
CAN Primary Metal Manufacturing
0
158
5,595
30
72
othar
350
CAN Water
0
120
2
0
4
othar
412
CAN Petroleum product wholesaler-distributors
0
0
0
0
45,257
othar
448
CAN clothing and clothing accessories stores
0
0
0
0
149
othar
482
CAN Rail Transportation
2
4,980
106
12
310
othar
562
CAN Waste management and remediation services
271
1,977
2,710
2,528
13,138
othar
901
CAN Airport
0
109
11
0
11
othar
921
CAN Commercial Fuel Combustion
243
23,628
2,333
2,821
1,091
othar
923
CAN TOTAL INSTITUTIONAL AND
GOVERNEMNT
0
0
0
0
14,859
othar
924
CAN Primary Industry
0
0
0
0
40,376
othar
925
CAN Manufacturing and Assembly
0
0
0
0
71,198
othar
926
CAN Distribtution and Retail (no petroleum)
0
0
0
0
7,461
othar
927
CAN Commercial Services
0
0
0
0
32,167
othar
932
CAN CANRAIL
61
132,985
3,107
485
6,567
othar
945
CAN Commercial Marine Vessels
69
53,264
966
549
2,659
68
-------
Sector
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
othar
946
CAN Construction and Mining
0
0
0
0
4,359
othar
951
CAN Wood Consumption Percentage
1,950
21,662
179,087
3,095
253,523
othar
990
CAN TOTFERT
48
4,456
0
9,881
164
othar
1251
CAN OFFR TOTFERT
81
77,166
5,671
58
7,176
othar
1252
CAN OFFR MINES
1
1,004
70
1
138
othar
1253
CAN OFFR Other Construction not Urban
66
53,671
6,096
47
12,159
othar
1254
CAN OFFR Commercial Services
40
17,791
2,552
34
44,338
othar
1255
CAN OFFR Oil Sands Mines
18
9,491
311
10
1,025
othar
1256
CAN OFFR Wood industries CANVEC
9
5,856
476
7
1,318
othar
1257
CAN OFFR Unpaved Roads Rural
32
11,866
1,169
28
49,975
othar
1258
CAN OFFR Utilities
8
5,579
349
7
1,087
othar
1259
CAN OFFR total dwelling
16
5,768
773
14
15,653
othar
1260
CAN OFFR water
15
4,356
451
29
28,411
othar
1261
CAN OFFR ALL INDUST
4
5,770
253
3
1,049
othar
1262
CAN OFFR Oil and Gas Extraction
0
368
29
0
143
othar
1263
CAN OFFR ALLROADS
3
2,418
244
2
582
othar
1265
CAN OFFR CANRAIL
0
85
9
0
15
othar
9450
CAN Commercial Marine Ports
1
5,690
148
473
199
onroad_
can
200
CAN Urban Primary Road Miles
1,619
85,558
2,851
329
8,396
onroad_
can
210
CAN Rural Primary Road Miles
683
51,307
1,673
139
3,807
onroad_
can
220
CAN Urban Secondary Road Miles
3,021
136,582
5,708
690
22,374
onroad_
can
230
CAN Rural Secondary Road Miles
1,769
96,911
3,238
374
10,370
onroad_
can
240
CAN Total Road Miles
43
57,401
1,355
77
103,658
onroad_
mex
11
MEX 2015 Population
0
281,317
1,873
533
291,992
onroad_
mex
22
MEX Total Road Miles
10,321
1,208,461
54,823
25,855
251,931
onroad_
mex
36
MEX Commercial plus Industrial Land
0
7,975
142
29
9,192
3.5 Preparation of Emissions for the CAMx model
3.5.1 Development of CAMx Emissions for Standard CAMx Runs
For this study, we perform air quality modeling with the Comprehensive Air Quality Model with
Extensions (CAMx model). Gridded hourly emissions output by the SMOKE model are output in the
format needed by the CMAQ model, but they cannot be used directly as emissions inputs to the CAMx
model. Instead, CMAQ-ready emissions must be converted to the format required by CAMx. For
"regular" CAMx modeling (i.e., without two-way nesting), the CAMx conversion process consists of the
following:
1) Convert all emissions file formats from the I/O API NetCDF format used by CMAQ to the UAM
format used by CAMx, including the merged, gridded low-level emissions files which include
biogenics
69
-------
2) Shift hourly emissions files from the 25 hour format used by CMAQ to the averaged 24 hour
format used by CAMx
3) Rename and aggregate model species for CAMx
4) Convert 3D wildland and agricultural fire emissions into CAMx point format
5) Merge all inline point source emissions files together for each day, including layered fire
emissions originally from SMOKE
6) Add sea salt aerosol emissions to the converted, gridded low-level emissions files
Conversion of file formats from I/O API to UAM is performed using a program called "cmaq2uam". In
the CAMx conversion process, all SMOKE outputs are passed through this step first. Unlike CMAQ, the
CAMx model does not have an inline biogenics option, and so for the purposes of CAMx modeling,
emissions from SMOKE must include biogenic emissions.
One difference between CMAQ-ready emissions files and CAMx-ready emissions files involves hourly
temporalization. A daily emissions file for CMAQ includes data for 25 hours, where the first hour is 0:00
GMT of a given day, and the last hour is 0:00 GMT of the following day. For the CAMx model, a daily
emissions file must only include data for 24 hours, not 25. Furthermore, to match the hourly configuration
expected by CAMx, each set of consecutive hourly timesteps from CMAQ-ready emissions files must be
averaged. For example, the first hour of a CAMx-ready emissions file will equal the average of the first
two hours from the corresponding CMAQ-ready emissions file, and the last (24th) hour of a CAMx-ready
emissions file will equal the average of the last two hours (24th and 25th) from the corresponding CMAQ-
ready emissions file. This time conversion is incorporated into each step of the CAMx-ready emissions
conversion process.
The CAMx model uses a slightly different version of the CB6 speciation mechanism than does the
CMAQ model. SMOKE prepares emissions files for the CB6 mechanism used by the CMAQ model
("CB6-CMAQ"), and therefore, the emissions must be converted to the CB6 mechanism used by the
CAMx model ("CB6-CAMx") during the CAMx conversion process. In addition to the mechanism
differences, CMAQ and CAMx also occasionally use different species naming conventions. For CAMx
modeling, we also create additional tracer species. A summary of the differences between CMAQ input
species and CAMx input species for CB6 (VOC), AE6 (PM2.5), and other model species, is provided in
Table 3-26. Each step of the CAMx-ready emissions conversion process includes conversion of CMAQ
species to CAMx species using a species mapping table which includes the mappings in Table 3-26.
70
-------
Table 3-26. Emission model species mappings for CMAQ and CAMx
Inventory Pollutant
CMAQ Model Species
CAMx Model Species
Cl2
CL2
CL2
HC1
HCL
HCL
CO
CO
CO
NOx
NO
NO
N02
N02
HONO
HONO
S02
S02
S02
SULF
SULF
nh3
NH3
NH3
NH3 FERT
n/a (not used in CAMx)
voc
ACET
ACET
ALD2
ALD2
ALDX
ALDX
BENZ
BENZ and BNZA (duplicate species)
CH4
CH4
ETH
ETH
ETHA
ETHA
ETHY
ETHY
ETOH
ETOH
FORM
FORM
IOLE
IOLE
ISOP
ISOP and ISP (duplicate species)
KET
KET
MEOH
MEOH
NAPH + XYLMN (sum)
XYL
NVOL
n/a (not used in CAMx)
OLE
OLE
PAR
PAR
PRPA
PRPA
SESQ
SQT
SOAALK
n/a (not used in CAMx)
TERP
TERP and TRP (duplicate species)
TOL
TOL and TOLA (duplicate species)
UNR + NR (sum)
NR
PM10
PMC
CPRM
PM2.5
PEC
PEC
PN03
PN03
POC
POC
PS04
PS04
PAL
PAL
PCA
PCA
PCL
PCL
PFE
PFE
PK
PK
PH20
PH20
PMG
PMG
PMN
PMN
PMOTHR
PMOTHR and FPRM (duplicate species)
PNA
NA
71
-------
Inventory Pollutant
CMAQ Model Species
CAMx Model Species
PNCOM
PNCOM
PNH4
PNH4
PSI
PSI
PTI
PTI
POC + PNCOM (sum)
POA1
PAL + PCA + PFE +
FCRS1
PMG + PK + PMN +
PSI + PTI (sum)
1 The POA species, which is the sum of POC and PNCOM, is passed to the CAMx model in addition to individual species POC
and PNCOM. The FCRS species, which is also a sum of multiple PM species, is passed to CAMx in addition to each of the
individual component species.
One feature which is part of CMAQ and is not part of CAMx involves plume rise for fires. For CMAQ
modeling, we process fire emissions through SMOKE as inline point sources, and plume rise for fires is
calculated within CMAQ using parameters from the inline emissions files (heat flux, etc). This is similar
to how non-fire point sources are handled, except that the fire parameters are used to calculate plume rise
instead of traditional stack parameters. The CAMx model supports inline plume rise calculations using
traditional stack parameters, but, does not support inline plume rise for fire sources. Therefore, for the
purposes of CAMx modeling, we must have SMOKE calculate plume rise for fires using the Laypoint
program. In this modeling platform, this must be done for the ptfire, ptfire othna, and ptagfire sectors. To
distinguish these layered fire emissions from inline fire emissions, layered fire emissions are processed
with the sector names "ptfire3D", "ptfire_othna3D", and "ptagfire3D". When converting layered fire
emissions files to CAMx format, stack parameters are added to the CAMx-ready fire emissions files to
force the correct amount of fire emissions into each layer for each fire location.
CMAQ modeling uses one gridded low-level emissions file, plus multiple inline point source emissions
files, per day. CAMx modeling also uses one gridded low-level emissions file per day - but instead of
reading multiple inline point source emissions files at once, CAMx can only read a single point source file
per day. Therefore, as part of the CAMx conversion process, all inline point source files are merged into a
single "mrgpt" file per day. The mrgpt file includes the layered fire emissions described in the previous
paragraph, in addition to all non-fire elevated point sources from the cmv_c3, othpt, ptegu, ptnonipm, and
pt oilgas sectors.
The remaining step in the CAMx emissions process is to generate sea salt aerosol emissions, which are
distinct from ocean chlorine emissions. Sea salt emissions do not need to be included in CMAQ-ready
emissions because they are calculated by the model, but, do need to be included in CAMx-ready
emissions. After the merged low-level emissions are converted to CAMx format, sea salt emissions are
generated using a program called "seasalt" and added to the low-level emissions. Sea salt emissions
depend on meteorology, vary on a daily and hourly basis, and exist for model species PCL, NA, PS04,
and SS (i.e., sea salt).
3.5.2 Development of CAMx Emissions for Two-Way Nested CAMx Runs in
This Study
Version 7 of the CAMx model supports a new type of modeling called two-way nested modeling. In a
standard model run, CAMx is run for the 36US3 grid first, and then run a second time for the 12US2 grid
using boundary conditions derived from the 36US3 run. In a two-way nested model run, CAMx is run for
both the 36US3 and 12US2 grids at the same time with feedback between the domains, eliminating the
72
-------
need for two separate model runs. CAMx modeling for this study was performed using the two-way
nesting feature.
For a regular CAMx model run, two emissions files per day are provided to the model: a gridded file of
low-level emissions (the "emis2d" file), and a file of point source emissions (the "mrgpt" file). For a two-
way nested CAMx model run, we provide two emis2d files per day; one for the 36US3 domain, and one
for the "12US2b" domain, not for the 12US2 domain as described below. A single mrgpt file is provided
to the model which covers all sources in the 36US3 domain. For all point sources except fires, the mrgpt
file has location and stack information for individual sources, and so for all point sources except fires, a
point source file developed for the 36US3 domain can be used for 12US2 modeling without losing
resolution.
For the ptfire, ptagfire, and ptfireothna sectors, support for two-way nested modeling requires additional
emissions modeling considerations. Fire emissions are unique from other point sectors in that CAMx
modeling does not support inline plume rise for fires, and so we calculate plume rise for fires within
SMOKE as described in the prior section. As part of calculating plume rise, it is necessary for the
emissions to be gridded by SMOKE as well. Therefore, layered fire emissions files for the 36US3 domain
output by SMOKE only have 36km resolution, and as such we cannot simply merge the 36US3 fire
emissions in the mrgpt file like we can for other point sectors, or else the fire emissions within the 12US2
domain will have 36km resolution. To support two-way nested modeling, we need the mrgpt file to
include fire emissions with 12km resolution in the area covered by the 12US2 domain, and 36km
resolution in the area outside of the 12US2 domain. To account for this, the following fire emissions are
included in the mrgpt file:
- Layered 12US2 emissions for the ptfire, ptagfire, and ptfire othna sectors
- Layered 36US3 emissions for the ptfire othna sector, but with the region of the domain which
overlaps 12US2 zeroed out to avoid a double count
- Layered 36US3 emissions for the Southeast Alaska portion of the ptfire sector (which only exist
on two days in 2016; ptagfire does not have any Southeast Alaska emissions)
Development of the emis2d files for two-way nested modeling is the same as for regular modeling, with
one exception: to support two-way nested modeling, the 12US2 emis2d file must have an extra row and
column of cells added to each edge of the domain, expanding the size of the domain by two rows and two
columns. The resulting 12km-resolution domain with two extra rows and columns is referred to as the
12US2b domain. The CAMx model requires these extra rows and columns to facilitate feedback between
the two domains. The emissions values in the extra rows and columns do not affect the model results, and
so it is not necessary to consider the 12US2b domain throughout the emissions modeling process. In other
words, it is valid to process emissions for 12US2, same as for a regular model run, and then convert the
12US2 emissions to the 12US2b domain in the last step. We do this with a utility which adds a row and
column to the edge of the domain, with zero emissions for all species in the extra rows and columns.
3.5.3 Development of CAMx Emissions for Source Apportionment CAMx Runs
The CAMx model supports source apportionment modeling for PM sources, using a technique called
Particulate Matter Source Apportionment Technology (PSAT). PSAT allows emissions from different
types of sources to be tracked through the CAMx model. For this study, PSAT modeling was performed
in CAMx with two-way nesting for the 2028, and a new set of emissions was developed specifically for
PSAT modeling with the case name "2028fg_secsa_16j".
73
-------
Source Apportionment modeling involves assigning tags to different categories of emissions. These tags
can be applied by region (e.g. state), by emissions type (e.g. SCC or sector), or a combination of the two.
For this study, emissions tagging was applied by sector, as shown in Table 3-27.
Table 3-27. Sector tags for 2028fg PSAT modeling
tag
emissions applied to tag
1
All biogenics (beis sector)
2
US EGUs (ptegu sector)
3
US onroad (onroad and onroad ca ad] sectors)
4
US nonroad (nonroad sector)
5
US CMV C1/C2, including Federal Waters (cmv clc2 sector)
6
US CMV C3 in state and federal waters (cmv c3 sector, except for FIPS 98001)
7
CMV C3 outside US and Canada federal waters (cmv c3 sector, FIPS 98001 only)
8
US rail (rail sector)
9
US ag fires (ptagfire sector)
10
US agriculture (ag sector)
11
US oil and gas (np oilgas and pt oilgas sectors)
12
US non-EGU point, including airports and rail yards (ptnonipm sector)
13
US residential wood combustion (rwc sector)
14
US wildfires (part of ptfire sector)
15
US prescribed fires (part of ptfire sector)
16
US fugitive dust (afdust adj sector)
17
US other nonpoint (nonpt sector)
18
Canada fires (part of ptfire othna sector)
19
Canada anthropogenics (part of othar and othpt sectors, plus all of onroad can,
othafdust adj, and othptdust adj)
20
Mexico fires (part of ptfire othna sector)
21
Mexico anthropogenics (part of othar and othpt sectors, plus all of onroad mex)
22
Oceanic sea salt (sulfate)
1BC
Boundary Conditions - International Anthropogenic
1IC
Initial Conditions - International Anthropogenic
2BC
Boundary Conditions - Natural
2IC
Initial Conditions - other
TOPCON
Top Concentrations
For PSAT modeling, all emissions must be input to CAMx in the form of a point source (mrgpt) file,
including low level sources. In addition, for two-way nested modeling, all emissions must be input in a
single mrgpt file, rather than separate mrgpt files for each of the two domains (36US3 and 12US2). As
described above, fire emissions require special consideration in two-way nested model runs; for PSAT
modeling, that same consideration must be given to any sector in which emissions are being gridded by
SMOKE.
There are two main approaches for tagging emissions for CAMx modeling. One approach is to tag
emissions within SMOKE. Here, SMOKE will output tagged point source files (SGINLN files), which
can then be converted to CAMx point source format with the tags applied by SMOKE carried forward
into the CAMx inputs. The second approach is to, if necessary depending on the nature of the tags, split
sectors into multiple components by tag so that each sector corresponds to a single tag. Then, the gridded
74
-------
and/or point source format SMOKE outputs from those split sectors are converted to CAMx point source
format, and then merged into the full mrgpt file, with the tags applied at that last step. Development of
the 2028fg_secsa_16j emissions includes a mix of the two approaches.
For most sectors, the second approach was used, meaning SMOKE is run normally with sectors split into
multiple parts if necessary, and with the SMOKE outputs converted to point source format and then
tagged on the back end. Two-way nested modeling requires additional considerations to ensure that, like
with fire emissions in a non-PSAT CAMx model run, gridded emissions have 12km resolution in the
12US2 area and 36km resolution elsewhere. Sectors that were processed and tagged this way include:
- For ag (10), np oilgas (11), onroad ca adj (3), ptagfire (9), rail (8), ptegu (2): These sectors have
a single tag and do not have any emissions which lie outside the 12US2 domain and inside the
36US3 domain (which as far as the US is concerned, only includes Southeast Alaska). So, the
gridded 12US2 emissions from the regular 2028fg run were used and a sector-wide tag applied.
- For afdust (16), onroad (3): These sectors have a single tag, but do have some Southeast Alaska
emissions in the 36US3 domain. Since the 36US3 emissions are derived from 12US2, the Alaska
emissions are already processed separately under the sector names afdustakadj and
onroadnonconus. The 12US2 afdust adj and onroad emissions are converted to CAMx format
with sector-wide tags applied. Then, the 36US3 Alaska-only afdust ak adj and onroad nonconus
emissions are converted to CAMx format with the same sector-wide tags applied.
- For nonpt (17), nonroad (4), rwc (13): These sectors have a single tag, but do have some Southeast
Alaska emissions in 36US3. Thus, a second set of 36US3 emissions was created for these sectors
that only include Alaska. Then, the 36US3 Alaska-only files and full 12US2 files are each
converted to CAMx format with sector-wide tags applied.
- For beis (1), cmv_clc2 (5), onroad can (19), onroadmex (21), othafdust (19), othptdust (19), sea
salt (22): These sectors have a single tag, and also have emissions that exist beyond the boundaries
of 12US2. Thus, a second set of 36US3 emissions was created for these sectors that has the
portion of the domain which overlaps 12US2 zeroed out, or "masked". Then, the masked 36US3
emissions and and full 12US2 files are each converted to CAMx format with sector-wide tags
applied.
- For ptfire (14/15), ptfire_othna (18/20): These are layered fire sectors, each with two tags, and
each with emissions outside of 12US2. (The ptfire does have some emissions in Southeast
Alaska.) For these sectors, the procedure is: 1) Split the sector into two parts, one part per tag. The
ptfire inventory is split into a wildfire component and a prescribed component, and the
ptfireothna inventory is split into Canada and Mexico components. 2) Process each component
through SMOKE separately for both 36US3 and 12US2, with layering. 3) Mask the 12US2
portion out of the 36US3 gridded and layered emissions. 4) Convert full 12US2 + masked 36US3
to CAMx format, preserving layering.
- For othar: This sector has two tags (Canada 19, Mexico 21). The procedure is the same as for
ptfire and ptfire othna, except without layering.
The cmv_c3 and othpt sectors were processed with the SGINLN approach using a tagging file applied by
SMOKE. The cmv_c3 and othpt sectors have two tags each, applied within SMOKE. Since these are
75
-------
point sectors which are not gridded by SMOKE, the sectors only needed to be processed for the 36US3
domain without special consideration for two-way nesting.
The ptnonipm and ptoilgas sectors are also point sectors that required special consideration for two-way
nesting when tagging. These sectors are normally processed through SMOKE as partially elevated
sectors, in which some sources are output to the inline point source file and other sources, depending on
stack parameters, are output to a gridded file. When creating SGINLN files for these sectors, sources
which would otherwise be output to the gridded file are also gridded in the SGINLN file. In other words,
the SGINLN file includes individual point source information for all elevated sources, but includes
gridded emissions for low-level sources. This means that unless every source in the sector is considered
an elevated source - which is normally the case in cmv_c3 and othpt, but not in ptnonipm and pt oilgas -
a 36US3 SGINLN file cannot be used for two-way nested modeling because the low-level sources in that
file will only have 36km resolution. To resolve this for the 2028fg_secsa_16j emissions, the ptnonipm and
pt oilgas sectors were reprocessed through SMOKE with all sources classified as elevated, so that the
resulting point source files would retain information for every point source in the sector rather than put
the low-level sources on a 36km grid.
Point source files for all of the sectors listed above are then merged together to create the mrgpt file for
PSAT modeling which includes all emissions, with the appropriate tags and appropriate resolution
throughout the domain.
76
-------
4 Emission Summaries
Tables 4-1 through 4-4 summarize emissions by sector for the 2016fg and 2028fg cases. These summaries
are provided at the national level by sector for the contiguous U.S. and for the portions of Canada and
Mexico inside the larger 12km domain (12US1) discussed in Section 3.1 and for the 36-km domain
(36US3). Note that totals for the 12US2 domain are not available here, but the sum of the U.S. sectors
would be essentially the same, only the Canadian and Mexican emissions would change according to how
far north/south the grids go. Note that the afdust sector emissions here represent the emissions after
application of both the land use (transport fraction) and meteorological adjustments; therefore, this sector
is called "afdust adj" in these summaries. The afdust emissions in the 36km domain are smaller than
those in the 12km domain due to how the adjustment factors are computed and the size of the grid cells.
The onroad sector totals are post-SMOKE-MOVES totals, representing air quality model-ready emission
totals, and include CARB emissions for California. The cmv sectors include U.S. emissions within state
waters only; these extend to roughly 3-5 miles offshore and includes CMV emissions at U.S. ports.
"Offshore" represents CMV emissions that are outside of U.S. state waters. Canadian CMV emissions are
included in the othar sector. The total of all US sectors is listed as "Con U.S. Total." State totals are
available in the reports area on the web and FTP site for the 2016 beta / regional haze platform
(https://www.epa.gov/air-emissions-modeling/2016v72-beta-platform) .
77
-------
Table 4-1. National by-sector CAP emissions summaries for the 2016fg case, 12US1 grid
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
7,202,127
1,006,412
ag
2,856,435
186,273
cmv clc2
46,873
120
241,103
5,813
5,521
2,231
4,582
cmv c3
10,780
25
106,234
1,743
1,516
3,757
4,995
nonpt
2,684,785
121,209
757,079
610,603
498,089
161,064
3,707,237
nonroad
10,881,052
1,794
1,090,157
108,882
103,015
2,209
1,151,547
np oilgas
740,254
12
565,202
14,398
14,311
23,592
2,908,396
onroad
20,330,093
100,841
4,065,702
272,770
130,564
27,547
1,985,763
ptagfire
278,701
54,442
10,824
41,115
28,632
3,908
18,323
ptfire
14,607,348
254,071
232,294
1,545,802
1,305,341
115,781
3,317,409
ptegu
658,287
23,972
1,290,226
163,956
133,491
1,540,557
33,757
ptnonipm
1,858,717
63,464
1,088,652
404,432
261,146
674,382
815,293
pt oilgas
167,933
4,338
339,440
11,474
10,974
33,224
127,636
rail
102,881
322
557,216
16,612
16,114
363
25,991
rwc
2,118,074
15,427
31,268
317,334
316,808
7,691
340,812
Con. U.S. Total
54,485,778
3,496,471
10,375,397
10,717,061
3,831,936
2,596,307
14,628,014
beis
7,163,806
966,421
42,095,853
CONUS + beis
61,649,584
3,496,471
11,341,818
10,717,061
3,831,936
2,596,307
56,723,867
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada othafdust
"
--
--
1,060,979
187,228
--
"
Canada othar
2,732,048
4,888
437,967
314,303
249,213
20,540
834,379
Canada onroadcan
1,665,792
6,877
404,856
25,204
14,076
1,556
143,213
Canada othpt
1,095,894
503,410
812,630
118,370
49,607
999,725
803,870
Canada othptdust
--
--
--
150,943
55,585
--
--
Canada ptfireothna
760,345
13,015
16,337
84,366
71,652
6,721
185,224
Mexico othar
241,571
201,994
220,491
115,460
54,294
7,717
522,236
Mexico onroad mex
1,828,101
2,789
442,410
15,151
10,836
6,247
158,812
Mexico othpt
205,083
5,049
447,675
73,256
57,440
476,079
71,031
Mexico ptfire othna
384,764
7,466
16,665
45,198
38,354
2,798
131,980
Offshore cmv in Federal
waters
99,386
254
715,163
14,061
13,220
12,013
24,428
Offshore cmv outside
Federal waters
34,966
0
411,067
34,920
32,119
258,869
14,804
Offshore ptoilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
9,098,003
745,757
3,973,953
2,052,880
834,290
1,792,766
2,938,186
78
-------
Table 4-2. National by-sector CAP emissions summaries for the 2028fg case, 12US1 grid
Sector
CO
NHs
NOx
PMio
PM25
SO2
VOC
afdust ad)
7,252,506
1,017,484
—
ag
2,983,996
197,459
cmv clc2
48,461
123
136,359
3,476
3,300
2,076
3,281
cmv c3
15,518
37
96,783
2,504
2,178
5,392
7,200
nonpt
2,746,495
122,505
760,352
663,580
541,739
118,619
3,925,951
nonroad
11,300,514
2,042
607,236
59,682
55,541
1,551
821,997
np oilgas
783,413
23
563,116
18,095
17,949
31,120
3,373,183
onroad
10,427,337
83,631
1,353,812
211,037
63,041
11,547
885,883
ptagfire
278,701
54,442
10,824
41,115
28,632
3,908
18,323
ptfire
14,607,348
254,071
232,294
1,545,802
1,305,341
115,781
3,317,409
Ptegu
671,029
39,533
804,093
147,663
111,617
878,681
29,823
ptnonipm
1,954,661
64,037
1,142,291
411,104
266,947
640,342
819,452
pt oilgas
170,020
4,344
316,719
12,656
12,086
40,365
146,288
rail
108,232
339
587,191
17,515
16,990
382
27,395
rwc
2,011,643
14,500
31,894
298,669
298,120
6,679
324,230
Con U.S. Total
45,123,373
3,623,622
6,642,964
10,685,403
3,740,967
1,856,443
13,897,875
beis
7,163,806
966,421
42,095,853
CONUS + beis
52,287,179
3,623,622
7,609,385
10,685,403
3,740,967
1,856,443
55,993,728
Can./Mex./Offshore
Canada othafdust
_
_
_
1,267,025
222,026
_
_
Canada othar
2,691,939
4,722
312,959
302,486
222,647
20,151
851,377
Canada onroad can
1,303,551
5,492
168,631
26,129
9,498
698
60,932
Canada othpt
1,149,091
696,115
565,743
96,966
52,822
861,704
758,931
Canada othptdust
151,271
55,706
Canada ptfire othna
760,345
13,015
16,337
84,366
71,652
6,721
185,224
Mexico othar
277,263
200,038
252,523
120,590
58,294
8,206
628,715
Mexico onroad mex
1,615,412
3,732
393,339
18,728
12,667
8,530
164,793
Mexico othpt
249,257
7,273
499,300
91,716
70,229
433,688
102,109
Mexico ptfire othna
384,764
7,466
16,665
45,198
38,354
2,798
131,980
Offshore cmv in Federal
waters
119,333
285
527,701
13,145
12,141
16,503
31,052
Offshore cmv outside
Federal waters
49,724
0
587,745
49,875
45,894
52,793
21,171
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
8,650,731
938,153
3,389,634
2,268,163
872,597
1,412,294
2,984,494
79
-------
Table 4-3. National by-sector CAP emissions summaries for the 2016fg case, 36US3 grid
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust ad)
7,204,014
1,006,603
ag
2,856,435
186,273
cmv clc2
48,591
124
249,496
6,016
5,717
2,233
4,675
cmv c3
11,361
26
112,318
1,821
1,587
3,911
5,254
nonpt
2,686,510
121,265
757,492
610,807
498,233
161,296
3,707,939
nonroad
10,884,434
1,795
1,090,387
108,917
103,048
2,210
1,152,385
np oilgas
740,254
12
565,202
14,398
14,311
23,592
2,908,396
onroad
20,335,564
100,856
4,066,978
272,851
130,614
27,550
1,986,602
ptagfire
278,701
54,442
10,824
41,115
28,632
3,908
18,323
ptfire
14,607,935
254,081
232,299
1,545,859
1,305,389
115,784
3,317,546
ptegu
658,287
23,972
1,290,226
163,956
133,491
1,540,557
33,757
ptnonipm
1,859,776
63,464
1,088,838
404,485
261,179
674,406
815,393
pt oilgas
167,933
4,338
339,440
11,474
10,974
33,224
127,636
rail
102,881
322
557,216
16,612
16,114
363
25,991
rwc
2,118,562
15,430
31,277
317,402
316,876
7,692
340,891
Con. U.S. Total
54,500,788
3,496,561
10,391,992
10,719,726
3,832,768
2,596,727
14,631,060
beis
7,225,877
969,510
42,184,034
CONUS + beis
61,726,665
3,496,561
11,361,502
10,719,726
3,832,768
2,596,727
56,815,095
Can./Mex./Offshore
Sector
CO
NH3
NOX
PM10
PM2 5
S02
VOC
Canada othafdust
1,101,762
194,352
Canada othar
2,939,311
5,211
489,313
328,383
261,298
21,337
888,110
Canada onroadcan
1,730,052
7,125
425,462
26,286
14,757
1,606
148,376
Canada othpt
1,329,655
521,321
1,011,385
153,243
59,833
1,124,147
986,821
Canada othptdust
150,113
54,659
Canada ptfireothna
6,282,821
104,683
134,301
685,165
580,958
60,914
1,501,988
Mexico othar
2,684,115
878,370
707,975
585,933
415,474
25,671
3,739,965
Mexico onroad mex
6,273,194
10,319
1,497,028
74,169
56,782
26,400
552,952
Mexico othpt
872,675
36,344
1,043,494
284,434
204,959
2,292,596
356,108
Mexico ptfire othna
7,136,168
120,627
347,132
1,155,991
746,107
45,222
2,260,695
Offshore cmv in Federal
waters
99,782
254
719,270
14,115
13,268
12,115
24,607
Offshore cmv outside
Federal waters
88,519
0
1,043,852
88,503
81,432
657,836
37,557
Offshore ptoilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
29,486,344
1,684,270
7,467,901
4,648,763
2,684,546
4,268,347
10,545,391
80
-------
Table 4-4. National by-sector CAP emissions summaries for the 2028fg case, 36US3 grid
Sector
CO
NHs
NOx
PMio
PM25
SO2
VOC
afdust ad)
-
-
-
7,254,396
1,017,675
-
-
ag
-
2,983,996
-
-
-
-
197,459
cmv clc2
50,185
127
141,008
3,591
3,411
2,076
3,330
cmv c3
16,339
39
102,619
2,614
2,278
5,610
7,566
nonpt
2,748,187
122,565
760,786
663,794
541,886
118,858
3,926,655
nonroad
11,303,516
2,043
607,391
59,702
55,559
1,551
822,511
np oilgas
783,413
23
563,116
18,095
17,949
31,120
3,373,183
onroad
10,429,919
83,643
1,354,242
211,087
63,060
11,549
886,243
ptagfire
278,701
54,442
10,824
41,115
28,632
3,908
18,323
ptfire
14,607,935
254,081
232,299
1,545,859
1,305,389
115,784
3,317,546
Ptegu
671,029
39,533
804,093
147,663
111,617
878,681
29,823
ptnonipm
1,955,711
64,037
1,142,485
411,156
266,978
640,367
819,548
pt oilgas
170,020
4,344
316,719
12,656
12,086
40,365
146,288
rail
108,232
339
587,191
17,515
16,990
382
27,395
rwc
2,012,100
14,503
31,903
298,731
298,182
6,680
324,303
Con U.S. Total
45,135,286
3,623,713
6,654,676
10,687,972
3,741,693
1,856,931
13,900,174
beis
7,225,877
969,510
42,184,034
CONUS + beis
52,361,164
3,623,713
7,624,186
10,687,972
3,741,693
1,856,931
56,084,208
Can./Mex./Offshore
Canada othafdust
1,314,491
230,228
Canada othar
2,902,592
5,034
358,016
314,906
232,768
21,205
904,910
Canada onroad can
1,353,512
5,692
177,653
27,234
9,960
723
63,284
Canada othpt
1,363,501
719,783
709,218
110,273
61,060
974,147
936,907
Canada othptdust
150,439
54,777
Canada ptfire othna
6,282,821
104,683
134,301
685,165
580,958
60,914
1,501,988
Mexico othar
2,995,073
871,163
800,519
627,824
454,427
27,308
4,263,367
Mexico onroad mex
5,496,594
13,807
1,336,088
108,810
83,255
36,064
574,688
Mexico othpt
1,136,851
51,548
1,215,901
374,281
265,263
2,370,238
511,462
Mexico ptfire othna
7,136,168
120,627
347,132
1,155,991
746,107
45,222
2,260,695
Offshore cmv in Federal
waters
119,908
286
530,669
13,222
12,210
16,651
31,311
Offshore cmv outside
Federal waters
126,309
0
1,482,984
126,183
116,059
133,509
53,535
Offshore pt oilgas
50,052
15
48,691
668
667
502
48,210
Non-US Total
28,963,379
1,892,638
7,141,172
5,009,485
2,847,741
3,686,482
11,150,358
81
-------
5 References
Adelman, Z. 2012. Memorandum: Fugitive Dust Modeling for the 2008 Emissions Modeling Platform.
UNC Institute for the Environment, Chapel Hill, NC. September, 28, 2012.
Adelman, Z. 2016. 2014 Emissions Modeling Platform Spatial Surrogate Documentation. UNC Institute
for the Environment, Chapel Hill, NC. October 1, 2016.
Adelman, Z., M. Omary, Q. He, J. Zhao and D. Yang, J. Boylan, 2012. "A Detailed Approach for
Improving Continuous Emissions Monitoring Data for Regulatory Air Quality Modeling."
Presented at the 2012 International Emission Inventory Conference, Tampa, Florida. Available
from http://www.epa.gOv/ttn/chief/conference/ei20/index.html#ses-5.
Anderson, G.K.; Sandberg, D.V; Norheim, R.A., 2004. Fire Emission Production Simulator (FEPS)
User's Guide. Available at http://www.fs.fed.us/pnw/fera/feps/FEPS users guide.pdf.
Appel, K.W., Napelenok, S., Hogrefe, C., Pouliot, G., Foley, K.M., Roselle, S.J., Pleim, J.E., Bash, J.,
Pye, H.O.T., Heath, N., Murphy, B., Mathur, R., 2018. Overview and evaluation of the
Community Multiscale Air Quality Model (CMAQ) modeling system version 5.2. In Mensink C.,
Kallos G. (eds), Air Pollution Modeling and its Application XXV. ITM 2016. Springer
Proceedings in Complexity. Springer, Cham. Available at https://doi.org/10.1007/978-3-319-
57645-9 11.
Bash, J.O., Baker, K.R., Beaver, M.R., Park, J.-H., Goldstein, A.H., 2016. Evaluation of improved land
use and canopy representation in BEIS with biogenic VOC measurements in California. Available
from http://www.geosci-model-dev.net/9/2191/2016/.
Bullock Jr., R, and K. A. Brehme (2002) "Atmospheric mercury simulation using the CMAQ model:
formulation description and analysis of wet deposition results." Atmospheric Environment 36, pp
2135-2146.
Coordinating Research Council (CRC). Report A-100. Improvement of Default Inputs for MOVES and
SMOKE-MOVES. Final Report. February 2017. Available at
https://crcao.org/reports/recentstudies2017/A-100/ERG FinalReport CRCA100 28Feb2017.pdf
Drillinginfo, Inc. 2015. "DI Desktop Database powered by HPDI." Internet address:
http://www.didesktop.com/
England et al., 2007, Dilution-Based Emissions Sampling from Stationary Sources: Part 2... J. Air &
Waste Manage. Assoc. 57.
England, G., Watson, J., Chow, J., Zielenska, B., Chang, M., Loos, K., Hidy, G. 2007. "Dilution-Based
Emissions Sampling from Stationary Sources: Part 2— Gas-Fired Combustors Compared with
Other Fuel-Fired Systems," Journal of the Air & Waste Management Association, 57:1, 65-78,
DOI: 10.1080/10473289.2007.1046529.
Environ Corp. 2008. Emission Profiles for EPA SPECIATE Database, Part 2: EPAct Fuels (Evaporative
Emissions). Prepared for U. S. EPA, Office of Transportation and Air Quality, September 30,
2008.
EPA, 1994. Onboard Refueling Vapor Recovery for Motor Vehicles, Fact Sheet. April, 1994. Available
at: http://www.epa.gov/oms/regs/ld-hwv/onboard/orvrfact.txt.
82
-------
EPA, 1998. AP-42, Fifth Edition, Compilation of Air Pollutant Emissions Factors, Volume 1: Stationary
Point and Area Sources. September 1998. Available at https://www.epa.gov/air-emissions-
factors-and-quantification/ap-42-compilation-air-emissions-factors.
EPA, 2000. Light-Duty Vehicle, Light-Duty Truck, and Medium-Duty Passenger Vehicle Tier 2 Exhaust
Emission Standards. Office of Transportation and Air Quality, Ann Arbor, MI 48105. Available
at: HYPERLINK "http://www.epa.gov/otaq/standards/light-
duty/tier2stds.htm"http://www.epa.gov/otaq/standards/light-duty/tier2stds.htm.
EPA. 2007a. National Scale Modeling for the Final Mobile Source Air Toxics Rule, Office of Air
Quality Planning and Standards, Emissions Analysis and Monitoring Division, Research Triangle
Park, NC 27711, EPA 454/R-07-002, February 2007. Available at
http://www.regulations.gov/#!documentDetail:D=EPA-HO-OAR-2005-0Q36-1134.
EPA, 2015a. Population and Activity of On-road Vehicles in MOVES2014 - Draft Report. EPA-420-D-
15-001. Available at http://www.epa.gov/otaq/models/moves/documents/420dl5001.pdf.
EPA, 2015b. Draft Report Speciation Profiles and Toxic Emission Factors for Nonroad Engines. EPA-
420-R-14-028. Available at
https://cfpub.epa.gov/si/si public record Report.cfm?dirEntryId=309339&CFID=83476290&CF
TOKEN=35281617.
EPA,2015c. Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles in
MOVES2014. EPA-420-R-15-022. Available at
https://nepis. epa.gov/Exe/ZyPDF. cgi?Dockev=P 100NQJG.pdf
EPA, 2016. SPECIATE Version 4.5 Database Development Documentation, U.S. Environmental
Protection Agency, Office of Research and Development, National Risk Management Research
Laboratory, Research Triangle Park, NC 27711, EPA/600/R-16/294, September 2016. Available
at https://www.epa.gov/sites/production/files/2016-Q9/documents/speciate 4.5.pdf
EPA, 2018. AERMOD Model Formulation and Evaluation Document. EPA-454/R-18-003. U.S.
Environmental Protection Agency, Research Triangle Park, North Carolina 27711.
ERG, 2014a. Develop Mexico Future Year Emissions Final Report. Available at
ftp://ftp.epa.gov/EmisInventory/201 Iv6/v2platform/2011 emissions/Mexico Emissions WA%204
-09 final report 121814.pdf.
ERG, 2014b. "Technical Memorandum: Modeling Allocation Factors for the 2011 NEI."
ERG, 2016b. "Technical Memorandum: Modeling Allocation Factors for the 2014 Oil and Gas Nonpoint
Tool."
ERG, 2017. "Technical Report: Development of Mexico Emission Inventories for the 2014 Modeling
Platform.". Available at ftp://newftp.epa.gov/Air/emismod/2014/v2/2014fd/emissions/EPA%205-
18%20Report Clean%20Final 01042017.pdf
Hildebrandt Ruiz, L. and Yarwood, G., 2013. Interactions between Organic Aerosol andNOy: Influence
on Oxidant Production, Final report for AQRP project 12-012. Available at
http://aqrp.ceer.utexas.edu/projectinfoFY12_13%5C12-012%5C12-012%20Final%20Report.pdf.
Joint Fire Science Program, 2009. Consume 3.0—a software tool for computing fuel consumption. Fire
Science Brief. 66, June 2009. Consume 3.0 is available at:
http://www.fs.fed.us/pnw/fera/research/smoke/consume/index.shtml.
83
-------
LADCO, 2012. "Regional Air Quality Analyses for Ozone, PM2.5, and Regional Haze: Base C
Emissions Inventory (September 12, 2011)". Lake Michigan Air Directors Consortium,
Rosemont, IL 60018. Available at:
http://www.ladco.org/tech/emis/basecv8/Base C Emissions Documentation Sept 12.pdf.
McCarty, J.L., Korontzi, S., Jutice, C.O., and T. Loboda. 2009. The spatial and temporal distribution of
crop residue burning in the contiguous United States. Science of the Total Environment, 407 (21):
5701-5712.
McQuilling, A. M. & Adams, P. J. Semi-empirical process-based models for ammonia emissions from
beef, swine, and poultry operations in the United States. Atmos. Environ. 120, 127-136 (2015).
MDNR, 2008; "A Minnesota 2008 Residential Fuelwood Assessment Survey of individual household
responses". Minnesota Department of Natural Resources. Available from
http://files.dnr.state.mn.us/forestry/um/residentialfuelwoodassessment07 08.pdf.
NCAR, 2016. FIRE EMISSION FACTORS AND EMISSION INVENTORIES, FINN Data, downloaded
2014 SAPRC99 version from http://bai.acom.ucar.edu/Data/fire/.
NESCAUM, 2006; "Assessment of Outdoor Wood-fired Boilers" (NESCAUM, 2006). Northeast States
for Coordinated Air Use Management (NESCAUM) report. Available from
http://www.nescaum.org/documents/assessment-of-outdoor-wood-fired-boilers/20Q6-1031-owb-
report revi sed-i une2006-appendix .pdf.
NYSERDA, 2012; "Environmental, Energy Market, and Health Characterization of Wood-Fired
Hydronic Heater Technologies, Final Report". New York State Energy Research and
Development Authority (NYSERDA). Available from:
http://www.nvserda.ny.gov/Publications/Case-Studies/-
/media/Files/Publications/Research/Environmental/Wood-Fired-Hvdronic-Heater-Tech.ashx.
Pinder, R., Strader, R., Davidson, C. & Adams, P. A temporally and spatially resolved ammonia emission
inventory for dairy cows in the United States. Atmos. Environ. 38.23, 3747-3756 (2004). 2.
Pinder, R., Pekney, N., Davidson, C. & Adams, P. A process-based model of ammonia emissions
from dairy cows: improved temporal and spatial resolution. Atmos. Environ. 38.9, 1357-1365
(2004).
Pinder, R., Pekney, N., Davidson, C. & Adams, P. A process-based model of ammonia emissions from
dairy cows: improved temporal and spatial resolution. Atmos. Environ. 38.9, 1357-1365 (2004).
Pouliot, G., H. Simon, P. Bhave, D. Tong, D. Mobley, T. Pace, and T. Pierce. (2010) "Assessing the
Anthropogenic Fugitive Dust Emission Inventory and Temporal Allocation Using an Updated
Speciation of Particulate Matter." International Emission Inventory Conference, San Antonio, TX.
Available at http://www3.epa.gov/ttn/chief/conference/eil9/session9/pouliot pres.pdf.
Pouliot, G. and J. Bash, 2015. Updates to Version 3.61 of the Biogenic Emission Inventory System
(BEIS). Presented at Air and Waste Management Association conference, Raleigh, NC, 2015.
Pye, H. and Pouliot, G. 2012. Modeling the Role of Alkanes, Polycyclic Aromatic Hydrocarbons, and
Their Oligomers in Secondary Organic Aerosol Formation. Environmental Science &
Technology, 46, 11, 6041-6047.
84
-------
Raffuse, S., D. Sullivan, L. Chinkin, S. Larkin, R. Solomon, A. Soja, 2007. Integration of Satellite-
Detected and Incident Command Reported Wildfire Information into BlueSky, June 27, 2007.
Available at: http://getbluesky.org/smartfire/docs.cfm.
Reichle, L.,R. Cook, C. Yanca, D. Sonntag, 2015. "Development of organic gas exhaust speciation
profiles for nonroad spark-ignition and compression-ignition engines and equipment", Journal of
the Air & Waste Management Association, 65:10, 1185-1193, DOI:
10.1080/10962247.2015.1020118.
Reff, A., Bhave, P., Simon, H., Pace, T., Pouliot, G., Mobley, J., Houyoux. M. "Emissions Inventory of
PM2.5 Trace Elements across the United States", Environmental Science & Technology 2009 43
(15), 5790-5796, DOI: 10.1021/es802930x
RIGDATA, 2015. U.S. Well Starts By Depth Range, January 2014 through December 2014. Used by
Permission and Approved for Publication by by Lori Spence at RIGDATA (www.rigdata.com) in
e-mail Regi Oommen, Eastern Research Group, Inc. July 9, 2015.
Russell, A.G. and G.R. Cass, 1986. Verification of a Mathematical Model for Aerosol Nitrate and Nitric
Acid Formation and Its Use for Control Measure Evaluation, Atmospheric Environment, 20:
2011-2025.
Sarwar, G., S. Roselle, R. Mathur, W. Apel, R. Dennis, "A Comparison of CMAQ HONO predictions
with observations from the Northeast Oxidant and Particle Study", Atmospheric Environment 42
(2008)5760-5770)
Schauer, J., G. Lough, M. Shafer, W. Christensen, M. Arndt, J. DeMinter, J. Park, "Characterization of
Metals Emitted from Motor Vehicles," Health Effects Institute, Research Report 133, March 2006.
Seigneur, C., K . Vigjayaraghavan, K. Lohman, P. Karamchandani, C. Scott. "Global Source Attribution
for Mercury Deposition in the United States." Environ. Sci. Technol. 2004, 38, 555-569.
Seigneur, C., P. Karamchandani, K. Lohman, K . Vigjayaraghavan. "Multiscale modeling of the
atmospheric fate and transport of mercury." Journal of Geophysical Research, Vol. 106, NO. D21,
Pages 27,795-27,809, November 16, 2001.
Skamarock, W., J. Klemp, J. Dudhia, D. Gill, D. Barker, M. Duda, X. Huang, W. Wang, J. Powers, 2008.
A Description of the Advanced Research WRF Version 3. NCAR Technical Note. National
Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, Boulder, CO.
June 2008. Available at: http://www2.mmm.ucar.edu/wrf/users/docs/arw v3.pdf.
Sullivan D.C., Raffuse S.M., Pryden D.A., Craig K.J., Reid S.B., Wheeler N.J.M., Chinkin L.R., Larkin
N.K., Solomon R., and Strand T. (2008) Development and applications of systems for modeling
emissions and smoke from fires: the BlueSky smoke modeling framework and SMARTFIRE: 17th
International Emissions Inventory Conference, Portland, OR, June 2-5. Available at:
http ://www. epa. gov/ttn/chief/conferences, html.
U.S. Census, 2012; "2012 Statistical Abstract, Construction and Housing: Housing Units and
Characteristics". U.S. Census Bureau. Available from:
http://www.census.gov/compendia/statab/cats/construction housing/housing units and characteri
stics.html.
85
-------
Wang, Y., P. Hopke, O. V. Rattigan, X. Xia, D. C. Chalupa, M. J. Utell. (2011) "Characterization of
Residential Wood Combustion Particles Using the Two-Wavelength Aethalometer", Environ. Sci.
Technol., 45 (17), pp 7387-7393.
Wiedinmyer, C., S.K. Akagi, R.J. Yokelson, L.K. Emmons, J.A. Al-Saadi3, J. J. Orlando1, and A. J. Soja.
(2011) "The Fire INventory from NCAR (FINN): a high resolution global model to estimate the
emissions from open burning", Geosci. Model Dev., 4, 625-641. http://www.geosci-model-
dev.net/4/625/2011/ doi: 10.5194/gmd-4-625-2011
Yarwood, G., J. Jung,, G. Whitten, G. Heo, J. Mellberg, and M. Estes,2010: Updates to the Carbon Bond
Chemical Mechanismfor Version 6 (CB6). Presented at the 9th Annual CMAS Conference, Chapel
Hill, NC. Available at
https://www.cmascenter.org/conference/2010/abstracts/emery updates carbon 2010.pdf.
Zue, Henze, et al, 2013. "Constraining U.S. Ammonia Emissions using TES Remote Sensing
Observations and the GEOS-Chem adjoint model", Journal of Geophysical Research:
Atmospheres, 118: 1-14.
86
-------
Appendix A: CB6 Assignment for New Species
September 27, 2016
MEMORANDUM
Tc: Aison Eyth and Madeleine Strum, OAQPS, £?A
From: Bess iearefsfey and GregYacwosd, Rsnrbo'l Environ
5u eject: Species r««p^r«> fc» CS6 ana CBGSfor use ».th SPECIATE4.5
SiMmmarv
Rarnboll Environ |RE| reviewed version 4,5 of the SPECifeTE date bass, and created €605 arc C5S
mechanism species mappings fat newly added con-.pc_r.ds. In addition, the map; ng fuieslines for
Garten Bond {CB] mechanisms were expanded to promote cons siency in curre-t ana fur. re vt art.
Background
The Environmental Protection Agency's 5PECIATE repository contains gas and particulate matter
sos aat on srafiles of air pollution sources, which are used in the generation of emissions data for air
quality n odels {AQMji such -as CMAQ liittp^'/wwwxifiascenter.orst'aiiaq/} and CMKx
lr.Kp://,,Vifrtvx3rnMxon|. However, the condensed chemical mechanisms jssi withir frsse
photochemical mosels y-tifee fewer species than SPEClATEto represent gas zhasa chemistry, a*nd
thus the SPECtATE zoTijreundi irust fee assigned to the AQM mcael szezies cft^e zonsiraed
nezhsnisms A c~e-i Ccl -napc-ng is used ru shcwtiie rezresenfat-on of crgaric zhemica' spec es by
the model compc-nds of tAacondensed "lecs'is'ii.
This memorandum describes how ctis^xat "iitnpings ',ve-e developed from SPEOATE 4.5
wr?KKite to swedes c't^eCS "¦edrerff* species'YCBOS
I ntrp://w,v\v.c sto .cc*' rub'/; df s/CG05_Fi r a_Repa rt_i20&05.2 df) anc Cfi-5
.-t":.--=q_c.:= = ' ¦_:=:? = = ed"Jr-z 'c =:t -f:--IZOII, LI-C12:'':2:r ¦= ::I0Report.pdf).
Mettifxfc
CB Mode; Species
Organic gases are mapped to the CI mechanism either as explicitly represented ind virus I
zompeunds |e.g. A J32. for Ecera dehyze], or is a combr-sticn c* rode' species that rep-esert
comma" structural =>-ducs is.g. ALQX ""cr other aldehydes, PAR for a kvl groupsi, "able i lists =11 of
the =;.pl ± ='r structuralmods' species ir* C£Cr =-d CB6 *a"isms, each cf wh-:h rtp'ssa-'tsa
defrs: n."z = 'zr csrton 3t?rrs si f.vrf'zr zs'b-z ¦ :z fce-zznse-^-ed ns csie:. Ci: c-z -tshs fzir
moree^p' :i: rze= ipse =iv-b~> CEC5 =«-d a-i = :r-i: o*3 str. tc -ep-s-s'-t .:«z"«.T-3
CB05 re: essn:a: ?-i :;t's 5d-zt:zrr :55 szszes s crz.'zrz: ' :_s "•¦'c'usss1505 cz'unmof
Table l.
"torts* Emirotx, 77'i Ssn Vtorin Drive. Saris 2:115, Novate, GVSfSSS
v^i4i3.sas>iB,iia f+i msmsiSTm
87
-------
a:5it s-itc the =:=pl r: 5-r.sructu -a -oec =5 t-sre a-s r-vr rcdelspec « t ~ at are used to
¦e-: '=:€<¦¦: :r§3^:-: fa^ei fat 3 "= net r rated t, the >:e -leca-iir
NVOL — Veiy low volatility SPECIATE compoundsthst -esids Dretzrrha't-v r the : = ~. :le zrs-s and
should be excluded from the gas phase mechanism. These compounds ere -mapped by setting
NVOL equal to the molecular weight |e„g. dscalsramodipfierif! oxide is mapped as 95S.2
N'.'C.. s Ic»'• £ i:~ t~ = tcta nr«i • y a WCL t: :e rete-^vnec
Lv^ -Ctr-.po.'dlsfst sra un«b-= tc De -n;p:a: c: C: us;-gthe 5 a lib a Tde zziizi ~~ :
approach should; be avoided unless, ats?!-tenecaii?ry, a ¦ r v. -1 lead to a warning message
in the speelatiGCt tool
TsDlei, Mode sparies n the CEZ5 end CB6 chemical mechanisms.
: ir
V:c t\
Njir-tie.*
:e : e-
:
nc ictc
'¦"sr*
Cs-rso-'t!
r?:: is
T Cif
£¦ z k : ••«
-zr~
-:f:: ^ j - j - s
--- :± i: ¦ ? * i
-
s:
::
E-£ iZ
Zrt "
s
^4g 11 , *
y«
Zu4
Y" I"
1
i:
Ves
z- _
Erri ?r r
s:
V>5
=¦-:
iTsl-sie
2
;;
= —
5:^* "t ¦**
2
HeliL
:tau
Etfa^c
"fes
=CRM
1
::
Tes
ID3
;3p*znt iZ-7^e?hyl-I,3-bi.:aC" *ie<
5
ees
¥es
YE -
I
t:
"to
®raparrg
3-
¦ IJWHl
i
•
- r 7 -1-1 - I
2.
?:
>GLE
rttsmal ots-fin croup I R-»>C~C< ^sR./'
4
¥es
yes
.^ET
ketone group |R.Rj »C=0|
1
Wo (1 PAR!
::
OLE
rerniinst olefin greiip' {R,Ri»e=Cf
2
¥tes
¥es
PAR
'sralfinic group- (B-,-£
^¦VQL
Very law volatility comf miiiSs
¥«
iiH*.
iinkfiowfi
"fes
'UK* wo.rapiESEiiB ig(nafairtlowwMittjfe:npcwCs»ret:: t-i: :: MUtt.basedonmftentar wefftt.UNKgiiwiMppal
atirt thus das: rot rsarMsr1: any osrtion.
%sr:*lwfl snurart US Capanftfe
¥-MI3.BS9jB?IJ0 ? 44415.!
.,Wv
88
-------
Mapping guidelines for noti-e* pi cit c rgamie pies using OB model species
s-sciate cc-h:dut:3 ftit s-re ist sraatse exp ic tly are nazpsd :d cb tic:?! spades t*az raoresert
cott-c" -t".'si | tu:= "=b-E 2 I =:::- = :3r::n r be" e^ d |£^e*= nip: rz f Jce : ¦¦Ei ee~
of fe structure Tiodel species..
Table 2, General Gudelines for mapping using CBS structural model species.
:e^
'• .:•*«: tz
'ar*
mirv&er a?
C-s'-^c^s
,11
1 m
• u
•>
- -- 1
-
^ ¦ 1 ¦ "BT" __ 'A'
•-OLE
.£
-itsrial cn'irr grcjo IG.S reprsserT: cs?for;-
: -"
i
ANnres tie ilfcfi groups. PAP; wssert: i. rsrtean, e.g. feutane is 4 MS, see UUi for
s
- r s s
10
*n rrsr-sts'peres era representee a: „ ,"=os'
TOL
7
T: lui.-is !ic stier iromatKS TOl represent 7 carbons s«d any soditewl
cartons are reprasenlM as iltfl gnnas t'ffioatt|* wwj,e.g. etfcfliJBKierie.»TQl
Crssefe s« 'represented is TOL and MIL Slfrsnss are reprssented using TOL. ¦Os.E : -;
jm
i
. - ':s:rL J',R sjif- ss qciftT!' alfcv g"5JF: 4 S lEC-sserdsie is 4-t"
^ «,"¦(*! ij-SC".- i sc J |: 5 . girstsc ;cd is t=Asi - Jil'^ jsts'grsijpi •« s. ntsf .1
Bieeste : I 3<'« - wvl:; is.g . tr zt v-sithj-it sc"iir; are 2 wN"|
¦£:'
Kit
S
iij atrs^cc ,'ilkj ar;io:ic. XV. S urtapi ana «r-» jr-dit :rjl
:srts:ii are rsp-iierstei si s 1 ti jrsLjs i'r"C5t fir-.eti,lt«->:en; csr.ir: art
) . - : :
Senile compounds that are multifunctional a«.d,»or include hsts->stc-:i 's:k ?b*,!crr C2 "-srp'-gi
We developed guidelines for some of these ccwsoyr classes :d p'omcte ccr.siste-t rspresentatior.
in this wort and future revisions. Approach as far several commune classes are expEa ned -n "able 2.
We developed guidelines as needed to add-ess new v addec spec ei in 5?ECIATE C.S but d : not
*; :=:iy r= %•;=•.¦.• s*r..ng Tiepj rg: fs,- ¦ c ~r c, t assign ' isrci-ndi :hs: :3w r -«-=* tfrom
de¥efcpi"f a fiiidet r.e.
Siftell Environ US CBpor»t«\ 773 San Marin lints, Sarte 2115, ftlwiitB, asai 3
89
-------
Tsclr I, M3pp ng gu ::e: n5=fo'so-i= :o ma.: cc~ic-o. nrf :i a =5-2 = s-d itr. a groups
li 1/ :i. - r
lis: i ::u i
i.'OUp
C2 i :>tc t: rt: ¦?:« -i:t : -
Chlotrabenzenei Brut
ottier twlogeiwteit
benzenes
Suide&ne:
« J or ieij hrfogens - 1 PAR. 7 ^l«
• 4 or more fwingsni - f .' "
E- s-: i:
« i,i,3-WlorBfc»siisiene - 1 3 3 J »^
« Tdjachlorgbemenes - 6 J \ »,
¦Suidewte:
• i iOll nwtft additions) cartons represented is sltf! groups (generally
:i =
SiBmoissi:
¦ MeftijJpycttf entodiene -1 -OLS, 2 PAR
• 1 IG;-E- 3
:. is/^frroles
Guideline:
« 2 OLE with Mtditnml coitions represented as ifitfl groups (generally
= 1 =
Stomples:
• 2-SlI%>mrar. -LC.E, 4 PAR
~ : •• • - : d.e. 3 par
* - j.i
« i-MeftftyffiyrFDh - 2 OLE, 1 PAR
-;t;-3c cl: aromatic
:c ".p 3 jr 3s
:c-lv res ::ive
functional group, 'if a confound contsnu mars tn»n one t- j!e bona
•Ml not other reactive fimttitmri groups, incn c-« :':¦>« s bwisfe
istreatedis quewithaddition!*carbonssrsstt; s:-altcj'l groups.
Em-.: s;
¦ l- = S*iteF-3-j."Se -13.5 3
* j 5— evsdisr-3-oe- I C.E, Z 3AR
« y.Z 5 "i*
Tfcese guidelines were used to map the new species from SPEICATE4.5, and also to revise so^e
previously mapped compounds, OveraH, a total of 175 new species from SPECIATEV4.5 '.vers rr.spped
ami 7 previously mapped species were revised based on the new guidelines.
Stpfeefl enar-m US Capeiitim 77i San \tiBB 3rkie, Suite 2115,. flowto, CA.9498B 4
¥««.iSS.O?Oa F414SJSSS7B7
JMC"#®P^-CSJ-SS
90
-------
JN
fteGcwiranendatian
I. co*tr =te 3«/5terr=tf: r= - i&.v :frrspr -§ x all spec ;• t; s-su-s :c *fcrtv
!"=:oi-| gj'rsl " *= su f"='U zr".[>-¦? irrpc." di z-st 3 ¦= s-rri = ¦:: new iirds- .vsrs
ra%»revisestoproiiioteconsissenct i- r-=z-pi-= apprcatnes,mutthe.-.ojvr.tyoi
existing species mappings were not reviewed as't was c utshfe the scope- of chis vwwfc.
Z. Develop a methodology for classifying and tracking larger organic caifipoufids based on their
vc stil tv sis'". hrs'Tiso str, ?r y.t m si" tv tc -nr p -z-ve s«.pzzt fV i€ zc " = i^prrz seres si
;5CA' rroc= inj -s t"-= -.->z =ti ty basis s=: ¦ 5S SOA i;:?!. •:-h;zi- s 5v=-i5z € i- oc:' C'.'AQ
3" CA'- ^ A p-s:hi !¦=".• i-vest jit .n cr;~ = pess z<:tv r rzi-| sz hs- perzr-isc, »tr is
ziic_ ===r h = ": = ~zrinc_ni.
91
-------
Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE4.5 that were used
in the 2016 alpha platform
Profile
SPECIATE
comment
Sector
Pollutant
code
Profile description
version
5.0 (not
Replacement for v4.5
yet
profile 95223; Used 70%
released)
methane, 20% ethane,
and the 10% remaining
Poultry Production - Average of Production
VOC is from profile
nonpt
voc
G95223TOG
Cycle with gapfilled methane and ethane
95223
5.0 (not
Replacement for v4.5
yet
profile 95240. Used 70%
released)
methane, 20% ethane;
Nonpt,
Beef Cattle Farm and Animal Waste with
the 10% remaining VOC
ptnonipm
voc
G95240TOG
gapfilled methane and ethane
is from profile 95240.
5.0 (not
Replacement for v4.5
yet
profile 95241. Used 70%
released)
methane, 20% ethane;
the 10% remaining VOC
nonpt
voc
G95241TOG
Swine Farm and Animal Waste
is from profile 95241
nonpt,
5.0 (not
Composite of AE6-ready
ptnonipm,
yet
versions of SPECIATE4.5
pt_oilgas,
Composite -Refinery Fuel Gas and Natural
released)
profies 95125, 95126,
ptegu
PM2.5
95475
Gas Combustion
and 95127
Spark-Ignition Exhaust Emissions from 2-
4.5
stroke off-road engines - E10 ethanol
nonroad
VOC
95328
gasoline
Spark-Ignition Exhaust Emissions from 4-
4.5
stroke off-road engines - E10 ethanol
nonroad
VOC
95330
gasoline
Diesel Exhaust Emissions from Pre-Tier 1
4.5
nonroad
VOC
95331
Off-road Engines
Diesel Exhaust Emissions from Tier 1 Off-
4.5
nonroad
VOC
95332
road Engines
Diesel Exhaust Emissions from Tier 2 Off-
4.5
nonroad
VOC
95333
road Engines
Oil and Gas - Composite - Oil Field - Oil
4.5
nP_oilgas
VOC
95087a
Tank Battery Vent Gas
Oil and Gas - Composite - Oil Field -
4.5
nP_oilgas
voc
95109a
Condensate Tank Battery Vent Gas
Composite Profile - Oil and Natural Gas
4.5
nP_oilgas
voc
95398
Production - Condensate Tanks
nP_oilgas
voc
95403
Composite Profile - Gas Wells
4.5
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95417
- Untreated Natural Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95418
- Condensate Tank Vent Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95419
- Oil Tank Vent Gas, Uinta Basin
Oil and Gas Production - Composite Profile
4.5
np_oilgas
voc
95420
- Glycol Dehydrator, Uinta Basin
92
-------
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
93
-------
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
94
-------
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
The table below provides a crosswalk between fuel distribution SCCs and classification type for portable
fuel containers (PFC), fuel distribution operations associated with the bulk-plant-to-pump (BTP),
refinery to bulk terminal (RBT) and bulk plant storage (BPS).
see
Type
Description
40301001
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Breathing Loss (67000 Bbl. Tank Size)
40301002
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 10: Breathing Loss (67000 Bbl. Tank Size)
40301003
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 7: Breathing Loss (67000 Bbl. Tank Size)
40301004
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Breathing Loss (250000 Bbl. Tank Size)
40301006
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 7: Breathing Loss (250000 Bbl. Tank Size)
40301007
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks
(Varying Sizes); Gasoline RVP 13: Working Loss (Tank Diameter Independent)
40301101
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 13: Standing Loss (67000 Bbl. Tank Size)
40301102
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 10: Standing Loss (67000 Bbl. Tank Size)
40301103
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 7: Standing Loss (67000 Bbl. Tank Size)
40301105
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline RVP 10: Standing Loss (250000 Bbl. Tank Size)
40301151
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks
(Varying Sizes); Gasoline: Standing Loss - Internal
40301202
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor
Space; Gasoline RVP 10: Filling Loss
40301203
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor
Space; Gasoline RVP 7: Filling Loss
40400101
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400102
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400103
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400104
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank
40400105
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank
40400106
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Breathing Loss (250000 Bbl Capacity) - Fixed Roof Tank
40400107
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Working Loss (Diam. Independent) - Fixed Roof Tank
40400108
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Working Loss (Diameter Independent) - Fixed Roof Tank
40400109
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Working Loss (Diameter Independent) - Fixed Roof Tank
40400110
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
40400111
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank
95
-------
see
Type
Description
40400112
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss (67000 Bbl Capacity)- Floating Roof Tank
40400113
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400114
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400115
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank
40400116
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float RfTnk
40400117
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss (250000 Bbl Cap.) - Float RfTnk
40400118
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400119
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400120
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400130
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
40400131
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400132
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400133
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
40400140
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Ext. Float Roof Tank w/ Secondy Seal
40400141
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400142
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400143
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400148
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
40400149
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
40400150
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Miscellaneous Losses/Leaks: Loading Racks
40400151
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Valves, Flanges, and Pumps
40400152
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Collection Losses
40400153
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Vapor Control Unit Losses
40400160
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
40400161
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
40400162
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
40400163
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
40400170
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
96
-------
see
Type
Description
40400171
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400172
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400173
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400178
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Gasoline RVP 13/10/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal)
40400179
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)
40400199
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals;
See Comment **
40400201
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400202
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank
40400203
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400204
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400205
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400206
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank
40400207
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank
40400208
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank
40400210
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float RfTnk
40400211
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400212
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400213
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space
40400230
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - External Floating Roof w/ Primary Seal
40400231
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Primary Seal
97
-------
see
Type
Description
40400232
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal
40400233
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal
40400240
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400241
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal
40400248
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10/13/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal)
40400249
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: External Floating Roof (Primary/Secondary Seal)
40400250
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Loading Racks
40400251
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Valves, Flanges, and Pumps
40400252
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Collection Losses
40400253
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Miscellaneous Losses/Leaks: Vapor Control Unit Losses
40400260
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Internal Floating Roof w/ Primary Seal
40400261
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal
40400262
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal
40400263
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal
40400270
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400271
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400272
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400273
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 7: Standing Loss - Int. Floating Roof w/ Secondary Seal
40400278
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Gasoline RVP 10/13/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal)
98
-------
see
Type
Description
40400279
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants;
Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)
40400401
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 13: Breathing Loss
40400402
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 13: Working Loss
40400403
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 10: Breathing Loss
40400404
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 10: Working Loss
40400405
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 7: Breathing Loss
40400406
BTP/
BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products
- Underground Tanks; Gasoline RVP 7: Working Loss
40600101
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading **
40600126
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading **
40600131
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Normal Service)
40600136
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading (Normal Service)
40600141
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Balanced Service)
40600144
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Splash Loading (Balanced Service)
40600147
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Submerged Loading (Clean Tanks)
40600162
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Loaded with Fuel (Transit Losses)
40600163
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Gasoline: Return with Vapor (Transit Losses)
40600199
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank
Cars and Trucks; Not Classified **
40600231
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Cleaned and Vapor Free Tanks
40600232
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers
99
-------
see
Type
Description
40600233
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Cleaned and Vapor Free Tanks
40600234
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Ballasted Tank
40600235
BTP/
BPS
Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum Products;Marine
Vessels;Gasoline: Ocean Barges Loading - Ballasted Tank
40600236
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Tankers: Uncleaned Tanks
40600237
RBT
Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum Products;Marine
Vessels;Gasoline: Ocean Barges Loading - Uncleaned Tanks
40600238
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Uncleaned Tanks
40600239
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tankers: Ballasted Tank
40600240
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Loading Barges: Average Tank Condition
40600241
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Gasoline: Tanker Ballasting
40600299
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine
Vessels; Not Classified **
40600301
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Splash Filling
40600302
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Submerged Filling w/o Controls
40600305
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Unloading **
40600306
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Balanced Submerged Filling
40600307
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Underground Tank Breathing and Emptying
40600399
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline
Retail Operations - Stage I; Not Classified **
40600401
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling
Vehicle Gas Tanks - Stage II; Vapor Loss w/o Controls
40600501
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Leaks
40600502
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pipeline Venting
40600503
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station
40600504
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline
Petroleum Transport - General - All Products; Pump Station Leaks
40600602
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage II; Liquid Spill Loss w/o Controls
100
-------
see
Type
Description
40600701
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Splash Filling
40600702
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Submerged Filling w/o Controls
40600706
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Balanced Submerged Filling
40600707
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products;
Consumer (Corporate) Fleet Refueling - Stage I; Underground Tank Breathing and Emptying
40688801
BTP/
BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Fugitive
Emissions; Specify in Comments Field
250105012
0
RBT
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Terminals: All Evaporative
Losses; Gasoline
250105512
0
BTP/
BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Plants: All Evaporative
Losses; Gasoline
250106005
0
BTP/
BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Total
250106005
1
BTP/
BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Submerged Filling
250106005
2
BTP/
BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Splash Filling
250106005
3
BTP/
BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage
1: Balanced Submerged Filling
250106020
0
BTP/
BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Total
250106020
1
BTP/
BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations;
Underground Tank: Breathing and Emptying
250199500
0
BTP/
BPS
Storage and Transport; Petroleum and Petroleum Product Storage; All Storage Types: Working
Loss; Total: All Products
250500012
0
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; All Transport Types; Gasoline
250502012
0
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline
250502012
1
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline -
Barge
250503012
0
BTP/
BPS
Storage and Transport; Petroleum and Petroleum Product Transport; Truck; Gasoline
250504012
0
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Pipeline; Gasoline
266000000
0
BTP/
BPS
Waste Disposal, Treatment, and Recovery; Leaking Underground Storage Tanks; Leaking
Underground Storage Tanks; Total: All Storage Types
101
-------
102
------- |